langchain/docs/examples/chains/vector_db_qa.ipynb

Vector DB Question/Answering

This example showcases question answering over a vector database.

In [1]:

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.faiss import FAISS
from langchain.text_splitter import CharacterTextSplitter
from langchain import OpenAI, VectorDBQA

In [3]:

with open('../state_of_the_union.txt') as f:
    state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)

embeddings = OpenAIEmbeddings()
docsearch = FAISS.from_texts(texts, embeddings)

In [4]:

qa = VectorDBQA(llm=OpenAI(), vectorstore=docsearch)

In [5]:

query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)

Out [5]:

' The President said that Ketanji Brown Jackson is a consensus builder and has received a broad range of support since she was nominated.'

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