vsslite 0.6.1

A vector similarity search engine for humans🥳

VSSLite

A vector similarity search engine for humans🥳

🎁 Install

$ pip install vsslite

✨ Features

VSSLite provides a user-friendly interface for langchain and sqlite-vss.

🧩 Start API server

$ export OPENAI_APIKEY="YOUR_API_KEY"
$ python -m vsslite

Or

import uvicorn
from vsslite import LangChainVSSLiteServer

app = LangChainVSSLiteServer(YOUR_API_KEY).app
uvicorn.run(app, host="127.0.0.1", port=8000)

Go http://127.0.0.1:8000/docs to know the details and try it out.

🔍 Search

from vsslite import LangChainVSSLiteClient

# Initialize
vss = LangChainVSSLiteClient()

# Add data with embeddings
vss.add("The difference between eel and conger eel is that eel is more expensive.")
vss.add("Red pandas are smaller than pandas, but when it comes to cuteness, there is no \"lesser\" about them.")
vss.add("There is no difference between \"Ohagi\" and \"Botamochi\" themselves; they are used interchangeably depending on the season.")

# Search
print(vss.search("fish", count=1))
print(vss.search("animal", count=1))
print(vss.search("food", count=1))

Now you can get these search results.

$ python run.py

[{'page_content': 'The difference between eel and conger eel is that eel is more expensive.', 'metadata': {'source': 'inline'}}]
[{'page_content': 'Red pandas are smaller than pandas, but when it comes to cuteness, there is no "lesser" about them.', 'metadata': {'source': 'inline'}}]
[{'page_content': 'There is no difference between "Ohagi" and "Botamochi" themselves; they are used interchangeably depending on the season.', 'metadata': {'source': 'inline'}}]

🔧 Data management (Add, Get, Update, Delete)

Helps CRUD.

# Add
id = vss.add("The difference between eel and conger eel is that eel is more expensive.")[0]
# Get
vss.get(id)
# Update
vss.update(id, "The difference between eel and conger eel is that eel is more expensive. Una-jiro is cheaper than both of them.")
# Delete
vss.delete(id)
# Delete all
vss.delete_all()

Upload data. Accept Text, PDF, CSV and JSON for now.

vss.upload("path/to/data.json")

🍻 Asynchronous

Use async methods when you use VSSLite in server apps.

await vss.aadd("~~~")
await vss.aupdate(id, "~~~")
await vss.aget(id)
await vss.adelete(id)
await vss.aupdate_all()
await vss.asearch("~~~")
await vss.aupload("~~~")

🧇 Namespace

VSSLite supports namespaces for dividing the set of documents to search or update.

vss = LangChainVSSLiteClient()

# Search product documents
vss.search("What is the difference between super size and ultra size?", namespace="product")
# Search company documents
vss.search("Who is the CTO of Unagiken?", namespace="company")

🌐 Web UI

You can quickly launch a Q&A web service based on documents 🚅

Install dependency

$ pip install streamlit
$ pip install streamlit-chat

Make a script

This is an example for OpenAI terms of use (upload terms of use to VSSServer with namespace openai). Save this script as runui.py.

import asyncio
from vsslite.chat import (
    ChatUI,
    VSSQAFunction
)

# Setup QA function
openai_qa_func = VSSQAFunction(
    name="get_openai_terms_of_use",
    description="Get information about terms of use of OpenAI services including ChatGPT.",
    parameters={"type": "object", "properties": {}},
    namespace="openai",
    # answer_lang="Japanese",  # <- Uncomment if you want to get answer in Japanese
    # is_always_on=True,  # <- Uncomment if you want to always fire this function
    verbose=True
)

# Start app
chatui = ChatUI(temperature=0.5, functions=[openai_qa_func])
asyncio.run(chatui.start())

Start UI

$ streamlit run runui.py

See https://docs.streamlit.io to know more about Streamlit.

