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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.