From 51ce8e4f752ccf97c41280339a48587d46036d5f Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 16:23:29 -0500 Subject: [PATCH 01/32] README intro updates. --- README.md | 35 ++++++++++++++++++++--------------- 1 file changed, 20 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 0a43c7a..841a81a 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,10 @@ -# Junjo 順序 +# Junjo Python SDK -> Japanese Translation: order, sequence, procedure +> 順序 (junjo): order, sequence, procedure Junjo is a modern Python library for designing, executing, testing, and debugging complex, graph-based AI workflows. -Whether you’re building a simple chatbot, a complex data manipulation pipeline, or a sophisticated workflow with dynamic branching and parallel execution, Junjo provides the tools to define your logic as a clear graph of nodes and edges. +Whether you’re building a simple chatbot, a complex data manipulation pipeline, or a sophisticated workflow with dynamic branching and parallel execution, Junjo provides the tools to define and debug your logic as a clear graph of nodes, edges, and state updates. #### Docs @@ -13,22 +13,27 @@ Whether you’re building a simple chatbot, a complex data manipulation pipeline #### Benefits: -- Asyncio & Pydantic native - Visualize your AI workflows -- Build in redux-inspired state machine -- Create robust and predictable conditional chains of LLM calls -- Organize complex workflow executions into a scalable clean Graph structure -- Manage execution order, loops, and concurrency -- Supports Eval-Driven Development techniques - - Test every node of your workflow to 100% evaluation accuracy - - Test your workflows with dozens or thousands of input cases +- Redux inspired state management and state debugging tools +- Concurrency and type safety native with asyncio and pydantic +- Organize conditional chains of LLM calls into observable graph workflows +- Easy patterns for directed graph loops, branching, and concurrency +- Eval-Driven Development focused + - Easily build massive eval sets with mocked node state + - Programmatically build and update eval sets with agentic code assistants + - Eval patterns are based on pytest, leveraging its testing framework and capabilities - Rapidly iterate on your AI capabilities knowing for sure you're making progress -- Native opentelemetry support for clear tracing, observability, and debugging - - Try our **optional, free, open source** companion [junjo-server](https://github.com/mdrideout/junjo-server) telemetry server. +- OpenTelemetry native + - Provides organized, structured traces to any OpenTelemetry provider + - Companion open source **[Junjo AI Studio](https://github.com/mdrideout/junjo-ai-studio)** enhances debugging and evaluation of production data + + +
+ +
Junjo AI Studio Screenshot +
- -_junjo-screenshot.png_ #### Decoupled From f1f22649bc256f61547ec6da794b5ff6fa724171 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 16:29:51 -0500 Subject: [PATCH 02/32] chore: lock grpcio-tools to 1.76.0 and add proto validation workflow --- .github/workflows/validate-proto.yml | 67 +++++++++++++++++++ Makefile | 17 ++++- PROTO_VERSIONS.md | 27 ++++++++ pyproject.toml | 2 +- .../junjo_server/proto_gen/node_log_pb2.py | 6 +- .../proto_gen/node_log_pb2_grpc.py | 2 +- .../proto_gen/workflow_log_pb2.py | 6 +- .../proto_gen/workflow_log_pb2_grpc.py | 2 +- .../proto_gen/workflow_metadata_pb2.py | 6 +- .../proto_gen/workflow_metadata_pb2_grpc.py | 2 +- 10 files changed, 122 insertions(+), 15 deletions(-) create mode 100644 .github/workflows/validate-proto.yml create mode 100644 PROTO_VERSIONS.md diff --git a/.github/workflows/validate-proto.yml b/.github/workflows/validate-proto.yml new file mode 100644 index 0000000..a0651a3 --- /dev/null +++ b/.github/workflows/validate-proto.yml @@ -0,0 +1,67 @@ +name: Validate Proto Files + +on: + pull_request: + push: + branches: + - main + - master + +jobs: + validate-proto: + name: Validate Proto Files Are Up-to-Date + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.13' + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + + - name: Install dependencies + run: | + # Install protoc v30.2 (locked version for consistency) + PROTOC_VERSION=30.2 + curl -LO https://github.com/protocolbuffers/protobuf/releases/download/v${PROTOC_VERSION}/protoc-${PROTOC_VERSION}-linux-x86_64.zip + sudo unzip -o protoc-${PROTOC_VERSION}-linux-x86_64.zip -d /usr/local bin/protoc + sudo unzip -o protoc-${PROTOC_VERSION}-linux-x86_64.zip -d /usr/local 'include/*' + rm protoc-${PROTOC_VERSION}-linux-x86_64.zip + + # Install Python tools (--system flag for CI environment) + uv pip install --system grpcio-tools==1.76.0 + + # Verify installation + protoc --version + python -m grpc_tools.protoc --version + + - name: Regenerate Proto Files + run: | + make proto + + - name: Check for uncommitted changes + run: | + if ! git diff --quiet; then + echo "❌ ERROR: Proto files are out of date!" + echo "" + echo "The following files have uncommitted changes:" + git diff --name-only + echo "" + echo "📋 Full diff:" + git diff + echo "" + echo "🔧 To fix this issue:" + echo " 1. Manually install protoc v30.2: see PROTO_VERSIONS.md" + echo " 2. Install grpcio-tools==1.76.0: uv pip install grpcio-tools==1.76.0" + echo " 3. Run: make proto" + echo " 4. Commit the updated files" + exit 1 + fi + echo "✅ Proto files are up-to-date" \ No newline at end of file diff --git a/Makefile b/Makefile index 3006b85..56bd260 100644 --- a/Makefile +++ b/Makefile @@ -1,9 +1,22 @@ -.PHONY: proto clean +.PHONY: proto clean check-version PROTO_SRC_DIR := ./src/junjo/telemetry/junjo_server/proto PROTO_OUT_DIR := ./src/junjo/telemetry/junjo_server/proto_gen +REQUIRED_PROTOC_VERSION := 30.2 -proto: +check-version: + @echo "Checking protoc version..." + @VERSION=$$(protoc --version 2>&1 | awk '{print $$2}'); \ + if [ "$$VERSION" != "$(REQUIRED_PROTOC_VERSION)" ]; then \ + echo "❌ Error: protoc version mismatch."; \ + echo " Expected: libprotoc $(REQUIRED_PROTOC_VERSION)"; \ + echo " Found: libprotoc $$VERSION"; \ + echo " Please ensure protoc v$(REQUIRED_PROTOC_VERSION) is installed and in your PATH (see PROTO_VERSIONS.md)."; \ + exit 1; \ + fi + @echo "✅ Version check passed: libprotoc $(REQUIRED_PROTOC_VERSION)" + +proto: check-version python -m grpc_tools.protoc \ -I$(PROTO_SRC_DIR) \ --python_out=$(PROTO_OUT_DIR) \ diff --git a/PROTO_VERSIONS.md b/PROTO_VERSIONS.md new file mode 100644 index 0000000..bc75fdc --- /dev/null +++ b/PROTO_VERSIONS.md @@ -0,0 +1,27 @@ +# Protocol Buffer Tool Versions + +This document specifies the **locked versions** of Protocol Buffer tools used in this project. +These versions MUST match the ones used in `junjo-ai-studio` to ensure compatibility. + +## Required Tool Versions + +| Tool | Version | Purpose | +|------|---------|---------| +| **protoc** | v30.2 | Protocol Buffer compiler (system-installed) | +| **grpcio-tools** | 1.76.0 | Python protobuf/gRPC code generator | + +## Why Lock Versions? + +Different versions of `protoc` and its plugins generate structurally different code. +By locking to the same version used in our backend services, we ensure: + +1. **Compatibility:** The generated SDK code works seamlessly with our ingestion services. +2. **Consistency:** CI validation passes without "diff noise" caused by minor generator differences. +3. **Reproducibility:** Builds are deterministic across all environments. + +## Updating Versions + +If you update the version here, you MUST: +1. Update `pyproject.toml` +2. Update `.github/workflows/validate-proto.yml` +3. Regenerate all proto files: `make proto` \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 86acec9..0dd80b7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,7 +47,7 @@ dev = [ "sphinx-rtd-theme", "simple-sphinx-xml-sitemap", "mypy", - "grpcio-tools", + "grpcio-tools==1.76.0", "build>=1.2.1", "twine>=6.1.0", "pre-commit", diff --git a/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.py b/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.py index fb19e62..d9c54aa 100644 --- a/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.py +++ b/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.py @@ -2,7 +2,7 @@ # Generated by the protocol buffer compiler. DO NOT EDIT! # NO CHECKED-IN PROTOBUF GENCODE # source: node_log.proto -# Protobuf Python Version: 5.29.0 +# Protobuf Python Version: 6.30.0 """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import descriptor_pool as _descriptor_pool @@ -11,8 +11,8 @@ from google.protobuf.internal import builder as _builder _runtime_version.ValidateProtobufRuntimeVersion( _runtime_version.Domain.PUBLIC, - 5, - 29, + 6, + 30, 0, '', 'node_log.proto' diff --git a/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2_grpc.py b/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2_grpc.py index 60c740d..ed12bee 100644 --- a/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2_grpc.py +++ b/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2_grpc.py @@ -6,7 +6,7 @@ from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 from . import node_log_pb2 as node__log__pb2 -GRPC_GENERATED_VERSION = '1.70.0' +GRPC_GENERATED_VERSION = '1.72.0' GRPC_VERSION = grpc.__version__ _version_not_supported = False diff --git a/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.py b/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.py index 7de7077..e00fd97 100644 --- a/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.py +++ b/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.py @@ -2,7 +2,7 @@ # Generated by the protocol buffer compiler. DO NOT EDIT! # NO CHECKED-IN PROTOBUF GENCODE # source: workflow_log.proto -# Protobuf Python Version: 5.29.0 +# Protobuf Python Version: 6.30.0 """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import descriptor_pool as _descriptor_pool @@ -11,8 +11,8 @@ from google.protobuf.internal import builder as _builder _runtime_version.ValidateProtobufRuntimeVersion( _runtime_version.Domain.PUBLIC, - 5, - 29, + 6, + 30, 0, '', 'workflow_log.proto' diff --git a/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2_grpc.py b/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2_grpc.py index 9c33ddc..57ba6a2 100644 --- a/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2_grpc.py +++ b/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2_grpc.py @@ -6,7 +6,7 @@ from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 from . import workflow_log_pb2 as workflow__log__pb2 -GRPC_GENERATED_VERSION = '1.70.0' +GRPC_GENERATED_VERSION = '1.72.0' GRPC_VERSION = grpc.__version__ _version_not_supported = False diff --git a/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.py b/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.py index 1ac18ac..a1a254b 100644 --- a/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.py +++ b/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.py @@ -2,7 +2,7 @@ # Generated by the protocol buffer compiler. DO NOT EDIT! # NO CHECKED-IN PROTOBUF GENCODE # source: workflow_metadata.proto -# Protobuf Python Version: 5.29.0 +# Protobuf Python Version: 6.30.0 """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import descriptor_pool as _descriptor_pool @@ -11,8 +11,8 @@ from google.protobuf.internal import builder as _builder _runtime_version.ValidateProtobufRuntimeVersion( _runtime_version.Domain.PUBLIC, - 5, - 29, + 6, + 30, 0, '', 'workflow_metadata.proto' diff --git a/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2_grpc.py b/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2_grpc.py index 59aad5e..424f84f 100644 --- a/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2_grpc.py +++ b/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2_grpc.py @@ -6,7 +6,7 @@ from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 from . import workflow_metadata_pb2 as workflow__metadata__pb2 -GRPC_GENERATED_VERSION = '1.70.0' +GRPC_GENERATED_VERSION = '1.72.0' GRPC_VERSION = grpc.__version__ _version_not_supported = False From 37c19ca020b0597ef4b5188c122d533b9448e608 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 16:58:51 -0500 Subject: [PATCH 03/32] fix: use 'uv run' for proto generation to resolve python path issues --- .github/workflows/validate-proto.yml | 6 +- Makefile | 2 +- README.md | 2 +- uv.lock | 268 ++++++++++++++------------- 4 files changed, 147 insertions(+), 131 deletions(-) diff --git a/.github/workflows/validate-proto.yml b/.github/workflows/validate-proto.yml index a0651a3..8e4877b 100644 --- a/.github/workflows/validate-proto.yml +++ b/.github/workflows/validate-proto.yml @@ -35,12 +35,10 @@ jobs: sudo unzip -o protoc-${PROTOC_VERSION}-linux-x86_64.zip -d /usr/local 'include/*' rm protoc-${PROTOC_VERSION}-linux-x86_64.zip - # Install Python tools (--system flag for CI environment) - uv pip install --system grpcio-tools==1.76.0 - # Verify installation protoc --version - python -m grpc_tools.protoc --version + # Use uv run to verify the tool version within the project environment + uv run python -m grpc_tools.protoc --version - name: Regenerate Proto Files run: | diff --git a/Makefile b/Makefile index 56bd260..e0fda39 100644 --- a/Makefile +++ b/Makefile @@ -17,7 +17,7 @@ check-version: @echo "✅ Version check passed: libprotoc $(REQUIRED_PROTOC_VERSION)" proto: check-version - python -m grpc_tools.protoc \ + uv run python -m grpc_tools.protoc \ -I$(PROTO_SRC_DIR) \ --python_out=$(PROTO_OUT_DIR) \ --grpc_python_out=$(PROTO_OUT_DIR) \ diff --git a/README.md b/README.md index 841a81a..eb0ccaf 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ Whether you’re building a simple chatbot, a complex data manipulation pipeline - [Python API Docs](https://python-api.junjo.ai/) - [PyPI](https://pypi.org/project/junjo/) -#### Benefits: +#### Benefits: - Visualize your AI workflows - Redux 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a complex data manipulation pipeline - [Python API Docs](https://python-api.junjo.ai/) - [PyPI](https://pypi.org/project/junjo/) -#### Benefits: +#### Benefits: - Visualize your AI workflows - Redux inspired state management and state debugging tools From f87fce3372b6ef4e2d0c379e29b84f835e0ca2bc Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 17:16:54 -0500 Subject: [PATCH 05/32] ci: add integration test to compare protos with junjo-ai-studio --- .github/workflows/proto-integration-test.yml | 47 ++++++++++++++ scripts/compare_protos.py | 66 ++++++++++++++++++++ 2 files changed, 113 insertions(+) create mode 100644 .github/workflows/proto-integration-test.yml create mode 100644 scripts/compare_protos.py diff --git a/.github/workflows/proto-integration-test.yml b/.github/workflows/proto-integration-test.yml new file mode 100644 index 0000000..adff344 --- /dev/null +++ b/.github/workflows/proto-integration-test.yml @@ -0,0 +1,47 @@ +name: Proto Integration Test + +on: + pull_request: + push: + branches: + - main + - master + # Allow manual trigger + workflow_dispatch: + +jobs: + compare-protos: + name: Compare Protos with Junjo AI Studio + runs-on: ubuntu-latest + + steps: + - name: Checkout Junjo SDK (this repo) + uses: actions/checkout@v4 + with: + path: junjo-sdk + + - name: Checkout Junjo AI Studio + uses: actions/checkout@v4 + with: + repository: mdrideout/junjo-ai-studio + path: junjo-ai-studio + # sparse-checkout can speed this up if the repo gets huge, + # but for now full checkout is fine for verifying latest main. + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.13' + + - name: Compare Proto Files + run: | + # Make script executable + chmod +x junjo-sdk/scripts/compare_protos.py + + # Run comparison + # SDK Protos: src/junjo/telemetry/junjo_server/proto + # Studio Protos: proto + python junjo-sdk/scripts/compare_protos.py \ + junjo-sdk/src/junjo/telemetry/junjo_server/proto \ + junjo-ai-studio/proto + diff --git a/scripts/compare_protos.py b/scripts/compare_protos.py new file mode 100644 index 0000000..9b525dc --- /dev/null +++ b/scripts/compare_protos.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +import os +import sys +import filecmp +import difflib + +def compare_protos(sdk_proto_dir, studio_proto_dir): + """ + Compares proto files in the SDK directory against the Studio directory. + Fail if: + 1. A file exists in SDK but not in Studio (SDK should follow Studio's contract). + 2. Content differs. + """ + print(f"Comparing SDK protos in '{sdk_proto_dir}' with Studio protos in '{studio_proto_dir}'...") + + # Get list of proto files in SDK + sdk_files = [f for f in os.listdir(sdk_proto_dir) if f.endswith('.proto')] + + if not sdk_files: + print("No proto files found in SDK directory.") + sys.exit(1) + + errors = 0 + + for filename in sdk_files: + sdk_path = os.path.join(sdk_proto_dir, filename) + studio_path = os.path.join(studio_proto_dir, filename) + + # Check existence + if not os.path.exists(studio_path): + print(f"❌ MISSING: '{filename}' exists in SDK but is missing in Studio ({studio_proto_dir})") + errors += 1 + continue + + # Check content equality + # We read files to handle potential whitespace/formatting differences if we wanted to be smarter, + # but for now, strict equality (or simple text diff) is best for protos. + with open(sdk_path, 'r') as f1, open(studio_path, 'r') as f2: + sdk_content = f1.readlines() + studio_content = f2.readlines() + + if sdk_content != studio_content: + print(f"❌ MISMATCH: '{filename}' differs between SDK and Studio.") + print("Diff:") + sys.stdout.writelines(difflib.unified_diff( + studio_content, sdk_content, + fromfile=f"Studio/{filename}", + tofile=f"SDK/{filename}", + )) + errors += 1 + else: + print(f"✅ MATCH: '{filename}'") + + if errors > 0: + print(f"\nFound {errors} discrepancies.") + sys.exit(1) + else: + print("\nAll proto files match successfully!") + sys.exit(0) + +if __name__ == "__main__": + if len(sys.argv) != 3: + print("Usage: compare_protos.py ") + sys.exit(1) + + compare_protos(sys.argv[1], sys.argv[2]) From 819b282de9ec417ae212c3261fe5c31962308c3e Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 18:21:02 -0500 Subject: [PATCH 06/32] Cleaned up and removed deprecated and dead code from pre-opentelemetry architecture. --- .github/workflows/proto-integration-test.yml | 47 - .github/workflows/publish.yml | 6 - .github/workflows/validate-proto.yml | 65 - .gitignore | 9 - .pre-commit-config.yaml | 9 - Makefile | 35 - PROTO_VERSIONS.md | 27 - README.md | 18 - pyproject.toml | 1 - scripts/compare_protos.py | 66 - src/junjo/telemetry/junjo_server/__init__.py | 0 src/junjo/telemetry/junjo_server/client.py | 113 - .../telemetry/junjo_server/proto/__init__.py | 0 .../junjo_server/proto/node_log.proto | 16 - .../junjo_server/proto/workflow_log.proto | 16 - .../proto/workflow_metadata.proto | 17 - .../junjo_server/proto_gen/node_log_pb2.py | 39 - .../junjo_server/proto_gen/node_log_pb2.pyi | 18 - .../proto_gen/node_log_pb2_grpc.py | 99 - .../proto_gen/workflow_log_pb2.py | 39 - .../proto_gen/workflow_log_pb2.pyi | 18 - .../proto_gen/workflow_log_pb2_grpc.py | 99 - .../proto_gen/workflow_metadata_pb2.py | 39 - .../proto_gen/workflow_metadata_pb2.pyi | 20 - .../proto_gen/workflow_metadata_pb2_grpc.py | 99 - uv.lock | 2153 ++++++++++------- 26 files changed, 1306 insertions(+), 1762 deletions(-) delete mode 100644 .github/workflows/proto-integration-test.yml delete mode 100644 .github/workflows/validate-proto.yml delete mode 100644 .pre-commit-config.yaml delete mode 100644 Makefile delete mode 100644 PROTO_VERSIONS.md delete mode 100644 scripts/compare_protos.py delete mode 100644 src/junjo/telemetry/junjo_server/__init__.py delete mode 100644 src/junjo/telemetry/junjo_server/client.py delete mode 100644 src/junjo/telemetry/junjo_server/proto/__init__.py delete mode 100644 src/junjo/telemetry/junjo_server/proto/node_log.proto delete mode 100644 src/junjo/telemetry/junjo_server/proto/workflow_log.proto delete mode 100644 src/junjo/telemetry/junjo_server/proto/workflow_metadata.proto delete mode 100644 src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.py delete mode 100644 src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.pyi delete mode 100644 src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2_grpc.py delete mode 100644 src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.py delete mode 100644 src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.pyi delete mode 100644 src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2_grpc.py delete mode 100644 src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.py delete mode 100644 src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.pyi delete mode 100644 src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2_grpc.py diff --git a/.github/workflows/proto-integration-test.yml b/.github/workflows/proto-integration-test.yml deleted file mode 100644 index adff344..0000000 --- a/.github/workflows/proto-integration-test.yml +++ /dev/null @@ -1,47 +0,0 @@ -name: Proto Integration Test - -on: - pull_request: - push: - branches: - - main - - master - # Allow manual trigger - workflow_dispatch: - -jobs: - compare-protos: - name: Compare Protos with Junjo AI Studio - runs-on: ubuntu-latest - - steps: - - name: Checkout Junjo SDK (this repo) - uses: actions/checkout@v4 - with: - path: junjo-sdk - - - name: Checkout Junjo AI Studio - uses: actions/checkout@v4 - with: - repository: mdrideout/junjo-ai-studio - path: junjo-ai-studio - # sparse-checkout can speed this up if the repo gets huge, - # but for now full checkout is fine for verifying latest main. - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.13' - - - name: Compare Proto Files - run: | - # Make script executable - chmod +x junjo-sdk/scripts/compare_protos.py - - # Run comparison - # SDK Protos: src/junjo/telemetry/junjo_server/proto - # Studio Protos: proto - python junjo-sdk/scripts/compare_protos.py \ - junjo-sdk/src/junjo/telemetry/junjo_server/proto \ - junjo-ai-studio/proto - diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 1e735f6..4934e6e 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -23,12 +23,6 @@ jobs: with: python-version: '3.x' - - name: Install protobuf compiler - run: pip install grpcio-tools - - - name: Generate protobuf files - run: make proto - - name: Build package run: | pip install build diff --git a/.github/workflows/validate-proto.yml b/.github/workflows/validate-proto.yml deleted file mode 100644 index 8e4877b..0000000 --- a/.github/workflows/validate-proto.yml +++ /dev/null @@ -1,65 +0,0 @@ -name: Validate Proto Files - -on: - pull_request: - push: - branches: - - main - - master - -jobs: - validate-proto: - name: Validate Proto Files Are Up-to-Date - runs-on: ubuntu-latest - - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.13' - - - name: Install uv - uses: astral-sh/setup-uv@v5 - with: - enable-cache: true - - - name: Install dependencies - run: | - # Install protoc v30.2 (locked version for consistency) - PROTOC_VERSION=30.2 - curl -LO https://github.com/protocolbuffers/protobuf/releases/download/v${PROTOC_VERSION}/protoc-${PROTOC_VERSION}-linux-x86_64.zip - sudo unzip -o protoc-${PROTOC_VERSION}-linux-x86_64.zip -d /usr/local bin/protoc - sudo unzip -o protoc-${PROTOC_VERSION}-linux-x86_64.zip -d /usr/local 'include/*' - rm protoc-${PROTOC_VERSION}-linux-x86_64.zip - - # Verify installation - protoc --version - # Use uv run to verify the tool version within the project environment - uv run python -m grpc_tools.protoc --version - - - name: Regenerate Proto Files - run: | - make proto - - - name: Check for uncommitted changes - run: | - if ! git diff --quiet; then - echo "❌ ERROR: Proto files are out of date!" - echo "" - echo "The following files have uncommitted changes:" - git diff --name-only - echo "" - echo "📋 Full diff:" - git diff - echo "" - echo "🔧 To fix this issue:" - echo " 1. Manually install protoc v30.2: see PROTO_VERSIONS.md" - echo " 2. Install grpcio-tools==1.76.0: uv pip install grpcio-tools==1.76.0" - echo " 3. Run: make proto" - echo " 4. Commit the updated files" - exit 1 - fi - echo "✅ Proto files are up-to-date" \ No newline at end of file diff --git a/.gitignore b/.gitignore index 887ba65..6412897 100644 --- a/.gitignore +++ b/.gitignore @@ -171,15 +171,6 @@ cython_debug/ # PyPI configuration file .pypirc -# Protobuf generated code -# We commit these files to the repository to ensure that the package can be -# installed directly from GitHub without requiring the user to have the -# protobuf compiler installed. -# src/junjo/telemetry/junjo_server/proto_gen/**.* - -# Keep the init -# !src/junjo/telemetry/junjo_server/proto_gen/__init__.py - # Misc .DS_Store .gitleaks/** diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml deleted file mode 100644 index 824d725..0000000 --- a/.pre-commit-config.yaml +++ /dev/null @@ -1,9 +0,0 @@ -repos: -- repo: local - hooks: - - id: proto-gen - name: Generate protobuf files - entry: make proto - language: system - files: ^src/junjo/telemetry/junjo_server/proto/.*\.proto$ - always_run: true \ No newline at end of file diff --git a/Makefile b/Makefile deleted file mode 100644 index e0fda39..0000000 --- a/Makefile +++ /dev/null @@ -1,35 +0,0 @@ -.PHONY: proto clean check-version - -PROTO_SRC_DIR := ./src/junjo/telemetry/junjo_server/proto -PROTO_OUT_DIR := ./src/junjo/telemetry/junjo_server/proto_gen -REQUIRED_PROTOC_VERSION := 30.2 - -check-version: - @echo "Checking protoc version..." - @VERSION=$$(protoc --version 2>&1 | awk '{print $$2}'); \ - if [ "$$VERSION" != "$(REQUIRED_PROTOC_VERSION)" ]; then \ - echo "❌ Error: protoc version mismatch."; \ - echo " Expected: libprotoc $(REQUIRED_PROTOC_VERSION)"; \ - echo " Found: libprotoc $$VERSION"; \ - echo " Please ensure protoc v$(REQUIRED_PROTOC_VERSION) is installed and in your PATH (see PROTO_VERSIONS.md)."; \ - exit 1; \ - fi - @echo "✅ Version check passed: libprotoc $(REQUIRED_PROTOC_VERSION)" - -proto: check-version - uv run python -m grpc_tools.protoc \ - -I$(PROTO_SRC_DIR) \ - --python_out=$(PROTO_OUT_DIR) \ - --grpc_python_out=$(PROTO_OUT_DIR) \ - --pyi_out=$(PROTO_OUT_DIR) \ - $(PROTO_SRC_DIR)/*.proto - - # Post-process every _pb2_grpc.py file for relative imports. - # Cross-platform sed command (works on both macOS and Linux) - @for file in $(PROTO_OUT_DIR)/*_pb2_grpc.py; do \ - echo "Fixing imports in $$file"; \ - sed -i.bak 's/^import \(.*_pb2\) as \(.*\)/from . import \1 as \2/' $$file && rm -f $$file.bak; \ - done - -clean: - rm -rf $(PROTO_OUT_DIR)/* diff --git a/PROTO_VERSIONS.md b/PROTO_VERSIONS.md deleted file mode 100644 index bc75fdc..0000000 --- a/PROTO_VERSIONS.md +++ /dev/null @@ -1,27 +0,0 @@ -# Protocol Buffer Tool Versions - -This document specifies the **locked versions** of Protocol Buffer tools used in this project. -These versions MUST match the ones used in `junjo-ai-studio` to ensure compatibility. - -## Required Tool Versions - -| Tool | Version | Purpose | -|------|---------|---------| -| **protoc** | v30.2 | Protocol Buffer compiler (system-installed) | -| **grpcio-tools** | 1.76.0 | Python protobuf/gRPC code generator | - -## Why Lock Versions? - -Different versions of `protoc` and its plugins generate structurally different code. -By locking to the same version used in our backend services, we ensure: - -1. **Compatibility:** The generated SDK code works seamlessly with our ingestion services. -2. **Consistency:** CI validation passes without "diff noise" caused by minor generator differences. -3. **Reproducibility:** Builds are deterministic across all environments. - -## Updating Versions - -If you update the version here, you MUST: -1. Update `pyproject.toml` -2. Update `.github/workflows/validate-proto.yml` -3. Regenerate all proto files: `make proto` \ No newline at end of file diff --git a/README.md b/README.md index 841a81a..267c7e1 100644 --- a/README.md +++ b/README.md @@ -170,22 +170,4 @@ $ sphinx-build -b html docs docs/_build ```bash # Run the tests with uv $ uv run pytest -``` - -## Code Generation - -### Protobuf schema generation - -1. Requires the optional `dev` dependencies to be installed via `uv pip install -e ".[dev]"` -2. Requires [protoc](https://grpc.io/docs/protoc-installation/) which can be installed into your developer environment host machine ([instructions](https://grpc.io/docs/protoc-installation/)). -3. Copy the .proto files from the junjo-server project to `src/telemetry/junjo_server/proto` -4. Run `make proto` from the project root to generate the `proto_gen` files for the client -5. Update any required changes to the `src/telemetry/junjo_server/client.py` file (type changes, fields, etc.) - -### Pre-commit Hook - -This project uses a pre-commit hook to automatically generate the protobuf files. To install the hook, run the following command: - -```bash -pre-commit install ``` \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 0dd80b7..087e19b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,7 +47,6 @@ dev = [ "sphinx-rtd-theme", "simple-sphinx-xml-sitemap", "mypy", - "grpcio-tools==1.76.0", "build>=1.2.1", "twine>=6.1.0", "pre-commit", diff --git a/scripts/compare_protos.py b/scripts/compare_protos.py deleted file mode 100644 index 9b525dc..0000000 --- a/scripts/compare_protos.py +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env python3 -import os -import sys -import filecmp -import difflib - -def compare_protos(sdk_proto_dir, studio_proto_dir): - """ - Compares proto files in the SDK directory against the Studio directory. - Fail if: - 1. A file exists in SDK but not in Studio (SDK should follow Studio's contract). - 2. Content differs. - """ - print(f"Comparing SDK protos in '{sdk_proto_dir}' with Studio protos in '{studio_proto_dir}'...") - - # Get list of proto files in SDK - sdk_files = [f for f in os.listdir(sdk_proto_dir) if f.endswith('.proto')] - - if not sdk_files: - print("No proto files found in SDK directory.") - sys.exit(1) - - errors = 0 - - for filename in sdk_files: - sdk_path = os.path.join(sdk_proto_dir, filename) - studio_path = os.path.join(studio_proto_dir, filename) - - # Check existence - if not os.path.exists(studio_path): - print(f"❌ MISSING: '{filename}' exists in SDK but is missing in Studio ({studio_proto_dir})") - errors += 1 - continue - - # Check content equality - # We read files to handle potential whitespace/formatting differences if we wanted to be smarter, - # but for now, strict equality (or simple text diff) is best for protos. - with open(sdk_path, 'r') as f1, open(studio_path, 'r') as f2: - sdk_content = f1.readlines() - studio_content = f2.readlines() - - if sdk_content != studio_content: - print(f"❌ MISMATCH: '{filename}' differs between SDK and Studio.") - print("Diff:") - sys.stdout.writelines(difflib.unified_diff( - studio_content, sdk_content, - fromfile=f"Studio/{filename}", - tofile=f"SDK/{filename}", - )) - errors += 1 - else: - print(f"✅ MATCH: '{filename}'") - - if errors > 0: - print(f"\nFound {errors} discrepancies.") - sys.exit(1) - else: - print("\nAll proto files match successfully!") - sys.exit(0) - -if __name__ == "__main__": - if len(sys.argv) != 3: - print("Usage: compare_protos.py ") - sys.exit(1) - - compare_protos(sys.argv[1], sys.argv[2]) diff --git a/src/junjo/telemetry/junjo_server/__init__.py b/src/junjo/telemetry/junjo_server/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/junjo/telemetry/junjo_server/client.py b/src/junjo/telemetry/junjo_server/client.py deleted file mode 100644 index 5dd834a..0000000 --- a/src/junjo/telemetry/junjo_server/client.py +++ /dev/null @@ -1,113 +0,0 @@ -from enum import StrEnum - -import grpc -from google.protobuf.empty_pb2 import Empty - -from junjo.telemetry.junjo_server.proto_gen import node_log_pb2, node_log_pb2_grpc - -from .proto_gen import workflow_log_pb2, workflow_log_pb2_grpc, workflow_metadata_pb2, workflow_metadata_pb2_grpc - - -class JunjoLogType(StrEnum): - START = "start" - END = "end" - -class JunjoUiClient: - """ - A gRPC client that connects to the WorkflowService - and provides a convenience method to call CreateWorkflow. - """ - - def __init__(self, host: str = "localhost", port: int = 50051): - """ - Initialize a gRPC channel and stub for the given host/port. - """ - self.channel = grpc.insecure_channel(f"{host}:{port}") - self.node_log_stub = node_log_pb2_grpc.NodeLogServiceStub(self.channel) - self.workflow_log_stub = workflow_log_pb2_grpc.WorkflowLogServiceStub(self.channel) - self.workflow_metadata_stub = workflow_metadata_pb2_grpc.WorkflowMetadataServiceStub(self.channel) - - def create_node_log(self, - exec_id: str, - type: JunjoLogType, - event_time_nano: int, - state: str - ) -> None: - """ - Make a CreateNodeLog gRPC call. - - Args: - exec_id: The id of the workflow execution (same for start and end) - type: The type of the workflow log ("start" or "end") - event_time_nano: The time of the event in nanoseconds - state: The state of the workflow execution - - """ - print("Sending grpc request to create workflow with id:", exec_id) - request = node_log_pb2.CreateNodeLogRequest( - exec_id=exec_id, - type=type, - event_time_nano=event_time_nano, - state=state - ) - - response: Empty = self.node_log_stub.CreateNodeLog(request) - print("CreateNodeLog RPC succeeded. Response is:", response) - - def create_workflow_log(self, - exec_id: str, - type: JunjoLogType, - event_time_nano: int, - state: str - ) -> None: - """ - Make a CreateWorkflowLog gRPC call. - - Args: - exec_id: The id of the workflow execution (same for start and end) - type: The type of the workflow log ("start" or "end") - event_time_nano: The time of the event in nanoseconds - state: The state of the workflow execution - - """ - print("Sending grpc request to create workflow with id:", exec_id) - request = workflow_log_pb2.CreateWorkflowLogRequest( - exec_id=exec_id, - type=type, - event_time_nano=event_time_nano, - state=state - ) - - response: Empty = self.workflow_log_stub.CreateWorkflowLog(request) - print("CreateWorkflowLog RPC succeeded. Response is:", response) - - def create_workflow_metadata(self, - exec_id: str, - app_name: str, - workflow_name: str, - event_time_nano: int, - structure: str - ) -> None: - """ - Make a CreateWorkflowMetadata gRPC call. - - Args: - exec_id: The id of the workflow execution - app_name: The name of the application - workflow_name: The name of the workflow - event_time_nano: The time of the event in nanoseconds - structure: The workflow structure as a JSON string - - """ - print("Sending grpc request to create workflow metadata with id:", exec_id) - request = workflow_metadata_pb2.CreateWorkflowMetadataRequest( - exec_id=exec_id, - app_name=app_name, - workflow_name=workflow_name, - event_time_nano=event_time_nano, - structure=structure - ) - - response: Empty = self.workflow_metadata_stub.CreateWorkflowMetadata(request) - print("CreateWorkflowMetadata RPC succeeded. Response is:", response) - diff --git a/src/junjo/telemetry/junjo_server/proto/__init__.py b/src/junjo/telemetry/junjo_server/proto/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/junjo/telemetry/junjo_server/proto/node_log.proto b/src/junjo/telemetry/junjo_server/proto/node_log.proto deleted file mode 100644 index 3a1da8e..0000000 --- a/src/junjo/telemetry/junjo_server/proto/node_log.proto +++ /dev/null @@ -1,16 +0,0 @@ -syntax = "proto3"; - -package workflows; - -import "google/protobuf/empty.proto"; - -service NodeLogService { - rpc CreateNodeLog (CreateNodeLogRequest) returns (google.protobuf.Empty); // Return Empty on success -} - -message CreateNodeLogRequest { - string exec_id = 1; - string type = 2; - int64 event_time_nano = 3; - string state = 4; // JSON data as a string -} diff --git a/src/junjo/telemetry/junjo_server/proto/workflow_log.proto b/src/junjo/telemetry/junjo_server/proto/workflow_log.proto deleted file mode 100644 index e41c141..0000000 --- a/src/junjo/telemetry/junjo_server/proto/workflow_log.proto +++ /dev/null @@ -1,16 +0,0 @@ -syntax = "proto3"; - -package workflows; - -import "google/protobuf/empty.proto"; - -service WorkflowLogService { - rpc CreateWorkflowLog (CreateWorkflowLogRequest) returns (google.protobuf.Empty); // Return Empty -} - -message CreateWorkflowLogRequest { - string exec_id = 1; - string type = 2; - int64 event_time_nano = 3; - string state = 5; // JSON data as a string -} \ No newline at end of file diff --git a/src/junjo/telemetry/junjo_server/proto/workflow_metadata.proto b/src/junjo/telemetry/junjo_server/proto/workflow_metadata.proto deleted file mode 100644 index 1e9d03e..0000000 --- a/src/junjo/telemetry/junjo_server/proto/workflow_metadata.proto +++ /dev/null @@ -1,17 +0,0 @@ -syntax = "proto3"; - -package workflows; - -import "google/protobuf/empty.proto"; - -service WorkflowMetadataService { - rpc CreateWorkflowMetadata (CreateWorkflowMetadataRequest) returns (google.protobuf.Empty); // Return Empty -} - -message CreateWorkflowMetadataRequest { - string exec_id = 1; - string app_name = 2; - string workflow_name = 3; - int64 event_time_nano = 4; - string structure = 5; // JSON data as a string -} \ No newline at end of file diff --git a/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.py b/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.py deleted file mode 100644 index d9c54aa..0000000 --- a/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.py +++ /dev/null @@ -1,39 +0,0 @@ -# -*- coding: utf-8 -*- -# Generated by the protocol buffer compiler. DO NOT EDIT! -# NO CHECKED-IN PROTOBUF GENCODE -# source: node_log.proto -# Protobuf Python Version: 6.30.0 -"""Generated protocol buffer code.""" -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pool as _descriptor_pool -from google.protobuf import runtime_version as _runtime_version -from google.protobuf import symbol_database as _symbol_database -from google.protobuf.internal import builder as _builder -_runtime_version.ValidateProtobufRuntimeVersion( - _runtime_version.Domain.PUBLIC, - 6, - 30, - 0, - '', - 'node_log.proto' -) -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 - - -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x0enode_log.proto\x12\tworkflows\x1a\x1bgoogle/protobuf/empty.proto\"]\n\x14\x43reateNodeLogRequest\x12\x0f\n\x07\x65xec_id\x18\x01 \x01(\t\x12\x0c\n\x04type\x18\x02 \x01(\t\x12\x17\n\x0f\x65vent_time_nano\x18\x03 \x01(\x03\x12\r\n\x05state\x18\x04 \x01(\t2Z\n\x0eNodeLogService\x12H\n\rCreateNodeLog\x12\x1f.workflows.CreateNodeLogRequest\x1a\x16.google.protobuf.Emptyb\x06proto3') - -_globals = globals() -_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) -_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'node_log_pb2', _globals) -if not _descriptor._USE_C_DESCRIPTORS: - DESCRIPTOR._loaded_options = None - _globals['_CREATENODELOGREQUEST']._serialized_start=58 - _globals['_CREATENODELOGREQUEST']._serialized_end=151 - _globals['_NODELOGSERVICE']._serialized_start=153 - _globals['_NODELOGSERVICE']._serialized_end=243 -# @@protoc_insertion_point(module_scope) diff --git a/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.pyi b/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.pyi deleted file mode 100644 index f55d708..0000000 --- a/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2.pyi +++ /dev/null @@ -1,18 +0,0 @@ -from google.protobuf import empty_pb2 as _empty_pb2 -from google.protobuf import descriptor as _descriptor -from google.protobuf import message as _message -from typing import ClassVar as _ClassVar, Optional as _Optional - -DESCRIPTOR: _descriptor.FileDescriptor - -class CreateNodeLogRequest(_message.Message): - __slots__ = ("exec_id", "type", "event_time_nano", "state") - EXEC_ID_FIELD_NUMBER: _ClassVar[int] - TYPE_FIELD_NUMBER: _ClassVar[int] - EVENT_TIME_NANO_FIELD_NUMBER: _ClassVar[int] - STATE_FIELD_NUMBER: _ClassVar[int] - exec_id: str - type: str - event_time_nano: int - state: str - def __init__(self, exec_id: _Optional[str] = ..., type: _Optional[str] = ..., event_time_nano: _Optional[int] = ..., state: _Optional[str] = ...) -> None: ... diff --git a/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2_grpc.py b/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2_grpc.py deleted file mode 100644 index ed12bee..0000000 --- a/src/junjo/telemetry/junjo_server/proto_gen/node_log_pb2_grpc.py +++ /dev/null @@ -1,99 +0,0 @@ -# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! -"""Client and server classes corresponding to protobuf-defined services.""" -import grpc -import warnings - -from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 -from . import node_log_pb2 as node__log__pb2 - -GRPC_GENERATED_VERSION = '1.72.0' -GRPC_VERSION = grpc.__version__ -_version_not_supported = False - -try: - from grpc._utilities import first_version_is_lower - _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION) -except ImportError: - _version_not_supported = True - -if _version_not_supported: - raise RuntimeError( - f'The grpc package installed is at version {GRPC_VERSION},' - + f' but the generated code in node_log_pb2_grpc.py depends on' - + f' grpcio>={GRPC_GENERATED_VERSION}.' - + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' - + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' - ) - - -class NodeLogServiceStub(object): - """Missing associated documentation comment in .proto file.""" - - def __init__(self, channel): - """Constructor. - - Args: - channel: A grpc.Channel. - """ - self.CreateNodeLog = channel.unary_unary( - '/workflows.NodeLogService/CreateNodeLog', - request_serializer=node__log__pb2.CreateNodeLogRequest.SerializeToString, - response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString, - _registered_method=True) - - -class NodeLogServiceServicer(object): - """Missing associated documentation comment in .proto file.""" - - def CreateNodeLog(self, request, context): - """Return Empty on success - """ - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - -def add_NodeLogServiceServicer_to_server(servicer, server): - rpc_method_handlers = { - 'CreateNodeLog': grpc.unary_unary_rpc_method_handler( - servicer.CreateNodeLog, - request_deserializer=node__log__pb2.CreateNodeLogRequest.FromString, - response_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString, - ), - } - generic_handler = grpc.method_handlers_generic_handler( - 'workflows.NodeLogService', rpc_method_handlers) - server.add_generic_rpc_handlers((generic_handler,)) - server.add_registered_method_handlers('workflows.NodeLogService', rpc_method_handlers) - - - # This class is part of an EXPERIMENTAL API. -class NodeLogService(object): - """Missing associated documentation comment in .proto file.""" - - @staticmethod - def CreateNodeLog(request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None): - return grpc.experimental.unary_unary( - request, - target, - '/workflows.NodeLogService/CreateNodeLog', - node__log__pb2.CreateNodeLogRequest.SerializeToString, - google_dot_protobuf_dot_empty__pb2.Empty.