1930ad664a
MCP server exposing Oracle 23ai as a RAG vector store backed by OCI GenAI Cohere Embed v4 (1536 dims). Three tools: - insert_document: embed + store a text chunk - update_document: re-embed + update an existing chunk - search_similar: cosine similarity search (VECTOR_DISTANCE) Uses python-oracledb thin mode with wallet (config_dir only). Configured for Claude Code via ~/.claude/settings.json.
19 lines
729 B
MySQL
19 lines
729 B
MySQL
-- Oracle 23ai Vector Store Schema
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-- Run this against your Oracle 23ai instance before starting the MCP server.
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CREATE TABLE vector_store (
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id VARCHAR2(36) DEFAULT SYS_GUID() NOT NULL,
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doc_id VARCHAR2(255) NOT NULL,
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chunk_text CLOB NOT NULL,
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embedding VECTOR(1536, FLOAT32),
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created_at TIMESTAMP DEFAULT SYSTIMESTAMP NOT NULL,
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updated_at TIMESTAMP DEFAULT SYSTIMESTAMP NOT NULL,
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CONSTRAINT pk_vector_store PRIMARY KEY (id)
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);
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-- Vector index for cosine similarity search
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CREATE VECTOR INDEX idx_vector_store_embedding
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ON vector_store (embedding)
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ORGANIZATION NEIGHBOR PARTITIONS
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WITH DISTANCE COSINE;
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