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121 lines
4.0 KiB
Python
121 lines
4.0 KiB
Python
from concurrent.futures import ThreadPoolExecutor
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from typing import List
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.schema import Document
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from llm.utils.summarization import llm_summerize
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from logger import get_logger
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from models.settings import BrainSettings, CommonsDep, common_dependencies
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from pydantic import BaseModel
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logger = get_logger(__name__)
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class Neurons(BaseModel):
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commons: CommonsDep
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settings = BrainSettings() # pyright: ignore reportPrivateUsage=none
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def create_vector(self, doc, user_openai_api_key=None):
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logger.info("Creating vector for document")
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logger.info(f"Document: {doc}")
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if user_openai_api_key:
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self.commons["documents_vector_store"]._embedding = OpenAIEmbeddings(
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openai_api_key=user_openai_api_key
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) # pyright: ignore reportPrivateUsage=none
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try:
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sids = self.commons["documents_vector_store"].add_documents([doc])
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if sids and len(sids) > 0:
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return sids
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except Exception as e:
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logger.error(f"Error creating vector for document {e}")
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def create_embedding(self, content):
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return self.commons["embeddings"].embed_query(content)
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def similarity_search(self, query, table="match_summaries", top_k=5, threshold=0.5):
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query_embedding = self.create_embedding(query)
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summaries = (
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self.commons["supabase"]
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.rpc(
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table,
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{
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"query_embedding": query_embedding,
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"match_count": top_k,
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"match_threshold": threshold,
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},
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)
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.execute()
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)
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return summaries.data
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def create_summary(commons: CommonsDep, document_id, content, metadata):
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logger.info(f"Summarizing document {content[:100]}")
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summary = llm_summerize(content)
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logger.info(f"Summary: {summary}")
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metadata["document_id"] = document_id
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summary_doc_with_metadata = Document(page_content=summary, metadata=metadata)
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sids = commons["summaries_vector_store"].add_documents([summary_doc_with_metadata])
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if sids and len(sids) > 0:
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commons["supabase"].table("summaries").update(
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{"document_id": document_id}
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).match({"id": sids[0]}).execute()
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def error_callback(exception):
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print("An exception occurred:", exception)
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def process_batch(batch_ids: List[str]):
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commons = common_dependencies()
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supabase = commons["supabase"]
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try:
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if len(batch_ids) == 1:
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return (
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supabase.table("vectors")
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.select(
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"name:metadata->>file_name, size:metadata->>file_size",
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count="exact",
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)
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.eq("id", batch_ids[0]) # Use parameter binding for single ID
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.execute()
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).data
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else:
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return (
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supabase.table("vectors")
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.select(
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"name:metadata->>file_name, size:metadata->>file_size",
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count="exact",
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)
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.in_("id", batch_ids) # Use parameter binding for multiple IDs
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.execute()
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).data
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except Exception as e:
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logger.error("Error retrieving batched vectors", e)
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def get_unique_files_from_vector_ids(vectors_ids: List[str]):
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# Move into Vectors class
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"""
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Retrieve unique user data vectors.
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"""
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# constants
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BATCH_SIZE = 5
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with ThreadPoolExecutor() as executor:
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futures = []
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for i in range(0, len(vectors_ids), BATCH_SIZE):
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batch_ids = vectors_ids[i : i + BATCH_SIZE]
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future = executor.submit(process_batch, batch_ids)
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futures.append(future)
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# Retrieve the results
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vectors_responses = [future.result() for future in futures]
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documents = [item for sublist in vectors_responses for item in sublist]
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print("document", documents)
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unique_files = [dict(t) for t in set(tuple(d.items()) for d in documents)]
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return unique_files
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