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - _registered_method=True) diff --git a/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.py b/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.py deleted file mode 100644 index e00fd97..0000000 --- a/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.py +++ /dev/null @@ -1,39 +0,0 @@ -# -*- coding: utf-8 -*- -# Generated by the protocol buffer compiler. DO NOT EDIT! -# NO CHECKED-IN PROTOBUF GENCODE -# source: workflow_log.proto -# Protobuf Python Version: 6.30.0 -"""Generated protocol buffer code.""" -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pool as _descriptor_pool -from google.protobuf import runtime_version as _runtime_version -from google.protobuf import symbol_database as _symbol_database -from google.protobuf.internal import builder as _builder -_runtime_version.ValidateProtobufRuntimeVersion( - _runtime_version.Domain.PUBLIC, - 6, - 30, - 0, - '', - 'workflow_log.proto' -) -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 - - -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x12workflow_log.proto\x12\tworkflows\x1a\x1bgoogle/protobuf/empty.proto\"a\n\x18\x43reateWorkflowLogRequest\x12\x0f\n\x07\x65xec_id\x18\x01 \x01(\t\x12\x0c\n\x04type\x18\x02 \x01(\t\x12\x17\n\x0f\x65vent_time_nano\x18\x03 \x01(\x03\x12\r\n\x05state\x18\x05 \x01(\t2f\n\x12WorkflowLogService\x12P\n\x11\x43reateWorkflowLog\x12#.workflows.CreateWorkflowLogRequest\x1a\x16.google.protobuf.Emptyb\x06proto3') - -_globals = globals() -_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) -_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'workflow_log_pb2', _globals) -if not _descriptor._USE_C_DESCRIPTORS: - DESCRIPTOR._loaded_options = None - _globals['_CREATEWORKFLOWLOGREQUEST']._serialized_start=62 - _globals['_CREATEWORKFLOWLOGREQUEST']._serialized_end=159 - _globals['_WORKFLOWLOGSERVICE']._serialized_start=161 - _globals['_WORKFLOWLOGSERVICE']._serialized_end=263 -# @@protoc_insertion_point(module_scope) diff --git a/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.pyi b/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.pyi deleted file mode 100644 index cab914b..0000000 --- a/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2.pyi +++ /dev/null @@ -1,18 +0,0 @@ -from google.protobuf import empty_pb2 as _empty_pb2 -from google.protobuf import descriptor as _descriptor -from google.protobuf import message as _message -from typing import ClassVar as _ClassVar, Optional as _Optional - -DESCRIPTOR: _descriptor.FileDescriptor - -class CreateWorkflowLogRequest(_message.Message): - __slots__ = ("exec_id", "type", "event_time_nano", "state") - EXEC_ID_FIELD_NUMBER: _ClassVar[int] - TYPE_FIELD_NUMBER: _ClassVar[int] - EVENT_TIME_NANO_FIELD_NUMBER: _ClassVar[int] - STATE_FIELD_NUMBER: _ClassVar[int] - exec_id: str - type: str - event_time_nano: int - state: str - def __init__(self, exec_id: _Optional[str] = ..., type: _Optional[str] = ..., event_time_nano: _Optional[int] = ..., state: _Optional[str] = ...) -> None: ... diff --git a/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2_grpc.py b/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2_grpc.py deleted file mode 100644 index 57ba6a2..0000000 --- a/src/junjo/telemetry/junjo_server/proto_gen/workflow_log_pb2_grpc.py +++ /dev/null @@ -1,99 +0,0 @@ -# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! -"""Client and server classes corresponding to protobuf-defined services.""" -import grpc -import warnings - -from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 -from . import workflow_log_pb2 as workflow__log__pb2 - -GRPC_GENERATED_VERSION = '1.72.0' -GRPC_VERSION = grpc.__version__ -_version_not_supported = False - -try: - from grpc._utilities import first_version_is_lower - _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION) -except ImportError: - _version_not_supported = True - -if _version_not_supported: - raise RuntimeError( - f'The grpc package installed is at version {GRPC_VERSION},' - + f' but the generated code in workflow_log_pb2_grpc.py depends on' - + f' grpcio>={GRPC_GENERATED_VERSION}.' - + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' - + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' - ) - - -class WorkflowLogServiceStub(object): - """Missing associated documentation comment in .proto file.""" - - def __init__(self, channel): - """Constructor. - - Args: - channel: A grpc.Channel. - """ - self.CreateWorkflowLog = channel.unary_unary( - '/workflows.WorkflowLogService/CreateWorkflowLog', - request_serializer=workflow__log__pb2.CreateWorkflowLogRequest.SerializeToString, - response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString, - _registered_method=True) - - -class WorkflowLogServiceServicer(object): - """Missing associated documentation comment in .proto file.""" - - def CreateWorkflowLog(self, request, context): - """Return Empty - """ - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - -def add_WorkflowLogServiceServicer_to_server(servicer, server): - rpc_method_handlers = { - 'CreateWorkflowLog': grpc.unary_unary_rpc_method_handler( - servicer.CreateWorkflowLog, - request_deserializer=workflow__log__pb2.CreateWorkflowLogRequest.FromString, - response_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString, - ), - } - generic_handler = grpc.method_handlers_generic_handler( - 'workflows.WorkflowLogService', rpc_method_handlers) - server.add_generic_rpc_handlers((generic_handler,)) - server.add_registered_method_handlers('workflows.WorkflowLogService', rpc_method_handlers) - - - # This class is part of an EXPERIMENTAL API. -class WorkflowLogService(object): - """Missing associated documentation comment in .proto file.""" - - @staticmethod - def CreateWorkflowLog(request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None): - return grpc.experimental.unary_unary( - request, - target, - '/workflows.WorkflowLogService/CreateWorkflowLog', - workflow__log__pb2.CreateWorkflowLogRequest.SerializeToString, - google_dot_protobuf_dot_empty__pb2.Empty.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - _registered_method=True) diff --git a/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.py b/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.py deleted file mode 100644 index a1a254b..0000000 --- a/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.py +++ /dev/null @@ -1,39 +0,0 @@ -# -*- coding: utf-8 -*- -# Generated by the protocol buffer compiler. DO NOT EDIT! -# NO CHECKED-IN PROTOBUF GENCODE -# source: workflow_metadata.proto -# Protobuf Python Version: 6.30.0 -"""Generated protocol buffer code.""" -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pool as _descriptor_pool -from google.protobuf import runtime_version as _runtime_version -from google.protobuf import symbol_database as _symbol_database -from google.protobuf.internal import builder as _builder -_runtime_version.ValidateProtobufRuntimeVersion( - _runtime_version.Domain.PUBLIC, - 6, - 30, - 0, - '', - 'workflow_metadata.proto' -) -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - -from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 - - -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x17workflow_metadata.proto\x12\tworkflows\x1a\x1bgoogle/protobuf/empty.proto\"\x85\x01\n\x1d\x43reateWorkflowMetadataRequest\x12\x0f\n\x07\x65xec_id\x18\x01 \x01(\t\x12\x10\n\x08\x61pp_name\x18\x02 \x01(\t\x12\x15\n\rworkflow_name\x18\x03 \x01(\t\x12\x17\n\x0f\x65vent_time_nano\x18\x04 \x01(\x03\x12\x11\n\tstructure\x18\x05 \x01(\t2u\n\x17WorkflowMetadataService\x12Z\n\x16\x43reateWorkflowMetadata\x12(.workflows.CreateWorkflowMetadataRequest\x1a\x16.google.protobuf.Emptyb\x06proto3') - -_globals = globals() -_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) -_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'workflow_metadata_pb2', _globals) -if not _descriptor._USE_C_DESCRIPTORS: - DESCRIPTOR._loaded_options = None - _globals['_CREATEWORKFLOWMETADATAREQUEST']._serialized_start=68 - _globals['_CREATEWORKFLOWMETADATAREQUEST']._serialized_end=201 - _globals['_WORKFLOWMETADATASERVICE']._serialized_start=203 - _globals['_WORKFLOWMETADATASERVICE']._serialized_end=320 -# @@protoc_insertion_point(module_scope) diff --git a/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.pyi b/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.pyi deleted file mode 100644 index ad0423e..0000000 --- a/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2.pyi +++ /dev/null @@ -1,20 +0,0 @@ -from google.protobuf import empty_pb2 as _empty_pb2 -from google.protobuf import descriptor as _descriptor -from google.protobuf import message as _message -from typing import ClassVar as _ClassVar, Optional as _Optional - -DESCRIPTOR: _descriptor.FileDescriptor - -class CreateWorkflowMetadataRequest(_message.Message): - __slots__ = ("exec_id", "app_name", "workflow_name", "event_time_nano", "structure") - EXEC_ID_FIELD_NUMBER: _ClassVar[int] - APP_NAME_FIELD_NUMBER: _ClassVar[int] - WORKFLOW_NAME_FIELD_NUMBER: _ClassVar[int] - EVENT_TIME_NANO_FIELD_NUMBER: _ClassVar[int] - STRUCTURE_FIELD_NUMBER: _ClassVar[int] - exec_id: str - app_name: str - workflow_name: str - event_time_nano: int - structure: str - def __init__(self, exec_id: _Optional[str] = ..., app_name: _Optional[str] = ..., workflow_name: _Optional[str] = ..., event_time_nano: _Optional[int] = ..., structure: _Optional[str] = ...) -> None: ... diff --git a/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2_grpc.py b/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2_grpc.py deleted file mode 100644 index 424f84f..0000000 --- a/src/junjo/telemetry/junjo_server/proto_gen/workflow_metadata_pb2_grpc.py +++ /dev/null @@ -1,99 +0,0 @@ -# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! -"""Client and server classes corresponding to protobuf-defined services.""" -import grpc -import warnings - -from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 -from . import workflow_metadata_pb2 as workflow__metadata__pb2 - -GRPC_GENERATED_VERSION = '1.72.0' -GRPC_VERSION = grpc.__version__ -_version_not_supported = False - -try: - from grpc._utilities import first_version_is_lower - _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION) -except ImportError: - _version_not_supported = True - -if _version_not_supported: - raise RuntimeError( - f'The grpc package installed is at version {GRPC_VERSION},' - + f' but the generated code in workflow_metadata_pb2_grpc.py depends on' - + f' grpcio>={GRPC_GENERATED_VERSION}.' - + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' - + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' - ) - - -class WorkflowMetadataServiceStub(object): - """Missing associated documentation comment in .proto file.""" - - def __init__(self, channel): - """Constructor. - - Args: - channel: A grpc.Channel. - """ - self.CreateWorkflowMetadata = channel.unary_unary( - '/workflows.WorkflowMetadataService/CreateWorkflowMetadata', - request_serializer=workflow__metadata__pb2.CreateWorkflowMetadataRequest.SerializeToString, - response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString, - _registered_method=True) - - -class WorkflowMetadataServiceServicer(object): - """Missing associated documentation comment in .proto file.""" - - def CreateWorkflowMetadata(self, request, context): - """Return Empty - """ - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - -def add_WorkflowMetadataServiceServicer_to_server(servicer, server): - rpc_method_handlers = { - 'CreateWorkflowMetadata': grpc.unary_unary_rpc_method_handler( - servicer.CreateWorkflowMetadata, - request_deserializer=workflow__metadata__pb2.CreateWorkflowMetadataRequest.FromString, - response_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString, - ), - } - generic_handler = grpc.method_handlers_generic_handler( - 'workflows.WorkflowMetadataService', rpc_method_handlers) - server.add_generic_rpc_handlers((generic_handler,)) - server.add_registered_method_handlers('workflows.WorkflowMetadataService', rpc_method_handlers) - - - # This class is part of an EXPERIMENTAL API. -class WorkflowMetadataService(object): - """Missing associated documentation comment in .proto file.""" - - @staticmethod - def CreateWorkflowMetadata(request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None): - return grpc.experimental.unary_unary( - request, - target, - '/workflows.WorkflowMetadataService/CreateWorkflowMetadata', - workflow__metadata__pb2.CreateWorkflowMetadataRequest.SerializeToString, - google_dot_protobuf_dot_empty__pb2.Empty.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - _registered_method=True) diff --git a/uv.lock b/uv.lock index be5858b..9cd2e27 100644 --- a/uv.lock +++ b/uv.lock @@ -14,6 +14,18 @@ members = [ "junjo", ] +[[package]] +name = "accessible-pygments" +version = "0.0.5" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pygments" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899, upload-time = "2024-05-10T11:23:10.216Z" } +wheels = [ + { url = 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[PATCH 07/32] Made publish flow more explicit --- .github/workflows/publish.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 4934e6e..9942262 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -16,12 +16,12 @@ jobs: contents: read steps: - name: Checkout - uses: actions/checkout@v4 + uses: actions/checkout@v5 - name: Set up Python uses: actions/setup-python@v5 with: - python-version: '3.x' + python-version: '3.11' - name: Build package run: | From c16d27347619dcf21a5fd41b9e7bc5055c457cd2 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 18:26:33 -0500 Subject: [PATCH 08/32] gitignore update --- .gitignore | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 6412897..2d47cd7 100644 --- a/.gitignore +++ b/.gitignore @@ -174,4 +174,7 @@ cython_debug/ # Misc .DS_Store .gitleaks/** -graphviz_out \ No newline at end of file +graphviz_out + +# Coding Agents +.claude \ No newline at end of file From 0cc6cadee9be282162a3fd80c31433408b7f47d7 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 18:46:42 -0500 Subject: [PATCH 09/32] Readme updates. --- README.md | 177 +++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 148 insertions(+), 29 deletions(-) diff --git a/README.md b/README.md index 267c7e1..8060c95 100644 --- a/README.md +++ b/README.md @@ -13,17 +13,17 @@ Whether you’re building a simple chatbot, a complex data manipulation pipeline #### Benefits: -- Visualize your AI workflows -- Redux inspired state management and state debugging tools -- Concurrency and type safety native with asyncio and pydantic -- Organize conditional chains of LLM calls into observable graph workflows -- Easy patterns for directed graph loops, branching, and concurrency -- Eval-Driven Development focused - - Easily build massive eval sets with mocked node state +- ✨ Visualize your AI workflows +- 🧠 Redux inspired state management and state debugging tools +- ⚡️ Concurrency and type safety native with asyncio and pydantic +- 🔗 Organize conditional chains of LLM calls into observable graph workflows +- 🏎️ Easy patterns for directed graph loops, branching, and concurrency +- 🧪 Eval-Driven Development focused + - Build massive eval sets by mocking node state - Programmatically build and update eval sets with agentic code assistants - Eval patterns are based on pytest, leveraging its testing framework and capabilities - - Rapidly iterate on your AI capabilities knowing for sure you're making progress -- OpenTelemetry native + - Rapidly iterate on your AI capabilities and avoid regressions +- 🔭 OpenTelemetry native - Provides organized, structured traces to any OpenTelemetry provider - Companion open source **[Junjo AI Studio](https://github.com/mdrideout/junjo-ai-studio)** enhances debugging and evaluation of production data @@ -72,26 +72,11 @@ Junjo prioritizes the following capabilities above all else to ensure these thin 1. Type safety (pydantic) 1. Concurrency safe (asyncio) - -## Contributing - -This project was made with the [uv](https://github.com/astral-sh/uv) python package manager. - -```bash -# Setup and activate the virtual environment -$ uv venv .venv -$ source .venv/bin/activate - -# Install optional development dependencies (graphviz is optional for running the graphviz visualizer) -# Graphviz, if utilized, must also be installed on the host system (see below) -$ uv pip install -e ".[dev,graphviz]" -``` - ## Visualizing Your Workflows -### Junjo Server +### Junjo AI Studio -[Junjo Server](https://github.com/mdrideout/junjo-server) is an optional, free, open-source companion telemetry visualization platform for debugging Junjo workflows. +[Junjo AI Studio](https://github.com/mdrideout/junjo-ai-studio) is our open source companion telemetry and debugging platform. It ingests Junjo's OpenTelemetry spans, providing execution visualizations and step-by-step debugging of state changes made by LLMs. **Quick Start:** @@ -112,7 +97,12 @@ docker compose up -d **Architecture:** Three-service Docker setup (backend, ingestion service, frontend) that runs on minimal resources (1GB RAM, shared vCPU). -See the [Junjo Server documentation](https://python-api.junjo.ai/junjo_server.html) for complete setup and configuration. +See the [Junjo AI Studio](https://python-api.junjo.ai/junjo_server.html) for complete setup and configuration. + +**Example Repositories:** + +- [Junjo AI Studio - Minimal Build](https://github.com/mdrideout/junjo-ai-studio-minimal-build) - a docker compose environment of the essential Junjo AI Studio services. +- [Junjo AI Studio - Deployment Example](https://github.com/mdrideout/junjo-ai-studio-deployment-example) - a production VM ready deployment example, including Caddy reverse proxy and SSL certificate handling. ### Graphviz @@ -143,11 +133,140 @@ python -m src.base.visualize -#### Full Example -**See the full example inside `examples/base`.** +## Code Examples + +_**Find several example Junjo applications inside the [examples](https://github.com/mdrideout/junjo/tree/main/examples) directory in this repository.**_ + +- [AI Chat](https://github.com/mdrideout/junjo/tree/main/examples/ai_chat) - a full featured chat application that can spawn new personas to chat with. This includes a react frontend and a FastAPI backend. +- [Getting Started](https://github.com/mdrideout/junjo/tree/main/examples/getting_started) - the basis of our getting started documentation +- [Base Example](https://github.com/mdrideout/junjo/tree/main/examples/base) - a minimal python example showcasing several Junjo patterns + +### Getting Started + +This is a single-file implementation of a basic Junjo powered python application. See the [getting started](https://github.com/mdrideout/junjo/tree/main/examples/getting_started) directory for dependencies, requirements, and instructions to run this. + +```python + +from junjo import BaseState, BaseStore, Condition, Edge, Graph, Node, Workflow + +# Run With +# python -m main +# uv run -m main + +async def main(): + """The main entry point for the application.""" + + # Define the workflow state + class SampleWorkflowState(BaseState): + count: int | None = None # Does not need an initial state value + items: list[str] # Does need an initial state value + + # Define the workflow store + class SampleWorkflowStore(BaseStore[SampleWorkflowState]): + # An immutable state update function + async def set_count(self, payload: int) -> None: + await self.set_state({"count": payload}) + + # Define the nodes + class FirstNode(Node[SampleWorkflowStore]): + async def service(self, store: SampleWorkflowStore) -> None: + print("First Node Executed") + + class CountItemsNode(Node[SampleWorkflowStore]): + async def service(self, store: SampleWorkflowStore) -> None: + # Get the state and count the items + state = await store.get_state() + items = state.items + count = len(items) + + # Perform a state update with the count + await store.set_count(count) + print(f"Counted {count} items") + + class EvenItemsNode(Node[SampleWorkflowStore]): + async def service(self, store: SampleWorkflowStore) -> None: + print("Path taken for even items count.") + + class OddItemsNode(Node[SampleWorkflowStore]): + async def service(self, store: SampleWorkflowStore) -> None: + print("Path taken for odd items count.") + + class FinalNode(Node[SampleWorkflowStore]): + async def service(self, store: SampleWorkflowStore) -> None: + print("Final Node Executed") + + class CountIsEven(Condition[SampleWorkflowState]): + def evaluate(self, state: SampleWorkflowState) -> bool: + count = state.count + if count is None: + return False + return count % 2 == 0 + + def create_graph() -> Graph: + """ + Factory function to create a new instance of the sample workflow graph. + This ensures that each workflow execution gets a fresh, isolated graph, + preventing state conflicts in concurrent environments. + """ + # Instantiate the nodes + first_node = FirstNode() + count_items_node = CountItemsNode() + even_items_node = EvenItemsNode() + odd_items_node = OddItemsNode() + final_node = FinalNode() + + # Create the workflow graph + return Graph( + source=first_node, + sink=final_node, + edges=[ + Edge(tail=first_node, head=count_items_node), + + # Branching based on the count of items + Edge(tail=count_items_node, head=even_items_node, condition=CountIsEven()), # Only transitions if count is even + Edge(tail=count_items_node, head=odd_items_node), # Fallback if first condition is not met + + # Branched paths converge to the final node + Edge(tail=even_items_node, head=final_node), + Edge(tail=odd_items_node, head=final_node), + ] + ) + + # Create the workflow + sample_workflow = Workflow[SampleWorkflowState, SampleWorkflowStore]( + name="Getting Started Example Workflow", + graph_factory=create_graph, + store_factory=lambda: SampleWorkflowStore( + initial_state=SampleWorkflowState( + items=["laser", "coffee", "horse"] + ) + ) + ) + + # Execute the workflow + await sample_workflow.execute() + print("Final state: ", await sample_workflow.get_state_json()) + +if __name__ == "__main__": + import asyncio + asyncio.run(main()) +``` + ## Contributing +This project was made with the [uv](https://github.com/astral-sh/uv) python package manager. + +```bash +# Setup and activate the virtual environment +$ uv venv .venv +$ source .venv/bin/activate + +# Install optional development dependencies (graphviz is optional for running the graphviz visualizer) +# Graphviz, if utilized, must also be installed on the host system (see below) +$ uv pip install -e ".[dev,graphviz]" +``` + ### Code Linting and Formatting This project utilizes [ruff](https://astral.sh/ruff) for linting and auto formatting. The VSCode settings.json in this project helps with additional formatting. From 1df4b42ed10b3ae3f731940e98f4a8fe699c8898 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 19:26:33 -0500 Subject: [PATCH 10/32] Readme updates, fixed getting_started examples to use a factory constructor method for creating the workflows. --- README.md | 203 +++++++++++++++++-------------- docs/getting_started.rst | 28 +++-- examples/getting_started/main.py | 28 +++-- 3 files changed, 143 insertions(+), 116 deletions(-) diff --git a/README.md b/README.md index 8060c95..75898dc 100644 --- a/README.md +++ b/README.md @@ -34,104 +34,39 @@ Whether you’re building a simple chatbot, a complex data manipulation pipeline +## Junjo's Philosophy -#### Decoupled +#### 🔍 Transparency -Junjo doesn't change how you create AI / LLM calls. Use any AI or LLM service provider or library. Junjo simply helps you organize your python functions into a clean organized graph structure with predictable, testable execution. +Junjo strives to be the opposite of a "black box". Transparency, observability, eval driven developmentm, and production data debugging are requirements for AI applications handling mission critical data, that need repeatable and high accuracy chained LLM logic. -Junjo provides the building blocks that let you make any sort of executable workflow. From linear function calls, to complex branching workflows with concurrent sublfows, to fully autonomous agents. +#### ⛓️‍💥 Decoupled -> -> There are zero proprietary AI / LLM implementations in Junjo. Use whatever LLM library you want. -> -> All logs produced are opentelemetry compatible. Existing otel spans are annotated with workflow and node execution span wrappers. -> +Junjo doesn't change how you implement LLM providers or make calls to their services. -It doesn't matter if the functions you add to a Junjo workflow are LLM API calls, database operations, or traditional business logic. You can write your business logic however you want. We just provide a convenient framework for organizing your desired flow into an executable graph. +Continue using [google-genai](https://github.com/googleapis/python-genai), [openai-python](https://github.com/openai/openai-python), [grok / xai sdk](https://github.com/xai-org/xai-sdk-python), [anthropic-sdk-python](https://github.com/anthropics/anthropic-sdk-python), [LiteLLM](https://github.com/BerriAI/litellm) or even REST API requests to any provider. -### Building AI Workflows and Agents as a Graph Workflow +Junjo remains decoupled from LLM providers. There are no proprietary implementations, no hijacking of python docstrings, no confusing or obfuscating decorators, and no middleman proxies. -Agentic AI applications use LLMs to determine the order of execution of python functions. These functions may involve LLM requests, API requests, database CRUD operations, etc. - -The simplest way to organize functions that can be / need to be executed in a certain order is in the form of a [directed graph](https://en.wikipedia.org/wiki/Directed_graph). +Junjo simply helps you organize your python functions (whether they be logic, LLM calls, RAG retrieval, REST API calls, etc.) into a clean organized graph structure with predictable, testable, and observable execution. -A directed graph gives one the building blocks to create any sort of agentic application, including: +#### 🥧 Conventional -- High precision workflows in the form of a Directed Acyclic Graph (DAG) -- Autonomous AI Agents in the form of dynamically determined directed graphs +Junjo provides primitive building blocks that let you build any sort of executable workflow. From linear chains of LLM calls, to complex branching workflows with concurrent sublfows, to fully autonomous agents. -### Priorities +Junjo uses conventional Pythonic architecture. Rather than obfuscating, proprietary decorators or runtime scripts that hijack execution, Junjo graph workflows are constructed conventionally with python classes and generics, and Pydantic models for type safe immutable state. -Test (eval) driven development, repeatability, debuggability, and telemetry are **CRITICAL** for rapid iteration and development of Agentic applications. +State is modeled after the conventional [Elm Architecture](https://guide.elm-lang.org/architecture/), and inspired by [Redux](https://redux.js.org/) for clean separation of concerns, concurrency safety, and debuggability. -Junjo prioritizes the following capabilities above all else to ensure these things are not an afterthought. +This helps your language server auto-complete methods and properties, and makes it easy for AI Coding agents to scaffold and understand massive Junjo workflows without needing to learn proprietary, library-specific logic patterns. -1. Eval driven development / Test driven development with pytest -1. Telemetry -1. Visualization -1. Type safety (pydantic) -1. Concurrency safe (asyncio) +Junjo organizes conventional OpenTelemetry spans into easy to understand groups. Your existing OpenTelemetry provider will continue to work, now with enhanced span organization. [Junjo AI Studio](https://github.com/mdrideout/junjo-ai-studio) is a companion OpenTelemetry platform with enhanced visuals and debugging tools for Junjo workflows. -## Visualizing Your Workflows +#### 🤝 Compatible -### Junjo AI Studio - -[Junjo AI Studio](https://github.com/mdrideout/junjo-ai-studio) is our open source companion telemetry and debugging platform. It ingests Junjo's OpenTelemetry spans, providing execution visualizations and step-by-step debugging of state changes made by LLMs. - -**Quick Start:** - -```bash -# Create docker-compose.yml (see docs for full example) -# Start services -docker compose up -d - -# Access UI at http://localhost:5153 -``` - -**Features:** -- Interactive graph visualization with execution path tracking -- State step debugging - see every state change in chronological order -- LLM decision tracking and trace timeline -- Multi-execution comparison -- Built specifically for graph-based AI workflows - -**Architecture:** Three-service Docker setup (backend, ingestion service, frontend) that runs on minimal resources (1GB RAM, shared vCPU). - -See the [Junjo AI Studio](https://python-api.junjo.ai/junjo_server.html) for complete setup and configuration. - -**Example Repositories:** - -- [Junjo AI Studio - Minimal Build](https://github.com/mdrideout/junjo-ai-studio-minimal-build) - a docker compose environment of the essential Junjo AI Studio services. -- [Junjo AI Studio - Deployment Example](https://github.com/mdrideout/junjo-ai-studio-deployment-example) - a production VM ready deployment example, including Caddy reverse proxy and SSL certificate handling. +Junjo can work alongside other AI Agent frameworks. Junjo Workflows can be setup as **tools** that Autonomous Agent frameworks can call for high-accuracy repeatable processes (like RAG retrieval or complex document parsing logic). -### Graphviz - -Junjo can render workflow graphs as images. It requires [Graphviz](https://graphviz.org/) to be installed on the underlying system (your developer computer or the docker image), as well as the above optional graphviz development dependencies in this python library. - -```bash -# Install Graphviz on MacOS with homebrew -$ brew install graphviz -``` - -```python -# visualize.py -from base.sample_workflow.graph import create_sample_workflow_graph - -def main(): - # Every graph can execute .export_graphviz_assets() to generate all graphs and subflow graphs in a workflow - # Creates .svg renderings, .dot notation files, and an HTML template to render the graphs - create_sample_workflow_graph().export_graphviz_assets() - -if __name__ == "__main__": - main() -``` - -```bash -# Run the visualizer -python -m src.base.visualize -``` - - +Junjo workflow nodes can also contain autonomous agents from other frameworks (like [smolagents](https://github.com/huggingface/smolagents)) if you need to introduce autonomous capabilities from other libraires into a structured Junjo AI workflow. ## Code Examples @@ -141,7 +76,7 @@ _**Find several example Junjo applications inside the [examples](https://github. - [Getting Started](https://github.com/mdrideout/junjo/tree/main/examples/getting_started) - the basis of our getting started documentation - [Base Example](https://github.com/mdrideout/junjo/tree/main/examples/base) - a minimal python example showcasing several Junjo patterns -### Getting Started +### Getting Started Code This is a single-file implementation of a basic Junjo powered python application. See the [getting started](https://github.com/mdrideout/junjo/tree/main/examples/getting_started) directory for dependencies, requirements, and instructions to run this. @@ -232,26 +167,106 @@ async def main(): ] ) - # Create the workflow - sample_workflow = Workflow[SampleWorkflowState, SampleWorkflowStore]( - name="Getting Started Example Workflow", - graph_factory=create_graph, - store_factory=lambda: SampleWorkflowStore( - initial_state=SampleWorkflowState( - items=["laser", "coffee", "horse"] + def create_workflow() -> Workflow[SampleWorkflowState, SampleWorkflowStore]: + """ + Factory function to create a new instance of the workflow. + This pattern is recommended to avoid concurrency issues and ensure + fresh state/store initialization for each execution. + """ + return Workflow[SampleWorkflowState, SampleWorkflowStore]( + name="Getting Started Example Workflow", + graph_factory=create_graph, + store_factory=lambda: SampleWorkflowStore( + initial_state=SampleWorkflowState( + items=["laser", "coffee", "horse"] + ) ) ) - ) - # Execute the workflow - await sample_workflow.execute() - print("Final state: ", await sample_workflow.get_state_json()) + # Create and execute the workflow + workflow = create_workflow() + await workflow.execute() + print("Final state: ", await workflow.get_state_json()) if __name__ == "__main__": import asyncio asyncio.run(main()) ``` +## Building AI Workflows and Agents as Graph Workflows + +Agentic AI applications use LLMs to determine the order of execution of python functions. These functions may involve LLM requests, API requests, database CRUD operations, etc. + +The simplest way to organize functions that can be / need to be conditionally executed in a certain order is in the form of a [directed graph](https://en.wikipedia.org/wiki/Directed_graph). + +A directed graph gives one the building blocks to create any sort of agentic application, including: + +- High precision workflows in the form of a Directed Acyclic Graph (DAG) +- Autonomous AI Agents in the form of dynamically determined directed graphs + +## Junjo AI Studio + +[Junjo AI Studio](https://github.com/mdrideout/junjo-ai-studio) is our open source companion telemetry and debugging platform. It ingests Junjo's OpenTelemetry spans, providing execution visualizations and step-by-step debugging of state changes made by LLMs. + +**Quick Start:** + +```bash +# Create docker-compose.yml (see docs for full example) +# Start services +docker compose up -d + +# Access UI at http://localhost:5153 +``` + +**Features:** +- Interactive graph visualization with execution path tracking +- State step debugging - see every state change in chronological order +- LLM decision tracking and trace timeline +- Multi-execution comparison +- Built specifically for graph-based AI workflows + +**Architecture:** Three-service Docker setup (backend, ingestion service, frontend) that runs on minimal resources (1GB RAM, shared vCPU). + +See the [Junjo AI Studio](https://python-api.junjo.ai/junjo_server.html) for complete setup and configuration. + +**Example Repositories:** + +- [Junjo AI Studio - Minimal Build](https://github.com/mdrideout/junjo-ai-studio-minimal-build) - a docker compose environment of the essential Junjo AI Studio services. +- [Junjo AI Studio - Deployment Example](https://github.com/mdrideout/junjo-ai-studio-deployment-example) - a production VM ready deployment example, including Caddy reverse proxy and SSL certificate handling. + + +## Graphviz + +Junjo can render workflow graphs as images. It requires [Graphviz](https://graphviz.org/) to be installed on the underlying system (your developer computer or the docker image), as well as the above optional graphviz development dependencies in this python library. + +```bash +# Install Graphviz on MacOS with homebrew +$ brew install graphviz +``` + +```python +# visualize.py +from base.sample_workflow.graph import create_sample_workflow_graph + +def main(): + # Every graph can execute .export_graphviz_assets() to generate all graphs and subflow graphs in a workflow + # Creates .svg renderings, .dot notation files, and an HTML template to render the graphs + create_sample_workflow_graph().export_graphviz_assets() + +if __name__ == "__main__": + main() +``` + +```bash +# Run the visualizer +python -m src.base.visualize +``` + +
+ +
Screenshot: Graphviz visualized Junjo workflow and subflow +
+ ## Contributing diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 1af40b8..7f34e94 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -117,20 +117,26 @@ More advanced examples can be found in the `examples directory Workflow[SampleWorkflowState, SampleWorkflowStore]: + """ + Factory function to create a new instance of the workflow. + This pattern is recommended to avoid concurrency issues and ensure + fresh state/store initialization for each execution. + """ + return Workflow[SampleWorkflowState, SampleWorkflowStore]( + name="Getting Started Example Workflow", + graph_factory=create_graph, + store_factory=lambda: SampleWorkflowStore( + initial_state=SampleWorkflowState( + items=["laser", "coffee", "horse"] + ) ) ) - ) - # Execute the workflow - await sample_workflow.execute() - print("Final state: ", await sample_workflow.get_state_json()) + # Create and execute the workflow + workflow = create_workflow() + await workflow.execute() + print("Final state: ", await workflow.get_state_json()) if __name__ == "__main__": import asyncio diff --git a/examples/getting_started/main.py b/examples/getting_started/main.py index 61379c3..2bd0f63 100644 --- a/examples/getting_started/main.py +++ b/examples/getting_started/main.py @@ -84,20 +84,26 @@ def create_graph() -> Graph: ] ) - # Create the workflow - sample_workflow = Workflow[SampleWorkflowState, SampleWorkflowStore]( - name="Getting Started Example Workflow", - graph_factory=create_graph, - store_factory=lambda: SampleWorkflowStore( - initial_state=SampleWorkflowState( - items=["laser", "coffee", "horse"] + def create_workflow() -> Workflow[SampleWorkflowState, SampleWorkflowStore]: + """ + Factory function to create a new instance of the workflow. + This pattern is recommended to avoid concurrency issues and ensure + fresh state/store initialization for each execution. + """ + return Workflow[SampleWorkflowState, SampleWorkflowStore]( + name="Getting Started Example Workflow", + graph_factory=create_graph, + store_factory=lambda: SampleWorkflowStore( + initial_state=SampleWorkflowState( + items=["laser", "coffee", "horse"] + ) ) ) - ) - # Execute the workflow - await sample_workflow.execute() - print("Final state: ", await sample_workflow.get_state_json()) + # Create and execute the workflow + workflow = create_workflow() + await workflow.execute() + print("Final state: ", await workflow.get_state_json()) if __name__ == "__main__": import asyncio From 7f7e15d748153cb5c8735f23e0001734cb77b6cf Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 19:28:44 -0500 Subject: [PATCH 11/32] Formatting adjustment --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 75898dc..ba14976 100644 --- a/README.md +++ b/README.md @@ -29,8 +29,8 @@ Whether you’re building a simple chatbot, a complex data manipulation pipeline
+Junjo AI Studio Screenshot -
Junjo AI Studio Screenshot
@@ -263,8 +263,8 @@ python -m src.base.visualize ```
+Screenshot: Graphviz visualized Junjo workflow and subflow -
Screenshot: Graphviz visualized Junjo workflow and subflow
