🤔 GPT-4 Mini: Agent Master or Mishap?
This exploration dives deep into using GPT-4 Mini for agentic RAG, comparing it to the reliable Cloud 3.5 Sonnet using the real-world Airbnb embeddings dataset from MongoDB 🏘️.
📊 Building the Agentic RAG Pipeline: A Step-by-Step Guide
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Laying the Foundation: Start by installing necessary libraries like LlamaIndex, LangChain, and ChromaDB. Don’t forget your OpenAI and HuggingFace tokens 🔑!
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Embeddings and LLMs: Opt for the efficient OpenAI TextEmbedding 3 small model for embeddings and, for this comparison, the GPT-4 Mini as our LLM. Remember, there’s a separate notebook showcasing Cloud 3.5 Sonnet 😉.
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Data Prep is Key: Load the Airbnb dataset, keeping around 2000 entries to manage costs. Drop unnecessary columns like existing text embeddings to avoid confusion 🧹.
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Crafting Metadata: Select key columns like amenities, room type, and description, and merge them into a single text blob for embedding. This gives your LLM a complete picture 🖼️.
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Chunking and Embedding: Break down the text into manageable 5000-character chunks. Embed each chunk and its corresponding metadata.
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Vector Store Setup: Employ ChromaDB to build a vector store housing your chunks, embeddings, and metadata. This searchable store is your agent’s knowledge base 📚.
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Empowering Your Agent: Define the ‘knowledge_base’ tool in Llama Index, pointing to your vector store. This equips your agent to retrieve relevant information when queried.
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Agent in Action: Instantiate Llama Index’s
FunctionCallingAgentWorker, passing in your GPT-4 Mini model and the defined tool. Watch as it tackles user queries! 🤖
⚔️ GPT-4 Mini vs. Cloud 3.5 Sonnet: The Showdown
The results are in! While GPT-4 Mini handles basic queries, its responses lack the depth and accuracy seen with Cloud 3.5 Sonnet. Sonnet excels in crafting detailed prompts and providing nuanced answers.
Example: Asking for the “worst” Airbnb listing in New York, GPT-4 Mini stumbled, while Sonnet cleverly pointed out the lack of Miami listings in the dataset and offered insightful comparisons between New York and Miami rentals 🏙️🌴.
🚀 Key Takeaways
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GPT-4 Mini shows promise but needs refinement for complex agentic RAG workflows.
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Cloud 3.5 Sonnet remains a powerful and reliable choice for agent orchestration.
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Carefully curate your metadata and optimize your embedding strategy for best results.
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Experiment with different LLMs and tools within Llama Index to fine-tune your agent’s performance.
🧰 Resource Roundup:
- Colab Notebook (GPT-4 Mini): https://tinyurl.com/3cy8f7b2
- Colab Notebook (Cloud 3.5 Sonnet): https://tinyurl.com/57f7sz5e
- Airbnb Embeddings Dataset: https://tinyurl.com/vcdatde3
- LlamaIndex Documentation: https://tinyurl.com/yc39zw8j
- RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/c…
🤔 Your Challenge:
Try building a similar agentic RAG pipeline using a different dataset and experiment with various prompt engineering techniques. Can you enhance GPT-4 Mini’s performance? Share your findings!
