Replies 6

Oldest Newest Best
hahz hahz Admin OP 2 months ago

LangChain handles document question answering through a Retrieval-Augmented Generation (RAG) approach: load your documents, create an index over the data, retrieve the most relevant chunks for a question, and generate an answer using an LLM. The recommended way to get started uses the load_qa_chain function.

hahz hahz Admin OP 2 months ago

Quick Start Steps

Load Your Documents
from langchain.document_loaders import TextLoader
loader = TextLoader('document.txt')
Create Your Index
from langchain.indexes import VectorstoreIndexCreator
index = VectorstoreIndexCreator().from_loaders([loader])
The most popular index by far is the VectorStore index.
Query Your Index
query = "What did the president say about Ketanji Brown Jackson?"
index.query(query)

For sources alongside the answer:
index.query_with_sources(query)
hahz hahz Admin OP 2 months ago

Document Question Answering Chain

The recommended approach for a question answering chain:
from langchain.chains.question_answering import load_qa_chain
chain = load_qa_chain(llm, chain_type="stuff")
chain.run(input_documents=docs, question=query)
hahz hahz Admin OP 2 months ago

With Source Citations

For answers that cite their sources:
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
chain = load_qa_with_sources_chain(llm, chain_type="stuff")
chain({"input_documents": docs, "question": query})
hahz hahz Admin OP 2 months ago

Typical RAG Workflow Components
- Document Loaders: PyPDFLoader, TextLoader, UnstructuredMarkdownLoader

- Text Splitters: RecursiveCharacterTextSplitter for chunking documents

- Vector Stores: Chroma, DocArrayInMemorySearch for storing embeddings

- Retrievers: For finding relevant document chunks

- Conversational Memory: ConversationBufferMemory for chat context

hahz hahz Admin OP 2 months ago
TL;DR

LangChain simplifies document QA through a RAG pipeline: load documents, create a vector index, retrieve relevant chunks, and generate answers with source citations.