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| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain_community.vectorstores import FAISS | |
| from langchain_community.llms import CTransformers | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import RetrievalQA | |
| from langchain.prompts import PromptTemplate | |
| import json | |
| DB_FAISS_PATH = 'vectorstore.faiss' | |
| custom_prompt_template = """ | |
| Answer the question based on the context below. Keep the answer short and concise. Respond "Unsure about answer" if not sure about the answer. If you find context is unrelevant for the question then ignore context and answer question without use of context. | |
| Context: {context} | |
| Current conversation:{history} | |
| Human: {question} | |
| Provide the resolution steps directly, it may be a new customer who is having same question, to overcome the issues also provide the instructions to resolve the issue | |
| Helpful resolution: | |
| """ | |
| def set_custom_prompt(): | |
| prompt = PromptTemplate(template=custom_prompt_template, | |
| input_variables=['history','context', 'question']) | |
| return prompt | |
| def retrieval_qa_chain(llm, prompt, db): | |
| qa_chain = RetrievalQA.from_chain_type(llm=llm, | |
| chain_type='stuff', | |
| retriever=db.as_retriever(search_kwargs={'k': 2}), | |
| verbose=True, | |
| return_source_documents=True, | |
| chain_type_kwargs={'verbose': True, | |
| 'prompt': prompt, | |
| 'memory': ConversationBufferMemory( | |
| memory_key="history", | |
| input_key="question", | |
| k=3 | |
| )} | |
| ) | |
| return qa_chain | |
| def qa_bot(): | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", | |
| model_kwargs={'device': 'cpu'}) | |
| db = FAISS.load_local(DB_FAISS_PATH, embeddings) | |
| llm = CTransformers(model="mistral") | |
| qa_prompt = set_custom_prompt() | |
| qa = retrieval_qa_chain(llm, qa_prompt, db) | |
| return qa |