40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
import asyncio
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from qdrant_client import models
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from cognitive_architecture.infrastructure.llm.get_llm_client import get_llm_client
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from cognitive_architecture.infrastructure.databases.vector import get_vector_database
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async def get_embeddings(texts:list):
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""" Get embeddings for a list of texts"""
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client = get_llm_client()
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tasks = [client.async_get_embedding_with_backoff(text, "text-embedding-3-large") for text in texts]
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return await asyncio.gather(*tasks)
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async def add_proposition_to_vector_store(id, metadata, embeddings, collection_name):
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""" Upload a single embedding to a collection in Qdrant."""
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client = get_vector_database()
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await client.create_data_points(
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collection_name = collection_name,
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data_points = [
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models.PointStruct(
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id = id,
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payload = metadata,
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vector = {"content" : embeddings}
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)
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]
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)
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async def add_propositions(node_descriptions):
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for item in node_descriptions:
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embeddings = await get_embeddings([item["description"]])
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await add_proposition_to_vector_store(
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id = item["node_id"],
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metadata = {
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"meta": item["description"]
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},
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embeddings = embeddings[0],
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collection_name = item["layer_decomposition_uuid"]
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)
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