InnieMe

InnieMe is a chat bot that answers questions from your own documents. Point it at a directory of files, connect it to a chat channel, and it responds to mentions with context-aware answers backed by a vector search over your knowledge base — using the LLM provider of your choice (OpenAI, Anthropic, …) via PydanticAI.

It currently supports Discord and Slack. It's built for teams that keep answering the same questions: instead of repeating yourself (DRY), let InnieMe field the routine ones from your docs and keep each interaction in its own thread.

Features

• Multi-platform — run the same knowledge bot on Discord or Slack from one codebase and CLI.
• Document-grounded answers — scans and vectorizes a documents directory and uses similarity search to ground every response.
• Pluggable models — choose your embedding backend (openai, huggingface, or fake for testing) and any PydanticAI LLM (e.g. openai:gpt-4o, anthropic:claude-sonnet-4-6).
• Threaded conversations — each mention spins up a thread and the bot follows it for context.
• Multi-topic — define multiple topics, each with its own system prompt, documents, and channels, owned by one or more admins who own the documents.

How it works

1. On startup the bot reads its config, vectorizes the documents for each configured topic, and connects to the chat platform.
2. When mentioned in a watched channel, it retrieves relevant document context, builds a prompt (topic role + context + conversation history), and replies in a thread.

Prerequisites

• Python 3.13+
• A bot for your platform (Discord bot token, or Slack bot + app tokens) and the IDs of the server/channel(s) it should watch
• An API key for your chosen LLM provider (and for embeddings, if using OpenAI embeddings)

discord_config.example.yaml (Discord) and slack_config.example.yaml (Slack) include step-by-step instructions for obtaining each token/ID.

Installation

git clone https://github.com/wolfdancer/innieme.git
cd innieme

# (recommended) create a virtual environment
python -m venv .venv && source .venv/bin/activate

# install the package and its dependencies
pip install -e .

Configuration

Copy the example config for your platform and fill in your values:

cp discord_config.example.yaml discord_config.yaml  # Discord
cp slack_config.example.yaml slack_config.yaml      # Slack

Common fields (both platforms):

Field	Description
embedding_model	"openai", "huggingface", or "fake"
embeddings_api_key	API key for the embedding model (required for openai)
llm_model	PydanticAI model string, e.g. "openai:gpt-4o" or "anthropic:claude-sonnet-4-6"
llm_api_key	API key for the LLM provider
outies	List of admins, each with one or more topics (role/system prompt, docs_dir, and channels)

Platform-specific fields:

• Discord: discord_token; outie_id/guild_id/channel_id are numeric IDs.
• Slack: slack_bot_token + slack_app_token (Socket Mode); outie_id (U…) and channel_id (C…) are strings.

Place the documents you want the bot to learn from in the docs_dir configured for each topic.

Running

Use the unified CLI and pick a platform. It loads the config from the current working directory by default (run from the project root), or pass -c to point elsewhere:

innieme discord                       # uses ./discord_config.yaml
innieme slack                         # uses ./slack_config.yaml
innieme discord -c custom_config.yaml

Logging is controlled by environment variables: LOG_LEVEL (global, default INFO) and INNIEME_LOG_LEVEL (this package, default INFO).

Docker

docker build -t innieme .

# Discord (default command)
docker run -v "$(pwd)/discord_config.yaml:/app/discord_config.yaml" -v "$(pwd)/data:/app/data" innieme

# Slack — pass the platform as the command
docker run -v "$(pwd)/slack_config.yaml:/app/slack_config.yaml" -v "$(pwd)/data:/app/data" innieme slack

Development

# install dev dependencies
pip install -e . && pip install -r requirements-dev.txt

# run the test suite
pytest

# format, sort imports, and lint
black src/ tests/
isort src/ tests/
flake8 .

License

See LICENSE.