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PROJECT
Build a RAG Using LangChain with Google Gemini
In this project, we’ll build a text-to-text RAG application using LangChain by experimenting with the configuration parameters of the Google Gemini Pro model.
You will learn to:
Build an LLM functionality using LangChain to generate high-quality text responses.
Understand and respond contextually to user prompts.
Fine-tune the LLM model with relevant data to improve the performance.
Explore different techniques for controlling the output of the model (e.g. temperature, top-k, top-p).
Skills
Machine Learning
Natural Language Processing
Data Science
Data Analysis
Generative AI
Prerequisites
Hands-on experience with Python NLP libraries
Good understanding of machine learning
Basic understanding of large language models
Basic understanding of LangChain
Technologies
Python
LangChain
Project Description
The project’s main objective is to develop a large language model (LLM) application using LangChain.
LLMs are AI-based models trained on massive amounts of data to understand complex human-like texts and generate human-like content. LangChain is an open source used to build AI applications driven by large language models (LLMs) like GPT-3. It has many resources that aid in building LLM-based applications, such as chatbots, translators, content writing tools, and summarizers.
We’ll build a text content generator application where the input prompt will be a few sentences and the output paragraphs of relevant text. We’ll use the Google Gemini Pro model to create this application. We need an API to access the model and perform operations like text generation, fine-tuning, chat history generation, and RAG. The model parameters can be fine-tuned for optimal performance.
Project Tasks
1
Introduction
Task 0: Get Started
Task 1: Import Libraries
2
Interact with Google Gemini
Task 2: Ask the Questions Using the Prompts
Task 3: Chat with Gemini and Retrieve the Chat History
3
Experiment with the Parameters
Task 4: Experiment with the temperature Parameter
Task 5: Experiment with the max_output_tokens Parameter
Task 6: Experiment with the top_k Parameter
Task 7: Experiment with the top_p Parameter
Task 8: Experiment with the candidate_count Parameter
4
Build a RAG System
Task 9: Get Started with Retrieval-Augmented Generation
Task 10: Load the PDF and Extract the Text
Task 11: Create the Gemini Model and Generate Embeddings
Task 12: Create the RAG Chain and Ask Query
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.