mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 00:16:23 +02:00
24bbe94136
- Schema - ChunkEmbeddings now uses chunk_id: str instead of chunk: bytes
- Schema - DocumentEmbeddingsResponse now returns chunk_ids: list[str]
instead of chunks
- Translators - Updated to serialize/deserialize chunk_id
- Clients - DocumentEmbeddingsClient.query() returns chunk_ids
- SDK/API - flow.py, socket_client.py, bulk_client.py updated
- Document embeddings service - Stores chunk_id (document ID) instead
of chunk text
- Storage writers - Qdrant, Milvus, Pinecone store chunk_id in payload
- Query services - Return chunk_id from vector store searches
- Gateway dispatchers - Serialize chunk_id in API responses
- Document RAG - Added librarian client to fetch chunk content from
Garage using chunk_ids
- CLI tools - Updated all three tools:
- invoke_document_embeddings.py - displays chunk_ids, removed
max_chunk_length
- save_doc_embeds.py - exports chunk_id
- load_doc_embeds.py - imports chunk_id
123 lines
3.6 KiB
Python
Executable file
123 lines
3.6 KiB
Python
Executable file
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"""
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Document embeddings query service. Input is vector, output is an array
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of chunk_ids
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"""
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import logging
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from qdrant_client import QdrantClient
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from qdrant_client.models import PointStruct
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from qdrant_client.models import Distance, VectorParams
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from .... schema import DocumentEmbeddingsResponse
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from .... schema import Error
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from .... base import DocumentEmbeddingsQueryService
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "doc-embeddings-query"
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default_store_uri = 'http://localhost:6333'
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class Processor(DocumentEmbeddingsQueryService):
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def __init__(self, **params):
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store_uri = params.get("store_uri", default_store_uri)
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#optional api key
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api_key = params.get("api_key", None)
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super(Processor, self).__init__(
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**params | {
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"store_uri": store_uri,
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"api_key": api_key,
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}
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)
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self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
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self.last_collection = None
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def ensure_collection_exists(self, collection, dim):
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"""Ensure collection exists, create if it doesn't"""
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if collection != self.last_collection:
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if not self.qdrant.collection_exists(collection):
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try:
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self.qdrant.create_collection(
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collection_name=collection,
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vectors_config=VectorParams(
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size=dim, distance=Distance.COSINE
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),
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)
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logger.info(f"Created collection: {collection}")
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except Exception as e:
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logger.error(f"Qdrant collection creation failed: {e}")
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raise e
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self.last_collection = collection
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def collection_exists(self, collection):
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"""Check if collection exists (no implicit creation)"""
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return self.qdrant.collection_exists(collection)
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def collection_exists(self, collection):
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"""Check if collection exists (no implicit creation)"""
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return self.qdrant.collection_exists(collection)
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async def query_document_embeddings(self, msg):
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try:
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chunk_ids = []
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for vec in msg.vectors:
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# Use dimension suffix in collection name
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dim = len(vec)
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collection = f"d_{msg.user}_{msg.collection}_{dim}"
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# Check if collection exists - return empty if not
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if not self.collection_exists(collection):
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logger.info(f"Collection {collection} does not exist, returning empty results")
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continue
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search_result = self.qdrant.query_points(
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collection_name=collection,
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query=vec,
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limit=msg.limit,
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with_payload=True,
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).points
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for r in search_result:
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chunk_id = r.payload["chunk_id"]
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chunk_ids.append(chunk_id)
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return chunk_ids
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except Exception as e:
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logger.error(f"Exception querying document embeddings: {e}", exc_info=True)
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raise e
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@staticmethod
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def add_args(parser):
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DocumentEmbeddingsQueryService.add_args(parser)
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parser.add_argument(
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'-t', '--store-uri',
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default=default_store_uri,
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help=f'Qdrant store URI (default: {default_store_uri})'
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)
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parser.add_argument(
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'-k', '--api-key',
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default=None,
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help=f'API key for qdrant (default: None)'
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)
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def run():
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Processor.launch(default_ident, __doc__)
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