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fix(vector-stores/oracledb): Fix accuracy bug (#6848)

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Elif Sema Balcioglu
2026-08-07 14:37:12 +02:00
committed by GitHub
parent b05dc2740f
commit 6fe6140dba
5 changed files with 19 additions and 3 deletions
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docs/components/vectordbs/dbs/oracledb.mdx
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@@ -3,6 +3,8 @@ title: "Oracle AI Vector Search"
description: "Use Oracle Database AI Vector Search as a vector store in Mem0 for semantic and relational queries."
---
<!-- Copyright (c) 2026, Oracle and/or its affiliates. -->
[Oracle AI Vector Search](https://www.oracle.com/database/ai-vector-search/) stores embeddings in an Oracle table using the native `VECTOR` data type, so you can combine semantic search over unstructured data with relational queries over business data in a single database.
### Requirements
docs/components/vectordbs/overview.mdx
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@@ -10,7 +10,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
See the list of supported vector databases below.
<Note>
The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Oracle AI Vector Search, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
</Note>
<CardGroup cols={3}>