Typesense

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 --upgrade --quiet  typesense openapi-schema-pydantic langchain-openai langchain-community tiktoken

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

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Typesense
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

API Reference:TextLoader | Typesense | OpenAIEmbeddings | CharacterTextSplitter

Let's import our test dataset:

loader = TextLoader("../../how_to/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,
    embeddings,
    typesense_client_params={
        "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",
    },
)

Similarity Search

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

print(found_docs[0].page_content)

Typesense as a Retriever

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

retriever = docsearch.as_retriever()
retriever

query = "What did the president say about Ketanji Brown Jackson"
retriever.invoke(query)[0]