mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-10-03 06:55:13 +02:00
Compare commits
1
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 William Fu-Hinthorn
|
47d42aacc9 |
@@ -17,11 +17,21 @@ jobs:
|
||||
- "3.11"
|
||||
- "3.12"
|
||||
- "3.13"
|
||||
core-version:
|
||||
- "latest"
|
||||
ff-send-v2:
|
||||
- "false"
|
||||
include:
|
||||
- python-version: "3.11"
|
||||
core-version: ">=0.2.42,<0.3.0"
|
||||
- python-version: "3.11"
|
||||
core-version: "latest"
|
||||
ff-send-v2: "true"
|
||||
|
||||
defaults:
|
||||
run:
|
||||
working-directory: libs/langgraph
|
||||
name: "test #${{ matrix.python-version }}"
|
||||
name: "test #${{ matrix.python-version }} (langchain-core: ${{ matrix.core-version }}, ff-send-v2: ${{ matrix.ff-send-v2 }})"
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
|
||||
@@ -41,9 +51,14 @@ jobs:
|
||||
shell: bash
|
||||
run: |
|
||||
poetry install --with dev
|
||||
if [ "${{ matrix.core-version }}" != "latest" ]; then
|
||||
poetry run pip install "langchain-core${{ matrix.core-version }}"
|
||||
fi
|
||||
|
||||
- name: Run tests
|
||||
shell: bash
|
||||
env:
|
||||
LANGGRAPH_FF_SEND_V2: ${{ matrix.ff-send-v2 }}
|
||||
run: |
|
||||
make test_parallel
|
||||
|
||||
|
||||
@@ -20,31 +20,7 @@ env:
|
||||
POETRY_VERSION: "1.7.1"
|
||||
|
||||
jobs:
|
||||
changes:
|
||||
runs-on: ubuntu-latest
|
||||
outputs:
|
||||
python: ${{ steps.filter.outputs.python }}
|
||||
sdk-js: ${{ steps.filter.outputs.sdk-js }}
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: dorny/paths-filter@v3
|
||||
id: filter
|
||||
with:
|
||||
filters: |
|
||||
python:
|
||||
- 'libs/langgraph/**'
|
||||
- 'libs/sdk-py/**'
|
||||
- 'libs/cli/**'
|
||||
- 'libs/checkpoint/**'
|
||||
- 'libs/checkpoint-sqlite/**'
|
||||
- 'libs/checkpoint-postgres/**'
|
||||
- 'libs/scheduler-kafka/**'
|
||||
- 'libs/prebuilt/**'
|
||||
sdk-js:
|
||||
- 'libs/sdk-js/**'
|
||||
|
||||
lint:
|
||||
needs: changes
|
||||
name: cd ${{ matrix.working-directory }}
|
||||
strategy:
|
||||
matrix:
|
||||
@@ -55,30 +31,26 @@ jobs:
|
||||
"libs/cli",
|
||||
"libs/checkpoint",
|
||||
"libs/checkpoint-sqlite",
|
||||
"libs/checkpoint-duckdb",
|
||||
"libs/checkpoint-postgres",
|
||||
"libs/scheduler-kafka",
|
||||
"libs/prebuilt",
|
||||
]
|
||||
if: needs.changes.outputs.python == 'true'
|
||||
uses: ./.github/workflows/_lint.yml
|
||||
with:
|
||||
working-directory: ${{ matrix.working-directory }}
|
||||
secrets: inherit
|
||||
|
||||
test:
|
||||
needs: changes
|
||||
name: cd ${{ matrix.working-directory }}
|
||||
strategy:
|
||||
matrix:
|
||||
working-directory:
|
||||
[
|
||||
working-directory: [
|
||||
"libs/cli",
|
||||
"libs/checkpoint",
|
||||
"libs/checkpoint-sqlite",
|
||||
"libs/checkpoint-postgres",
|
||||
"libs/prebuilt",
|
||||
"libs/checkpoint-duckdb",
|
||||
"libs/checkpoint-postgres"
|
||||
]
|
||||
if: needs.changes.outputs.python == 'true'
|
||||
uses: ./.github/workflows/_test.yml
|
||||
with:
|
||||
working-directory: ${{ matrix.working-directory }}
|
||||
@@ -86,23 +58,17 @@ jobs:
|
||||
|
||||
# NOTE: we're testing langgraph separately because it requires a different matrix
|
||||
test-langgraph:
|
||||
needs: changes
|
||||
if: needs.changes.outputs.python == 'true'
|
||||
name: "cd libs/langgraph"
|
||||
uses: ./.github/workflows/_test_langgraph.yml
|
||||
secrets: inherit
|
||||
|
||||
# NOTE: we're testing scheduler-kafka separately because it requires a different matrix
|
||||
test-scheduler-kafka:
|
||||
needs: changes
|
||||
if: needs.changes.outputs.python == 'true'
|
||||
name: "cd libs/scheduler-kafka"
|
||||
uses: ./.github/workflows/_test_scheduler_kafka.yml
|
||||
secrets: inherit
|
||||
|
||||
check-sdk-methods:
|
||||
needs: changes
|
||||
if: needs.changes.outputs.python == 'true'
|
||||
name: "Check SDK methods matching"
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
@@ -110,20 +76,16 @@ jobs:
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
python-version: '3.11'
|
||||
- name: Run check_sdk_methods script
|
||||
run: python .github/scripts/check_sdk_methods.py
|
||||
|
||||
integration-test:
|
||||
needs: changes
|
||||
if: needs.changes.outputs.python == 'true'
|
||||
name: CLI integration test
|
||||
uses: ./.github/workflows/_integration_test.yml
|
||||
secrets: inherit
|
||||
|
||||
lint-js:
|
||||
needs: changes
|
||||
if: needs.changes.outputs.sdk-js == 'true'
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
@@ -147,42 +109,9 @@ jobs:
|
||||
- name: Build
|
||||
run: yarn build
|
||||
|
||||
test-js:
|
||||
needs: changes
|
||||
if: needs.changes.outputs.sdk-js == 'true'
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
working-directory:
|
||||
- "libs/sdk-js"
|
||||
defaults:
|
||||
run:
|
||||
working-directory: ${{ matrix.working-directory }}
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- name: Setup Node.js (LTS)
|
||||
uses: actions/setup-node@v3
|
||||
with:
|
||||
node-version: "20"
|
||||
cache: "yarn"
|
||||
cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
|
||||
- name: Install dependencies
|
||||
run: yarn install
|
||||
- name: Run tests
|
||||
run: yarn test
|
||||
|
||||
ci_success:
|
||||
name: "CI Success"
|
||||
needs:
|
||||
[
|
||||
lint,
|
||||
lint-js,
|
||||
test,
|
||||
test-langgraph,
|
||||
test-scheduler-kafka,
|
||||
integration-test,
|
||||
test-js,
|
||||
]
|
||||
needs: [lint, lint-js, test, test-langgraph, test-scheduler-kafka, integration-test]
|
||||
if: |
|
||||
always()
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
@@ -9,11 +9,7 @@
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
defaults:
|
||||
run:
|
||||
working-directory: docs
|
||||
|
||||
|
||||
jobs:
|
||||
codespell:
|
||||
name: (Check for spelling errors)
|
||||
@@ -25,18 +21,18 @@
|
||||
|
||||
- name: Install Dependencies
|
||||
run: |
|
||||
pip install toml codespell==2.3.0 jupytext
|
||||
pip install toml codespell jupytext
|
||||
|
||||
- name: Extract Ignore Words List
|
||||
run: |
|
||||
# Use a Python script to extract the ignore words list from pyproject.toml
|
||||
python ../.github/workflows/extract_ignored_words_list.py
|
||||
python .github/workflows/extract_ignored_words_list.py
|
||||
id: extract_ignore_words
|
||||
|
||||
- name: Codespell
|
||||
uses: codespell-project/actions-codespell@v2
|
||||
with:
|
||||
skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.md'
|
||||
skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib'
|
||||
ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
|
||||
# We do this to avoid spellchecking cell outputs
|
||||
- name: Codespell Notebooks
|
||||
|
||||
@@ -21,10 +21,6 @@ concurrency:
|
||||
group: "pages"
|
||||
cancel-in-progress: false
|
||||
|
||||
defaults:
|
||||
run:
|
||||
working-directory: docs
|
||||
|
||||
jobs:
|
||||
get-changed-files:
|
||||
runs-on: ubuntu-latest
|
||||
@@ -48,7 +44,6 @@ jobs:
|
||||
deploy:
|
||||
# needs: run-changed-notebooks
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 10 # Job will be cancelled if it runs for more than 10 minutes
|
||||
env:
|
||||
GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
|
||||
steps:
|
||||
@@ -63,50 +58,26 @@ jobs:
|
||||
poetry-version: ${{ env.POETRY_VERSION }}
|
||||
cache-key: docs
|
||||
|
||||
- name: Use Node.js
|
||||
uses: actions/setup-node@v3
|
||||
with:
|
||||
node-version: "22"
|
||||
cache: "yarn"
|
||||
cache-dependency-path: docs/yarn.lock
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
yarn
|
||||
poetry install --with test --with docs --no-root
|
||||
poetry install --with test --no-root
|
||||
poetry run pip install -U \
|
||||
pytest \
|
||||
pytest-check-links \
|
||||
langsmith \
|
||||
langchain \
|
||||
GitPython \
|
||||
"git+https://github.com/benjamincburns/markdown-exec.git@cc0d39d737e5ffd4b83d23cd8729d7ea16e363c8"
