Compare commits

..
Author SHA1 Message Date
Sydney Runkle 4de4abbe29 comments 2026-01-21 15:37:53 -05:00
Sydney Runkle 834fa8932f async 2026-01-21 12:43:32 -05:00
Sydney Runkle 9458700fe9 lint 2026-01-21 09:08:21 -05:00
Sydney Runkle 122a63fc83 simplify injected args 2026-01-21 09:07:05 -05:00
Sydney Runkle d05eac67f8 simplify 2026-01-21 09:02:24 -05:00
Sydney Runkle 31b5a9c4fe dynamic tools 2026-01-21 08:41:30 -05:00
241 changed files with 12498 additions and 36098 deletions
+6
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@@ -0,0 +1,6 @@
# Contributing to LangGraph
Hi there! Thank you for even being interested in contributing to LangGraph.
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
To learn how to contribute to LangGraph, please follow the [contribution guide here](https://docs.langchain.com/oss/python/contributing).
+24 -48
View File
@@ -1,60 +1,43 @@
name: "\U0001F41B Bug Report"
description: Report a bug in LangGraph. To report a security issue, please instead use the security option (below). For questions, please use the LangChain forum (below).
labels: ["bug"]
type: bug
description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the LangChain Forum at forum.langchain.com.
labels: [pending, bug]
body:
- type: markdown
attributes:
value: |
Thank you for taking the time to file a bug report.
Thank you for taking the time to file a bug report.
For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
Check these before submitting to see if your issue has already been reported, fixed or if there's another way to solve your problem:
Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
if there's another way to solve your problem:
* [Documentation](https://docs.langchain.com/oss/python/langgraph/overview),
* [API Reference Documentation](https://reference.langchain.com/python/),
* [LangChain ChatBot](https://chat.langchain.com/)
* [GitHub search](https://github.com/langchain-ai/langgraph),
* [LangChain Forum](https://forum.langchain.com/),
* [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
* [LangChain documentation with the integrated search](https://docs.langchain.com/),
* [GitHub search](https://github.com/langchain-ai/langgraph),
- type: checkboxes
id: checks
attributes:
label: Checked other resources
description: Please confirm and check all the following options.
description: Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
options:
- label: This is a bug, not a usage question.
- label: This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
required: true
- label: I added a clear and descriptive title that summarizes this issue.
- label: I added a clear and detailed title that summarizes the issue.
required: true
- label: I used the GitHub search to find a similar question and didn't find it.
- label: I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
required: true
- label: I am sure that this is a bug in LangGraph rather than my code.
required: true
- label: The bug is not resolved by updating to the latest stable version of LangGraph (or the specific integration package).
required: true
- label: This is not related to the langchain-community package.
required: true
- label: I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.
- label: I included a self-contained, minimal example that demonstrates the issue INCLUDING all the relevant imports. The code run AS IS to reproduce the issue.
required: true
- type: textarea
id: reproduction
validations:
required: true
attributes:
label: Reproduction Steps / Example Code (Python)
label: Example Code
description: |
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
**Important!**
* Avoid screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
* Reduce your code to the minimum required to reproduce the issue if possible.
(This will be automatically formatted into code, so no need for backticks.)
render: python
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case. Replace this code with your own!
placeholder: |
from langgraph.graph import StateGraph
@@ -63,13 +46,17 @@ body:
chain = StateGraph(list)
chain.invoke('Hello!')
render: python
- type: textarea
id: error
validations:
required: false
attributes:
label: Error Message and Stack Trace (if applicable)
description: |
If you are reporting an error, please copy and paste the full error message and
stack trace.
(This will be automatically formatted into code, so no need for backticks.)
If you are reporting an error, please include the full error message and stack trace.
placeholder: |
Exception + full stack trace
render: shell
- type: textarea
id: description
@@ -90,18 +77,7 @@ body:
attributes:
label: System Info
description: |
Please share your system info with us.
Run the following command in your terminal and paste the output here:
`python -m langchain_core.sys_info`
or if you have an existing python interpreter running:
```python
from langchain_core import sys_info
sys_info.print_sys_info()
```
Run on your machine: `python -m langchain_core.sys_info`
placeholder: |
python -m langchain_core.sys_info
validations:
+4 -10
View File
@@ -1,15 +1,9 @@
blank_issues_enabled: false
version: 2.1
contact_links:
- name: 💬 LangChain Forum
- name: Documentation
url: https://github.com/langchain-ai/docs/issues/new?template=langgraph.yml
about: Report an issue related to the LangGraph documentation
- name: LangChain Forum
url: https://forum.langchain.com/
about: General community discussions and support
- name: 📚 LangGraph Documentation
url: https://docs.langchain.com/oss/python/langgraph/overview
about: View the official LangGraph documentation
- name: 📚 API Reference Documentation
url: https://reference.langchain.com/python/
about: View the official LangGraph API reference documentation
- name: 📚 Documentation issue
url: https://github.com/langchain-ai/docs/issues/new?template=02-langgraph.yml
about: Report an issue related to the LangGraph documentation
+1 -1
View File
@@ -21,7 +21,7 @@ Thank you for contributing to LangGraph! Follow these steps to mark your pull re
1. A test for the integration, preferably unit tests that do not rely on network access,
2. An example notebook showing its use. It lives in `docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://docs.langchain.com/oss/python/contributing/overview) for more.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md) for more.
Additional guidelines:
+9 -102
View File
@@ -4,108 +4,15 @@ updates:
directory: "/"
schedule:
interval: "weekly"
day: "monday"
groups:
all-dependencies:
patterns:
- "*"
- package-ecosystem: "uv"
directory: "/libs/checkpoint"
- package-ecosystem: "pip"
directories:
- "libs/checkpoint"
- "libs/checkpoint-postgres"
- "libs/checkpoint-sqlite"
- "libs/cli"
- "libs/langgraph"
- "libs/prebuilt"
- "libs/sdk-py"
schedule:
interval: "weekly"
day: "monday"
groups:
all-dependencies:
patterns:
- "*"
- package-ecosystem: "uv"
directory: "/libs/checkpoint-conformance"
schedule:
interval: "weekly"
day: "monday"
groups:
all-dependencies:
patterns:
- "*"
- package-ecosystem: "uv"
directory: "/libs/checkpoint-postgres"
schedule:
interval: "weekly"
day: "monday"
groups:
all-dependencies:
patterns:
- "*"
- package-ecosystem: "uv"
directory: "/libs/checkpoint-sqlite"
schedule:
interval: "weekly"
day: "monday"
groups:
all-dependencies:
patterns:
- "*"
- package-ecosystem: "uv"
directory: "/libs/cli"
schedule:
interval: "weekly"
day: "monday"
groups:
all-dependencies:
patterns:
- "*"
- package-ecosystem: "uv"
directory: "/libs/langgraph"
schedule:
interval: "weekly"
day: "monday"
groups:
all-dependencies:
patterns:
- "*"
- package-ecosystem: "uv"
directory: "/libs/prebuilt"
schedule:
interval: "weekly"
day: "monday"
groups:
all-dependencies:
patterns:
- "*"
- package-ecosystem: "uv"
directory: "/libs/sdk-py"
schedule:
interval: "weekly"
day: "monday"
groups:
all-dependencies:
patterns:
- "*"
- package-ecosystem: "npm"
directory: "/libs/cli/js-examples"
schedule:
interval: "weekly"
day: "monday"
groups:
all-dependencies:
patterns:
- "*"
- package-ecosystem: "npm"
directory: "/libs/cli/js-monorepo-example"
schedule:
interval: "weekly"
day: "monday"
groups:
all-dependencies:
patterns:
- "*"
-5
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@@ -1,5 +0,0 @@
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+2 -2
View File
@@ -63,7 +63,7 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
try:
sys.stderr.write("\n== docker compose ps ==\n")
runner.run(
subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=True)
subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=False)
)
except Exception:
pass
@@ -76,7 +76,7 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
"logs",
"langgraph-api",
input=stdin,
verbose=True,
verbose=False,
)
)
except Exception:
+18 -39
View File
@@ -2,9 +2,6 @@ name: CLI integration test
on:
workflow_call:
secrets:
LANGSMITH_API_KEY:
required: false
permissions:
contents: read
@@ -31,8 +28,6 @@ jobs:
workdir: libs/cli/examples/graphs_reqs_b
tag: langgraph-test-d
name: "CLI integration test"
env:
HAS_LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY != '' }}
defaults:
run:
working-directory: libs/cli
@@ -40,12 +35,11 @@ jobs:
- uses: actions/checkout@v6
- name: Get changed files
id: changed-files
if: github.event_name != 'workflow_dispatch'
uses: Ana06/get-changed-files@v2.3.0
with:
filter: "libs/cli/**"
- name: Set up Python ${{ matrix.python-version }}
if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
if: steps.changed-files.outputs.all
uses: astral-sh/setup-uv@v7
with:
python-version: ${{ matrix.python-version }}
@@ -53,72 +47,57 @@ jobs:
cache-suffix: "cli-integration-test"
ignore-nothing-to-cache: true
- name: Install cli globally
if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
if: steps.changed-files.outputs.all
run: pip install -e .
