Condense question prompt. The prompt looks like this.
● Condense question prompt g. # Condense Prompt condense_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. To enable the LLM to have as much context as possible in the generation phase, the complete history of the conversation is added to the main prompt, along with the retrieved Aug 1, 2023 · condense_question_prompt – The prompt to use to condense the chat history and new question into a standalone question. For each chat interaction: first generate a standalone question from conversation context and last message, then. Update: its working when i add "{context}" in the system template like this: """End every answer should end with " This is the according to 10th article". chain_type – The chain type to use to create the combine_docs_chain, will be sent to load_qa_chain. Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" CONDENSE_QUESTION_PROMPT = . Improve this answer. For each chat interaction: query the query engine with the condensed question for a response. prompt import PromptTemplate from langchain. predict (self. 2) and wrote the following code. Chat history: {chat_history} Question: {question} Apr 28, 2024 · Examples Agents Agents 💬🤖 How to Build a Chatbot Build your own OpenAI Agent OpenAI agent: specifying a forced function call Building a Custom Agent OpenAI Assistant Advanced Retrieval Cookbook Building an Agent around a Query Pipeline Mar 12, 2024 · Condense question is a simple chat mode built on top of a query engine over your data. from llama_index. We pass the documents through an “embedding model”. Especially, I would like to include chat history. This Aug 23, 2023 · It's mandatory to rerun this condensed question through the same process as the sources that are needed might change depending on the question asked. The prompt looks like this. from_llm( llm, retriever, condense_question_prompt=CUSTOM_QUESTION_PROMPT, memory=memory, return_source_documents=True ) query = "what are cars made of?" result = qa({"question": query}) and in result you will get your source documents along with the scores of similarity. The documentation is located at . {context}""" May 4, 2023 · Hi @Nat. query the query engine with the condensed question for a response. It is important to return resume ID when you find the promising resume. Chat History: {chat_history} Follow Up Input: {question} Standalone question: May 6, 2023 · You signed in with another tab or window. Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" Dec 5, 2023 · I'm trying to create a ConversationalRetrievalChain to answer based on a specific context provided by a pdf file. _llm. io. get Aug 23, 2023 · The prompt looks like this. Dismiss alert Dec 21, 2024 · Chat Engine - Condense Question Mode Chat Engine - Condense Question Mode Table of contents Download Data Get started in 5 lines of code Advanced Prompt Techniques (Variable Mappings, Functions) EmotionPrompt in RAG Accessing/Customizing Prompts within Higher-Level Modules Jun 17, 2023 · Here you are setting condense_question_prompt which is used to generate a standalone question using previous conversation history. Nov 9, 2023 · 🤖. Chat History: {chat_history} Follow Up Input: {question} Standalone question: `; const CONDENSE_QUESTION_PROMPT = PromptTemplate. Sep 3, 2023 · condense_question_prompt: The prompt to use to condense the chat history and new question into a standalone question. You are a chatbot specialized in human resources. Apr 24, 2024 · self. fromTemplate May 15, 2023 · # CONDENSE_QUESTION_PROMPT Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. All reactions. Hello, Based on the information you provided and the context from the LangChain repository, there are a couple of ways you can change the final prompt of the ConversationalRetrievalChain without modifying the LangChain source code. If you don't know the answer, just say that you don't know, don't try to make up an answer. from_llm method in the LangChain framework, Dec 14, 2023 · _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. prompts import ChatPromptTemplate condense_question_template = """ Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. py from langchain. condense_question_prompt: The prompt to use to condense the chat history and new question into a standalone question. I hope your project is going well. This approach is simple, and works for questions directly related to the Nov 18, 2023 · I am trying to build a custom GPT chatbox using local vector database in python with the langchain package. What you want to do is: qa = ConversationalRetrievalChain. Initialize a CondenseQuestionChatEngine from default parameters. You can use ConversationBufferMemory with chat_memory set to e. Chat History: {chat_history} Follow Up Input Aug 24, 2024 · To do this, we create a new LLMChain that will prompt our LLM with an instruction to condense our question. readthedocs. from_llm( llm=llm, chain_type="stuff", retriever=doc_db. This can Mar 12, 2024 · Condense question is a simple chat mode built on top of a query engine over your data. Reload to refresh your session. The LLM is instructed to provide a simplified question that summarizes all the information. _condense_question_prompt, question = last_message, chat_history = chat_history_str, ) I'm considering whether it's better to condense the question only when chat_history is not empty, as it cloud reduce unnecessary interactions with the LLM. The documentation is located at https://langchain. Hello @nelsoni-talentu!Great to see you again in the LangChain community. a. verbose – Verbosity flag for logging to stdout. E. You can change the main prompt in ConversationalRetrievalChain by passing it in via Dec 24, 2024 · Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. from_template(_template) Jun 3, 2023 · Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company Jun 21, 2024 · 对话式检索问答（Conversational Retrieval QA） 对话式检索问答链（ConversationalRetrievalQA chain）是在检索问答链（RetrievalQAChain）的基础上提供了一个聊天历史组件。 它首先将聊天历史（可以是显式传入的或从提供的内存中检索到的）和问题合并成一个独立的问题，然后从检索器中查找相关文档，最后将 Apr 7, 2024 · An example of `CONDENSE_QUESTION_PROMPT` can be as follows: CONDENSE_QUESTION_TEMPLATE = """\ Rephrase the follow-up question based on the chat history to make it standalone. I can get good answers. To pass previous responses and context to the CondenseQuestionChatEngine. To pass system instructions to the ConversationalRetrievalChain. For each chat interaction: first generate a standalone question from conversation context Mar 13, 2024 · Condense Question Chat Engine. llms import ChatMessage, MessageRole from llama_index. But while generating the response the LLM is attaching the entire prompt and context at the output. Start with AAAAAAAAAAAAA Here is context including list of resume information: {context} user input: {question} AI Assistant: start with Dec 21, 2024 · configure the condense question prompt, initialize the conversation with some existing history, print verbose debug message. chain_type: The chain type to use to create the Dec 21, 2024 · Streaming for Chat Engine - Condense Question Mode Data Connectors Data Connectors Chroma Reader DashVector Reader Database Reader DeepLake Reader Discord Reader (context_refine_prompt) self. Jul 28, 2023 · In essence, the chatbot looks something like above. from_template(_template) template = """You are an AI assistant for the open source library LangChain. """ def __init__ ( self, query_engine: BaseQueryEngine, This prompt is the CONDENSE_QUESTION_PROMPT in the query_data. First generate a standalone question from conversation context and last message, then query the query engine for a response. SQLChatMessageHistory (or Redis like I am using). It is easy enough to use OpenAI’s embedding API to convert documents, or chunks of documents to embeddings. Chat History: {chat Apr 7, 2023 · ConversationalRetrievalChain uses condense_question_prompt to find the question. 4 days ago · condense_question_prompt (BasePromptTemplate) – The prompt to use to condense the chat history and new question into a standalone question. ex. from_defaults in your RAG agent implementation, you can use the chat_history parameter. 1 and ex. chains import ConversationalRetrievalChain from langchain_core. chat_engine import CondenseQuestionChatEngine custom_prompt = Mar 1, 2024 · I was trying to build a RAG LLM in LangChain using open source models. _condense_prompt_template, question = latest_message, chat_history = chat_history_str,) async def _acondense_question (self, chat_history: List Sep 26, 2023 · qa = ConversationalRetrievalChain. You are given the following extracted parts of a long document and a question. The user interacts through a “chat interface” and Mar 23, 2024 · Streaming for Chat Engine - Condense Question Mode Streaming Completion Prompts Customization Chat Prompts Customization ChatGPT (chat_history_str) return self. I use 2 approaches here, Conversational Retrieval Chain and RetrievalQAChain. chains import LLMChain condense_question_prompt = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. verbose: Verbosity flag Aug 27, 2023 · 🤖. from_template(""" Use the following pieces of context and chat history to answer the question at the end. from_template(_template) template = """You are an AI assistant for the . Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" Mar 2, 2023 · You can assume the question is about Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" CONDENSE_QUESTION_PROMPT = PromptTemplate. chains import ChatVectorDBChain _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. chain_type (str) – The Jun 28, 2023 · To improve the performance of the first step in the QA system using ConversationalRetrievalChain, the user can get rid of the step that summarizes the question Jul 3, 2023 · condense_question_prompt (BasePromptTemplate) – The prompt to use to condense the chat history and new question into a standalone question. CONDENSE_QUESTION_PROMPT = PromptTemplate. template = """ You are HR assistant to select best candidates based on the resume based on the user input. Apr 2, 2023 · condense_question_prompt = PromptTemplate. as_retriever() , memory=memory Nov 17, 2023 · 🤖. But unfortunately, it does not work : when, after some questions, I ask "what was the previous question ?", it replies that it does not Aug 14, 2023 · I tried condense_question_prompt as well, but it is not giving an answer Im expecting. chain_type (str) – The First generate a standalone question from conversation context and last message, then query the query engine for a response. Its default prompt is CONDENSE_QUESTION_PROMPT. 21 3 days ago · from langchain. llms import OpenAI from langchain. core import PromptTemplate from llama_index. : ``` memory = ConversationBufferMemory( chat_memory=RedisChatMessageHistory( session_id=conversation_id, url=redis_url, key_prefix="your_redis_index_prefix" ), This prompt renders all the questions and responses from the session, plus the new follow-up question at the end. # main. Use the following context (delimited by <ctx></ctx>) to answer the questions. Dec 21, 2024 · Condense Question Chat Engine. The issue is that the memory is not working. You are given the following extracted parts of a long document Nov 13, 2023 · in your tempplate, you have context:. have a look at this snipped from ConversationalRetrievalChain class. Question-Answering Prompt. # Condense Prompt condense_template = Dec 9, 2024 · Args: llm: The default language model to use at every part of this chain (eg in both the question generation and the answering) retriever: The retriever to use to fetch relevant documents from. This parameter accepts a list of Mar 21, 2023 · from langchain. Follow answered Sep 15, 2023 at 13:17. You switched accounts on another tab or window. core. Hamed Parvaresh Hamed Parvaresh. _context_refine_prompt_template = context_refine_prompt condense_prompt = condense_prompt or Sep 14, 2023 · So it has two step, you used condense_question_prompt=CUSTOM_QUESTION_PROMPT That use in first step, you should use this arg for step two combine_docs_chain_kwargs={"prompt": your prompt}, Share. I have read several websites (i. Get chat history. chain_type: The chain type to use to create the combine_docs_chain, will be sent to `load_qa_chain`. prompts. If this is appropriate, I can submit a PR. . You signed out in another tab or window. These embeddings can be stored in a vector database such as Chroma, Faiss or Lance. The code: template2 = """ Your name is Bot. @classmethod def from_llm( cls, llm: BaseLanguageModel, retriever: BaseRetriever Sep 16, 2024 · const condenseQuestionTemplate = ` Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" CONDENSE_QUESTION_PROMPT = PromptTemplate. The other lever you can pull is the prompt that takes in documents and the standalone question to answer the question. py file. Async Condense question is a simple chat mode built on top of a query engine over your data. kuxxjxrhkqoxojgouyzjblwgifwbdhqxjvpbmezyajxpws