From c750238cc7aceccfea2ca2ac68f134618d706711 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 23:19:58 -0500 Subject: [PATCH 12/32] Docs: Update terminology, fix typos, and refactor code examples\n\n- Renamed 'Junjo Server' to 'Junjo AI Studio' across documentation and code examples.\n- Updated 'Bare-Bones' to 'Minimal Build' in repository links and text.\n- Renamed to and its corresponding file.\n- Refactored documentation code examples to consistently use the workflow factory pattern.\n- Fixed typos and a broken image link in .\n- Converted docstrings in to Sphinx-compatible format. --- README.md | 8 +- docs/concurrency.rst | 4 +- docs/core_concepts.rst | 34 +++--- docs/deployment.rst | 22 ++-- docs/docker_reference.rst | 78 ++++++------- docs/index.rst | 10 +- .../{junjo_server.rst => junjo_ai_studio.rst} | 106 +++++++++--------- docs/opentelemetry.rst | 40 +++---- docs/subflows.rst | 80 +++++++------ docs/tutorial.rst | 28 +++-- docs/visualizing_workflows.rst | 23 ++-- .../ai_chat/backend/src/app/otel_config.py | 24 ++-- examples/base/src/base/otel_config.py | 28 ++--- ...tel_exporter.py => junjo_otel_exporter.py} | 42 +++---- 14 files changed, 274 insertions(+), 253 deletions(-) rename docs/{junjo_server.rst => junjo_ai_studio.rst} (75%) rename src/junjo/telemetry/{junjo_server_otel_exporter.py => junjo_otel_exporter.py} (69%) diff --git a/README.md b/README.md index ba14976..c7a5d05 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ Whether you’re building a simple chatbot, a complex data manipulation pipeline #### 🔍 Transparency -Junjo strives to be the opposite of a "black box". Transparency, observability, eval driven developmentm, and production data debugging are requirements for AI applications handling mission critical data, that need repeatable and high accuracy chained LLM logic. +Junjo strives to be the opposite of a "black box". Transparency, observability, eval driven development, and production data debugging are requirements for AI applications handling mission critical data, that need repeatable and high accuracy chained LLM logic. #### ⛓️‍💥 Decoupled @@ -52,7 +52,7 @@ Junjo simply helps you organize your python functions (whether they be logic, LL #### 🥧 Conventional -Junjo provides primitive building blocks that let you build any sort of executable workflow. From linear chains of LLM calls, to complex branching workflows with concurrent sublfows, to fully autonomous agents. +Junjo provides primitive building blocks that let you build any sort of executable workflow. From linear chains of LLM calls, to complex branching workflows with concurrent subflows, to fully autonomous agents. Junjo uses conventional Pythonic architecture. Rather than obfuscating, proprietary decorators or runtime scripts that hijack execution, Junjo graph workflows are constructed conventionally with python classes and generics, and Pydantic models for type safe immutable state. @@ -66,7 +66,7 @@ Junjo organizes conventional OpenTelemetry spans into easy to understand groups. Junjo can work alongside other AI Agent frameworks. Junjo Workflows can be setup as **tools** that Autonomous Agent frameworks can call for high-accuracy repeatable processes (like RAG retrieval or complex document parsing logic). -Junjo workflow nodes can also contain autonomous agents from other frameworks (like [smolagents](https://github.com/huggingface/smolagents)) if you need to introduce autonomous capabilities from other libraires into a structured Junjo AI workflow. +You can execute autonomous agent capabilities from other libraries inside a Junjo AI workflow. For example, a Junjo workflow node can run a [smolagents](https://github.com/huggingface/smolagents) tool calling agent as a single step within a greater Junjo workflow or subflow. ## Code Examples @@ -227,7 +227,7 @@ docker compose up -d **Architecture:** Three-service Docker setup (backend, ingestion service, frontend) that runs on minimal resources (1GB RAM, shared vCPU). -See the [Junjo AI Studio](https://python-api.junjo.ai/junjo_server.html) for complete setup and configuration. +See the [Junjo AI Studio](https://python-api.junjo.ai/junjo_ai_studio.html) for complete setup and configuration. **Example Repositories:** diff --git a/docs/concurrency.rst b/docs/concurrency.rst index 07340e9..4b0d72f 100644 --- a/docs/concurrency.rst +++ b/docs/concurrency.rst @@ -59,13 +59,13 @@ When defining your workflow's graph or subflow, you can easily execute nodes and Example execution visualization: .. image:: _static/concurrent-visual.png - :alt: A screenshot of a Junjo workflow graph's telemetry on Junjo Server, featuring a RunConcurrent node in the workflow's graph. + :alt: A screenshot of a Junjo workflow graph's telemetry on Junjo AI Studio, featuring a RunConcurrent node in the workflow's graph. :align: center :width: 600px This example demonstrates how `RunConcurrent` can be utilized to execute nodes in parallel. You can also execute entire `Subflow` instances in parallel. -All state updates made by these nodes are concurrency safe due to the immutable nature of state updates. **Junjo Server** allows you to step through state updates incrementally to see which nodes update state and when, even during high concurrency. +All state updates made by these nodes are concurrency safe due to the immutable nature of state updates. **Junjo AI Studio** allows you to step through state updates incrementally to see which nodes update state and when, even during high concurrency. Immutable State: Ensuring Concurrency Safety ============================================ diff --git a/docs/core_concepts.rst b/docs/core_concepts.rst index 78ee556..bb6e2cc 100644 --- a/docs/core_concepts.rst +++ b/docs/core_concepts.rst @@ -151,14 +151,18 @@ A `Workflow` is the main executable component that takes a `graph_factory` and a # ... (graph creation logic) return workflow_graph - sample_workflow = Workflow[MyWorkflowState, MyWorkflowStore]( - name="My First Workflow", - graph_factory=create_graph, - store_factory=lambda: MyWorkflowStore( - initial_state=MyWorkflowState(user_input="hello") + def create_workflow() -> Workflow[MyWorkflowState, MyWorkflowStore]: + """Factory function to create a new instance of the workflow.""" + return Workflow[MyWorkflowState, MyWorkflowStore]( + name="My First Workflow", + graph_factory=create_graph, + store_factory=lambda: MyWorkflowStore( + initial_state=MyWorkflowState(user_input="hello") + ) ) - ) + # Create and execute the workflow + sample_workflow = create_workflow() await sample_workflow.execute() **Passing Parameters to Factories** @@ -179,14 +183,16 @@ concurrency safety. # ... setup graph using the api_key return Graph(...) - # Instantiate the workflow, using a lambda to create the factory - workflow = Workflow[MyState, MyStore]( - name="configured_workflow", - graph_factory=lambda: create_graph_with_dependency( - api_key="your-secret-key" - ), - store_factory=lambda: MyStore(initial_state=MyState()) - ) + def create_workflow() -> Workflow[MyState, MyStore]: + # Instantiate the workflow, using a lambda to create the factory + return Workflow[MyState, MyStore]( + name="configured_workflow", + graph_factory=lambda: create_graph_with_dependency( + api_key="your-secret-key" + ), + store_factory=lambda: MyStore(initial_state=MyState()) + ) # The workflow can now be executed normally + workflow = create_workflow() await workflow.execute() \ No newline at end of file diff --git a/docs/deployment.rst b/docs/deployment.rst index fff54ed..67fd6fe 100644 --- a/docs/deployment.rst +++ b/docs/deployment.rst @@ -1,12 +1,12 @@ -Junjo Server Deployment +Junjo AI Studio Deployment ======================= -Junjo Server can be deployed in various ways depending on your needs. Being based on docker containers, it's easy to deploy Junjo Server anywhere with docker compose. +Junjo AI Studio can be deployed in various ways depending on your needs. Being based on docker containers, it's easy to deploy Junjo AI Studio anywhere with docker compose. -Bare-Bones Template (Recommended Starting Point) +Minimal Build Template (Recommended Starting Point) ------------------------------------------------- -For a minimal, flexible Docker Compose setup that you can customize for your specific infrastructure needs, use the `Junjo Server Bare-Bones Template `_. +For a minimal, flexible Docker Compose setup that you can customize for your specific infrastructure needs, use the `Junjo AI Studio Minimal Build Template `_. This GitHub template repository provides: @@ -20,7 +20,7 @@ This GitHub template repository provides: - Teams with existing infrastructure - Custom deployment requirements - Local development environments -- Learning Junjo Server architecture +- Learning Junjo AI Studio architecture - Integration into existing docker-compose.yml files **Quick start:** @@ -28,8 +28,8 @@ This GitHub template repository provides: .. code-block:: bash # Use as GitHub template or clone - git clone https://github.com/mdrideout/junjo-server-bare-bones.git - cd junjo-server-bare-bones + git clone https://github.com/mdrideout/junjo-ai-studio-minimal-build.git + cd junjo-ai-studio-minimal-build # Configure environment cp .env.example .env @@ -44,16 +44,16 @@ This GitHub template repository provides: Digital Ocean VM Deployment Example ------------------------------------ -For a complete production junjo server deployment example to a fresh virtual machine using Docker Compose, Caddy reverse proxy, and automatic HTTPS, see the `Junjo Server Deployment Example `_. +For a complete production Junjo AI Studio deployment example to a fresh virtual machine using Docker Compose, Caddy reverse proxy, and automatic HTTPS, see the `Junjo AI Studio Deployment Example `_. This example demonstrates: - **Low-cost VM setup** - Runs on a 1GB RAM VM with all services - **Microservices architecture** - Separate containers for: - - Junjo Server Ingestion (gRPC telemetry endpoint) - - Junjo Server Backend (API & authentication) - - Junjo Server Frontend (Web UI) + - Junjo AI Studio Ingestion (gRPC opentelemetry endpoint) + - Junjo AI Studio Backend (API & authentication) + - Junjo AI Studio Frontend (Web UI) - **Automatic HTTPS** - Caddy reverse proxy with Let's Encrypt SSL - **Subdomain routing** - Clean URLs for different services: diff --git a/docs/docker_reference.rst b/docs/docker_reference.rst index c456605..104ade0 100644 --- a/docs/docker_reference.rst +++ b/docs/docker_reference.rst @@ -1,9 +1,9 @@ Docker Reference ================ -Junjo Server is distributed as three Docker images that work together to provide a complete observability platform for AI workflows. This page provides detailed reference information for deploying and configuring these services. +Junjo AI Studio is distributed as three Docker images that work together to provide a complete observability platform for AI workflows. This page provides detailed reference information for deploying and configuring these services. -**Quick Start:** For a ready-to-use setup, see the `Junjo Server Bare-Bones Template `_. For a complete production example with reverse proxy and HTTPS, see the `Junjo Server Deployment Example `_. More details in :doc:`deployment`. +**Quick Start:** For a ready-to-use setup, see the `Junjo AI Studio Minimal Build Template `_. For a complete production example with reverse proxy and HTTPS, see the `Junjo AI Studio Deployment Example `_. More details in :doc:`deployment`. Docker Images ------------- @@ -11,7 +11,7 @@ Docker Images Backend Service ~~~~~~~~~~~~~~~ -**Image:** `mdrideout/junjo-server-backend `_ +**Image:** `mdrideout/junjo-ai-studio-backend `_ The backend service provides the HTTP API, authentication, and data processing capabilities. It uses SQLite for metadata storage and DuckDB for analytical queries. @@ -34,7 +34,7 @@ The backend service provides the HTTP API, authentication, and data processing c Ingestion Service ~~~~~~~~~~~~~~~~~ -**Image:** `mdrideout/junjo-server-ingestion-service `_ +**Image:** `mdrideout/junjo-ai-studio-ingestion `_ The ingestion service provides high-throughput OpenTelemetry data reception using BadgerDB as a write-ahead log. This decoupled architecture ensures telemetry data is never lost, even under heavy load. @@ -56,7 +56,7 @@ The ingestion service provides high-throughput OpenTelemetry data reception usin Frontend Service ~~~~~~~~~~~~~~~~ -**Image:** `mdrideout/junjo-server-frontend `_ +**Image:** `mdrideout/junjo-ai-studio-frontend `_ The frontend service provides the interactive web UI for visualizing and debugging AI workflows. @@ -83,9 +83,9 @@ This configuration includes all three services with best practices for productio services: # Backend API and data processing service - junjo-server-backend: - image: mdrideout/junjo-server-backend:latest - container_name: junjo-server-backend + junjo-ai-studio-backend: + image: mdrideout/junjo-ai-studio-backend:latest + container_name: junjo-ai-studio-backend restart: unless-stopped ports: - "1323:1323" # HTTP API (public) @@ -113,9 +113,9 @@ This configuration includes all three services with best practices for productio start_period: 40s # OpenTelemetry data ingestion service - junjo-server-ingestion: - image: mdrideout/junjo-server-ingestion-service:latest - container_name: junjo-server-ingestion + junjo-ai-studio-ingestion: + image: mdrideout/junjo-ai-studio-ingestion:latest + container_name: junjo-ai-studio-ingestion restart: unless-stopped ports: - "50051:50051" # OTel gRPC ingestion (public, your apps connect here) @@ -125,11 +125,11 @@ This configuration includes all three services with best practices for productio - ./dbdata/badgerdb:/dbdata/badgerdb environment: # API key for authentication (generate in web UI) - - JUNJO_SERVER_API_KEY=${JUNJO_SERVER_API_KEY} + - JUNJO_AI_STUDIO_API_KEY=${JUNJO_AI_STUDIO_API_KEY} networks: - junjo-network depends_on: - junjo-server-backend: + junjo-ai-studio-backend: condition: service_healthy # Wait for backend server to be healthy before starting healthcheck: test: ["CMD", "grpc_health_probe", "-addr=:50051"] @@ -139,9 +139,9 @@ This configuration includes all three services with best practices for productio start_period: 40s # Web UI for visualization and debugging - junjo-server-frontend: - image: mdrideout/junjo-server-frontend:latest - container_name: junjo-server-frontend + junjo-ai-studio-frontend: + image: mdrideout/junjo-ai-studio-frontend:latest + container_name: junjo-ai-studio-frontend restart: unless-stopped ports: - "5153:80" # Web UI @@ -151,9 +151,9 @@ This configuration includes all three services with best practices for productio networks: - junjo-network depends_on: - junjo-server-backend: + junjo-ai-studio-backend: condition: service_healthy - junjo-server-ingestion: + junjo-ai-studio-ingestion: condition: service_healthy networks: @@ -169,8 +169,8 @@ For local development, you can use a simpler configuration: :caption: docker-compose.dev.yml services: - junjo-server-backend: - image: mdrideout/junjo-server-backend:latest + junjo-ai-studio-backend: + image: mdrideout/junjo-ai-studio-backend:latest ports: - "1323:1323" - "50053:50053" @@ -183,8 +183,8 @@ For local development, you can use a simpler configuration: networks: - junjo-network - junjo-server-ingestion: - image: mdrideout/junjo-server-ingestion-service:latest + junjo-ai-studio-ingestion: + image: mdrideout/junjo-ai-studio-ingestion:latest ports: - "50051:50051" - "50052:50052" @@ -193,8 +193,8 @@ For local development, you can use a simpler configuration: networks: - junjo-network - junjo-server-frontend: - image: mdrideout/junjo-server-frontend:latest + junjo-ai-studio-frontend: + image: mdrideout/junjo-ai-studio-frontend:latest ports: - "5153:80" environment: @@ -246,12 +246,12 @@ Backend Service Ingestion Service ~~~~~~~~~~~~~~~~~ -``JUNJO_SERVER_API_KEY`` +``JUNJO_AI_STUDIO_API_KEY`` **Required.** API key for authenticating telemetry ingestion requests - **Default:** None - - **Example:** ``JUNJO_SERVER_API_KEY=js_1234567890abcdef...`` - - **Generate:** Create in the Junjo Server web UI under Settings → API Keys + - **Example:** ``JUNJO_AI_STUDIO_API_KEY=js_1234567890abcdef...`` + - **Generate:** Create in the Junjo AI Studio web UI under Settings → API Keys Frontend Service ~~~~~~~~~~~~~~~~ @@ -348,7 +348,7 @@ See :doc:`deployment` for production deployment examples with Caddy reverse prox Resource Requirements --------------------- -Junjo Server is designed to run on minimal resources: +Junjo AI Studio is designed to run on minimal resources: **Minimum:** @@ -414,20 +414,20 @@ Example Caddyfile: # Web UI junjo.example.com { - reverse_proxy junjo-server-frontend:80 + reverse_proxy junjo-ai-studio-frontend:80 } # Backend API api.junjo.example.com { - reverse_proxy junjo-server-backend:1323 + reverse_proxy junjo-ai-studio-backend:1323 } # Ingestion gRPC grpc.junjo.example.com { - reverse_proxy h2c://junjo-server-ingestion:50051 + reverse_proxy h2c://junjo-ai-studio-ingestion:50051 } -See the `Junjo Server Deployment Example `_ for a complete production setup. +See the `Junjo AI Studio Deployment Example `_ for a complete production setup. Backup and Recovery ~~~~~~~~~~~~~~~~~~~ @@ -496,8 +496,8 @@ Example multi-instance configuration: .. code-block:: yaml services: - junjo-server-ingestion: - image: mdrideout/junjo-server-ingestion-service:latest + junjo-ai-studio-ingestion: + image: mdrideout/junjo-ai-studio-ingestion:latest deploy: replicas: 3 # ... rest of configuration @@ -514,7 +514,7 @@ For most deployments, vertical scaling is sufficient: .. code-block:: yaml services: - junjo-server-backend: + junjo-ai-studio-backend: # ... existing config deploy: resources: @@ -535,9 +535,9 @@ Check logs for specific errors: .. code-block:: bash - docker compose logs junjo-server-backend - docker compose logs junjo-server-ingestion - docker compose logs junjo-server-frontend + docker compose logs junjo-ai-studio-backend + docker compose logs junjo-ai-studio-ingestion + docker compose logs junjo-ai-studio-frontend Common issues: @@ -579,6 +579,6 @@ If the web UI shows API errors: Next Steps ---------- -- See :doc:`junjo_server` for setup and configuration +- See :doc:`junjo_ai_studio` for setup and configuration - Review :doc:`deployment` for production deployment examples - Explore :doc:`opentelemetry` for application instrumentation \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index ac79b97..81a30b5 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -15,10 +15,10 @@ .. toctree:: :maxdepth: 2 - :caption: Junjo Server: + :caption: Junjo AI Studio: :hidden: - junjo_server + junjo_ai_studio docker_reference deployment opentelemetry @@ -30,7 +30,7 @@ Junjo PyPI GitHub - Junjo - GitHub - Junjo Server + GitHub - Junjo AI Studio .. toctree:: @@ -53,7 +53,7 @@ Junjo makes it easy to build a graph of possible paths for an AI to take. When t Whether you're building a simple chatbot, a complex data manipulation pipeline, or a sophisticated workflow with dynamic branching and parallel execution, Junjo provides the tools to define your logic as a clear graph of nodes and edges, and telemetry to make it easy to debug. .. image:: _static/junjo-screenshot.png - :alt: A screenshot of a Junjo workflow graph's telemetry on Junjo Server + :alt: A screenshot of a Junjo workflow graph's telemetry on Junjo AI Studio :align: center :width: 600px @@ -61,7 +61,7 @@ Whether you're building a simple chatbot, a complex data manipulation pipeline, Junjo remains decoupled from any specific AI model or framework. Simply wrap your existing business logic in a Junjo node, organize them into a Graph with conditional edges, and then execute the graph. Junjo will handle the rest, including task orchestration, error handling, and logging to any OpenTelemetry destination. -Junjo's optional companion library, `Junjo Server `_, provides additional features for observing graph execution telemetry, and visually stepping through state changes made by each node. This is particularly useful for debugging complex workflows or understanding how data flows through your application. +Junjo's optional companion library, `Junjo AI Studio `_, provides additional features for observing graph execution telemetry, and visually stepping through state changes made by each node. This is particularly useful for debugging complex workflows or understanding how data flows through your application. With built-in support for fully type-safe (Pydantic) redux-inspired state management, conditional execution, concurrent tasks (asyncio), and native OpenTelemetry support, Junjo empowers you to create robust, observable, and scalable Python workflows. Dive into our documentation to learn how to streamline your task orchestration and gain deeper insights into your application's execution. diff --git a/docs/junjo_server.rst b/docs/junjo_ai_studio.rst similarity index 75% rename from docs/junjo_server.rst rename to docs/junjo_ai_studio.rst index d3ad5a9..c53d28c 100644 --- a/docs/junjo_server.rst +++ b/docs/junjo_ai_studio.rst @@ -1,17 +1,17 @@ -.. _junjo_server: +.. _junjo_ai_studio: ############################################################## -Junjo Server Intro +Junjo AI Studio Intro ############################################################## .. meta:: - :description: Debug and visualize Junjo AI workflows with Junjo Server's interactive telemetry platform. Step through LLM decisions, trace state changes, and understand agentic behavior. - :keywords: junjo server, workflow debugging, AI observability, LLM tracing, state machine debugging, workflow visualization, opentelemetry + :description: Debug and visualize Junjo AI workflows with Junjo AI Studio's interactive telemetry platform. Step through LLM decisions, trace state changes, and understand agentic behavior. + :keywords: junjo ai studio, workflow debugging, AI observability, LLM tracing, state machine debugging, workflow visualization, opentelemetry -Junjo Server is a free, open-source telemetry visualization platform built specifically for debugging graph-based AI workflows. It ingests OpenTelemetry traces from your Junjo workflows and provides interactive tools to understand exactly what your LLMs are doing and why. +Junjo AI Studio is a free, open-source telemetry visualization platform built specifically for debugging graph-based AI workflows. It ingests OpenTelemetry traces from your Junjo workflows and provides interactive tools to understand exactly what your LLMs are doing and why. -What is Junjo Server? -===================== +What is Junjo AI Studio? +========================= **Key Capabilities:** @@ -21,8 +21,8 @@ What is Junjo Server? - **Trace Timeline:** Visualize concurrent execution and performance bottlenecks - **Multi-Execution Comparison:** Compare different runs to identify issues -Why Use Junjo Server for AI Workflows? -======================================= +Why Use Junjo AI Studio for AI Workflows? +========================================== LLM-powered applications are inherently non-deterministic. Traditional debugging doesn't work well when: @@ -31,10 +31,10 @@ LLM-powered applications are inherently non-deterministic. Traditional debugging - You're testing complex agentic behaviors - You need to verify eval-driven development results -Junjo Server solves this by providing **complete execution transparency**. +Junjo AI Studio solves this by providing **complete execution transparency**. .. image:: _static/junjo-screenshot.png - :alt: Junjo Server interactive workflow visualization + :alt: Junjo AI Studio interactive workflow visualization :align: center :width: 800px @@ -43,7 +43,7 @@ Junjo Server solves this by providing **complete execution transparency**. Installation & Setup ==================== -Junjo Server is composed of three Docker services that work together: +Junjo AI Studio is composed of three Docker services that work together: 1. **Backend**: API server and data processing (SQLite + DuckDB) 2. **Ingestion Service**: High-throughput OpenTelemetry data receiver (BadgerDB) @@ -52,16 +52,16 @@ Junjo Server is composed of three Docker services that work together: Quick Start Options ------------------- -Option 1: Use the Bare-Bones Template (Recommended) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Option 1: Use the Minimal Build Template (Recommended) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The easiest way to get started is with the `Junjo Server Bare-Bones Template `_, a GitHub template repository with a ready-to-use Docker Compose configuration: +The easiest way to get started is with the `Junjo AI Studio Minimal Build Template `_, a GitHub template repository with a ready-to-use Docker Compose configuration: .. code-block:: bash # Clone the template repository - git clone https://github.com/mdrideout/junjo-server-bare-bones.git - cd junjo-server-bare-bones + git clone https://github.com/mdrideout/junjo-ai-studio-minimal-build.git + cd junjo-ai-studio-minimal-build # Configure environment cp .env.example .env @@ -78,14 +78,14 @@ This template provides a minimal, flexible foundation you can customize for your Option 2: Create Your Own Docker Compose File ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -If you prefer to integrate Junjo Server into an existing project, here's a minimal Docker Compose example: +If you prefer to integrate Junjo AI Studio into an existing project, here's a minimal Docker Compose example: .. code-block:: yaml :caption: docker-compose.yml services: - junjo-server-backend: - image: mdrideout/junjo-server-backend:latest + junjo-ai-studio-backend: + image: mdrideout/junjo-ai-studio-backend:latest ports: - "1323:1323" # HTTP API - "50053:50053" # Internal gRPC @@ -96,8 +96,8 @@ If you prefer to integrate Junjo Server into an existing project, here's a minim networks: - junjo-network - junjo-server-ingestion: - image: mdrideout/junjo-server-ingestion-service:latest + junjo-ai-studio-ingestion: + image: mdrideout/junjo-ai-studio-ingestion:latest ports: - "50051:50051" # OTel data ingestion (your app connects here) - "50052:50052" # Internal gRPC @@ -107,16 +107,16 @@ If you prefer to integrate Junjo Server into an existing project, here's a minim networks: - junjo-network - junjo-server-frontend: - image: mdrideout/junjo-server-frontend:latest + junjo-ai-studio-frontend: + image: mdrideout/junjo-ai-studio-frontend:latest ports: - "5153:80" # Web UI env_file: .env networks: - junjo-network depends_on: - - junjo-server-backend - - junjo-server-ingestion + - junjo-ai-studio-backend + - junjo-ai-studio-ingestion networks: junjo-network: @@ -138,7 +138,7 @@ If you prefer to integrate Junjo Server into an existing project, here's a minim Resource Requirements --------------------- -Junjo Server is designed to run on minimal resources: +Junjo AI Studio is designed to run on minimal resources: - **CPU**: Single shared vCPU is sufficient - **RAM**: 1GB minimum @@ -152,14 +152,14 @@ Configuration Step 1: Generate an API Key ---------------------------- -1. Open Junjo Server UI at http://localhost:5153 +1. Open Junjo AI Studio UI at http://localhost:5153 2. Navigate to Settings → API Keys 3. Create a new API key 4. Copy the key to your environment .. code-block:: bash - export JUNJO_SERVER_API_KEY="your-api-key-here" + export JUNJO_AI_STUDIO_API_KEY="your-api-key-here" Step 2: Configure OpenTelemetry in Your Application ---------------------------------------------------- @@ -176,20 +176,20 @@ Create an OpenTelemetry configuration file: :caption: otel_config.py import os - from junjo.telemetry.junjo_server_otel_exporter import JunjoServerOtelExporter + from junjo.telemetry.junjo_otel_exporter import JunjoOtelExporter from opentelemetry import trace, metrics from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.metrics import MeterProvider from opentelemetry.sdk.resources import Resource def init_telemetry(service_name: str): - """Configure OpenTelemetry for Junjo Server.""" + """Configure OpenTelemetry for Junjo AI Studio.""" # Get API key from environment - api_key = os.getenv("JUNJO_SERVER_API_KEY") + api_key = os.getenv("JUNJO_AI_STUDIO_API_KEY") if not api_key: - raise ValueError("JUNJO_SERVER_API_KEY environment variable not set. " - "Generate a new API key in the Junjo Server UI.") + raise ValueError("JUNJO_AI_STUDIO_API_KEY environment variable not set. " + "Generate a new API key in the Junjo AI Studio UI.") # Create OpenTelemetry resource resource = Resource.create({"service.name": service_name}) @@ -197,9 +197,9 @@ Create an OpenTelemetry configuration file: # Set up tracer provider tracer_provider = TracerProvider(resource=resource) - # Configure Junjo Server exporter - junjo_exporter = JunjoServerOtelExporter( - host="localhost", # Junjo Server ingestion service host + # Configure Junjo AI Studio exporter + junjo_exporter = JunjoOtelExporter( + host="localhost", # Junjo AI Studio ingestion service host port="50051", # Port 50051 receives OpenTelemetry data api_key=api_key, insecure=True # Use False in production with TLS @@ -291,7 +291,7 @@ Compare executions side-by-side: Using with OpenInference for LLM Tracing ========================================= -Junjo Server automatically displays LLM-specific data when you instrument with OpenInference: +Junjo AI Studio automatically displays LLM-specific data when you instrument with OpenInference: .. code-block:: bash @@ -305,7 +305,7 @@ Junjo Server automatically displays LLM-specific data when you instrument with O # After setting up OpenTelemetry tracer provider GoogleGenAIInstrumentor().instrument(tracer_provider=tracer_provider) -You'll see in Junjo Server: +You'll see in Junjo AI Studio: - Full prompt text - LLM responses @@ -342,31 +342,31 @@ Subflow Spans - ``junjo.parent_id``: Parent workflow ID - ``junjo.workflow.parent_store.id``: Parent store ID -These attributes power Junjo Server's specialized visualization and debugging features. +These attributes power Junjo AI Studio's specialized visualization and debugging features. Complete Example ================ See working examples in the repository: -- `Base Example with Junjo Server `_ +- `Base Example with Junjo AI Studio `_ - `AI Chat Example `_ Using Other OpenTelemetry Platforms ==================================== -**Important:** Junjo's telemetry works with **any** OpenTelemetry platform. The ``JunjoServerOtelExporter`` is specifically for Junjo Server, but all Junjo-specific span attributes are automatically included when you use standard OTLP exporters. +**Important:** Junjo's telemetry works with **any** OpenTelemetry platform. The ``JunjoOtelExporter`` is specifically for Junjo AI Studio, but all Junjo-specific span attributes are automatically included when you use standard OTLP exporters. -You can use Junjo Server alongside other platforms: +You can use Junjo AI Studio alongside other platforms: .. code-block:: python - # Use both Junjo Server AND Jaeger + # Use both Junjo AI Studio AND Jaeger from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.trace.export import BatchSpanProcessor - # Junjo Server - junjo_exporter = JunjoServerOtelExporter( + # Junjo AI Studio + junjo_exporter = JunjoOtelExporter( host="localhost", port="50051", api_key=api_key, @@ -378,12 +378,12 @@ You can use Junjo Server alongside other platforms: jaeger_exporter = OTLPSpanExporter(endpoint="http://jaeger:4317") tracer_provider.add_span_processor(BatchSpanProcessor(jaeger_exporter)) -Platforms like Jaeger, Grafana, Honeycomb, etc. will receive all Junjo spans with their custom attributes, though they won't have Junjo Server's specialized workflow visualization. +Platforms like Jaeger, Grafana, Honeycomb, etc. will receive all Junjo spans with their custom attributes, though they won't have Junjo AI Studio's specialized workflow visualization. Architecture Details ==================== -Junjo Server uses a three-service architecture for scalability and reliability: +Junjo AI Studio uses a three-service architecture for scalability and reliability: .. code-block:: text @@ -409,14 +409,14 @@ Junjo Server uses a three-service architecture for scalability and reliability: Troubleshooting =============== -No data appearing in Junjo Server ----------------------------------- +No data appearing in Junjo AI Studio +------------------------------------- -- Verify API key is set correctly: ``echo $JUNJO_SERVER_API_KEY`` +- Verify API key is set correctly: ``echo $JUNJO_AI_STUDIO_API_KEY`` - Check services are running: ``docker compose ps`` - Ensure ingestion service is accessible on port 50051 - Look for connection errors in your application logs -- Check ingestion service logs: ``docker compose logs junjo-server-ingestion`` +- Check ingestion service logs: ``docker compose logs junjo-ai-studio-ingestion`` Missing LLM data ---------------- @@ -431,7 +431,7 @@ Performance issues - Use sampling for high-volume workflows - The ingestion service uses BadgerDB as a write-ahead log for durability - Backend polls and indexes data asynchronously -- See `Junjo Server repository `_ for tuning options +- See `Junjo AI Studio repository `_ for tuning options Docker Compose not starting ---------------------------- diff --git a/docs/opentelemetry.rst b/docs/opentelemetry.rst index 1d4bd39..9a47da4 100644 --- a/docs/opentelemetry.rst +++ b/docs/opentelemetry.rst @@ -54,8 +54,8 @@ Choosing an OpenTelemetry Exporter Junjo works with any OpenTelemetry-compatible platform. Choose based on your needs: -1. Junjo Server (Recommended for AI Workflows) ------------------------------------------------ +1. Junjo AI Studio (Recommended for AI Workflows) +-------------------------------------------------- Built specifically for graph workflow debugging with: @@ -63,14 +63,14 @@ Built specifically for graph workflow debugging with: - Workflow-specific visualization - LLM decision tracking -See :doc:`junjo_server` for complete setup. +See :doc:`junjo_ai_studio` for complete setup. .. code-block:: python - from junjo.telemetry.junjo_server_otel_exporter import JunjoServerOtelExporter + from junjo.telemetry.junjo_otel_exporter import JunjoOtelExporter - junjo_exporter = JunjoServerOtelExporter( - host="localhost", # Junjo Server ingestion service + junjo_exporter = JunjoOtelExporter( + host="localhost", # Junjo AI Studio ingestion service port="50051", # gRPC port for receiving spans api_key=api_key, insecure=True # Use False in production with TLS @@ -131,7 +131,7 @@ You can send telemetry to multiple platforms simultaneously: .. code-block:: python - from junjo.telemetry.junjo_server_otel_exporter import JunjoServerOtelExporter + from junjo.telemetry.junjo_otel_exporter import JunjoOtelExporter from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.sdk.trace import TracerProvider @@ -144,9 +144,9 @@ You can send telemetry to multiple platforms simultaneously: # Set up tracer provider tracer_provider = TracerProvider(resource=resource) - # Add Junjo Server exporter - junjo_exporter = JunjoServerOtelExporter( - host="localhost", # Junjo Server ingestion service + # Add Junjo AI Studio exporter + junjo_exporter = JunjoOtelExporter( + host="localhost", # Junjo AI Studio ingestion service port="50051", # gRPC port for receiving spans api_key=junjo_api_key, insecure=True # Use False in production with TLS @@ -211,19 +211,19 @@ Here's a complete OpenTelemetry setup for Junjo: :caption: otel_config.py import os - from junjo.telemetry.junjo_server_otel_exporter import JunjoServerOtelExporter + from junjo.telemetry.junjo_otel_exporter import JunjoOtelExporter from opentelemetry import trace, metrics from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.metrics import MeterProvider from opentelemetry.sdk.resources import Resource def init_telemetry(service_name: str): - """Configure OpenTelemetry with Junjo Server.""" + """Configure OpenTelemetry with Junjo AI Studio.""" # Get API key and determine environment - api_key = os.getenv("JUNJO_SERVER_API_KEY") + api_key = os.getenv("JUNJO_AI_STUDIO_API_KEY") if not api_key: - raise ValueError("JUNJO_SERVER_API_KEY environment variable not set") + raise ValueError("JUNJO_AI_STUDIO_API_KEY environment variable not set") is_production = os.getenv("ENV", "development") == "production" @@ -237,9 +237,9 @@ Here's a complete OpenTelemetry setup for Junjo: # Set up tracer provider tracer_provider = TracerProvider(resource=resource) - # Configure Junjo Server exporter - junjo_exporter = JunjoServerOtelExporter( - host="localhost", # Junjo Server ingestion service + # Configure Junjo AI Studio exporter + junjo_exporter = JunjoOtelExporter( + host="localhost", # Junjo AI Studio ingestion service port="50051", # gRPC port for receiving spans api_key=api_key, insecure=not is_production # True for local dev, False for production @@ -252,7 +252,7 @@ Here's a complete OpenTelemetry setup for Junjo: trace.set_tracer_provider(tracer_provider) print(f"OpenTelemetry configured for {service_name}") - print(f"Sending traces to Junjo Server at localhost:50051") + print(f"Sending traces to Junjo AI Studio at localhost:50051") Use in your application: @@ -335,7 +335,7 @@ When viewing Junjo traces in your observability platform, you'll see: - Workflow total duration - Concurrent execution overlap -**Platforms without Junjo Server** will receive all this data but display it in their standard trace viewer. **Junjo Server** provides specialized visualization for these workflow-specific attributes. +**Platforms without Junjo AI Studio** will receive all this data but display it in their standard trace viewer. **Junjo AI Studio** provides specialized visualization for these workflow-specific attributes. Troubleshooting =============== @@ -368,7 +368,7 @@ Performance impact Next Steps ========== -- Set up :doc:`junjo_server` for AI workflow-specific debugging +- Set up :doc:`junjo_ai_studio` for AI workflow-specific debugging - Explore :doc:`visualizing_workflows` for static diagrams - Learn about :doc:`concurrency` to understand parallel execution traces - Review :doc:`eval_driven_dev` for testing workflows \ No newline at end of file diff --git a/docs/subflows.rst b/docs/subflows.rst index f135763..d9dc439 100644 --- a/docs/subflows.rst +++ b/docs/subflows.rst @@ -126,30 +126,32 @@ Now, implement the `SampleSubflow`: await parent_store.set_fact(subflow_state.fact) print("Subflow: post_run_actions executed, updated parent store.") - # Instantiate nodes for the subflow's internal graph - get_joke_node = GetJokeNode() - get_fact_node = GetFactNode() - - # Define the internal graph for the SampleSubflow - sample_subflow_graph = Graph( - source=get_joke_node, - sink=get_fact_node, # For simplicity, joke then fact - edges=[ - Edge(tail=get_joke_node, head=get_fact_node) - ] - ) - - # Instantiate the Subflow - sample_subflow_instance = SampleSubflow( - name="Joke and Fact Finder", - graph=sample_subflow_graph, - store_factory=lambda: SampleSubflowStore(initial_state=SampleSubflowState()) - ) + def create_subflow_graph() -> Graph: + # Instantiate nodes for the subflow's internal graph + get_joke_node = GetJokeNode() + get_fact_node = GetFactNode() + + # Define the internal graph for the SampleSubflow + return Graph( + source=get_joke_node, + sink=get_fact_node, # For simplicity, joke then fact + edges=[ + Edge(tail=get_joke_node, head=get_fact_node) + ] + ) + + def create_subflow_instance() -> SampleSubflow: + # Instantiate the Subflow + return SampleSubflow( + name="Joke and Fact Finder", + graph_factory=create_subflow_graph, + store_factory=lambda: SampleSubflowStore(initial_state=SampleSubflowState()) + ) In this example: - `pre_run_actions` copies `items` from the parent's state into the Subflow's state. - `post_run_actions` takes the `joke` and `fact` produced within the Subflow and updates the parent's state. -- The `SampleSubflow` is instantiated with its own `Graph` (`sample_subflow_graph`) and a factory for its `SampleSubflowStore`. +- The `SampleSubflow` is instantiated with its own `Graph` factory (`create_subflow_graph`) and a factory for its `SampleSubflowStore`. The `Graph` class is used consistently for both main workflows and Subflows, providing a unified way to define execution logic. @@ -165,7 +167,7 @@ Example Parent Workflow Graph with a Subflow: from junjo import Workflow, Node, Edge, Graph # Assuming SampleWorkflowStore, SampleWorkflowState are defined - # Assuming sample_subflow_instance is the one created earlier + # Assuming create_subflow_instance is defined # Nodes for the parent workflow class ParentStartNode(Node[SampleWorkflowStore]): @@ -178,21 +180,25 @@ Example Parent Workflow Graph with a Subflow: parent_state = await store.get_state() print(f"Parent Workflow: Finished. Joke: {parent_state.joke_from_subflow}, Fact: {parent_state.fact_from_subflow}") - parent_start_node = ParentStartNode() - parent_end_node = ParentEndNode() - - # Define the parent workflow's graph, including the Subflow instance - parent_workflow_graph = Graph( - source=parent_start_node, - sink=parent_end_node, - edges=[ - Edge(tail=parent_start_node, head=sample_subflow_instance), # Subflow as a node - Edge(tail=sample_subflow_instance, head=parent_end_node) - ] - ) + def create_parent_graph() -> Graph: + parent_start_node = ParentStartNode() + parent_end_node = ParentEndNode() + + # Create a fresh subflow instance for this execution + sample_subflow_instance = create_subflow_instance() + + # Define the parent workflow's graph, including the Subflow instance + return Graph( + source=parent_start_node, + sink=parent_end_node, + edges=[ + Edge(tail=parent_start_node, head=sample_subflow_instance), # Subflow as a node + Edge(tail=sample_subflow_instance, head=parent_end_node) + ] + ) In this structure: -- `sample_subflow_instance` (our `Subflow`) is used directly as the `head` of one edge and the `tail` of another in the `parent_workflow_graph`. +- `sample_subflow_instance` (our `Subflow`) is created fresh in `create_parent_graph` and used directly as the `head` of one edge and the `tail` of another. - The parent workflow's `Graph` orchestrates the flow *to* and *from* the Subflow, but the Subflow manages its own internal execution. Visualizing Subflows @@ -209,13 +215,13 @@ This hierarchical visualization makes it easier to understand complex workflows :align: center :width: 600px -Subflows on Junjo Server +Subflows on Junjo AI Studio ======================== -`Junjo Server `_ will also intelligently render subflows, allowing one to step through node executions and state updates even in highly concurrent subflow executions. +`Junjo AI Studio `_ will also intelligently render subflows, allowing one to step through node executions and state updates even in highly concurrent subflow executions. .. image:: _static/subflow-junjo-server-visual.png - :alt: A screenshot of a Junjo workflow graph's visual in Junjo Server, featuring a subflow rendering. + :alt: A screenshot of a Junjo workflow graph's visual in Junjo AI Studio, featuring a subflow rendering. :align: center :width: 600px diff --git a/docs/tutorial.rst b/docs/tutorial.rst index fc0eb1c..4e7fb34 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -137,20 +137,26 @@ Finally, we'll create a `Workflow` instance and execute it. We'll pass our `grap import asyncio async def main(): - # Create the workflow - sample_workflow = Workflow[SampleWorkflowState, SampleWorkflowStore]( - name="Getting Started Example Workflow", - graph_factory=create_graph, - store_factory=lambda: SampleWorkflowStore( - initial_state=SampleWorkflowState( - items=["laser", "coffee", "horse"] + def create_workflow() -> Workflow[SampleWorkflowState, SampleWorkflowStore]: + """ + Factory function to create a new instance of the workflow. + This pattern is recommended to avoid concurrency issues and ensure + fresh state/store initialization for each execution. + """ + return Workflow[SampleWorkflowState, SampleWorkflowStore]( + name="Getting Started Example Workflow", + graph_factory=create_graph, + store_factory=lambda: SampleWorkflowStore( + initial_state=SampleWorkflowState( + items=["laser", "coffee", "horse"] + ) ) ) - ) - # Execute the workflow - await sample_workflow.execute() - print("Final state: ", await sample_workflow.get_state_json()) + # Create and execute the workflow + workflow = create_workflow() + await workflow.execute() + print("Final state: ", await workflow.get_state_json()) if __name__ == "__main__": asyncio.run(main()) diff --git a/docs/visualizing_workflows.rst b/docs/visualizing_workflows.rst index c1c162a..ba8c8bc 100644 --- a/docs/visualizing_workflows.rst +++ b/docs/visualizing_workflows.rst @@ -67,20 +67,20 @@ The ``Graph`` object in Junjo provides an ``export_graphviz_assets()`` method. T Example Usage ~~~~~~~~~~~~~ -Let's assume you have a workflow graph defined, for instance, ``sample_workflow_graph`` from one of the Junjo examples. +Let's assume you have a workflow graph factory defined, for instance, ``create_sample_workflow_graph`` from one of the Junjo examples. In this example, we create an execute a visualize.py script to generate the Graphviz rendered assets. .. code-block:: python :caption: visualize.py - # Import the graph you want to visualize - from base.sample_workflow.workflow import sample_workflow_graph + # Import the graph factory you want to visualize + from base.sample_workflow.graph import create_sample_workflow_graph def main(): # Every graph can execute .export_graphviz_assets() to generate all graphs and subflow graphs in a workflow # Creates .svg renderings, .dot notation files, and an HTML template to render the graphs - sample_workflow_graph.export_graphviz_assets() + create_sample_workflow_graph().export_graphviz_assets() if __name__ == "__main__": main() @@ -94,29 +94,30 @@ Visual Elements: * **Clusters (for RunConcurrent):** ``RunConcurrent`` nodes are rendered as distinct clusters, visually grouping the concurrently executing nodes. * **Subflows:** Subflows are initially shown as a single "component" shape in the overview graph. A separate diagram is generated for each subflow, detailing its internal structure. This allows for a clean, hierarchical drill-down approach to understanding complex workflows. -Dynamic Telemetry with Junjo Server ------------------------------------ -For real-time observation and debugging of workflow executions, Junjo integrates seamlessly with OpenTelemetry. The optional, open-source `Junjo Server `_ ingests these telemetry traces and provides a web interface to: +Dynamic Telemetry with Junjo AI Studio +-------------------------------------- +For real-time observation and debugging of workflow executions, Junjo integrates seamlessly with OpenTelemetry. The optional, open-source `Junjo AI Studio `_ ingests these telemetry traces and provides a web interface to: * **Visualize live execution graphs:** See the path taken by a specific execution. * **Step through state changes:** Observe how the redux-inspired state machine is updated by each node. * **Inspect inputs and outputs:** Understand the data flowing through your workflow at each step. .. image:: _static/junjo-screenshot.png - :alt: A screenshot of a Junjo workflow graph's telemetry on Junjo Server + :alt: A screenshot of a Junjo workflow graph's telemetry on Junjo AI Studio :align: center :width: 600px -While Graphviz provides a static "blueprint" of your workflow's potential paths, Junjo Server offers a dynamic view of actual executions, making it an invaluable tool for debugging and fine-tuning your AI applications. +While Graphviz provides a static "blueprint" of your workflow's potential paths, Junjo AI Studio offers a dynamic view of actual executions, making it an invaluable tool for debugging and fine-tuning your AI applications. .. note:: Junjo remains decoupled from any specific AI model or framework. The visualization tools help you organize and understand the flow of your Python functions, regardless of whether they are LLM calls, database operations, or other business logic. -By combining static Graphviz diagrams for architectural understanding with dynamic Junjo Server telemetry for execution analysis, developers can build, test, and maintain complex AI workflows with greater confidence and clarity. +By combining static Graphviz diagrams for architectural understanding with dynamic Junjo AI Studio telemetry for execution analysis, developers can build, test, and maintain complex AI workflows with greater confidence and clarity. --- Next Steps: - Explore the :doc:`getting_started` guide for installation and basic usage. - Dive into the :doc:`api` reference for detailed information on Junjo's components. - - Learn about :doc:`eval_driven_dev` for robust testing of your workflows. \ No newline at end of file + - Learn about :doc:`eval_driven_dev` for robust testing of your workflows. + - Set up :doc:`junjo_ai_studio` for dynamic telemetry visualization. \ No newline at end of file diff --git a/examples/ai_chat/backend/src/app/otel_config.py b/examples/ai_chat/backend/src/app/otel_config.py index e030170..a954c43 100644 --- a/examples/ai_chat/backend/src/app/otel_config.py +++ b/examples/ai_chat/backend/src/app/otel_config.py @@ -1,6 +1,6 @@ import os -from junjo.telemetry.junjo_server_otel_exporter import JunjoServerOtelExporter +from junjo.telemetry.junjo_otel_exporter import JunjoOtelExporter from openinference.instrumentation.google_genai import GoogleGenAIInstrumentor from opentelemetry import metrics, trace from opentelemetry.sdk.metrics import MeterProvider @@ -11,11 +11,11 @@ def init_otel(service_name: str): """Configure OpenTelemetry for this application.""" - # Load the JUNJO_SERVER_API_KEY from the environment variable - JUNJO_SERVER_API_KEY = os.getenv("JUNJO_SERVER_API_KEY") - if JUNJO_SERVER_API_KEY is None: - raise ValueError("JUNJO_SERVER_API_KEY environment variable is not set." - "Generate a new API key in the Junjo Server UI.") + # Load the JUNJO_AI_STUDIO_API_KEY from the environment variable + JUNJO_AI_STUDIO_API_KEY = os.getenv("JUNJO_AI_STUDIO_API_KEY") + if JUNJO_AI_STUDIO_API_KEY is None: + raise ValueError("JUNJO_AI_STUDIO_API_KEY environment variable is not set." + "Generate a new API key in the Junjo AI Studio UI.") # Configure OpenTelemetry for this application # Create the OpenTelemetry Resource to identify this service @@ -27,25 +27,25 @@ def init_otel(service_name: str): # Instrument Google GenAI GoogleGenAIInstrumentor().instrument(tracer_provider=tracer_provider) - # Construct a Junjo exporter for Junjo Server (see junjo-server docker-compose.yml) - junjo_server_exporter = JunjoServerOtelExporter( + # Construct a Junjo exporter for Junjo AI Studio + junjo_ai_studio_exporter = JunjoOtelExporter( host="localhost", port="50051", - api_key=JUNJO_SERVER_API_KEY, + api_key=JUNJO_AI_STUDIO_API_KEY, insecure=True, ) # Set up span processors # Add the Junjo span processor # Add more span processors if desired - tracer_provider.add_span_processor(junjo_server_exporter.span_processor) + tracer_provider.add_span_processor(junjo_ai_studio_exporter.span_processor) trace.set_tracer_provider(tracer_provider) # Set up metrics # - Construct with the Junjo metric reader # - Add more metric readers if desired - junjo_server_metric_reader = junjo_server_exporter.metric_reader + junjo_metric_reader = junjo_ai_studio_exporter.metric_reader meter_provider = MeterProvider( - resource=resource, metric_readers=[junjo_server_metric_reader] + resource=resource, metric_readers=[junjo_metric_reader] ) metrics.set_meter_provider(meter_provider) diff --git a/examples/base/src/base/otel_config.py b/examples/base/src/base/otel_config.py index 588f308..716aaa8 100644 --- a/examples/base/src/base/otel_config.py +++ b/examples/base/src/base/otel_config.py @@ -1,6 +1,6 @@ import os -from junjo.telemetry.junjo_server_otel_exporter import JunjoServerOtelExporter +from junjo.telemetry.junjo_otel_exporter import JunjoOtelExporter from openinference.instrumentation.google_genai import GoogleGenAIInstrumentor from opentelemetry import metrics, trace from opentelemetry.sdk.metrics import MeterProvider @@ -8,14 +8,14 @@ from opentelemetry.sdk.trace import TracerProvider -def init_otel(service_name: str) -> JunjoServerOtelExporter | None: +def init_otel(service_name: str) -> JunjoOtelExporter | None: """Configure OpenTelemetry for this application.""" - # Load the JUNJO_SERVER_API_KEY from the environment variable - JUNJO_SERVER_API_KEY = os.getenv("JUNJO_SERVER_API_KEY") - if JUNJO_SERVER_API_KEY is None: - print("JUNJO_SERVER_API_KEY environment variable is not set. " - "Generate a new API key in the Junjo Server UI.") + # Load the JUNJO_AI_STUDIO_API_KEY from the environment variable + JUNJO_AI_STUDIO_API_KEY = os.getenv("JUNJO_AI_STUDIO_API_KEY") + if JUNJO_AI_STUDIO_API_KEY is None: + print("JUNJO_AI_STUDIO_API_KEY environment variable is not set. " + "Generate a new API key in the Junjo AI Studio UI.") return None # Configure OpenTelemetry for this application @@ -25,26 +25,26 @@ def init_otel(service_name: str) -> JunjoServerOtelExporter | None: # Set up tracing for this application tracer_provider = TracerProvider(resource=resource) - # Construct a Junjo exporter for Junjo Server (see junjo-server docker-compose.yml) - junjo_server_exporter = JunjoServerOtelExporter( + # Construct a Junjo exporter for Junjo AI Studio + junjo_ai_studio_exporter = JunjoOtelExporter( host="localhost", port="50051", - api_key=JUNJO_SERVER_API_KEY, + api_key=JUNJO_AI_STUDIO_API_KEY, insecure=True, ) # Set up span processors # Add the Junjo span processor # Add more span processors if desired - tracer_provider.add_span_processor(junjo_server_exporter.span_processor) + tracer_provider.add_span_processor(junjo_ai_studio_exporter.span_processor) trace.set_tracer_provider(tracer_provider) # Set up metrics # - Construct with the Junjo metric reader # - Add more metric readers if desired - junjo_server_metric_reader = junjo_server_exporter.metric_reader + junjo_metric_reader = junjo_ai_studio_exporter.metric_reader meter_provider = MeterProvider( - resource=resource, metric_readers=[junjo_server_metric_reader] + resource=resource, metric_readers=[junjo_metric_reader] ) metrics.set_meter_provider(meter_provider) @@ -52,4 +52,4 @@ def init_otel(service_name: str) -> JunjoServerOtelExporter | None: # Google genai GoogleGenAIInstrumentor().instrument(tracer_provider=tracer_provider) - return junjo_server_exporter + return junjo_ai_studio_exporter diff --git a/src/junjo/telemetry/junjo_server_otel_exporter.py b/src/junjo/telemetry/junjo_otel_exporter.py similarity index 69% rename from src/junjo/telemetry/junjo_server_otel_exporter.py rename to src/junjo/telemetry/junjo_otel_exporter.py index eb96232..13279f4 100644 --- a/src/junjo/telemetry/junjo_server_otel_exporter.py +++ b/src/junjo/telemetry/junjo_otel_exporter.py @@ -4,12 +4,9 @@ from opentelemetry.sdk.trace.export import BatchSpanProcessor -class JunjoServerOtelExporter: +class JunjoOtelExporter: """ - Export OpenTelemetry data to Junjo Server. - - Junjo Server is capable of parsing and displaying enhanced - Graph Workflow visualizations, and data specific to Graph Workflow debugging. + An OpenTelemetry SpanExporter that sends spans to the Junjo AI Studio. """ def __init__( @@ -20,7 +17,7 @@ def __init__( insecure: bool = False, ): """ - Export OpenTelemetry data to Junjo Server. + Export OpenTelemetry data to Junjo AI Studio. Junjo is designed to be compatible with existing OpenTelemetry configurations, by adding to an existing configuration instead of creating a new one. @@ -28,11 +25,15 @@ def __init__( If you have not already configured OpenTelemetry for your application, follow these instructions: TODO: Add instructions. - Args: - - host: the hostname of the Junjo Server. - - port: the port of the Junjo Server. - - api_key: the API key for the Junjo Server. - - insecure: whether to allow insecure connections to the Junjo Server. + :param host: The hostname of the Junjo AI Studio. + :type host: str + :param port: The port of the Junjo AI Studio. + :type port: str + :param api_key: The API key for the Junjo AI Studio. + :type api_key: str + :param insecure: Whether to allow insecure connections to the Junjo AI Studio. + Defaults to ``False``. + :type insecure: bool """ # Set Class Instance Vars @@ -41,13 +42,13 @@ def __init__( self._api_key = api_key self._insecure = insecure - # Set the endpoint for the Junjo Server + # Set the endpoint for the Junjo AI Studio self._endpoint = f"{self._host}:{self._port}" # Define headers exporter_headers = (("x-junjo-api-key", self._api_key),) - # Set OTLP Span Exporter for Junjo Server + # Set OTLP Span Exporter for Junjo AI Studio oltp_exporter = OTLPSpanExporter( endpoint=self._endpoint, insecure=self._insecure, @@ -67,10 +68,12 @@ def __init__( @property def span_processor(self): + """Returns the configured span processor.""" return self._span_processor @property def metric_reader(self): + """Returns the configured metric reader.""" return self._metric_reader def flush(self, timeout_millis: float = 120000) -> bool: @@ -81,13 +84,12 @@ def flush(self, timeout_millis: float = 120000) -> bool: It leverages the existing retry/timeout logic in the underlying gRPC exporters. It can be used to force a flush of all pending telemetry before the application exits. - Args: - timeout_millis: Maximum time to wait for flush in milliseconds. - Defaults to 120000ms (120 seconds) to match the - exporter timeout and allow for retries. - - Returns: - True if all telemetry was flushed successfully, False otherwise. + :param timeout_millis: Maximum time to wait for flush in milliseconds. + Defaults to 120000ms (120 seconds) to match the + exporter timeout and allow for retries. + :type timeout_millis: float + :returns: ``True`` if all telemetry was flushed successfully, ``False`` otherwise. + :rtype: bool """ success = True From 7bae914bbecef81fb502b6ec070d09e17fcfef2a Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 23:22:24 -0500 Subject: [PATCH 13/32] Docs: Move docstrings to class level in JunjoOtelExporter and add example --- src/junjo/telemetry/junjo_otel_exporter.py | 51 ++++++++++++++-------- 1 file changed, 34 insertions(+), 17 deletions(-) diff --git a/src/junjo/telemetry/junjo_otel_exporter.py b/src/junjo/telemetry/junjo_otel_exporter.py index 13279f4..6c1bdee 100644 --- a/src/junjo/telemetry/junjo_otel_exporter.py +++ b/src/junjo/telemetry/junjo_otel_exporter.py @@ -7,6 +7,39 @@ class JunjoOtelExporter: """ An OpenTelemetry SpanExporter that sends spans to the Junjo AI Studio. + + Junjo is designed to be compatible with existing OpenTelemetry configurations, + by adding to an existing configuration instead of creating a new one. + + :param host: The hostname of the Junjo AI Studio. + :type host: str + :param port: The port of the Junjo AI Studio. + :type port: str + :param api_key: The API key for the Junjo AI Studio. + :type api_key: str + :param insecure: Whether to allow insecure connections to the Junjo AI Studio. + Defaults to ``False``. + :type insecure: bool + + Example: + .. code-block:: python + + from junjo.telemetry.junjo_otel_exporter import JunjoOtelExporter + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry import trace + + # Initialize the exporter + junjo_exporter = JunjoOtelExporter( + host="localhost", + port="50051", + api_key="your_api_key", + insecure=True + ) + + # Add to your tracer provider + provider = TracerProvider() + provider.add_span_processor(junjo_exporter.span_processor) + trace.set_tracer_provider(provider) """ def __init__( @@ -17,23 +50,7 @@ def __init__( insecure: bool = False, ): """ - Export OpenTelemetry data to Junjo AI Studio. - - Junjo is designed to be compatible with existing OpenTelemetry configurations, - by adding to an existing configuration instead of creating a new one. - - If you have not already configured OpenTelemetry for your application, - follow these instructions: TODO: Add instructions. - - :param host: The hostname of the Junjo AI Studio. - :type host: str - :param port: The port of the Junjo AI Studio. - :type port: str - :param api_key: The API key for the Junjo AI Studio. - :type api_key: str - :param insecure: Whether to allow insecure connections to the Junjo AI Studio. - Defaults to ``False``. - :type insecure: bool + Initializes the JunjoOtelExporter. """ # Set Class Instance Vars From 97ea1b1cba9712543a6e2a8985ef76d561abad4a Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 23:24:39 -0500 Subject: [PATCH 14/32] Docs: Enhance JunjoOtelExporter example with local/production schemes --- src/junjo/telemetry/junjo_otel_exporter.py | 52 +++++++++++++++++++--- 1 file changed, 47 insertions(+), 5 deletions(-) diff --git a/src/junjo/telemetry/junjo_otel_exporter.py b/src/junjo/telemetry/junjo_otel_exporter.py index 6c1bdee..5c706aa 100644 --- a/src/junjo/telemetry/junjo_otel_exporter.py +++ b/src/junjo/telemetry/junjo_otel_exporter.py @@ -22,23 +22,65 @@ class JunjoOtelExporter: :type insecure: bool Example: + **Local Development:** + + To send telemetry to a local Junjo AI Studio instance (e.g., running via Docker Compose): + .. code-block:: python + import os from junjo.telemetry.junjo_otel_exporter import JunjoOtelExporter from opentelemetry.sdk.trace import TracerProvider from opentelemetry import trace - # Initialize the exporter - junjo_exporter = JunjoOtelExporter( + # Retrieve API key from environment + JUNJO_AI_STUDIO_API_KEY = os.getenv("JUNJO_AI_STUDIO_API_KEY") + + # Option 1: Using localhost + junjo_exporter_local = JunjoOtelExporter( host="localhost", port="50051", - api_key="your_api_key", - insecure=True + api_key=JUNJO_AI_STUDIO_API_KEY, + insecure=True, + ) + + # Option 2: Using Docker service name (if running in same Docker network) + junjo_exporter_docker = JunjoOtelExporter( + host="junjo-ai-studio-ingestion", # Docker service name + port="50051", + api_key=JUNJO_AI_STUDIO_API_KEY, + insecure=True, + ) + + # Add to your tracer provider + provider = TracerProvider() + provider.add_span_processor(junjo_exporter_local.span_processor) # or junjo_exporter_docker.span_processor + trace.set_tracer_provider(provider) + + **Production Deployment:** + + For a production environment with TLS enabled (e.g., using a reverse proxy like Caddy): + + .. code-block:: python + + import os + from junjo.telemetry.junjo_otel_exporter import JunjoOtelExporter + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry import trace + + # Retrieve API key from environment + JUNJO_AI_STUDIO_API_KEY = os.getenv("JUNJO_AI_STUDIO_API_KEY") + + junjo_exporter_prod = JunjoOtelExporter( + host="ingestion.junjo.example.com", # Your domain + port="443", # HTTPS port + api_key=JUNJO_AI_STUDIO_API_KEY, + insecure=False, # TLS enabled ) # Add to your tracer provider provider = TracerProvider() - provider.add_span_processor(junjo_exporter.span_processor) + provider.add_span_processor(junjo_exporter_prod.span_processor) trace.set_tracer_provider(provider) """ From 10772036031276a7b40ee02125c7d22aca02bcf4 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 23:26:08 -0500 Subject: [PATCH 15/32] Improved JunjoOtelExporter doc string. --- src/junjo/telemetry/junjo_otel_exporter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/junjo/telemetry/junjo_otel_exporter.py b/src/junjo/telemetry/junjo_otel_exporter.py index 5c706aa..62924de 100644 --- a/src/junjo/telemetry/junjo_otel_exporter.py +++ b/src/junjo/telemetry/junjo_otel_exporter.py @@ -73,9 +73,9 @@ class JunjoOtelExporter: junjo_exporter_prod = JunjoOtelExporter( host="ingestion.junjo.example.com", # Your domain - port="443", # HTTPS port + port="443", # HTTPS port api_key=JUNJO_AI_STUDIO_API_KEY, - insecure=False, # TLS enabled + insecure=False, # TLS enabled ) # Add to your tracer provider From d5b6ab987225773d238cfe654630a99a44369d27 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 22 Nov 2025 23:55:48 -0500 Subject: [PATCH 16/32] Refined documentation on the docker images and configurations. --- docs/docker_reference.rst | 417 ++++++++++++-------------------------- 1 file changed, 135 insertions(+), 282 deletions(-) diff --git a/docs/docker_reference.rst b/docs/docker_reference.rst index 104ade0..8bf291a 100644 --- a/docs/docker_reference.rst +++ b/docs/docker_reference.rst @@ -15,19 +15,8 @@ Backend Service The backend service provides the HTTP API, authentication, and data processing capabilities. It uses SQLite for metadata storage and DuckDB for analytical queries. -**Ports:** - -- ``1323`` - HTTP API server (public) -- ``50053`` - Internal gRPC for service communication (internal) - -**Volumes:** - -- ``/dbdata/sqlite`` - SQLite database directory (persistent) -- ``/dbdata/duckdb`` - DuckDB database directory (persistent) - **Notes:** -- Requires root user or appropriate permissions for database write operations - Polls the ingestion service for new telemetry data - Processes and indexes traces for querying via the web UI @@ -43,10 +32,6 @@ The ingestion service provides high-throughput OpenTelemetry data reception usin - ``50051`` - OpenTelemetry gRPC ingestion endpoint (public, your applications connect here) - ``50052`` - Internal gRPC for backend polling (internal) -**Volumes:** - -- ``/dbdata/badgerdb`` - BadgerDB write-ahead log directory (persistent) - **Notes:** - This is the primary endpoint where your Junjo workflows send telemetry @@ -73,195 +58,180 @@ The frontend service provides the interactive web UI for visualizing and debuggi Complete Docker Compose Configuration -------------------------------------- -Production-Ready Configuration -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Minimal Build Configuration +~~~~~~~~~~~~~~~~~~~~~~~~~~~ -This configuration includes all three services with best practices for production deployment: +This is the standard configuration for running Junjo AI Studio, suitable for both local development and as a starting point for production. .. code-block:: yaml :caption: docker-compose.yml + # Junjo AI Studio - Minimal Build + # A lightweight, self-hostable AI workflow debugging platform. + # https://github.com/mdrideout/junjo-ai-studio-minimal-build + services: - # Backend API and data processing service junjo-ai-studio-backend: - image: mdrideout/junjo-ai-studio-backend:latest + image: mdrideout/junjo-ai-studio-backend:0.70.3 container_name: junjo-ai-studio-backend restart: unless-stopped - ports: - - "1323:1323" # HTTP API (public) - - "50053:50053" # Internal gRPC (backend communication) volumes: - # Persistent database storage - - ./dbdata/sqlite:/dbdata/sqlite - - ./dbdata/duckdb:/dbdata/duckdb - environment: - # Environment (production|development) - - JUNJO_ENV=${JUNJO_ENV:-production} - # CORS origins (comma-separated) - - JUNJO_ALLOW_ORIGINS=${JUNJO_ALLOW_ORIGINS} - # Session secret for authentication (generate with: openssl rand -hex 32) - - JUNJO_SESSION_SECRET=${JUNJO_SESSION_SECRET} - # Production authentication domain - - JUNJO_PROD_AUTH_DOMAIN=${JUNJO_PROD_AUTH_DOMAIN} + # Database storage (required for all modes) + - ${JUNJO_HOST_DB_DATA_PATH:-./.dbdata}:/app/.dbdata + ports: + - "1323:1323" # HTTP API (public) networks: - junjo-network - healthcheck: - test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://localhost:1323/health"] - interval: 30s - timeout: 10s - retries: 3 - start_period: 40s + env_file: + - .env + environment: + # Override host/port for Docker network communication + - INGESTION_HOST=junjo-ai-studio-ingestion + - INGESTION_PORT=50052 + # Enable migrations on startup + - RUN_MIGRATIONS=true + # Database paths (hardcoded for Docker, users configure host mount via JUNJO_HOST_DB_DATA_PATH) + - JUNJO_SQLITE_PATH=/app/.dbdata/sqlite/junjo.db + - JUNJO_DUCKDB_PATH=/app/.dbdata/duckdb/traces.duckdb - # OpenTelemetry data ingestion service junjo-ai-studio-ingestion: - image: mdrideout/junjo-ai-studio-ingestion:latest + image: mdrideout/junjo-ai-studio-ingestion:0.70.3 container_name: junjo-ai-studio-ingestion restart: unless-stopped - ports: - - "50051:50051" # OTel gRPC ingestion (public, your apps connect here) - - "50052:50052" # Internal gRPC (backend polling) volumes: - # BadgerDB write-ahead log - - ./dbdata/badgerdb:/dbdata/badgerdb - environment: - # API key for authentication (generate in web UI) - - JUNJO_AI_STUDIO_API_KEY=${JUNJO_AI_STUDIO_API_KEY} + # Database storage (required for all modes) + - ${JUNJO_HOST_DB_DATA_PATH:-./.dbdata}:/app/.dbdata + ports: + - "50051:50051" # Public OTLP endpoint (authenticated via API key) networks: - junjo-network + env_file: + - .env + environment: + - BACKEND_GRPC_HOST=junjo-ai-studio-backend + - BACKEND_GRPC_PORT=50053 + # Database path (hardcoded for Docker, users configure host mount via JUNJO_HOST_DB_DATA_PATH) + - JUNJO_BADGERDB_PATH=/app/.dbdata/badgerdb depends_on: junjo-ai-studio-backend: - condition: service_healthy # Wait for backend server to be healthy before starting + condition: service_started healthcheck: - test: ["CMD", "grpc_health_probe", "-addr=:50051"] - interval: 30s - timeout: 10s - retries: 3 - start_period: 40s + test: ["CMD", "grpc_health_probe", "-addr=localhost:50052"] + interval: 5s + timeout: 3s + retries: 5 + start_period: 5s - # Web UI for visualization and debugging junjo-ai-studio-frontend: - image: mdrideout/junjo-ai-studio-frontend:latest + image: mdrideout/junjo-ai-studio-frontend:0.70.3 container_name: junjo-ai-studio-frontend restart: unless-stopped ports: - - "5153:80" # Web UI - environment: - # Backend API URL (adjust for your deployment) - - VITE_API_URL=${VITE_API_URL:-http://localhost:1323} + - "5153:80" # Public frontend (production build - nginx serving static files on port 80) + env_file: + - .env networks: - junjo-network depends_on: junjo-ai-studio-backend: - condition: service_healthy - junjo-ai-studio-ingestion: - condition: service_healthy + condition: service_started networks: junjo-network: + name: junjo_network driver: bridge -Development Configuration -~~~~~~~~~~~~~~~~~~~~~~~~~ - -For local development, you can use a simpler configuration: - -.. code-block:: yaml - :caption: docker-compose.dev.yml - - services: - junjo-ai-studio-backend: - image: mdrideout/junjo-ai-studio-backend:latest - ports: - - "1323:1323" - - "50053:50053" - volumes: - - ./.dbdata/sqlite:/dbdata/sqlite - - ./.dbdata/duckdb:/dbdata/duckdb - environment: - - JUNJO_ENV=development - - JUNJO_ALLOW_ORIGINS=http://localhost:5153 - networks: - - junjo-network +Production Deployment +--------------------- - junjo-ai-studio-ingestion: - image: mdrideout/junjo-ai-studio-ingestion:latest - ports: - - "50051:50051" - - "50052:50052" - volumes: - - ./.dbdata/badgerdb:/dbdata/badgerdb - networks: - - junjo-network +For a complete production setup on a Virtual Machine (VM) including: - junjo-ai-studio-frontend: - image: mdrideout/junjo-ai-studio-frontend:latest - ports: - - "5153:80" - environment: - - VITE_API_URL=http://localhost:1323 - networks: - - junjo-network +* **Caddy Reverse Proxy** for automatic HTTPS +* **Subdomain routing** (e.g., ``junjo.example.com``) +* **Block storage** configuration - networks: - junjo-network: - driver: bridge +Please refer to the `Junjo AI Studio Deployment Example `_ repository. Environment Variables Reference -------------------------------- -Backend Service -~~~~~~~~~~~~~~~ +This section details the environment variables used to configure Junjo AI Studio. These are typically defined in a ``.env`` file. + +Common Configuration +~~~~~~~~~~~~~~~~~~~~ ``JUNJO_ENV`` - **Required.** Environment mode: ``production`` or ``development`` + **Required.** Environment mode. - - **Default:** None - - **Example:** ``JUNJO_ENV=production`` + - ``development`` (default): Uses localhost and standard ports. + - ``production``: Expects production hostname and subdomains. ``JUNJO_ALLOW_ORIGINS`` - **Required.** Comma-separated list of allowed CORS origins for API requests + **Optional.** Comma-separated list of allowed CORS origins for API requests. + + - **Development Default:** ``http://localhost:5151,http://localhost:5153`` + - **Production:** Auto-derived from ``JUNJO_PROD_FRONTEND_URL`` if not set. + +Security (Backend) +~~~~~~~~~~~~~~~~~~ - - **Default:** None - - **Example:** ``JUNJO_ALLOW_ORIGINS=https://junjo.example.com,https://www.example.com`` +These keys secure session cookies and must be Base64-encoded strings. ``JUNJO_SESSION_SECRET`` - **Required for production.** Secret key for session management and JWT tokens + **Required.** Signing key for session integrity (prevents tampering). - - **Default:** None - - **Example:** ``JUNJO_SESSION_SECRET=a1b2c3d4e5f6...`` (64 character hex string) - - **Generate with:** ``openssl rand -hex 32`` + - **Generate:** ``openssl rand -base64 32`` -``JUNJO_PROD_AUTH_DOMAIN`` - **Required for production.** The primary domain for authentication cookies +``JUNJO_SECURE_COOKIE_KEY`` + **Required.** Encryption key for session confidentiality (prevents reading). - - **Default:** None - - **Example:** ``JUNJO_PROD_AUTH_DOMAIN=junjo.example.com`` + - **Generate:** ``openssl rand -base64 32`` -``JUNJO_BUILD_TARGET`` - **Optional.** Build target for development purposes +Database Storage +~~~~~~~~~~~~~~~~ - - **Default:** ``production`` - - **Example:** ``JUNJO_BUILD_TARGET=development`` +``JUNJO_HOST_DB_DATA_PATH`` + **Required.** Path on the host machine where database files are stored. -Ingestion Service -~~~~~~~~~~~~~~~~~ + - **Development Default:** ``./.dbdata`` (local directory) + - **Production:** Use a mounted block storage path (e.g., ``/mnt/junjo-data``). -``JUNJO_AI_STUDIO_API_KEY`` - **Required.** API key for authenticating telemetry ingestion requests +Logging +~~~~~~~ - - **Default:** None - - **Example:** ``JUNJO_AI_STUDIO_API_KEY=js_1234567890abcdef...`` - - **Generate:** Create in the Junjo AI Studio web UI under Settings → API Keys +``JUNJO_LOG_LEVEL`` + Minimum severity level for logs. -Frontend Service -~~~~~~~~~~~~~~~~ + - Values: ``debug``, ``info`` (default), ``warn``, ``error``. + +``JUNJO_LOG_FORMAT`` + Output format for logs. + + - ``json`` (default): Machine-readable, recommended for production. + - ``text``: Human-readable, colored output for development. + +Production URLs (Required for Production) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +When ``JUNJO_ENV=production``, these variables configure public access points. + +``JUNJO_PROD_FRONTEND_URL`` + The public URL where users access the web UI (e.g., ``https://app.example.com``). -``VITE_API_URL`` - **Optional.** Backend API URL for the frontend to connect to +``JUNJO_PROD_BACKEND_URL`` + The public URL for the backend API (e.g., ``https://api.example.com``). Must share the same root domain as the frontend. - - **Default:** ``http://localhost:1323`` (development) - - **Example:** ``VITE_API_URL=https://api.junjo.example.com`` - - **Note:** In production with reverse proxy, this may be a relative path +``JUNJO_PROD_INGESTION_URL`` + The public URL for the ingestion service (e.g., ``https://ingestion.example.com``). + +AI Service Keys (Optional) +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +API keys for LLM features in the prompt playground. + +- ``GEMINI_API_KEY`` +- ``OPENAI_API_KEY`` +- ``ANTHROPIC_API_KEY`` Volume Mounts ------------- @@ -341,7 +311,7 @@ These ports need to be accessible: - Frontend: ``https://junjo.example.com`` (Web UI) - Backend API: ``https://api.junjo.example.com`` (API) -- Ingestion: ``grpc.junjo.example.com:443`` (gRPC with TLS) +- Ingestion: ``ingestion.junjo.example.com`` (gRPC with TLS - see Caddyfile routing example) See :doc:`deployment` for production deployment examples with Caddy reverse proxy. @@ -350,26 +320,12 @@ Resource Requirements Junjo AI Studio is designed to run on minimal resources: -**Minimum:** +**Minimum Recommended Resources:** - **CPU:** 1 shared vCPU - **RAM:** 1 GB - **Storage:** 10 GB (more for long-term trace retention) -**Recommended for Production:** - -- **CPU:** 1-2 dedicated vCPUs -- **RAM:** 2-4 GB -- **Storage:** 25+ GB on block storage -- **Network:** 1 Gbps - -Database Sizes -~~~~~~~~~~~~~~ - -- **BadgerDB (Ingestion):** Write-ahead log, periodically compacted -- **SQLite (Backend):** Metadata and indexes, grows slowly -- **DuckDB (Backend):** Analytical data, grows with trace retention - Network Configuration --------------------- @@ -382,19 +338,9 @@ All services should run on the same Docker network for internal communication: networks: junjo-network: + name: junjo_network driver: bridge -Firewall Rules -~~~~~~~~~~~~~~ - -If using a firewall, allow these ports: - -- ``1323/tcp`` - Backend API (if accessing directly) -- ``5153/tcp`` - Frontend web UI -- ``50051/tcp`` - Ingestion gRPC endpoint -- ``443/tcp`` - HTTPS (if using reverse proxy) -- ``80/tcp`` - HTTP (for Let's Encrypt challenges) - Production Deployment --------------------- @@ -412,96 +358,36 @@ Example Caddyfile: .. code-block:: text :caption: Caddyfile - # Web UI - junjo.example.com { - reverse_proxy junjo-ai-studio-frontend:80 - } + # Junjo AI Studio routing block + junjo.example.com, *.junjo.example.com { + tls your.email@gmail.com { + dns cloudflare {env.CLOUDFLARE_API_TOKEN} # Created inside CloudFlare and set in the .env file + resolvers 1.1.1.1 # Cloudflare DNS is recommended for this plugin + } - # Backend API - api.junjo.example.com { + # backend: api.junjo.example.com + @api host api.junjo.example.com + handle @api { reverse_proxy junjo-ai-studio-backend:1323 - } + } - # Ingestion gRPC - grpc.junjo.example.com { + # ingestion: ingestion.junjo.example.com + @ingestion host ingestion.junjo.example.com + handle @ingestion { reverse_proxy h2c://junjo-ai-studio-ingestion:50051 + } + + # frontend: Fallback for the root domain + handle { + reverse_proxy junjo-ai-studio-frontend:80 + } } See the `Junjo AI Studio Deployment Example `_ for a complete production setup. -Backup and Recovery -~~~~~~~~~~~~~~~~~~~ - -Backup all three database directories regularly: - -.. code-block:: bash - - # Backup script example - tar -czf junjo-backup-$(date +%Y%m%d).tar.gz \ - dbdata/sqlite \ - dbdata/duckdb \ - dbdata/badgerdb - -To restore, stop services, extract backup, and restart: - -.. code-block:: bash - - docker compose down - tar -xzf junjo-backup-20241012.tar.gz - docker compose up -d - -Monitoring -~~~~~~~~~~ - -Monitor these key metrics: - -- **Disk usage:** Database directories -- **Memory usage:** Each service container -- **Network traffic:** Port 50051 (ingestion load) -- **API response time:** Backend health endpoint -- **Error logs:** ``docker compose logs`` - -Health Checks -~~~~~~~~~~~~~ - -Services include health check endpoints: - -- **Backend:** ``http://localhost:1323/health`` -- **Ingestion:** gRPC health probe on port 50051 - -Example monitoring with curl: - -.. code-block:: bash - - # Check backend health - curl http://localhost:1323/health - - # Check all services - docker compose ps - Scaling Considerations ---------------------- -Horizontal Scaling -~~~~~~~~~~~~~~~~~~ - -For high-volume deployments: - -1. **Ingestion Service:** Can run multiple instances behind a load balancer -2. **Backend Service:** Can run multiple instances with shared database volumes -3. **Frontend Service:** Static assets, easily cached or CDN-distributed - -Example multi-instance configuration: - -.. code-block:: yaml - - services: - junjo-ai-studio-ingestion: - image: mdrideout/junjo-ai-studio-ingestion:latest - deploy: - replicas: 3 - # ... rest of configuration - Vertical Scaling ~~~~~~~~~~~~~~~~ @@ -510,20 +396,7 @@ For most deployments, vertical scaling is sufficient: - Increase CPU/RAM allocation - Use faster disk (NVMe SSDs) - Increase Docker memory limits - -.. code-block:: yaml - - services: - junjo-ai-studio-backend: - # ... existing config - deploy: - resources: - limits: - cpus: '2' - memory: 4G - reservations: - cpus: '1' - memory: 2G +- Split frontend, backend, and ingestion services to separate virtual machines, scaling each's resources as necessary. Troubleshooting --------------- @@ -546,26 +419,6 @@ Common issues: - Permission issues with volume mounts - Network not created (``docker network create junjo-network``) -High Memory Usage -~~~~~~~~~~~~~~~~~ - -If services consume excessive memory: - -- Check BadgerDB size (``du -sh dbdata/badgerdb``) -- Implement trace retention policies -- Increase disk swap if needed -- Add memory limits to docker-compose.yml - -Cannot Connect to Ingestion -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -If applications can't send telemetry: - -- Verify port 50051 is accessible: ``telnet localhost 50051`` -- Check firewall rules -- Verify API key in application matches server -- Check ingestion service logs for auth errors - API Errors ~~~~~~~~~~ From 03cd29b44a3dce1c168213aeaddbbdb621dc913b Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sun, 23 Nov 2025 00:13:05 -0500 Subject: [PATCH 17/32] Getting started improvements and other minor formatting fixes. --- docs/deployment.rst | 4 +-- docs/getting_started.rst | 2 +- docs/index.rst | 64 +++++++++++++++++++++++++++++++++++----- 3 files changed, 59 insertions(+), 11 deletions(-) diff --git a/docs/deployment.rst b/docs/deployment.rst index 67fd6fe..9a3003d 100644 --- a/docs/deployment.rst +++ b/docs/deployment.rst @@ -1,10 +1,10 @@ Junjo AI Studio Deployment -======================= +=========================== Junjo AI Studio can be deployed in various ways depending on your needs. Being based on docker containers, it's easy to deploy Junjo AI Studio anywhere with docker compose. Minimal Build Template (Recommended Starting Point) -------------------------------------------------- +------------------------------------------------------ For a minimal, flexible Docker Compose setup that you can customize for your specific infrastructure needs, use the `Junjo AI Studio Minimal Build Template `_. diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 7f34e94..b1e46f1 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -24,7 +24,7 @@ The following is a basic, single file example of how to use Junjo to create a si .. image:: _static/junjo-base-example-screenshot.png :alt: Example telemetry visualization of the getting started example :align: center - :width: 75% + :width: 100% *The above screenshot shows the telemetry visualization of this getting started example. Visually step through the workflow nodes and individual state updates.* diff --git a/docs/index.rst b/docs/index.rst index 81a30b5..836b130 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -41,29 +41,77 @@ Base Example AI Chat -Junjo 順序 - Python API Reference +Junjo Python SDK - API Reference ================================== `Junjo on PyPI `_ -Junjo is a modern Python library for building, executing, testing, and debugging complex, graph-based AI workflows. + 順序 (junjo): order, sequence, procedure -Junjo makes it easy to build a graph of possible paths for an AI to take. When the graph executed, the LLM will autonomously traverse the graph, choosing the next node as it goes based on application state and edge conditions. +Junjo is a modern Python library for designing, executing, testing, and debugging complex, graph-based AI workflows. -Whether you're building a simple chatbot, a complex data manipulation pipeline, or a sophisticated workflow with dynamic branching and parallel execution, Junjo provides the tools to define your logic as a clear graph of nodes and edges, and telemetry to make it easy to debug. +Whether you’re building a simple chatbot, a complex data manipulation pipeline, or a sophisticated workflow with dynamic branching and parallel execution, Junjo provides the tools to define and debug your logic as a clear graph of nodes, edges, and state updates. .. image:: _static/junjo-screenshot.png :alt: A screenshot of a Junjo workflow graph's telemetry on Junjo AI Studio :align: center - :width: 600px + :width: 800px *A screenshot of Junjo's companion open-telemetry ingestion server to make debugging graph workflow state easy.* -Junjo remains decoupled from any specific AI model or framework. Simply wrap your existing business logic in a Junjo node, organize them into a Graph with conditional edges, and then execute the graph. Junjo will handle the rest, including task orchestration, error handling, and logging to any OpenTelemetry destination. +Benefits +--------- -Junjo's optional companion library, `Junjo AI Studio `_, provides additional features for observing graph execution telemetry, and visually stepping through state changes made by each node. This is particularly useful for debugging complex workflows or understanding how data flows through your application. +* ✨ Visualize your AI workflows +* 🧠 Redux inspired state management and state debugging tools +* ⚡️ Concurrency and type safety native with asyncio and pydantic +* 🔗 Organize conditional chains of LLM calls into observable graph workflows +* 🏎️ Easy patterns for directed graph loops, branching, and concurrency +* 🧪 Eval-Driven Development focused + * Build massive eval sets by mocking node state + * Programmatically build and update eval sets with agentic code assistants + * Eval patterns are based on pytest, leveraging its testing framework and capabilities + * Rapidly iterate on your AI capabilities and avoid regressions +* 🔭 OpenTelemetry native + * Provides organized, structured traces to any OpenTelemetry provider + * Companion open source **`Junjo AI Studio `_** enhances debugging and evaluation of production data + + +Junjo's Philosophy +------------------- + +**🔍 Transparency** + +Junjo strives to be the opposite of a "black box". Transparency, observability, eval driven development, and production data debugging are requirements for AI applications handling mission critical data, that need repeatable and high accuracy chained LLM logic. + +**⛓️‍💥 Decoupled** + +Junjo doesn't change how you implement LLM providers or make calls to their services. + +Continue using `google-genai `_, `openai-python `_, `grok / xai sdk `_, `anthropic-sdk-python `_, `LiteLLM `_ or even REST API requests to any provider. + +Junjo remains decoupled from LLM providers. There are no proprietary implementations, no hijacking of python docstrings, no confusing or obfuscating decorators, and no middleman proxies. + +Junjo simply helps you organize your python functions (whether they be logic, LLM calls, RAG retrieval, REST API calls, etc.) into a clean organized graph structure with predictable, testable, and observable execution. + +**🥧 Conventional** + +Junjo provides primitive building blocks that let you build any sort of executable workflow. From linear chains of LLM calls, to complex branching workflows with concurrent subflows, to fully autonomous agents. + +Junjo uses conventional Pythonic architecture. Rather than obfuscating, proprietary decorators or runtime scripts that hijack execution, Junjo graph workflows are constructed conventionally with python classes and generics, and Pydantic models for type safe immutable state. + +State is modeled after the conventional `Elm Architecture `_, and inspired by `Redux `_ for clean separation of concerns, concurrency safety, and debuggability. + +This helps your language server auto-complete methods and properties, and makes it easy for AI Coding agents to scaffold and understand massive Junjo workflows without needing to learn proprietary, library-specific logic patterns. + +Junjo organizes conventional OpenTelemetry spans into easy to understand groups. Your existing OpenTelemetry provider will continue to work, now with enhanced span organization. `Junjo AI Studio `_ is a companion OpenTelemetry platform with enhanced visuals and debugging tools for Junjo workflows. + +**🤝 Compatible** + +Junjo can work alongside other AI Agent frameworks. Junjo Workflows can be setup as **tools** that Autonomous Agent frameworks can call for high-accuracy repeatable processes (like RAG retrieval or complex document parsing logic). + +You can execute autonomous agent capabilities from other libraries inside a Junjo AI workflow. For example, a Junjo workflow node can run a `smolagents `_ tool calling agent as a single step within a greater Junjo workflow or subflow. -With built-in support for fully type-safe (Pydantic) redux-inspired state management, conditional execution, concurrent tasks (asyncio), and native OpenTelemetry support, Junjo empowers you to create robust, observable, and scalable Python workflows. Dive into our documentation to learn how to streamline your task orchestration and gain deeper insights into your application's execution. Getting Started --------------- From abad43a14e83adf451913f1f963711432a2edbb8 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sun, 23 Nov 2025 00:22:58 -0500 Subject: [PATCH 18/32] Incorporated JunjoOtelExporter into generated API docs. Deleted deprecated / commented out code. --- docs/api.rst | 11 +++++++++++ src/junjo/app.py | 29 ----------------------------- 2 files changed, 11 insertions(+), 29 deletions(-) delete mode 100644 src/junjo/app.py diff --git a/docs/api.rst b/docs/api.rst index 1cdc957..e0173a6 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -3,8 +3,19 @@ API Reference ============= +Core API +-------- + .. automodule:: junjo :members: :undoc-members: :show-inheritance: +Telemetry API +------------- + +.. automodule:: junjo.telemetry.junjo_otel_exporter + :members: + :undoc-members: + :show-inheritance: + diff --git a/src/junjo/app.py b/src/junjo/app.py deleted file mode 100644 index 801f368..0000000 --- a/src/junjo/app.py +++ /dev/null @@ -1,29 +0,0 @@ -# from typing import Optional - - -# class JunjoApp: -# """ -# -# """ -# _instance: Optional["JunjoApp"] = None -# _app_name: str = "" # Class-level attribute for app_name - -# def __new__(cls, app_name: str | None = None): -# if cls._instance is None: -# if app_name is None: -# raise ValueError("app_name must be provided on first initialization") -# cls._instance = super().