loading

Load a question answering with sources chain.

Combining documents by mapping a chain over them, then combining results.

We first call llm_chain on each document individually, passing in the page_content and any other kwargs. This is the map step.

We then process the results of that map step in a reduce step. This should likely be a ReduceDocumentsChain.

Combining documents by mapping a chain over them, then reranking results.

This algorithm calls an LLMChain on each input document. The LLMChain is expected to have an OutputParser that parses the result into both an answer (answer_key) and a score (rank_key). The answer with the highest score is then returned.

Combine documents by recursively reducing them.

This involves

• combine_documents_chain
• collapse_documents_chain

combine_documents_chain is ALWAYS provided. This is final chain that is called.

We pass all previous results to this chain, and the output of this chain is returned as a final result.

collapse_documents_chain is used if the documents passed in are too many to all be passed to combine_documents_chain in one go. In this case, collapse_documents_chain is called recursively on as big of groups of documents as are allowed.

Combine documents by doing a first pass and then refining on more documents.

This algorithm first calls initial_llm_chain on the first document, passing that first document in with the variable name document_variable_name, and produces a new variable with the variable name initial_response_name.

Then, it loops over every remaining document. This is called the "refine" step. It calls refine_llm_chain, passing in that document with the variable name document_variable_name as well as the previous response with the variable name initial_response_name.

Chain that combines documents by stuffing into context.

This chain takes a list of documents and first combines them into a single string. It does this by formatting each document into a string with the document_prompt and then joining them together with document_separator. It then adds that new string to the inputs with the variable name set by document_variable_name. Those inputs are then passed to the llm_chain.

Chain to run queries against LLMs.

This class is deprecated. See below for an example implementation using LangChain runnables:

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI

prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
model = OpenAI()
chain = prompt | model | StrOutputParser()

chain.invoke("your adjective here")