|
||||
"git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
|
||||
|
||||
# we run this installation only for internal PRs
|
||||
# as GITHUB_TOKEN is not available for PRs from outside contributors
|
||||
if [ -n "${GITHUB_TOKEN}" ]; then
|
||||
poetry run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
|
||||
fi
|
||||
|
||||
poetry run jupyter kernelspec list
|
||||
poetry run python3 -m ipykernel install --user --name=python3
|
||||
npm install -g tslab
|
||||
poetry run tslab install --python=python3
|
||||
poetry run jupyter kernelspec list
|
||||
|
||||
- name: Run unit tests
|
||||
# Run unit tests on the docs build pipeline
|
||||
run: make tests
|
||||
- name: Lint Docs
|
||||
# This step lints the docs using the existing linting set up.
|
||||
# It should be very fast and should not require any external services.
|
||||
run: make lint-docs
|
||||
- name: Build llms-text
|
||||
run: make llms-text
|
||||
- name: Build site
|
||||
run: make build-docs
|
||||
env:
|
||||
MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
|
||||
OPENAI_API_KEY: sf-proj-1234567890 # fake placeholder, shouldn't actually be used
|
||||
ANTHROPIC_API_KEY: sk-ant-api03-1234567890 # fake placeholder, shouldn't actually be used
|
||||
|
||||
- name: Check links in notebooks
|
||||
env:
|
||||
LANGCHAIN_API_KEY: test
|
||||
@@ -115,36 +86,27 @@ jobs:
|
||||
echo "Running link check on all HTML files matching notebooks in docs directory..."
|
||||
poetry run pytest -v \
|
||||
--check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
|
||||
--check-links-ignore "https://academy\.langchain\.com/.*" \
|
||||
--check-links-ignore "https://x.com/.*" \
|
||||
--check-links-ignore "https://twitter.com/.*" \
|
||||
--check-links-ignore "https://github\.com/.*" \
|
||||
--check-links-ignore "http://localhost:8123/.*" \
|
||||
--check-links-ignore "http://localhost:2024.*" \
|
||||
--check-links-ignore "http://127.0.0.1:.*" \
|
||||
--check-links-ignore "/.*\.(ipynb|html)$" \
|
||||
--check-links-ignore "https://python\.langchain\.com/.*" \
|
||||
--check-links-ignore "https://openai\.com/.*" \
|
||||
--check-links-ignore "https://www\.uber\.com/.*" \
|
||||
--check-links-ignore "https://pepy\.tech/.*" \
|
||||
--check-links $(find site -name "index.html" | grep -v 'storm/index.html')
|
||||
--check-links $(find docs/site -name "index.html" | grep -v 'storm/index.html')
|
||||
|
||||
else
|
||||
echo "Fetching changes from origin/main..."
|
||||
git fetch origin main
|
||||
echo "Checking for changed notebook files..."
|
||||
CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|site/|; s/\.ipynb$/\/index.html/' || true)
|
||||
CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|docs/site/|; s/\.ipynb$/\/index.html/' || true)
|
||||
echo "Changed files: ${CHANGED_FILES}"
|
||||
if [ -n "${CHANGED_FILES}" ]; then
|
||||
echo "Running link check on HTML files matching changed notebook files..."
|
||||
poetry run pytest -v \
|
||||
--check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
|
||||
--check-links-ignore "https://academy\.langchain\.com/.*" \
|
||||
--check-links-ignore "http://localhost:8123/.*" \
|
||||
--check-links-ignore "http://localhost:2024.*" \
|
||||
--check-links-ignore "http://127.0.0.1:.*" \
|
||||
--check-links-ignore "https://x.com/.*" \
|
||||
--check-links-ignore "https://twitter.com/.*" \
|
||||
--check-links-ignore "https://github\.com/.*" \
|
||||
--check-links-ignore "/.*\.(ipynb|html)$" \
|
||||
--check-links ${CHANGED_FILES} \
|
||||
@@ -159,7 +121,7 @@ jobs:
|
||||
uses: actions/configure-pages@v4
|
||||
|
||||
- name: Upload Pages Artifact
|
||||
# if: github.ref == 'refs/heads/main'
|
||||
if: github.ref == 'refs/heads/main'
|
||||
uses: actions/upload-pages-artifact@v3
|
||||
with:
|
||||
path: ./docs/site/
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import toml
|
||||
|
||||
pyproject_toml = toml.load("pyproject.toml")
|
||||
pyproject_toml = toml.load("libs/langgraph/pyproject.toml")
|
||||
|
||||
# Extract the ignore words list (adjust the key as per your TOML structure)
|
||||
ignore_words_list = (
|
||||
|
||||
@@ -195,11 +195,7 @@ jobs:
|
||||
"$PKG_NAME==$VERSION" \
|
||||
)
|
||||
|
||||
if [[ "$PKG_NAME" == *prebuilt* ]]; then
|
||||
poetry run pip install langgraph
|
||||
fi
|
||||
|
||||
if [[ "$PKG_NAME" == *checkpoint* || "$PKG_NAME" == *prebuilt* ]]; then
|
||||
if [[ "$PKG_NAME" == *checkpoint* ]]; then
|
||||
# since checkpoint packages are namespace packages, import them with . convention
|
||||
# i.e. import langgraph.checkpoint or langgraph.checkpoint.sqlite
|
||||
IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/./g)"
|
||||
|
||||
@@ -11,10 +11,6 @@ on:
|
||||
schedule:
|
||||
- cron: '0 13 * * *'
|
||||
|
||||
defaults:
|
||||
run:
|
||||
working-directory: docs
|
||||
|
||||
jobs:
|
||||
build:
|
||||
runs-on: ubuntu-latest
|
||||
@@ -43,14 +39,14 @@ jobs:
|
||||
|
||||
- name: Pre-download tiktoken files
|
||||
run: |
|
||||
poetry run python _scripts/download_tiktoken.py
|
||||
poetry run python docs/_scripts/download_tiktoken.py
|
||||
|
||||
- name: Prepare notebooks
|
||||
run: |
|
||||
if [ "${{ matrix.lib-version }}" = "development" ]; then
|
||||
poetry run python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
|
||||
poetry run python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
|
||||
else
|
||||
poetry run python _scripts/prepare_notebooks_for_ci.py
|
||||
poetry run python docs/_scripts/prepare_notebooks_for_ci.py
|
||||
fi
|
||||
|
||||
- name: Run notebooks
|
||||
@@ -67,12 +63,12 @@ jobs:
|
||||
run: |
|
||||
if [ "${{ github.event_name }}" = "workflow_dispatch" ] || [ "${{ github.event_name }}" = "schedule" ]; then
|
||||
echo "Running all notebooks"
|
||||
./_scripts/execute_notebooks.sh
|
||||
./docs/_scripts/execute_notebooks.sh
|
||||
else
|
||||
CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | sed 's|^docs/docs/|docs/|' | grep '\.ipynb$' || true)
|
||||
CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | grep '\.ipynb$' || true)
|
||||
if [ -n "$CHANGED_FILES" ]; then
|
||||
echo "Running changed notebooks: $CHANGED_FILES"
|
||||
./_scripts/execute_notebooks.sh $CHANGED_FILES
|
||||
./docs/_scripts/execute_notebooks.sh $CHANGED_FILES
|
||||
else
|
||||
echo "No notebook files changed, skipping execution"
|
||||
fi
|
||||
|
||||
+1
-2
@@ -178,5 +178,4 @@ Untitled*.ipynb
|
||||
|
||||
Chinook.db
|
||||
|
||||
.vercel
|
||||
.turbo
|
||||
libs/langgraph/out
|
||||
|
||||
@@ -1,142 +0,0 @@
|
||||
# LangGraph Coding Guide
|
||||
|
||||
## Repository Structure
|
||||
|
||||
LangGraph follows a monorepo organization, with the following structure:
|
||||
|
||||
- `libs/langgraph` is the main Python library, published to pypi as `langgraph`. This contains the majority of the code for the framework, as well as the majority of the unit tests.