- name: Build service ${{ matrix.example.name }}
if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
working-directory: ${{ matrix.example.workdir }}
run: |
langgraph build -t ${{ matrix.example.tag }}
- name: Test service ${{ matrix.example.name }}
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&env.HAS_LANGSMITH_API_KEY == 'true' }}
- name: Build and test service ${{ matrix.example.name }}
if: steps.changed-files.outputs.all
working-directory: ${{ matrix.example.workdir }}
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
# Build the image for this example
langgraph build -t ${{ matrix.example.tag }}
# Prepare environment file from local or parent example directory
if [ -f .env.example ]; then cp .env.example .env; elif [ -f ../.env.example ]; then cp ../.env.example .env && cp ../.env.example ../.env; fi
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi
if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi; fi
# Run the integration test using the built tag
# Compute repo root to reference the shared script robustly
REPO_ROOT=$(git rev-parse --show-toplevel)
timeout 60 python "$REPO_ROOT/.github/scripts/run_langgraph_cli_test.py" -t ${{ matrix.example.tag }}
- name: Build JS service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
working-directory: libs/cli/js-examples
run: |
langgraph build -t langgraph-test-e
- name: Build JS monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
working-directory: libs/cli/js-monorepo-example
run: |
langgraph build -t langgraph-test-f -c apps/agent/langgraph.json --build-command "yarn run turbo build" --install-command "yarn install"
- name: Build Python monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
working-directory: libs/cli/python-monorepo-example
run: |
langgraph build -t langgraph-test-g -c apps/agent/langgraph.json
- name: Test Python monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/python-monorepo-example
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp apps/agent/.env.example apps/agent/.env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env
if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env; fi
timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-g -c apps/agent/langgraph.json
- name: Build prerelease reqs service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
- name: Build and test prerelease reqs service
if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
working-directory: libs/cli/examples/graph_prerelease_reqs
run: |
langgraph build -t langgraph-test-h
- name: Test prerelease reqs service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/examples/graph_prerelease_reqs
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp ../.env.example .env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h
echo "Finished starting up langgraph-test-h"
LANGGRAPH_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langgraph'); print(v);")
if [ "$LANGGRAPH_VERSION" != "1.0.8" ]; then
echo "LANGGRAPH_VERSION != 1.0.8; $LANGGRAPH_VERSION"
if [ "$LANGGRAPH_VERSION" != "1.0.2" ]; then
echo "LANGGRAPH_VERSION != 1.0.2; $LANGGRAPH_VERSION"
exit 1
fi
LANGCHAIN_OPENAI_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-openai'); print(v);")
@@ -133,7 +112,7 @@ jobs:
fi
- name: Build and test prerelease reqs fail service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
working-directory: libs/cli/examples/graph_prerelease_reqs_fail
run: |
langgraph build -t langgraph-test-i || [ $? -eq 1 ]
+7 -8
View File
@@ -34,12 +34,11 @@ jobs:
- uses: actions/checkout@v6
- name: Get changed files
id: changed-files
if: github.event_name != 'workflow_dispatch'
uses: Ana06/get-changed-files@v2.3.0
with:
filter: "${{ inputs.working-directory }}/**"
- name: Set up Python ${{ matrix.python-version }}
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
uses: astral-sh/setup-uv@v7
with:
python-version: ${{ matrix.python-version }}
@@ -47,12 +46,12 @@ jobs:
cache-suffix: lint-${{ inputs.working-directory }}
- name: Install dependencies
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: uv sync --frozen --group lint
- name: Get .mypy_cache to speed up mypy
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
uses: actions/cache@v5
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
@@ -62,7 +61,7 @@ jobs:
key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
- name: Analysing package code with our lint
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: |
if make lint_package > /dev/null 2>&1; then
@@ -73,12 +72,12 @@ jobs:
fi
- name: Install test dependencies
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: uv sync --group lint
- name: Get .mypy_cache_test to speed up mypy
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
uses: actions/cache@v5
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
@@ -88,7 +87,7 @@ jobs:
key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
- name: Analysing tests with our lint
if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
if: steps.changed-files.outputs.all
working-directory: ${{ inputs.working-directory }}
run: |
if make lint_tests > /dev/null 2>&1; then
+1 -1
View File
@@ -33,7 +33,7 @@ jobs:
enable-cache: true
cache-suffix: test-${{ inputs.working-directory }}
- name: Login to Docker Hub
uses: docker/login-action@v4
uses: docker/login-action@v3
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
+1 -8
View File
@@ -31,7 +31,7 @@ jobs:
enable-cache: true
cache-suffix: "test-langgraph"
- name: Login to Docker Hub
uses: docker/login-action@v4
uses: docker/login-action@v3
if: ${{ !github.event.pull_request.head.repo.fork }}
with:
username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -45,13 +45,6 @@ jobs:
shell: bash
run: make test_parallel
- name: Run strict msgpack pregel tests
if: ${{ matrix.python-version == '3.13' }}
shell: bash
env:
LANGGRAPH_STRICT_MSGPACK: "true"
run: make test TEST="tests/test_pregel.py tests/test_pregel_async.py"
- name: Ensure the tests did not create any additional files
shell: bash
run: |
+2 -2
View File
@@ -48,7 +48,7 @@ jobs:
working-directory: ${{ inputs.working-directory }}
- name: Upload build
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v6
with:
name: test-dist
path: ${{ inputs.working-directory }}/dist/
@@ -76,7 +76,7 @@ jobs:
steps:
- uses: actions/checkout@v6
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v7
with:
name: test-dist
path: ${{ inputs.working-directory }}/dist/
+3 -8
View File
@@ -2,12 +2,10 @@
name: CI
on:
workflow_dispatch:
push:
branches:
- main
pull_request:
permissions:
contents: read
@@ -26,12 +24,11 @@ jobs:
changes:
runs-on: ubuntu-latest
outputs:
python: ${{ steps.filter.outputs.python || 'true' }}
deps: ${{ steps.filter.outputs.deps || 'true' }}
python: ${{ steps.filter.outputs.python }}
deps: ${{ steps.filter.outputs.deps }}
steps:
- uses: actions/checkout@v6
- uses: dorny/paths-filter@v3
if: github.event_name != 'workflow_dispatch'
id: filter
with:
filters: |
@@ -42,7 +39,6 @@ jobs:
- 'libs/checkpoint/**'
- 'libs/checkpoint-sqlite/**'
- 'libs/checkpoint-postgres/**'
- 'libs/checkpoint-conformance/**'
- 'libs/prebuilt/**'
deps:
- '**/pyproject.toml'
@@ -61,7 +57,7 @@ jobs:
"libs/checkpoint",
"libs/checkpoint-sqlite",
"libs/checkpoint-postgres",
"libs/checkpoint-conformance",
"libs/prebuilt",
]
if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
@@ -81,7 +77,6 @@ jobs:
"libs/checkpoint",
"libs/checkpoint-sqlite",
"libs/checkpoint-postgres",
"libs/checkpoint-conformance",
"libs/prebuilt",
"libs/sdk-py",
]
-49
View File
@@ -1,49 +0,0 @@
name: Deploy Redirects to GitHub Pages
on:
push:
branches:
- main
paths:
- 'docs/**'
- '.github/workflows/deploy-redirects.yml'
workflow_dispatch:
permissions:
contents: read
pages: write
id-token: write
concurrency:
group: "pages"
cancel-in-progress: false
jobs:
deploy:
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v6
- name: Setup Python
uses: actions/setup-python@v6
with:
python-version: '3.11'
- name: Generate redirect files
run: python docs/generate_redirects.py
- name: Setup Pages
uses: actions/configure-pages@v5
- name: Upload artifact
uses: actions/upload-pages-artifact@v4
with:
path: 'docs/_site'
- name: Deploy to GitHub Pages
id: deployment
uses: actions/deploy-pages@v4
+3 -3
View File
@@ -50,7 +50,7 @@ jobs:
working-directory: ${{ inputs.working-directory }}
- name: Upload build
uses: actions/upload-artifact@v7
uses: actions/upload-artifact@v6
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
@@ -269,7 +269,7 @@ jobs:
enable-cache: true
cache-suffix: "release"
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v7
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
@@ -310,7 +310,7 @@ jobs:
enable-cache: true
cache-suffix: "release"
- uses: actions/download-artifact@v8
- uses: actions/download-artifact@v7
with:
name: dist
path: ${{ inputs.working-directory }}/dist/
-2
View File
@@ -53,5 +53,3 @@ sdk-js (standalone)
```
Changes to a library may impact all of its dependents shown above.
- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.
-2
View File
@@ -53,5 +53,3 @@ sdk-js (standalone)
```
Changes to a library may impact all of its dependents shown above.
- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.
+3 -6
View File
@@ -1,7 +1,7 @@
<picture class="github-only">
<source media="(prefers-color-scheme: light)" srcset=".github/images/logo-light.svg">
<source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-dark.svg">
<img alt="LangGraph Logo" src=".github/images/logo-dark.svg" width="50%">
<source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
<source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
<img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
</picture>
<div>
@@ -56,9 +56,6 @@ Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/pytho
To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
> [!TIP]
> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
## Core benefits
LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
-1
View File
@@ -1 +0,0 @@
_site/
-142
View File
@@ -1,142 +0,0 @@
#!/usr/bin/env python3
"""
Generate HTML redirect files from redirects.json.
Usage:
python generate_redirects.py
This script reads redirects.json and generates individual HTML files
for each redirect path. Each HTML file uses meta refresh (0 delay)
which is SEO-friendly and treated similarly to 301 redirects by Google.
To add new redirects, simply edit redirects.json and re-run this script.
"""
import json
import os
from pathlib import Path
# Default fallback URL for any path not in the redirect map
DEFAULT_REDIRECT = "https://docs.langchain.com/oss/python/langgraph/overview"
HTML_TEMPLATE = """<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Redirecting...</title>
<link rel="canonical" href="{url}">
<meta name="robots" content="noindex">
<script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
<meta http-equiv="refresh" content="0; url={url}">
</head>
<body>
Redirecting...
</body>
</html>
"""
ROOT_HTML_TEMPLATE = """<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Redirecting to LangGraph Documentation</title>
<link rel="canonical" href="{url}">
<meta name="robots" content="noindex">
<script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
<meta http-equiv="refresh" content="0; url={url}">
</head>
<body>
<h1>Documentation has moved</h1>
<p>The LangGraph documentation has moved to <a href="{url}">docs.langchain.com</a>.</p>
<p>Redirecting you now...</p>
</body>
</html>
"""
CATCHALL_404_TEMPLATE = """<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Redirecting to LangGraph Documentation</title>
<link rel="canonical" href="{default_url}">
<meta name="robots" content="noindex">
<script>
// Catchall redirect for any unmapped paths
window.location.replace("{default_url}");
</script>
<meta http-equiv="refresh" content="0; url={default_url}">
</head>
<body>
<h1>Documentation has moved</h1>
<p>The LangGraph documentation has moved to <a href="{default_url}">docs.langchain.com</a>.</p>
<p>Redirecting you now...</p>
</body>
</html>
"""
def generate_redirects():
script_dir = Path(__file__).parent
output_dir = script_dir / "_site"
# Load redirects
with open(script_dir / "redirects.json") as f:
redirects = json.load(f)
# Clean output directory
if output_dir.exists():
import shutil
shutil.rmtree(output_dir)
output_dir.mkdir(parents=True)
# Generate individual HTML files for each redirect
for old_path, new_url in redirects.items():
# Remove leading slash and create directory structure
path = old_path.lstrip("/")
# Check if path has a file extension (e.g., .txt, .xml)
# If so, create the file directly instead of a directory with index.html
path_obj = Path(path)
has_extension = path_obj.suffix and len(path_obj.suffix) <= 5
if not path:
html_path = output_dir / "index.html"
elif has_extension:
# For files with extensions, create the file directly
html_path = output_dir / path
else:
# For directory-style URLs, create index.html inside
html_path = output_dir / path / "index.html"
# Create parent directories
html_path.parent.mkdir(parents=True, exist_ok=True)
# Write the redirect HTML
html_path.write_text(HTML_TEMPLATE.format(url=new_url))
print(f"Created: {html_path}")
# Create root index.html
root_index = output_dir / "index.html"
if not root_index.exists():
root_index.write_text(ROOT_HTML_TEMPLATE.format(url=DEFAULT_REDIRECT))
print(f"Created: {root_index}")
# Create 404.html for catchall
catchall_404 = output_dir / "404.html"
catchall_404.write_text(CATCHALL_404_TEMPLATE.format(default_url=DEFAULT_REDIRECT))
print(f"Created: {catchall_404}")
# Copy static files (like llms.txt) that can't be redirected via HTML
static_files = ["llms.txt"]
for static_file in static_files:
src = script_dir / static_file
if src.exists():
dst = output_dir / static_file
dst.write_text(src.read_text())
print(f"Copied: {dst}")
print(f"\nGenerated {len(redirects)} redirect files in {output_dir}")
if __name__ == "__main__":
generate_redirects()
-35
View File
@@ -1,35 +0,0 @@
# LangGraph
LangGraph documentation has moved to docs.langchain.com.
## Overview
- [LangGraph Overview](https://docs.langchain.com/oss/python/langgraph/overview): Introduction to LangGraph, a library for building stateful, multi-actor applications with LLMs.
- [Why LangGraph?](https://docs.langchain.com/oss/python/langgraph/why-langgraph): Motivation for LangGraph and its key features.
## Core Concepts
- [Graph API](https://docs.langchain.com/oss/python/langgraph/graph-api): Learn how to define state, create nodes, and connect them with edges.
- [Streaming](https://docs.langchain.com/oss/python/langgraph/streaming): Stream outputs from your graph for better UX.
- [Persistence](https://docs.langchain.com/oss/python/langgraph/persistence): Add memory and checkpointing to your graphs.
- [Add Memory](https://docs.langchain.com/oss/python/langgraph/add-memory): Implement short-term and long-term memory.
- [Workflows & Agents](https://docs.langchain.com/oss/python/langgraph/workflows-agents): Build agents and workflows with LangGraph.
## How-To Guides
- [Use Subgraphs](https://docs.langchain.com/oss/python/langgraph/use-subgraphs): Compose graphs using subgraphs.
- [Observability](https://docs.langchain.com/oss/python/langgraph/observability): Add tracing and debugging to your graphs.
- [Common Errors](https://docs.langchain.com/oss/python/langgraph/common-errors): Troubleshoot common LangGraph errors.
## Tutorials
- [Agentic RAG](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Build an agentic RAG system with LangGraph.
- [SQL Agent](https://docs.langchain.com/oss/python/langgraph/sql-agent): Create a SQL agent with LangGraph.
## Reference
- [API Reference](https://reference.langchain.com/python/langgraph/): Complete API documentation for LangGraph.
## LangGraph Platform
For deploying LangGraph applications in production, see the [LangSmith documentation](https://docs.langchain.com/langsmith/agent-server).
