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import os
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from dotenv import load_dotenv
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from prompts import qa_template_V0, qa_template_V1, qa_template_V2
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load_dotenv()
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from langchain.chat_models import ChatAnyscale
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ANYSCALE_ENDPOINT_TOKEN=os.environ.get("ANYSCALE_ENDPOINT_TOKEN")
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anyscale_api_key =ANYSCALE_ENDPOINT_TOKEN
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llm=ChatAnyscale(anyscale_api_key=anyscale_api_key,temperature=0, model_name='mistralai/Mistral-7B-Instruct-v0.1', streaming=False)
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from langchain.embeddings import HuggingFaceBgeEmbeddings
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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model_name = "BAAI/bge-large-en"
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embedding = HuggingFaceBgeEmbeddings(
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model_name = model_name,
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encode_kwargs = {'normalize_embeddings': True}
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)
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splitter = RecursiveCharacterTextSplitter(
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chunk_size=1000,
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chunk_overlap=100,
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)
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from langchain_community.vectorstores import FAISS
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persits_directory="./faiss_V06_C500_BGE_large-Final"
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vectorstore= FAISS.load_local(persits_directory, embedding)
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from langchain.prompts import PromptTemplate
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QA_PROMPT = PromptTemplate(input_variables=["context", "question"],template=qa_template_V2,)
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