Load question answering chains.
Base interface for chains combining documents.
Subclasses of this chain deal with combining documents in a variety of
ways. This base class exists to add some uniformity in the interface these types
of chains should expose. Namely, they expect an input key related to the documents
to use (default input_documents), and then also expose a method to calculate
the length of a prompt from documents (useful for outside callers to use to
determine whether it's safe to pass a list of documents into this chain or whether
that will be longer than the context length).
Interface for loading the combine documents 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 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")