-296
View File
@@ -1,296 +0,0 @@
{
"/how-tos/stream-values": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/stream-updates": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/streaming-content": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/stream-multiple": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/streaming-tokens-without-langchain": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/streaming-from-final-node": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/streaming-events-from-within-tools-without-langchain": "https://docs.langchain.com/oss/python/langgraph/streaming",
"/how-tos/state-reducers": "https://docs.langchain.com/oss/python/langgraph/graph-api#define-and-update-state",
"/how-tos/sequence": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-a-sequence-of-steps",
"/how-tos/branching": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-branches",
"/how-tos/recursion-limit": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-and-control-loops",
"/how-tos/visualization": "https://docs.langchain.com/oss/python/langgraph/graph-api#visualize-your-graph",
"/how-tos/input_output_schema": "https://docs.langchain.com/oss/python/langgraph/graph-api#define-input-and-output-schemas",
"/how-tos/pass_private_state": "https://docs.langchain.com/oss/python/langgraph/graph-api#pass-private-state-between-nodes",
"/how-tos/state-model": "https://docs.langchain.com/oss/python/langgraph/graph-api#use-pydantic-models-for-graph-state",
"/how-tos/map-reduce": "https://docs.langchain.com/oss/python/langgraph/graph-api#map-reduce-and-the-send-api",
"/how-tos/command": "https://docs.langchain.com/oss/python/langgraph/graph-api#combine-control-flow-and-state-updates-with-command",
"/how-tos/configuration": "https://docs.langchain.com/oss/python/langgraph/graph-api#add-runtime-configuration",
"/how-tos/node-retries": "https://docs.langchain.com/oss/python/langgraph/graph-api#add-retry-policies",
"/how-tos/return-when-recursion-limit-hits": "https://docs.langchain.com/oss/python/langgraph/graph-api#impose-a-recursion-limit",
"/how-tos/async": "https://docs.langchain.com/oss/python/langgraph/graph-api#async",
"/how-tos/memory/manage-conversation-history": "https://docs.langchain.com/oss/python/langgraph/add-memory",
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"/how-tos/multi-agent-multi-turn-convo-functional": "https://docs.langchain.com/oss/python/langgraph/graph-api",
"/how-tos/multi-agent-network-functional": "https://docs.langchain.com/oss/python/langgraph/graph-api",
"/how-tos/persistence-functional": "https://docs.langchain.com/oss/python/langgraph/add-memory",
"/how-tos/react-agent-from-scratch-functional": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
"/reference": "https://reference.langchain.com/python/langgraph/",
"/troubleshooting/errors": "https://docs.langchain.com/oss/python/langgraph/common-errors",
"/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/chatbots/information-gather-prompting": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/extraction/retries": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/langgraph-platform/local-server": "https://docs.langchain.com/langsmith/agent-server",
"/tutorials/lats/lats": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/llm-compiler/LLMCompiler": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/rag/langgraph_adaptive_rag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
"/tutorials/rag/langgraph_crag": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
"/tutorials/rag/langgraph_crag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
"/tutorials/rag/langgraph_self_rag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
"/tutorials/reflection/reflection": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/reflexion/reflexion": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/rewoo/rewoo": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/self-discover/self-discover": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/tnt-llm/tnt-llm": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/tot/tot": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/usaco/usaco": "https://docs.langchain.com/oss/python/langgraph/overview",
"/tutorials/web-navigation/web_voyager": "https://docs.langchain.com/oss/python/langgraph/overview"
}
+1 -1
View File
@@ -1,3 +1,3 @@
# LangGraph examples
This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview). Please refer to the LangChain docs for the most up-to-date examples and usage guidelines for LangGraph.
This directory should NOT be used for documentation. All new documentation must be added to `docs/docs/` directory.
+33
View File
@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "23544406",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/async.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
+33
View File
@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "14f7ca50",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/branching.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@@ -5,15 +5,7 @@
"id": "10251c1c",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "c5fc63df",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb"
]
}
],
@@ -5,15 +5,7 @@
"id": "a4351a24",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "4cc9af1e",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb"
]
}
],
@@ -5,15 +5,7 @@
"id": "a9014f94",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "f47ce992",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb"
]
}
],
@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "2b789e16",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/cloud/how-tos/langgraph_to_langgraph_cloud.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@@ -5,15 +5,7 @@
"id": "1f2f13ca",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "5e4c9bfe",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb"
]
}
],
@@ -1,13 +1,5 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "1d38cbab",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
]
},
{
"attachments": {
"15d3ac32-cdf3-4800-a30c-f26d828d69c8.png": {
@@ -41,9 +33,7 @@
"id": "e501686f-323f-4b87-8f9c-8ba89133078b",
"metadata": {},
"outputs": [],
"source": [
"! pip install -U langchain_community langchain-mistralai langchain langgraph"
]
"source": ["! pip install -U langchain_community langchain-mistralai langchain langgraph"]
},
{
"cell_type": "markdown",
@@ -61,12 +51,7 @@
"id": "982e4609-86e4-4934-828f-e03d89c20393",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
"mistral_api_key = os.getenv(\"MISTRAL_API_KEY\") # Ensure this is set"
]
"source": ["import os\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\nmistral_api_key = os.getenv(\"MISTRAL_API_KEY\") # Ensure this is set"]
},
{
"cell_type": "markdown",
@@ -84,12 +69,7 @@
"id": "37b172d2-3a9d-49a8-898c-22ed0cb45c88",
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\"\n",
"os.environ[\"LANGCHAIN_PROJECT\"] = \"Mistral-code-gen-testing\""
]
"source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\"\nos.environ[\"LANGCHAIN_PROJECT\"] = \"Mistral-code-gen-testing\""]
},
{
"cell_type": "markdown",
@@ -107,42 +87,7 @@
"id": "a188c8ca-c053-4e6d-b7af-38a3b6b371c7",
"metadata": {},
"outputs": [],
"source": [
"# Select LLM\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain_mistralai import ChatMistralAI\n",
"\n",
"mistral_model = \"mistral-large-latest\"\n",
"llm = ChatMistralAI(model=mistral_model, temperature=0)\n",
"\n",
"# Prompt\n",
"code_gen_prompt_claude = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"\"\"You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables \\n\n",
" defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.\n",
" \\n Here is the user question:\"\"\",\n",
" ),\n",
" (\"placeholder\", \"{messages}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"# Data model\n",
"class code(BaseModel):\n",
" \"\"\"Code output\"\"\"\n",
"\n",
" prefix: str = Field(description=\"Description of the problem and approach\")\n",
" imports: str = Field(description=\"Code block import statements\")\n",
" code: str = Field(description=\"Code block not including import statements\")\n",
" description = \"Schema for code solutions to questions about LCEL.\"\n",
"\n",
"\n",
"# LLM\n",
"code_gen_chain = llm.with_structured_output(code, include_raw=False)"
]
"source": ["# Select LLM\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.pydantic_v1 import BaseModel, Field\nfrom langchain_mistralai import ChatMistralAI\n\nmistral_model = \"mistral-large-latest\"\nllm = ChatMistralAI(model=mistral_model, temperature=0)\n\n# Prompt\ncode_gen_prompt_claude = ChatPromptTemplate.from_messages(\n [\n (\n \"system\",\n \"\"\"You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables \\n\n defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.\n \\n Here is the user question:\"\"\",\n ),\n (\"placeholder\", \"{messages}\"),\n ]\n)\n\n\n# Data model\nclass code(BaseModel):\n \"\"\"Code output\"\"\"\n\n prefix: str = Field(description=\"Description of the problem and approach\")\n imports: str = Field(description=\"Code block import statements\")\n code: str = Field(description=\"Code block not including import statements\")\n description = \"Schema for code solutions to questions about LCEL.\"\n\n\n# LLM\ncode_gen_chain = llm.with_structured_output(code, include_raw=False)"]
},
{
"cell_type": "code",
@@ -150,10 +95,7 @@
"id": "9fc0290d-5a04-4514-8664-91f9dbf2da7b",
"metadata": {},
"outputs": [],
"source": [
"question = \"Write a function for fibonacci.\"\n",
"messages = [(\"user\", question)]"
]
"source": ["question = \"Write a function for fibonacci.\"\nmessages = [(\"user\", question)]"]
},
{
"cell_type": "code",
@@ -172,11 +114,7 @@
"output_type": "execute_result"
}
],
"source": [
"# Test\n",