__new__(cls) -# cls._instance._app_name = app_name.strip() # Initialize directly -# elif app_name is not None and app_name.strip() != cls._instance._app_name: -# print("Warning: app_name cannot be changed once set") -# return cls._instance - -# def __init__(self, app_name: str | None = None): -# # The constructor will always be called. -# # Even though this does nothing, it is conventional -# # to include it. -# pass - -# @property # Make app_name a read-only property -# def app_name(self) -> str: -# return self._app_name From 79cb247e4816b0524ff9a59e8c19da9f338d47da Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sun, 23 Nov 2025 00:26:25 -0500 Subject: [PATCH 19/32] Removed deprecated code. Lint fix. --- .../src/app/ai_services/gemini/gemini_tool.py | 5 ++- src/junjo/react_flow/schemas.py | 31 ------------------- 2 files changed, 4 insertions(+), 32 deletions(-) delete mode 100644 src/junjo/react_flow/schemas.py diff --git a/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py b/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py index f5abacf..439baef 100644 --- a/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py +++ b/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py @@ -214,7 +214,10 @@ async def gemini_image_edit_request(self, image_bytes: bytes) -> tuple[bytes | N if not response.candidates[0].content: # If prohibited content if response.candidates[0].finish_reason is not None: - logger.warning(f"Prohibited content detected in response. Finish Reason: {response.candidates[0].finish_reason}") + logger.warning( + "Prohibited content detected in response." + f"Finish Reason: {response.candidates[0].finish_reason}" + ) return None, str(response.candidates[0].finish_reason) logger.error(f"No content in response: {response}") diff --git a/src/junjo/react_flow/schemas.py b/src/junjo/react_flow/schemas.py deleted file mode 100644 index d1d4bc7..0000000 --- a/src/junjo/react_flow/schemas.py +++ /dev/null @@ -1,31 +0,0 @@ - -from pydantic import BaseModel - - -class ReactFlowPosition(BaseModel): - x: float - y: float - -class ReactFlowNodeData(BaseModel): - label: str - -class ReactFlowNode(BaseModel): - id: str - data: ReactFlowNodeData - position: ReactFlowPosition - -class ReactFlowEdge(BaseModel): - id: str - source: str - target: str - label: str | None = None - -class ReactFlowViewport(BaseModel): - x: float - y: float - zoom: float - -class ReactFlowJsonObject(BaseModel): - nodes: list[ReactFlowNode] - edges: list[ReactFlowEdge] - viewport: ReactFlowViewport From 8d2249adbe0340dde797772809c9513759762109 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sun, 23 Nov 2025 00:31:45 -0500 Subject: [PATCH 20/32] Docs renaming fixes. --- ...isual.png => subflow-junjo-ai-studio-visual.png} | Bin docs/subflows.rst | 4 ++-- docs/visualizing_workflows.rst | 2 +- examples/ai_chat/README.md | 2 +- examples/base/README.md | 6 +++--- pyproject.toml | 2 +- 6 files changed, 8 insertions(+), 8 deletions(-) rename docs/_static/{subflow-junjo-server-visual.png => subflow-junjo-ai-studio-visual.png} (100%) diff --git a/docs/_static/subflow-junjo-server-visual.png b/docs/_static/subflow-junjo-ai-studio-visual.png similarity index 100% rename from docs/_static/subflow-junjo-server-visual.png rename to docs/_static/subflow-junjo-ai-studio-visual.png diff --git a/docs/subflows.rst b/docs/subflows.rst index d9dc439..f23517d 100644 --- a/docs/subflows.rst +++ b/docs/subflows.rst @@ -216,11 +216,11 @@ This hierarchical visualization makes it easier to understand complex workflows :width: 600px Subflows on Junjo AI Studio -======================== +============================ `Junjo AI Studio `_ will also intelligently render subflows, allowing one to step through node executions and state updates even in highly concurrent subflow executions. -.. image:: _static/subflow-junjo-server-visual.png +.. image:: _static/subflow-junjo-ai-studio-visual.png :alt: A screenshot of a Junjo workflow graph's visual in Junjo AI Studio, featuring a subflow rendering. :align: center :width: 600px diff --git a/docs/visualizing_workflows.rst b/docs/visualizing_workflows.rst index ba8c8bc..8a2ab7f 100644 --- a/docs/visualizing_workflows.rst +++ b/docs/visualizing_workflows.rst @@ -6,7 +6,7 @@ Visualizing AI Workflows Understanding the structure and execution flow of your AI workflows and agents is crucial for development, debugging, and optimization. -This guide focuses on Junjo's built-in Graphviz integration to generate static diagrams of your workflow architecture as you code, and touches upon the dynamic visualization capabilities of Junjo Server. +This guide focuses on Junjo's built-in Graphviz integration to generate static diagrams of your workflow architecture as you code, and touches upon the dynamic visualization capabilities of Junjo AI Studio. .. image:: _static/junjo-screenshot-graphviz.png :alt: Basic example of a Junjo workflow graph rendered by Graphviz diff --git a/examples/ai_chat/README.md b/examples/ai_chat/README.md index 80e6b3b..5e47a95 100644 --- a/examples/ai_chat/README.md +++ b/examples/ai_chat/README.md @@ -46,7 +46,7 @@ $ python -m src.app.visualize ### Telemetry -Have the [junjo-server](https://github.com/mdrideout/junjo-server?tab=readme-ov-file#docker-commands) running on your machine to see the telemetry and graph visualizations. +Have the [junjo-ai-studio](https://github.com/mdrideout/junjo-ai-studio) running on your machine to see the telemetry and graph visualizations. ### Clearing the SQLite database diff --git a/examples/base/README.md b/examples/base/README.md index 46dc95b..408a353 100644 --- a/examples/base/README.md +++ b/examples/base/README.md @@ -8,7 +8,7 @@ This is a basic single-file implementation of Junjo's Graph Workflow Execution. - Concurrent execution - A Subflow - A Conditional that determines the next node based on the results of a state update -- An opentelemetry exporter for Junjo Server (optional / not required) +- An opentelemetry exporter for Junjo AI Studio (optional / not required) See the **ai_chat** example for a more advanced frontend / backend E2E experience that utilizes LLM API calls. @@ -17,8 +17,8 @@ See the **ai_chat** example for a more advanced frontend / backend E2E experienc > Note: the following commands assume your terminal is located in this directory. - The graph workflow will run, logging node executions and state changes. -- If [Junjo Server](https://github.com/mdrideout/junjo-server) is running, it will receive telemetry. - - Requires you to generate an API key inside the Junjo Server interface, and add it as a `.env` variable here. +- If [Junjo AI Studio](https://github.com/mdrideout/junjo-ai-studio) is running, it will receive telemetry. + - Requires you to generate an API key inside the Junjo AI Studio interface, and add it as a `.env` variable here. ```bash # Run commands from this directory diff --git a/pyproject.toml b/pyproject.toml index 087e19b..cdff8e3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,7 +35,7 @@ dependencies = [ Docs = "https://python-api.junjo.ai/" Repository = "https://github.com/mdrideout/junjo" "Bug Tracker" = "https://github.com/mdrideout/junjo/issues" -"Telemetry Server" = "https://github.com/mdrideout/junjo-server" +"Telemetry + AI Studio" = "https://github.com/mdrideout/junjo-ai-studio" [project.optional-dependencies] dev = [ From acfb47542ff06f83627ebf06e46b2ebb35aae8cf Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sun, 23 Nov 2025 00:39:31 -0500 Subject: [PATCH 21/32] Minor docs improvements. --- docs/api.rst | 2 +- docs/conf.py | 4 ++-- docs/index.rst | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/api.rst b/docs/api.rst index e0173a6..0b93b87 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -1,7 +1,7 @@ .. _api: API Reference -============= +============================ Core API -------- diff --git a/docs/conf.py b/docs/conf.py index b32773e..b94582a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,7 +25,7 @@ # -- Options for HTML output ------------------------------------------------- html_theme = "furo" -html_title = "Junjo Docs" +html_title = "Junjo AI Framework Python SDK Docs - API Reference | Junjo AI Studio" html_baseurl = "https://python-api.junjo.ai/" # Hide the link to the rst source files @@ -41,7 +41,7 @@ # "color-brand-primary": "orange", # "color-brand-content": "#FF8800", # }, - "sidebar_hide_name": False, # Show the project name in the sidebar + "sidebar_hide_name": True, # Show the project name in the sidebar # "navigation_with_keys": True, # Allow navigation with arrow keys # "announcement": "Important announcement!", # Add a banner at the top } diff --git a/docs/index.rst b/docs/index.rst index 836b130..bf0bae3 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,6 +1,6 @@ .. toctree:: :maxdepth: 2 - :caption: Contents: + :caption: Junjo Python SDK Docs: :hidden: getting_started From 63f46755534765e009e4d722c64faf54235aa0a2 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sun, 23 Nov 2025 00:44:14 -0500 Subject: [PATCH 22/32] Renaming fixes. --- examples/ai_chat/backend/.env.example | 2 +- examples/base/.env.example | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/ai_chat/backend/.env.example b/examples/ai_chat/backend/.env.example index 4423ea9..2eed407 100644 --- a/examples/ai_chat/backend/.env.example +++ b/examples/ai_chat/backend/.env.example @@ -1,2 +1,2 @@ -JUNJO_SERVER_API_KEY="[junjo_server_api_key_here]" +JUNJO_AI_STUDIO_API_KEY="[junjo_ai_studio_api_key_here]" GEMINI_API_KEY="[gemini_api_key_here]" \ No newline at end of file diff --git a/examples/base/.env.example b/examples/base/.env.example index 749d909..990df95 100644 --- a/examples/base/.env.example +++ b/examples/base/.env.example @@ -1 +1 @@ -JUNJO_SERVER_API_KEY="[junjo_server_api_key_here]" \ No newline at end of file +JUNJO_AI_STUDIO_API_KEY="[junjo_ai_studio_api_key_here]" \ No newline at end of file From dba9b848a5fd429ee55c88aa6449d58220e91ed3 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sun, 23 Nov 2025 11:03:05 -0500 Subject: [PATCH 23/32] Overrode sidebar template for branding. --- .DS_Store | Bin 8196 -> 8196 bytes docs/_templates/sidebar/brand.html | 11 +++++++++++ docs/conf.py | 1 + 3 files changed, 12 insertions(+) create mode 100644 docs/_templates/sidebar/brand.html diff --git a/.DS_Store b/.DS_Store index 431d349fc488a2bf857355bee4d63b77fa191673..70a8636c68488cf5b50ddee6548d76759c76c83c 100644 GIT binary patch delta 669 zcmdVWJxJ7X9LMqR`!|>x$fy)&aM6gkO< zO!O9?aZ?gOy~wB0JV~l0M1%;s9C8Q+aR|cv2`z2n=6CtPXZpOpe!JhkIA*$$ZOyXS zZs!DMOsxqurnq*u>xr|a&E4$rJ#u+{Is;Y4)1(S_L$jyM{mf;^|3`2swx7;)yY*im zDQ3wQhdh*8@k)mb%BalCTiKG3?8-Melq2~KL=Z(ZH!(1if`wEvv2l<4;15=yD0 zmU9BvVW?o5vzcEVIE5ANj;*_6YNX1Ag&Wi9$syLnWw0 zm7**vO{J?l%BJk9Kou!x;4HrUYV;Gsd|CRwgyp9k%O6dTA)Z@gVC63PJfH}t#+Fe| zH8s>}@&(vK3$6Hg&I^t1qMHF;@tPrqHGD$THJrIL-Z0Ms?^t1#bvAiVh;2UTSA5|s T-w6lT5*-o2QzQABj4k9GA0?&v delta 643 zcmdVVPe{{o9LDj_`x~0ljE!wMjZqXxNVv^l?9kyC$Yu>s>(5GWv9aYp1f?2?jz*Dm z+D`>f8%m%aR9cr|E{gIZB0>nQ4tfx~20{&C_?v=9p<~bCf#>$TQd&w|nlhht`>tR1 zdRq0Yd7w5Nf85^}iYFp^%=TQjSnRb|HMblkm$!^DVmlrujns%!n#3oX+?A;GOH$rQ zM&8Sc6l7htWJmVox0Iv|D9&KQPCX79xPl80t=!-y?cC-LA;LVOlP)4Wp@&}jc*-y% zjFM!GbQ|MLFiD14=9nkPXTI>2RW>N{gP-j3hrg7RQYKZW&Z~OmP>t%MYEdrbQ@4~~ z1@yA@-l@_9oAY0#n^Kf*`6c^OqMCD~xSr D2FR3A diff --git a/docs/_templates/sidebar/brand.html b/docs/_templates/sidebar/brand.html new file mode 100644 index 0000000..e33b54e --- /dev/null +++ b/docs/_templates/sidebar/brand.html @@ -0,0 +1,11 @@ +{% extends "furo/sidebar/brand.html" %} + +{% block brand_content %} + {# Check if a logo exists, if so, display it #} + {% if logo_url %} + Logo + {% endif %} + + {# Use shorttitle if it exists, otherwise fallback to project or html_title #} + {{ shorttitle|default(project)|default(docstitle) }} +{% endblock %} \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index b94582a..b126cae 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -26,6 +26,7 @@ html_theme = "furo" html_title = "Junjo AI Framework Python SDK Docs - 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API Reference | Junjo AI Studio" html_short_title = "Junjo Python Docs" html_baseurl = "https://python-api.junjo.ai/" From 6838c7588c6b635e87168e6722d49b7ba4ae69d8 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 31 Jan 2026 14:46:22 -0500 Subject: [PATCH 25/32] Example updates. --- .gitignore | 8 +++- .../ai_chat/backend/.vscode/settings.json | 3 +- examples/base/.env.example | 11 ++++- examples/base/README.md | 26 +++++----- examples/base/pyproject.toml | 6 +-- examples/base/src/base/gemini_tool.py | 48 ++++++++++++------- examples/base/src/base/otel_config.py | 2 + .../nodes/create_fact_node/node.py | 2 +- .../nodes/create_joke_node/node.py | 2 +- .../create_joke_node/test/test_prompt.py | 9 +--- .../create_joke_node/test/test_service.py | 2 +- examples/getting_started/main.py | 24 +++++----- 12 files changed, 85 insertions(+), 58 deletions(-) diff --git a/.gitignore b/.gitignore index 2d47cd7..af8bd7b 100644 --- a/.gitignore +++ b/.gitignore @@ -177,4 +177,10 @@ cython_debug/ graphviz_out # Coding Agents -.claude \ No newline at end of file +.claude +# Viberag index +.viberag/ +# MCP config (local, not committed) +.cursor/mcp.json +# MCP config (local, not committed) +.zed/settings.json diff --git a/examples/ai_chat/backend/.vscode/settings.json b/examples/ai_chat/backend/.vscode/settings.json index b907d45..a4607ff 100644 --- a/examples/ai_chat/backend/.vscode/settings.json +++ b/examples/ai_chat/backend/.vscode/settings.json @@ -14,5 +14,6 @@ "python.testing.pytestArgs": ["./src"], "python.analysis.extraPaths": ["./src"], "python.testing.unittestEnabled": false, - "python.testing.pytestEnabled": true + "python.testing.pytestEnabled": true, + "cursorpyright.analysis.extraPaths": ["./src"] } diff --git a/examples/base/.env.example b/examples/base/.env.example index 990df95..99accc1 100644 --- a/examples/base/.env.example +++ b/examples/base/.env.example @@ -1 +1,10 @@ -JUNJO_AI_STUDIO_API_KEY="[junjo_ai_studio_api_key_here]" \ No newline at end of file +# === Example Junjo App - Telemetry API Key =========================================================> +# This API key allows the example Junjo App to send telemetry to the Junjo AI Studio instance. +# Setup an API key inside Junjo AI Studio and replace the default value. +# Turn-Key Junjo AI Studio Repo: https://github.com/mdrideout/junjo-ai-studio-minimal-build +JUNJO_AI_STUDIO_API_KEY="junjo_ai_studio_api_key_here" + +# === Gemini API Key =====================================================> +# This example app uses the Gemini API to make an LLM request. +# Get a Gemini API key from https://aistudio.google.com/app/api-keys +GEMINI_API_KEY="your_gemini_api_key_here" \ No newline at end of file diff --git a/examples/base/README.md b/examples/base/README.md index 408a353..7cc7b8a 100644 --- a/examples/base/README.md +++ b/examples/base/README.md @@ -1,6 +1,6 @@ -# Junjo Example - Base +# Junjo Example: Base -This is a basic single-file implementation of Junjo's Graph Workflow Execution. It includes: +This is a baseline Junjo example application meant to showcase: - A single workflow - The Graph @@ -8,16 +8,20 @@ This is a basic single-file implementation of Junjo's Graph Workflow Execution. - Concurrent execution - A Subflow - A Conditional that determines the next node based on the results of a state update -- An opentelemetry exporter for Junjo AI Studio (optional / not required) +- Streaming telemetry to Junjo AI Studio for visualization and debugging See the **ai_chat** example for a more advanced frontend / backend E2E experience that utilizes LLM API calls. +### Recommended Setup: Junjo AI Studio + +Start an instance of [Junjo AI Studio Minimal Build](https://github.com/mdrideout/junjo-ai-studio-minimal-build) for a turn-key way to see how this example streams debugging telemetry. This is optional. Junjo works with any OpenTelemetry provider. + ### Run the example -> Note: the following commands assume your terminal is located in this directory. +> Note: the following commands assume your terminal is located in this example project's directory root. -- The graph workflow will run, logging node executions and state changes. -- If [Junjo AI Studio](https://github.com/mdrideout/junjo-ai-studio) is running, it will receive telemetry. +- The graph workflow will run, logging node executions and state changes to your console +- If [Junjo AI Studio](https://github.com/mdrideout/junjo-ai-studio-minimal-build) is running, it will receive telemetry. - Requires you to generate an API key inside the Junjo AI Studio interface, and add it as a `.env` variable here. ```bash @@ -63,12 +67,12 @@ On mission critical workflows, this setup can be used to orchestrate hundreds or This is also a great way to evaluate whether changing LLM models increases or decreases eval pass / fail rates, or changes the speed at which evals are completed. -#### Running The `create_joke_node` eval: +## Running The Sample Evals: `create_joke_node` + +This eval is strict and likely to fail all cases. This is to demonstrate the information provided by this eval pattern, that can inform improvements to prompts and workflow steps. ```bash # Run the pytest command from this directory. # Ensure you have setup the appropriate environment from the above "Run the example" instructions -$ python -m pytest src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test/test_node.py - -# This test is intentially tough to fail at least a few times for demonstration. -``` +$ python -m pytest src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test/test_node.py -v +``` \ No newline at end of file diff --git a/examples/base/pyproject.toml b/examples/base/pyproject.toml index 9229d0e..44a468b 100644 --- a/examples/base/pyproject.toml +++ b/examples/base/pyproject.toml @@ -4,10 +4,10 @@ version = "0.1.0" description = "Development example for junjo" requires-python = ">=3.11" dependencies = [ - "google-genai>=1.3.0", - "junjo", # TODO: Convert to github link when package is published + "google-genai>=1.48.0", + "junjo", "load-dotenv>=0.1.0", - "openinference-instrumentation-google-genai>=0.1.1", + "openinference-instrumentation-google-genai>=0.1.6", ] [project.optional-dependencies] diff --git a/examples/base/src/base/gemini_tool.py b/examples/base/src/base/gemini_tool.py index cfa7b0b..a056841 100644 --- a/examples/base/src/base/gemini_tool.py +++ b/examples/base/src/base/gemini_tool.py @@ -9,28 +9,27 @@ T = TypeVar("T", bound=BaseModel) class GeminiTool: - """A tool for making requests to the Google Gemini LLM via the google genai library.""" + """A tool for making requests to the Google Gemini LLM via the google genai library. + + :param prompt: The prompt to send in the request to Gemini. + :type prompt: str + :param model: The gemini model to use in the request. + See https://ai.google.dev/gemini-api/docs/models for available models. + Examples: "gemini-2.5-flash", "gemini-2.5-flash-lite" + :type model: str + :raises ValueError: If GEMINI_API_KEY environment variable is not set + + **Methods:** + + - ``text_request()``: Sends a text request to the Gemini AI model. + - ``schema_request(schema)``: Sends a request to the Gemini AI model and validates, returns a Pydantic model. + """ _prompt: str _model: str _client: genai.Client def __init__(self, prompt: str, model: str): - """ - A tool that uses the Gemini AI model to generate content based on a prompt. - - Attributes: - prompt (str): The prompt to send in the request to Gemini. - model (str): The gemini model to use in the request - - Docs: - Model Options: https://ai.google.dev/gemini-api/docs/models/gemini - Model Examples: "gemini-2.0-flash", "gemini-2.0-flash-lite-001" - - Methods: - text_request(): Sends a text request to the Gemini AI model. - model_request(): Sends a request to the Gemini AI model and validates, returns a Pydantic model. - """ # Get the API Key from env file gemini_api_key = os.getenv("GEMINI_API_KEY") @@ -66,18 +65,33 @@ async def schema_request(self, schema: type[T]) -> T: """ print(f"Making schema request with prompt: {self._prompt}") + # Extract JSON schema for better telemetry capture + schema_dict = schema.model_json_schema() + response = await self._client.aio.models.generate_content( model=self._model, contents=self._prompt, config=types.GenerateContentConfig( - max_output_tokens=500, temperature=2, response_mime_type="application/json", response_schema=schema + max_output_tokens=8192, # Increased to accommodate thinking + output tokens + temperature=0.0, # Low temperature for deterministic, consistent outputs + response_mime_type="application/json", + response_json_schema=schema_dict, # Use dict for full telemetry capture ), ) print(f"Raw response: {response}") + # Check for MAX_TOKENS finish reason + if response.candidates and str(response.candidates[0].finish_reason) == "FinishReason.MAX_TOKENS": + raise ValueError( + f"Response exceeded max_output_tokens. Increase the limit or simplify the schema. " + f"Thoughts: {response.usage_metadata.thoughts_token_count if response.usage_metadata else 'N/A'}, " + f"Total: {response.usage_metadata.total_token_count if response.usage_metadata else 'N/A'}" + ) + schema_response = response.parsed if schema_response is None: print(f"Parsed schema response is None: {response}") + raise ValueError(f"Failed to parse structured output. Response: {response.text}") # Validate again using the provided model validated = schema.model_validate(schema_response) diff --git a/examples/base/src/base/otel_config.py b/examples/base/src/base/otel_config.py index 716aaa8..4a4c627 100644 --- a/examples/base/src/base/otel_config.py +++ b/examples/base/src/base/otel_config.py @@ -26,6 +26,8 @@ def init_otel(service_name: str) -> JunjoOtelExporter | None: tracer_provider = TracerProvider(resource=resource) # Construct a Junjo exporter for Junjo AI Studio + # This example is configured for a local instance of Junjo AI Studio + # See https://github.com/mdrideout/junjo-ai-studio-minimal-build junjo_ai_studio_exporter = JunjoOtelExporter( host="localhost", port="50051", diff --git a/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_fact_node/node.py b/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_fact_node/node.py index 825d9bb..45c25ff 100644 --- a/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_fact_node/node.py +++ b/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_fact_node/node.py @@ -21,7 +21,7 @@ async def service(self, store: SampleSubflowStore) -> None: prompt = create_fact_prompt(joke) # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") + gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.5-flash") gemini_result = await gemini_tool.text_request() # Update the store with the new joke diff --git a/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/node.py b/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/node.py index 96b54a3..d95f0aa 100644 --- a/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/node.py +++ b/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/node.py @@ -21,7 +21,7 @@ async def service(self, store: SampleSubflowStore) -> None: prompt = create_joke_prompt(items) # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") + gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.5-flash") gemini_result = await gemini_tool.text_request() # Update the store with the new joke diff --git a/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test/test_prompt.py b/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test/test_prompt.py index 2ffaaec..b1e7056 100644 --- a/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test/test_prompt.py +++ b/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test/test_prompt.py @@ -15,7 +15,7 @@ def test_evaluate_joke_prompt(joke: str, items: list[str]) -> str: Requirements: - There must be only one joke in the response - The joke must be funny. -- On a scale of 1 (very bad) to 10 (worthy of an award), the joke must be a perfect 10. +- On a scale of 1 (very bad) to 10 (worthy of an award), the joke must be at least a 7. - The joke must incorporate all of the items - There is no conversational response, just the joke. - There are no wrapping quotes or any special formatting characters, only the joke. @@ -25,11 +25,4 @@ def test_evaluate_joke_prompt(joke: str, items: list[str]) -> str: Be harsh in your evaluation. If the joke is not funny enough, or does not incorporate all of the items, or is not a joke then it fails to pass the requirements. - -Output only JSON according to the following schema: - -{{ - "passed": boolean, // whether the joke passed the requirements - "reason": string, // a short concise reason why the joke failed to pass the requirements -}} """.strip() diff --git a/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test/test_service.py b/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test/test_service.py index 455304c..1676bb0 100644 --- a/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test/test_service.py +++ b/examples/base/src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test/test_service.py @@ -11,7 +11,7 @@ async def eval_create_joke_node(joke: str, items: list[str]) -> TestCreateJokeSc prompt = test_evaluate_joke_prompt(joke, items) # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") + gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.5-flash") gemini_result = await gemini_tool.schema_request(schema=TestCreateJokeSchema) return gemini_result diff --git a/examples/getting_started/main.py b/examples/getting_started/main.py index 2bd0f63..55f6dba 100644 --- a/examples/getting_started/main.py +++ b/examples/getting_started/main.py @@ -1,17 +1,17 @@ - from junjo import BaseState, BaseStore, Condition, Edge, Graph, Node, Workflow # Run With # python -m main # uv run -m main + async def main(): - """The main entry point for the application.""" + """Entrypoint for the Junjo "Getting Started" example app.""" # Define the workflow state class SampleWorkflowState(BaseState): - count: int | None = None # Does not need an initial state value - items: list[str] # Does need an initial state value + count: int | None = None # Does not need an initial state value + items: list[str] # Does need an initial state value # Define the workflow store class SampleWorkflowStore(BaseStore[SampleWorkflowState]): @@ -73,15 +73,13 @@ def create_graph() -> Graph: sink=final_node, edges=[ Edge(tail=first_node, head=count_items_node), - # Branching based on the count of items - Edge(tail=count_items_node, head=even_items_node, condition=CountIsEven()), # Only transitions if count is even - Edge(tail=count_items_node, head=odd_items_node), # Fallback if first condition is not met - + Edge(tail=count_items_node, head=even_items_node, condition=CountIsEven()), # Only if count is even + Edge(tail=count_items_node, head=odd_items_node), # Fallback if first condition is not met # Branched paths converge to the final node Edge(tail=even_items_node, head=final_node), Edge(tail=odd_items_node, head=final_node), - ] + ], ) def create_workflow() -> Workflow[SampleWorkflowState, SampleWorkflowStore]: @@ -94,10 +92,8 @@ def create_workflow() -> Workflow[SampleWorkflowState, SampleWorkflowStore]: name="Getting Started Example Workflow", graph_factory=create_graph, store_factory=lambda: SampleWorkflowStore( - initial_state=SampleWorkflowState( - items=["laser", "coffee", "horse"] - ) - ) + initial_state=SampleWorkflowState(items=["laser", "coffee", "horse"]) + ), ) # Create and execute the workflow @@ -105,6 +101,8 @@ def create_workflow() -> Workflow[SampleWorkflowState, SampleWorkflowStore]: await workflow.execute() print("Final state: ", await workflow.get_state_json()) + if __name__ == "__main__": import asyncio + asyncio.run(main()) From c617a70178f31259e2690d16645271a43a01d13e Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 31 Jan 2026 14:58:40 -0500 Subject: [PATCH 26/32] Documentation Improvements. --- README.md | 6 +++- docs/docker_reference.rst | 64 ++++++++++++++++++++++++--------------- docs/index.rst | 4 +-- docs/junjo_ai_studio.rst | 52 +++++++++++++++++-------------- uv.lock | 6 ++-- 5 files changed, 79 insertions(+), 53 deletions(-) diff --git a/README.md b/README.md index c7a5d05..45dd65e 100644 --- a/README.md +++ b/README.md @@ -279,7 +279,11 @@ $ source .venv/bin/activate # Install optional development dependencies (graphviz is optional for running the graphviz visualizer) # Graphviz, if utilized, must also be installed on the host system (see below) +# Option 1: $ uv pip install -e ".[dev,graphviz]" + +# Option 2: +$ uv sync --all-extras ``` ### Code Linting and Formatting @@ -296,7 +300,7 @@ This project utilizes [ruff](https://astral.sh/ruff) for linting and auto format # Execute the build command to preview the new docs. # They will appear in a .gitignored folder docs/_build -$ sphinx-build -b html docs docs/_build +$ uv run sphinx-build -b html docs docs/_build ``` ### Tests diff --git a/docs/docker_reference.rst b/docs/docker_reference.rst index 8bf291a..a3edb92 100644 --- a/docs/docker_reference.rst +++ b/docs/docker_reference.rst @@ -13,30 +13,38 @@ Backend Service **Image:** `mdrideout/junjo-ai-studio-backend `_ -The backend service provides the HTTP API, authentication, and data processing capabilities. It uses SQLite for metadata storage and DuckDB for analytical queries. +The backend service provides the HTTP API, authentication, and query capabilities. + +**Storage and query model (current):** + +- **SQLite** for users / sessions / API keys +- **SQLite metadata index** for fast lookup of which Parquet files may contain a trace/service +- **Parquet** files for span storage (cold tier) +- **DataFusion** to query Parquet (cold + hot snapshot) and deduplicate results **Notes:** -- Polls the ingestion service for new telemetry data -- Processes and indexes traces for querying via the web UI +- Indexes new Parquet files asynchronously (polls the shared Parquet directory) +- Calls ingestion internal gRPC (``:50052``) per query to generate a hot snapshot and return a bounded list of recently-flushed cold Parquet paths (bridges flush→index lag) Ingestion Service ~~~~~~~~~~~~~~~~~ **Image:** `mdrideout/junjo-ai-studio-ingestion `_ -The ingestion service provides high-throughput OpenTelemetry data reception using BadgerDB as a write-ahead log. This decoupled architecture ensures telemetry data is never lost, even under heavy load. +The ingestion service provides high-throughput OpenTelemetry data reception using a segmented Arrow IPC write-ahead log (WAL) that flushes to date-partitioned Parquet files (constant memory flush). **Ports:** - ``50051`` - OpenTelemetry gRPC ingestion endpoint (public, your applications connect here) -- ``50052`` - Internal gRPC for backend polling (internal) +- ``50052`` - Internal gRPC for backend queries (PrepareHotSnapshot / FlushWAL) (internal) **Notes:** - This is the primary endpoint where your Junjo workflows send telemetry -- Uses BadgerDB for durable, high-performance data ingestion -- Backend service polls this service to retrieve new data +- Uses segmented Arrow IPC WAL for durability and throughput +- Flushes WAL → Parquet and maintains a bounded list of recently flushed Parquet paths for query bridging +- Validates API keys by calling the backend's internal auth gRPC (``:50053``) Frontend Service ~~~~~~~~~~~~~~~~ @@ -72,7 +80,7 @@ This is the standard configuration for running Junjo AI Studio, suitable for bot services: junjo-ai-studio-backend: - image: mdrideout/junjo-ai-studio-backend:0.70.3 + image: mdrideout/junjo-ai-studio-backend:latest container_name: junjo-ai-studio-backend restart: unless-stopped volumes: @@ -92,10 +100,11 @@ This is the standard configuration for running Junjo AI Studio, suitable for bot - RUN_MIGRATIONS=true # Database paths (hardcoded for Docker, users configure host mount via JUNJO_HOST_DB_DATA_PATH) - JUNJO_SQLITE_PATH=/app/.dbdata/sqlite/junjo.db - - JUNJO_DUCKDB_PATH=/app/.dbdata/duckdb/traces.duckdb + - JUNJO_METADATA_DB_PATH=/app/.dbdata/sqlite/metadata.db + - JUNJO_PARQUET_STORAGE_PATH=/app/.dbdata/spans/parquet junjo-ai-studio-ingestion: - image: mdrideout/junjo-ai-studio-ingestion:0.70.3 + image: mdrideout/junjo-ai-studio-ingestion:latest container_name: junjo-ai-studio-ingestion restart: unless-stopped volumes: @@ -110,8 +119,12 @@ This is the standard configuration for running Junjo AI Studio, suitable for bot environment: - BACKEND_GRPC_HOST=junjo-ai-studio-backend - BACKEND_GRPC_PORT=50053 - # Database path (hardcoded for Docker, users configure host mount via JUNJO_HOST_DB_DATA_PATH) - - JUNJO_BADGERDB_PATH=/app/.dbdata/badgerdb + # Arrow IPC WAL directory + - WAL_DIR=/app/.dbdata/spans/wal + # Hot snapshot path (backend reads this file directly) + - SNAPSHOT_PATH=/app/.dbdata/spans/hot_snapshot.parquet + # Parquet output directory (backend indexer watches this) + - PARQUET_OUTPUT_DIR=/app/.dbdata/spans/parquet depends_on: junjo-ai-studio-backend: condition: service_started @@ -123,7 +136,7 @@ This is the standard configuration for running Junjo AI Studio, suitable for bot start_period: 5s junjo-ai-studio-frontend: - image: mdrideout/junjo-ai-studio-frontend:0.70.3 + image: mdrideout/junjo-ai-studio-frontend:latest container_name: junjo-ai-studio-frontend restart: unless-stopped ports: @@ -246,17 +259,19 @@ Local Directory Structure . ├── docker-compose.yml ├── .env - └── dbdata/ - ├── sqlite/ # Backend metadata - ├── duckdb/ # Backend analytics - └── badgerdb/ # Ingestion WAL + └── .dbdata/ + ├── sqlite/ # Backend SQLite (junjo.db + metadata.db) + └── spans/ + ├── wal/ # Arrow IPC WAL segments (hot) + ├── parquet/ # Date-partitioned Parquet files (cold) + └── hot_snapshot.parquet **Create directories:** .. code-block:: bash - mkdir -p dbdata/{sqlite,duckdb,badgerdb} - chmod -R 755 dbdata + mkdir -p .dbdata/sqlite .dbdata/spans/wal .dbdata/spans/parquet + chmod -R 755 .dbdata Block Storage (Production) ~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -271,7 +286,7 @@ For production deployments, mount block storage at a consistent location: sudo mount -o defaults,nofail /dev/disk/by-id/scsi-0DO_Volume_junjo-data /mnt/junjo-data # Create database directories - sudo mkdir -p /mnt/junjo-data/{sqlite,duckdb,badgerdb} + sudo mkdir -p /mnt/junjo-data/sqlite /mnt/junjo-data/spans/wal /mnt/junjo-data/spans/parquet sudo chown -R $USER:$USER /mnt/junjo-data Update volume paths in docker-compose.yml: @@ -279,9 +294,7 @@ Update volume paths in docker-compose.yml: .. code-block:: yaml volumes: - - /mnt/junjo-data/sqlite:/dbdata/sqlite - - /mnt/junjo-data/duckdb:/dbdata/duckdb - - /mnt/junjo-data/badgerdb:/dbdata/badgerdb + - /mnt/junjo-data:/app/.dbdata Port Mappings ------------- @@ -294,7 +307,8 @@ Services communicate internally via the Docker network. These ports do not need .. code-block:: text Backend (1323) ←→ Frontend - Backend (50053) ←→ Ingestion (50052) + Backend (50052) ←→ Ingestion (internal RPC) + Backend (50053) ←→ Ingestion (internal auth RPC) External Access ~~~~~~~~~~~~~~~ @@ -434,4 +448,4 @@ Next Steps - See :doc:`junjo_ai_studio` for setup and configuration - Review :doc:`deployment` for production deployment examples -- Explore :doc:`opentelemetry` for application instrumentation \ No newline at end of file +- Explore :doc:`opentelemetry` for application instrumentation diff --git a/docs/index.rst b/docs/index.rst index bf0bae3..91af925 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -57,7 +57,7 @@ Whether you’re building a simple chatbot, a complex data manipulation pipeline :align: center :width: 800px -*A screenshot of Junjo's companion open-telemetry ingestion server to make debugging graph workflow state easy.* +*A screenshot of Junjo AI Studio, Junjo's companion OpenTelemetry observability platform for debugging graph workflow state.* Benefits --------- @@ -126,4 +126,4 @@ See the :doc:`api` page for the full API reference. Eval-Driven Development ------------------------ -See the :doc:`eval_driven_dev` page for more information on how to use Junjo for eval-driven development. \ No newline at end of file +See the :doc:`eval_driven_dev` page for more information on how to use Junjo for eval-driven development. diff --git a/docs/junjo_ai_studio.rst b/docs/junjo_ai_studio.rst index c53d28c..5b75691 100644 --- a/docs/junjo_ai_studio.rst +++ b/docs/junjo_ai_studio.rst @@ -45,8 +45,8 @@ Installation & Setup Junjo AI Studio is composed of three Docker services that work together: -1. **Backend**: API server and data processing (SQLite + DuckDB) -2. **Ingestion Service**: High-throughput OpenTelemetry data receiver (BadgerDB) +1. **Backend**: FastAPI HTTP API + auth, DataFusion queries over Parquet, plus a SQLite metadata index (and SQLite for users / API keys) +2. **Ingestion Service**: High-throughput OTLP receiver (Rust) with segmented Arrow IPC WAL → Parquet (cold), and on-demand hot snapshots for real-time queries 3. **Frontend**: Web UI for visualization and debugging Quick Start Options @@ -88,11 +88,16 @@ If you prefer to integrate Junjo AI Studio into an existing project, here's a mi image: mdrideout/junjo-ai-studio-backend:latest ports: - "1323:1323" # HTTP API - - "50053:50053" # Internal gRPC volumes: - - ./.dbdata/sqlite:/dbdata/sqlite - - ./.dbdata/duckdb:/dbdata/duckdb + - ${JUNJO_HOST_DB_DATA_PATH:-./.dbdata}:/app/.dbdata env_file: .env + environment: + - INGESTION_HOST=junjo-ai-studio-ingestion + - INGESTION_PORT=50052 + - RUN_MIGRATIONS=true + - JUNJO_SQLITE_PATH=/app/.dbdata/sqlite/junjo.db + - JUNJO_METADATA_DB_PATH=/app/.dbdata/sqlite/metadata.db + - JUNJO_PARQUET_STORAGE_PATH=/app/.dbdata/spans/parquet networks: - junjo-network @@ -100,12 +105,19 @@ If you prefer to integrate Junjo AI Studio into an existing project, here's a mi image: mdrideout/junjo-ai-studio-ingestion:latest ports: - "50051:50051" # OTel data ingestion (your app connects here) - - "50052:50052" # Internal gRPC volumes: - - ./.dbdata/badgerdb:/dbdata/badgerdb + - ${JUNJO_HOST_DB_DATA_PATH:-./.dbdata}:/app/.dbdata env_file: .env + environment: + - BACKEND_GRPC_HOST=junjo-ai-studio-backend + - BACKEND_GRPC_PORT=50053 + - WAL_DIR=/app/.dbdata/spans/wal + - SNAPSHOT_PATH=/app/.dbdata/spans/hot_snapshot.parquet + - PARQUET_OUTPUT_DIR=/app/.dbdata/spans/parquet networks: - junjo-network + depends_on: + - junjo-ai-studio-backend junjo-ai-studio-frontend: image: mdrideout/junjo-ai-studio-frontend:latest @@ -142,7 +154,7 @@ Junjo AI Studio is designed to run on minimal resources: - **CPU**: Single shared vCPU is sufficient - **RAM**: 1GB minimum -- **Storage**: Uses SQLite, DuckDB, and BadgerDB (all embedded databases) +- **Storage**: Uses SQLite + Parquet (cold storage) + Arrow IPC WAL segments (hot storage) This makes it affordable to deploy on small cloud VMs. @@ -155,11 +167,7 @@ Step 1: Generate an API Key 1. Open Junjo AI Studio UI at http://localhost:5153 2. Navigate to Settings → API Keys 3. Create a new API key -4. Copy the key to your environment - -.. code-block:: bash - - export JUNJO_AI_STUDIO_API_KEY="your-api-key-here" +4. Set the key in your application's environment as ``JUNJO_AI_STUDIO_API_KEY`` Step 2: Configure OpenTelemetry in Your Application ---------------------------------------------------- @@ -388,12 +396,12 @@ Junjo AI Studio uses a three-service architecture for scalability and reliabilit .. code-block:: text Your Application (Junjo Python Library) - ↓ (sends OTel spans via gRPC) + ↓ (sends OTel spans via gRPC + x-junjo-api-key) Ingestion Service :50051 - ↓ (writes to BadgerDB WAL) - ↓ (backend polls via internal gRPC :50052) + ↓ (writes segmented Arrow IPC WAL, flushes to Parquet) + ↓ (backend calls internal gRPC :50052 for hot snapshot + recent cold paths) Backend Service :1323 - ↓ (stores in SQLite + DuckDB) + ↓ (indexes cold Parquet metadata into SQLite, queries Parquet via DataFusion) ↓ (serves HTTP API) Frontend :5153 (web UI) @@ -401,8 +409,8 @@ Junjo AI Studio uses a three-service architecture for scalability and reliabilit **Port Reference:** - **50051**: Public gRPC - Your application sends telemetry here -- **50052**: Internal gRPC - Backend reads from ingestion service -- **50053**: Internal gRPC - Backend server communication +- **50052**: Internal gRPC - Backend calls ingestion (PrepareHotSnapshot / FlushWAL) +- **50053**: Internal gRPC - Ingestion calls backend (ValidateApiKey) - **1323**: Public HTTP - API server - **5153**: Public HTTP - Web UI @@ -429,8 +437,8 @@ Performance issues ------------------ - Use sampling for high-volume workflows -- The ingestion service uses BadgerDB as a write-ahead log for durability -- Backend polls and indexes data asynchronously +- The ingestion service uses a segmented Arrow IPC WAL and streams flushes to Parquet (constant memory) +- The backend indexes new Parquet files asynchronously and queries cold + hot data with deduplication - See `Junjo AI Studio repository `_ for tuning options Docker Compose not starting @@ -447,4 +455,4 @@ Next Steps - Explore :doc:`opentelemetry` for general OpenTelemetry configuration - Learn about :doc:`visualizing_workflows` for static Graphviz diagrams - See :doc:`eval_driven_dev` for testing workflows -- Review :doc:`concurrency` for understanding parallel execution traces \ No newline at end of file +- Review :doc:`concurrency` for understanding parallel execution traces diff --git a/uv.lock b/uv.lock index 9cd2e27..4c53edd 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.13'", @@ -186,10 +186,10 @@ dev = [ [package.metadata] requires-dist = [ - { name = "google-genai", specifier = ">=1.3.0" }, + { name = "google-genai", specifier = ">=1.48.0" }, { name = "junjo", editable = "." }, { name = "load-dotenv", specifier = ">=0.1.0" }, - { name = "openinference-instrumentation-google-genai", specifier = ">=0.1.1" }, + { name = "openinference-instrumentation-google-genai", specifier = ">=0.1.6" }, { name = "pytest", marker = "extra == 'dev'" }, { name = "pytest-asyncio", marker = "extra == 'dev'" }, { name = "ruff", marker = "extra == 'dev'", specifier = "~=0.9.5" }, From b05e78e2d5ab11f004adc2aac2e8f946f0ba3bcc Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sat, 31 Jan 2026 15:54:26 -0500 Subject: [PATCH 27/32] Added description. --- examples/getting_started/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/getting_started/pyproject.toml b/examples/getting_started/pyproject.toml index ec92f23..26d5c7a 100644 --- a/examples/getting_started/pyproject.toml +++ b/examples/getting_started/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "getting-started" version = "0.1.0" -description = "Add your description here" +description = "The Junjo Getting Started documentation example." readme = "README.md" requires-python = ">=3.11" dependencies = ["junjo", "ruff>=0.9.5"] From 41722ac96f7e51f0c2a649fe1d5dfce748f9a502 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Mon, 2 Feb 2026 00:12:32 -0500 Subject: [PATCH 28/32] examples/ai_chat: switch to xAI Grok via xai-sdk - Add GrokTool for chat + image gen/edit with store_messages disabled\n- Update workflow nodes to use grok-4-1-non-reasoning and grok-imagine-image\n- Document non-storage behavior with ADR\n- Update .env.example and README\n- Improve frontend chat UX for images/read state --- examples/ai_chat/README.md | 31 +- examples/ai_chat/backend/.env.example | 11 +- examples/ai_chat/backend/pyproject.toml | 8 + .../src/app/ai_services/gemini/gemini_tool.py | 208 ++++++++---- .../ADR-0001-non-storage-responses-api.md | 26 ++ .../src/app/ai_services/grok/__init__.py | 3 + .../src/app/ai_services/grok/grok_tool.py | 298 ++++++++++++++++++ .../src/app/db/models/contact/routes.py | 8 +- .../src/app/db/models/message/repository.py | 14 +- examples/ai_chat/backend/src/app/main.py | 4 +- .../ai_chat/backend/src/app/otel_config.py | 21 +- examples/ai_chat/backend/src/app/visualize.py | 2 + .../nodes/avatar_inspiration/node.py | 15 +- .../nodes/create_avatar/node.py | 9 +- .../create_contact/nodes/create_bio/node.py | 11 +- .../nodes/create_bio/test/test_service.py | 10 +- .../create_contact/nodes/create_name/node.py | 20 +- .../services/get_nearest_city_state.py | 11 +- .../create_contact/nodes/select_sex/node.py | 8 +- .../nodes/select_sex/test/__init__.py | 1 + .../nodes/select_sex/test/test_node.py | 36 +++ .../app/workflows/create_contact/workflow.py | 5 +- .../nodes/create_image/node.py | 36 +-- .../nodes/image_inspiration/node.py | 16 +- .../nodes/image_inspiration/prompt.py | 8 +- .../nodes/assess_message_directive/node.py | 18 +- .../nodes/create_date_idea_response/node.py | 17 +- .../nodes/create_general_response/node.py | 11 +- .../nodes/create_work_response/node.py | 17 +- .../sub_sub_flow/sub_flow_node_1/node.py | 12 +- .../sub_sub_flow/sub_flow_node_2/node.py | 11 +- .../sub_sub_flow/sub_flow_node_3/node.py | 12 +- .../test_sub_flow/sub_flow_node_1/node.py | 14 +- .../test_sub_flow/sub_flow_node_2/node.py | 14 +- .../test_sub_flow/sub_flow_node_3/node.py | 14 +- examples/ai_chat/frontend/src/App.tsx | 8 + .../ai_chat/frontend/src/api/chat/hook.ts | 10 +- .../frontend/src/api/chat/read-store.ts | 42 +++ .../ai_chat/frontend/src/api/chat/store.ts | 38 ++- .../ai_chat/frontend/src/api/contact/fetch.ts | 11 +- .../hooks/create-upsert-contact-hook.ts | 7 +- .../api/message/hooks/get-messages-hook.ts | 10 + .../frontend/src/components/ChatForm.tsx | 3 + .../frontend/src/components/ChatWindow.tsx | 17 +- .../src/components/avatars/SidebarAvatar.tsx | 10 +- .../bubbles/ChatReceiveImageBubble.tsx | 9 +- .../modals/FullscreenImageModal.tsx | 51 +++ .../src/components/sidebar/ChatSidebar.tsx | 5 + .../components/sidebar/NewContactButton.tsx | 40 ++- 49 files changed, 925 insertions(+), 296 deletions(-) create mode 100644 examples/ai_chat/backend/src/app/ai_services/grok/ADR-0001-non-storage-responses-api.md create mode 100644 examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/test/__init__.py create mode 100644 examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/test/test_node.py create mode 100644 examples/ai_chat/frontend/src/api/chat/read-store.ts create mode 100644 examples/ai_chat/frontend/src/components/modals/FullscreenImageModal.tsx diff --git a/examples/ai_chat/README.md b/examples/ai_chat/README.md index 5e47a95..1617a47 100644 --- a/examples/ai_chat/README.md +++ b/examples/ai_chat/README.md @@ -12,6 +12,12 @@ This is a more complete example of Junjo, showcasing: - FastAPI Backend - SQLite for persistence +## AI provider + +This example is currently wired up to use **xAI (Grok)** for text + image generation (via `GrokTool`). + +If you'd rather use **Gemini**, you can swap the workflow nodes to use `GeminiTool` instead (both live in `backend/src/app/ai_services/`). + ## Run the example ```bash @@ -25,24 +31,23 @@ $ npm run dev # -------------------------# # BACKEND (from ./backend) -# - Using uv package manager https://docs.astral.sh/uv/) +# - Using uv package manager https://docs.astral.sh/uv/ # -# Create a virtual environment if one doesn't exist yet (recommend python 3.11) -$ uv venv --python 3.11 - -# Make sure the backend virtual environment is activated -$ source .venv/bin/activate - -# Ensure all packages are installed -$ uv pip install -e . +# This repo is a `uv` workspace. The virtual environment lives at the repo root +# (`../../../.venv` from here), not inside this backend directory. +# +# Ensure all packages are installed (Python 3.11) +$ uv sync --python 3.11 --package app # Start the backend -$ fastapi dev src/app/main.py +$ uv run --package app fastapi dev src/app/main.py # Visualize the graph -$ python -m src.app.visualize +$ uv run --package app -m app.visualize ``` +Environment variables live in `backend/.env.example` (copy to `backend/.env` and fill in the keys you want to use). + ### Telemetry @@ -50,6 +55,6 @@ Have the [junjo-ai-studio](https://github.com/mdrideout/junjo-ai-studio) running ### Clearing the SQLite database -The SQLite database (see `.backend/src/app/db/db_config.py`) should persist inside the `.backend/sqlite-data` folder in this project. +The SQLite database (see `backend/src/app/db/db_config.py`) should persist inside the `backend/sqlite-data` folder in this project. -**Delete `.backend/sqlite-data`** to clear the sqlite database and start fresh. A new database will be created automatically at the next app startup. \ No newline at end of file +**Delete `backend/sqlite-data`** to clear the sqlite database and start fresh. A new database will be created automatically at the next app startup. diff --git a/examples/ai_chat/backend/.env.example b/examples/ai_chat/backend/.env.example index 2eed407..38edc05 100644 --- a/examples/ai_chat/backend/.env.example +++ b/examples/ai_chat/backend/.env.example @@ -1,2 +1,11 @@ +# Create your Junjo AI Studio API Key inside Junjo Studio JUNJO_AI_STUDIO_API_KEY="[junjo_ai_studio_api_key_here]" -GEMINI_API_KEY="[gemini_api_key_here]" \ No newline at end of file + +# xAI is the currently setup provider of chat and image generation responses. +XAI_API_KEY="[xai_api_key_here]" + +# Junjo nodes can be configured to use GeminiTool instead of GrokTool +GEMINI_API_KEY="[gemini_api_key_here]" + +# Required by xAI SDK to produce opentelemetry +OTEL_EXPORTER_OTLP_PROTOCOL="grpc" \ No newline at end of file diff --git a/examples/ai_chat/backend/pyproject.toml b/examples/ai_chat/backend/pyproject.toml index 489fbbd..d6be038 100644 --- a/examples/ai_chat/backend/pyproject.toml +++ b/examples/ai_chat/backend/pyproject.toml @@ -1,3 +1,7 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + [project] name = "app" version = "0.1.0" @@ -23,6 +27,7 @@ dependencies = [ "junjo", "pillow>=11.3.0", "openinference-instrumentation-google-genai>=0.1.1", + "xai-sdk[telemetry-grpc]>=1.6.1", ] [tool.ruff] @@ -49,3 +54,6 @@ junjo = { workspace = true } [dependency-groups] dev = ["ruff>=0.9.5"] + +[tool.setuptools.packages.find] +where = ["src"] diff --git a/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py b/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py index 439baef..e87fde2 100644 --- a/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py +++ b/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py @@ -46,12 +46,137 @@ def __init__(self, prompt: str, model: str): self._prompt = prompt self._model = model + @staticmethod + def _safety_settings_off() -> list[types.SafetySetting]: + """ + Turn off all adjustable Gemini safety filters. + + Note: The Gemini API still enforces non-adjustable protections (for example, child safety). + """ + + return [ + types.SafetySetting( + category=types.HarmCategory.HARM_CATEGORY_HARASSMENT, + threshold=types.HarmBlockThreshold.OFF, + ), + types.SafetySetting( + category=types.HarmCategory.HARM_CATEGORY_HATE_SPEECH, + threshold=types.HarmBlockThreshold.OFF, + ), + types.SafetySetting( + category=types.