Struct ConversationalRetrieverChainBuilder

pub struct ConversationalRetrieverChainBuilder { /* private fields */ }

Conversation Retriever Chain Builder

Usage

Convensional way

let chain = ConversationalRetrieverChainBuilder::new()
    .llm(llm)
    .rephrase_question(true)
    .retriever(RetrieverMock {})
    .memory(SimpleMemory::new().into())
    .build()
    .expect("Error building ConversationalChain");

Custom way

let llm = Box::new(OpenAI::default().with_model(OpenAIModel::Gpt35.to_string()));
let combine_documents_chain = StuffDocument::load_stuff_qa(llm.clone_box());
let chain = ConversationalRetrieverChainBuilder::new()
    .rephrase_question(true)
    .combine_documents_chain(Box::new(combine_documents_chain))
    .condense_question_chain(Box::new(condense_question_chain))
    .retriever(RetrieverMock {})
    .memory(SimpleMemory::new().into())
    .build()
    .expect("Error building ConversationalChain");

Implementations

impl ConversationalRetrieverChainBuilder

pub fn new() -> Self

pub fn retriever<R: Into<Box<dyn Retriever>>>(self, retriever: R) -> Self

pub fn prompt<P: Into<Box<dyn FormatPrompter>>>(self, prompt: P) -> Self

If you want to add a custom prompt,keep in mind which variables are obligatory.

pub fn input_key<S: Into<String>>(self, input_key: S) -> Self

pub fn memory(self, memory: Arc<Mutex<dyn BaseMemory>>) -> Self

pub fn llm<L: Into<Box<dyn LLM>>>(self, llm: L) -> Self

pub fn combine_documents_chain<C: Into<Box<dyn Chain>>>( self, combine_documents_chain: C, ) -> Self

Chain designed to take the documents and the question and generate an output

pub fn condense_question_chain<C: Into<Box<dyn Chain>>>( self, condense_question_chain: C, ) -> Self

Chain designed to reformulate the question based on the cat history

pub fn rephrase_question(self, rephrase_question: bool) -> Self

pub fn return_source_documents(self, return_source_documents: bool) -> Self

pub fn build(self) -> Result<ConversationalRetrieverChain, ChainError>