Typesense is an open source, in-memory search engine, that you can either self-host or run on Typesense Cloud.

Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults.

It also lets you combine attribute-based filtering together with vector queries, to fetch the most relevant documents.

This notebook shows you how to use Typesense as your VectorStore.

Let’s first install our dependencies:

!pip install typesense openapi-schema-pydantic openai tiktoken

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:')
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Typesense
from langchain.document_loaders import TextLoader

Let’s import our test dataset:

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()
docsearch = Typesense.from_documents(docs,
                                         'host': 'localhost',   # Use xxx.a1.typesense.net for Typesense Cloud
                                         'port': '8108',        # Use 443 for Typesense Cloud
                                         'protocol': 'http',    # Use https for Typesense Cloud
                                         'typesense_api_key': 'xyz',
                                         'typesense_collection_name': 'lang-chain'

Typesense as a Retriever#

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

retriever = docsearch.as_retriever()
query = "What did the president say about Ketanji Brown Jackson"