|
||||
- `libs/checkpoint` , published to pypi as `langgraph-checkpoint` contains the base classes for the persistence layer of langgraph. The two main abstractions are BaseCheckpointSaver (base class for persistence of workflow runs step-by-step) and BaseStore (base class for "long-term memory" operations, offering a key-value interface combined with semantic search over documents, used for persisting information across distinct workflow runs). This library is a dependency of both the main langgraph library, as well as implementations of these storage interfaces for specific databases. This library also contains reference implementations
|
||||
- `libs/checkpoint-postgres` published to pypi as langgraph-checkpoint-postgres, contains implementations of checkpoint and store backed by postgres. Majority of the test coverage is in `libs/langgraph` in the form of tests that run over all storage implementations in the repo.
|
||||
- `langgraph-java` contains a Java implementation of the langgraph framework, which is in the early stages of development.
|
||||
|
||||
## Feature Overview
|
||||
|
||||
langgraph is an orchestration framework (in the style of airflow or temporal) designed for LLM applications, with a focus on streaming output, cyclical and parallel workflows, and interrupt/resume capabilities. Applications built with langgraph are variously called workflows, graphs, cognitive architectures, agents. Key features:
|
||||
|
||||
1. **Graph-based Architecture**: Build directed computation graphs with nodes and edges
|
||||
2. **State Management**: Type-safe state schema with custom reducers and transformations
|
||||
3. **Human-in-the-loop**: Support for interrupts, checkpoints, and tool call review
|
||||
4. **Persistence**: Save and resume execution with in-memory or database storage
|
||||
5. **Streaming**: Multiple modes (values, updates, custom) for real-time feedback
|
||||
6. **Multi-agent Patterns**: Support for network, supervisor, and hierarchical architectures
|
||||
|
||||
## Python Development
|
||||
|
||||
### Build/Test/Lint Commands
|
||||
|
||||
(in the respective subdirectory)
|
||||
|
||||
- Run all tests: `make test`
|
||||
- Run single test: `make test TEST=path/to/test_file.py::test_function`
|
||||
- Watch mode tests: `make test_watch`
|
||||
- Run tests in parallel: `make test_parallel`
|
||||
- Generate coverage report: `make coverage`
|
||||
- Format code: `make format`
|
||||
- Lint code: `make lint`
|
||||
- Check spelling: `make spell_check`
|
||||
- Fix spelling: `make spell_fix`
|
||||
- Build documentation: `make serve-docs` (from repo root)
|
||||
- Run benchmarks: `make benchmark` or `make benchmark-fast`
|
||||
|
||||
### Code Style Guidelines
|
||||
|
||||
- Follow [ruff](https://github.com/astral-sh/ruff) formatting/linting rules
|
||||
- Use [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html) for docstrings
|
||||
- Enforce type annotations with mypy (`disallow_untyped_defs = True`)
|
||||
- Use double quotes for strings
|
||||
- Maximum line length of 88 characters
|
||||
- Follow imports sorting with `ruff`
|
||||
- All functions/classes must have proper docstrings with args/returns
|
||||
- Write comprehensive unit tests for new features
|
||||
- Keep backward compatibility
|
||||
- PR scope should be isolated (changes shouldn't affect multiple packages)
|
||||
- Use descriptive variable names following Python conventions
|
||||
- Error handling should use appropriate exception types and messaging
|
||||
|
||||
## Java Development
|
||||
|
||||
(in the `langgraph-java` subdirectory)
|
||||
|
||||
### Build/Test/Lint Commands
|
||||
|
||||
- Build the project: `./gradlew build`
|
||||
- Run tests: `./gradlew test`
|
||||
- Run a specific test: `./gradlew test --tests "com.langgraph.package.TestClass.testMethod"`
|
||||
- Check formatting: `./gradlew spotlessCheck`
|
||||
- Apply formatting: `./gradlew spotlessApply`
|
||||
- Run all checks: `./gradlew check`
|
||||
- Generate Javadoc: `./gradlew javadoc`
|
||||
|
||||
### Code Style Guidelines
|
||||
|
||||
- Follow standard Java code style (Google Java Style Guide)
|
||||
- Use 4 spaces for indentation
|
||||
- Maximum line length of 100 characters
|
||||
- All public methods/classes must have proper Javadoc with @param/@return tags
|
||||
- Use descriptive variable names following Java conventions (camelCase)
|
||||
- Exception handling should use appropriate exception types with descriptive messages
|
||||
- Favor composition over inheritance
|
||||
- Use the Builder pattern for complex object creation
|
||||
- Write comprehensive unit tests for new features
|
||||
|
||||
### Python Compatibility Guidelines
|
||||
|
||||
- When implementing features from the Python version:
|
||||
- Maintain semantic equivalence with the Python implementation
|
||||
- Preserve the same behavior for all public APIs
|
||||
- Document any intentional differences in behavior with comments
|
||||
- Pay special attention to collections handling (Python lists vs Java Lists)
|
||||
- Ensure that iteration order and value handling match Python where relevant
|
||||
- Use the same test cases as the Python version when possible
|
||||
- Do not introduce Java-specific shortcuts that would break Python compatibility
|
||||
- Never add test-specific code to source files - tests should adapt to implementation, not vice versa
|
||||
|
||||
### Implementation Mapping
|
||||
|
||||
- Always consult and update the `PYTHON_JAVA_MAPPING.md` file when:
|
||||
- Adding new Java files or classes
|
||||
- Updating existing Java implementations
|
||||
- Fixing test failures in Java
|
||||
- Implementing Python features in Java
|
||||
- This mapping file documents:
|
||||
- Where to find equivalent functionality in Python and Java
|
||||
- Any intentional deviations between implementations
|
||||
- Implementation status and compatibility notes
|
||||
- When tests fail, check if the Java implementation matches Python behavior:
|
||||
- Fix the implementation to match Python semantics whenever possible
|
||||
- Update tests only if the Python version also differs
|
||||
- Never create special cases or workarounds in Java just to make tests pass
|
||||
- Document any implementation differences clearly in the mapping file
|
||||
- For new features, implement the Python behavior first, then adapt to Java idioms
|
||||
|
||||
### Backward Compatibility and API Design
|
||||
|
||||
- LangGraph Java has not been released publicly, so there is no need to maintain backward compatibility
|
||||
- When renaming methods, members, or classes:
|
||||
- Use the clearest, most intuitive names that match Python semantics
|
||||
- Remove old/deprecated methods completely rather than marking them as deprecated
|
||||
- Update all tests and documentation to use the new names
|
||||
- Do not leave deprecated methods or tests for backward compatibility
|
||||
|
||||
### API Design Principles
|
||||
|
||||
- Prefer a single, clear way to accomplish each task rather than multiple convenience methods
|
||||
- Prefer builder patterns over static factory methods where appropriate
|
||||
- For collections, prefer methods that operate on collections rather than having both single-item and collection variants
|
||||
- Choose method names that clearly express their purpose and align with Java conventions
|
||||
- Maintain consistent naming patterns across similar components
|
||||
- Document the recommended usage pattern in JavaDoc
|
||||
|
||||
### Project Structure
|
||||
|
||||
- `langgraph-core`: Core functionality of the framework
|
||||
- `langgraph-checkpoint`: Persistence layer for checkpoints and state management
|
||||
- `langgraph-examples`: Example applications and usage patterns
|
||||
|
||||
### Error Handling
|
||||
|
||||
- Use runtime exceptions for unexpected errors
|
||||
- Use checked exceptions for recoverable errors
|
||||
- Provide clear error messages that include context about what went wrong
|
||||
- Validate inputs early to prevent cascading errors
|
||||
- Ensure all resources are properly closed even in error conditions
|
||||
@@ -0,0 +1,39 @@
|
||||
.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc
|
||||
|
||||
build-typedoc:
|
||||
cd libs/sdk-js && yarn install --include-dev && yarn typedoc
|
||||
cd libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
|
||||
# Add links to the monorepo
|
||||
sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md
|
||||
|
||||
build-docs: build-typedoc
|
||||
poetry run python -m mkdocs build --clean -f docs/mkdocs.yml --strict
|
||||
|
||||
serve-clean-docs: clean-docs
|
||||
poetry run python -m mkdocs serve -c -f docs/mkdocs.yml --strict -w ./libs/langgraph
|
||||
|
||||
serve-docs: build-typedoc
|
||||
poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph -w ./libs/checkpoint --dirty
|
||||
|
||||
clean-docs:
|
||||
find ./docs/docs -name "*.ipynb" -type f -delete
|
||||
rm -rf docs/site
|
||||
|
||||
## Run format against the project documentation.
|
||||
format-docs:
|
||||
poetry run ruff format docs/docs
|
||||
poetry run ruff check --fix docs/docs
|
||||
|
||||
# Check the docs for linting violations
|
||||
lint-docs:
|
||||
poetry run ruff format --check docs/docs
|
||||
poetry run ruff check docs/docs
|
||||
|
||||
codespell:
|
||||
./docs/codespell_notebooks.sh .
