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# Astra DB Vector store integration - **Description:** This PR adds a `VectorStore` implementation for DataStax Astra DB using its HTTP API - **Issue:** (no related issue) - **Dependencies:** A new required dependency is `astrapy` (`>=0.5.3`) which was added to pyptoject.toml, optional, as per guidelines - **Tag maintainer:** I recently mentioned to @baskaryan this integration was coming - **Twitter handle:** `@rsprrs` if you want to mention me This PR introduces the `AstraDB` vector store class, extensive integration test coverage, a reworking of the documentation which conflates Cassandra and Astra DB on a single "provider" page and a new, completely reworked vector-store example notebook (common to the Cassandra store, since parts of the flow is shared by the two APIs). I also took care in ensuring docs (and redirects therein) are behaving correctly. All style, linting, typechecks and tests pass as far as the `AstraDB` integration is concerned. I could build the documentation and check it all right (but ran into trouble with the `api_docs_build` makefile target which I could not verify: `Error: Unable to import module 'plan_and_execute.agent_executor' with error: No module named 'langchain_experimental'` was the first of many similar errors) Thank you for a review! Stefano --------- Co-authored-by: Erick Friis <erick@langchain.dev>
79 lines
2.3 KiB
Markdown
79 lines
2.3 KiB
Markdown
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# rag-astradb
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This template will perform RAG using Astra DB (`AstraDB` vector store class)
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## Environment Setup
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An [Astra DB](https://astra.datastax.com) database is required; free tier is fine.
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- You need the database **API endpoint** (such as `https://0123...-us-east1.apps.astra.datastax.com`) ...
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- ... and a **token** (`AstraCS:...`).
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Also, an **OpenAI API Key** is required. _Note that out-of-the-box this demo supports OpenAI only, unless you tinker with the code._
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Provide the connection parameters and secrets through environment variables. Please refer to `.env.template` for the variable names.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U "langchain-cli[serve]"
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package rag-astradb
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add rag-astradb
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```
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And add the following code to your `server.py` file:
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```python
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from astradb_entomology_rag import chain as astradb_entomology_rag_chain
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add_routes(app, astradb_entomology_rag_chain, path="/rag-astradb")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/rag-astradb/playground](http://127.0.0.1:8000/rag-astradb/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-astradb")
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```
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## Reference
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Stand-alone repo with LangServe chain: [here](https://github.com/hemidactylus/langserve_astradb_entomology_rag).
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