mirror of
https://github.com/hwchase17/langchain.git
synced 2026-02-18 04:25:22 +00:00
Compare commits
1 Commits
langchain-
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|---|---|---|---|
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|
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4
.github/CODEOWNERS
vendored
4
.github/CODEOWNERS
vendored
@@ -1,2 +1,2 @@
|
||||
/.github/ @baskaryan @ccurme
|
||||
/libs/packages.yml @ccurme
|
||||
/.github/ @efriis @baskaryan @ccurme
|
||||
/libs/packages.yml @efriis
|
||||
|
||||
2
.github/PULL_REQUEST_TEMPLATE.md
vendored
2
.github/PULL_REQUEST_TEMPLATE.md
vendored
@@ -26,4 +26,4 @@ Additional guidelines:
|
||||
- Changes should be backwards compatible.
|
||||
- If you are adding something to community, do not re-import it in langchain.
|
||||
|
||||
If no one reviews your PR within a few days, please @-mention one of baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
|
||||
If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
|
||||
|
||||
1
.github/workflows/_integration_test.yml
vendored
1
.github/workflows/_integration_test.yml
vendored
@@ -6,7 +6,6 @@ on:
|
||||
working-directory:
|
||||
required: true
|
||||
type: string
|
||||
description: "From which folder this pipeline executes"
|
||||
python-version:
|
||||
required: true
|
||||
type: string
|
||||
|
||||
111
.github/workflows/_release.yml
vendored
111
.github/workflows/_release.yml
vendored
@@ -12,7 +12,6 @@ on:
|
||||
working-directory:
|
||||
required: true
|
||||
type: string
|
||||
description: "From which folder this pipeline executes"
|
||||
default: 'libs/langchain'
|
||||
dangerous-nonmaster-release:
|
||||
required: false
|
||||
@@ -101,32 +100,15 @@ jobs:
|
||||
PKG_NAME: ${{ needs.build.outputs.pkg-name }}
|
||||
VERSION: ${{ needs.build.outputs.version }}
|
||||
run: |
|
||||
# Handle regular versions and pre-release versions differently
|
||||
if [[ "$VERSION" == *"-"* ]]; then
|
||||
# This is a pre-release version (contains a hyphen)
|
||||
# Extract the base version without the pre-release suffix
|
||||
BASE_VERSION=${VERSION%%-*}
|
||||
# Look for the latest release of the same base version
|
||||
REGEX="^$PKG_NAME==$BASE_VERSION\$"
|
||||
PREV_TAG=$(git tag --sort=-creatordate | (grep -P "$REGEX" || true) | head -1)
|
||||
|
||||
# If no exact base version match, look for the latest release of any kind
|
||||
if [ -z "$PREV_TAG" ]; then
|
||||
REGEX="^$PKG_NAME==\\d+\\.\\d+\\.\\d+\$"
|
||||
PREV_TAG=$(git tag --sort=-creatordate | (grep -P "$REGEX" || true) | head -1)
|
||||
fi
|
||||
else
|
||||
# Regular version handling
|
||||
PREV_TAG="$PKG_NAME==${VERSION%.*}.$(( ${VERSION##*.} - 1 ))"; [[ "${VERSION##*.}" -eq 0 ]] && PREV_TAG=""
|
||||
PREV_TAG="$PKG_NAME==${VERSION%.*}.$(( ${VERSION##*.} - 1 ))"; [[ "${VERSION##*.}" -eq 0 ]] && PREV_TAG=""
|
||||
|
||||
# backup case if releasing e.g. 0.3.0, looks up last release
|
||||
# note if last release (chronologically) was e.g. 0.1.47 it will get
|
||||
# that instead of the last 0.2 release
|
||||
if [ -z "$PREV_TAG" ]; then
|
||||
REGEX="^$PKG_NAME==\\d+\\.\\d+\\.\\d+\$"
|
||||
echo $REGEX
|
||||
PREV_TAG=$(git tag --sort=-creatordate | (grep -P $REGEX || true) | head -1)
|
||||
fi
|
||||
# backup case if releasing e.g. 0.3.0, looks up last release
|
||||
# note if last release (chronologically) was e.g. 0.1.47 it will get
|
||||
# that instead of the last 0.2 release
|
||||
if [ -z "$PREV_TAG" ]; then
|
||||
REGEX="^$PKG_NAME==\\d+\\.\\d+\\.\\d+\$"
|
||||
echo $REGEX
|
||||
PREV_TAG=$(git tag --sort=-creatordate | (grep -P $REGEX || true) | head -1)
|
||||
fi
|
||||
|
||||
# if PREV_TAG is empty, let it be empty
|
||||
@@ -330,89 +312,12 @@ jobs:
|
||||
run: make integration_tests
|
||||
working-directory: ${{ inputs.working-directory }}
|
||||
|
||||
# Test select published packages against new core
|
||||
test-prior-published-packages-against-new-core:
|
||||
needs:
|
||||
- build
|
||||
- release-notes
|
||||
- test-pypi-publish
|
||||
- pre-release-checks
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
partner: [openai, anthropic]
|
||||
fail-fast: false # Continue testing other partners if one fails
|
||||
env:
|
||||
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
|
||||
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
|
||||
AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
|
||||
AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
|
||||
AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
|
||||
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
|
||||
AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME }}
|
||||
AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
|
||||
AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
|
||||
# We implement this conditional as Github Actions does not have good support
|
||||
# for conditionally needing steps. https://github.com/actions/runner/issues/491
|
||||
- name: Check if libs/core
|
||||
run: |
|
||||
if [ "${{ startsWith(inputs.working-directory, 'libs/core') }}" != "true" ]; then
|
||||
echo "Not in libs/core. Exiting successfully."