|
||||
|
||||
start-services:
|
||||
docker compose -f docs/test-compose.yml up -V --force-recreate --wait --remove-orphans
|
||||
|
||||
stop-services:
|
||||
docker compose -f docs/test-compose.yml down
|
||||
@@ -8,49 +8,29 @@
|
||||
⚡ Building language agents as graphs ⚡
|
||||
|
||||
> [!NOTE]
|
||||
> Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
|
||||
> Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)).
|
||||
|
||||
## Overview
|
||||
|
||||
[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
|
||||
stateful, multi-actor applications with LLMs, used to create agent and multi-agent
|
||||
workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
|
||||
|
||||
[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
|
||||
|
||||
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
|
||||
|
||||
### Why use LangGraph?
|
||||
[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger),
|
||||
|
||||
LangGraph powers [production-grade agents](https://www.langchain.com/built-with-langgraph), trusted by Linkedin, Uber, Klarna, GitLab, and many more. LangGraph provides fine-grained control over both the flow and state of your agent applications. It implements a central [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), enabling features that are common to most agent architectures:
|
||||
To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
|
||||
|
||||
- **Memory**: LangGraph persists arbitrary aspects of your application's state,
|
||||
supporting memory of conversations and other updates within and across user
|
||||
interactions;
|
||||
- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
|
||||
and resumed, allowing for decisions, validation, and corrections at key stages via
|
||||
human input.
|
||||
### Key Features
|
||||
|
||||
Standardizing these components allows individuals and teams to focus on the behavior
|
||||
of their agent, instead of its supporting infrastructure.
|
||||
|
||||
Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
|
||||
the development, deployment, debugging, and monitoring of your applications.
|
||||
|
||||
LangGraph integrates seamlessly with
|
||||
[LangChain](https://python.langchain.com/docs/introduction/) and
|
||||
[LangSmith](https://docs.smith.langchain.com/) (but does not require them).
|
||||
|
||||
To learn more about LangGraph, check out our first LangChain Academy
|
||||
course, *Introduction to LangGraph*, available for free
|
||||
[here](https://academy.langchain.com/courses/intro-to-langgraph).
|
||||
- **Cycles and Branching**: Implement loops and conditionals in your apps.
|
||||
- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
|
||||
- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
|
||||
- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
|
||||
- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
|
||||
|
||||
### LangGraph Platform
|
||||
|
||||
[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
|
||||
|
||||
See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
|
||||
(includes a free tier).
|
||||
|
||||
LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework.
|
||||
Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
|
||||
|
||||
- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
|
||||
@@ -67,7 +47,9 @@ pip install -U langgraph
|
||||
|
||||
## Example
|
||||
|
||||
Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
|
||||
One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
|
||||
|
||||
Let's take a look at a simple example of an agent that can use a search tool.
|
||||
|
||||
```shell
|
||||
pip install langchain-anthropic
|
||||
@@ -84,72 +66,10 @@ export LANGSMITH_TRACING=true
|
||||
export LANGSMITH_API_KEY=lsv2_sk_...
|
||||
```
|
||||
|
||||
The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
|
||||
|
||||
<details open>
|
||||
<summary>High-level implementation</summary>
|
||||
|
||||
```python
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
from langchain_anthropic import ChatAnthropic
|
||||
from langchain_core.tools import tool
|
||||
|
||||
# Define the tools for the agent to use
|
||||
@tool
|
||||
def search(query: str):
|
||||
"""Call to surf the web."""
|
||||
# This is a placeholder, but don't tell the LLM that...
|
||||
if "sf" in query.lower() or "san francisco" in query.lower():
|
||||
return "It's 60 degrees and foggy."
|
||||
return "It's 90 degrees and sunny."
|
||||
|
||||
|
||||
tools = [search]
|
||||
model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
|
||||
|
||||
# Initialize memory to persist state between graph runs
|
||||
checkpointer = MemorySaver()
|
||||
|
||||
app = create_react_agent(model, tools, checkpointer=checkpointer)
|
||||
|
||||
# Use the agent
|
||||
final_state = app.invoke(
|
||||
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
|
||||
config={"configurable": {"thread_id": 42}}
|
||||
)
|
||||
final_state["messages"][-1].content
|
||||
```
|
||||
```
|
||||
"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
|
||||
```
|
||||
|
||||
Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
|
||||
|
||||
```python
|
||||
final_state = app.invoke(
|
||||
{"messages": [{"role": "user", "content": "what about ny"}]},
|
||||
config={"configurable": {"thread_id": 42}}
|
||||
)
|
||||
final_state["messages"][-1].content
|
||||
```
|
||||
|
||||
```
|
||||
"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
|
||||
```
|
||||
</details>
|
||||
|
||||
> [!TIP]
|
||||
> LangGraph is a **low-level** framework that allows you to implement any custom agent
|
||||
architectures. Click on the low-level implementation below to see how to implement a
|
||||
tool-calling agent from scratch.
|
||||
|
||||
<details>
|
||||
<summary>Low-level implementation</summary>
|
||||
|
||||
```python
|
||||
from typing import Literal
|
||||
from typing import Annotated, Literal, TypedDict
|
||||
|
||||
from langchain_core.messages import HumanMessage
|
||||
from langchain_anthropic import ChatAnthropic
|
||||
from langchain_core.tools import tool
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
@@ -171,7 +91,7 @@ tools = [search]
|
||||
|
||||
tool_node = ToolNode(tools)
|
||||
|
||||
model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
|
||||
model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)
|
||||
|
||||
# Define the function that determines whether to continue or not
|
||||
def should_continue(state: MessagesState) -> Literal["tools", END]:
|
||||
@@ -225,102 +145,92 @@ checkpointer = MemorySaver()
|
||||
# Note that we're (optionally) passing the memory when compiling the graph
|
||||
app = workflow.compile(checkpointer=checkpointer)
|
||||
|
||||
# Use the agent
|
||||
# Use the Runnable
|
||||
final_state = app.invoke(
|
||||
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
|
||||
{"messages": [HumanMessage(content="what is the weather in sf")]},
|
||||
config={"configurable": {"thread_id": 42}}
|
||||
)
|
||||
final_state["messages"][-1].content
|
||||
```
|
||||
|
||||
<b>Step-by-step Breakdown</b>:
|
||||
```
|
||||
"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Initialize the model and tools.</summary>
|
||||
<ul>
|
||||
<li>
|
||||
We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
|
||||
</li>
|
||||
<li>
|
||||
We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/how_to/custom_tools/">here</a>.
