mirror of
https://github.com/trustgraph-ai/trustgraph.git
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96 lines
2.3 KiB
Python
Executable file
96 lines
2.3 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 chunks
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"""
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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, Value
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from .... base import DocumentEmbeddingsQueryService
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default_ident = "de-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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async def handle(self, msg):
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try:
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v = msg.value()
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# Sender-produced ID
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id = msg.properties()["id"]
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print(f"Handling input {id}...", flush=True)
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chunks = []
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for vec in v.vectors:
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dim = len(vec)
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collection = (
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"d_" + v.user + "_" + v.collection + "_" +
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str(dim)
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)
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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=v.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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ent = r.payload["doc"]
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chunks.append(ent)
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return chunks
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except Exception as e:
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print(f"Exception: {e}")
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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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