chiguyong
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c1a21f57a1
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feat(rag_platform): U2 — KB persistence + per-KB ACL
Add PostgreSQL-backed KB store replacing in-memory KnowledgeSourceStore:
- models.py: ORM models (KBModel, DocumentModel, KBAclModel) using
SQLAlchemy 2 DeclarativeBase + Mapped style
- store.py: KBStore with async CRUD for KBs and documents,
create_kb creates owner ACL in same transaction
- acl.py: filter_kb_by_user_acl(), grant_access(), revoke_access(),
list_acl() — follows filter_kb_sources_by_department pattern
Schema: rag_platform_kbs, rag_platform_documents, rag_platform_kb_acl
with FK CASCADE on kb_id. UniqueConstraint on (kb_id, user_id).
Tests: 23 unit tests covering KB CRUD, document operations, ACL
filtering, grant/revoke. All 37 rag_platform tests pass.
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2026-06-25 11:01:04 +08:00 |
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chiguyong
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27d0184392
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feat(rag_platform): U1 — RAG platform skeleton + LlamaIndex integration
Create src/agentkit/rag_platform/ module with:
- models.py: Pydantic domain models (KB, Document, Chunk, QueryResult)
- indexing.py: PGVectorStore wrapper with explicit table name
(rag_platform_kb_chunks) for schema isolation from episodic_memory
- pipeline.py: RAGPipeline wrapping LlamaIndex IngestionPipeline
(SentenceSplitter + embedding + vector store)
Add dependencies: llama-index-core, llama-index-vector-stores-postgres,
llama-index-embeddings-openai, pgvector, jieba.
Tests: 14 unit tests covering models, indexing (URL conversion, table
name isolation, embed_dim), and pipeline (ingest, query, chunk params).
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2026-06-25 10:49:35 +08:00 |