"result = code_gen_chain.invoke(messages)\n",
"result"
]
"source": ["# Test\nresult = code_gen_chain.invoke(messages)\nresult"]
},
{
"cell_type": "markdown",
@@ -192,28 +130,7 @@
"id": "183d77b8-f180-4815-b39f-8ef507ec0534",
"metadata": {},
"outputs": [],
"source": [
"from typing import Annotated, TypedDict\n",
"\n",
"from langgraph.graph.message import AnyMessage, add_messages\n",
"\n",
"\n",
"class GraphState(TypedDict):\n",
" \"\"\"\n",
" Represents the state of our graph.\n",
"\n",
" Attributes:\n",
" error : Binary flag for control flow to indicate whether test error was tripped\n",
" messages : With user question, error messages, reasoning\n",
" generation : Code solution\n",
" iterations : Number of tries\n",
" \"\"\"\n",
"\n",
" error: str\n",
" messages: Annotated[list[AnyMessage], add_messages]\n",
" generation: str\n",
" iterations: int"
]
"source": ["from typing import Annotated, TypedDict\n\nfrom langgraph.graph.message import AnyMessage, add_messages\n\n\nclass GraphState(TypedDict):\n \"\"\"\n Represents the state of our graph.\n\n Attributes:\n error : Binary flag for control flow to indicate whether test error was tripped\n messages : With user question, error messages, reasoning\n generation : Code solution\n iterations : Number of tries\n \"\"\"\n\n error: str\n messages: Annotated[list[AnyMessage], add_messages]\n generation: str\n iterations: int"]
},
{
"cell_type": "markdown",
@@ -229,163 +146,7 @@
"id": "14bc89d1-3ca6-4847-a048-1803e0e4600e",
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"### Parameters\n",
"max_iterations = 3\n",
"\n",
"\n",
"### Nodes\n",
"def generate(state: GraphState):\n",
" \"\"\"\n",
" Generate a code solution\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, generation\n",
" \"\"\"\n",
"\n",
" print(\"---GENERATING CODE SOLUTION---\")\n",
"\n",
" # State\n",
" messages = state[\"messages\"]\n",
" iterations = state[\"iterations\"]\n",
"\n",
" # Solution\n",
" code_solution = code_gen_chain.invoke(messages)\n",
" messages += [\n",
" (\n",
" \"assistant\",\n",
" f\"Here is my attempt to solve the problem: {code_solution.prefix} \\n Imports: {code_solution.imports} \\n Code: {code_solution.code}\",\n",
" )\n",
" ]\n",
"\n",
" # Increment\n",
" iterations = iterations + 1\n",
" return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n",
"\n",
"\n",
"def code_check(state: GraphState):\n",
" \"\"\"\n",
" Check code\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, error\n",
" \"\"\"\n",
"\n",
" print(\"---CHECKING CODE---\")\n",
"\n",
" # State\n",
" messages = state[\"messages\"]\n",
" code_solution = state[\"generation\"]\n",
" iterations = state[\"iterations\"]\n",
"\n",
" # Get solution components\n",
" imports = code_solution.imports\n",
" code = code_solution.code\n",
"\n",
" # Check imports\n",
" try:\n",
" exec(imports)\n",
" except Exception as e:\n",
" print(\"---CODE IMPORT CHECK: FAILED---\")\n",
" error_message = [\n",
" (\n",
" \"user\",\n",
" f\"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
" )\n",
" ]\n",
" messages += error_message\n",
" return {\n",
" \"generation\": code_solution,\n",
" \"messages\": messages,\n",
" \"iterations\": iterations,\n",
" \"error\": \"yes\",\n",
" }\n",
"\n",
" # Check execution\n",
" try:\n",
" combined_code = f\"{imports}\\n{code}\"\n",
" print(f\"CODE TO TEST: {combined_code}\")\n",
" # Use a shared scope for exec\n",
" global_scope = {}\n",
" exec(combined_code, global_scope)\n",
" except Exception as e:\n",
" print(\"---CODE BLOCK CHECK: FAILED---\")\n",
" error_message = [\n",
" (\n",
" \"user\",\n",
" f\"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
" )\n",
" ]\n",
" messages += error_message\n",
" return {\n",
" \"generation\": code_solution,\n",
" \"messages\": messages,\n",
" \"iterations\": iterations,\n",
" \"error\": \"yes\",\n",
" }\n",
"\n",
" # No errors\n",
" print(\"---NO CODE TEST FAILURES---\")\n",
" return {\n",
" \"generation\": code_solution,\n",
" \"messages\": messages,\n",
" \"iterations\": iterations,\n",
" \"error\": \"no\",\n",
" }\n",
"\n",
"\n",
"### Conditional edges\n",
"\n",
"\n",
"def decide_to_finish(state: GraphState):\n",
" \"\"\"\n",
" Determines whether to finish.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" str: Next node to call\n",
" \"\"\"\n",
" error = state[\"error\"]\n",
" iterations = state[\"iterations\"]\n",
"\n",
" if error == \"no\" or iterations == max_iterations:\n",
" print(\"---DECISION: FINISH---\")\n",
" return \"end\"\n",
" else:\n",
" print(\"---DECISION: RE-TRY SOLUTION---\")\n",
" return \"generate\"\n",
"\n",
"\n",
"### Utilities\n",
"\n",
"\n",
"def _print_event(event: dict, _printed: set, max_length=1500):\n",
" current_state = event.get(\"dialog_state\")\n",
" if current_state:\n",
" print(\"Currently in: \", current_state[-1])\n",
" message = event.get(\"messages\")\n",
" if message:\n",
" if isinstance(message, list):\n",
" message = message[-1]\n",
" if message.id not in _printed:\n",
" msg_repr = message.pretty_repr(html=True)\n",
" if len(msg_repr) > max_length:\n",
" msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n",
" print(msg_repr)\n",
" _printed.add(message.id)"
]
"source": ["import uuid\n\nfrom langchain_core.pydantic_v1 import BaseModel, Field\n\n### Parameters\nmax_iterations = 3\n\n\n### Nodes\ndef generate(state: GraphState):\n \"\"\"\n Generate a code solution\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, generation\n \"\"\"\n\n print(\"---GENERATING CODE SOLUTION---\")\n\n # State\n messages = state[\"messages\"]\n iterations = state[\"iterations\"]\n\n # Solution\n code_solution = code_gen_chain.invoke(messages)\n messages += [\n (\n \"assistant\",\n f\"Here is my attempt to solve the problem: {code_solution.prefix} \\n Imports: {code_solution.imports} \\n Code: {code_solution.code}\",\n )\n ]\n\n # Increment\n iterations = iterations + 1\n return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n\n\ndef code_check(state: GraphState):\n \"\"\"\n Check code\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, error\n \"\"\"\n\n print(\"---CHECKING CODE---\")\n\n # State\n messages = state[\"messages\"]\n code_solution = state[\"generation\"]\n iterations = state[\"iterations\"]\n\n # Get solution components\n imports = code_solution.imports\n code = code_solution.code\n\n # Check imports\n try:\n exec(imports)\n except Exception as e:\n print(\"---CODE IMPORT CHECK: FAILED---\")\n error_message = [\n (\n \"user\",\n f\"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n )\n ]\n messages += error_message\n return {\n \"generation\": code_solution,\n \"messages\": messages,\n \"iterations\": iterations,\n \"error\": \"yes\",\n }\n\n # Check execution\n try:\n combined_code = f\"{imports}\\n{code}\"\n print(f\"CODE TO TEST: {combined_code}\")\n # Use a shared scope for exec\n global_scope = {}\n exec(combined_code, global_scope)\n except Exception as e:\n print(\"---CODE BLOCK CHECK: FAILED---\")\n error_message = [\n (\n \"user\",\n f\"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n )\n ]\n messages += error_message\n return {\n \"generation\": code_solution,\n \"messages\": messages,\n \"iterations\": iterations,\n \"error\": \"yes\",\n }\n\n # No errors\n print(\"---NO CODE TEST FAILURES---\")\n return {\n \"generation\": code_solution,\n \"messages\": messages,\n \"iterations\": iterations,\n \"error\": \"no\",\n }\n\n\n### Conditional edges\n\n\ndef decide_to_finish(state: GraphState):\n \"\"\"\n Determines whether to finish.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n str: Next node to call\n \"\"\"\n error = state[\"error\"]\n iterations = state[\"iterations\"]\n\n if error == \"no\" or iterations == max_iterations:\n print(\"---DECISION: FINISH---\")\n return \"end\"\n else:\n print(\"---DECISION: RE-TRY SOLUTION---\")\n return \"generate\"\n\n\n### Utilities\n\n\ndef _print_event(event: dict, _printed: set, max_length=1500):\n current_state = event.get(\"dialog_state\")\n if current_state:\n print(\"Currently in: \", current_state[-1])\n message = event.get(\"messages\")\n if message:\n if isinstance(message, list):\n message = message[-1]\n if message.id not in _printed:\n msg_repr = message.pretty_repr(html=True)\n if len(msg_repr) > max_length:\n msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n print(msg_repr)\n _printed.add(message.id)"]
},
{
"cell_type": "code",
@@ -393,31 +154,7 @@
"id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.checkpoint.memory import InMemorySaver\n",
"from langgraph.graph import END, StateGraph, START\n",
"\n",
"builder = StateGraph(GraphState)\n",
"\n",
"# Define the nodes\n",
"builder.add_node(\"generate\", generate) # generation solution\n",
"builder.add_node(\"check_code\", code_check) # check code\n",
"\n",
"# Build graph\n",
"builder.add_edge(START, \"generate\")\n",
"builder.add_edge(\"generate\", \"check_code\")\n",
"builder.add_conditional_edges(\n",
" \"check_code\",\n",
" decide_to_finish,\n",
" {\n",
" \"end\": END,\n",
" \"generate\": \"generate\",\n",
" },\n",
")\n",
"\n",
"memory = InMemorySaver()\n",
"graph = builder.compile(checkpointer=memory)"
]
"source": ["from langgraph.checkpoint.memory import InMemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate) # generation solution\nbuilder.add_node(\"check_code\", code_check) # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n \"check_code\",\n decide_to_finish,\n {\n \"end\": END,\n \"generate\": \"generate\",\n },\n)\n\nmemory = InMemorySaver()\ngraph = builder.compile(checkpointer=memory)"]
},
{
"cell_type": "code",
@@ -436,15 +173,7 @@
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Image, display\n",
"\n",
"try:\n",
" display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\n",
"except Exception:\n",
" # This requires some extra dependencies and is optional\n",
