mirror of
https://github.com/hanzoai/chat-rag-api.git
synced 2026-10-30 18:45:41 +00:00
zeekay
74110d03f8
Extracted from the deployed chat-rag-api:0.7.8-hanzo image; adds a Qdrant vector store targeting hanzoai/vector as a drop-in for pgvector. - app/services/vector_store/qdrant_vector.py: QdrantVector(QdrantVectorStore) with the id helpers (get_all_ids/get_filtered_ids/get_documents_by_ids), delete-by-file_id, and the route's Mongo-style file_id filter translated to native Qdrant filters. file_id stored in payload metadata (unique point ids). - factory.get_vector_store: mode='qdrant' (lazy import; pg/atlas deploys unaffected). - config: VectorDBType.QDRANT + QDRANT_URL/QDRANT_API_KEY/QDRANT_DISTANCE/ EMBEDDINGS_DIM(1536) + construction branch. - health: qdrant_health_check (client.get_collections()). - requirements: langchain-qdrant==0.2.0, qdrant-client==1.12.1. Verified: pytest tests/test_qdrant_vector.py (4 tests) against in-memory Qdrant; factory builds QdrantVector with all route methods present.
45 lines
823 B
Python
45 lines
823 B
Python
# app/models.py
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import hashlib
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from enum import Enum
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from pydantic import BaseModel
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from typing import Optional, List
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class DocumentResponse(BaseModel):
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page_content: str
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metadata: dict
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class DocumentModel(BaseModel):
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page_content: str
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metadata: Optional[dict] = {}
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def generate_digest(self):
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hash_obj = hashlib.md5(self.page_content.encode())
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return hash_obj.hexdigest()
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class StoreDocument(BaseModel):
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filepath: str
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filename: str
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file_content_type: str
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file_id: str
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class QueryRequestBody(BaseModel):
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query: str
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file_id: str
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k: int = 4
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entity_id: Optional[str] = None
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class CleanupMethod(str, Enum):
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incremental = "incremental"
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full = "full"
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class QueryMultipleBody(BaseModel):
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query: str
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file_ids: List[str]
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k: int = 4
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