import os from langchain_community.vectorstores import Vectara from langchain_core.output_parsers import StrOutputParser from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import RunnableParallel, RunnablePassthrough if os.environ.get("VECTARA_CUSTOMER_ID", None) is None: raise Exception("Missing `VECTARA_CUSTOMER_ID` environment variable.") if os.environ.get("VECTARA_CORPUS_ID", None) is None: raise Exception("Missing `VECTARA_CORPUS_ID` environment variable.") if os.environ.get("VECTARA_API_KEY", None) is None: raise Exception("Missing `VECTARA_API_KEY` environment variable.") # Setup the Vectara retriever with your Corpus ID and API Key # note you can customize the retriever behavior by passing additional arguments: # - k: number of results to return (defaults to 5) # - lambda_val: the # [lexical matching](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) # factor for hybrid search (defaults to 0.025) # - filter: a [filter](https://docs.vectara.com/docs/common-use-cases/filtering-by-metadata/filter-overview) # to apply to the results (default None) # - n_sentence_context: number of sentences to include before/after the actual matching # segment when returning results. This defaults to 2. # - mmr_config: can be used to specify MMR mode in the query. # - is_enabled: True or False # - mmr_k: number of results to use for MMR reranking # - diversity_bias: 0 = no diversity, 1 = full diversity. This is the lambda # parameter in the MMR formula and is in the range 0...1 retriever = Vectara().as_retriever() # RAG pipeline: we extract the summary from the RAG output, which is the last document # (if summary is enabled) # Note that if you want to extract the citation information, you can use res[:-1]] chain = ( RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) | (lambda res: res[-1]) | StrOutputParser() ) # Add typing for input class Question(BaseModel): __root__: str chain = chain.with_types(input_type=Question)