- **Description:**
- Change the docs flag for v0.3 branch to list Multi-tenancy as a
MongoDBAtlasVectorStore supported feature
- **Issue:** N/A
- **Dependencies:** None
- [x] **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) for
more.
Additional guidelines:
- Most PRs should not touch more than one package.
- Please do not add dependencies to `pyproject.toml` files (even
optional ones) unless they are **required** for unit tests. Likewise,
please do not update the `uv.lock` files unless you are adding a
required dependency.
- Changes should be backwards compatible.
- Make sure optional dependencies are imported within a function.
Mostly adding a descriptive frontmatter to workflow files. Also address
some formatting and outdated artifacts
No functional changes outside of
[d5457c3](d5457c39ee),
[90708a0](90708a0d99),
and
[338c82d](338c82d21e)
- Removes Codespell from deps, docs, and `Makefile`s
- Python version requirements in all `pyproject.toml` files now use the
`~=` (compatible release) specifier
- All dependency groups and main dependencies now use explicit lower and
upper bounds, reducing potential for breaking changes
- **Description:** Updated the dead/unreachable links to Docling from
the additional resources section of the langchain-docling docs
- **Issue:** Fixes langchain-ai/docs/issues/574
- **Dependencies:** None
Adds documentation for the integration langchain-scraperapi, which
contains 3 tools using the ScraperAPI service.
The tools give AI agents the ability to
Scrape the web and return HTML/text/markdown
Perform Google search and return json output
Perform Amazon search and return json output
For reference, here is the official repo for langchain_scraperapi:
https://github.com/scraperapi/langchain-scraperapi
Replaced `input_message` parameter with a directly called tuple, e.g.
`{"messages": [("user", "What is my name?")]}`
Before, the memory function wasn't working with the agent, using the
format of the input_message parameter.
Specifically, on page [Build an
Agent#adding-in-memory](https://python.langchain.com/docs/tutorials/agents/#adding-in-memory)
In the previous code, the query "What's my name?" wasn't working, as the
agent could not recall memory correctly.
<img width="860" height="679" alt="image"
src="https://github.com/user-attachments/assets/dfbca21e-ffe9-4645-a810-3be7a46d81d5"
/>
This PR improves navigation in the summarization how-to section by
adding
cross-links from the single-call guide to the related map-reduce and
refine
guides. This mirrors the docs style guide’s emphasis on clear
cross-references
and should help readers discover the appropriate pattern for longer
texts.
- Source edited: docs/docs/how_to/summarize_stuff.ipynb
- Links added:
- /docs/how_to/summarize_map_reduce/
- /docs/how_to/summarize_refine/
Type: docs-only (no code changes)
## Description
This PR adds documentation for the new ZeusDB vector store integration
with LangChain.
## Motivation
ZeusDB is a high-performance vector database (Python/Rust backend)
designed for AI applications that need fast similarity search and
real-time vector ops. This integration brings ZeusDB's capabilities to
the LangChain ecosystem, giving developers another production-oriented
option for vector storage and retrieval.
**Key Features:**
- **User-Friendly Python API**: Intuitive interface that integrates
seamlessly with Python ML workflows
- **High Performance**: Powered by a robust Rust backend for
lightning-fast vector operations
- **Enterprise Logging**: Comprehensive logging capabilities for
monitoring and debugging production systems
- **Advanced Features**: Includes product quantization and persistence
capabilities
- **AI-Optimized**: Purpose-built for modern AI applications and RAG
pipelines
## Changes
- Added provider documentation:
`docs/docs/integrations/providers/zeusdb.mdx` (installation, setup).
- Added vector store documentation:
`docs/docs/integrations/vectorstores/zeusdb.ipynb` (quickstart for
creating/querying a ZeusDBVectorStore).
- Registered langchain-zeusdb in `libs/packages.yml` for discovery.
## Target users
- AI/ML engineers building RAG pipelines
- Data scientists working with large document collections
- Developers needing high-throughput vector search
- Teams requiring near real-time vector operations
## Testing
- Followed LangChain's "How to add standard tests to an integration"
guidance.
- Code passes format, lint, and test checks locally.
- Tested with LangChain Core 0.3.74
- Works with Python 3.10 to 3.13
## Package Information
**PyPI:** https://pypi.org/project/langchain-zeusdb
**Github:** https://github.com/ZeusDB/langchain-zeusdb
**Description:**
Introduces documentation notebooks for AI/ML API integration covering
the following use cases:
- Chat models (`ChatAimlapi`)
- Text completion models (`AimlapiLLM`)
- Provider usage examples
- Text embedding models (`AimlapiEmbeddings`)
Additionally, adds the `langchain-aimlapi` package entry to
`libs/packages.yml` for package management.
This PR aims to provide a comprehensive starting point for developers
integrating AI/ML API models with LangChain via the new
`langchain-aimlapi` package.
**Issue:** N/A
**Dependencies:** None
**Twitter handle:** @aimlapi
---
### **To-Do Before Submitting PR:**
* [x] Run `make format`
* [x] Run `make lint`
* [x] Confirm all documentation notebooks are in
`docs/docs/integrations/`
* [x] Double-check `libs/packages.yml` has the correct repo path
* [x] Confirm no `pyproject.toml` modifications were made unnecessarily
Co-authored-by: Mason Daugherty <mason@langchain.dev>
**Description:**
This PR updates the free searches per month from **100** to **250** and
renames SerpAPI to [SerpApi](https://serpapi.com/) to prevent confusion.