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
|
||||
|
||||
<details>
|
||||
<summary>Initialize graph with state.</summary>
|
||||
```python
|
||||
final_state = app.invoke(
|
||||
{"messages": [HumanMessage(content="what about ny")]},
|
||||
config={"configurable": {"thread_id": 42}}
|
||||
)
|
||||
final_state["messages"][-1].content
|
||||
```
|
||||
|
||||
<ul>
|
||||
<li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
|
||||
<li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
|
||||
</ul>
|
||||
</details>
|
||||
```
|
||||
"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Define graph nodes.</summary>
|
||||
### Step-by-step Breakdown
|
||||
|
||||
There are two main nodes we need:
|
||||
1. <details>
|
||||
<summary>Initialize the model and tools.</summary>
|
||||
|
||||
<ul>
|
||||
<li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
|
||||
<li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
|
||||
</ul>
|
||||
</details>
|
||||
- we use `ChatAnthropic` as our LLM. **NOTE:** we need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the `.bind_tools()` method.
|
||||
- we define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Define entry point and graph edges.</summary>
|
||||
2. <details>
|
||||
<summary>Initialize graph with state.</summary>
|
||||
|
||||
First, we need to set the entry point for graph execution - <code>agent</code> node.
|
||||
- we initialize graph (`StateGraph`) by passing state schema (in our case `MessagesState`)
|
||||
- `MessagesState` is a prebuilt state schema that has one attribute -- a list of LangChain `Message` objects, as well as logic for merging the updates from each node into the state
|
||||
</details>
|
||||
|
||||
Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
|
||||
3. <details>
|
||||
<summary>Define graph nodes.</summary>
|
||||
|
||||
<ul>
|
||||
<li>Conditional edge: after the agent is called, we should either:
|
||||
<ul>
|
||||
<li>a. Run tools if the agent said to take an action, OR</li>
|
||||
<li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
|
||||
</ul>
|
||||
</details>
|
||||
There are two main nodes we need:
|
||||
|
||||
<details>
|
||||
<summary>Compile the graph.</summary>
|
||||
- The `agent` node: responsible for deciding what (if any) actions to take.
|
||||
- The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
|
||||
</details>
|
||||
|
||||
<ul>
|
||||
<li>
|
||||
When we compile the graph, we turn it into a LangChain
|
||||
<a href="https://python.langchain.com/docs/concepts/runnables/">Runnable</a>,
|
||||
which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code>
|
||||
with your inputs
|
||||
</li>
|
||||
<li>
|
||||
We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory,
|
||||
human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> -
|
||||
a simple in-memory checkpointer
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
4. <details>
|
||||
<summary>Define entry point and graph edges.</summary>
|
||||
|
||||
<details>
|
||||
<summary>Execute the graph.</summary>
|
||||
First, we need to set the entry point for graph execution - `agent` node.
|
||||
|
||||
<ol>
|
||||
<li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
|
||||
<li>The <code>"agent"</code> node executes, invoking the chat model.</li>
|
||||
<li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
|
||||
<li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
|
||||
<ul>
|
||||
<li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
|
||||
<li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
|
||||
</ol>
|
||||
</details>
|
||||
Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (`MessageState`). In our case, the destination is not known until the agent (LLM) decides.
|
||||
|
||||
- Conditional edge: after the agent is called, we should either:
|
||||
- a. Run tools if the agent said to take an action, OR
|
||||
- b. Finish (respond to the user) if the agent did not ask to run tools
|
||||
- Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
|
||||
</details>
|
||||
|
||||
5. <details>
|
||||
<summary>Compile the graph.</summary>
|
||||
|
||||
- When we compile the graph, we turn it into a LangChain [Runnable](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface), which automatically enables calling `.invoke()`, `.stream()` and `.batch()` with your inputs
|
||||
- We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, human-in-the-loop workflows, time travel and more. In our case we use `MemorySaver` - a simple in-memory checkpointer
|
||||
</details>
|
||||
|
||||
6. <details>
|
||||
<summary>Execute the graph.</summary>
|
||||
|
||||
1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, `"agent"`.
|
||||
2. The `"agent"` node executes, invoking the chat model.
|
||||
3. The chat model returns an `AIMessage`. LangGraph adds this to the state.
|
||||
4. Graph cycles the following steps until there are no more `tool_calls` on `AIMessage`:
|
||||
|
||||
- If `AIMessage` has `tool_calls`, `"tools"` node executes
|
||||
- The `"agent"` node executes again and returns `AIMessage`
|
||||
|
||||
5. Execution progresses to the special `END` value and outputs the final state.
|
||||
And as a result, we get a list of all our chat messages as output.
|
||||
</details>
|
||||
|
||||
</details>
|
||||
|
||||
## Documentation
|
||||
|
||||
@@ -330,10 +240,6 @@ Then we define one normal and one conditional edge. Conditional edge means that
|
||||
* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
|
||||
* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
|
||||
|
||||
## Resources
|
||||
|
||||
* [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
|
||||
|
||||
## Contributing
|
||||
|
||||
For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).
|
||||
|
||||
@@ -0,0 +1,2 @@
|
||||
site/
|
||||
docs/cloud/reference/sdk/js_ts_sdk_ref.md
|
||||
@@ -0,0 +1,61 @@
|
||||
# Setup
|
||||
|
||||
To setup requirements for building docs you can run:
|
||||
|
||||
```bash
|
||||
poetry install --with test
|
||||
```
|
||||
|
||||
## Serving documentation locally
|
||||
|
||||
To run the documentation server locally you can run:
|
||||
|
||||
```bash
|
||||
make serve-docs
|
||||
```
|
||||
|
||||
## Execute notebooks
|
||||
|
||||
If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
|
||||
|
||||
```bash
|
||||
python docs/_scripts/prepare_notebooks_for_ci.py
|
||||
./docs/_scripts/execute_notebooks.sh
|
||||
```
|
||||
|
||||
**Note**: if you want to run the notebooks without `%pip install` cells, you can run:
|
||||
|
||||
```bash
|
||||
python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
|
||||
./docs/_scripts/execute_notebooks.sh
|
||||
```
|
||||
|
||||
`prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
|
||||
* when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
|
||||
* when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
|
||||
|
||||
**Note**: this is currently limited only to the notebooks in `docs/docs/how-tos`
|
||||
|
||||
## Adding new notebooks
|
||||
|
||||
If you are adding a notebook with API requests, it's **recommended** to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
|
||||
|
||||
To record network requests, please make sure to first run `prepare_notebooks_for_ci.py` script.
|
||||
|
||||
Then, run
|
||||
|
||||
```bash
|
||||
jupyter execute <path_to_notebook>
|
||||
```
|
||||
|
||||
Once the notebook is executed, you should see the new VCR cassettes recorded in `docs/cassettes` directory and discard the updated notebook.
|
||||
|
||||
## Updating existing notebooks
|
||||
|
||||
If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `docs/cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
|
||||
|
||||
To delete cassettes for a notebook, you can run:
|
||||
|
||||
```bash
|
||||
rm docs/cassettes/<notebook_name>*
|
||||
```
|
||||
@@ -0,0 +1,5 @@
|
||||
import tiktoken
|
||||
|
||||
# This will trigger the download and caching of the necessary files
|
||||
for encoding in ("gpt2", "gpt-3.5"):
|
||||
tiktoken.encoding_for_model(encoding)
|
||||
Executable
+36
@@ -0,0 +1,36 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Read the list of notebooks to skip from the JSON file
|
||||
SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('docs/notebooks_no_execution.json'))))")
|
||||
|
||||
# Function to execute a single notebook
|
||||
execute_notebook() {
|
||||
file="$1"
|
||||
echo "Starting execution of $file"
|
||||
start_time=$(date +%s)
|
||||
