This repository explores advanced orchestration within the LangChain ecosystem, specifically focusing on Sequential Chains and Agentic Workflows. It demonstrates how to move beyond single-prompt interactions to build complex, multi-step AI systems that can reason and interact with data.
The project is divided into two major architectural patterns:
- Sequential Skill-Building System: A workflow built using LangChain Expression Language (LCEL) that sequentially processes user preferences and constraints to generate tailored learning plans.
- Autonomous Customer Agents: An implementation of an AI Agent designed to act as a natural language interface for databases. It can autonomously retrieve, filter, and summarize specific customer entities (e.g., "Peak Performance Co.") based on conversational input.
- LCEL Implementation: Utilizing the LangChain Expression Language for clean, readable, and efficient chain composition.
- Sequential Logic: Designing multi-stage prompt templates where the output of one step informs the context of the next.
- Tool-Augmented Agents: Building agents that can "think" and use external tools to fetch real-world data before formulating a response.
- Data Summarization: Automated extraction of key metrics (ID, Subscription Type, Active Users) from structured data via natural language.
- Language: Python
- Framework: LangChain (LCEL)
- LLM Integration: Google Gemini / ChatGoogleGenerativeAI
- Tools: LangChain Community Tools, Pandas
It is recommended to use a dedicated environment for LangChain projects:
# Create and activate the environment
git clone https://github.com/Joe-Naz01/seq_chains_ai.git
cd seq_chains_ai
conda create -n chains_agents python=3.10 -y
conda activate chains_agents
jupyter notebook
# Install dependencies
pip install -r requirements.txt