Add import API keys and enhance usage instructions in the Jupyter
notebook
**Issue:** N/A
**Dependencies:** N/A
- [x] **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://python.langchain.com/docs/contributing/) for more.
**Description:**
This PR updated links to the latest Anthropic documentation. Changes
include revised links for model overview, tool usage, web search tool,
text editor tool, and more.
**Issue:**
N/A
**Dependencies:**
None
**Twitter handle:**
N/A
- **Description:** The `langchain-yugabytedb` package implementations of
core LangChain abstractions using `YugabyteDB` Distributed SQL Database.
YugabyteDB is a cloud-native distributed PostgreSQL-compatible database
that combines strong consistency with ultra-resilience, seamless
scalability, geo-distribution, and highly flexible data locality to
deliver business-critical, transactional applications.
[YugabyteDB](https://www.yugabyte.com/ai/) combines the power of the
`pgvector` PostgreSQL extension with an inherently distributed
architecture. This future-proofed foundation helps you build GenAI
applications using RAG retrieval that demands high-performance vector
search.
- [ ] **tests and docs**:
1. `langchain-yugabytedb`
[github](https://github.com/yugabyte/langchain-yugabytedb) repo.
2. YugabyteDB VectorStore example notebook showing its use. It lives in
`langchain/docs/docs/integrations/vectorstores/yugabytedb.ipynb`
directory.
3. Running `langchain-yugabytedb` unit tests
- Setting up a Development Environment
This document details how to set up a local development environment that
will
allow you to contribute changes to the project.
Acquire sources and create virtualenv.
```shell
git clone https://github.com/yugabyte/langchain-yugabytedb
cd langchain-yugabytedb
uv venv --python=3.13
source .venv/bin/activate
```
Install package in editable mode.
```shell
uv pip install pipx
pipx install poetry
poetry install
uv pip install pytest pytest_asyncio pytest-timeout langchain-core langchain_tests sqlalchemy psycopg psycopg-binary numpy pgvector
```
Start YugabyteDB RF-1 Universe.
```shell
docker run -d --name yugabyte_node01 --hostname yugabyte01 \
-p 7000:7000 -p 9000:9000 -p 15433:15433 -p 5433:5433 -p 9042:9042 \
yugabytedb/yugabyte:2.25.2.0-b359 bin/yugabyted start --background=false \
--master_flags="allowed_preview_flags_csv=ysql_yb_enable_advisory_locks,ysql_yb_enable_advisory_locks=true" \
--tserver_flags="allowed_preview_flags_csv=ysql_yb_enable_advisory_locks,ysql_yb_enable_advisory_locks=true"
docker exec -it yugabyte_node01 bin/ysqlsh -h yugabyte01 -c "CREATE extension vector;"
```
Invoke test cases.
```shell
pytest -vvv tests/unit_tests/yugabytedb_tests
```
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [x] **feat(docs)**: add Bigtable Key-value store doc
- [X] **feat(docs)**: add Bigtable Vector store doc
This PR adds a doc for Bigtable and LangChain Key-value store
integration. It contains guides on how to add, delete, get, and yield
key-value pairs from Bigtable Key-value Store for LangChain.
- [x] **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://python.langchain.com/docs/contributing/) for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even
optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
# feat(integrations): Add Timbr tools integration
## DESCRIPTION
This PR adds comprehensive documentation and integration support for
Timbr's semantic layer tools in LangChain.
[Timbr](https://timbr.ai/) provides an ontology-driven semantic layer
that enables natural language querying of databases through
business-friendly concepts. It connects raw data to governed business
measures for consistent access across BI, APIs, and AI applications.
[`langchain-timbr`](https://pypi.org/project/langchain-timbr/) is a
Python SDK that extends
[LangChain](https://github.com/WPSemantix/Timbr-GenAI/tree/main/LangChain)
and
[LangGraph](https://github.com/WPSemantix/Timbr-GenAI/tree/main/LangGraph)
with custom agents, chains, and nodes for seamless integration with the
Timbr semantic layer. It enables converting natural language prompts
into optimized semantic-SQL queries and executing them directly against
your data.
**What's Added:**
- Complete integration documentation for `langchain-timbr` package
- Tool documentation page with usage examples and API reference
**Integration Components:**
- `IdentifyTimbrConceptChain` - Identify relevant concepts from user
prompts
- `GenerateTimbrSqlChain` - Generate SQL queries from natural language
- `ValidateTimbrSqlChain` - Validate queries against knowledge graph
schemas
- `ExecuteTimbrQueryChain` - Execute queries against semantic databases
- `GenerateAnswerChain` - Generate human-readable answers from results
## Documentation Added
- `/docs/integrations/providers/timbr.mdx` - Provider overview and
configuration
- `/docs/integrations/tools/timbr.ipynb` - Comprehensive tool usage
examples
## Links
- [PyPI Package](https://pypi.org/project/langchain-timbr/)
- [GitHub Repository](https://github.com/WPSemantix/langchain-timbr)
- [Official
Documentation](https://docs.timbr.ai/doc/docs/integration/langchain-sdk/)
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
**Description:**
Add documentation for Qwen integration in LangChain, including setup
instructions, usage examples, and configuration details. Update related
qwq documentation to reflect current best practices and improve clarity
for users.
This PR enhances the documentation ecosystem by:
- Adding a new guide for integrating Qwen models
- Updating outdated or incomplete qwq documentation
- Improving structure and readability of relevant sections
**Issue:** N/A
**Dependencies:** None
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