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT, + threshold=types.HarmBlockThreshold.OFF, + ), + types.SafetySetting( + category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, + threshold=types.HarmBlockThreshold.OFF, + ), + types.SafetySetting( + category=types.HarmCategory.HARM_CATEGORY_CIVIC_INTEGRITY, + threshold=types.HarmBlockThreshold.OFF, + ), + ] + + def _log_block_reason(self, response: object, *, context: str) -> None: + """ + Best-effort logging for Gemini content blocks. + + Useful when the API returns no content or omits image parts due to safety / policy restrictions. + """ + + try: + response_id = getattr(response, "response_id", None) + model_version = getattr(response, "model_version", None) + logger.warning( + f"{context}: Gemini response blocked/empty (response_id={response_id}, model_version={model_version})" + ) + + prompt_feedback = getattr(response, "prompt_feedback", None) + if prompt_feedback: + logger.warning( + f"{context}: prompt_feedback.block_reason={getattr(prompt_feedback, 'block_reason', None)} " + f"message={getattr(prompt_feedback, 'block_reason_message', None)}" + ) + for rating in getattr(prompt_feedback, "safety_ratings", []) or []: + logger.warning( + f"{context}: prompt safety_rating " + f"category={getattr(rating, 'category', None)} " + f"blocked={getattr(rating, 'blocked', None)} " + f"probability={getattr(rating, 'probability', None)} " + f"severity={getattr(rating, 'severity', None)}" + ) + + candidates = getattr(response, "candidates", []) or [] + if candidates: + candidate = candidates[0] + logger.warning( + f"{context}: candidate.finish_reason={getattr(candidate, 'finish_reason', None)} " + f"finish_message={getattr(candidate, 'finish_message', None)}" + ) + for rating in getattr(candidate, "safety_ratings", []) or []: + logger.warning( + f"{context}: candidate safety_rating " + f"category={getattr(rating, 'category', None)} " + f"blocked={getattr(rating, 'blocked', None)} " + f"probability={getattr(rating, 'probability', None)} " + f"severity={getattr(rating, 'severity', None)}" + ) + + # If available, log raw prompt feedback / safety ratings from the SDK's HTTP response. + # This helps when the client library doesn't fully map newer enum values (e.g. IMAGE_OTHER). + sdk_http_response = getattr(response, "sdk_http_response", None) + if sdk_http_response is not None: + raw = getattr(sdk_http_response, "json", None) + if isinstance(raw, dict): + prompt_feedback_raw = raw.get("promptFeedback") or raw.get("prompt_feedback") + if isinstance(prompt_feedback_raw, dict): + logger.warning( + f"{context}: raw.prompt_feedback.block_reason=" + f"{prompt_feedback_raw.get('blockReason') or prompt_feedback_raw.get('block_reason')} " + f"message={prompt_feedback_raw.get('blockReasonMessage') or prompt_feedback_raw.get('block_reason_message')}" + ) + for rating in ( + prompt_feedback_raw.get("safetyRatings") + or prompt_feedback_raw.get("safety_ratings") + or [] + ): + logger.warning( + f"{context}: raw.prompt safety_rating " + f"category={rating.get('category')} " + f"blocked={rating.get('blocked')} " + f"probability={rating.get('probability')} " + f"severity={rating.get('severity')}" + ) + + candidates_raw = raw.get("candidates") or [] + if candidates_raw: + candidate_raw = candidates_raw[0] + logger.warning( + f"{context}: raw.candidate.finish_reason=" + f"{candidate_raw.get('finishReason') or candidate_raw.get('finish_reason')} " + f"finish_message={candidate_raw.get('finishMessage') or candidate_raw.get('finish_message')}" + ) + for rating in candidate_raw.get("safetyRatings") or candidate_raw.get("safety_ratings") or []: + logger.warning( + f"{context}: raw.candidate safety_rating " + f"category={rating.get('category')} " + f"blocked={rating.get('blocked')} " + f"probability={rating.get('probability')} " + f"severity={rating.get('severity')}" + ) + except Exception: + logger.exception(f"{context}: failed to log Gemini block reason") + async def text_request(self) -> str: """ Sends a text request to the Gemini AI model. """ logger.debug(f"Making text request with model: {self._model}, prompt: {self._prompt}") - response = await self._client.aio.models.generate_content(model=self._model, contents=self._prompt) + response = await self._client.aio.models.generate_content( + model=self._model, + contents=self._prompt, + config=types.GenerateContentConfig(safety_settings=self._safety_settings_off()), + ) text = response.text if text is None: @@ -76,7 +201,11 @@ async def schema_request(self, schema: type[T]) -> T: model=self._model, contents=self._prompt, config=types.GenerateContentConfig( - max_output_tokens=500, temperature=2, response_mime_type="application/json", response_schema=schema + max_output_tokens=500, + temperature=2, + response_mime_type="application/json", + response_schema=schema, + safety_settings=self._safety_settings_off(), ), ) logger.info(f"Raw response: {response}") @@ -90,40 +219,6 @@ async def schema_request(self, schema: type[T]) -> T: return validated - - async def imagen_3_request(self) -> bytes: - """ - Sends a request to the Gemini AI model for an image. - """ - bytes = None - - logger.debug(f"Making imagen_3_request with model: {self._model}, prompt: {self._prompt}") - response = await self._client.aio.models.generate_images( - model=self._model, - prompt=self._prompt, - config=types.GenerateImagesConfig( - number_of_images=1, aspect_ratio="1:1", person_generation=types.PersonGeneration.ALLOW_ADULT - ), - ) - - if not response.generated_images: - logger.error(f"No generated images in response: {response}") - raise ValueError("No generated images in response") - - for generated_image in response.generated_images: - if not generated_image.image: - logger.error(f"No image in generated image: {generated_image}") - raise ValueError("No image in generated image") - - # Get the image bytes - bytes = generated_image.image.image_bytes - - if not bytes: - logger.error(f"No image bytes in generated image: {generated_image}") - raise ValueError("No image bytes in generated image") - - return bytes - async def gemini_image_request(self) -> bytes: """ Sends a request to the Gemini AI model for an image. @@ -134,18 +229,24 @@ async def gemini_image_request(self) -> bytes: response = await self._client.aio.models.generate_content( model=self._model, contents=self._prompt, - config=types.GenerateContentConfig(response_modalities=["TEXT", "IMAGE"]), + config=types.GenerateContentConfig( + response_modalities=["TEXT", "IMAGE"], + safety_settings=self._safety_settings_off(), + ), ) if not response.candidates: + self._log_block_reason(response, context="gemini_image_request:no_candidates") logger.error(f"No candidates in response: {response}") raise ValueError("No candidates in response") if not response.candidates[0].content: + self._log_block_reason(response, context="gemini_image_request:no_content") logger.error(f"No content in response: {response}") raise ValueError("No content in response") if not response.candidates[0].content.parts: + self._log_block_reason(response, context="gemini_image_request:no_parts") logger.error(f"No parts in response: {response}") raise ValueError("No parts in response") @@ -162,6 +263,7 @@ async def gemini_image_request(self) -> bytes: bytes = part.inline_data.data if not bytes: + self._log_block_reason(response, context="gemini_image_request:no_image_bytes") logger.error(f"No image bytes in part: {part}") raise ValueError("No image bytes in part") @@ -182,48 +284,31 @@ async def gemini_image_edit_request(self, image_bytes: bytes) -> tuple[bytes | N model=self._model, contents=[self._prompt, image], config=types.GenerateContentConfig( - safety_settings=[ - types.SafetySetting( - category=types.HarmCategory.HARM_CATEGORY_HATE_SPEECH, - threshold=types.HarmBlockThreshold.BLOCK_NONE, - ), - types.SafetySetting( - category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, - threshold=types.HarmBlockThreshold.BLOCK_NONE, - ), - types.SafetySetting( - category=types.HarmCategory.HARM_CATEGORY_HARASSMENT, - threshold=types.HarmBlockThreshold.BLOCK_NONE, - ), - types.SafetySetting( - category=types.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT, - threshold=types.HarmBlockThreshold.BLOCK_NONE, - ), - types.SafetySetting( - category=types.HarmCategory.HARM_CATEGORY_CIVIC_INTEGRITY, - threshold=types.HarmBlockThreshold.BLOCK_NONE, - ), - ] + response_modalities=["TEXT", "IMAGE"], + safety_settings=self._safety_settings_off(), ), ) if not response.candidates: + self._log_block_reason(response, context="gemini_image_edit_request:no_candidates") logger.error(f"No candidates in response: {response}") raise ValueError("No candidates in response") if not response.candidates[0].content: # If prohibited content if response.candidates[0].finish_reason is not None: + self._log_block_reason(response, context="gemini_image_edit_request:blocked") logger.warning( - "Prohibited content detected in response." - f"Finish Reason: {response.candidates[0].finish_reason}" + f"Prohibited content detected in response.Finish Reason: {response.candidates[0].finish_reason}" ) return None, str(response.candidates[0].finish_reason) + self._log_block_reason(response, context="gemini_image_edit_request:no_content") logger.error(f"No content in response: {response}") raise ValueError("No content in response") if not response.candidates[0].content.parts: + self._log_block_reason(response, context="gemini_image_edit_request:no_parts") logger.error(f"No parts in response: {response}") raise ValueError("No parts in response") @@ -238,6 +323,7 @@ async def gemini_image_edit_request(self, image_bytes: bytes) -> tuple[bytes | N bytes_response = part.inline_data.data if not bytes_response: + self._log_block_reason(response, context="gemini_image_edit_request:no_image_bytes") logger.error(f"No image bytes in response: {response}") return bytes_response, text_response diff --git a/examples/ai_chat/backend/src/app/ai_services/grok/ADR-0001-non-storage-responses-api.md b/examples/ai_chat/backend/src/app/ai_services/grok/ADR-0001-non-storage-responses-api.md new file mode 100644 index 0000000..1230e16 --- /dev/null +++ b/examples/ai_chat/backend/src/app/ai_services/grok/ADR-0001-non-storage-responses-api.md @@ -0,0 +1,26 @@ +# ADR-0001: Disable xAI Responses API storage + +- Status: Accepted +- Date: 2026-02-01 + +## Context + +The xAI Responses API supports server-side conversation state by storing previous request/response messages. This can be convenient, but this app explicitly manages and submits chat history on the client side (or within the workflow) and does **not** want xAI to store conversation context. + +## Decision + +`GrokTool` always disables server-side conversation storage by creating chats with: + +- `store_messages=False` + +Additionally, `GrokTool` does not use `previous_response_id`-style chaining; callers must provide the full message history they want the model to consider. + +## Consequences + +- Privacy/retention: xAI is instructed not to store conversation state for our responses requests. +- Request size: callers must send the full conversation context each time (or a summarized version). +- No server-side continuity: features that rely on stored history or `previous_response_id` will not work by design. + +## References + +- xAI Chat/Responses guide (disable storing): https://docs.x.ai/docs/guides/chat#disable-storing-previous-requestresponse-on-server diff --git a/examples/ai_chat/backend/src/app/ai_services/grok/__init__.py b/examples/ai_chat/backend/src/app/ai_services/grok/__init__.py index e69de29..5414b0c 100644 --- a/examples/ai_chat/backend/src/app/ai_services/grok/__init__.py +++ b/examples/ai_chat/backend/src/app/ai_services/grok/__init__.py @@ -0,0 +1,3 @@ +from app.ai_services.grok.grok_tool import GrokAPIError, GrokTool + +__all__ = ["GrokAPIError", "GrokTool"] diff --git a/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py b/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py index e69de29..af0a0d7 100644 --- a/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py +++ b/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py @@ -0,0 +1,298 @@ +import base64 +import inspect +import os +from io import BytesIO +from typing import Any, TypeVar + +from loguru import logger +from PIL import Image +from pydantic import BaseModel +from xai_sdk import AsyncClient +from xai_sdk.chat import assistant, image as xai_image, system, user + +T = TypeVar("T", bound=BaseModel) + + +class GrokAPIError(RuntimeError): + pass + + +class GrokTool: + """ + A tool for making requests to xAI Grok via the official xAI Python SDK (`xai-sdk`). + + Notes: + - This tool is intentionally stateless: callers provide the full message history each call. + - Server-side conversation storage is disabled (`store_messages=False`). + """ + + _prompt: str | None + _model: str + _client: AsyncClient + + def __init__( + self, + *, + prompt: str | None = None, + model: str, + timeout_s: float = 60.0, + api_key: str | None = None, + ) -> None: + xai_api_key = api_key or os.getenv("XAI_API_KEY") + if not xai_api_key: + raise ValueError("XAI_API_KEY environment variable not set") + + # The xAI SDK uses seconds for timeouts (default: 900s). + timeout = max(1, int(timeout_s)) + + self._client = AsyncClient(api_key=xai_api_key, timeout=timeout) + self._prompt = prompt + self._model = model + + @staticmethod + async def _maybe_await(value: Any) -> Any: + if inspect.isawaitable(value): + return await value + return value + + @staticmethod + def build_messages_from_prompt(prompt: str) -> list[dict[str, Any]]: + """ + Convenience helper to build a minimal message list from a single prompt. + """ + + return [{"role": "user", "content": prompt}] + + @staticmethod + def build_image_input_message(*, image_url: str, text: str, mime_type: str | None = None) -> dict[str, Any]: + """ + Convenience helper for vision-style chat messages (image understanding). + + If `mime_type` is provided, `image_url` is assumed to be raw base64 (no prefix) and will be converted to a + `data:;base64,...` data URL. + """ + + url = image_url + if mime_type is not None and not image_url.startswith("data:"): + url = f"data:{mime_type};base64,{image_url}" + + return { + "role": "user", + "content": [ + {"type": "input_image", "image_url": url}, + {"type": "input_text", "text": text}, + ], + } + + @staticmethod + def _stringify(value: Any) -> str: + if value is None: + return "" + if isinstance(value, str): + return value + return str(value) + + @classmethod + def _to_xai_message(cls, message: dict[str, Any]) -> Any: + role = cls._stringify(message.get("role")).strip().lower() + content = message.get("content") + + if role == "system": + return system(cls._stringify(content)) + + if role == "assistant": + return assistant(cls._stringify(content)) + + if role != "user": + logger.warning(f"Unknown message role '{role}', treating as user.") + + # user messages can interleave text + images: user("...", image("..."), image("...")) + if isinstance(content, str): + return user(content) + + if isinstance(content, list): + text_parts: list[str] = [] + image_parts: list[Any] = [] + + for part in content: + if not isinstance(part, dict): + continue + + part_type = part.get("type") + if part_type in ("input_text", "text"): + text = part.get("text") + if isinstance(text, str) and text: + text_parts.append(text) + continue + + if part_type in ("input_image", "image"): + image_url = part.get("image_url") + if isinstance(image_url, str) and image_url: + image_parts.append(xai_image(image_url=image_url)) + continue + + text = "\n".join(text_parts).strip() + if image_parts: + return user(text, *image_parts) + + return user(text) + + return user(cls._stringify(content)) + + def _xai_messages(self, messages: list[dict[str, Any]] | None) -> list[Any]: + if messages is None: + if not self._prompt: + raise ValueError("Either `messages` must be provided or `prompt` must be set in GrokTool().") + messages = self.build_messages_from_prompt(self._prompt) + + if not isinstance(messages, list) or not messages: + raise ValueError("messages must be a non-empty list") + + return [self._to_xai_message(m) for m in messages] + + async def text_request(self, *, messages: list[dict[str, Any]] | None = None) -> str: + """ + Create a text response using xAI's chat API. + + Server-side message storage is disabled (`store_messages=False`). + """ + + xai_messages = self._xai_messages(messages) + + try: + chat = self._client.chat.create(model=self._model, messages=xai_messages, store_messages=False) + response = await self._maybe_await(chat.sample()) + except Exception as e: + logger.exception("xAI chat request failed") + raise GrokAPIError(str(e)) from e + + text = getattr(response, "content", None) + if not isinstance(text, str) or not text.strip(): + raise ValueError("No text in xAI response") + + return text.strip() + + async def schema_request(self, schema: type[T], *, messages: list[dict[str, Any]] | None = None) -> T: + """ + Request structured output and validate it against a Pydantic schema. + """ + + xai_messages = self._xai_messages(messages) + + try: + chat = self._client.chat.create(model=self._model, messages=xai_messages, store_messages=False) + parse_result = chat.parse(schema) + response, parsed = await self._maybe_await(parse_result) + except Exception as e: + logger.exception("xAI schema request failed") + raise GrokAPIError(str(e)) from e + + _ = response # reserved for future logging/telemetry + if parsed is None: + raise ValueError("Parsed schema result is None") + + return parsed + + @staticmethod + def _data_url(image_bytes: bytes, *, mime_type: str) -> str: + b64 = base64.b64encode(image_bytes).decode("ascii") + return f"data:{mime_type};base64,{b64}" + + @staticmethod + def _ensure_png_bytes(image_bytes: bytes) -> bytes: + """ + Our app persists generated images as `.png`. Convert bytes to PNG when possible. + """ + + try: + with Image.open(BytesIO(image_bytes)) as img: + out = BytesIO() + img.save(out, format="PNG") + return out.getvalue() + except Exception: + return image_bytes + + async def image_request( + self, + *, + prompt: str | None = None, + n: int = 1, + aspect_ratio: str | None = None, + ) -> bytes: + """ + Generate an image and return the first image bytes. + + Uses `image_format="base64"` to avoid xAI-hosted image URLs. + """ + + image_prompt = prompt or self._prompt + if not image_prompt: + raise ValueError("Either `prompt` must be provided or `prompt` must be set in GrokTool().") + + try: + if n != 1 or aspect_ratio is not None: + results = await self._maybe_await( + self._client.image.sample_batch( + model=self._model, + prompt=image_prompt, + n=n, + image_format="base64", + aspect_ratio=aspect_ratio, + ) + ) + result = results[0] + else: + result = await self._maybe_await( + self._client.image.sample( + model=self._model, + prompt=image_prompt, + image_format="base64", + ) + ) + except Exception as e: + logger.exception("xAI image generation request failed") + raise GrokAPIError(str(e)) from e + + image_bytes = await self._maybe_await(getattr(result, "image", None)) + if not isinstance(image_bytes, (bytes, bytearray)) or not image_bytes: + raise ValueError("No image bytes in xAI image response") + + return self._ensure_png_bytes(bytes(image_bytes)) + + async def image_edit_request( + self, + image_bytes: bytes, + *, + image_mime_type: str = "image/png", + prompt: str | None = None, + ) -> bytes: + """ + Edit an image and return the edited image bytes. + + Uses `image_format="base64"` to avoid xAI-hosted image URLs. + """ + + edit_prompt = prompt or self._prompt + if not edit_prompt: + raise ValueError("Either `prompt` must be provided or `prompt` must be set in GrokTool().") + + data_url = self._data_url(image_bytes, mime_type=image_mime_type) + + try: + result = await self._maybe_await( + self._client.image.sample( + model=self._model, + image_url=data_url, + prompt=edit_prompt, + image_format="base64", + ) + ) + except Exception as e: + logger.exception("xAI image edit request failed") + raise GrokAPIError(str(e)) from e + + edited_bytes = await self._maybe_await(getattr(result, "image", None)) + if not isinstance(edited_bytes, (bytes, bytearray)) or not edited_bytes: + raise ValueError("No image bytes in xAI image edit response") + + return self._ensure_png_bytes(bytes(edited_bytes)) diff --git a/examples/ai_chat/backend/src/app/db/models/contact/routes.py b/examples/ai_chat/backend/src/app/db/models/contact/routes.py index de72f93..6d58bc2 100644 --- a/examples/ai_chat/backend/src/app/db/models/contact/routes.py +++ b/examples/ai_chat/backend/src/app/db/models/contact/routes.py @@ -2,7 +2,7 @@ from loguru import logger from app.db.models.contact.repository import ContactRepository -from app.db.models.contact.schemas import ContactRead +from app.db.models.contact.schemas import ContactRead, Sex from app.db.queries.create_setup_contact.schemas import CreateSetupContactResponse from app.workflows.create_contact.workflow import run_create_contact_workflow @@ -10,14 +10,14 @@ @contact_router.post("/") -async def post_contact() -> CreateSetupContactResponse: +async def post_contact(sex: Sex | None = None) -> CreateSetupContactResponse: """ Create a contact. """ - logger.info("Creating a contact") + logger.info(f"Creating a contact (sex={sex})") # Execute the create contact workflow - new_contact = await run_create_contact_workflow() + new_contact = await run_create_contact_workflow(sex=sex) return new_contact diff --git a/examples/ai_chat/backend/src/app/db/models/message/repository.py b/examples/ai_chat/backend/src/app/db/models/message/repository.py index 35565aa..fa67a31 100644 --- a/examples/ai_chat/backend/src/app/db/models/message/repository.py +++ b/examples/ai_chat/backend/src/app/db/models/message/repository.py @@ -1,10 +1,11 @@ # app/db/models/message/repository.py -from sqlalchemy import exists, select +from sqlalchemy import exists, select, update, func from sqlalchemy.exc import SQLAlchemyError from sqlalchemy.orm import aliased from app.db.db_config import async_session # You'll need to define this +from app.db.models.chat.model import ChatsTable from app.db.models.message import model, schemas @@ -20,8 +21,15 @@ async def create(message: schemas.MessageCreate) -> schemas.MessageRead: ) async with async_session() as session: - session.add(db_obj) - await session.commit() + async with session.begin(): + session.add(db_obj) + await session.flush() + + # Keep chat ordering correct by tracking the most recent message time. + await session.execute( + update(ChatsTable).where(ChatsTable.id == message.chat_id).values(last_message_time=func.now()) + ) + await session.refresh(db_obj) return schemas.MessageRead.model_validate(db_obj) diff --git a/examples/ai_chat/backend/src/app/main.py b/examples/ai_chat/backend/src/app/main.py index 474855a..2c21683 100644 --- a/examples/ai_chat/backend/src/app/main.py +++ b/examples/ai_chat/backend/src/app/main.py @@ -4,7 +4,6 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor -from opentelemetry.instrumentation.google_genai import GoogleGenAiSdkInstrumentor from app.avatar.routes import avatar_router from app.chat_image.routes import chat_image_router @@ -49,8 +48,7 @@ async def lifespan(app: FastAPI): # OTEL: Instrument FastAPI tracing FastAPIInstrumentor.instrument_app(app) -# OTEL: Instrument Gemini AI tracing -# GoogleGenAiSdkInstrumentor().instrument() +# OTEL: Gemini (Google GenAI) instrumentation is configured in `app.otel_config`. origins = [ "http://localhost:5173", diff --git a/examples/ai_chat/backend/src/app/otel_config.py b/examples/ai_chat/backend/src/app/otel_config.py index a954c43..129d655 100644 --- a/examples/ai_chat/backend/src/app/otel_config.py +++ b/examples/ai_chat/backend/src/app/otel_config.py @@ -6,16 +6,16 @@ from opentelemetry.sdk.metrics import MeterProvider from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace import TracerProvider +from xai_sdk.telemetry import Telemetry def init_otel(service_name: str): """Configure OpenTelemetry for this application.""" - # Load the JUNJO_AI_STUDIO_API_KEY from the environment variable - JUNJO_AI_STUDIO_API_KEY = os.getenv("JUNJO_AI_STUDIO_API_KEY") - if JUNJO_AI_STUDIO_API_KEY is None: - raise ValueError("JUNJO_AI_STUDIO_API_KEY environment variable is not set." - "Generate a new API key in the Junjo AI Studio UI.") + # Load the Junjo API key from the environment variable. + junjo_api_key = os.getenv("JUNJO_AI_STUDIO_API_KEY") + if junjo_api_key is None: + raise ValueError("JUNJO_AI_STUDIO_API_KEY environment variable is not set.") # Configure OpenTelemetry for this application # Create the OpenTelemetry Resource to identify this service @@ -27,11 +27,16 @@ def init_otel(service_name: str): # Instrument Google GenAI GoogleGenAIInstrumentor().instrument(tracer_provider=tracer_provider) + # Instrument xAI + # https://github.com/xai-org/xai-sdk-python?tab=readme-ov-file#advanced-configuration + telemetry = Telemetry(provider=tracer_provider) + telemetry.setup_otlp_exporter() + # Construct a Junjo exporter for Junjo AI Studio junjo_ai_studio_exporter = JunjoOtelExporter( host="localhost", port="50051", - api_key=JUNJO_AI_STUDIO_API_KEY, + api_key=junjo_api_key, insecure=True, ) @@ -45,7 +50,5 @@ def init_otel(service_name: str): # - Construct with the Junjo metric reader # - Add more metric readers if desired junjo_metric_reader = junjo_ai_studio_exporter.metric_reader - meter_provider = MeterProvider( - resource=resource, metric_readers=[junjo_metric_reader] - ) + meter_provider = MeterProvider(resource=resource, metric_readers=[junjo_metric_reader]) metrics.set_meter_provider(meter_provider) diff --git a/examples/ai_chat/backend/src/app/visualize.py b/examples/ai_chat/backend/src/app/visualize.py index cff0d46..3a9b744 100644 --- a/examples/ai_chat/backend/src/app/visualize.py +++ b/examples/ai_chat/backend/src/app/visualize.py @@ -11,6 +11,8 @@ def main(): # Export graphviz assets create_handle_message_graph().export_graphviz_assets() + print("\n\nCheck `graphviz_out` folder for visualizations.\n") + if __name__ == "__main__": main() diff --git a/examples/ai_chat/backend/src/app/workflows/create_contact/avatar_subflow/nodes/avatar_inspiration/node.py b/examples/ai_chat/backend/src/app/workflows/create_contact/avatar_subflow/nodes/avatar_inspiration/node.py index 637825a..82cad96 100644 --- a/examples/ai_chat/backend/src/app/workflows/create_contact/avatar_subflow/nodes/avatar_inspiration/node.py +++ b/examples/ai_chat/backend/src/app/workflows/create_contact/avatar_subflow/nodes/avatar_inspiration/node.py @@ -1,8 +1,7 @@ - from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.create_contact.avatar_subflow.nodes.avatar_inspiration.prompt import avatar_inspiration_prompt from app.workflows.create_contact.avatar_subflow.store import AvatarSubflowStore @@ -36,7 +35,6 @@ async def service(self, store: AvatarSubflowStore) -> None: last_name = "last_name here" - # Create the request to gemini for avatar inspiration prompt = avatar_inspiration_prompt( parent_state.personality_traits, @@ -44,16 +42,15 @@ async def service(self, store: AvatarSubflowStore) -> None: parent_state.location.city, parent_state.location.state, first_name, - last_name + last_name, ) logger.info(f"Creating response with prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-lite-001") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Update the state with the avatar id - await store.set_inspiration_prompt(gemini_result) + await store.set_inspiration_prompt(grok_result) return diff --git a/examples/ai_chat/backend/src/app/workflows/create_contact/avatar_subflow/nodes/create_avatar/node.py b/examples/ai_chat/backend/src/app/workflows/create_contact/avatar_subflow/nodes/create_avatar/node.py index aa686dc..64e4efc 100644 --- a/examples/ai_chat/backend/src/app/workflows/create_contact/avatar_subflow/nodes/create_avatar/node.py +++ b/examples/ai_chat/backend/src/app/workflows/create_contact/avatar_subflow/nodes/create_avatar/node.py @@ -2,7 +2,7 @@ from loguru import logger from nanoid import generate -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.util.save_image_file import save_image_file from app.workflows.create_contact.avatar_subflow.nodes.create_avatar.prompt import create_avatar_prompt from app.workflows.create_contact.avatar_subflow.store import AvatarSubflowStore @@ -51,10 +51,9 @@ async def service(self, store: AvatarSubflowStore) -> None: ) logger.info(f"Creating image with prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.5-flash-image-preview") - image_bytes = await gemini_tool.gemini_image_request() - logger.info(f"Gemini result image size: {len(image_bytes) / 1024} kb") + grok_tool = GrokTool(prompt=prompt, model="grok-imagine-image") + image_bytes = await grok_tool.image_request() + logger.info(f"Grok result image size: {len(image_bytes) / 1024} kb") # Create an id for the avatar avatar_id = generate() diff --git a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_bio/node.py b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_bio/node.py index de8a1ee..fe01937 100644 --- a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_bio/node.py +++ b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_bio/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.create_contact.nodes.create_bio.prompt import create_bio_prompt from app.workflows.create_contact.store import CreateContactStore @@ -38,12 +38,11 @@ async def service(self, store: CreateContactStore) -> None: ) logger.info(f"Creating response with prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.5-flash") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Update the state with the bio - await store.set_bio(gemini_result) + await store.set_bio(grok_result) return diff --git a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_bio/test/test_service.py b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_bio/test/test_service.py index 6d7854a..58072b7 100644 --- a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_bio/test/test_service.py +++ b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_bio/test/test_service.py @@ -1,5 +1,4 @@ - -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.create_contact.nodes.create_bio.test.test_prompt import test_evaluate_bio_prompt from app.workflows.create_contact.nodes.create_bio.test.test_schema import TestCreateBioSchema @@ -10,8 +9,7 @@ async def eval_create_bio_node(bio: str) -> TestCreateBioSchema: # Create the request to gemini for avatar inspiration prompt = test_evaluate_bio_prompt(bio) - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") - gemini_result = await gemini_tool.schema_request(schema=TestCreateBioSchema) + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.schema_request(schema=TestCreateBioSchema) - return gemini_result + return grok_result diff --git a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_name/node.py b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_name/node.py index d086427..24be0c7 100644 --- a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_name/node.py +++ b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/create_name/node.py @@ -1,8 +1,7 @@ - from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.create_contact.nodes.create_name.prompt import create_name_prompt from app.workflows.create_contact.nodes.create_name.schema import CreateNameSchema from app.workflows.create_contact.store import CreateContactStore @@ -36,21 +35,16 @@ async def service(self, store: CreateContactStore) -> None: # Create the request to gemini for avatar inspiration prompt = create_name_prompt( - state.personality_traits, - state.location.city, - state.location.state, - state.age, - state.sex + state.personality_traits, state.location.city, state.location.state, state.age, state.sex ) logger.info(f"Creating response with prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-lite-001") - gemini_result = await gemini_tool.schema_request(CreateNameSchema) - logger.info(f"Gemini result: {gemini_result}") + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.schema_request(CreateNameSchema) + logger.info(f"Grok result: {grok_result}") # Update the state with the first name - await store.set_first_name(gemini_result.first_name) - await store.set_last_name(gemini_result.last_name) + await store.set_first_name(grok_result.first_name) + await store.set_last_name(grok_result.last_name) return diff --git a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_location/services/get_nearest_city_state.py b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_location/services/get_nearest_city_state.py index 14a8330..3b98b97 100644 --- a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_location/services/get_nearest_city_state.py +++ b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_location/services/get_nearest_city_state.py @@ -1,6 +1,6 @@ from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.create_contact.nodes.select_location.schemas import LocCityState from app.workflows.create_contact.nodes.select_location.services.get_nearest_city_state_prompt import ( get_nearest_city_state_prompt, @@ -16,9 +16,8 @@ async def get_nearest_city_state(lat: float, long: float) -> LocCityState: prompt = get_nearest_city_state_prompt(lat, long) logger.info(f"Creating response with prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-lite-001") - gemini_result = await gemini_tool.schema_request(schema=LocCityState) - logger.info(f"Gemini result: {gemini_result}") + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.schema_request(schema=LocCityState) + logger.info(f"Grok result: {grok_result}") - return gemini_result + return grok_result diff --git a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/node.py b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/node.py index 4a40994..c22f12c 100644 --- a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/node.py +++ b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/node.py @@ -14,10 +14,14 @@ async def service(self, store: CreateContactStore) -> None: Service method to select the sex of a contact. """ - # Determine sex + state = await store.get_state() + + # Respect a pre-selected sex (e.g. passed in from the API) + if state.sex is not None: + return + sex = select_sex() # Update state await store.set_sex(sex) - diff --git a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/test/__init__.py b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/test/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/test/__init__.py @@ -0,0 +1 @@ + diff --git a/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/test/test_node.py b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/test/test_node.py new file mode 100644 index 0000000..8b2cfb3 --- /dev/null +++ b/examples/ai_chat/backend/src/app/workflows/create_contact/nodes/select_sex/test/test_node.py @@ -0,0 +1,36 @@ +import pytest + +from app.db.models.contact.schemas import Sex +from app.workflows.create_contact.nodes.select_sex.node import SelectSexNode +from app.workflows.create_contact.store import CreateContactState, CreateContactStore + + +class TestSelectSexNode: + @pytest.mark.asyncio(loop_scope="session") + async def test_does_not_override_preselected_sex(self, monkeypatch: pytest.MonkeyPatch) -> None: + import app.workflows.create_contact.nodes.select_sex.node as select_sex_node_module + + monkeypatch.setattr(select_sex_node_module, "select_sex", lambda: Sex.FEMALE) + + store = CreateContactStore(initial_state=CreateContactState(sex=Sex.MALE)) + node = SelectSexNode() + + await node.service(store) + state = await store.get_state() + + assert state.sex == Sex.MALE + + @pytest.mark.asyncio(loop_scope="session") + async def test_sets_sex_when_missing(self, monkeypatch: pytest.MonkeyPatch) -> None: + import app.workflows.create_contact.nodes.select_sex.node as select_sex_node_module + + monkeypatch.setattr(select_sex_node_module, "select_sex", lambda: Sex.MALE) + + store = CreateContactStore(initial_state=CreateContactState()) + node = SelectSexNode() + + await node.service(store) + state = await store.get_state() + + assert state.sex == Sex.MALE + diff --git a/examples/ai_chat/backend/src/app/workflows/create_contact/workflow.py b/examples/ai_chat/backend/src/app/workflows/create_contact/workflow.py index 2e7fa85..1874bfb 100644 --- a/examples/ai_chat/backend/src/app/workflows/create_contact/workflow.py +++ b/examples/ai_chat/backend/src/app/workflows/create_contact/workflow.py @@ -1,12 +1,13 @@ from junjo.telemetry.hook_manager import HookManager from junjo.workflow import Workflow +from app.db.models.contact.schemas import Sex from app.db.queries.create_setup_contact.schemas import CreateSetupContactResponse from app.workflows.create_contact.graph import create_create_contact_graph from app.workflows.create_contact.store import CreateContactState, CreateContactStore -async def run_create_contact_workflow() -> CreateSetupContactResponse: +async def run_create_contact_workflow(sex: Sex | None = None) -> CreateSetupContactResponse: """ Create Contact Workflow @@ -21,7 +22,7 @@ async def run_create_contact_workflow() -> CreateSetupContactResponse: name="Create Contact Workflow", graph_factory=create_create_contact_graph, store_factory=lambda: CreateContactStore( - initial_state=CreateContactState() + initial_state=CreateContactState(sex=sex) ), hook_manager=HookManager(verbose_logging=True, open_telemetry=True), ) diff --git a/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/create_image/node.py b/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/create_image/node.py index b69af3c..de9e2e0 100644 --- a/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/create_image/node.py +++ b/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/create_image/node.py @@ -2,7 +2,7 @@ from loguru import logger from nanoid import generate -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.util.get_image_bytes import get_image_bytes from app.util.save_image_file import save_image_file from app.workflows.handle_message.create_response_with_image_subflow.store import ( @@ -42,28 +42,22 @@ async def service(self, store: CreateResponseWithImageSubflowStore) -> None: # Get the avatar image bytes avatar_image_bytes = get_image_bytes("avatars", contact.avatar_id, "png") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.5-flash-image-preview") - image_bytes, text_response = await gemini_tool.gemini_image_edit_request(avatar_image_bytes) + grok_tool = GrokTool(prompt=prompt, model="grok-imagine-image") + image_bytes = await grok_tool.image_edit_request(avatar_image_bytes, image_mime_type="image/png") + logger.info(f"Grok result image size: {len(image_bytes) / 1024} kb") - if image_bytes: - logger.info(f"Gemini result image size: {len(image_bytes) / 