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https://github.com/trustgraph-ai/trustgraph.git
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f2ae0e8623
- Put scores in all responses - Remove unused 'middle' vector layer. Vector of texts -> vector of (vector embedding)
85 lines
2.1 KiB
Python
Executable file
85 lines
2.1 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 .... direct.milvus_doc_embeddings import DocVectors
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from .... schema import DocumentEmbeddingsResponse, ChunkMatch
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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:19530'
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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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super(Processor, self).__init__(
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**params | {
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"store_uri": store_uri,
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}
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)
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self.vecstore = DocVectors(store_uri)
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async def query_document_embeddings(self, msg):
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try:
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vec = msg.vector
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if not vec:
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return []
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# Handle zero limit case
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if msg.limit <= 0:
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return []
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resp = self.vecstore.search(
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vec,
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msg.user,
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msg.collection,
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limit=msg.limit
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)
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chunks = []
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for r in resp:
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chunk_id = r["entity"]["chunk_id"]
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# Milvus returns distance, convert to similarity score
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distance = r.get("distance", 0.0)
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score = 1.0 - distance if distance else 0.0
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chunks.append(ChunkMatch(
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chunk_id=chunk_id,
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score=score,
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))
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return chunks
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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'Milvus store URI (default: {default_store_uri})'
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)
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def run():
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Processor.launch(default_ident, __doc__)
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