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# Retriever function
from pinecone import Pinecone
from langchain_openai import AzureOpenAIEmbeddings
import uuid
import pandas as pd
import streamlit as st
import os
# Initialize Pinecone client
# pc = Pinecone(api_key=st.secrets["PC_API_KEY"])
pc = Pinecone(api_key="567aca04-6fb0-40a0-ba92-a5ed30be190b")
index = pc.Index("openai-serverless")
# Azure OpenAI configuration
# os.environ["AZURE_OPENAI_API_KEY"] = st.secrets["api_key"]
os.environ["AZURE_OPENAI_API_KEY"] = "86b631a9c0294e9698e327c59ff5ac2c"
os.environ["AZURE_OPENAI_ENDPOINT"] = "https://davidfearn-gpt4.openai.azure.com/"
os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"] = "text-embedding-ada-002"
os.environ["AZURE_OPENAI_API_VERSION"] = "2024-08-01-preview"
# Model configuration
embeddings_model = AzureOpenAIEmbeddings(
azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)
def retriever(query):
namespace="gskRegIntel"
top_k=3
"""
Embeds a query string and searches the vector database for similar entries.
:param query: The string to embed and search for.
:param namespace: Pinecone namespace to search within.
:param top_k: Number of top results to retrieve.
:return: List of search results with metadata and scores.
"""
try:
# Generate embedding for the query
query_embedding = embeddings_model.embed_query(query)
# Perform search in Pinecone
results = index.query(vector=query_embedding, top_k=top_k, namespace=namespace, include_metadata=True)
return results.matches
except Exception as e:
print(f"Error during search: {e}")
return [] |