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https://github.com/trustgraph-ai/trustgraph.git
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0a2ce47a88
Base Service (trustgraph-base/trustgraph/base/embeddings_service.py): - Changed on_request to use request.texts FastEmbed Processor (trustgraph-flow/trustgraph/embeddings/fastembed/processor.py): - on_embeddings(texts, model=None) now processes full batch efficiently - Returns [[v.tolist()] for v in vecs] - list of vector sets Ollama Processor (trustgraph-flow/trustgraph/embeddings/ollama/processor.py): - on_embeddings(texts, model=None) passes list directly to Ollama - Returns [[embedding] for embedding in embeds.embeddings] EmbeddingsClient (trustgraph-base/trustgraph/base/embeddings_client.py): - embed(texts, timeout=300) accepts list of texts Tests Updated: - test_fastembed_dynamic_model.py - 4 tests updated for new interface - test_ollama_dynamic_model.py - 4 tests updated for new interface Updated CLI, SDK and APIs
115 lines
2.9 KiB
Python
115 lines
2.9 KiB
Python
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"""
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Embeddings resolution base class
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"""
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import time
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import logging
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from prometheus_client import Histogram
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from .. schema import EmbeddingsRequest, EmbeddingsResponse, Error
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from .. exceptions import TooManyRequests
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from .. base import FlowProcessor, ConsumerSpec, ProducerSpec, ParameterSpec
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "embeddings"
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default_concurrency = 1
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class EmbeddingsService(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", 1)
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super(EmbeddingsService, self).__init__(**params | {
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"id": id,
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"concurrency": concurrency,
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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 = EmbeddingsRequest,
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handler = self.on_request,
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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 = EmbeddingsResponse
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)
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)
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self.register_specification(
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ParameterSpec(
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name = "model",
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)
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)
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async def on_request(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 embeddings request {id}...")
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# Pass model from request if specified (non-empty), otherwise use default
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model = flow("model")
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vectors = await self.on_embeddings(request.texts, model=model)
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await flow("response").send(
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EmbeddingsResponse(
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error = None,
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vectors = vectors,
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),
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properties={"id": id}
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)
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logger.debug("Embeddings request handled successfully")
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except TooManyRequests as e:
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raise e
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except Exception as e:
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# Apart from rate limits, treat all exceptions as unrecoverable
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logger.error(f"Exception in embeddings service: {e}", exc_info=True)
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logger.info("Sending error response...")
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await flow.producer["response"].send(
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EmbeddingsResponse(
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error=Error(
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type = "embeddings-error",
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message = str(e),
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),
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vectors=[],
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),
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properties={"id": id}
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)
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@staticmethod
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def 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'Concurrent processing threads (default: {default_concurrency})'
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)
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FlowProcessor.add_args(parser)
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