RAG + Langchain Python Project: Easy AI/Chat For Your Docs
TLDRThis tutorial demonstrates how to build a retrieval augmented generation (RAG) application using Langchain and OpenAI in Python. It's ideal for interacting with large text datasets like books, documents, or lectures. The video guides viewers through preparing data, creating a vector database with ChromaDB, and querying it for relevant information. It also covers generating responses with OpenAI, ensuring the AI uses provided data without fabricating answers. The process includes splitting documents into chunks, creating a database, and using vector embeddings for efficient data retrieval. The tutorial is practical, showing how to switch data sources and handle different types of queries, with a GitHub link provided for further exploration.
Takeaways
- 😀 The video demonstrates how to build a retrieval augmented generation (RAG) app using Langchain and OpenAI to interact with documents or data sources.
- 🔍 RAG is beneficial for handling large text data, such as books, documents, or lectures, and enables AI interaction like asking questions or building chatbots.
- 📚 The example data source used is the AWS Lambda documentation, which the AI can use to provide responses and quote the original source.
- 📁 The process starts with preparing a data source, such as PDFs or markdown files, and organizing them into a structured folder.
- 🔑 The script uses a directory loader module from Langchain to load markdown data and convert it into documents with metadata.
- 📑 Documents are then split into smaller chunks to improve search relevance and focus, using a recursive character text splitter.
- 🗄️ A vector database, ChromaDB, is created using embeddings generated by OpenAI, which helps in efficiently querying relevant data chunks.
- 🧭 Embedding vectors represent text meanings numerically, allowing for easy comparison of text similarity using cosine similarity or Euclidean distance.
- 🔎 The video explains how to query the Chroma database to find the most relevant chunks of information in response to a given query.
- 💬 Finally, the AI uses the retrieved data chunks to craft a coherent response to the query, providing both an answer and source references.
Q & A
What is the purpose of the application demonstrated in the video?
-The purpose of the application is to build a retrieval augmented generation (RAG) app using Langchain and OpenAI, which allows users to interact with their own documents or data sources using AI, such as asking questions or building customer support chatbots.
What does RAG stand for and how is it used in the application?
-RAG stands for Retrieval Augmented Generation. It is used in the application to enhance the AI's ability to provide responses by first retrieving relevant information from a database and then generating a response based on that retrieved data.
What is the data source used in the example provided in the video?
-The data source used in the example is the AWS documentation for Lambda, which is used to demonstrate how the AI can provide responses based on specific documentation.
How does the AI agent ensure the responses are based on the provided data sources?
-The AI agent ensures responses are based on provided data sources by quoting the source of the information, which allows users to verify that the responses are not fabricated but are derived from the data sources.
What is the role of Langchain in this project?
-Langchain plays a crucial role in this project by providing the necessary tools and modules to load, process, and manage the data, as well as to interact with OpenAI's API for generating vector embeddings and creating the database.
How is the data prepared for use in the RAG application?
-The data is prepared by loading it into Python using the directory loader module, splitting it into chunks of text, and then turning those chunks into a vector database using ChromaDB and OpenAI's embeddings function.
What is a vector embedding in the context of this video?
-A vector embedding is a numerical representation of text that captures its meaning. It is used to determine the similarity between pieces of text by calculating the distance between their vector representations.
How does the video demonstrate the process of querying the database for relevant data?
-The video demonstrates querying the database by showing how to use the embedding function to convert a user's query into a vector, and then searching the database to find the chunks of information that are closest in embedding distance to the query.
What is the significance of the metadata associated with each document chunk?
-The metadata associated with each document chunk is significant because it provides information about the source of the text, such as the file path and the starting index, which helps in attributing the AI's responses back to the original data sources.
How does the video show the integration of retrieved data into a coherent AI response?
-The video shows the integration of retrieved data into a coherent AI response by using a prompt template that includes the retrieved context and the user's query, which is then used to generate a response with OpenAI's LLM, ensuring the response is based on the provided data.
Outlines
🤖 Building a Retrieval-Augmented Generation App
The video introduces a tutorial on constructing an app that utilizes retrieval augmented generation (RAG) with Langchain and OpenAI. This app enables interaction with personal documents or data sources, ideal for large text datasets like books or documentation. The presenter demonstrates using AWS Lambda documentation as a data source and asks a question based on this data. The app not only provides a response but also cites the original source, ensuring the information is derived from provided sources rather than fabricated. The tutorial promises to guide viewers step-by-step, starting from data preparation to creating a vector database, querying the database, and forming coherent responses.
📚 Data Preparation and Vector Database Creation
The second paragraph delves into the process of preparing data for the app. It emphasizes the need for a data source, such as PDFs or text files, and suggests examples like software documentation or podcast transcripts. The video then instructs on loading markdown files into Python using Langchain's directory loader module, transforming each file into a 'document' containing text and metadata. The challenge of managing long documents is addressed by introducing a text splitter that divides documents into smaller, more focused 'chunks'. This aids in relevance when searching through data. The paragraph concludes with a demonstration of the text-splitting process, showing how one document was split into many chunks, each with its own metadata.
🔍 Querying the Database for Relevant Information
Paragraph three explains the necessity of converting text chunks into a queryable database using ChromaDB, a vector embeddings-based database. It details the creation of a Chroma database with the help of OpenAI's embeddings function, which generates vector embeddings for each chunk. The video also touches on the concept of vector embeddings, describing them as multi-dimensional coordinates that represent text meaning. The distance between these vectors, calculated through cosine similarity or Euclidean distance, determines the relevance of text snippets to a query. The paragraph concludes with a demonstration of querying the database to find the most relevant chunks in response to a question, aiming to construct a customized AI response based on the source material.
📝 Crafting Responses with Source References
The final paragraph showcases how to use the found data chunks to craft AI-generated responses. It discusses loading the Chroma database and using the same embedding function to search for the best matches to a query. The process involves creating a prompt template, formatting it with the context and query, and then using an LLM model like OpenAI to generate a response. The paragraph also addresses the importance of providing source references by extracting metadata from the document chunks. The video concludes with a demonstration of the complete process, including a switch to a different data source to illustrate the app's versatility. The presenter encourages viewers to try the tutorial with their own datasets and provides a GitHub link for the code in the video description.
Mindmap
Keywords
💡Retrieval Augmented Generation (RAG)
💡Langchain
💡OpenAI
💡Vector Database
💡Embeddings
💡ChromaDB
💡Metadata
💡LLM (Large Language Model)
💡Query
💡Context
Highlights
Build a retrieval augmented generation app using Langchain and OpenAI.
Interact with your own documents or data source using AI.
Suitable for large text data like books, documents, or lectures.
Use AI to answer questions or build customer support chatbots.
Learn to build an app step-by-step with detailed instructions.
Prepare data sources like PDFs, text, or markdown files.
Use directory loader module from Langchain to load markdown data.
Split documents into chunks for more focused search results.
Use recursive character text splitter with customizable chunk size and overlap.
Create a ChromaDB database using vector embeddings as keys.
Require an OpenAI account for using OpenAI embeddings function.
Save the database to disk for easy deployment.
Understand vector embeddings for text representation.
Use cosine similarity or Euclidean distance to calculate vector distances.
Generate vector embeddings using an LLM like OpenAI.
Use Langchain's evaluator to compare embedding distances.
Query the database to find chunks most relevant to a question.
Craft a custom response based on the retrieved chunks.
Use a prompt template to create a prompt for OpenAI.
Extract source references from document metadata.
Switch data sources to demonstrate versatility of the app.
Summarize information from multiple sources for comprehensive responses.
GitHub code link provided for hands-on learning.