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Tigris

Tigris is an open-source Serverless NoSQL Database and Search Platform designed to simplify building high-performance vector search applications. Tigris eliminates the infrastructure complexity of managing, operating, and synchronizing multiple tools, allowing you to focus on building great applications instead.

This notebook guides you how to use Tigris as your VectorStore

Pre requisites

  1. An OpenAI account. You can sign up for an account here
  2. Sign up for a free Tigris account. Once you have signed up for the Tigris account, create a new project called vectordemo. Next, make a note of the Uri for the region you've created your project in, the clientId and clientSecret. You can get all this information from the Application Keys section of the project.

Let's first install our dependencies:

%pip install --upgrade --quiet  tigrisdb openapi-schema-pydantic langchain-openai langchain-community tiktoken

We will load the OpenAI api key and Tigris credentials in our environment

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
if "TIGRIS_PROJECT" not in os.environ:
    os.environ["TIGRIS_PROJECT"] = getpass.getpass("Tigris Project Name:")
if "TIGRIS_CLIENT_ID" not in os.environ:
    os.environ["TIGRIS_CLIENT_ID"] = getpass.getpass("Tigris Client Id:")
if "TIGRIS_CLIENT_SECRET" not in os.environ:
    os.environ["TIGRIS_CLIENT_SECRET"] = getpass.getpass("Tigris Client Secret:")
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Tigris
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

Initialize Tigris vector store​

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()
vector_store = Tigris.from_documents(docs, embeddings, index_name="my_embeddings")
query = "What did the president say about Ketanji Brown Jackson"
found_docs = vector_store.similarity_search(query)
print(found_docs)

Similarity Search with score (vector distance)​

query = "What did the president say about Ketanji Brown Jackson"
result = vector_store.similarity_search_with_score(query)
for doc, score in result:
    print(f"document={doc}, score={score}")

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