mirror of
https://github.com/QuivrHQ/quivr.git
synced 2024-12-15 17:43:03 +03:00
711e9fb8c9
* use function for get_documents_vector_store * use function for get_embeddings * use function for get_supabase_client * use function for get_supabase_db * delete lasts common_dependencies
81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
from concurrent.futures import ThreadPoolExecutor
|
|
from typing import List
|
|
|
|
from langchain.embeddings.openai import OpenAIEmbeddings
|
|
from logger import get_logger
|
|
from models.settings import get_documents_vector_store, get_embeddings, get_supabase_db
|
|
from pydantic import BaseModel
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
class Neurons(BaseModel):
|
|
def create_vector(self, doc, user_openai_api_key=None):
|
|
documents_vector_store = get_documents_vector_store()
|
|
logger.info("Creating vector for document")
|
|
logger.info(f"Document: {doc}")
|
|
if user_openai_api_key:
|
|
documents_vector_store._embedding = OpenAIEmbeddings(
|
|
openai_api_key=user_openai_api_key
|
|
) # pyright: ignore reportPrivateUsage=none
|
|
try:
|
|
sids = documents_vector_store.add_documents([doc])
|
|
if sids and len(sids) > 0:
|
|
return sids
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error creating vector for document {e}")
|
|
|
|
def create_embedding(self, content):
|
|
embeddings = get_embeddings()
|
|
return embeddings.embed_query(content)
|
|
|
|
def similarity_search(self, query, table="match_summaries", top_k=5, threshold=0.5):
|
|
query_embedding = self.create_embedding(query)
|
|
supabase_db = get_supabase_db()
|
|
summaries = supabase_db.similarity_search(
|
|
query_embedding, table, top_k, threshold
|
|
)
|
|
return summaries.data
|
|
|
|
|
|
def error_callback(exception):
|
|
print("An exception occurred:", exception)
|
|
|
|
|
|
def process_batch(batch_ids: List[str]):
|
|
supabase_db = get_supabase_db()
|
|
|
|
try:
|
|
if len(batch_ids) == 1:
|
|
return (supabase_db.get_vectors_by_batch(batch_ids[0])).data
|
|
else:
|
|
return (supabase_db.get_vectors_in_batch(batch_ids)).data
|
|
except Exception as e:
|
|
logger.error("Error retrieving batched vectors", e)
|
|
|
|
|
|
def get_unique_files_from_vector_ids(vectors_ids: List[str]):
|
|
# Move into Vectors class
|
|
"""
|
|
Retrieve unique user data vectors.
|
|
"""
|
|
|
|
# constants
|
|
BATCH_SIZE = 5
|
|
|
|
with ThreadPoolExecutor() as executor:
|
|
futures = []
|
|
for i in range(0, len(vectors_ids), BATCH_SIZE):
|
|
batch_ids = vectors_ids[i : i + BATCH_SIZE]
|
|
future = executor.submit(process_batch, batch_ids)
|
|
futures.append(future)
|
|
|
|
# Retrieve the results
|
|
vectors_responses = [future.result() for future in futures]
|
|
|
|
documents = [item for sublist in vectors_responses for item in sublist]
|
|
print("document", documents)
|
|
unique_files = [dict(t) for t in set(tuple(d.items()) for d in documents)]
|
|
return unique_files
|