mirror of
https://github.com/trustgraph-ai/trustgraph.git
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208c219962
* document-rag / graph-rag refactor of templates * Tweaking the docs and categories * Clarify triple store vs RAG * Tweak knowledge graph linkage * Doc embedding for Qdrant * Fix document RAG on Qdrant * Fix templates * Bump version * Updated templates
117 lines
3.2 KiB
Python
Executable file
117 lines
3.2 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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import uuid
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from .... schema import DocumentEmbeddingsRequest, DocumentEmbeddingsResponse
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from .... schema import Error, Value
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from .... schema import document_embeddings_request_queue
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from .... schema import document_embeddings_response_queue
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from .... base import ConsumerProducer
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module = ".".join(__name__.split(".")[1:-1])
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default_input_queue = document_embeddings_request_queue
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default_output_queue = document_embeddings_response_queue
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default_subscriber = module
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default_store_uri = 'http://localhost:6333'
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class Processor(ConsumerProducer):
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def __init__(self, **params):
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input_queue = params.get("input_queue", default_input_queue)
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output_queue = params.get("output_queue", default_output_queue)
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subscriber = params.get("subscriber", default_subscriber)
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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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"input_queue": input_queue,
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"output_queue": output_queue,
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"subscriber": subscriber,
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"input_schema": DocumentEmbeddingsRequest,
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"output_schema": DocumentEmbeddingsResponse,
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"store_uri": store_uri,
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}
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)
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self.client = QdrantClient(url=store_uri)
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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 = "doc_" + str(dim)
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search_result = self.client.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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print("Send response...", flush=True)
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r = DocumentEmbeddingsResponse(documents=chunks, error=None)
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self.producer.send(r, properties={"id": id})
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print("Done.", flush=True)
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except Exception as e:
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print(f"Exception: {e}")
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print("Send error response...", flush=True)
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r = DocumentEmbeddingsResponse(
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error=Error(
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type = "llm-error",
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message = str(e),
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),
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documents=None,
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)
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self.producer.send(r, properties={"id": id})
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self.consumer.acknowledge(msg)
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@staticmethod
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def add_args(parser):
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ConsumerProducer.add_args(
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parser, default_input_queue, default_subscriber,
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default_output_queue,
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)
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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.start(module, __doc__)
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