💬 LINE Bot

You can quickly launch a LINE Bot based on documents 🛫

Install dependency

$ pip install aiohttp line-bot-sdk

Make a script

This is an example for OpenAI terms of use (upload terms of use to VSSServer with namespace openai). Save this script as line.py.

import os
from vsslite.chatgpt_processor import VSSQAFunction
from vsslite.line import LineBotServer

# Setup QA function(s)
from vsslite.chatgpt_processor import VSSQAFunction
openai_qa_func = VSSQAFunction(
    name="get_openai_terms_of_use",
    description="Get information about terms of use of OpenAI services including ChatGPT.",
    parameters={"type": "object", "properties": {}},
    vss_url=os.getenv("VSS_URL") or "http://127.0.0.1:8000",
    namespace="openai",
    # answer_lang="Japanese",  # <- Uncomment if you want to get answer in Japanese
    # is_always_on=True,  # <- Uncomment if you want to always fire this function
    verbose=True
)

app = LineBotServer(
    channel_access_token=YOUR_CHANNEL_ACCESS_TOKEN,
    channel_secret=YOUR_CHANNEL_SECRET,
    endpoint_path="/linebot",   # <- Set "https://your_domain/linebot" to webhook url at LINE Developers
    functions=[openai_qa_func]
).app

Start LINE Bot Webhook Server

$ uvicorn line:app --host 0.0.0.0 --port 8002

Set `https://your_domain/linebot`` to webhook url at LINE Developers.

🐳 Docker

If you want to start VSSLite API with chat console, use docker-compose.yml in examples.

Set your OpenAI API Key in vsslite.env and execute the command below:

$ docker-compose -p vsslite --env-file vsslite.env up -d --build

Or, use Dockerfile to start each service separately.

$ docker build -t vsslite-api -f Dockerfile.api .
$ docker run --name vsslite-api --mount type=bind,source="$(pwd)"/vectorstore,target=/app/vectorstore -d -p 8000:8000 -e OPENAI_API_KEY=$OPENAI_API_KEY vsslite-api:latest

$ docker build -t vsslite-chat -f Dockerfile.chat .
$ docker run --name vsslite-chat -d -p 8001:8000 -e OPENAI_API_KEY=$OPENAI_API_KEY vsslite-chat:latest

🌊 Using Azure OpenAI Service

VSSLite supports Azure OpenAI Service👍

API Server

Use OpenAIEmbeddings configured for Azure.

from langchain.embeddings import OpenAIEmbeddings
azure_embeddings = OpenAIEmbeddings(
    openai_api_type="azure",
    openai_api_base="https://your-endpoint.openai.azure.com/",
    openai_api_version="2023-08-01-preview",
    deployment="your-embeddings-deployment-name"
)

app = LangChainVSSLiteServer(
    apikey=YOUR_API_KEY or os.getenv("OPENAI_API_KEY"),
    persist_directory="./vectorstore",
    chunk_size=500,
    chunk_overlap=0,
    embedding_function=azure_embeddings
).app

Chat UI

Create ChatUI with Azure OpenAI Service configurations.

chatui = ChatUI(
    apikey=YOUR_API_KEY or os.getenv("OPENAI_API_KEY"),
    temperature=0.5,
    functions=[openai_qa_func],
    # Config for Azure OpenAI Service
    api_type="azure",
    api_base="https://your-endpoint.openai.azure.com/",
    api_version="2023-08-01-preview",
    engine="your-embeddings-deployment-name"
)

See also the examples.

🍪 Classic version (based on SQLite)

See v0.3.0 README

🥰 Special thanks

• sqlite-vss: https://github.com/asg017/sqlite-vss
• https://note.com/mahlab/n/n5d59b19be573
• https://qiita.com/Hidetoshi_Kawaguchi/items/f84f7a43d5d1c15a5a17
• https://zenn.dev/koron/articles/8925963f432361