if ! output=$(time poetry run jupyter execute "$file" 2>&1); then
|
||||
end_time=$(date +%s)
|
||||
execution_time=$((end_time - start_time))
|
||||
echo "Error in $file. Execution time: $execution_time seconds"
|
||||
echo "Error details: $output"
|
||||
exit 1
|
||||
fi
|
||||
end_time=$(date +%s)
|
||||
execution_time=$((end_time - start_time))
|
||||
echo "Finished $file. Execution time: $execution_time seconds"
|
||||
}
|
||||
|
||||
export -f execute_notebook
|
||||
|
||||
# Check if custom notebook paths are provided
|
||||
if [ $# -gt 0 ]; then
|
||||
notebooks=$(echo "$@" | tr ' ' '\n' | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
|
||||
else
|
||||
# Find all notebooks and filter out those in the skip list
|
||||
notebooks=$(find docs/docs/tutorials docs/docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
|
||||
fi
|
||||
|
||||
# Execute notebooks sequentially
|
||||
for file in $notebooks; do
|
||||
execute_notebook "$file"
|
||||
done
|
||||
@@ -0,0 +1,246 @@
|
||||
import importlib
|
||||
import inspect
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
from typing import List, Literal, Optional
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
import nbformat
|
||||
from nbconvert.preprocessors import Preprocessor
|
||||
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
# Base URL for all class documentation
|
||||
_LANGCHAIN_API_REFERENCE = "https://python.langchain.com/api_reference/"
|
||||
_LANGGRAPH_API_REFERENCE = "https://langchain-ai.github.io/langgraph/reference/"
|
||||
|
||||
|
||||
# (alias/re-exported modules, source module, class, docs namespace)
|
||||
MANUAL_API_REFERENCES_LANGGRAPH = [
|
||||
(
|
||||
["langgraph.prebuilt"],
|
||||
"langgraph.prebuilt.chat_agent_executor",
|
||||
"create_react_agent",
|
||||
"prebuilt",
|
||||
),
|
||||
(["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
|
||||
(
|
||||
["langgraph.prebuilt"],
|
||||
"langgraph.prebuilt.tool_node",
|
||||
"tools_condition",
|
||||
"prebuilt",
|
||||
),
|
||||
(
|
||||
["langgraph.prebuilt"],
|
||||
"langgraph.prebuilt.tool_node",
|
||||
"InjectedState",
|
||||
"prebuilt",
|
||||
),
|
||||
# Graph
|
||||
(["langgraph.graph"], "langgraph.graph.message", "add_messages", "graphs"),
|
||||
(["langgraph.graph"], "langgraph.graph.state", "StateGraph", "graphs"),
|
||||
(["langgraph.graph"], "langgraph.graph.state", "CompiledStateGraph", "graphs"),
|
||||
([], "langgraph.types", "StreamMode", "types"),
|
||||
(["langgraph.graph"], "langgraph.constants", "START", "constants"),
|
||||
(["langgraph.graph"], "langgraph.constants", "END", "constants"),
|
||||
(["langgraph.constants"], "langgraph.types", "Send", "types"),
|
||||
(["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
|
||||
([], "langgraph.types", "RetryPolicy", "types"),
|
||||
([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"),
|
||||
([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"),
|
||||
([], "langgraph.checkpoint.base", "BaseCheckpointSaver", "checkpoints"),
|
||||
([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
|
||||
([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
|
||||
([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
|
||||
([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
|
||||
([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
|
||||
([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
|
||||
([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
|
||||
]
|
||||
|
||||
WELL_KNOWN_LANGGRAPH_OBJECTS = {
|
||||
(module_, class_): (source_module, namespace)
|
||||
for (modules, source_module, class_, namespace) in MANUAL_API_REFERENCES_LANGGRAPH
|
||||
for module_ in modules + [source_module]
|
||||
}
|
||||
|
||||
|
||||
def _make_regular_expression(pkg_prefix: str) -> re.Pattern:
|
||||
if not pkg_prefix.isidentifier():
|
||||
raise ValueError(f"Invalid package prefix: {pkg_prefix}")
|
||||
return re.compile(
|
||||
r"from\s+(" + pkg_prefix + "(?:_\w+)?(?:\.\w+)*?)\s+import\s+"
|
||||
r"((?:\w+(?:,\s*)?)*" # Match zero or more words separated by a comma+optional ws
|
||||
r"(?:\s*\(.*?\))?)", # Match optional parentheses block
|
||||
re.DOTALL, # Match newlines as well
|
||||
)
|
||||
|
||||
|
||||
# Regular expression to match langchain import lines
|
||||
_IMPORT_LANGCHAIN_RE = _make_regular_expression("langchain")
|
||||
_IMPORT_LANGGRAPH_RE = _make_regular_expression("langgraph")
|
||||
|
||||
|
||||
def _get_full_module_name(module_path, class_name) -> Optional[str]:
|
||||
"""Get full module name using inspect"""
|
||||
try:
|
||||
module = importlib.import_module(module_path)
|
||||
class_ = getattr(module, class_name)
|
||||
module = inspect.getmodule(class_)
|
||||
if module is None:
|
||||
# For constants, inspect.getmodule() might return None
|
||||
# In this case, we'll return the original module_path
|
||||
return module_path
|
||||
return module.__name__
|
||||
except AttributeError as e:
|
||||
logger.warning(f"Could not find module for {class_name}, {e}")
|
||||
return None
|
||||
except ImportError as e:
|
||||
logger.warning(f"Failed to load for class {class_name}, {e}")
|
||||
return None
|
||||
|
||||
|
||||
def _get_doc_title(data: str, file_name: str) -> str:
|
||||
try:
|
||||
return re.findall(r"^#\s*(.*)", data, re.MULTILINE)[0]
|
||||
except IndexError:
|
||||
pass
|
||||
# Parse the rst-style titles
|
||||
try:
|
||||
return re.findall(r"^(.*)\n=+\n", data, re.MULTILINE)[0]
|
||||
except IndexError:
|
||||
return file_name
|
||||
|
||||
|
||||
class ImportInformation(TypedDict):
|
||||
imported: str # imported class name
|
||||
source: str # module path
|
||||
docs: str # URL to the documentation
|
||||
title: str # Title of the document
|
||||
|
||||
|
||||
def _get_imports(
|
||||
code: str, doc_title: str, package_ecosystem: Literal["langchain", "langgraph"]
|
||||
) -> List[ImportInformation]:
|
||||
"""Get imports from the given code block.
|
||||
|
||||
Args:
|
||||
code: Python code block from which to extract imports
|
||||
doc_title: Title of the document
|
||||
package_ecosystem: "langchain" or "langgraph". The two live in different
|
||||
repositories and have separate documentation sites.
|
||||
|
||||
Returns:
|
||||
List of import information for the given code block
|
||||
"""
|
||||
imports = []
|
||||
|
||||
if package_ecosystem == "langchain":
|
||||
pattern = _IMPORT_LANGCHAIN_RE
|
||||
elif package_ecosystem == "langgraph":
|
||||
pattern = _IMPORT_LANGGRAPH_RE
|
||||
else:
|
||||
raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
|
||||
|
||||
for import_match in pattern.finditer(code):
|
||||
module = import_match.group(1)
|
||||
if "pydantic_v1" in module:
|
||||
continue
|
||||
imports_str = (
|
||||
import_match.group(2).replace("(\n", "").replace("\n)", "")
|
||||
) # Handle newlines within parentheses
|
||||
# remove any newline and spaces, then split by comma
|
||||
imported_classes = [
|
||||
imp.strip()
|
||||
for imp in re.split(r",\s*", imports_str.replace("\n", ""))
|
||||
if imp.strip()
|
||||
]
|
||||
for class_name in imported_classes:
|
||||
module_path = _get_full_module_name(module, class_name)
|
||||
if not module_path:
|
||||
continue
|
||||
if len(module_path.split(".")) < 2:
|
||||
continue
|
||||
|
||||
if package_ecosystem == "langchain":
|
||||
pkg = module_path.split(".")[0].replace("langchain_", "")
|
||||
top_level_mod = module_path.split(".")[1]
|
||||
|
||||
url = (
|
||||
_LANGCHAIN_API_REFERENCE
|
||||
+ pkg
|
||||
+ "/"
|
||||
+ top_level_mod
|
||||
+ "/"
|
||||
+ module_path
|
||||
+ "."
|
||||
+ class_name
|
||||
+ ".html"
|
||||
)
|
||||
elif package_ecosystem == "langgraph":
|
||||
if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
|
||||
# Likely not documented yet
|
||||
continue
|
||||
|
||||
source_module, namespace = WELL_KNOWN_LANGGRAPH_OBJECTS[
|
||||
(module, class_name)
|
||||
]
|
||||
url = (
|
||||
_LANGGRAPH_API_REFERENCE
|
||||
+ namespace
|
||||
+ "/#"
|
||||
+ source_module
|
||||
+ "."
|
||||
+ class_name
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
|
||||
|
||||
# Add the import information to our list
|
||||
imports.append(
|
||||
{
|
||||
"imported": class_name,
|
||||
"source": module,
|
||||
"docs": url,
|
||||
"title": doc_title,
|
||||
}
|
||||
)
|
||||
|
||||
return imports
|
||||
|
||||
|
||||
class ImportPreprocessor(Preprocessor):
|
||||
"""A preprocessor to replace imports in each Python code cell with links to their
|
||||
documentation and append the import info in a comment."""