|
||||
exit 0
|
||||
fi
|
||||
|
||||
- name: Set up Python + uv
|
||||
if: startsWith(inputs.working-directory, 'libs/core')
|
||||
uses: "./.github/actions/uv_setup"
|
||||
with:
|
||||
python-version: ${{ env.PYTHON_VERSION }}
|
||||
|
||||
- uses: actions/download-artifact@v4
|
||||
if: startsWith(inputs.working-directory, 'libs/core')
|
||||
with:
|
||||
name: dist
|
||||
path: ${{ inputs.working-directory }}/dist/
|
||||
|
||||
- name: Test against ${{ matrix.partner }}
|
||||
if: startsWith(inputs.working-directory, 'libs/core')
|
||||
run: |
|
||||
# Identify latest tag
|
||||
LATEST_PACKAGE_TAG="$(
|
||||
git ls-remote --tags origin "langchain-${{ matrix.partner }}*" \
|
||||
| awk '{print $2}' \
|
||||
| sed 's|refs/tags/||' \
|
||||
| sort -Vr \
|
||||
| head -n 1
|
||||
)"
|
||||
echo "Latest package tag: $LATEST_PACKAGE_TAG"
|
||||
|
||||
# Shallow-fetch just that single tag
|
||||
git fetch --depth=1 origin tag "$LATEST_PACKAGE_TAG"
|
||||
|
||||
# Checkout the latest package files
|
||||
rm -rf $GITHUB_WORKSPACE/libs/partners/${{ matrix.partner }}/*
|
||||
cd $GITHUB_WORKSPACE/libs/partners/${{ matrix.partner }}
|
||||
git checkout "$LATEST_PACKAGE_TAG" -- .
|
||||
|
||||
# Print as a sanity check
|
||||
echo "Version number from pyproject.toml: "
|
||||
cat pyproject.toml | grep "version = "
|
||||
|
||||
# Run tests
|
||||
uv sync --group test --group test_integration
|
||||
uv pip install ../../core/dist/*.whl
|
||||
make integration_tests
|
||||
|
||||
publish:
|
||||
needs:
|
||||
- build
|
||||
- release-notes
|
||||
- test-pypi-publish
|
||||
- pre-release-checks
|
||||
- test-prior-published-packages-against-new-core
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
# This permission is used for trusted publishing:
|
||||
|
||||
1
.github/workflows/check_diffs.yml
vendored
1
.github/workflows/check_diffs.yml
vendored
@@ -1,3 +1,4 @@
|
||||
---
|
||||
name: CI
|
||||
|
||||
on:
|
||||
|
||||
1
.github/workflows/check_new_docs.yml
vendored
1
.github/workflows/check_new_docs.yml
vendored
@@ -1,3 +1,4 @@
|
||||
---
|
||||
name: Integration docs lint
|
||||
|
||||
on:
|
||||
|
||||
1
.github/workflows/codespell.yml
vendored
1
.github/workflows/codespell.yml
vendored
@@ -1,3 +1,4 @@
|
||||
---
|
||||
name: CI / cd . / make spell_check
|
||||
|
||||
on:
|
||||
|
||||
@@ -1,7 +1,12 @@
|
||||
# Read the Docs configuration file
|
||||
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
|
||||
|
||||
# Required
|
||||
version: 2
|
||||
|
||||
formats:
|
||||
- pdf
|
||||
|
||||
# Set the version of Python and other tools you might need
|
||||
build:
|
||||
os: ubuntu-22.04
|
||||
@@ -10,16 +15,15 @@ build:
|
||||
commands:
|
||||
- mkdir -p $READTHEDOCS_OUTPUT
|
||||
- cp -r api_reference_build/* $READTHEDOCS_OUTPUT
|
||||
|
||||
# Build documentation in the docs/ directory with Sphinx
|
||||
sphinx:
|
||||
configuration: docs/api_reference/conf.py
|
||||
|
||||
# If using Sphinx, optionally build your docs in additional formats such as PDF
|
||||
formats:
|
||||
- pdf
|
||||
# formats:
|
||||
# - pdf
|
||||
|
||||
# Optionally declare the Python requirements required to build your docs
|
||||
python:
|
||||
install:
|
||||
- requirements: docs/api_reference/requirements.txt
|
||||
- requirements: docs/api_reference/requirements.txt
