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Implement Qdrant vector store

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- Implement QdrantVectorStore.
   Uses a custom parser for converting Spring AI metadata(Map<String, Object>) to Qdrant GRPC payload.
 - Implement Qdrant Expression Filter support.
   Uses a custom parser for converting Spring AI filters to Qdrant-compatible GRPC filters.
 - Add ITs using testcontainers.
 - Add antora docs adrant.adoc.
 - Add Qdrant vector store auto-configuraton and boot starter.

Additional (review) change:

 - Fix poms parent to 0.8.1-SNAPSHOT.
 - Rename ObjectFactory into QdrantObjectFactor.
 - Rename ValueFactory into QdrantValueFactory.
 - Move the org.springframework.ai.vectorstore package into org.springframework.ai.vectorstore.qdrant.
 - Add missing Autoconfigure definition.
 - Add missing license and JavaDocs.
 - Minor code style improvmentes.
 - Move the qdrant version to the main pom
 - Add QdrantVectorStoreAutoConfigurationIT
 - Remove guava dependency
 - Improve gdrant.adoc conent and structure.
 - Remove the grpc-protobuf dependency

Resolves #331
This commit is contained in:
Anush008
2024-02-22 10:30:04 +05:30
committed by Christian Tzolov
parent e1462b86e3
commit ea0b439dac
21 changed files with 1686 additions and 3 deletions
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README.md
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@@ -162,7 +162,7 @@ Though the `DocumentWriter` interface isn't exclusively for Vector Database writ
**Vector Stores:** Vector Databases are instrumental in incorporating your data with AI models.
They ascertain which document sections the AI should use for generating responses.
Examples of Vector Databases include Chroma, Postgres, Pinecone, Weaviate, Mongo Atlas, and Redis. Spring AI's `VectorStore` abstraction permits effortless transitions between database implementations.
Examples of Vector Databases include Chroma, Postgres, Pinecone, Qdrant, Weaviate, Mongo Atlas, and Redis. Spring AI's `VectorStore` abstraction permits effortless transitions between database implementations.