|
||||
|
||||
def preprocess(self, nb, resources):
|
||||
self.all_imports = []
|
||||
file_name = os.path.basename(resources.get("metadata", {}).get("name", ""))
|
||||
_DOC_TITLE = _get_doc_title(nb.cells[0].source, file_name)
|
||||
|
||||
cells = []
|
||||
for cell in nb.cells:
|
||||
if cell.cell_type == "code":
|
||||
cells.append(cell)
|
||||
imports = _get_imports(
|
||||
cell.source, _DOC_TITLE, "langchain"
|
||||
) + _get_imports(cell.source, _DOC_TITLE, "langgraph")
|
||||
if not imports:
|
||||
continue
|
||||
|
||||
cells.append(
|
||||
nbformat.v4.new_markdown_cell(
|
||||
source=f"""
|
||||
<div>
|
||||
<b>API Reference:</b>
|
||||
{' | '.join(f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports)}
|
||||
</div>
|
||||
"""
|
||||
)
|
||||
)
|
||||
else:
|
||||
cells.append(cell)
|
||||
nb.cells = cells
|
||||
return nb, resources
|
||||
@@ -0,0 +1,126 @@
|
||||
import os
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
import nbformat
|
||||
from nbconvert.exporters import MarkdownExporter
|
||||
from nbconvert.preprocessors import Preprocessor
|
||||
|
||||
from generate_api_reference_links import ImportPreprocessor
|
||||
|
||||
|
||||
class EscapePreprocessor(Preprocessor):
|
||||
def preprocess_cell(self, cell, resources, cell_index):
|
||||
if cell.cell_type == "markdown":
|
||||
# rewrite markdown links to html links (excluding image links)
|
||||
cell.source = re.sub(
|
||||
r"(?<!!)\[([^\]]*)\]\((?![^\)]*//)([^)]*)(?:\.ipynb)?\)",
|
||||
r'<a href="\2">\1</a>',
|
||||
cell.source,
|
||||
)
|
||||
# Fix image paths in <img> tags
|
||||
cell.source = re.sub(
|
||||
r'<img\s+src="\.?/img/([^"]+)"', r'<img src="../img/\1"', cell.source
|
||||
)
|
||||
|
||||
elif cell.cell_type == "code":
|
||||
# escape ``` in code
|
||||
cell.source = cell.source.replace("```", r"\`\`\`")
|
||||
# escape ``` in output
|
||||
if "outputs" in cell:
|
||||
filter_out = set()
|
||||
for i, output in enumerate(cell["outputs"]):
|
||||
if "text" in output:
|
||||
if not output["text"].strip():
|
||||
filter_out.add(i)
|
||||
continue
|
||||
|
||||
value = output["text"].replace("```", r"\`\`\`")
|
||||
# handle a funky case w/ references in text
|
||||
value = re.sub(r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value)
|
||||
output["text"] = value
|
||||
elif "data" in output:
|
||||
for key, value in output["data"].items():
|
||||
if isinstance(value, str):
|
||||
value = value.replace("```", r"\`\`\`")
|
||||
# handle a funky case w/ references in text
|
||||
output["data"][key] = re.sub(
|
||||
r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value
|
||||
)
|
||||
cell["outputs"] = [
|
||||
output
|
||||
for i, output in enumerate(cell["outputs"])
|
||||
if i not in filter_out
|
||||
]
|
||||
|
||||
return cell, resources
|
||||
|
||||
|
||||
class ExtractAttachmentsPreprocessor(Preprocessor):
|
||||
"""
|
||||
Extracts all of the outputs from the notebook file. The extracted
|
||||
outputs are returned in the 'resources' dictionary.
|
||||
"""
|
||||
|
||||
def preprocess_cell(self, cell, resources, cell_index):
|
||||
"""
|
||||
Apply a transformation on each cell,
|
||||
Parameters
|
||||
----------
|
||||
cell : NotebookNode cell
|
||||
Notebook cell being processed
|
||||
resources : dictionary
|
||||
Additional resources used in the conversion process. Allows
|
||||
preprocessors to pass variables into the Jinja engine.
|
||||
cell_index : int
|
||||
Index of the cell being processed (see base.py)
|
||||
"""
|
||||
|
||||
# Get files directory if it has been specified
|
||||
|
||||
# Make sure outputs key exists
|
||||
if not isinstance(resources["outputs"], dict):
|
||||
resources["outputs"] = {}
|
||||
|
||||
# Loop through all of the attachments in the cell
|
||||
for name, attach in cell.get("attachments", {}).items():
|
||||
for mime, data in attach.items():
|
||||
if mime not in {
|
||||
"image/png",
|
||||
"image/jpeg",
|
||||
"image/svg+xml",
|
||||
"application/pdf",
|
||||
}:
|
||||
continue
|
||||
|
||||
# attachments are pre-rendered. Only replace markdown-formatted
|
||||
# images with the following logic
|
||||
attach_str = f"({name})"
|
||||
if attach_str in cell.source:
|
||||
data = f"(data:{mime};base64,{data})"
|
||||
cell.source = cell.source.replace(attach_str, data)
|
||||
|
||||
return cell, resources
|
||||
|
||||
|
||||
exporter = MarkdownExporter(
|
||||
preprocessors=[
|
||||
EscapePreprocessor,
|
||||
ExtractAttachmentsPreprocessor,
|
||||
ImportPreprocessor,
|
||||
],
|
||||
template_name="mdoutput",
|
||||
extra_template_basedirs=[
|
||||
os.path.join(os.path.dirname(__file__), "notebook_convert_templates")
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
def convert_notebook(
|
||||
notebook_path: Path,
|
||||
) -> Path:
|
||||
with open(notebook_path) as f:
|
||||
nb = nbformat.read(f, as_version=4)
|
||||
|
||||
body, _ = exporter.from_notebook_node(nb)
|
||||
return body
|
||||
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"mimetypes": {
|
||||
"text/markdown": true
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,33 @@
|
||||
{% extends 'markdown/index.md.j2' %}
|
||||
|
||||
{%- block traceback_line -%}
|
||||
```output
|
||||
{{ line.rstrip() | strip_ansi }}
|
||||
```
|
||||
{%- endblock traceback_line -%}
|
||||
|
||||
{%- block stream -%}
|
||||
```output
|
||||
{{ output.text.rstrip() }}
|
||||
```
|
||||
{%- endblock stream -%}
|
||||
|
||||
{%- block data_text scoped -%}
|
||||
```output
|
||||
{{ output.data['text/plain'].rstrip() }}
|
||||
```
|
||||
{%- endblock data_text -%}
|
||||
|
||||
{%- block data_html scoped -%}
|
||||
```html
|
||||
{{ output.data['text/html'] | safe }}
|
||||
```
|
||||
{%- endblock data_html -%}
|
||||
|
||||
{%- block data_jpg scoped -%}
|
||||
|
||||
{%- endblock data_jpg -%}
|
||||
|
||||
{%- block data_png scoped -%}
|
||||
|
||||
{%- endblock data_png -%}
|
||||
@@ -0,0 +1,40 @@
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
|
||||
from mkdocs.structure.pages import Page
|
||||
from mkdocs.structure.files import Files, File
|
||||
from notebook_convert import convert_notebook
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logging.basicConfig()
|
||||
logger.setLevel(logging.INFO)
|
||||
|
||||
|
||||
class NotebookFile(File):
|
||||
def is_documentation_page(self):
|
||||
return True
|
||||
|
||||
|
||||
def on_files(files: Files, **kwargs: Dict[str, Any]):
|
||||
new_files = Files([])
|
||||
for file in files:
|
||||
if file.src_path.endswith(".ipynb"):
|
||||
new_file = NotebookFile(
|
||||
path=file.src_path,
|
||||
src_dir=file.src_dir,
|
||||
dest_dir=file.dest_dir,
|
||||
use_directory_urls=file.use_directory_urls,
|
||||
)
|
||||
new_files.append(new_file)
|
||||
else:
|
||||
new_files.append(file)
|
||||
return new_files
|
||||
|
||||
|
||||
def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
|
||||
if page.file.src_path.endswith(".ipynb"):
|
||||
logger.info("Processing Jupyter notebook: %s", page.file.src_path)
|
||||
body = convert_notebook(page.file.abs_src_path)
|
||||
return body
|
||||
|
||||
return markdown
|
||||
@@ -0,0 +1,215 @@
|
||||
"""Preprocess notebooks for CI. Currently adds VCR cassettes and optionally removes pip install cells."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import json
|
||||
import click
|
||||
import nbformat
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
NOTEBOOK_DIRS = ("docs/docs/how-tos","docs/docs/tutorials")
|
||||
DOCS_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
CASSETTES_PATH = os.path.join(DOCS_PATH, "cassettes")
|
||||
|
||||
BLOCKLIST_COMMANDS = (
|
||||
# skip if has WebBaseLoader to avoid caching web pages
|
||||
"WebBaseLoader",
|
||||
# skip if has draw_mermaid_png to avoid generating mermaid images via API
|
||||
"draw_mermaid_png",
|
||||
)
|
||||
|
||||
NOTEBOOKS_NO_CASSETTES = (
|
||||
"docs/docs/how-tos/visualization.ipynb",
|
||||
"docs/docs/how-tos/many-tools.ipynb"
|
||||
)
|
||||
|
||||
NOTEBOOKS_NO_EXECUTION = [
|
||||
# this uses a user provided project name for langsmith
|
||||
"docs/docs/tutorials/tnt-llm/tnt-llm.ipynb",
|
||||
# this uses langsmith datasets
|
||||
"docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
|
||||
# this uses browser APIs
|
||||
"docs/docs/tutorials/web-navigation/web_voyager.ipynb",
|
||||
# these RAG guides use an ollama model
|
||||
"docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
|
||||
"docs/docs/tutorials/rag/langgraph_crag_local.ipynb",
|
||||
"docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
|
||||
# this loads a massive dataset from gcp
|
||||
"docs/docs/tutorials/usaco/usaco.ipynb",
|
||||
# TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
|
||||
"docs/docs/how-tos/autogen-integration.ipynb",
|
||||
# TODO: need to update these notebooks to make sure they are runnable in CI
|
||||
"docs/docs/tutorials/storm/storm.ipynb", # issues only when running with VCR
|
||||
"docs/docs/tutorials/lats/lats.ipynb", # issues only when running with VCR
|
||||
"docs/docs/tutorials/rag/langgraph_crag.ipynb", # flakiness from tavily
|
||||
"docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb", # Cannot create a consistent method resolution error from VCR
|
||||
"docs/docs/how-tos/map-reduce.ipynb" # flakiness from structured output, only when running with VCR
|
||||
]
|
||||
|
||||
|
||||
def comment_install_cells(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
|
||||
for cell in notebook.cells:
|
||||
if cell.cell_type != "code":
|
||||
continue
|
||||
|
||||
if "pip install" in cell.source:
|
||||
# Comment out the lines in cells containing "pip install"
|
||||
cell.source = "\n".join(
|
||||
f"# {line}" if line.strip() else line
|
||||
for line in cell.source.splitlines()
|
||||
)
|
||||
|
||||
return notebook
|
||||
|
||||
|
||||
def is_magic_command(code: str) -> bool:
|
||||
return code.strip().startswith("%") or code.strip().startswith("!")
