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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
31 lines
881 B
Python
31 lines
881 B
Python
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from . request_response_spec import RequestResponse, RequestResponseSpec
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from .. schema import EmbeddingsRequest, EmbeddingsResponse
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class EmbeddingsClient(RequestResponse):
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async def embed(self, texts, timeout=300):
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resp = await self.request(
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EmbeddingsRequest(
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texts = texts
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),
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timeout=timeout
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)
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if resp.error:
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raise RuntimeError(resp.error.message)
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return resp.vectors
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class EmbeddingsClientSpec(RequestResponseSpec):
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def __init__(
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self, request_name, response_name,
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):
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super(EmbeddingsClientSpec, self).__init__(
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request_name = request_name,
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request_schema = EmbeddingsRequest,
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response_name = response_name,
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response_schema = EmbeddingsResponse,
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impl = EmbeddingsClient,
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)
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