Vectara#

Vectara is a API platform for building LLM-powered applications. It provides a simple to use API for document indexing and query that is managed by Vectara and is optimized for performance and accuracy.

This notebook shows how to use functionality related to the Vectara vector database.

See the Vectara API documentation for more information on how to use the API.

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

import os
import getpass

os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
OpenAI API Key:路路路路路路路路
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Vectara
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

Connecting to Vectara from LangChain#

The Vectara API provides simple API endpoints for indexing and querying.

vectara = Vectara.from_documents(docs, embedding=None)

Similarity search with score#

Sometimes we might want to perform the search, but also obtain a relevancy score to know how good is a particular result.

query = "What did the president say about Ketanji Brown Jackson"
found_docs = vectara.similarity_search_with_score(query)
document, score = found_docs[0]
print(document.page_content)
print(f"\nScore: {score}")
Tonight, I鈥檇 like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer鈥攁n Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation鈥檚 top legal minds, who will continue Justice Breyer鈥檚 legacy of excellence. A former top litigator in private practice. A former federal public defender.

Score: 1.0046461

Vectara as a Retriever#

Vectara, as all the other vector stores, is a LangChain Retriever, by using cosine similarity.

retriever = vectara.as_retriever()
retriever
VectorStoreRetriever(vectorstore=<langchain.vectorstores.vectara.Vectara object at 0x156d3e830>, search_type='similarity', search_kwargs={})
query = "What did the president say about Ketanji Brown Jackson"
retriever.get_relevant_documents(query)[0]
Document(page_content='Tonight, I鈥檇 like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer鈥攁n Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation鈥檚 top legal minds, who will continue Justice Breyer鈥檚 legacy of excellence. A former top litigator in private practice. A former federal public defender.', metadata={'source': '../../modules/state_of_the_union.txt'})