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A RAG API with all the data, tools, and an opinionated stack that just works. Both vector and structured data, secure, compliant, scalable, and supported. Integrated with LangChain, Vercel, GitHub Copilot and AI ecosystem leaders.
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An intuitive API and powerful integrations for production-level RAG and FLARE.
Install the Astra library
pip install astrapy
Create or connect to existing collection
# The return of create_collection() will return the collection collection = astra_db.create_collection( collection_name="collection_test", dimension=5 ) # Or you can connect to an existing connection directly collection = AstraDBCollection( collection_name="collection_test", astra_db=astra_db ) # You don't even need the astra_db object collection = AstraDBCollection( collection_name="collection_test", token=token, api_endpoint=api_endpoint )
Inserting a vector object into your vector store (collection)
collection.insert_one( { "_id": "5", "name": "Coded Cleats Copy", "description": "ChatGPT integrated sneakers that talk to you", "$vector": [0.25, 0.25, 0.25, 0.25, 0.25], } )
Find documents using vector search
documents = collection.vector_find( [0.15, 0.1, 0.1, 0.35, 0.55], limit=100, )
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