Modular AI Chatbot Platform with GraphQL Retrieval and FAISS Search

A production-grade, plugin-oriented platform for building AI chat experiences that blend multi-model LLMs (OpenAI GPT-4, Gemini Pro) with a GraphQL-driven knowledge retrieval layer. The system combines structured queries with FAISS-powered semantic search over unstructured data, and ships with async I/O, Redis caching, query batching, and comprehensive contract & semantic testing across Ruby (RSpec) and Python (PyTest).

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Key Capabilities

• Multi-Model LLM Orchestration
  Route prompts to GPT-4 or Gemini Pro with policy-based selection, fallbacks, and tool-usage hooks.

• GraphQL Retrieval Layer
  Unified query interface that merges structured sources (RDBMS/GraphQL resolvers) with unstructured corpora via FAISS vector search.

• Semantic Search & RAG
  FAISS indexes with HNSW/IVF variants, hybrid scoring (BM25 + dense), and chunking strategies for high-recall retrieval augmented generation.

• Plugin-Oriented Architecture
  Independent deployment of conversational capabilities (retrieval, summarization, persona reasoning). Hot-load plugins without touching core runtime.

• Low-Latency Serving
  Async I/O pipelines with Redis caching and query batching, delivering ~34% lower average API latency under 1,000+ concurrent sessions.

• Quality Gates & Regression Safety
  Cross-language test harness: RSpec for API contract & GraphQL schema checks; PyTest for semantic response validation and regression detection (250+ tests).

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Architecture Overview

        ┌────────────────────────────────────────────┐
        │              Client Channels               │
        │  Web, Mobile, Slack, Zendesk, Custom UIs  │
        └───────────────────────┬────────────────────┘
                                │
                      ┌─────────▼──────────┐
                      │  API Gateway (REST)│
                      │  / GraphQL Router  │
                      └─────────┬──────────┘
                                │
            ┌───────────────────▼───────────────────┐
            │      Conversation Orchestrator        │
            │  • Policy Routing (GPT-4/Gemini)      │
            │  • Tool/Function Calls                │
            │  • Query Batching & Caching           │
            └───────────┬───────────┬───────────────┘
                        │           │
       ┌────────────────▼─┐     ┌───▼─────────────────┐
       │ Retrieval Plugin │     │ Persona/Reasoning    │
       │ (RAG)           │     │ Plugins               │
       └───────┬─────────┘     └───────────┬──────────┘
               │                           │
┌──────────────▼───────────────┐     ┌─────▼───────────┐
│ GraphQL Knowledge Layer      │     │ Summarization    │
│ • Structured resolvers       │     │ • Map/Reduce     │
│ • Joins & filters            │     │ • Long-context   │
└──────────────┬───────────────┘     └─────┬───────────┘
               │                           │
    ┌──────────▼───────────┐     ┌────────▼───────────┐
    │ FAISS Vector Store    │     │ LLM Providers      │
    │ • Dense indices       │     │ • OpenAI GPT-4     │
    │ • Hybrid BM25 + dense │     │ • Gemini Pro       │
    └──────────┬───────────┘     └────────┬───────────┘
               │                           │
         ┌─────▼───────┐             ┌─────▼─────────┐
         │ Blob Store  │             │ Redis Cache    │
         │ (docs/chunks│             │ + Rate Limits  │
         └─────────────┘             └───────────────┘

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Features in Detail

• Document Processing

  • Ingestion pipelines (PDF, HTML, Markdown, CSV).
  • Chunking by semantic boundaries; metadata tagging (source, timestamp, access level).
  • Embeddings via OpenAI/Text-Embeddings or local models; FAISS index build & refresh tasks.

• LLM Applications

  • Retrieval, summarization (map-reduce & refine), persona-based reasoning.
  • Deterministic tool-calling contracts for GraphQL lookups (schema-validated).
  • Hallucination guards: grounded-only mode, citation injection, and answerability classification.

• Sentiment & Analytics

  • Sentiment classification for customer feedback streams.
  • Topic clustering, intent detection, and trend reporting through nightly jobs.

• Performance

  • Async HTTP clients; request coalescing; Redis object & embedding cache.
  • Query batching to LLMs and FAISS to reduce cold latency spikes.
  • Horizontal autoscaling of plugin workers.

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Tech Stack

• Languages: Python, Ruby
• APIs/Providers: OpenAI (GPT-4), Google Gemini Pro
• Search/Vector: FAISS, optional BM25 hybrid
• Orchestration: Async I/O (Python), Sidekiq/Celery (as applicable)
• Cache/Queue: Redis
• API: REST + GraphQL (schema-first, codegen for contracts)
• Testing: RSpec (Ruby), PyTest (Python), golden files for semantic diffs
• Ops: Docker, CI with test matrices, load testing scripts (k6/Locust)

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Getting Started

Prerequisites

• Python 3.10+, Ruby 3.1+
• Docker & Docker Compose
• Redis
• API keys: OPENAI_API_KEY, GEMINI_API_KEY

Quickstart

# 1) Clone
git clone https://github.com/your-org/ai-chat-platform.git
cd ai-chat-platform

# 2) Environment
cp .env.example .env
# set OPENAI_API_KEY, GEMINI_API_KEY, REDIS_URL, DB_URL, etc.

# 3) Build & run
docker compose up --build

# 4) Seed FAISS index (example)
make ingest DOCS_PATH=./samples

# 5) Hit the API
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message":"Summarize latest refund policy","persona":"support"}'