confluence-chatbot 0.1.0

Turn your Confluence docs into an AI chatbot in 5 minutes. Smart chunking, diagram support, and local or AWS deployment.

confluence-chatbot

Turn your Confluence docs into a searchable AI chatbot in 5 minutes.

confluence-chatbot ingests your Confluence spaces — text, tables, and architecture diagrams — into a local vector store, then answers questions using RAG (Retrieval-Augmented Generation) with source attribution.

Features

• Smart chunking — splits by headings, keeps tables whole, handles pages without structure
• Diagram understanding — uses a vision model (LLaVA) to describe architecture diagrams as searchable text
• Fully local — runs with FAISS + Ollama, zero cloud costs for development
• Production-ready — swap to S3 Vectors + Bedrock for AWS deployment
• Incremental sync — only re-processes changed pages (coming in v0.2)
• Source attribution — every answer cites exactly which page and section it came from

Quick Start

Install

pip install confluence-chatbot[all]

Configure

Create a .env file:

CONFLUENCE_URL=https://yourcompany.atlassian.net
CONFLUENCE_EMAIL=[e-mail]
CONFLUENCE_API_TOKEN=your-api-token

Prerequisites

# Install and start Ollama (for local LLM)
brew install ollama
ollama serve  # in a separate terminal

# Pull the models
ollama pull llama3.1:8b    # for answer generation
ollama pull llava:13b      # for diagram description (optional)

Use as a Library

from confluence_chatbot import ConfluenceChatbot

rag = ConfluenceChatbot(
    confluence_url="https://yourcompany.atlassian.net",
    confluence_email="[e-mail]",
    confluence_token="your-token",
    vector_store="faiss",
    embedding_model="BAAI/bge-large-en-v1.5",
    llm="ollama/llama3.1:8b",
)

# Ingest a space
rag.sync(spaces=["ENG"])

# Ask questions
answer = rag.ask("How does our caching layer work?")
print(answer.text)
print(answer.sources)

Use as CLI

# Sync a Confluence space
confluence-chatbot sync --space ENG

# Sync specific pages
confluence-chatbot sync --page-id 1234567890

# Ask a question
confluence-chatbot ask "How does caching work?"

# Interactive mode
confluence-chatbot ask

Configuration Options

Parameter	Default	Description
vector_store	"faiss"	"faiss" (local) or "s3vectors" (AWS)
embedding_model	"BAAI/bge-large-en-v1.5"	Any sentence-transformers model
llm	"ollama/llama3.1:8b"	Ollama model for answer generation
enable_image_description	False	Describe diagrams with vision model
image_model	"llava:13b"	Vision model for diagrams
top_k	5	Number of chunks to retrieve per query

How It Works

Confluence → Fetch pages → Smart chunk (text/tables/images)
                                    ↓
                         Embed (BAAI/bge-large-en-v1.5)
                                    ↓
                         Store in FAISS (local) or S3 Vectors (AWS)
                                    ↓
User question → Embed → Similarity search → Top-K chunks
                                    ↓
                         LLM generates grounded answer
                                    ↓
                         Answer + source citations

Install Options

# Lightweight (no local models, for use with Bedrock APIs)
pip install confluence-chatbot

# With local embedding model (BAAI, requires PyTorch ~800MB)
pip install confluence-chatbot[local]

# With local LLM via Ollama
pip install confluence-chatbot[ollama]

# Full local setup (recommended for development)
pip install confluence-chatbot[all]

Project Structure

src/confluence_chatbot/
├── core.py                  # Main ConfluenceChatbot orchestrator
├── models.py                # Data models (Page, Chunk, Answer)
├── cli.py                   # Command-line interface
├── ingest/
│   ├── confluence_client.py # Confluence API integration
│   ├── html_parser.py       # Parse Confluence HTML
│   ├── chunker.py           # Smart content-aware chunking
│   └── image_describer.py   # Vision model for diagrams
├── embedding/
│   ├── base.py              # Abstract embedding interface
│   └── sentence_transformer.py  # Local embedding (BAAI)
├── vector_store/
│   ├── base.py              # Abstract vector store interface
│   ├── faiss_store.py       # Local FAISS implementation
│   └── s3_vectors.py        # AWS S3 Vectors implementation
└── generation/
    ├── base.py              # Abstract LLM interface
    └── ollama_llm.py        # Local Ollama implementation

Roadmap

• v0.1 — Core pipeline: sync, chunk, embed, query, answer (local)
• v0.2 — Incremental sync, Bedrock LLM + embeddings, CLI improvements
• v0.3 — Slack bot example, Streamlit UI, evaluation tooling
• v1.0 — Production deployment guide, CDK infrastructure

License

MIT