Skip to content
Prompt Engineering
0:12:51
2 991
105
21
Last update : 28/08/2024

Unlocking the Power of GPT-4 Mini for Agentic RAG πŸš€

πŸ€” 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 🏘️.

πŸ“Š Building the Agentic RAG Pipeline: A Step-by-Step Guide

  1. Laying the Foundation: Start by installing necessary libraries like LlamaIndex, LangChain, and ChromaDB. Don’t forget your OpenAI and HuggingFace tokens πŸ”‘!

  2. 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 πŸ˜‰.

  3. 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 🧹.

  4. 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 πŸ–ΌοΈ.

  5. Chunking and Embedding: Break down the text into manageable 5000-character chunks. Embed each chunk and its corresponding metadata.

  6. 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 πŸ“š.

  7. 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.

  8. 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! πŸ€–

βš”οΈ 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 πŸ™οΈπŸŒ΄.

πŸš€ Key Takeaways

  • GPT-4 Mini shows promise but needs refinement for complex agentic RAG workflows.

  • Cloud 3.5 Sonnet remains a powerful and reliable choice for agent orchestration.

  • Carefully curate your metadata and optimize your embedding strategy for best results.

  • Experiment with different LLMs and tools within Llama Index to fine-tune your agent’s performance.

🧰 Resource Roundup:

πŸ€” 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!

Other videos of

Play Video
Prompt Engineering
0:12:36
1 100
95
14
Last update : 30/01/2025
Play Video
Prompt Engineering
0:13:53
799
65
6
Last update : 28/01/2025
Play Video
Prompt Engineering
0:16:03
813
66
16
Last update : 27/01/2025
Play Video
Prompt Engineering
0:20:19
1 018
60
4
Last update : 23/01/2025
Play Video
Prompt Engineering
0:19:57
0
0
0
Last update : 22/01/2025
Play Video
Prompt Engineering
0:08:54
1 037
76
9
Last update : 21/01/2025
Play Video
Prompt Engineering
0:05:56
175
15
3
Last update : 17/01/2025
Play Video
Prompt Engineering
0:08:46
149
8
2
Last update : 16/01/2025
Play Video
Prompt Engineering
0:17:31
187
10
2
Last update : 15/01/2025