1024} kb") + # Create an id for the image + image_id = generate() - # Create an id for the image - image_id = generate() + # Save the image to the file system + save_image_file( + image_bytes, + f"chat-images/{state.parent_state.received_message.chat_id}", + image_id, + "png", + ) - # Save the image to the file system - save_image_file( - image_bytes, - f"chat-images/{state.parent_state.received_message.chat_id}", - image_id, - "png", - ) - - # Update the state with the image id - await store.set_image_id(image_id) - - if text_response: - await store.set_text_response(text_response) + # Update the state with the image id + await store.set_image_id(image_id) return diff --git a/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/node.py b/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/node.py index 2f25904..ff85cfd 100644 --- a/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/node.py +++ b/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.handle_message.create_response_with_image_subflow.nodes.image_inspiration.prompt import ( image_inspiration_prompt, ) @@ -30,18 +30,14 @@ async def service(self, store: CreateResponseWithImageSubflowStore) -> None: raise ValueError("Contact is required to execute this node.") # Create the request to gemini for image inspiration - prompt = image_inspiration_prompt( - parent_state.conversation_history, - parent_state.contact - ) + prompt = image_inspiration_prompt(parent_state.conversation_history, parent_state.contact) logger.info(f"Creating response with prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.5-flash") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Update the state with the image id - await store.set_inspiration_prompt(gemini_result) + await store.set_inspiration_prompt(grok_result) return diff --git a/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/prompt.py b/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/prompt.py index 980fd86..5f6c59f 100644 --- a/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/prompt.py +++ b/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/prompt.py @@ -33,19 +33,19 @@ def image_inspiration_prompt( # Create and include the following photo specifications with the prompt: - the camera / phone used to take the photo +- If: selfie, mirror selfie, or taken by someone else - the location of the photo - the time of day - the weather -- the set and setting of the photo -- the clothes or lack of clothes the person is wearing +- the set, setting, and pose for the photo +- the exact clothes the person should be wearing (try to adhere to requests from the conversation history) - Specify the text that the image + text generation model should respond with to go with the photo # Include the following verbatim in the prompt: - "Photo Subject Age: {contact.age}" - "ultra-realistic" - "Aspect Ratio: 1:1 square" -- "You must generate safe, non-prohibited content" -- "The person in the provided image is the basis for this new photo, and is the person sending the message." +- "The AI-generated person in the provided image is the person sending the message." - "No text, no watermarks" # DO NOT: diff --git a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/assess_message_directive/node.py b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/assess_message_directive/node.py index d13d141..22c3771 100644 --- a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/assess_message_directive/node.py +++ b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/assess_message_directive/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.handle_message.nodes.assess_message_directive.prompt_gemini import ( assess_message_directive_prompt, ) @@ -19,20 +19,18 @@ async def service(self, store) -> None: prompt = assess_message_directive_prompt(state.conversation_history) logger.info(f"Prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Validate the result is a MessageDirective enum - if not gemini_result: - raise ValueError("Gemini result is empty.") + if not grok_result: + raise ValueError("Grok result is empty.") try: - # Attempt to construct the enum value with the gemini_result - message_directive = MessageDirective(gemini_result) + message_directive = MessageDirective(grok_result) except ValueError as e: - raise ValueError(f"Gemini result '{gemini_result}' is not a valid MessageDirective.") from e + raise ValueError(f"Grok result '{grok_result}' is not a valid MessageDirective.") from e # Update state await store.set_message_directive(message_directive) diff --git a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_date_idea_response/node.py b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_date_idea_response/node.py index 9d55bbf..41fbf63 100644 --- a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_date_idea_response/node.py +++ b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_date_idea_response/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.db.models.message.repository import MessageRepository from app.db.models.message.schemas import MessageCreate from app.workflows.handle_message.nodes.create_date_idea_response.prompt_gemini import ( @@ -21,19 +21,18 @@ async def service(self, store) -> None: raise ValueError("Contact is required to execute this node.") # Construct the prompt - prompt = create_date_idea_response_workflow_prompt(state.conversation_history, contact, state.received_message.message) + prompt = create_date_idea_response_workflow_prompt( + state.conversation_history, contact, state.received_message.message + ) logger.info(f"Prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Create a message for the database message_create = MessageCreate( - chat_id=state.received_message.chat_id, - contact_id=contact.id, - message=gemini_result + chat_id=state.received_message.chat_id, contact_id=contact.id, message=grok_result ) # Insert the message into the database diff --git a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_general_response/node.py b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_general_response/node.py index 2e745fb..9dfff58 100644 --- a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_general_response/node.py +++ b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_general_response/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.db.models.message.repository import MessageRepository from app.db.models.message.schemas import MessageCreate from app.workflows.handle_message.nodes.create_general_response.prompt_gemini import ( @@ -26,14 +26,13 @@ async def service(self, store) -> None: ) logger.info(f"Creating response with prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.5-flash") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Create a message for the database message_create = MessageCreate( - chat_id=state.received_message.chat_id, contact_id=contact.id, message=gemini_result + chat_id=state.received_message.chat_id, contact_id=contact.id, message=grok_result ) # Insert the message into the database diff --git a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_work_response/node.py b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_work_response/node.py index be01f54..c1bb4cd 100644 --- a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_work_response/node.py +++ b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_work_response/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.db.models.message.repository import MessageRepository from app.db.models.message.schemas import MessageCreate from app.workflows.handle_message.nodes.create_work_response.prompt_gemini import ( @@ -21,19 +21,18 @@ async def service(self, store) -> None: raise ValueError("Contact is required to execute this node.") # Construct the prompt - prompt = create_work_response_workflow_prompt(state.conversation_history, contact, state.received_message.message) + prompt = create_work_response_workflow_prompt( + state.conversation_history, contact, state.received_message.message + ) logger.info(f"Prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Create a message for the database message_create = MessageCreate( - chat_id=state.received_message.chat_id, - contact_id=contact.id, - message=gemini_result + chat_id=state.received_message.chat_id, contact_id=contact.id, message=grok_result ) # Insert the message into the database diff --git a/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_1/node.py b/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_1/node.py index 7beeea3..35e95d6 100644 --- a/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_1/node.py +++ b/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_1/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.sub_sub_flow.store import SubSubFlowStore @@ -13,13 +13,11 @@ async def service(self, store) -> None: prompt = "Output a fact about apples." logger.info(f"Prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") - + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Update state - await store.append_fact(gemini_result) + await store.append_fact(grok_result) return diff --git a/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_2/node.py b/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_2/node.py index 32f2ed1..b66ddb3 100644 --- a/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_2/node.py +++ b/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_2/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.sub_sub_flow.store import SubSubFlowStore @@ -13,12 +13,11 @@ async def service(self, store) -> None: prompt = "Output a fact about whales." logger.info(f"Prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Update state - await store.append_fact(gemini_result) + await store.append_fact(grok_result) return diff --git a/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_3/node.py b/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_3/node.py index 9318171..d03076f 100644 --- a/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_3/node.py +++ b/examples/ai_chat/backend/src/app/workflows/sub_sub_flow/sub_flow_node_3/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.sub_sub_flow.store import SubSubFlowStore @@ -13,13 +13,11 @@ async def service(self, store) -> None: prompt = "Output a fact about cellular division." logger.info(f"Prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") - + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Update state - await store.append_fact(gemini_result) + await store.append_fact(grok_result) return diff --git a/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_1/node.py b/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_1/node.py index c6eb088..3fe946b 100644 --- a/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_1/node.py +++ b/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_1/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.test_sub_flow.store import TestSubFlowStore @@ -10,16 +10,14 @@ class SubFlowNode1(Node[TestSubFlowStore]): async def service(self, store) -> None: # Construct the prompt - prompt = "Output a joke about a piece of butter prepended by \"SubFlowNode1:\"" + prompt = 'Output a joke about a piece of butter prepended by "SubFlowNode1:"' logger.info(f"Prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") - + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Update state - await store.append_joke(gemini_result) + await store.append_joke(grok_result) return diff --git a/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_2/node.py b/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_2/node.py index 47bfa7c..fe2c66f 100644 --- a/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_2/node.py +++ b/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_2/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.test_sub_flow.store import TestSubFlowStore @@ -10,16 +10,14 @@ class SubFlowNode2(Node[TestSubFlowStore]): async def service(self, store) -> None: # Construct the prompt - prompt = "Output a joke about a very large lasagna prepended by \"SubFlowNode2:\"" + prompt = 'Output a joke about a very large lasagna prepended by "SubFlowNode2:"' logger.info(f"Prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") - + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Update state - await store.append_joke(gemini_result) + await store.append_joke(grok_result) return diff --git a/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_3/node.py b/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_3/node.py index d4a25b4..8a172f5 100644 --- a/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_3/node.py +++ b/examples/ai_chat/backend/src/app/workflows/test_sub_flow/sub_flow_node_3/node.py @@ -1,7 +1,7 @@ from junjo.node import Node from loguru import logger -from app.ai_services.gemini.gemini_tool import GeminiTool +from app.ai_services.grok import GrokTool from app.workflows.test_sub_flow.store import TestSubFlowStore @@ -10,16 +10,14 @@ class SubFlowNode3(Node[TestSubFlowStore]): async def service(self, store) -> None: # Construct the prompt - prompt = "Output a joke about New York City Hotdogs prepended by \"SubFlowNode3:\"" + prompt = 'Output a joke about New York City Hotdogs prepended by "SubFlowNode3:"' logger.info(f"Prompt: {prompt}") - # Create a request to gemini - gemini_tool = GeminiTool(prompt=prompt, model="gemini-2.0-flash-001") - gemini_result = await gemini_tool.text_request() - logger.info(f"Gemini result: {gemini_result}") - + grok_tool = GrokTool(prompt=prompt, model="grok-4-1-fast-non-reasoning") + grok_result = await grok_tool.text_request() + logger.info(f"Grok result: {grok_result}") # Update state - await store.append_joke(gemini_result) + await store.append_joke(grok_result) return diff --git a/examples/ai_chat/frontend/src/App.tsx b/examples/ai_chat/frontend/src/App.tsx index 18d2906..cf17789 100644 --- a/examples/ai_chat/frontend/src/App.tsx +++ b/examples/ai_chat/frontend/src/App.tsx @@ -3,9 +3,17 @@ import ChatHeader from './components/ChatHeader' import ChatSidebar from './components/sidebar/ChatSidebar' import ChatWindow from './components/ChatWindow' import { useParams } from 'react-router' +import { useEffect } from 'react' +import { useChatReadStateStore } from './api/chat/read-store' function App() { const { chat_id } = useParams() + const markChatRead = useChatReadStateStore((state) => state.markChatRead) + + useEffect(() => { + if (!chat_id) return + markChatRead(chat_id) + }, [chat_id, markChatRead]) return (
diff --git a/examples/ai_chat/frontend/src/api/chat/hook.ts b/examples/ai_chat/frontend/src/api/chat/hook.ts index 895373b..3eb238a 100644 --- a/examples/ai_chat/frontend/src/api/chat/hook.ts +++ b/examples/ai_chat/frontend/src/api/chat/hook.ts @@ -1,6 +1,7 @@ import { useState, useEffect } from 'react' import { fetchChatsWithMembers } from './fetch' import { useChatsWithMembersStore } from './store' +import { useChatReadStateStore } from './read-store' interface UseGetContactsResult { isLoading: boolean @@ -9,14 +10,17 @@ interface UseGetContactsResult { } const useGetChatsWithMembers = (): UseGetContactsResult => { - const { lastFetch, set } = useChatsWithMembersStore() + const { set } = useChatsWithMembersStore() + const initializeFromChats = useChatReadStateStore((state) => state.initializeFromChats) const [isLoading, setIsLoading] = useState(false) const [error, setError] = useState(null) // Cache duration const CACHE_DURATION = 3 * 1000 // 3 seconds + const POLL_INTERVAL = 7500 const fetchData = async () => { + const lastFetch = useChatsWithMembersStore.getState().lastFetch if (lastFetch && Date.now() - lastFetch < CACHE_DURATION) return setIsLoading(true) @@ -24,6 +28,7 @@ const useGetChatsWithMembers = (): UseGetContactsResult => { try { const data = await fetchChatsWithMembers() set(data) + initializeFromChats(data) } catch (error: any) { setError(error.message) } finally { @@ -34,6 +39,9 @@ const useGetChatsWithMembers = (): UseGetContactsResult => { // Automatically run useEffect(() => { fetchData() + + const intervalId = window.setInterval(fetchData, POLL_INTERVAL) + return () => window.clearInterval(intervalId) }, []) return { isLoading, error, refetch: fetchData } diff --git a/examples/ai_chat/frontend/src/api/chat/read-store.ts b/examples/ai_chat/frontend/src/api/chat/read-store.ts new file mode 100644 index 0000000..6f98544 --- /dev/null +++ b/examples/ai_chat/frontend/src/api/chat/read-store.ts @@ -0,0 +1,42 @@ +import { create } from 'zustand' +import { createJSONStorage, devtools, persist } from 'zustand/middleware' + +import { ChatWithMembersRead } from './schemas' + +interface ChatReadState { + lastReadAtByChatId: Record + + initializeFromChats: (chats: ChatWithMembersRead[]) => void + markChatRead: (chatId: string, at?: number) => void +} + +export const useChatReadStateStore = create()( + devtools( + persist( + (set) => ({ + lastReadAtByChatId: {}, + + initializeFromChats: (chats) => + set((state) => { + const next = { ...state.lastReadAtByChatId } + for (const chat of chats) { + if (next[chat.id] == null) { + next[chat.id] = chat.last_message_time.getTime() + } + } + return { lastReadAtByChatId: next } + }), + + markChatRead: (chatId, at) => + set((state) => ({ + lastReadAtByChatId: { ...state.lastReadAtByChatId, [chatId]: at ?? Date.now() }, + })), + }), + { + name: 'ai-chat:last-read-at', + storage: createJSONStorage(() => localStorage), + } + ) + ) +) + diff --git a/examples/ai_chat/frontend/src/api/chat/store.ts b/examples/ai_chat/frontend/src/api/chat/store.ts index 6e06eb3..4a225a8 100644 --- a/examples/ai_chat/frontend/src/api/chat/store.ts +++ b/examples/ai_chat/frontend/src/api/chat/store.ts @@ -8,6 +8,7 @@ interface ContactState { set: (chats: ChatWithMembersRead[]) => void upsertChat: (chat: ChatWithMembersRead) => void + touchChat: (chat_id: string, last_message_time: Date) => void } export const useChatsWithMembersStore = create()( @@ -15,24 +16,33 @@ export const useChatsWithMembersStore = create()( chats: [], lastFetch: null, - set: (chats) => set({ chats, lastFetch: Date.now() }), + set: (chats) => + set({ + chats: [...chats].sort((a, b) => b.last_message_time.getTime() - a.last_message_time.getTime()), + lastFetch: Date.now(), + }), upsertChat: (chat) => set((state) => { - // Check if the chat already exists const index = state.chats.findIndex((c) => c.id === chat.id) - if (index === -1) { - // If it doesn't, add it - state.chats.push(chat) - } else { - // If it does, update it - state.chats[index] = chat - } - - // Sort for most recent first - state.chats.sort((a, b) => b.last_message_time.getTime() - a.last_message_time.getTime()) - - return { chats: state.chats } + const next = [...state.chats] + if (index === -1) next.push(chat) + else next[index] = chat + + next.sort((a, b) => b.last_message_time.getTime() - a.last_message_time.getTime()) + return { chats: next } + }), + + touchChat: (chat_id, last_message_time) => + set((state) => { + const next = state.chats.map((chat) => { + if (chat.id !== chat_id) return chat + if (chat.last_message_time.getTime() >= last_message_time.getTime()) return chat + return { ...chat, last_message_time } + }) + + next.sort((a, b) => b.last_message_time.getTime() - a.last_message_time.getTime()) + return { chats: next } }), })) ) diff --git a/examples/ai_chat/frontend/src/api/contact/fetch.ts b/examples/ai_chat/frontend/src/api/contact/fetch.ts index 75788be..4533cfd 100644 --- a/examples/ai_chat/frontend/src/api/contact/fetch.ts +++ b/examples/ai_chat/frontend/src/api/contact/fetch.ts @@ -1,8 +1,13 @@ import { z } from 'zod' -import { ContactRead, ContactReadSchema, CreateSetupContactResponse, CreateSetupContactResponseSchema } from './schemas' +import { ContactRead, ContactReadSchema, CreateSetupContactResponse, CreateSetupContactResponseSchema, Sex } from './schemas' -export const createSetupContact = async (): Promise => { - const response = await fetch(`http://127.0.0.1:8000/api/contact`, { +export const createSetupContact = async (sex?: Sex): Promise => { + const url = new URL('http://127.0.0.1:8000/api/contact') + if (sex) { + url.searchParams.set('sex', sex) + } + + const response = await fetch(url.toString(), { method: 'POST', mode: 'cors', headers: { diff --git a/examples/ai_chat/frontend/src/api/contact/hooks/create-upsert-contact-hook.ts b/examples/ai_chat/frontend/src/api/contact/hooks/create-upsert-contact-hook.ts index a3372fd..a9aed23 100644 --- a/examples/ai_chat/frontend/src/api/contact/hooks/create-upsert-contact-hook.ts +++ b/examples/ai_chat/frontend/src/api/contact/hooks/create-upsert-contact-hook.ts @@ -1,12 +1,13 @@ import { useState } from 'react' import { createSetupContact } from '../fetch' +import { Sex } from '../schemas' import { useContactStore } from '../store' import { useChatsWithMembersStore } from '../../chat/store' interface UseCreateContactResult { isLoading: boolean error: string | null - createContact: () => Promise // Added createContact function + createContact: (sex?: Sex) => Promise } const useCreateAndUpsertContact = (): UseCreateContactResult => { @@ -15,12 +16,12 @@ const useCreateAndUpsertContact = (): UseCreateContactResult => { const [isLoading, setIsLoading] = useState(false) const [error, setError] = useState(null) - const createContactAndUpsert = async (): Promise => { + const createContactAndUpsert = async (sex?: Sex): Promise => { setIsLoading(true) setError(null) try { // Create and upsert the new contact into state - const response = await createSetupContact() + const response = await createSetupContact(sex) upsertContacts([response.contact]) upsertChat(response.chat_with_members) diff --git a/examples/ai_chat/frontend/src/api/message/hooks/get-messages-hook.ts b/examples/ai_chat/frontend/src/api/message/hooks/get-messages-hook.ts index 423bae8..c120b87 100644 --- a/examples/ai_chat/frontend/src/api/message/hooks/get-messages-hook.ts +++ b/examples/ai_chat/frontend/src/api/message/hooks/get-messages-hook.ts @@ -1,6 +1,7 @@ import { useRef, useState } from 'react' import { useMessagesStore } from '../store' import { fetchChatMessages, fetchNewerChatMessages } from '../fetch' +import { useChatsWithMembersStore } from '../../chat/store' interface UseGetMessagesResult { isLoading: boolean @@ -12,6 +13,7 @@ interface UseGetMessagesResult { const useGetMessages = (): UseGetMessagesResult => { const { upsertMessages } = useMessagesStore() + const touchChat = useChatsWithMembersStore((state) => state.touchChat) const [isLoading, setIsLoading] = useState(false) const [error, setError] = useState(null) const pollingActive = useRef(false) @@ -24,6 +26,10 @@ const useGetMessages = (): UseGetMessagesResult => { try { const newMessages = await fetchChatMessages(chat_id) upsertMessages(chat_id, newMessages) + if (newMessages.length > 0) { + const latest = new Date(Math.max(...newMessages.map((m) => m.created_at.getTime()))) + touchChat(chat_id, latest) + } } catch (error: any) { setError(error.message) } finally { @@ -37,6 +43,10 @@ const useGetMessages = (): UseGetMessagesResult => { try { const newMessages = await fetchNewerChatMessages(chat_id, message_id) upsertMessages(chat_id, newMessages) + if (newMessages.length > 0) { + const latest = new Date(Math.max(...newMessages.map((m) => m.created_at.getTime()))) + touchChat(chat_id, latest) + } } catch (error: any) { setError(error.message) } finally { diff --git a/examples/ai_chat/frontend/src/components/ChatForm.tsx b/examples/ai_chat/frontend/src/components/ChatForm.tsx index ab00188..e29cb46 100644 --- a/examples/ai_chat/frontend/src/components/ChatForm.tsx +++ b/examples/ai_chat/frontend/src/components/ChatForm.tsx @@ -1,6 +1,7 @@ import { useState } from 'react' import useSendMessage from '../api/message/hooks/send-message-hook' import useGetMessages from '../api/message/hooks/get-messages-hook' +import { useChatReadStateStore } from '../api/chat/read-store' export interface ChatFormProps { chat_id: string | undefined @@ -11,6 +12,7 @@ export default function ChatForm(props: ChatFormProps) { const [message, setMessage] = useState('') const { isLoading, error, sendMessage } = useSendMessage() const { getChatMessages } = useGetMessages() + const markChatRead = useChatReadStateStore((state) => state.markChatRead) const handleSubmit = async (e: React.FormEvent) => { e.preventDefault() @@ -19,6 +21,7 @@ export default function ChatForm(props: ChatFormProps) { try { await sendMessage({ chat_id, message }) setMessage('') // Clear the input field after sending + markChatRead(chat_id) // Await a delay await new Promise((resolve) => setTimeout(resolve, 150)) diff --git a/examples/ai_chat/frontend/src/components/ChatWindow.tsx b/examples/ai_chat/frontend/src/components/ChatWindow.tsx index a5f3bdb..80232d3 100644 --- a/examples/ai_chat/frontend/src/components/ChatWindow.tsx +++ b/examples/ai_chat/frontend/src/components/ChatWindow.tsx @@ -4,6 +4,7 @@ import ChatReceiveBubble from './bubbles/ChatReceiveBubble' import ChatReceiveImageBubble from './bubbles/ChatReceiveImageBubble' import ChatSendBubble from './bubbles/ChatSendBubble' import useGetMessages from '../api/message/hooks/get-messages-hook' +import { useChatReadStateStore } from '../api/chat/read-store' interface ChatWindowProps { chat_id: string | undefined @@ -13,11 +14,13 @@ export default function ChatWindow(props: ChatWindowProps) { const { chat_id } = props const chatWindowRef = useRef(null) const { getChatMessages, pollForNewerMessages } = useGetMessages() + const markChatRead = useChatReadStateStore((state) => state.markChatRead) const messages = useMessagesStore((state) => state.messages[chat_id ?? '']) const messagesList = Object.values(messages ?? {}) + const sortedMessagesList = [...messagesList].sort((a, b) => a.created_at.getTime() - b.created_at.getTime()) // Latest message - const latestMessageId = messagesList[messagesList.length - 1]?.id ?? null + const latestMessageId = sortedMessagesList[sortedMessagesList.length - 1]?.id ?? null // Automatically fetch initial round of messages if there are none useEffect(() => { @@ -30,13 +33,21 @@ export default function ChatWindow(props: ChatWindowProps) { if (!chat_id) return if (!latestMessageId) return - if (messagesList.length > 0) { + if (sortedMessagesList.length > 0) { pollForNewerMessages(chat_id, latestMessageId) } }, [latestMessageId]) + // While viewing a chat, treat any incoming messages as read + useEffect(() => { + if (!chat_id) return + if (sortedMessagesList.length === 0) return + const latestReadAt = sortedMessagesList[sortedMessagesList.length - 1].created_at.getTime() + markChatRead(chat_id, latestReadAt) + }, [chat_id, latestMessageId, sortedMessagesList.length, markChatRead]) + // Reverse the messages list to show the newest at the bottom - const reversedList = [...messagesList].reverse() + const reversedList = [...sortedMessagesList].reverse() return (
diff --git a/examples/ai_chat/frontend/src/components/avatars/SidebarAvatar.tsx b/examples/ai_chat/frontend/src/components/avatars/SidebarAvatar.tsx index 45cad20..930db9b 100644 --- a/examples/ai_chat/frontend/src/components/avatars/SidebarAvatar.tsx +++ b/examples/ai_chat/frontend/src/components/avatars/SidebarAvatar.tsx @@ -6,10 +6,11 @@ interface SidebarAvatarProps { contact_id: string lastMessage: string isActive: boolean + hasUnread?: boolean } export default function SidebarAvatar(props: SidebarAvatarProps) { - const { contact_id, lastMessage, isActive } = props + const { contact_id, lastMessage, isActive, hasUnread } = props const [isModalOpen, setIsModalOpen] = useState(false) // Get contact from store @@ -42,6 +43,13 @@ export default function SidebarAvatar(props: SidebarAvatarProps) { View Profile
+ {hasUnread && ( +
+ )} {isModalOpen && setIsModalOpen(false)} />} diff --git a/examples/ai_chat/frontend/src/components/bubbles/ChatReceiveImageBubble.tsx b/examples/ai_chat/frontend/src/components/bubbles/ChatReceiveImageBubble.tsx index cca5388..38f63ea 100644 --- a/examples/ai_chat/frontend/src/components/bubbles/ChatReceiveImageBubble.tsx +++ b/examples/ai_chat/frontend/src/components/bubbles/ChatReceiveImageBubble.tsx @@ -1,8 +1,11 @@ +import { useState } from 'react' import { formatDateForChat } from '../../util/date-utils' +import FullscreenImageModal from '../modals/FullscreenImageModal' import { ChatBubbleProps } from './schemas' export default function ChatReceiveImageBubble(props: ChatBubbleProps) { const { message } = props + const [isFullScreen, setIsFullScreen] = useState(false) if (!message.image_id) { return null @@ -20,12 +23,16 @@ export default function ChatReceiveImageBubble(props: ChatBubbleProps) { chat content setIsFullScreen(true)} /> {message.message &&
{message.message}
}
{dateString}
+ {isFullScreen && ( + setIsFullScreen(false)} /> + )} ) } diff --git a/examples/ai_chat/frontend/src/components/modals/FullscreenImageModal.tsx b/examples/ai_chat/frontend/src/components/modals/FullscreenImageModal.tsx new file mode 100644 index 0000000..7f23022 --- /dev/null +++ b/examples/ai_chat/frontend/src/components/modals/FullscreenImageModal.tsx @@ -0,0 +1,51 @@ +import { useEffect } from 'react' + +interface FullscreenImageModalProps { + src: string + alt?: string + onClose: () => void +} + +export default function FullscreenImageModal(props: FullscreenImageModalProps) { + const { src, alt = 'image', onClose } = props + + useEffect(() => { + const onKeyDown = (e: KeyboardEvent) => { + if (e.key === 'Escape') { + onClose() + } + } + + const previousOverflow = document.body.style.overflow + document.body.style.overflow = 'hidden' + + window.addEventListener('keydown', onKeyDown) + return () => { + window.removeEventListener('keydown', onKeyDown) + document.body.style.overflow = previousOverflow + } + }, [onClose]) + + return ( +
+
e.stopPropagation()}> +
+ +
+ {alt} +
+
+ ) +} + diff --git a/examples/ai_chat/frontend/src/components/sidebar/ChatSidebar.tsx b/examples/ai_chat/frontend/src/components/sidebar/ChatSidebar.tsx index 0ff1b31..fb23676 100644 --- a/examples/ai_chat/frontend/src/components/sidebar/ChatSidebar.tsx +++ b/examples/ai_chat/frontend/src/components/sidebar/ChatSidebar.tsx @@ -3,6 +3,7 @@ import NewContactButton from './NewContactButton' import useGetChatsWithMembers from '../../api/chat/hook' import { useChatsWithMembersStore } from '../../api/chat/store' import useGetContacts from '../../api/contact/hooks/get-contacts-hook' +import { useChatReadStateStore } from '../../api/chat/read-store' import { Link, useParams } from 'react-router' import { formatDateForChat } from '../../util/date-utils' @@ -14,6 +15,7 @@ export default function ChatSidebar() { const { error: contactsError, refetch: refetchContacts } = useGetContacts() const { error: chatsWithMembersError, refetch: refetchChatsWithMembers } = useGetChatsWithMembers() const { chats } = useChatsWithMembersStore() + const lastReadAtByChatId = useChatReadStateStore((state) => state.lastReadAtByChatId) async function handleRefetch() { await refetchContacts() @@ -44,6 +46,8 @@ export default function ChatSidebar() { const lastMessageTime = new Date(chat.last_message_time) const dateString = formatDateForChat(lastMessageTime) const isActive = chat.id == chat_id + const lastReadAt = lastReadAtByChatId[chat.id] + const hasUnread = !isActive && lastReadAt != null && lastMessageTime.getTime() > lastReadAt return ( @@ -53,6 +57,7 @@ export default function ChatSidebar() { lastMessage={dateString} contact_id={member.contact_id} isActive={isActive} + hasUnread={hasUnread} /> ))} diff --git a/examples/ai_chat/frontend/src/components/sidebar/NewContactButton.tsx b/examples/ai_chat/frontend/src/components/sidebar/NewContactButton.tsx index 0de5319..2db2ace 100644 --- a/examples/ai_chat/frontend/src/components/sidebar/NewContactButton.tsx +++ b/examples/ai_chat/frontend/src/components/sidebar/NewContactButton.tsx @@ -1,15 +1,14 @@ import { useNavigate } from 'react-router' import useCreateAndUpsertContact from '../../api/contact/hooks/create-upsert-contact-hook' -import { PlusCircledIcon } from '@radix-ui/react-icons' +import { Sex } from '../../api/contact/schemas' export default function NewContactButton() { const navigate = useNavigate() const { isLoading, error, createContact } = useCreateAndUpsertContact() - async function handleOnClick() { - // Create and upsert the contact + async function handleOnClick(sex: Sex) { try { - const chat_id = await createContact() + const chat_id = await createContact(sex) navigate(`/${chat_id}`) } catch (e) { // do nothing @@ -17,15 +16,28 @@ export default function NewContactButton() { } return ( - +
+ + + +
) } From 5b0675f11ae8824fcf2c8b3a0322897f1c538d0a Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Mon, 2 Feb 2026 00:14:57 -0500 Subject: [PATCH 29/32] chore: pin Python 3.11 and update examples --- .python-version | 2 +- README.md | 7 +- examples/base/README.md | 37 +- examples/base/pyproject.toml | 7 + examples/base/src/base/visualize.py | 2 + examples/getting_started/README.md | 20 +- pyproject.toml | 1 - uv.lock | 594 +++++++++++++++++++++++++++- 8 files changed, 632 insertions(+), 38 deletions(-) diff --git a/.python-version b/.python-version index 24ee5b1..2c07333 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.13 +3.11 diff --git a/README.md b/README.md index 45dd65e..525f3bc 100644 --- a/README.md +++ b/README.md @@ -259,7 +259,8 @@ if __name__ == "__main__": ```bash # Run the visualizer -python -m src.base.visualize +$ cd examples/base +$ uv run --package base -m base.visualize ```
@@ -274,7 +275,7 @@ This project was made with the [uv](https://github.com/astral-sh/uv) python pack ```bash # Setup and activate the virtual environment -$ uv venv .venv +$ uv venv --python 3.11 .venv $ source .venv/bin/activate # Install optional development dependencies (graphviz is optional for running the graphviz visualizer) @@ -308,4 +309,4 @@ $ uv run sphinx-build -b html docs docs/_build ```bash # Run the tests with uv $ uv run pytest -``` \ No newline at end of file +``` diff --git a/examples/base/README.md b/examples/base/README.md index 7cc7b8a..8f2a4b2 100644 --- a/examples/base/README.md +++ b/examples/base/README.md @@ -18,30 +18,29 @@ Start an instance of [Junjo AI Studio Minimal Build](https://github.com/mdrideou ### Run the example -> Note: the following commands assume your terminal is located in this example project's directory root. +> Note: you can run the following commands from this directory; `uv` will discover the workspace root automatically. - The graph workflow will run, logging node executions and state changes to your console - If [Junjo AI Studio](https://github.com/mdrideout/junjo-ai-studio-minimal-build) is running, it will receive telemetry. - Requires you to generate an API key inside the Junjo AI Studio interface, and add it as a `.env` variable here. ```bash -# Run commands from this directory -# - (Using uv package manager https://docs.astral.sh/uv/) +# (use uv package manager https://docs.astral.sh/uv/) # -# Create a virtual environment if one doesn't exist yet (tested down to python 3.11) -$ uv venv --python 3.11 - -# Make sure the backend virtual environment is activated -$ source .venv/bin/activate - -# Ensure all packages are installed -$ uv pip install -e ".[dev]" - -# Run from this directory -$ python -m src.base.main - -# Generate Graphviz renderings of the graph (outputs to graphviz_out dir in root) -$ python -m src.base.visualize +# Create a .env file from the .env.example +# +# This repo is a `uv` workspace. The virtual environment lives at the repo root +# (`../../.venv` from here), not inside this example directory. +# +# Recommended (run from this directory): +$ uv sync --python 3.11 --package base --all-extras +$ uv run --package base -m base.main +$ uv run --package base -m base.visualize + +# Standalone (if you copied this example out of the workspace): +# $ uv sync --all-extras +# $ uv run -m base.main +# $ uv run -m base.visualize ``` ## Eval Driven Development @@ -50,7 +49,7 @@ Eval-Driven Development (EDD) is a critical development strategy for application EDD accelerates complex workflow development by allowing one to iterate on their LLM prompts with many test inputs, and immediately see how the prompt changes impact the evaluation results. -**Example:** Check out `src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test` to see an example eval system, setup to evaluate the joke created. +**Example:** Open `src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test` to see an example eval system, setup to evaluate the joke created. - The eval system is powered by **pytest**' - No third party tools or platforms are required - everything happens directly in your codebase @@ -75,4 +74,4 @@ This eval is strict and likely to fail all cases. This is to demonstrate the inf # Run the pytest command from this directory. # Ensure you have setup the appropriate environment from the above "Run the example" instructions $ python -m pytest src/base/sample_workflow/sample_subflow/nodes/create_joke_node/test/test_node.py -v -``` \ No newline at end of file +``` diff --git a/examples/base/pyproject.toml b/examples/base/pyproject.toml index 44a468b..fe8c169 100644 --- a/examples/base/pyproject.toml +++ b/examples/base/pyproject.toml @@ -1,3 +1,7 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + [project] name = "base" version = "0.1.0" @@ -37,3 +41,6 @@ junjo = { workspace = true } [dependency-groups] dev = ["ruff>=0.9.5"] + +[tool.setuptools.packages.find] +where = ["src"] diff --git a/examples/base/src/base/visualize.py b/examples/base/src/base/visualize.py index f0c6600..feb7d53 100644 --- a/examples/base/src/base/visualize.py +++ b/examples/base/src/base/visualize.py @@ -11,6 +11,8 @@ def main(): # Export graphviz assets create_sample_workflow_graph().export_graphviz_assets() + print("\n\nCheck graphviz_out directory for visualization.