" pass"
]
"source": ["from IPython.display import Image, display\n\ntry:\n display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"]
},
{
"cell_type": "code",
@@ -452,23 +181,7 @@
"id": "242aa2f0-2c31-462f-a958-ff9ae0cf7c62",
"metadata": {},
"outputs": [],
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"Write a Python program that prints 'Hello, World!' to the console.\"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"Write a Python program that prints 'Hello, World!' to the console.\"\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
},
{
"cell_type": "markdown",
@@ -486,31 +199,7 @@
"id": "390b2768-f395-4aea-8b0e-9d36212a31ac",
"metadata": {},
"outputs": [],
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"Create a Python program that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).\n",
"\n",
"Requirements:\n",
"The program should define a function is_palindrome(s) that takes a string s as input.\n",
"The function should return True if the string is a palindrome and False otherwise.\n",
"Ignore spaces, punctuation, and case differences when checking for palindromes.\n",
"\n",
"Give an example of it working on an example input word.\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"Create a Python program that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).\n\nRequirements:\nThe program should define a function is_palindrome(s) that takes a string s as input.\nThe function should return True if the string is a palindrome and False otherwise.\nIgnore spaces, punctuation, and case differences when checking for palindromes.\n\nGive an example of it working on an example input word.\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
},
{
"cell_type": "markdown",
@@ -528,26 +217,7 @@
"id": "0a3f946b-e2f2-44d9-905b-09f36980cf9f",
"metadata": {},
"outputs": [],
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"Write a program that prints the numbers from 1 to 100. \n",
"But for multiples of three, print \"Fizz\" instead of the number, and for the multiples of five, print \"Buzz\". \n",
"For numbers which are multiples of both three and five, print \"FizzBuzz\".\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"Write a program that prints the numbers from 1 to 100. \nBut for multiples of three, print \"Fizz\" instead of the number, and for the multiples of five, print \"Buzz\". \nFor numbers which are multiples of both three and five, print \"FizzBuzz\".\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
},
{
"cell_type": "markdown",
@@ -565,37 +235,7 @@
"id": "2bb883df-540b-46ab-9415-fe27db68456f",
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"\n",
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"I want to vectorize a function\n",
"\n",
" frame = np.zeros((out_h, out_w, 3), dtype=np.uint8)\n",
" for i, val1 in enumerate(rows):\n",
" for j, val2 in enumerate(cols):\n",
" for j, val3 in enumerate(ch):\n",
" # Assuming you want to store the pair as tuples in the matrix\n",
" frame[i, j, k] = image[val1, val2, val3]\n",
"\n",
" out.write(np.array(frame))\n",
"\n",
"with a simple numpy function that does something like this what is it called. Show me a test case with this working.\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
"source": ["import uuid\n\n_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"I want to vectorize a function\n\n frame = np.zeros((out_h, out_w, 3), dtype=np.uint8)\n for i, val1 in enumerate(rows):\n for j, val2 in enumerate(cols):\n for j, val3 in enumerate(ch):\n # Assuming you want to store the pair as tuples in the matrix\n frame[i, j, k] = image[val1, val2, val3]\n\n out.write(np.array(frame))\n\nwith a simple numpy function that does something like this what is it called. Show me a test case with this working.\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
},
{
"cell_type": "markdown",
@@ -613,34 +253,7 @@
"id": "ee05da1f-c272-405d-8a7b-552cfc3106e1",
"metadata": {},
"outputs": [],
"source": [
"_printed = set()\n",
"thread_id = str(uuid.uuid4())\n",
"config = {\n",
" \"configurable\": {\n",
" # Checkpoints are accessed by thread_id\n",
" \"thread_id\": thread_id,\n",
" }\n",
"}\n",
"\n",
"question = \"\"\"Create a Python program that allows two players to play a game of Tic-Tac-Toe. The game should be played on a 3x3 grid. The program should:\n",
"\n",
"- Allow players to take turns to input their moves.\n",
"- Check for invalid moves (e.g., placing a marker on an already occupied space).\n",
"- Determine and announce the winner or if the game ends in a draw.\n",
"\n",
"Requirements:\n",
"- Use a 2D list to represent the Tic-Tac-Toe board.\n",
"- Use functions to modularize the code.\n",
"- Validate player input.\n",
"- Check for win conditions and draw conditions after each move.\"\"\"\n",
"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" _print_event(event, _printed)"
]
"source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n \"configurable\": {\n # Checkpoints are accessed by thread_id\n \"thread_id\": thread_id,\n }\n}\n\nquestion = \"\"\"Create a Python program that allows two players to play a game of Tic-Tac-Toe. The game should be played on a 3x3 grid. The program should:\n\n- Allow players to take turns to input their moves.\n- Check for invalid moves (e.g., placing a marker on an already occupied space).\n- Determine and announce the winner or if the game ends in a draw.\n\nRequirements:\n- Use a 2D list to represent the Tic-Tac-Toe board.\n- Use functions to modularize the code.\n- Validate player input.\n- Check for win conditions and draw conditions after each move.\"\"\"\n\nevents = graph.stream(\n {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n _print_event(event, _printed)"]
},
{
"cell_type": "markdown",
@@ -658,7 +271,7 @@
"id": "814fc2a4-8e5b-4faa-8f52-3977226bd09a",
"metadata": {},
"outputs": [],
"source": []
"source": [""]
}
],
"metadata": {
+33
View File
@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "e9a58c69",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/configuration.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
+33
View File
@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "a1e6efeb",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-hitl.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
+33
View File
@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "1ef41a89",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-memory.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "9e2f7902",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-system-prompt.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
+33
View File
@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "eb07372e",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent.ipynb"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@@ -5,15 +5,7 @@
"id": "a8232bc9",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/customer-support/customer-support.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "63da8671",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/customer-support/customer-support.ipynb"
]
}
],
+1 -9
View File
@@ -5,15 +5,7 @@
"id": "8dbdba5b",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/extraction/retries.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "1d444b7f",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/extraction/retries.ipynb"
]
}
],
@@ -5,15 +5,7 @@
"id": "3ecab357",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "3f2866bd",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb"
]
}
],
+33
View File
@@ -0,0 +1,33 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fc0793cb",
"metadata": {},
"source": [
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/input_output_schema.ipynb"
]
}
],
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"kernelspec": {
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"pygments_lexer": "ipython3",
"version": "3.11.1"
}
},
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"nbformat_minor": 5
}
+1 -9
View File
@@ -5,15 +5,7 @@
"id": "09038b53",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/lats/lats.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "b1669748",
"metadata": {},
"source": [
"This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/lats/lats.ipynb"
]
}
],
+1 -9
View File
@@ -5,15 +5,7 @@
"id": "85205e97",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb)"
]
},
{
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@@ -0,0 +1,33 @@
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@@ -5,15 +5,7 @@
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@@ -1,13 +1,5 @@
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@@ -1,13 +1,5 @@
{
"cells": [
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"cell_type": "markdown",
"id": "425fb020-e864-40ce-a31f-8da40c73d14b",
@@ -208,11 +200,11 @@
"output_type": "stream",
"text": [
"********************Prompt[rlm/rag-prompt]********************\n",
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"================================\u001B[1m Human Message \u001B[0m=================================\n",
"\n",
"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n",
"Question: \u001b[33;1m\u001b[1;3m{question}\u001b[0m \n",
"Context: \u001b[33;1m\u001b[1;3m{context}\u001b[0m \n",
"Question: \u001B[33;1m\u001B[1;3m{question}\u001B[0m \n",
"Context: \u001B[33;1m\u001B[1;3m{context}\u001B[0m \n",
"Answer:\n"
]
}
-8
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@@ -1,13 +1,5 @@
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{
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@@ -1,13 +1,5 @@
{
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@@ -1,13 +1,5 @@
{
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@@ -62,11 +54,7 @@
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
