Virtual TA Chatbot Implementation Pathways

Implementation Stack

Data Processing → Vector Storage → Query Processing → LLM Generation → Response + Context

1. Paid/Premium Path - Cloud-Based with Managed Services

Core Architecture

• Vector Database: Pinecone (free tier: 1M vectors) or Weaviate Cloud
• Embedding Model: OpenAI text-embedding-3-small or Cohere
• LLM: OpenAI GPT-4 or Anthropic Claude via API
• Backend: FastAPI (Python) or Express.js (Node.js)
• Hosting: Vercel, Railway, or Google Cloud Run

Tools & Services:

• LangChain/LlamaIndex: Framework for RAG pipeline
• Unstructured.io: Parse HTML/JSON content
• Pinecone/Weaviate: Vector search
• OpenAI/Anthropic APIs: Text generation
• FastAPI: API endpoint creation

Estimated Monthly Cost: $20-50 (depending on usage)

Key Features

• High accuracy responses
• Semantic search across content
• Source attribution with URLs
• Scalable infrastructure
• Easy deployment

2. Free Tier Path - Mixed Cloud/Local Services

Core Architecture

• Vector Database: Weaviate local instance or Qdrant
• Embedding Model: OpenAI free tier or HuggingFace sentence-transformers
• LLM: OpenAI or local Llama via Ollama
• Backend: FastAPI or Flask
• Hosting: Railway free tier, Render, or local deployment

Implementation Stack

Primary Option:

• Ollama: Local LLM deployment (Llama 3.1, Mistral)
• Qdrant: Local vector database
• HuggingFace Transformers: Free embedding models
• Backend: FastAPI or Django

Hybrid Option:

• Supabase: Free PostgreSQL with pgvector extension
• OpenAI free tier: $5 credit for embeddings/generation
• Vercel: Free hosting for API endpoints

Estimated Monthly Cost: $0-10

Key Features

• No recurring costs after setup
• Good performance with optimization
• Full control over data
• Requires more technical setup

3. Open Source Path - Fully Self-Hosted

Core Architecture

• Vector Database: ChromaDB, FAISS, or Qdrant
• Embedding Model: sentence-transformers (all-MiniLM-L6-v2)
• LLM: Llama 3.1, Mistral 7B, or Code Llama via Ollama
• Backend: FastAPI or Django
• Hosting: Self-hosted or free cloud instances

Implementation Stack

Recommended Stack:

• Ollama: Local LLM management
• ChromaDB: Simple vector storage
• sentence-transformers: Free embeddings
• FastAPI: API framework
• Docker: Containerized deployment

Alternative Stack:

• FAISS: Facebook's vector search
• Transformers: HuggingFace model library

Estimated Monthly Cost: $0 (hardware costs only)

Key Features

• Complete data privacy
• No API costs
• Customizable models
• Requires significant local resources

Implementation Approaches Beyond RAG

1. Fine-tuning Approach

• Method: Fine-tune smaller models (Mistral 7B, Llama 3.1) on your course content
• Tools:
  • Unsloth (efficient fine-tuning)
  • LoRA/QLoRA for parameter-efficient training
  • HuggingFace Trainer
• Pros: Model learns course-specific patterns
• Cons: Harder to update, less flexible

2. Prompt Engineering + Context Injection

• Method: Use large context models (Claude 3.5, GPT-4 Turbo) with full course content
• Tools:
  • Anthropic Claude (200K context)
  • OpenAI GPT-4 Turbo (128K context)
• Implementation: Chunk content, inject relevant chunks into prompt
• Pros: Simple implementation, high accuracy
• Cons: Higher token costs

3. Graph-Based Knowledge Extraction

• Method: Extract entities and relationships from content, build knowledge graph
• Tools:
  • Neo4j (graph database)
  • spaCy (NER)
  • NetworkX (graph processing)
• Implementation: Query graph for relevant context, generate responses
• Pros: Structured knowledge representation
• Cons: Complex setup, requires NLP expertise

4. Elasticsearch + LLM Hybrid

• Method: Use Elasticsearch for keyword/semantic search, LLM for generation
• Tools:
  • Elasticsearch (free tier available)
  • sentence-transformers for dense vectors
  • Local LLM via Ollama
• Pros: Powerful search capabilities
• Cons: Additional infrastructure complexity

Data Processing Pipeline (All Paths)

1. Content Extraction

• HTML Parsing: Use Unstructured.io or BeautifulSoup
• JSON Parsing: Use built-in JSON libraries
• Text Cleaning: Remove HTML tags, scripts, etc.

2. Chunking Strategy

• Semantic chunking: Split by topics/sections
• Fixed-size chunking: 512-1024 tokens with overlap
• Discourse-aware: Keep Q&A pairs together

3. Metadata Preservation

• Store original URLs
• Keep course section/category info
• Maintain creation timestamps
• Track content type (forum post vs. course material)