Add observability support for the existing Vector Stores #1244
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Add observability support to: - Cassandra - Chroma - Elasticsearch - Milvus - Neo4j - OpenSearch - Qdrant - Redis - Typesense - Weaviate - Pinecone - Oracle - Gemifire - MongoDB - HanaDB Add autoconfiguration obsrvability for the above vector stores. Add integration tests for all vector stores.
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| return VectorStoreObservationContext.builder(VectorStoreProvider.AZURE_VECTOR_STORE.value(), operationName) | ||
| .withDimensions(this.embeddingModel.dimensions()) | ||
| .withSimilarityMetric(this.initializeSchema ? VectorStoreSimilarityMetric.COSINE.value() : "Unknown") |
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Should it be null instead of "Unknown", so that the observation value will end up being KeyValue.NONE? For other attributes, when we can't infer the value, we fall back to "none".
| return VectorStoreObservationContext.builder(VectorStoreProvider.CHROMA_VECTOR_STORE.value(), operationName) | ||
| .withDimensions(this.embeddingModel.dimensions()) | ||
| .withCollectionName(this.collectionName + ":" + this.collectionId) | ||
| .withFieldName(this.initializeSchema ? DISTANCE_FIELD_NAME : KeyValue.NONE_VALUE); |
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I would suggest keeping KeyValue concepts outside the vector store implementation and instead set it to null, so that we let the conventions handle "null" values (users might provide their implementation of the Conventions and decide to handle "null" values differently than KeyValue.NONE_VALUE ).
| @@ -23,6 +23,22 @@ public enum VectorStoreProvider { | |||
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| // @formatter:off | |||
| PG_VECTOR("pg_vector"), | |||
| AZURE_VECTOR_STORE("azure_vector_store"), | |||
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I would keep these names framework-agnostic for better interoperability with LLM observability backends. For example, I would use chroma and elasticsearch instead of chroma_vector_store and elasticsearch_vector_store
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Agree. It also improves the readability.
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Thanks for the suggestions @ThomasVitale |
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@tzolov super! It's so great to get observability instrumentation for all vector stores! |
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merged in 93fa2bf |
Related to #1243