"import os\n\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
]
},
{
@@ -76,9 +64,7 @@
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
"import os\n\nos.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
]
},
{
@@ -98,18 +84,7 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_pinecone import PineconeVectorStore\n",
"\n",
"# use pinecone movies database\n",
"\n",
"# Add to vectorDB\n",
"vectorstore = PineconeVectorStore(\n",
" embedding=OpenAIEmbeddings(),\n",
" index_name=\"sample-movies\",\n",
" text_key=\"summary\",\n",
")\n",
"retriever = vectorstore.as_retriever()"
"from langchain_openai import OpenAIEmbeddings\nfrom langchain_pinecone import PineconeVectorStore\n\n# use pinecone movies database\n\n# Add to vectorDB\nvectorstore = PineconeVectorStore(\n embedding=OpenAIEmbeddings(),\n index_name=\"sample-movies\",\n text_key=\"summary\",\n)\nretriever = vectorstore.as_retriever()"
]
},
{
@@ -138,11 +113,7 @@
}
],
"source": [
"docs = retriever.invoke(\"James Cameron\")\n",
"for doc in docs:\n",
" print(\"# \" + doc.metadata[\"title\"])\n",
" print(doc.page_content)\n",
" print()"
"docs = retriever.invoke(\"James Cameron\")\nfor doc in docs:\n print(\"# \" + doc.metadata[\"title\"])\n print(doc.page_content)\n print()"
]
},
{
@@ -202,12 +173,7 @@
}
],
"source": [
"# Test the retrieval grader\n",
"question = \"movies starring jason momoa\"\n",
"docs = retriever.invoke(question)\n",
"doc_txt = docs[0].page_content\n",
"print(doc_txt)\n",
"print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
"# Test the retrieval grader\nquestion = \"movies starring jason momoa\"\ndocs = retriever.invoke(question)\ndoc_txt = docs[0].page_content\nprint(doc_txt)\nprint(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
]
},
{
@@ -235,23 +201,7 @@
}
],
"source": [
"### Generate\n",
"\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"\n",
"# Prompt\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"# LLM\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"# Chain\n",
"rag_chain = prompt | llm | StrOutputParser()\n",
"\n",
"# Run\n",
"generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
"print(generation)"
"### Generate\n\nfrom langchain import hub\nfrom langchain_core.output_parsers import StrOutputParser\n\n# Prompt\nprompt = hub.pull(\"rlm/rag-prompt\")\n\n# LLM\nllm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n\n# Chain\nrag_chain = prompt | llm | StrOutputParser()\n\n# Run\ngeneration = rag_chain.invoke({\"context\": docs, \"question\": question})\nprint(generation)"
]
},
{
@@ -379,17 +329,7 @@
}
],
"source": [
"### Question Re-writer\n",
"\n",
"# LLM\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"\n",
"# Prompt\n",
"re_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n",
"\n",
"question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
"print(question)\n",
"question_rewriter.invoke({\"question\": question})"
"### Question Re-writer\n\n# LLM\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n\n# Prompt\nre_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n\nquestion_rewriter = re_write_prompt | llm | StrOutputParser()\nprint(question)\nquestion_rewriter.invoke({\"question\": question})"
]
},
{
@@ -411,24 +351,7 @@
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"\n",
"from typing_extensions import TypedDict\n",
"\n",
"\n",
"class GraphState(TypedDict):\n",
" \"\"\"\n",
" Represents the state of our graph.\n",
"\n",
" Attributes:\n",
" question: question\n",
" generation: LLM generation\n",
" documents: list of documents\n",
" \"\"\"\n",
"\n",
" question: str\n",
" generation: str\n",
" documents: List[str]"
"from typing import List\n\nfrom typing_extensions import TypedDict\n\n\nclass GraphState(TypedDict):\n \"\"\"\n Represents the state of our graph.\n\n Attributes:\n question: question\n generation: LLM generation\n documents: list of documents\n \"\"\"\n\n question: str\n generation: str\n documents: List[str]"
]
},
{
@@ -438,95 +361,7 @@
"metadata": {},
"outputs": [],
"source": [
"### Nodes\n",
"\n",
"\n",
"def retrieve(state):\n",
" \"\"\"\n",
" Retrieve documents\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, documents, that contains retrieved documents\n",
" \"\"\"\n",
" print(\"---RETRIEVE---\")\n",
" question = state[\"question\"]\n",
"\n",
" # Retrieval\n",
" documents = retriever.invoke(question)\n",
" return {\"documents\": documents, \"question\": question}\n",
"\n",
"\n",
"def generate(state):\n",
" \"\"\"\n",
" Generate answer\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): New key added to state, generation, that contains LLM generation\n",
" \"\"\"\n",
" print(\"---GENERATE---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
"\n",
" # RAG generation\n",
" generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
" return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
"\n",
"\n",
"def grade_documents(state):\n",
" \"\"\"\n",
" Determines whether the retrieved documents are relevant to the question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): Updates documents key with only filtered relevant documents\n",
" \"\"\"\n",
"\n",
" print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
"\n",
" # Score each doc\n",
" filtered_docs = []\n",
" for d in documents:\n",
" score = retrieval_grader.invoke(\n",
" {\"question\": question, \"document\": d.page_content}\n",
" )\n",
" grade = score.binary_score\n",
" if grade == \"yes\":\n",
" print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
" filtered_docs.append(d)\n",
" else:\n",
" print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
" continue\n",
" return {\"documents\": filtered_docs, \"question\": question}\n",
"\n",
"\n",
"def transform_query(state):\n",
" \"\"\"\n",
" Transform the query to produce a better question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" state (dict): Updates question key with a re-phrased question\n",
" \"\"\"\n",
"\n",
" print(\"---TRANSFORM QUERY---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
"\n",
" # Re-write question\n",
" better_question = question_rewriter.invoke({\"question\": question})\n",
" return {\"documents\": documents, \"question\": better_question}"
"### Nodes\n\n\ndef retrieve(state):\n \"\"\"\n Retrieve documents\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, documents, that contains retrieved documents\n \"\"\"\n print(\"---RETRIEVE---\")\n question = state[\"question\"]\n\n # Retrieval\n documents = retriever.invoke(question)\n return {\"documents\": documents, \"question\": question}\n\n\ndef generate(state):\n \"\"\"\n Generate answer\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): New key added to state, generation, that contains LLM generation\n \"\"\"\n print(\"---GENERATE---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n\n # RAG generation\n generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n return {\"documents\": documents, \"question\": question, \"generation\": generation}\n\n\ndef grade_documents(state):\n \"\"\"\n Determines whether the retrieved documents are relevant to the question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): Updates documents key with only filtered relevant documents\n \"\"\"\n\n print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n\n # Score each doc\n filtered_docs = []\n for d in documents:\n score = retrieval_grader.invoke(\n {\"question\": question, \"document\": d.page_content}\n )\n grade = score.binary_score\n if grade == \"yes\":\n print(\"---GRADE: DOCUMENT RELEVANT---\")\n filtered_docs.append(d)\n else:\n print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n continue\n return {\"documents\": filtered_docs, \"question\": question}\n\n\ndef transform_query(state):\n \"\"\"\n Transform the query to produce a better question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n state (dict): Updates question key with a re-phrased question\n \"\"\"\n\n print(\"---TRANSFORM QUERY---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n\n # Re-write question\n better_question = question_rewriter.invoke({\"question\": question})\n return {\"documents\": documents, \"question\": better_question}"
]
},
{
@@ -536,74 +371,7 @@
"metadata": {},
"outputs": [],
"source": [
"### Edges\n",
"\n",
"\n",
"def decide_to_generate(state):\n",
" \"\"\"\n",
" Determines whether to generate an answer, or re-generate a question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" str: Binary decision for next node to call\n",
" \"\"\"\n",
"\n",
" print(\"---ASSESS GRADED DOCUMENTS---\")\n",
" state[\"question\"]\n",
" filtered_documents = state[\"documents\"]\n",
"\n",
" if not filtered_documents:\n",
" # All documents have been filtered check_relevance\n",
" # We will re-generate a new query\n",
" print(\n",
" \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
" )\n",
" return \"transform_query\"\n",
" else:\n",
" # We have relevant documents, so generate answer\n",
" print(\"---DECISION: GENERATE---\")\n",
" return \"generate\"\n",
"\n",
"\n",
"def grade_generation_v_documents_and_question(state):\n",
" \"\"\"\n",
" Determines whether the generation is grounded in the document and answers question.\n",
"\n",
" Args:\n",
" state (dict): The current graph state\n",
"\n",
" Returns:\n",
" str: Decision for next node to call\n",
" \"\"\"\n",
"\n",
" print(\"---CHECK HALLUCINATIONS---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
" generation = state[\"generation\"]\n",
"\n",
" score = hallucination_grader.invoke(\n",
" {\"documents\": documents, \"generation\": generation}\n",
" )\n",
" grade = score.binary_score\n",
"\n",
" # Check hallucination\n",
" if grade == \"yes\":\n",
" print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
" # Check question-answering\n",
" print(\"---GRADE GENERATION vs QUESTION---\")\n",
