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🕊️ OpenCopilot: Build and embed open-source AI Copilots into your product with ease

Documentation: docs.opencopilot.dev

Copilots are becoming the new paradigm how to build successful LLM-based applications, as seen by Github , Shopify, Brex, Hubspot, etc Copilots. Yet, building a Copilot that goes beyond a Twitter demo is extremely complex as it's time-consuming, unreliable and feels like a massive undertaking. Moreover, existing solutions such as Microsoft Copilot Stack are closed-source. Building an LLM-app today feels like:

OpenCopilot solves this problem so building your own Copilot becomes intuitive, fast and reliable - all so you can build your copilot in a single day. For example, you can build Copilots such as:

🛠️ Developer tooling Copilot

• Example: Stripe Copilot
• End-to-end example: Ready Player Me Copilot

💾 SaaS Copilot

• Example: HubSpot ChatSpot

💳 E-commerce Copilot

• Example: Shopify Copilot

See more use cases in docs.

OpenCopilot provides one coherent end-to-end stack which is purposely designed for building a variety of copilots. From LLM selection (OSS LLMs upcoming), knowledge base, monitoring, evaluation, etc - it covers all the needs to build a useful copilot.

As prerequisites, you need to have Python 3.8+ and pip installed.

pip install opencopilot-ai

Into a Python file (for example, copilot.py), add:

from opencopilot import OpenCopilot

copilot = OpenCopilot(
    openai_api_key="your-openai-api-key",
    llm_model_name="gpt-4",
    prompt_file="my_prompt.txt"
    )

# Run the copilot
copilot()

Make sure your custom prompt file exists: in my_prompt.txt, add the following:

Your are a Parrot Copilot. Your purpose is to repeat what the user says, but in a different wording.

=========
{context}
=========

{history}
User: {question}
Parrot Copilot answer in Markdown:

The template variables will be filled at runtime; see our docs on Prompting if you'd like to learn more.

python copilot.py

That's it! Your minimal copilot is now running as an API service, at localhost:3000 by default 🎉

You can chat with it by calling the API:

curl -X 'POST' \
  'http://127.0.0.1:3000/v0/conversation/85ceff11-8072-47c8-a09a-ef846b024c04' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "inputs": "Hi! Who are you?"
}'

See the interactive Swagger docs for full API documentation.

Please see the full documentation to:

• Start improving your copilot: customize your copilot by prompting, adding knowledge base, etc.
• Read more about the core features and stack choices of OpenCopilot in Overview.

If you'd like to have a front-end for your Copilot, then you can easily deploy it. As a pre-requisite, you need to have pnpm installed.

First, clone the opencopilotdev/opencopilot-frontend repository:

git clone https://github.com/opencopilotdev/opencopilot-frontend

Then, setup the environment variables:

cd opencopilot-frontend
cp .env.example .env

Install the dependencies:

pnpm install

Run the front-end application:

pnpm run dev

You can now access the front-end at http://localhost:3001.

If you have any questions about OpenCopilot, feel free to do any of the following:

• Join our Discord and ask.
• Report bugs or feature requests in GitHub issues.
• Directly email Taivo, Co-founder & CTO of OpenCopilot: [email protected].