|
||||
|
||||
|
||||
def is_comment(code: str) -> bool:
|
||||
return code.strip().startswith("#")
|
||||
|
||||
|
||||
def has_blocklisted_command(code: str, metadata: dict) -> bool:
|
||||
if 'hide_from_vcr' in metadata:
|
||||
return True
|
||||
|
||||
code = code.strip()
|
||||
for blocklisted_pattern in BLOCKLIST_COMMANDS:
|
||||
if blocklisted_pattern in code:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def add_vcr_to_notebook(
|
||||
notebook: nbformat.NotebookNode, cassette_prefix: str
|
||||
) -> nbformat.NotebookNode:
|
||||
"""Inject `with vcr.cassette` into each code cell of the notebook."""
|
||||
|
||||
# Inject VCR context manager into each code cell
|
||||
for idx, cell in enumerate(notebook.cells):
|
||||
if cell.cell_type != "code":
|
||||
continue
|
||||
|
||||
lines = cell.source.splitlines()
|
||||
# skip if empty cell
|
||||
if not lines:
|
||||
continue
|
||||
|
||||
are_magic_lines = [is_magic_command(line) for line in lines]
|
||||
|
||||
# skip if all magic
|
||||
if all(are_magic_lines):
|
||||
continue
|
||||
|
||||
if any(are_magic_lines):
|
||||
raise ValueError(
|
||||
"Cannot process code cells with mixed magic and non-magic code."
|
||||
)
|
||||
|
||||
# skip if just comments
|
||||
if all(is_comment(line) or not line.strip() for line in lines):
|
||||
continue
|
||||
|
||||
if has_blocklisted_command(cell.source, cell.metadata):
|
||||
continue
|
||||
|
||||
cell_id = cell.get("id", idx)
|
||||
cassette_name = f"{cassette_prefix}_{cell_id}.msgpack.zlib"
|
||||
cell.source = f"with custom_vcr.use_cassette('{cassette_name}', filter_headers=['x-api-key', 'authorization'], record_mode='once', serializer='advanced_compressed'):\n" + "\n".join(
|
||||
f" {line}" for line in lines
|
||||
)
|
||||
|
||||
# Add import statement
|
||||
vcr_import_lines = [
|
||||
"import nest_asyncio",
|
||||
"nest_asyncio.apply()",
|
||||
"import vcr",
|
||||
"import msgpack",
|
||||
"import base64",
|
||||
"import zlib",
|
||||
"import os",
|
||||
"os.environ.pop(\"LANGCHAIN_TRACING_V2\", None)",
|
||||
"custom_vcr = vcr.VCR()",
|
||||
"",
|
||||
"def compress_data(data, compression_level=9):",
|
||||
" packed = msgpack.packb(data, use_bin_type=True)",
|
||||
" compressed = zlib.compress(packed, level=compression_level)",
|
||||
" return base64.b64encode(compressed).decode('utf-8')",
|
||||
"",
|
||||
"def decompress_data(compressed_string):",
|
||||
" decoded = base64.b64decode(compressed_string)",
|
||||
" decompressed = zlib.decompress(decoded)",
|
||||
" return msgpack.unpackb(decompressed, raw=False)",
|
||||
"",
|
||||
"class AdvancedCompressedSerializer:",
|
||||
" def serialize(self, cassette_dict):",
|
||||
" return compress_data(cassette_dict)",
|
||||
"",
|
||||
" def deserialize(self, cassette_string):",
|
||||
" return decompress_data(cassette_string)",
|
||||
"",
|
||||
"custom_vcr.register_serializer('advanced_compressed', AdvancedCompressedSerializer())",
|
||||
"custom_vcr.serializer = 'advanced_compressed'",
|
||||
]
|
||||
import_cell = nbformat.v4.new_code_cell(source="\n".join(vcr_import_lines))
|
||||
import_cell.pop("id", None)
|
||||
notebook.cells.insert(0, import_cell)
|
||||
return notebook
|
||||
|
||||
|
||||
def process_notebooks(should_comment_install_cells: bool) -> None:
|
||||
for directory in NOTEBOOK_DIRS:
|
||||
for root, _, files in os.walk(directory):
|
||||
for file in files:
|
||||
if not file.endswith(".ipynb") or "ipynb_checkpoints" in root:
|
||||
continue
|
||||
|
||||
notebook_path = os.path.join(root, file)
|
||||
try:
|
||||
notebook = nbformat.read(notebook_path, as_version=4)
|
||||
|
||||
if should_comment_install_cells:
|
||||
notebook = comment_install_cells(notebook)
|
||||
|
||||
base_filename = os.path.splitext(os.path.basename(file))[0]
|
||||
cassette_prefix = os.path.join(CASSETTES_PATH, base_filename)
|
||||
if notebook_path not in NOTEBOOKS_NO_CASSETTES:
|
||||
notebook = add_vcr_to_notebook(
|
||||
notebook, cassette_prefix=cassette_prefix
|
||||
)
|
||||
|
||||
if notebook_path in NOTEBOOKS_NO_EXECUTION:
|
||||
# Add a cell at the beginning to indicate that this notebook should not be executed
|
||||
warning_cell = nbformat.v4.new_markdown_cell(
|
||||
source="**Warning:** This notebook is not meant to be executed automatically."
|
||||
)
|
||||
notebook.cells.insert(0, warning_cell)
|
||||
|
||||
# Add a special tag to the first code cell
|
||||
if notebook.cells and notebook.cells[1].cell_type == "code":
|
||||
notebook.cells[1].metadata["tags"] = notebook.cells[1].metadata.get("tags", []) + ["no_execution"]
|
||||
|
||||
nbformat.write(notebook, notebook_path)
|
||||
logger.info(f"Processed: {notebook_path}")
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing {notebook_path}: {e}")
|
||||
|
||||
with open(os.path.join(DOCS_PATH, "notebooks_no_execution.json"), "w") as f:
|
||||
json.dump(NOTEBOOKS_NO_EXECUTION, f)
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.option(
|
||||
"--comment-install-cells",
|
||||
is_flag=True,
|
||||
default=False,
|
||||
help="Whether to comment out install cells",
|
||||
)
|
||||
def main(comment_install_cells):
|
||||
process_notebooks(should_comment_install_cells=comment_install_cells)
|
||||
logger.info("All notebooks processed successfully.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
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main()
|
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|
||||
@@ -0,0 +1 @@
|
||||
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|
||||
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|
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Reference in New Issue
Block a user