🚀 Augmented Generation Experiments with LLMs

Welcome to Augmented Generation with LLMs, a curated collection of interactive Colab notebooks exploring different approaches to enhance Large Language Model (LLM) outputs through context, cache, and retrieval-based techniques. Built using LangChain, Ollama, Vector Databases, and more, this repo demonstrates powerful patterns to improve LLM performance and memory capabilities.

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🧠 Notebooks Included

📌 1. Cache_Augmented_Generation.ipynb

• Concept: Enhances LLM inference by reusing previously computed results with a smart cache layer.
• Tech Stack:
  • Pickling for storing & retrieving cached outputs
  • In-memory caching logic
  • Minimal recomputation, blazing speed ⚡
• ✅ Ideal for repetitive or FAQ-style inputs.

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🧩 2. Context_Augmented_Generation.ipynb

• Concept: Augments responses using relevant context from previous interactions or documents.
• Tech Stack:
  • Custom context memory
  • LangChain’s prompt management
  • Context-based generation pipeline
• 📚 Boosts response richness and continuity.

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🔍 3. Simple_Retrieval_Augmented_Generation.ipynb

• Concept: Integrates Vector DBs and Embeddings to retrieve relevant chunks from external data for precise answering.
• Tech Stack:
  • LangChain + FAISS/Chroma
  • Embedding models via Ollama
  • Retrieval-Augmented Generation (RAG) flow
• 🔎 Perfect for knowledge-based systems and document Q&A.

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🛠️ Tech Stack

Tech / Tool	Purpose
LLMs	Generative responses
LangChain	Chaining prompts, memory, and tools
Ollama	Lightweight local LLMs
Vector DB	Fast document retrieval
Embeddings	Semantic search capability
Pickling	Output caching
Cache Memory	Efficient reuse of responses
Jupyter Notebook	Interactive development

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📸 Screenshots

🧪 Live Testing with Cached Responses

🔗 Context Injection Flow

📚 Retrieval-Augmented Answer

💡 Tip: You can replace the above links with your actual GitHub asset paths or embed Colab previews using badges.

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🧭 How to Run

1. Open any notebook in Jupyter Notebook or Google Colab .
2. Follow the instructions in each cell.
3. Make sure you have the required models via Ollama and libraries installed (langchain, faiss-cpu, etc.)

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Questions or contributions? Open an issue or PR anytime.