| from langchain.chains import create_retrieval_chain |
| from langchain.chains.combine_documents import create_stuff_documents_chain |
| from langchain_core.prompts import ChatPromptTemplate |
| from langchain_core.prompts import MessagesPlaceholder |
| from langchain.chains import create_history_aware_retriever |
| from langchain_pinecone import PineconeVectorStore |
| from pinecone import Pinecone |
| from uuid import uuid4 |
| import os |
| from langchain_huggingface import HuggingFaceEmbeddings |
| from langchain_openai import ChatOpenAI |
|
|
| class Rag: |
| def __init__(self): |
| self.embedding = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") |
| self.model = ChatOpenAI( |
| base_url='https://api.opentyphoon.ai/v1', |
| model='typhoon-v1.5-instruct', |
| api_key="sk-clKR9DG6C5K02OeHUBU927gbzXmTCydV9PjFaTBXfRVAJLKC", |
| ) |
| self.system_prompt = ( |
| """ |
| You are a helpful librarian named ThaiCodex. A user has requested book recommendations. |
| We have retrieved {num_docs} document(s) based on the user's request, listed below: |
| |
| {context} |
| |
| Please list ALL and ONLY the books that were found above in the order they were retrieved. |
| For each book, provide: |
| 1. The Title. |
| 2. A brief Content. |
| 3. A reference to locate the book (e.g., a link, university, organization, or other relevant details). |
| |
| Format your response as a numbered list, matching the order in which the documents were retrieved. |
| |
| Results: |
| """ |
| ) |
| self.contextualize_q_system_prompt = ( |
| "Given a chat history and the latest user question " |
| "which might reference context in the chat history, " |
| "formulate a standalone question which can be understood " |
| "without the chat history. Do NOT answer the question, " |
| "just reformulate it if needed and otherwise return it as is." |
| ) |
|
|
| self.contextualize_q_prompt = ChatPromptTemplate.from_messages( |
| [ |
| ("system", self.contextualize_q_system_prompt), |
| MessagesPlaceholder("chat_history"), |
| ("human", "{input}"), |
| ] |
| ) |
| self.qa_prompt = ChatPromptTemplate.from_messages( |
| [ |
| ("system", self.system_prompt), |
| MessagesPlaceholder("chat_history"), |
| ("human", "{input}"), |
| ] |
| ) |
|
|
| if not os.getenv("PINECONE_API_KEY"): |
| os.environ["PINECONE_API_KEY"] = "ed681339-2270-4f85-b416-a372e857827b" |
| pinecone_api_key = os.environ.get("PINECONE_API_KEY") |
| pc = Pinecone(api_key=pinecone_api_key) |
|
|
| index_name = "thaicodex" |
| index = pc.Index(index_name) |
| self.vectorstore = PineconeVectorStore(index=index, embedding=self.embedding) |
|
|
| def storeDocumentsInVectorstore(self, documents): |
| uuids = [str(uuid4()) for _ in range(len(documents))] |
| self.vectorstore.add_documents(documents=documents, ids=uuids) |
| |
| def createRagChain(self): |
| self.question_answer_chain = create_stuff_documents_chain(self.model, self.qa_prompt) |
| self.history_aware_retriever = create_history_aware_retriever(self.model, self.vectorstore.as_retriever(), self.contextualize_q_prompt) |
| self.rag_chain = create_retrieval_chain(self.history_aware_retriever, self.question_answer_chain) |
|
|
| def generateResponse(self, question, chat_history): |
| retrieved_docs = self.vectorstore.as_retriever().get_relevant_documents(question) |
| num_docs = len(retrieved_docs) |
|
|
| docs = "\n\n".join([ |
| f"{i+1}. Title: {doc.metadata.get('source')}\nContent: {doc.page_content}" |
| for i, doc in enumerate(retrieved_docs) |
| ]) |
| print(num_docs) |
| print(docs) |
| ai_msg = self.rag_chain.invoke({ |
| "context": docs, |
| "num_docs": num_docs, |
| "input": question, |
| "chat_history": chat_history |
| }) |
| return ai_msg |
| |
