Using LangChain ReAct Agents with Qdrant and Llama3 for Intelligent Information Retrieval

Introduction

In the world of natural language processing (NLP) and information retrieval, combining cutting-edge tools and models is crucial for delivering efficient query handling and insightful responses. This tutorial explores how three powerful technologies — LangChain’s ReAct Agents, the Qdrant Vector Database, and the Llama3 large language model (LLM) from the Groq endpoint — can work together to supercharge intelligent information retrieval systems.

Fact: Agents are nothing but LLMs with a complex prompt.

Source

The documents I will be referring to in this article include:

1. https://policies.google.com/terms?hl=en-US
2. https://openai.com/policies/terms-of-use/
3. https://www.facebook.com/legal/terms?paipv=0&eav=AfYU7-7Cf-zij8FiJxMbZUIw3eF6mj9sXRTd01_PiZSBjEuKOE3VHDVPzP31EkYsVZk&_rdr
4. https://students.ucsd.edu/_files/sls/handbook/SLSHandbook-Contract_Law.pdf

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