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llm-agents

1: intro_to_llm_agents

It is structured in three parts :

  1. Planning / Reasoning
  2. Make use of Memory - Long, Short, Sensory
  3. Link Multiple Tools to Agent/s

2: base_code_llm_agents_langchain

To build llm agents using langchain, refer this repository for base code. It covers :

  1. Popular LLM initialization
  2. Search tools initialization
  3. Agent with multiple tools using different LLMs.

This makes it easy to understand the flow and extend codebase.

4: text2pandas-query-pl

Talk to your Dataset. This project uses 'LlamaIndex Query Pipelines'.

  1. Initialize LLM. Read dataset file.
  2. Define Query Pipeline, which is actually a DAG flow. Take care of output links. Visualise the DAG.
  3. Talk to your llm app.

input text -> pandas commands -> eval -> result