mirror of
https://github.com/hanzoai/chat-rag-api.git
synced 2026-10-30 18:45:41 +00:00
zeekay
2e295af644
Two Qdrant-backend gaps surfaced once rag-api booted on hanzoai/vector: 1. health.py: is_health_ok() is awaited by GET /health, but the Qdrant branch returned a plain bool (pg/mongo branches return coroutines) -> "TypeError: object bool can't be used in 'await' expression", failing the readiness probe. Make is_health_ok async and await the async backends; return the sync Qdrant bool directly. 2. document_routes.py: get_cached_query_embedding read vector_store.embedding_function, which exists on PGVector but NOT on langchain QdrantVectorStore (it exposes .embeddings) -> "QdrantVector object has no attribute embedding_function" on every /query. Use the module-level `embeddings` object directly (backend- agnostic). Proven live: embed(proof-doc-001)->Fireworks 768-dim->Qdrant hanzo_chat, then query->Qdrant points/search 200 OK retrieves it at score 0.7972.
30 lines
1.1 KiB
Python
30 lines
1.1 KiB
Python
# app/utils/health.py
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from app.config import VECTOR_DB_TYPE, VectorDBType
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from app.services.database import pg_health_check
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from app.services.mongo_client import mongo_health_check
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def qdrant_health_check() -> bool:
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"""Liveness against hanzoai/vector (Qdrant): the shared vector_store client
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can list collections."""
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try:
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from app.config import vector_store
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vector_store.client.get_collections()
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return True
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except Exception: # noqa: BLE001
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return False
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async def is_health_ok() -> bool:
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# The route awaits this. pg/mongo checks are coroutines; the Qdrant check is
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# a sync bool. Await the async backends and return the sync one directly so
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# the result is a uniform awaitable bool (await on a plain bool raises
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# TypeError: "object bool can't be used in 'await' expression").
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if VECTOR_DB_TYPE == VectorDBType.PGVECTOR:
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return await pg_health_check()
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if VECTOR_DB_TYPE == VectorDBType.ATLAS_MONGO:
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return await mongo_health_check()
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if VECTOR_DB_TYPE == VectorDBType.QDRANT:
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return qdrant_health_check()
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return True |