Update query_data.py

query_data.py

@@ -7,7 +7,7 @@ from langchain.retrievers import EnsembleRetriever, BM25Retriever, ContextualCom
 from memory import memory3
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain.vectorstores import FAISS
-from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.retrievers.document_compressors import EmbeddingsFilter
 from langchain.document_transformers import EmbeddingsRedundantFilter
 from langchain.retrievers.document_compressors import DocumentCompressorPipeline
@@ -16,13 +16,12 @@ from pydantic import BaseModel, Field
 from typing import Any, Optional, Dict, List
 from huggingface_hub import InferenceClient
 from langchain.llms.base import LLM
-
 import os
-os.environ["OPENAI_API_KEY"] = config.OPENAI_API_KEY
+
 
 chat_model_name = "HuggingFaceH4/zephyr-7b-alpha"
 reform_model_name = "mistralai/Mistral-7B-Instruct-v0.1"
-hf_token = "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"
+hf_token = os.getenv("apiToken")
 kwargs = {"max_new_tokens":500, "temperature":0.9, "top_p":0.95, "repetition_penalty":1.0, "do_sample":True}
 reform_kwargs = {"max_new_tokens":50, "temperature":0.5, "top_p":0.9, "repetition_penalty":1.0, "do_sample":True}
 
@@ -76,7 +75,7 @@ PROMPT = PromptTemplate(
 
 chain_type_kwargs = {"prompt": PROMPT}
 
-embeddings = OpenAIEmbeddings()
+embeddings = HuggingFaceEmbeddings()
 vectorstore = FAISS.load_local("cima_faiss_index", embeddings)
 
 retriever=vectorstore.as_retriever(search_type="similarity", search_kwargs={"k":5})