|
||||
|
||||
1
Makefile
1
Makefile
@@ -2,6 +2,7 @@
|
||||
|
||||
.EXPORT_ALL_VARIABLES:
|
||||
UV_FROZEN = true
|
||||
UV_NO_SYNC = true
|
||||
|
||||
## help: Show this help info.
|
||||
help: Makefile
|
||||
|
||||
176
README.md
176
README.md
@@ -1,12 +1,6 @@
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: light)" srcset="docs/static/img/logo-dark.svg">
|
||||
<source media="(prefers-color-scheme: dark)" srcset="docs/static/img/logo-light.svg">
|
||||
<img alt="LangChain Logo" src="docs/static/img/logo-dark.svg" width="80%">
|
||||
</picture>
|
||||
# 🦜️🔗 LangChain
|
||||
|
||||
<div>
|
||||
<br>
|
||||
</div>
|
||||
⚡ Build context-aware reasoning applications ⚡
|
||||
|
||||
[](https://github.com/langchain-ai/langchain/releases)
|
||||
[](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml)
|
||||
@@ -18,65 +12,131 @@
|
||||
[](https://codespaces.new/langchain-ai/langchain)
|
||||
[](https://twitter.com/langchainai)
|
||||
|
||||
> [!NOTE]
|
||||
> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
|
||||
Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
|
||||
|
||||
LangChain is a framework for building LLM-powered applications. It helps you chain
|
||||
together interoperable components and third-party integrations to simplify AI
|
||||
application development — all while future-proofing decisions as the underlying
|
||||
technology evolves.
|
||||
To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
|
||||
[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.
|
||||
Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team.
|
||||
|
||||
## Quick Install
|
||||
|
||||
With pip:
|
||||
|
||||
```bash
|
||||
pip install -U langchain
|
||||
pip install langchain
|
||||
```
|
||||
|
||||
To learn more about LangChain, check out
|
||||
[the docs](https://python.langchain.com/docs/introduction/). If you’re looking for more
|
||||
advanced customization or agent orchestration, check out
|
||||
[LangGraph](https://langchain-ai.github.io/langgraph/), our framework for building
|
||||
controllable agent workflows.
|
||||
With conda:
|
||||
|
||||
## Why use LangChain?
|
||||
```bash
|
||||
conda install langchain -c conda-forge
|
||||
```
|
||||
|
||||
LangChain helps developers build applications powered by LLMs through a standard
|
||||
interface for models, embeddings, vector stores, and more.
|
||||
## 🤔 What is LangChain?
|
||||
|
||||
Use LangChain for:
|
||||
- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and
|
||||
external / internal systems, drawing from LangChain’s vast library of integrations with
|
||||
model providers, tools, vector stores, retrievers, and more.
|
||||
- **Model interoperability**. Swap models in and out as your engineering team
|
||||
experiments to find the best choice for your application’s needs. As the industry
|
||||
frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without
|
||||
losing momentum.
|
||||
**LangChain** is a framework for developing applications powered by large language models (LLMs).
|
||||
|
||||
## LangChain’s ecosystem
|
||||
While the LangChain framework can be used standalone, it also integrates seamlessly
|
||||
with any LangChain product, giving developers a full suite of tools when building LLM
|
||||
applications.