\n") + if __name__ == "__main__": main() diff --git a/examples/getting_started/README.md b/examples/getting_started/README.md index 374dbd0..503dd29 100644 --- a/examples/getting_started/README.md +++ b/examples/getting_started/README.md @@ -4,18 +4,14 @@ This is the example Junjo workflow from the Junjo Python API docs. ```bash # Run commands from this directory -# - (Using uv package manager https://docs.astral.sh/uv/) +# - Using uv package manager https://docs.astral.sh/uv/ # -# Create a virtual environment if one doesn't exist yet (tested down to python 3.11) -$ uv venv --python 3.11 - -# Make sure the backend virtual environment is activated -$ source .venv/bin/activate - -# Ensure all packages are installed -$ uv pip install -e . +# This repo is a `uv` workspace. The virtual environment lives at the repo root +# (`../../.venv` from here), not inside this example directory. +# +# Ensure all packages are installed (Python 3.11) +$ uv sync --python 3.11 --package getting-started # Run from this directory -$ python -m main -$ uv run -m main -``` \ No newline at end of file +$ uv run --package getting-started -m main +``` diff --git a/pyproject.toml b/pyproject.toml index cdff8e3..5904b00 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -90,7 +90,6 @@ ignore = ["B006"] members = [ "examples/ai_chat/backend", "examples/base", - "examples/getting_started/getting_started", "examples/getting_started", ] diff --git a/uv.lock b/uv.lock index 4c53edd..99c9909 100644 --- a/uv.lock +++ b/uv.lock @@ -26,6 +26,130 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = 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2df2e3cee954b2ad77ca0985bd4f687418d76176 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sun, 8 Feb 2026 12:14:27 -0500 Subject: [PATCH 30/32] chore: bump ruff to 0.15.0 and apply unsafe fixes --- examples/ai_chat/backend/pyproject.toml | 2 +- .../src/app/ai_services/grok/grok_tool.py | 3 +- .../src/app/db/models/message/repository.py | 2 +- .../app/workflows/handle_message/workflow.py | 2 +- examples/base/pyproject.toml | 4 +- examples/getting_started/pyproject.toml | 2 +- pyproject.toml | 2 +- src/junjo/graph.py | 2 +- uv.lock | 52 +++++++++---------- 9 files changed, 36 insertions(+), 35 deletions(-) diff --git a/examples/ai_chat/backend/pyproject.toml b/examples/ai_chat/backend/pyproject.toml index d6be038..d9bb780 100644 --- a/examples/ai_chat/backend/pyproject.toml +++ b/examples/ai_chat/backend/pyproject.toml @@ -53,7 +53,7 @@ ignore = ["B006"] junjo = { workspace = true } [dependency-groups] -dev = ["ruff>=0.9.5"] +dev = ["ruff>=0.15.0"] [tool.setuptools.packages.find] where = ["src"] diff --git a/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py b/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py index af0a0d7..90e3591 100644 --- a/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py +++ b/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py @@ -8,7 +8,8 @@ from PIL import Image from pydantic import BaseModel from xai_sdk import AsyncClient -from xai_sdk.chat import assistant, image as xai_image, system, user +from xai_sdk.chat import assistant, system, user +from xai_sdk.chat import image as xai_image T = TypeVar("T", bound=BaseModel) diff --git a/examples/ai_chat/backend/src/app/db/models/message/repository.py b/examples/ai_chat/backend/src/app/db/models/message/repository.py index fa67a31..246e9f0 100644 --- a/examples/ai_chat/backend/src/app/db/models/message/repository.py +++ b/examples/ai_chat/backend/src/app/db/models/message/repository.py @@ -1,6 +1,6 @@ # app/db/models/message/repository.py -from sqlalchemy import exists, select, update, func +from sqlalchemy import exists, func, select, update from sqlalchemy.exc import SQLAlchemyError from sqlalchemy.orm import aliased diff --git a/examples/ai_chat/backend/src/app/workflows/handle_message/workflow.py b/examples/ai_chat/backend/src/app/workflows/handle_message/workflow.py index 2a7793f..fd1235a 100644 --- a/examples/ai_chat/backend/src/app/workflows/handle_message/workflow.py +++ b/examples/ai_chat/backend/src/app/workflows/handle_message/workflow.py @@ -22,7 +22,7 @@ async def run_handle_message_workflow(message: MessageCreate) -> None: # Execute the workflow print("Executing handle_message_workflow") await handle_message_workflow.execute() - final_state = await handle_message_workflow.get_state() + await handle_message_workflow.get_state() print("handle_message_workflow is done") return diff --git a/examples/base/pyproject.toml b/examples/base/pyproject.toml index fe8c169..e8ff9aa 100644 --- a/examples/base/pyproject.toml +++ b/examples/base/pyproject.toml @@ -15,7 +15,7 @@ dependencies = [ ] [project.optional-dependencies] -dev = ["pytest", "pytest-asyncio", "ruff~=0.9.5"] +dev = ["pytest", "pytest-asyncio", "ruff~=0.15.0"] [tool.ruff] # https://docs.astral.sh/ruff/settings/ @@ -40,7 +40,7 @@ ignore = ["B006"] junjo = { workspace = true } [dependency-groups] -dev = ["ruff>=0.9.5"] +dev = ["ruff>=0.15.0"] [tool.setuptools.packages.find] where = ["src"] diff --git a/examples/getting_started/pyproject.toml b/examples/getting_started/pyproject.toml index 26d5c7a..306887b 100644 --- a/examples/getting_started/pyproject.toml +++ b/examples/getting_started/pyproject.toml @@ -4,7 +4,7 @@ version = "0.1.0" description = "The Junjo Getting Started documentation example." readme = "README.md" requires-python = ">=3.11" -dependencies = ["junjo", "ruff>=0.9.5"] +dependencies = ["junjo", "ruff>=0.15.0"] [tool.uv.sources] junjo = { workspace = true } diff --git a/pyproject.toml 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src/junjo/workflow.py | 4 +- 12 files changed, 465 insertions(+), 493 deletions(-) diff --git a/examples/ai_chat/backend/pyproject.toml b/examples/ai_chat/backend/pyproject.toml index d9bb780..c1ef4ab 100644 --- a/examples/ai_chat/backend/pyproject.toml +++ b/examples/ai_chat/backend/pyproject.toml @@ -48,6 +48,7 @@ ignore = ["B006"] [tool.ruff.lint.per-file-ignores] "tests/junjo/test_*.py" = ["D", "F401"] # ignore docstring rules in tests +"**/node.py" = ["C901"] # allow higher complexity in workflow node services [tool.uv.sources] junjo = { workspace = true } diff --git a/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py b/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py index e87fde2..d644917 100644 --- a/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py +++ b/examples/ai_chat/backend/src/app/ai_services/gemini/gemini_tool.py @@ -1,6 +1,6 @@ import os from io import BytesIO -from typing import TypeVar +from typing import Any, TypeVar from google import genai from google.genai import types @@ -77,6 +77,46 @@ def _safety_settings_off() -> list[types.SafetySetting]: ), ] + @staticmethod + def _coalesce(mapping: dict[str, Any], *keys: str) -> Any: + for key in keys: + value = mapping.get(key) + if value is not None: + return value + return None + + @staticmethod + def _log_ratings(context: str, prefix: str, ratings: list[Any]) -> None: + for rating in ratings or []: + if isinstance(rating, dict): + category = rating.get("category") + blocked = rating.get("blocked") + probability = rating.get("probability") + severity = rating.get("severity") + else: + category = getattr(rating, "category", None) + blocked = getattr(rating, "blocked", None) + probability = getattr(rating, "probability", None) + severity = getattr(rating, "severity", None) + + logger.warning( + f"{context}: {prefix} safety_rating " + f"category={category} blocked={blocked} " + f"probability={probability} severity={severity}" + ) + + def _log_reason_and_ratings( + self, + *, + context: str, + prefix: str, + reason: Any, + message: Any, + ratings: list[Any], + ) -> None: + logger.warning(f"{context}: {prefix}={reason} message={message}") + self._log_ratings(context, prefix, ratings) + def _log_block_reason(self, response: object, *, context: str) -> None: """ Best-effort logging for Gemini content blocks. @@ -93,77 +133,47 @@ def _log_block_reason(self, response: object, *, context: str) -> None: prompt_feedback = getattr(response, "prompt_feedback", None) if prompt_feedback: - logger.warning( - f"{context}: prompt_feedback.block_reason={getattr(prompt_feedback, 'block_reason', None)} " - f"message={getattr(prompt_feedback, 'block_reason_message', None)}" + self._log_reason_and_ratings( + context=context, + prefix="prompt_feedback.block_reason", + reason=getattr(prompt_feedback, "block_reason", None), + message=getattr(prompt_feedback, "block_reason_message", None), + ratings=getattr(prompt_feedback, "safety_ratings", []) or [], ) - for rating in getattr(prompt_feedback, "safety_ratings", []) or []: - logger.warning( - f"{context}: prompt safety_rating " - f"category={getattr(rating, 'category', None)} " - f"blocked={getattr(rating, 'blocked', None)} " - f"probability={getattr(rating, 'probability', None)} " - f"severity={getattr(rating, 'severity', None)}" - ) candidates = getattr(response, "candidates", []) or [] if candidates: candidate = candidates[0] - logger.warning( - f"{context}: candidate.finish_reason={getattr(candidate, 'finish_reason', None)} " - f"finish_message={getattr(candidate, 'finish_message', None)}" + self._log_reason_and_ratings( + context=context, + prefix="candidate.finish_reason", + reason=getattr(candidate, "finish_reason", None), + message=getattr(candidate, "finish_message", None), + ratings=getattr(candidate, "safety_ratings", []) or [], ) - for rating in getattr(candidate, "safety_ratings", []) or []: - logger.warning( - f"{context}: candidate safety_rating " - f"category={getattr(rating, 'category', None)} " - f"blocked={getattr(rating, 'blocked', None)} " - f"probability={getattr(rating, 'probability', None)} " - f"severity={getattr(rating, 'severity', None)}" - ) - # If available, log raw prompt feedback / safety ratings from the SDK's HTTP response. - # This helps when the client library doesn't fully map newer enum values (e.g. IMAGE_OTHER). sdk_http_response = getattr(response, "sdk_http_response", None) - if sdk_http_response is not None: - raw = getattr(sdk_http_response, "json", None) - if isinstance(raw, dict): - prompt_feedback_raw = raw.get("promptFeedback") or raw.get("prompt_feedback") - if isinstance(prompt_feedback_raw, dict): - logger.warning( - f"{context}: raw.prompt_feedback.block_reason=" - f"{prompt_feedback_raw.get('blockReason') or prompt_feedback_raw.get('block_reason')} " - f"message={prompt_feedback_raw.get('blockReasonMessage') or prompt_feedback_raw.get('block_reason_message')}" - ) - for rating in ( - prompt_feedback_raw.get("safetyRatings") - or prompt_feedback_raw.get("safety_ratings") - or [] - ): - logger.warning( - f"{context}: raw.prompt safety_rating " - f"category={rating.get('category')} " - f"blocked={rating.get('blocked')} " - f"probability={rating.get('probability')} " - f"severity={rating.get('severity')}" - ) - - candidates_raw = raw.get("candidates") or [] - if candidates_raw: - candidate_raw = candidates_raw[0] - logger.warning( - f"{context}: raw.candidate.finish_reason=" - f"{candidate_raw.get('finishReason') or candidate_raw.get('finish_reason')} " - f"finish_message={candidate_raw.get('finishMessage') or candidate_raw.get('finish_message')}" - ) - for rating in candidate_raw.get("safetyRatings") or candidate_raw.get("safety_ratings") or []: - logger.warning( - f"{context}: raw.candidate safety_rating " - f"category={rating.get('category')} " - f"blocked={rating.get('blocked')} " - f"probability={rating.get('probability')} " - f"severity={rating.get('severity')}" - ) + raw = getattr(sdk_http_response, "json", None) if sdk_http_response else None + if isinstance(raw, dict): + prompt_feedback_raw = raw.get("promptFeedback") or raw.get("prompt_feedback") + if isinstance(prompt_feedback_raw, dict): + self._log_reason_and_ratings( + context=context, + prefix="raw.prompt_feedback.block_reason", + reason=self._coalesce(prompt_feedback_raw, "blockReason", "block_reason"), + message=self._coalesce(prompt_feedback_raw, "blockReasonMessage", "block_reason_message"), + ratings=self._coalesce(prompt_feedback_raw, "safetyRatings", "safety_ratings") or [], + ) + + candidate_raw = (raw.get("candidates") or [None])[0] + if isinstance(candidate_raw, dict): + self._log_reason_and_ratings( + context=context, + prefix="raw.candidate.finish_reason", + reason=self._coalesce(candidate_raw, "finishReason", "finish_reason"), + message=self._coalesce(candidate_raw, "finishMessage", "finish_message"), + ratings=self._coalesce(candidate_raw, "safetyRatings", "safety_ratings") or [], + ) except Exception: logger.exception(f"{context}: failed to log Gemini block reason") diff --git a/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py b/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py index 90e3591..01bceea 100644 --- a/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py +++ b/examples/ai_chat/backend/src/app/ai_services/grok/grok_tool.py @@ -93,53 +93,62 @@ def _stringify(value: Any) -> str: return value return str(value) - @classmethod - def _to_xai_message(cls, message: dict[str, Any]) -> Any: - role = cls._stringify(message.get("role")).strip().lower() - content = message.get("content") + @staticmethod + def _collect_user_parts(content: list[Any]) -> tuple[str, list[Any]]: + text_parts: list[str] = [] + image_parts: list[Any] = [] - if role == "system": - return system(cls._stringify(content)) + for part in content: + if not isinstance(part, dict): + continue - if role == "assistant": - return assistant(cls._stringify(content)) + part_type = part.get("type") + if part_type in ("input_text", "text"): + text = part.get("text") + if isinstance(text, str) and text: + text_parts.append(text) + continue - if role != "user": - logger.warning(f"Unknown message role '{role}', treating as user.") + if part_type in ("input_image", "image"): + image_url = part.get("image_url") + if isinstance(image_url, str) and image_url: + image_parts.append(xai_image(image_url=image_url)) + continue + + return "\n".join(text_parts).strip(), image_parts + @classmethod + def _to_xai_user_message(cls, content: Any) -> Any: # user messages can interleave text + images: user("...", image("..."), image("...")) if isinstance(content, str): return user(content) if isinstance(content, list): - text_parts: list[str] = [] - image_parts: list[Any] = [] - - for part in content: - if not isinstance(part, dict): - continue - - part_type = part.get("type") - if part_type in ("input_text", "text"): - text = part.get("text") - if isinstance(text, str) and text: - text_parts.append(text) - continue - - if part_type in ("input_image", "image"): - image_url = part.get("image_url") - if isinstance(image_url, str) and image_url: - image_parts.append(xai_image(image_url=image_url)) - continue - - text = "\n".join(text_parts).strip() + text, image_parts = cls._collect_user_parts(content) if image_parts: return user(text, *image_parts) - return user(text) return user(cls._stringify(content)) + @classmethod + def _to_xai_message(cls, message: dict[str, Any]) -> Any: + role = cls._stringify(message.get("role")).strip().lower() + content = message.get("content") + + role_handlers = { + "assistant": assistant, + "system": system, + } + role_handler = role_handlers.get(role) + if role_handler: + return role_handler(cls._stringify(content)) + + if role != "user": + logger.warning(f"Unknown message role '{role}', treating as user.") + + return cls._to_xai_user_message(content) + def _xai_messages(self, messages: list[dict[str, Any]] | None) -> list[Any]: if messages is None: if not self._prompt: diff --git a/examples/ai_chat/backend/src/app/db/db_config.py b/examples/ai_chat/backend/src/app/db/db_config.py index 78d9a43..d1f0242 100644 --- a/examples/ai_chat/backend/src/app/db/db_config.py +++ b/examples/ai_chat/backend/src/app/db/db_config.py @@ -1,8 +1,6 @@ from pathlib import Path -from urllib.parse import urlparse from loguru import logger -from opentelemetry import trace from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor from sqlalchemy import text from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine @@ -71,94 +69,8 @@ async def init_db(): # --- OpenTelemetry Instrumentation --- -def add_details(span, conn, cursor, statement, parameters, context, exception_): - """Adds more details to the SQLAlchemy span.""" - # **Crucial: Set db.system** - db_system = conn.dialect.name - span.set_attribute("db.system", db_system) - - # Database Name - parsed_url = urlparse(str(conn.engine.url)) - db_name = parsed_url.path.lstrip("/") # Remove leading slash - if not db_name: - db_name = "Unknown SQLite" - span.set_attribute("db.name", db_name) - - # db.user - span.set_attribute("db.user", conn.engine.url.username or "default") - - # net.peer.name - span.set_attribute("net.peer.name", parsed_url.hostname) - - # net.peer.port - span.set_attribute("net.peer.port", parsed_url.port or (3306 if db_system == "mysql" else 5432 if db_system == "postgresql" else 0)) - - # SQL Statement Type (db.operation) - statement_lower = statement.lower() - if statement_lower.startswith("select"): - span.set_attribute("db.operation", "SELECT") - elif statement_lower.startswith("insert"): - span.set_attribute("db.operation", "INSERT") - elif statement_lower.startswith("update"): - span.set_attribute("db.operation", "UPDATE") - elif statement_lower.startswith("delete"): - span.set_attribute("db.operation", "DELETE") - elif statement_lower.startswith("create"): - span.set_attribute("db.operation", "CREATE") - elif statement_lower.startswith("alter"): - span.set_attribute("db.operation", "ALTER") - elif statement_lower.startswith("drop"): - span.set_attribute("db.operation", "DROP") - else: - span.set_attribute("db.operation", "OTHER") - - # Table Name (basic extraction) - if "from" in statement_lower: - parts = statement_lower.split("from") - if len(parts) > 1: - table_part = parts[1].split()[0] # Get the first word after "from" - span.set_attribute("db.sql.table", table_part) - elif "into" in statement_lower: - parts = statement_lower.split("into") - if len(parts) > 1: - table_part = parts[1].split()[0] # Get the first word after "into" - span.set_attribute("db.sql.table", table_part) - elif "table" in statement_lower: - parts = statement_lower.split("table") - if len(parts) > 1: - table_part = parts[1].split()[0] # Get the first word after "table" - span.set_attribute("db.sql.table", table_part) - - # Row Count (if available) - if cursor and hasattr(cursor, "rowcount"): - span.set_attribute("db.rowcount", cursor.rowcount) - - # Parameters - if parameters: - span.set_attribute("db.params", str(parameters)) - - # db.statement - span.set_attribute("db.statement", statement) - - # Exception Handling - if exception_: - span.set_status(trace.Status(trace.StatusCode.ERROR)) - span.record_exception(exception_) - - # Get isolation level - try: - isolation_level = conn.get_isolation_level() - span.set_attribute("db.connection.isolation_level", isolation_level) - except AttributeError: - span.set_attribute("db.connection.isolation_level", "N/A") - - # db.connection.string - span.set_attribute("db.connection.string", str(conn.engine.url)) - SQLAlchemyInstrumentor().instrument( - engine=engine.sync_engine, # `.sync_engine` is required since this instrumentation does not support the async engine - before_cursor_execute=add_details, - after_cursor_execute=add_details, - dbapi_level_span=True + # `.sync_engine` is required since this instrumentation does not support the async engine. + engine=engine.sync_engine, ) # --- End OpenTelemetry Instrumentation --- diff --git a/examples/ai_chat/backend/src/app/db/models/chat_members/repository.py b/examples/ai_chat/backend/src/app/db/models/chat_members/repository.py index 1303c0d..de3638a 100644 --- a/examples/ai_chat/backend/src/app/db/models/chat_members/repository.py +++ b/examples/ai_chat/backend/src/app/db/models/chat_members/repository.py @@ -70,7 +70,12 @@ async def read_by_chat_id(chat_id: str, skip: int = 0, limit: int = 100) -> list """Reads multiple chat member records by chat_id with pagination.""" try: async with async_session() as session: - stmt = select(model.ChatMembersTable).where(model.ChatMembersTable.chat_id == chat_id).offset(skip).limit(limit) + stmt = ( + select(model.ChatMembersTable) + .where(model.ChatMembersTable.chat_id == chat_id) + .offset(skip) + .limit(limit) + ) db_chat_members = (await session.execute(stmt)).scalars().all() return [schemas.ChatMemberRead.model_validate(db_chat_member) for db_chat_member in db_chat_members] except SQLAlchemyError as e: diff --git a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_date_idea_response/prompt_gemini.py b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_date_idea_response/prompt_gemini.py index e3f8bb0..3392566 100644 --- a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_date_idea_response/prompt_gemini.py +++ b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_date_idea_response/prompt_gemini.py @@ -2,7 +2,11 @@ from app.db.models.message.schemas import MessageRead -def create_date_idea_response_workflow_prompt(history: list[MessageRead], contact: ContactRead, most_recent_message: str) -> str: +def create_date_idea_response_workflow_prompt( + history: list[MessageRead], + contact: ContactRead, + most_recent_message: str, +) -> str: """Create a message response using history and contact bio information.""" # Convert the message history into stringified JSON @@ -26,7 +30,8 @@ def create_date_idea_response_workflow_prompt(history: list[MessageRead], contac {most_recent_message} RESPONSE DIRECTIVE: -You need to create a date idea response. Analyze the context for anything you have previously disucssed about finding a place to go on a date. +You need to create a date idea response. Analyze the context for anything you have +previously disucssed about finding a place to go on a date. The date ideas should be real places that you have been to or would like to go to in your geographic area. Make sure you can give specifics. diff --git a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_work_response/prompt_gemini.py b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_work_response/prompt_gemini.py index fcfbd3a..0375ae7 100644 --- a/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_work_response/prompt_gemini.py +++ b/examples/ai_chat/backend/src/app/workflows/handle_message/nodes/create_work_response/prompt_gemini.py @@ -2,7 +2,11 @@ from app.db.models.message.schemas import MessageRead -def create_work_response_workflow_prompt(history: list[MessageRead], contact: ContactRead, most_recent_message: str) -> str: +def create_work_response_workflow_prompt( + history: list[MessageRead], + contact: ContactRead, + most_recent_message: str, +) -> str: """Create a message response using history and contact bio information.""" # Convert the message history into stringified JSON @@ -26,8 +30,10 @@ def create_work_response_workflow_prompt(history: list[MessageRead], contact: Co {most_recent_message} RESPONSE DIRECTIVE: -You need to create a work-related response. Analyze the context for anything you have previously disucssed about your own work history. -This is to continue building your personal narrative for future chats. Come up with new work history if you have not previously discussed it. +You need to create a work-related response. Analyze the context for anything you have +previously disucssed about your own work history. +This is to continue building your personal narrative for future chats. +Come up with new work history if you have not previously discussed it. If you have previously discussed it, make sure you are furthering the narrative and not just repeating yourself. diff --git a/examples/base/pyproject.toml b/examples/base/pyproject.toml index e8ff9aa..79094ad 100644 --- a/examples/base/pyproject.toml +++ b/examples/base/pyproject.toml @@ -35,6 +35,7 @@ ignore = ["B006"] [tool.ruff.lint.per-file-ignores] "tests/junjo/test_*.py" = ["D", "F401"] # ignore docstring rules in tests +"**/node.py" = ["C901"] # allow higher complexity in workflow node services [tool.uv.sources] junjo = { workspace = true } diff --git a/examples/getting_started/main.py b/examples/getting_started/main.py index 55f6dba..c2b1560 100644 --- a/examples/getting_started/main.py +++ b/examples/getting_started/main.py @@ -5,98 +5,104 @@ # uv run -m main +class SampleWorkflowState(BaseState): + count: int | None = None # Does not need an initial state value + items: list[str] # Does need an initial state value + + +class SampleWorkflowStore(BaseStore[SampleWorkflowState]): + # An immutable state update function + async def set_count(self, payload: int) -> None: + await self.set_state({"count": payload}) + + +class FirstNode(Node[SampleWorkflowStore]): + async def service(self, store: SampleWorkflowStore) -> None: + print("First Node Executed") + + +class CountItemsNode(Node[SampleWorkflowStore]): + async def service(self, store: SampleWorkflowStore) -> None: + # Get the state and count the items + state = await store.get_state() + items = state.items + count = len(items) + + # Perform a state update with the count + await store.set_count(count) + print(f"Counted {count} items") + + +class EvenItemsNode(Node[SampleWorkflowStore]): + async def service(self, store: SampleWorkflowStore) -> None: + print("Path taken for even items count.") + + +class OddItemsNode(Node[SampleWorkflowStore]): + async def service(self, store: SampleWorkflowStore) -> None: + print("Path taken for odd items count.") + + +class FinalNode(Node[SampleWorkflowStore]): + async def service(self, store: SampleWorkflowStore) -> None: + print("Final Node Executed") + + +class CountIsEven(Condition[SampleWorkflowState]): + def evaluate(self, state: SampleWorkflowState) -> bool: + count = state.count + if count is None: + return False + return count % 2 == 0 + + +def create_graph() -> Graph: + """ + Factory function to create a new instance of the sample workflow graph. + This ensures that each workflow execution gets a fresh, isolated graph, + preventing state conflicts in concurrent environments. + """ + # Instantiate the nodes + first_node = FirstNode() + count_items_node = CountItemsNode() + even_items_node = EvenItemsNode() + odd_items_node = OddItemsNode() + final_node = FinalNode() + + # Create the workflow graph + return Graph( + source=first_node, + sink=final_node, + edges=[ + Edge(tail=first_node, head=count_items_node), + # Branching based on the count of items + Edge(tail=count_items_node, head=even_items_node, condition=CountIsEven()), + Edge(tail=count_items_node, head=odd_items_node), # Fallback if first condition is not met + # Branched paths converge to the final node + Edge(tail=even_items_node, head=final_node), + Edge(tail=odd_items_node, head=final_node), + ], + ) + + +def create_workflow() -> Workflow[SampleWorkflowState, SampleWorkflowStore]: + """ + Factory function to create a new instance of the workflow. + This pattern is recommended to avoid concurrency issues and ensure + fresh state/store initialization for each execution. + """ + return Workflow[SampleWorkflowState, SampleWorkflowStore]( + name="Getting Started Example Workflow", + graph_factory=create_graph, + store_factory=lambda: SampleWorkflowStore( + initial_state=SampleWorkflowState(items=["laser", "coffee", "horse"]) + ), + ) + + async def main(): """Entrypoint for the Junjo "Getting Started" example app.""" - # Define the workflow state - class SampleWorkflowState(BaseState): - count: int | None = None # Does not need an initial state value - items: list[str] # Does need an initial state value - - # Define the workflow store - class SampleWorkflowStore(BaseStore[SampleWorkflowState]): - # An immutable state update function - async def set_count(self, payload: int) -> None: - await self.set_state({"count": payload}) - - # Define the nodes - class FirstNode(Node[SampleWorkflowStore]): - async def service(self, store: SampleWorkflowStore) -> None: - print("First Node Executed") - - class CountItemsNode(Node[SampleWorkflowStore]): - async def service(self, store: SampleWorkflowStore) -> None: - # Get the state and count the items - state = await store.get_state() - items = state.items - count = len(items) - - # Perform a state update with the count - await store.set_count(count) - print(f"Counted {count} items") - - class EvenItemsNode(Node[SampleWorkflowStore]): - async def service(self, store: SampleWorkflowStore) -> None: - print("Path taken for even items count.") - - class OddItemsNode(Node[SampleWorkflowStore]): - async def service(self, store: SampleWorkflowStore) -> None: - print("Path taken for odd items count.") - - class FinalNode(Node[SampleWorkflowStore]): - async def service(self, store: SampleWorkflowStore) -> None: - print("Final Node Executed") - - class CountIsEven(Condition[SampleWorkflowState]): - def evaluate(self, state: SampleWorkflowState) -> bool: - count = state.count - if count is None: - return False - return count % 2 == 0 - - def create_graph() -> Graph: - """ - Factory function to create a new instance of the sample workflow graph. - This ensures that each workflow execution gets a fresh, isolated graph, - preventing state conflicts in concurrent environments. - """ - # Instantiate the nodes - first_node = FirstNode() - count_items_node = CountItemsNode() - even_items_node = EvenItemsNode() - odd_items_node = OddItemsNode() - final_node = FinalNode() - - # Create the workflow graph - return Graph( - source=first_node, - sink=final_node, - edges=[ - Edge(tail=first_node, head=count_items_node), - # Branching based on the count of items - Edge(tail=count_items_node, head=even_items_node, condition=CountIsEven()), # Only if count is even - Edge(tail=count_items_node, head=odd_items_node), # Fallback if first condition is not met - # Branched paths converge to the final node - Edge(tail=even_items_node, head=final_node), - Edge(tail=odd_items_node, head=final_node), - ], - ) - - def create_workflow() -> Workflow[SampleWorkflowState, SampleWorkflowStore]: - """ - Factory function to create a new instance of the workflow. - This pattern is recommended to avoid concurrency issues and ensure - fresh state/store initialization for each execution. - """ - return Workflow[SampleWorkflowState, SampleWorkflowStore]( - name="Getting Started Example Workflow", - graph_factory=create_graph, - store_factory=lambda: SampleWorkflowStore( - initial_state=SampleWorkflowState(items=["laser", "coffee", "horse"]) - ), - ) - - # Create and execute the workflow workflow = create_workflow() await workflow.execute() print("Final state: ", await workflow.get_state_json()) diff --git a/pyproject.toml b/pyproject.toml index 1e636a3..82691cd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -85,6 +85,7 @@ ignore = ["B006"] [tool.ruff.lint.per-file-ignores] "tests/junjo/test_*.py" = ["D", "F401"] # ignore docstring rules in tests +"**/node.py" = ["C901"] # allow higher complexity in workflow node services [tool.uv.workspace] members = [ diff --git a/src/junjo/graph.py b/src/junjo/graph.py index 559f96b..dfa9ddb 100644 --- a/src/junjo/graph.py +++ b/src/junjo/graph.py @@ -30,7 +30,8 @@ class Graph: :type source: Node | _NestableWorkflow :param sink: The terminal node or subflow of the graph. Reaching this node signifies the completion of the workflow. :type sink: Node | _NestableWorkflow - :param edges: A list of :class:`~.Edge` instances that define the connections and transition logic between nodes in the graph. + :param edges: A list of :class:`~.Edge` instances that define the + connections and transition logic between nodes in the graph. :type edges: list[Edge] Example: @@ -273,159 +274,234 @@ def to_mermaid(self) -> str: """ raise NotImplementedError("Mermaid conversion is not implemented yet.") - def to_dot_notation(self) -> str: # noqa: C901 (complexity fine for helper) - """ - Render the Junjo graph as a *main* overview digraph plus one additional - digraph for **each Subflow**. - - Strategy - -------- - • In the **overview** we treat every Subflow node as an atomic component - (shape=component, fillcolour light-yellow). - • Any `RunConcurrent` node is rendered as a cluster, exactly like before. - • For every Subflow we emit a *second* `digraph subflow_` that expands - its internal graph, again treating nested Subflows as atomic macro nodes - (so the drill-down is recursive). + def _build_dot_render_context( + self, + graph: dict, + edge_filter: Callable[[dict], bool], + ) -> tuple[dict[str, dict], list[dict], set[str], dict[str, dict], dict[str, str], dict[str, str]]: + nodes_by_id = {node["id"]: node for node in graph["nodes"]} + edges = [edge for edge in graph["edges"] if edge_filter(edge)] + + node_ids: set[str] = set() + for edge in edges: + node_ids.update((edge["source"], edge["target"])) + + for node_id in list(node_ids): + node = nodes_by_id.get(node_id) + if node and node.get("isSubgraph"): + node_ids.update(node["children"]) + + clusters = { + node["id"]: node + for node in graph["nodes"] + if node.get("isSubgraph") and node["id"] in node_ids + } + entry_anchor = {cluster_id: f"{cluster_id}__entry" for cluster_id in clusters} + exit_anchor = {cluster_id: f"{cluster_id}__exit" for cluster_id in clusters} + return nodes_by_id, edges, node_ids, clusters, entry_anchor, exit_anchor + + @staticmethod + def _dot_anchor( + node_id: str, + *, + is_src: bool, + clusters: dict[str, dict], + entry_anchor: dict[str, str], + exit_anchor: dict[str, str], + ) -> str: + if node_id in clusters: + return exit_anchor[node_id] if is_src else entry_anchor[node_id] + return node_id + + def _append_dot_header(self, output: list[str], graph_name: str) -> None: + append = output.append + append(f'digraph "{graph_name}" {{') + append(" rankdir=LR;") + append(" compound=true;") + append(' node [shape=box, style="rounded,filled", fillcolor="#EFEFEF", fontname="Helvetica", fontsize=10];') + append(' edge [fontname="Helvetica", fontsize=9];') + + def _append_dot_clusters( + self, + output: list[str], + clusters: dict[str, dict], + entry_anchor: dict[str, str], + exit_anchor: dict[str, str], + nodes_by_id: dict[str, dict], + ) -> None: + append = output.append + for cluster_id, cluster_node in clusters.items(): + append(f' subgraph "cluster_{cluster_id}" {{') + append(f' label="{self._safe_label(cluster_node["label"])} (Concurrent)";') + append(' style="filled"; fillcolor="lightblue"; color="blue";') + append(' node [fillcolor="lightblue", style="filled,rounded"];') + append(f' "{entry_anchor[cluster_id]}" [label="", shape=point, width=0.01, style=invis];') + append(f' "{exit_anchor[cluster_id]}" [label="", shape=point, width=0.01, style=invis];') + for child_id in cluster_node["children"]: + child = nodes_by_id[child_id] + append(f' {self._q(child_id)} [label="{self._safe_label(child["label"])}"];') + append(" }") + + def _append_dot_nodes( + self, + output: list[str], + node_ids: set[str], + clusters: dict[str, dict], + nodes_by_id: dict[str, dict], + ) -> None: + append = output.append + for node_id in node_ids: + if node_id in clusters: + continue + node = nodes_by_id[node_id] + if node.get("isSubflow"): + append( + f' {self._q(node_id)} [label="{self._safe_label(node["label"])}", ' + 'shape=component, style="filled,rounded", fillcolor="lightyellow"];' + ) + else: + append(f' {self._q(node_id)} [label="{self._safe_label(node["label"])}"];') + + def _dot_edge_attrs(self, edge: dict, clusters: dict[str, dict]) -> list[str]: + attrs: list[str] = [] + if edge["source"] in clusters: + attrs.append(f'ltail="cluster_{edge["source"]}"') + if edge["target"] in clusters: + attrs.append(f'lhead="cluster_{edge["target"]}"') + if edge.get("condition"): + attrs.extend(('style="dashed"', f'label="{self._safe_label(edge["condition"])}"')) + else: + attrs.append('style="solid"') + return attrs + def _append_dot_edges( + self, + output: list[str], + edges: list[dict], + clusters: dict[str, dict], + entry_anchor: dict[str, str], + exit_anchor: dict[str, str], + ) -> None: + append = output.append + for edge in edges: + src = self._dot_anchor( + edge["source"], + is_src=True, + clusters=clusters, + entry_anchor=entry_anchor, + exit_anchor=exit_anchor, + ) + target = self._dot_anchor( + edge["target"], + is_src=False, + clusters=clusters, + entry_anchor=entry_anchor, + exit_anchor=exit_anchor, + ) + attrs = self._dot_edge_attrs(edge, clusters) + append(f' {self._q(src)} -> {self._q(target)} [{", ".join(attrs)}];') + + def _render_dot_graph( + self, + graph: dict, + graph_name: str, + edge_filter: Callable[[dict], bool], + ) -> str: + nodes_by_id, edges, node_ids, clusters, entry_anchor, exit_anchor = self._build_dot_render_context( + graph, edge_filter + ) + output: list[str] = [] + self._append_dot_header(output, graph_name) + self._append_dot_clusters(output, clusters, entry_anchor, exit_anchor, nodes_by_id) + self._append_dot_nodes(output, node_ids, clusters, nodes_by_id) + self._append_dot_edges(output, edges, clusters, entry_anchor, exit_anchor) + output.append("}") + return "\n".join(output) + + def to_dot_notation(self) -> str: + """ + Render the Junjo graph as a main overview digraph plus one additional + digraph for each subflow. """ graph = json.loads(self.serialize_to_json_string()) - # ----------------------------------------------------------------------- # - # Render helpers # - # ----------------------------------------------------------------------- # - def render_graph( - graph_name: str, - edge_filter: Callable[[dict], bool], - ) -> str: - """ - Build a single digraph string with: - * RunConcurrent → clusters - * Subflow → macro node (no cluster) - Only edges for which `edge_filter(edge)` is True are included. - """ - nodes_by_id = {n["id"]: n for n in graph["nodes"]} - edges = [e for e in graph["edges"] if edge_filter(e)] - - # ---- gather node‑ids that really participate in *this* drawing ---- # - node_ids: set[str] = set() - for e in edges: - node_ids.update((e["source"], e["target"])) - - # if a RunConcurrent appears, also pull in its children - for nid in list(node_ids): - n = nodes_by_id.get(nid) - if n and n.get("isSubgraph"): - node_ids.update(n["children"]) - - # --------------- meta for RunConcurrent clusters ------------------- # - clusters = { - n["id"]: n - for n in graph["nodes"] - if n.get("isSubgraph") and n["id"] in node_ids - } - - entry_anchor = {cid: f"{cid}__entry" for cid in clusters} - exit_anchor = {cid: f"{cid}__exit" for cid in clusters} - - def _anchor(nid: str, *, is_src: bool) -> str: - if nid in clusters: - return exit_anchor[nid] if is_src else entry_anchor[nid] - return nid - - # ------------------------------------------------------------------- # - out: list[str] = [] - a = out.append - - a(f'digraph "{graph_name}" {{') - a(" rankdir=LR;") - a(" compound=true;") - a(' node [shape=box, style="rounded,filled", fillcolor="#EFEFEF", ' - 'fontname="Helvetica", fontsize=10];') - a(' edge [fontname="Helvetica", fontsize=9];') - - # ---------------------- RunConcurrent clusters --------------------- # - for cid, n in clusters.items(): - a(f' subgraph "cluster_{cid}" {{') - a(f' label="{self._safe_label(n["label"])} (Concurrent)";') - a(' style="filled"; fillcolor="lightblue"; color="blue";') - a(' node [fillcolor="lightblue", style="filled,rounded"];') - # invisible entry/exit points - a(f' "{entry_anchor[cid]}" [label="", shape=point, width=0.01, ' - 'style=invis];') - a(f' "{exit_anchor[cid]}" [label="", shape=point, width=0.01, ' - 'style=invis];') - for child_id in n["children"]: - child = nodes_by_id[child_id] - a(f' {self._q(child_id)} ' - f'[label="{self._safe_label(child["label"])}"];') - a(" }") - - # ------------------- ordinary & Subflow macro nodes ---------------- # - for nid in node_ids: - if nid in clusters: - continue # already rendered inside its cluster - n = nodes_by_id[nid] - if n.get("isSubflow"): - # macro representation - a(f' {self._q(nid)} [label="{self._safe_label(n["label"])}", ' - 'shape=component, style="filled,rounded", ' - 'fillcolor="lightyellow"];') - else: - a(f' {self._q(nid)} [label="{self._safe_label(n["label"])}"];') - - # ------------------------------ edges ------------------------------ # - for e in edges: - src = _anchor(e["source"], is_src=True) - tgt = _anchor(e["target"], is_src=False) - - attrs: list[str] = [] - if e["source"] in clusters: - attrs.append(f'ltail="cluster_{e["source"]}"') - if e["target"] in clusters: - attrs.append(f'lhead="cluster_{e["target"]}"') - - if e.get("condition"): - attrs.extend( - ('style="dashed"', f'label="{self._safe_label(e["condition"])}"') - ) - else: - attrs.append('style="solid"') - - a(f' {self._q(src)} -> {self._q(tgt)} [{", ".join(attrs)}];') - - a("}") - return "\n".join(out) - - # ----------------------------------------------------------------------- # - # 1) overview graph (explicit edges only) # - # ----------------------------------------------------------------------- # dot_parts: list[str] = [ - render_graph( + self._render_dot_graph( + graph=graph, graph_name="G", - edge_filter=lambda e: e["type"] == "explicit" + edge_filter=lambda edge: edge["type"] == "explicit", ) ] - # ----------------------------------------------------------------------- # - # 2) one digraph per sub‑flow # - # ----------------------------------------------------------------------- # - subflows = [ - n for n in graph["nodes"] if n.get("isSubflow") - ] - for sf in subflows: + subflows = [node for node in graph["nodes"] if node.get("isSubflow")] + for subflow in subflows: + subflow_id = subflow["id"] dot_parts.append( - render_graph( - graph_name=f'subflow_{sf["id"]}', - edge_filter=lambda e, sid=sf["id"]: ( - e["type"] == "subflow" and e["subflowId"] == sid - ), + self._render_dot_graph( + graph=graph, + graph_name=f"subflow_{subflow_id}", + edge_filter=lambda edge, sid=subflow_id: edge["type"] == "subflow" and edge["subflowId"] == sid, ) ) - # join with blank lines so graphviz treats them as separate digraphs return "\n\n".join(dot_parts) + @staticmethod + def _fname(name: str) -> str: + """Turn any string into a filesystem-friendly stem.""" + return re.sub(r"[^A-Za-z0-9_.-]", "_", name) + + @staticmethod + def _split_dot_blocks(dot_text: str) -> list[str]: + return re.split(r"\n(?=digraph )", dot_text.lstrip()) + + @staticmethod + def _extract_digraph_id(dot_block: str) -> str | None: + match = re.match(r'digraph\s+"?([A-Za-z0-9_]+)"?', dot_block) + if not match: + return None + return match.group(1) + + @staticmethod + def _clean_graphviz_outputs(out_dir: Path, fmt: str, clean: bool) -> None: + if not clean: + return + for path in out_dir.iterdir(): + if path.suffix in (".dot", f".{fmt}") and path.is_file(): + path.unlink() + + @staticmethod + def _render_digraph_asset(dot_block: str, out_dir: Path, stem: str, fmt: str, dot_cmd: str) -> Path: + dot_path = out_dir / f"{stem}.dot" + img_path = out_dir / f"{stem}.{fmt}" + dot_path.write_text(dot_block, encoding="utf-8") + subprocess.run([dot_cmd, "-T", fmt, str(dot_path), "-o", str(img_path)], check=True) + return img_path + + def _build_graphviz_label_lookup(self) -> dict[str, str]: + label_lookup = {"G": "Overview"} + json_graph = json.loads(self.serialize_to_json_string()) + for node in json_graph["nodes"]: + if node.get("isSubflow"): + label_lookup[f"subflow_{node['id']}"] = node["label"] + return label_lookup + @staticmethod + def _write_graphviz_index(html_path: Path, digraph_files: dict[str, Path], label_lookup: dict[str, str]) -> None: + html_parts = [ + "", + 'Junjo Graphs', + "", + "", + "

Junjo workflow diagrams

", + ] + for name, img in digraph_files.items(): + heading = html.escape(label_lookup.get(name, name)) + html_parts.append(f"

{heading}

") + html_parts.append(f'{heading} diagram') + html_parts.append("") + html_path.write_text("\n".join(html_parts), encoding="utf-8") def export_graphviz_assets( self, @@ -446,88 +522,26 @@ def export_graphviz_assets( """ out_dir = Path(out_dir) out_dir.mkdir(parents=True, exist_ok=True) + self._clean_graphviz_outputs(out_dir, fmt, clean) - # --- delete old artefacts -------------------------------------- - if clean: - for p in out_dir.iterdir(): - if p.suffix in (".dot", f".{fmt}") and p.is_file(): - p.unlink() - - # ------------------------------------------------------------------ # - # 1. Build "digraph name" → human‑readable label lookup # - # ------------------------------------------------------------------ # - label_lookup = {"G": "Overview"} # first graph - json_graph = json.loads(self.serialize_to_json_string()) - for node in json_graph["nodes"]: - if node.get("isSubflow"): - label_lookup[f"subflow_{node['id']}"] = node["label"] - - # ------------------------------------------------------------------ # - # 2. Split the combined DOT text into individual blocks # - # (order preserved) # - # ------------------------------------------------------------------ # - dot_text = self.to_dot_notation().lstrip() - blocks: list[str] = re.split(r"\n(?=digraph )", dot_text) + label_lookup = self._build_graphviz_label_lookup() + blocks = self._split_dot_blocks(self.to_dot_notation()) + top_stem = self._fname(type(self).__name__ or "Overview") - # ------------------------------------------------------------------ # - # choose a safe filename stem # - # ------------------------------------------------------------------ # - def _fname(s: str) -> str: - """Turn any string into a filesystem‑friendly stem.""" - return re.sub(r"[^A-Za-z0-9_.-]", "_", s) - - top_stem = _fname(type(self).__name__ or "Overview") # e.g. MyWorkflow - - digraph_files: dict[str, Path] = {} # preserves order + digraph_files: dict[str, Path] = {} for block in blocks: - m = re.match(r'digraph\s+"?([A-Za-z0-9_]+)"?', block) - if not m: + dgraph_id = self._extract_digraph_id(block) + if not dgraph_id: continue - dgraph_id = m.group(1) # raw identifier: "G", "subflow_", … - - # --------------------------------------------- # - # decide the output filename stem # - # --------------------------------------------- # - if dgraph_id == "G": # the primary overview graph - stem = top_stem - else: # keep sub‑flow identifier for others - stem = dgraph_id - - dot_path = out_dir / f"{stem}.dot" - img_path = out_dir / f"{stem}.{fmt}" - - dot_path.write_text(block, encoding="utf-8") - - subprocess.run( - [dot_cmd, "-T", fmt, str(dot_path), "-o", str(img_path)], - check=True, - ) - - # keep mapping by digraph‑identifier, not by filename - digraph_files[dgraph_id] = img_path + stem = top_stem if dgraph_id == "G" else dgraph_id + digraph_files[dgraph_id] = self._render_digraph_asset(block, out_dir, stem, fmt, dot_cmd) - # ------------------------------------------------------------------ # - # 3. Build index.html (respect encounter order) # - # ------------------------------------------------------------------ # html_path = out_dir / "index.html" - html_parts = [ - "", - 'Junjo Graphs', - "", - "", - "

Junjo workflow diagrams

", - ] - for name, img in digraph_files.items(): - heading = html.escape(label_lookup.get(name, name)) - html_parts.append(f"

{heading}

") - html_parts.append(f'{heading} diagram') - html_parts.append("") - - html_path.write_text("\n".join(html_parts), encoding="utf-8") + self._write_graphviz_index(html_path, digraph_files, label_lookup) if open_html: import webbrowser + webbrowser.open(html_path.as_uri()) return digraph_files diff --git a/src/junjo/workflow.py b/src/junjo/workflow.py index 5620a91..f32d738 100644 --- a/src/junjo/workflow.py +++ b/src/junjo/workflow.py @@ -195,7 +195,9 @@ async def execute( # noqa: C901 # Increment the execution counter for Node executions else: - self.node_execution_counter[current_executable.id] = self.node_execution_counter.get(current_executable.id, 0) + 1 + self.node_execution_counter[current_executable.id] = ( + self.node_execution_counter.get(current_executable.id, 0) + 1 + ) if self.node_execution_counter[current_executable.id] > self.max_iterations: raise ValueError( f"Node '{current_executable}' exceeded maximum execution count. \ From ba94632693db29e45db95a5644968ab0ee322c32 Mon Sep 17 00:00:00 2001 From: Matthew Rideout Date: Sun, 8 Feb 2026 15:33:01 -0500 Subject: [PATCH 32/32] improved prompts for realism. --- .../nodes/image_inspiration/prompt.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/prompt.py b/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/prompt.py index 5f6c59f..4712c3f 100644 --- a/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/prompt.py +++ b/examples/ai_chat/backend/src/app/workflows/handle_message/create_response_with_image_subflow/nodes/image_inspiration/prompt.py @@ -43,9 +43,10 @@ def image_inspiration_prompt( # Include the following verbatim in the prompt: - "Photo Subject Age: {contact.age}" -- "ultra-realistic" +- "Make sure the photo is ultra-realistic, real life, as if taken with a real camera. Not cartoonish or perfect." +- "Do not overly embelish the human physique. Make it realistic, normal, average and aligned with the profile." - "Aspect Ratio: 1:1 square" -- "The AI-generated person in the provided image is the person sending the message." +- "The person in the provided image is the person sending the message." - "No text, no watermarks" # DO NOT: