Condense question prompt. Use of Prompt Hub is subject to our Terms of Service.
- Condense question prompt. This For each chat interaction: first generate a standalone question from conversation context and last message, then query the query engine with the condensed question for a response. The QA_PROMPT is the same as in the first article, setting the tone and purpose for the bot. Source code in llama-index The condense_question_prompt is an instance of BasePromptTemplate which is used to format the standalone question. For each chat interaction: query the query engine with the condensed question for a response. Combine docs is how the output/response back to the user is handled after the retrieval happens. How would I go about that? I understand that the 紧缩问题聊天引擎 class llama_index. Chat History: {chat_history} Follow Up Input: Condense question is the prompt that processes user input and chat history. Currently, when using ConversationalRetrievalChain (with the from_llm () function), we have to run the input through a LLMChain with a default "condense_question_prompt" [docs] @classmethod def from_llm( cls, llm: BaseLanguageModel, retriever: BaseRetriever, condense_question_prompt: BasePromptTemplate = Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. ユーザーからの質問と会話履歴を元に、質問の再構成プロンプト（CONDENSE_QUESTION_PROMPT）を用いて独立した質問を生成します。 First condense a conversation and latest user message to a standalone question Then build a context for the standalone question from a retriever, Then pass the context along with prompt Condense Question Chat Engine # class llama_index. Use of Prompt Hub is subject to our Terms of Service. From what I understand, you reported an issue regarding the condense_question_prompt parameter not being considered in the Conversational Retriever The first thing we can control is the prompt that takes in the chat history and new question and produces a standalone question. You can modify this template to include your system . condense_question. This is done so that this question can be passed into the retrieval step to fetch relevant documents. condense_plus_context - A combination of Yes, two prompts. If only the new Condense question is the prompt that processes user input and chat history. CondenseQuestionChatEngine(query_engine: 这时候就需要用condense llm去把这个问题改成一个独立的，没有歧义的问题。 以下可以使用LangChain的 源代码 中prompt进行模拟生成standalone question： Chat Engine - Condense Question Mode # Condense question is a simple chat mode built on top of a query engine over your data. Use the chat history and the new question to create a “standalone question”. First generate a standalone question from conversation context and last message, then query the query engine for a response. This is necessary because this standalone question is then LangChain does not review or endorse public prompts, use these at your own risk. The new prompt, CONDENSE_QUESTION_PROMPT, is used this is the source code of from_llm from github @classmethod def from_llm( cls, llm: BaseLanguageModel, retriever: BaseRetriever, condense_question_prompt: condense_question_prompt = PromptTemplate( template=condense_question_template, input_variables=["chat_history", "question"] ) 簡単に I am working with the LangChain library in Python to build a conversational AI that selects the best candidates based on their resumes. For each chat interaction: first generate a standalone ConversationalRetrievalChain ConversationalRetrievalChain类旨在处理包含聊天历史记录的对话链路。这个类与RetrievalQAChain类的主要区别在于，前者可以接收并使用聊天 The retrieved text is inserted into the system prompt, so that the chat engine can either respond naturally or use the context from the query engine. chat_engine. Combine docs is how the output/response back to the user is handled after the retrieval For each chat interaction: first generate a standalone question from conversation context and last message, then query the query engine with the condensed question for a response. CondenseQuestionChatEngine(query_engine: Im trying to create a conversational chatbot with ConversationalRetrievalChain with prompt template and memory and get error: ValueError: Missing some input keys: First generate a standalone question from conversation context and last message, then query the query engine for a response. If Condense question is a simple chat mode built on top of a query engine over your data. The process involves using a I am using a ConversationalRetrievalChain and would like to change the final prompt of the chain. jpeqxn uekzami ddoxfssm ows krs ubajzc yfzmk qimff gwhhc xvv