" score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
" grade = score.binary_score\n",
" if grade == \"yes\":\n",
" print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
" return \"useful\"\n",
" else:\n",
" print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
" return \"not useful\"\n",
" else:\n",
" pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
" return \"not supported\""
"### Edges\n\n\ndef decide_to_generate(state):\n \"\"\"\n Determines whether to generate an answer, or re-generate a question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n str: Binary decision for next node to call\n \"\"\"\n\n print(\"---ASSESS GRADED DOCUMENTS---\")\n state[\"question\"]\n filtered_documents = state[\"documents\"]\n\n if not filtered_documents:\n # All documents have been filtered check_relevance\n # We will re-generate a new query\n print(\n \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n )\n return \"transform_query\"\n else:\n # We have relevant documents, so generate answer\n print(\"---DECISION: GENERATE---\")\n return \"generate\"\n\n\ndef grade_generation_v_documents_and_question(state):\n \"\"\"\n Determines whether the generation is grounded in the document and answers question.\n\n Args:\n state (dict): The current graph state\n\n Returns:\n str: Decision for next node to call\n \"\"\"\n\n print(\"---CHECK HALLUCINATIONS---\")\n question = state[\"question\"]\n documents = state[\"documents\"]\n generation = state[\"generation\"]\n\n score = hallucination_grader.invoke(\n {\"documents\": documents, \"generation\": generation}\n )\n grade = score.binary_score\n\n # Check hallucination\n if grade == \"yes\":\n print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n # Check question-answering\n print(\"---GRADE GENERATION vs QUESTION---\")\n score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n grade = score.binary_score\n if grade == \"yes\":\n print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n return \"useful\"\n else:\n print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n return \"not useful\"\n else:\n pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n return \"not supported\""
]
},
{
@@ -622,42 +390,7 @@
"id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.graph import END, StateGraph, START\n",
"\n",
"workflow = StateGraph(GraphState)\n",
"\n",
"# Define the nodes\n",
"workflow.add_node(\"retrieve\", retrieve) # retrieve\n",
"workflow.add_node(\"grade_documents\", grade_documents) # grade documents\n",
"workflow.add_node(\"generate\", generate) # generate\n",
"workflow.add_node(\"transform_query\", transform_query) # transform_query\n",
"\n",
"# Build graph\n",
"workflow.add_edge(START, \"retrieve\")\n",
"workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
"workflow.add_conditional_edges(\n",
" \"grade_documents\",\n",
" decide_to_generate,\n",
" {\n",
" \"transform_query\": \"transform_query\",\n",
" \"generate\": \"generate\",\n",
" },\n",
")\n",
"workflow.add_edge(\"transform_query\", \"retrieve\")\n",
"workflow.add_conditional_edges(\n",
" \"generate\",\n",
" grade_generation_v_documents_and_question,\n",
" {\n",
" \"not supported\": \"generate\",\n",
" \"useful\": END,\n",
" \"not useful\": \"transform_query\",\n",
" },\n",
")\n",
"\n",
"# Compile\n",
"app = workflow.compile()"
]
"source": ["from langgraph.graph import END, StateGraph, START\n\nworkflow = StateGraph(GraphState)\n\n# Define the nodes\nworkflow.add_node(\"retrieve\", retrieve) # retrieve\nworkflow.add_node(\"grade_documents\", grade_documents) # grade documents\nworkflow.add_node(\"generate\", generate) # generate\nworkflow.add_node(\"transform_query\", transform_query) # transform_query\n\n# Build graph\nworkflow.add_edge(START, \"retrieve\")\nworkflow.add_edge(\"retrieve\", \"grade_documents\")\nworkflow.add_conditional_edges(\n \"grade_documents\",\n decide_to_generate,\n {\n \"transform_query\": \"transform_query\",\n \"generate\": \"generate\",\n },\n)\nworkflow.add_edge(\"transform_query\", \"retrieve\")\nworkflow.add_conditional_edges(\n \"generate\",\n grade_generation_v_documents_and_question,\n {\n \"not supported\": \"generate\",\n \"useful\": END,\n \"not useful\": \"transform_query\",\n },\n)\n\n# Compile\napp = workflow.compile()"]
},
{
"cell_type": "code",
@@ -693,18 +426,7 @@
}
],
"source": [
"from pprint import pprint\n",
"\n",
"# Run\n",
"inputs = {\"question\": \"Movies that star Daniel Craig\"}\n",
"for output in app.stream(inputs):\n",
" for key, value in output.items():\n",
" # Node\n",
" pprint(f\"Node '{key}':\")\n",
" pprint(\"\\n---\\n\")\n",
"\n",
"# Final generation\n",
"pprint(value[\"generation\"])"
"from pprint import pprint\n\n# Run\ninputs = {\"question\": \"Movies that star Daniel Craig\"}\nfor output in app.stream(inputs):\n for key, value in output.items():\n # Node\n pprint(f\"Node '{key}':\")\n pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
]
},
{
@@ -714,15 +436,7 @@
"metadata": {},
"outputs": [],
"source": [
"inputs = {\"question\": \"Which movies are about aliens?\"}\n",
"for output in app.stream(inputs):\n",
" for key, value in output.items():\n",
" # Node\n",
" pprint(f\"Node '{key}':\")\n",
" pprint(\"\\n---\\n\")\n",
"\n",
"# Final generation\n",
"pprint(value[\"generation\"])"
"inputs = {\"question\": \"Which movies are about aliens?\"}\nfor output in app.stream(inputs):\n for key, value in output.items():\n # Node\n pprint(f\"Node '{key}':\")\n pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
]
},
{
@@ -731,7 +445,9 @@
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"metadata": {},
"outputs": [],
"source": []
"source": [
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@@ -0,0 +1,33 @@
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{
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}
],
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{
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"id": "f49876e1",
"metadata": {},
"source": [
"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/subgraph.md)"
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"This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/subgraph.ipynb"
]
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{
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{
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"id": "83c2223f",
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.PHONY: format lint test
format:
uv run ruff format .
uv run ruff check --fix .
lint:
uv run ruff check .
uv run ty check
test:
uv run pytest $(TEST)
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# langgraph-checkpoint-conformance
Conformance test suite for [LangGraph](https://github.com/langchain-ai/langgraph) checkpointer implementations.
Validates that a `BaseCheckpointSaver` subclass correctly implements the checkpoint storage contract — blob round-trips, metadata preservation, namespace isolation, incremental channel updates, and more.
## Installation
```bash
pip install langgraph-checkpoint-conformance
```
## Quick start
Register your checkpointer with `@checkpointer_test` and run `validate()`:
```python
import asyncio
from langgraph.checkpoint.conformance import checkpointer_test, validate
@checkpointer_test(name="MyCheckpointer")
async def my_checkpointer():
saver = MyCheckpointer(...)
yield saver
# cleanup runs after yield
async def main():
report = await validate(my_checkpointer)
report.print_report()
assert report.passed_all_base()
asyncio.run(main())
```
Or in a pytest test:
```python
import pytest
from langgraph.checkpoint.conformance import checkpointer_test, validate
@checkpointer_test(name="MyCheckpointer")
async def my_checkpointer():
yield MyCheckpointer(...)
@pytest.mark.asyncio
async def test_conformance():
report = await validate(my_checkpointer)
report.print_report()
assert report.passed_all_base()
```
## Capabilities
The suite tests **base** capabilities (required) and **extended** capabilities (optional, auto-detected):
| Capability | Required | Method |
|---|---|---|
| `put` | yes | `aput` |
| `put_writes` | yes | `aput_writes` |
| `get_tuple` | yes | `aget_tuple` |
| `list` | yes | `alist` |
| `delete_thread` | yes | `adelete_thread` |
| `delete_for_runs` | no | `adelete_for_runs` |
| `copy_thread` | no | `acopy_thread` |
| `prune` | no | `aprune` |
Extended capabilities are detected by checking whether the method is overridden from `BaseCheckpointSaver`. If not overridden, those tests are skipped.
## Options
### Progress output
```python
from langgraph.checkpoint.conformance.report import ProgressCallbacks
# Dot-style progress (. per pass, F per fail)
report = await validate(my_checkpointer, progress=ProgressCallbacks.default())
# Verbose (per-test names + stacktraces on failure)
report = await validate(my_checkpointer, progress=ProgressCallbacks.verbose())
```
### Skip capabilities
```python
@checkpointer_test(name="MyCheckpointer", skip_capabilities={"prune"})
async def my_checkpointer():
yield MyCheckpointer(...)
```
### Run specific capabilities
```python
report = await validate(my_checkpointer, capabilities={"put", "list"})
```
### Lifespan (one-time setup/teardown)
For expensive setup like database creation:
```python
async def db_lifespan():
await create_database()
yield
await drop_database()
@checkpointer_test(name="PostgresSaver", lifespan=db_lifespan)
async def pg_checkpointer():
async with PostgresSaver.from_conn_string(CONN_STRING) as saver:
yield saver
```
@@ -1,9 +0,0 @@
"""langgraph-checkpoint-conformance: conformance test suite for checkpointer implementations."""
from langgraph.checkpoint.conformance.initializer import checkpointer_test
from langgraph.checkpoint.conformance.validate import validate
__all__ = [
"checkpointer_test",
"validate",
]

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