Context Engineering Tutorial

This repo contains the source code from my Youtube course on Context Engineering with DSPy.

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Getting Started

Prerequisites

• Python 3.10+ (required)
• uv (recommended) or pip for package management

Installation

1. Clone the repository:

   git clone https://github.com/avbiswas/context-engineering-dspy
   cd context-engineering-dspy/tutorial

2. Install dependencies:

   # Using uv
   uv sync

3. Set up your API keys:

   Required API Keys:

   • OPENAI_API_KEY - For OpenAI models
   • GEMINI_API_KEY - For Google Gemini models
   • TAVILY_API_KEY - For web search functionality

   Environment Management Options:

   Option 1: Using direnv (Recommended)

   # Install direnv first, then create .envrc file
   echo "export OPENAI_API_KEY=your_key_here" >> .envrc
   echo "export GEMINI_API_KEY=your_key_here" >> .envrc
   echo "export TAVILY_API_KEY=your_key_here" >> .envrc
   direnv allow

   Option 2: Using .env file with python-dotenv

   # Create .env file
   touch .env

   Add your keys to .env:

   OPENAI_API_KEY=your_key_here
   GEMINI_API_KEY=your_key_here
   TAVILY_API_KEY=your_key_here

   Note: This requires adding dotenv.load_dotenv() to your Python scripts.

   Option 3: Global environment variables (Not recommended for security)

   export OPENAI_API_KEY=your_key_here
   # Repeat for other keys...

4. Run the examples: Navigate to any level directory and run the Python scripts:

   cd level2_multi_interaction
   uv run t1_sequential_flow.py

File Descriptions

Level 1: Atomic Prompts

• level1_atomic_prompts/level1.ipynb: Introduces the basics of prompting and interacting with language models.

Level 2: Multi-Interaction

• level2_multi_interaction/t1_sequential_flow.py: Demonstrates a sequential flow of interactions with the language model.
• level2_multi_interaction/t2_iterative_refinement.py: Shows how to iteratively refine the output from the model.
• level2_multi_interaction/t3_conditional_branch.py: Illustrates how to use conditional logic to guide the conversation with the model.
• level2_multi_interaction/t3-multi_out.py: Multiple output handling example.
• level2_multi_interaction/t3-multi_out_refine.py: Refined multiple output handling.
• level2_multi_interaction/t4_reflection.py: An example of how to make the model reflect on its own output.

Level 3: Evaluation

To run mlflow server, use the command: uv run mlflow server --backend-store-uri sqlite:///mydb.sqlite --port 5000

Uncomment the below lines to track experiments in mlflow

# import mlflow
# mlflow.autolog()
# mlflow.set_tracking_uri("http://127.0.0.1:5000")
# mlflow.set_experiment("Tool calling")

You can visit localhost:5000 to track experiments from the mlflow dashboard.

• level3_evaluation/reflection.py: Shows how to use reflection for evaluation to generate dataset of results with different hyperparams.
• level3_evaluation/pairwise_elo.py: Use pairwise comparison of model outputs (not actual elo, but similar motives)
• level3_evaluation/analysis.ipynb: Analysis notebook for evaluation techniques.

Level 4: Tools

You will need the TAVILY_API_KEY to run web search. You can sign up for a free account from their website.

• level4_tools/main.py: Main tool usage examples.
• level4_tools/tool_calling_agent.py: An example of a tool-calling agent.
• level4_tools/tools.py: Tool definitions and implementations.
• level4_tools/idea_gen.py: Idea generation tool example.
• level4_tools/joke_gen.py: Joke generation tool example.

Level 5: RAGs (Retrieval-Augmented Generation)

First, download this dataset: https://www.kaggle.com/datasets/abhinavmoudgil95/short-jokes

Unzip inside level5/data Next, prepare the embeddings:

cd level5_rags
uv run vector_embedding.py

This code looks for the file level5_rags/data/shortjokes.csv This will create some files inside the data/ directory. You should now be able to run scripts to play with retrieval.

Core RAG Implementations:

• level5_rags/basic_rag.py: A basic RAG implementation.
• level5_rags/hyde.py: An implementation of the HyDE (Hypothetical Document Embeddings) technique.
• level5_rags/annoy_rag.py: RAG implementation using Annoy for vector similarity.

Retrieval Components:

• level5_rags/bm25_retriever.py: BM25-based retrieval implementation.
• level5_rags/rank_fusion.py: An example of fusing ranks from multiple retrievers.
• level5_rags/vector_embedding.py: Vector embedding utilities.

Tools & Applications:

• level5_rags/main.py: Main application with RAG-powered tools.
• level5_rags/tools.py: Tool definitions for RAG applications.
• level5_rags/joke_gen.py: Joke generation using RAG.
• level5_rags/idea_gen.py: Idea generation using RAG.

Utilities:

• level5_rags/prepare_data.py: Data preparation utilities for RAG systems.
• level5_rags/data/: Directory containing data files for RAG examples.

Quick Start Patterns

To run examples from each level:

# Level 2 - Multi-interaction examples
cd level2_multi_interaction
uv run t1_sequential_flow.py
uv run t2_iterative_refinement.py