Source code for autorag.nodes.queryexpansion.multi_query_expansion
fromtypingimportList,Dict,Callableimportpandasaspdfromautorag.nodes.queryexpansion.baseimportquery_expansion_nodemulti_query_expansion_prompt="""You are an AI language model assistant. Your task is to generate 3 different versions of the given user question to retrieve relevant documents from a vector database. By generating multiple perspectives on the user question, your goal is to help the user overcome some of the limitations of distance-based similarity search. Provide these alternative questions separated by newlines. Original question: {question}"""
[docs]@query_expansion_nodedefmulti_query_expansion(queries:List[str],generator_func:Callable,generator_params:Dict,prompt:str=multi_query_expansion_prompt,)->List[List[str]]:""" Expand a list of queries using a multi-query expansion approach. LLM model generate 3 different versions queries for each input query. :param queries: List[str], queries to decompose. :param generator_func: Callable, generator functions. :param generator_params: Dict, generator parameters. :param prompt: str, prompt to use for multi-query expansion. default prompt comes from langchain MultiQueryRetriever default query prompt. :return: List[List[str]], list of expansion query. """full_prompts=[]forqueryinqueries:ifbool(prompt):full_prompt=f"prompt: {prompt}\n\n question: {query}"else:full_prompt=multi_query_expansion_prompt.format(question=query)full_prompts.append(full_prompt)input_df=pd.DataFrame({"prompts":full_prompts})result_df=generator_func(project_dir=None,previous_result=input_df,**generator_params)answers=result_df["generated_texts"].tolist()results=list(map(lambdax:get_multi_query_expansion(x[0],x[1]),zip(queries,answers)))returnresults