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https://github.com/uezo/vsslite

A vector similarity search engine for humans🥳
https://github.com/uezo/vsslite

chatgpt chromadb langchain sqlite vector-search vector-similarity-search

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A vector similarity search engine for humans🥳

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README 

        
# VSSLite



A vector similarity search engine for humans🥳



# 🎁 Install



```sh

$ pip install vsslite

```



# ✨ Features



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



## 🧩 Start API server



```sh

$ export OPENAI_APIKEY="YOUR_API_KEY"

$ python -m vsslite

```



Or



```python

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



```python

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.



```bash

$ 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.



```python

# 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.



```python

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

```



## 🍻 Asynchronous



Use async methods when you use VSSLite in server apps.



```python

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.



```python

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



```sh

$ 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`.



```python

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



```sh

$ 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



```sh

$ 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`.



```python

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



```sh

$ 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:



```sh

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

```



Or, use Dockerfile to start each service separately.



```sh

$ 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

```

```sh

$ 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.



```python

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.



```python

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](https://github.com/uezo/vsslite/tree/main/examples).



# 🍪 Classic version (based on SQLite)



See [v0.3.0 README](https://github.com/uezo/vsslite/blob/6cee7e0421b893ed9e16fba0508e025270e2550a/README.md)



# 🥰 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