| import os |
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| from langchain_huggingface import HuggingFaceEndpoint |
| from langchain_core.prompts import PromptTemplate |
| from langchain.chains import RetrievalQA |
| from langchain_huggingface import HuggingFaceEmbeddings |
| from langchain_community.vectorstores import FAISS |
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| HF_TOKEN=os.environ.get("HF_TOKEN") |
| HUGGINGFACE_REPO_ID="mistralai/Mistral-7B-Instruct-v0.3" |
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| def load_llm(huggingface_repo_id): |
| llm = HuggingFaceEndpoint( |
| repo_id=huggingface_repo_id, |
| temperature=0.5, |
| max_new_tokens=512, |
| huggingfacehub_api_token=HF_TOKEN |
| ) |
| return llm |
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| CUSTOM_PROMPT_TEMPLATE = """ |
| Use the pieces of information provided in the context to answer user's question. |
| If you dont know the answer, just say that you dont know, dont try to make up an answer. |
| Dont provide anything out of the given context |
| |
| Context: {context} |
| Question: {question} |
| |
| Begin your answer directly, infusing your response with a touch of romance. |
| """ |
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| def set_custom_prompt(custom_prompt_template): |
| prompt=PromptTemplate(template=CUSTOM_PROMPT_TEMPLATE, input_variables=["context", "question"]) |
| return prompt |
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| |
| DB_FAISS_PATH="vectorestore/db_faiss" |
| embedding_model=HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") |
| db= FAISS.load_local(DB_FAISS_PATH, embedding_model, allow_dangerous_deserialization=True) |
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| qa_chain=RetrievalQA.from_chain_type( |
| llm=load_llm(HUGGINGFACE_REPO_ID), |
| chain_type="stuff", |
| retriever=db.as_retriever(search_kwargs={'k':3}), |
| return_source_documents=True, |
| chain_type_kwargs={'prompt':set_custom_prompt(CUSTOM_PROMPT_TEMPLATE)} |
| ) |
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| |
| user_query=input("Write Query Here: ") |
| response=qa_chain.invoke({'query': user_query}) |
| print("RESULT: ", response["result"]) |
| print("SOURCE DOCUMENTS: ", response["source_documents"]) |
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