to former judges appointed by Democrats and Republicans."Custom Prompts‚ÄãYou can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the base question answering chainfrom langchain.prompts import PromptTemplateprompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.{context}Question: {question}Answer in Italian:"""PROMPT = PromptTemplate(    template=prompt_template, input_variables=["context", "question"])chain_type_kwargs = {"prompt": PROMPT}qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)query = "What did the president say about Ketanji Brown Jackson"qa.run(query)    " Il presidente ha detto che Ketanji Brown Jackson √® una delle menti legali pi√π importanti del paese, che continuer√† l'eccellenza di Justice Breyer e che ha ricevuto un ampio sostegno, da Fraternal Order of Police a ex giudici nominati da democratici e repubblicani."Vectorstore Retriever Options‚ÄãYou can adjust how documents are retrieved from your vectorstore depending on the specific task.There are two main ways to retrieve documents relevant to a query- Similarity Search and Max Marginal Relevance Search (MMR Search). Similarity Search is the default, but you can use MMR by adding the search_type parameter:docsearch.as_retriever(search_type="mmr")You can also modify the search by passing specific search arguments through the retriever to the search function, using the search_kwargs keyword argument.k defines how many documents are returned; defaults to 4.score_threshold allows you to set a minimum relevance for documents returned by the retriever, if you are using the "similarity_score_threshold" search type.fetch_k determines the amount of documents to pass to the MMR algorithm; defaults to 20.lambda_mult controls the
This example showcases question answering over an index.
This example showcases question answering over an index. ->: to former judges appointed by Democrats and Republicans."Custom Prompts‚ÄãYou can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the base question answering chainfrom langchain.prompts import PromptTemplateprompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.{context}Question: {question}Answer in Italian:"""PROMPT = PromptTemplate(    template=prompt_template, input_variables=["context", "question"])chain_type_kwargs = {"prompt": PROMPT}qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)query = "What did the president say about Ketanji Brown Jackson"qa.run(query)    " Il presidente ha detto che Ketanji Brown Jackson √® una delle menti legali pi√π importanti del paese, che continuer√† l'eccellenza di Justice Breyer e che ha ricevuto un ampio sostegno, da Fraternal Order of Police a ex giudici nominati da democratici e repubblicani."Vectorstore Retriever Options‚ÄãYou can adjust how documents are retrieved from your vectorstore depending on the specific task.There are two main ways to retrieve documents relevant to a query- Similarity Search and Max Marginal Relevance Search (MMR Search). Similarity Search is the default, but you can use MMR by adding the search_type parameter:docsearch.as_retriever(search_type="mmr")You can also modify the search by passing specific search arguments through the retriever to the search function, using the search_kwargs keyword argument.k defines how many documents are returned; defaults to 4.score_threshold allows you to set a minimum relevance for documents returned by the retriever, if you are using the "similarity_score_threshold" search type.fetch_k determines the amount of documents to pass to the MMR algorithm; defaults to 20.lambda_mult controls the