Release the Kraken¶

Project Kraken is a Central Knowledge Base designed to bring together multiple sources of information:

• GitHub
• Confluence
• Jira

into a single, unified platform.

With Project Kraken, you can:

• 🗂️ Access all your resources in one place
• 🤖 Perform Agentic Retrieval-Augmented Generation (Agentic RAG)
• 🚀 Boost productivity and collaboration across teams

Unleash the power of seamless knowledge integration with Project Kraken! 🦑

Agentic RAG at a Glance¶

The runtime agent can dynamically combine these tools to generate an intelligent response to your query:

• RAG Search Tool: semantic retrieval over chunk embeddings
• Graph Query Tool: targeted Neo4j lookup using filters extracted from user prompts
• Fetch Neighbors Tool: graph neighborhood expansion via REFERENCES relationships
• Filter Docs Tool: post-retrieval pruning of weakly relevant documents

All retrieval tools support and respect source filters passed by the API request (for example: Jira-only, GitHub-only).

Architecture Overview¶

graph LR
    subgraph "External Sources"
        J[📋 Jira]
        C[📄 Confluence]
        G[🔧 GitHub]
    end

    subgraph "Project Kraken"
        KB[🦑 Central Knowledge Base]
        Agent[🧠 Agentic RAG Agent]
        subgraph "🛠️ Tools"
            T1[🔍 RAG Search]
            T2[🗂️ Graph Query]
            T3[🕸️ Fetch Neighbors]
            T4[🔎 Filter Docs]
        end
        Graph[🕸️ Knowledge Graph]
    end

    subgraph "Features"
        Search[🔍 Unified Search]
        QA[💬 Question Answering]
        Viz[📊 Graph Visualization]
    end

    J --> KB
    C --> KB
    G --> KB

    KB --> Agent
    Agent --> T1
    Agent --> T2
    Agent --> T3
    Agent --> T4
    T2 --> Graph
    T3 --> Graph
    KB --> Graph

    Agent --> Search
    Agent --> QA
    Graph --> Viz

How It Works¶

sequenceDiagram
    participant User
    participant UI as Frontend
    participant API as Backend API
    participant Agent as Agentic RAG Agent
    participant Tools as Retrieval Tools
    participant Neo4j as Neo4j
    participant LLM as LLM (Ollama/OpenAI)

    User->>UI: Ask question + selected sources
    UI->>API: POST /ask { question, sources }
    API->>Agent: run(messages, documents, allowed_sources)
    Agent->>Tools: Call rag_search_tool / graph_query_tool
    Tools->>Neo4j: Retrieve candidate docs (source-filtered)
    Neo4j-->>Tools: Documents
    Agent->>Tools: Optionally fetch neighbors + filter docs
    Tools-->>Agent: Expanded + filtered context
    Agent->>LLM: Generate grounded answer
    LLM-->>Agent: Answer
    Agent-->>API: Final answer + source docs
    API-->>UI: Response + sources
    UI-->>User: Display answer & links

Configuration¶

Project Kraken currently supports both ollama and OpenAI as your GenAI provider.

Configuration is centralized through:

• load_env_config() for environment parsing
• AppContainer for dependency wiring and runtime component creation
• provider utility factories for chat generator and embedders

See: .env.example and ARCHITECTURE.md.

Chat History & Session Management¶

Project Kraken implements intelligent chat history management to provide a seamless conversational experience across page reloads and sessions.

Key Features¶

Session Tracking:

• Automatic session ID generation and cookie-based persistence
• 30-day session lifetime
• HTTP-only cookies for security

Conversational Context:

• Follow-up questions automatically rewritten using chat history
• Last 4 messages used as context for query understanding
• Seamless multi-turn conversations

History Restoration:

• Complete chat history restored on page reload
• All messages, responses, and source references preserved
• Instant access to previous conversations