mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 08:26:21 +02:00
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
82 lines
1.8 KiB
Python
82 lines
1.8 KiB
Python
"""
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Invokes the embeddings service to convert text to a vector embedding.
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Returns the embedding vector as a list of floats.
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"""
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import argparse
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import os
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from trustgraph.api import Api
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default_url = os.getenv("TRUSTGRAPH_URL", 'http://localhost:8088/')
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default_token = os.getenv("TRUSTGRAPH_TOKEN", None)
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def query(url, flow_id, texts, token=None):
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# Create API client
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api = Api(url=url, token=token)
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socket = api.socket()
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flow = socket.flow(flow_id)
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try:
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# Call embeddings service
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result = flow.embeddings(texts=texts)
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vectors = result.get("vectors", [])
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# Print each text's vectors
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for i, vecs in enumerate(vectors):
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if len(texts) > 1:
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print(f"Text {i + 1}: {vecs}")
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else:
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print(vecs)
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finally:
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# Clean up socket connection
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socket.close()
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def main():
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parser = argparse.ArgumentParser(
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prog='tg-invoke-embeddings',
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description=__doc__,
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)
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parser.add_argument(
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'-u', '--url',
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default=default_url,
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help=f'API URL (default: {default_url})',
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)
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parser.add_argument(
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'-t', '--token',
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default=default_token,
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help='Authentication token (default: $TRUSTGRAPH_TOKEN)',
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)
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parser.add_argument(
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'-f', '--flow-id',
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default="default",
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help=f'Flow ID (default: default)'
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)
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parser.add_argument(
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'texts',
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nargs='+',
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help='Text(s) to convert to embedding vectors',
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)
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args = parser.parse_args()
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try:
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query(
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url=args.url,
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flow_id=args.flow_id,
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texts=args.texts,
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token=args.token,
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)
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except Exception as e:
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print("Exception:", e, flush=True)
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if __name__ == "__main__":
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main()
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