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This pull request adds documentation and a tutorial for integrating the [Vectorize](https://vectorize.io/) service with LangChain. The most important changes include adding a new documentation page for Vectorize and creating a Jupyter notebook that demonstrates how to use the Vectorize retriever. The source code for the langchain-vectorize package can be found [here](https://github.com/vectorize-io/integrations-python/tree/main/langchain). Previews: * https://langchain-git-fork-cbornet-vectorize-langchain.vercel.app/docs/integrations/providers/vectorize/ * https://langchain-git-fork-cbornet-vectorize-langchain.vercel.app/docs/integrations/retrievers/vectorize/ Documentation updates: * [`docs/docs/integrations/providers/vectorize.mdx`](diffhunk://#diff-7e00d4ce4768f73b4d381a7c7b1f94d138f1b27ebd08e3666b942630a0285606R1-R40): Added a new documentation page for Vectorize, including an overview of its features, installation instructions, and a basic usage example. Tutorial updates: * [`docs/docs/integrations/retrievers/vectorize.ipynb`](diffhunk://#diff-ba5bb9a1b4586db7740944b001bcfeadc88be357640ded0c82a329b11d8d6e29R1-R294): Created a Jupyter notebook tutorial that shows how to set up the Vectorize environment, create a RAG pipeline, and use the LangChain Vectorize retriever. The notebook includes steps for account creation, token generation, environment setup, and pipeline deployment.
LangChain Documentation
For more information on contributing to our documentation, see the Documentation Contributing Guide