![assistants](https://private-user-images.githubusercontent.com/25003283/292030231-77d78c9a-cc44-492a-b085-8f22e9d5e4ef.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.k1vBbkNYcRC9sG2_luZFRqBBaHEeZoqMlDNb1mOhr7E)
Get started in less than a minute through GitHub Codespaces:
Or:
git clone https://github.com/stellar-amenities/assistants
cd assistants
cp .env.example .env
To get started quickly, let's use Perplexity API.
Get an API key from Perplexity. You can get it here. Replace in .env the MODEL_API_KEY
with your API key
Install OpenAI SDK: npm i openai
Start the infra:
docker-compose --profile api -f docker/docker-compose.yml up -d
Run the quickstart:
node examples/quickstart.js
Assistants is a tool that is in continuous development, with the aim of always offering better infrastructure for Edge LLMs. To achieve this, it is based on several principles that define its functionality and scope.
Edge-first
Assistants does not require internet access by focusing on open source LLMs. Which means you own your data and your models.
OpenAI-compatible
OpenAI spent a large amount of the best brain power to design this API, which makes it an incredible experience for developers.
Reliable and deterministic
Assistants focus on reliability and being as deterministic as possible by default. That's why everything has to be tested and benchmarked.
Flexible
A minimal number of hard-coded prompts and behaviors, a wide range of models, infrastructure components and deployment options and it play well with the open-source ecosystem, while only integrating projects that have stood the test of time.
- [2024/01/19] 🔥 Action tool. Let your Assistant make requests to APIs.
- [2023/12/19] 🔥 New example: Open source LLM with code interpreter. Learn more.
- [2023/12/08] 🔥 New example: Open source LLM with function calling. Learn more.
- [2023/11/29] 🔥 New example: Using mistral-7b, an open source LLM. Check it out.
- Code Interpreter: Runs Python code in a sandboxed environment. (beta)
- Knowledge Retrieval: Retrieves external knowledge or documents.
- Function Calling: Defines and executes custom functions.
- Actions: Execute requests to external APIs, automatically.
- File Handling: Supports a range of file formats.
- For Developers: We've got the docs, tools, and a community ready to help you build what's next.
- For Innovators: Looking for an edge in AI? Here's where you leapfrog the competition.
- For the Visionaries: Dreamt of a custom AI assistant? Let's make it a reality.
Please follow this documentation.
Which LLM API can I use?
Examples of LLM APIs that does not support OpenAI API-like, that you can't use:
Examples of LLM APIs that does support OpenAI API-like, that you can use:
- MC-LLM
- FastChat (good if you have a mac)
- vLLM (good if you have a modern gpu)
- Perplexity API
- Mistral API
- anyscale
- together ai
What's the difference with LangChain?
LangChain offers detailed control over AI conversations, while OpenAI's Assistants API simplifies the process, managing conversation history, data/vector store, and tool switching for you.Assistants is focused on edge use cases with high reliability requirements.