|
||||
For these applications, LangChain simplifies the entire application lifecycle:
|
||||
|
||||
To improve your LLM application development, pair LangChain with:
|
||||
|
||||
- [LangSmith](http://www.langchain.com/langsmith) - Helpful for agent evals and
|
||||
observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain
|
||||
visibility in production, and improve performance over time.
|
||||
- [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can
|
||||
reliably handle complex tasks with LangGraph, our low-level agent orchestration
|
||||
framework. LangGraph offers customizable architecture, long-term memory, and
|
||||
human-in-the-loop workflows — and is trusted in production by companies like LinkedIn,
|
||||
Uber, Klarna, and GitLab.
|
||||
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) - Deploy
|
||||
and scale agents effortlessly with a purpose-built deployment platform for long
|
||||
running, stateful workflows. Discover, reuse, configure, and share agents across
|
||||
teams — and iterate quickly with visual prototyping in
|
||||
[LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
|
||||
- **Open-source libraries**: Build your applications using LangChain's open-source
|
||||
[components](https://python.langchain.com/docs/concepts/) and
|
||||
[third-party integrations](https://python.langchain.com/docs/integrations/providers/).
|
||||
Use [LangGraph](https://langchain-ai.github.io/langgraph/) to build stateful agents with first-class streaming and human-in-the-loop support.
|
||||
- **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
|
||||
- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/).
|
||||
|
||||
## Additional resources
|
||||
- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with
|
||||
guided examples on getting started with LangChain.
|
||||
- [How-to Guides](https://python.langchain.com/docs/how_to/): Quick, actionable code
|
||||
snippets for topics such as tool calling, RAG use cases, and more.
|
||||
- [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key
|
||||
concepts behind the LangChain framework.
|
||||
- [API Reference](https://python.langchain.com/api_reference/): Detailed reference on
|
||||
navigating base packages and integrations for LangChain.
|
||||
### Open-source libraries
|
||||
|
||||
- **`langchain-core`**: Base abstractions.
|
||||
- **Integration packages** (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
|
||||
- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
|
||||
- **`langchain-community`**: Third-party integrations that are community maintained.
|
||||
- **[LangGraph](https://langchain-ai.github.io/langgraph)**: LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more. Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it. To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).
|
||||
|
||||
### Productionization:
|
||||
|
||||
- **[LangSmith](https://docs.smith.langchain.com/)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
|
||||
|
||||
### Deployment:
|
||||
|
||||
- **[LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/)**: Turn your LangGraph applications into production-ready APIs and Assistants.
|
||||
|
||||

|
||||

|
||||
|
||||
## 🧱 What can you build with LangChain?
|
||||
|
||||
**❓ Question answering with RAG**
|
||||
|
||||
- [Documentation](https://python.langchain.com/docs/tutorials/rag/)
|
||||
- End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
|
||||
|
||||
**🧱 Extracting structured output**
|
||||
|
||||
- [Documentation](https://python.langchain.com/docs/tutorials/extraction/)
|
||||
- End-to-end Example: [LangChain Extract](https://github.com/langchain-ai/langchain-extract/)
|
||||
|
||||
**🤖 Chatbots**
|
||||
|
||||
- [Documentation](https://python.langchain.com/docs/tutorials/chatbot/)
|
||||
- End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
|
||||
|
||||
And much more! Head to the [Tutorials](https://python.langchain.com/docs/tutorials/) section of the docs for more.
|
||||
|
||||
## 🚀 How does LangChain help?
|
||||
|
||||
The main value props of the LangChain libraries are:
|
||||
|
||||
1. **Components**: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not.
|
||||
2. **Easy orchestration with LangGraph**: [LangGraph](https://langchain-ai.github.io/langgraph/),
|
||||
built on top of `langchain-core`, has built-in support for [messages](https://python.langchain.com/docs/concepts/messages/), [tools](https://python.langchain.com/docs/concepts/tools/),
|
||||
and other LangChain abstractions. This makes it easy to combine components into
|
||||
production-ready applications with persistence, streaming, and other key features.
|
||||
Check out the LangChain [tutorials page](https://python.langchain.com/docs/tutorials/#orchestration) for examples.
