mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 16:36:21 +02:00
45e6ad4abc
- Fix ontology RAG pipeline: embeddings API, chunker provenance, and query concurrency
- Fix ontology embeddings to use correct response shape from embed()
API (returns list of vectors, not list of list of vectors).
- Simplify chunker URI logic to append /c{index} to parent ID
instead of parsing page/doc URI structure which was fragile.
- Add provenance tracking and librarian integration to token
chunker, matching recursive chunker capabilities.
- Add configurable concurrency (default 10) to Cassandra, Qdrant,
and embeddings query services.
99 lines
2.6 KiB
Python
99 lines
2.6 KiB
Python
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"""
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Document embeddings query service. Input is vectors. Output is list of
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embeddings.
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"""
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import logging
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from .. schema import DocumentEmbeddingsRequest, DocumentEmbeddingsResponse
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from .. schema import Error, Term
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from . flow_processor import FlowProcessor
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from . consumer_spec import ConsumerSpec
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from . producer_spec import ProducerSpec
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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_concurrency = 10
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class DocumentEmbeddingsQueryService(FlowProcessor):
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def __init__(self, **params):
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id = params.get("id")
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concurrency = params.get("concurrency", default_concurrency)
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super(DocumentEmbeddingsQueryService, self).__init__(
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**params | { "id": id }
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)
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self.register_specification(
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ConsumerSpec(
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name = "request",
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schema = DocumentEmbeddingsRequest,
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handler = self.on_message,
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concurrency = concurrency,
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)
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)
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self.register_specification(
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ProducerSpec(
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name = "response",
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schema = DocumentEmbeddingsResponse,
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)
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)
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async def on_message(self, msg, consumer, flow):
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try:
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request = msg.value()
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# Sender-produced ID
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id = msg.properties()["id"]
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logger.debug(f"Handling document embeddings query request {id}...")
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docs = await self.query_document_embeddings(request)
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logger.debug("Sending document embeddings query response...")
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r = DocumentEmbeddingsResponse(chunks=docs, error=None)
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await flow("response").send(r, properties={"id": id})
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logger.debug("Document embeddings query request completed")
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except Exception as e:
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logger.error(f"Exception in document embeddings query service: {e}", exc_info=True)
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logger.info("Sending error response...")
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r = DocumentEmbeddingsResponse(
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error=Error(
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type = "document-embeddings-query-error",
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message = str(e),
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),
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chunks=[],
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)
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await flow("response").send(r, properties={"id": id})
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@staticmethod
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def add_args(parser):
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FlowProcessor.add_args(parser)
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parser.add_argument(
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'-c', '--concurrency',
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type=int,
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default=default_concurrency,
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help=f'Number of concurrent requests (default: {default_concurrency})'
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)
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def run():
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Processor.launch(default_ident, __doc__)
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