|
||||
|
||||
## Components
|
||||
|
||||
Components fall into the following **modules**:
|
||||
|
||||
**📃 Model I/O**
|
||||
|
||||
This includes [prompt management](https://python.langchain.com/docs/concepts/prompt_templates/)
|
||||
and a generic interface for [chat models](https://python.langchain.com/docs/concepts/chat_models/), including a consistent interface for [tool-calling](https://python.langchain.com/docs/concepts/tool_calling/) and [structured output](https://python.langchain.com/docs/concepts/structured_outputs/) across model providers.
|
||||
|
||||
**📚 Retrieval**
|
||||
|
||||
Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/concepts/document_loaders/) from a variety of sources, [preparing it](https://python.langchain.com/docs/concepts/text_splitters/), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/docs/concepts/retrievers/) it for use in the generation step.
|
||||
|
||||
**🤖 Agents**
|
||||
|
||||
Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. [LangGraph](https://langchain-ai.github.io/langgraph/) makes it easy to use
|
||||
LangChain components to build both [custom](https://langchain-ai.github.io/langgraph/tutorials/)
|
||||
and [built-in](https://langchain-ai.github.io/langgraph/how-tos/create-react-agent/)
|
||||
LLM agents.
|
||||
|
||||
## 📖 Documentation
|
||||
|
||||
Please see [here](https://python.langchain.com) for full documentation, which includes:
|
||||
|
||||
- [Introduction](https://python.langchain.com/docs/introduction/): Overview of the framework and the structure of the docs.
|
||||
- [Tutorials](https://python.langchain.com/docs/tutorials/): If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
|
||||
- [How-to guides](https://python.langchain.com/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
|
||||
- [Conceptual guide](https://python.langchain.com/docs/concepts/): Conceptual explanations of the key parts of the framework.
|
||||
- [API Reference](https://python.langchain.com/api_reference/): Thorough documentation of every class and method.
|
||||
|
||||
## 🌐 Ecosystem
|
||||
|
||||
- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
|
||||
- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
|
||||
- [🦜🕸️ LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): Deploy LLM applications built with LangGraph into production.
|
||||
|
||||
## 💁 Contributing
|
||||
|
||||
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
|
||||
|
||||
For detailed information on how to contribute, see [here](https://python.langchain.com/docs/contributing/).
|
||||
|
||||
## 🌟 Contributors
|
||||
|
||||
[](https://github.com/langchain-ai/langchain/graphs/contributors)
|
||||
|
||||
@@ -60,7 +60,7 @@
|
||||
"id": "CI8Elyc5gBQF"
|
||||
},
|
||||
"source": [
|
||||
"Go to the VertexAI Model Garden on Google Cloud [console](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/335), and deploy the desired version of Gemma to VertexAI. It will take a few minutes, and after the endpoint is ready, you need to copy its number."
|
||||
"Go to the VertexAI Model Garden on Google Cloud [console](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/335), and deploy the desired version of Gemma to VertexAI. It will take a few minutes, and after the endpoint it ready, you need to copy its number."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -115,7 +115,7 @@
|
||||
"\n",
|
||||
"PROMPT_TEMPLATE = \"\"\"Given an input question, create a syntactically correct Elasticsearch query to run. Unless the user specifies in their question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n",
|
||||
"\n",
|
||||
"Unless told to do not query for all the columns from a specific index, only ask for a few relevant columns given the question.\n",
|
||||
"Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question.\n",
|
||||
"\n",
|
||||
"Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json.\n",
|
||||
"\n",
|
||||
|
||||
@@ -233,7 +233,7 @@ Question: {input}"""
|
||||
|
||||
_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
|
||||
|
||||
Never query for all the columns from a specific table, only ask for a few relevant columns given the question.
|
||||
Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
|
||||
|
||||
Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
|
||||
|
||||
@@ -328,7 +328,7 @@ html[data-theme=dark] .MathJax_SVG * {
|
||||
}
|
||||
|
||||
.bd-sidebar-primary {
|
||||
width: max-content; /* Adjust this value to your preference */
|
||||
width: 22%; /* Adjust this value to your preference */
|
||||
line-height: 1.4;
|
||||
}
|
||||
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
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
|
||||
@@ -1 +0,0 @@
|
||||
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
|
||||
@@ -1 +1 @@
|
||||
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
|
||||
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@@ -1 +1 @@
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@@ -1 +1 @@
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|
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@@ -1 +1 @@
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|
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@@ -1 +1 @@
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|
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1
docs/cassettes/sql_qa_10.msgpack.zlib
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docs/cassettes/sql_qa_10.msgpack.zlib
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docs/cassettes/sql_qa_32.msgpack.zlib
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Reference in New Issue
Block a user