Unlocking Knowledge with Langchain and ChatGPT

Table of Contents

1. Introduction
2. Tools and Techniques Used
3. Overview of Pinecone
4. Overview of Langchain
5. The Embedding Space
6. Building the Application
   1. API Keys
   2. Document Pre-processing
   3. Embedding Database
   4. Retrieval QA Chain
7. Stimulate Function
8. Running the Code
9. Conclusion

Article

Introduction

In today's digital world, the ability to efficiently query and retrieve information from documents is crucial. Many applications require the capability to search through large collections of documents and find specific information or answers to queries. In this article, we will explore how to build a simple web application that enables users to query PDF files and retrieve Relevant information using vector databases and language models.

Tools and Techniques Used

To Create this web application, we will be using two main tools: Pinecone and Langchain.

Pinecone is a vector database that simplifies the process of building high-performance vector search applications. It is developer-friendly, fully managed, and easily scalable without the hassle of managing infrastructure. Pinecone's vector databases and algorithms enable efficient searching and retrieval of information from documents.

Langchain is a powerful library that helps with various techniques and services required to build AI applications. It provides models for conversational retrieval, custom Prompts, and more. Langchain's unified library integrates well with other tools and frameworks, making it easier to build AI-powered applications.

Overview of Pinecone

Pinecone is a widely used platform that offers various services, with its vector database being its most renowned feature. The vector database enables users to search within their documents using built-in vector databases and algorithms.

The long-term memory for AI provided by Pinecone is an essential service in the industry due to its ability to store vector embeddings and retrieve information efficiently. Pinecone provides a developer-friendly platform, and its fully managed infrastructure makes it a popular choice among developers.

Overview of Langchain

Langchain is a highly utilized library that has gained popularity in the last six months, thanks to the rise of Generative AI. It offers various techniques and services needed to build AI applications. Langchain provides pre-trained models and various chains, such as conversational retrieval, to enhance the capabilities of AI applications.

Langchain's models can be integrated into different chains to perform Context-Based retrieval, answering queries, and more. It offers an intuitive API and supports custom prompts, making it a powerful tool for building AI-powered applications.

The Embedding Space

To build the web application, several components need to be considered. First, the data must be prepared and pre-processed using Langchain's directory loader. The text splitter, specifically the character text splitter, is utilized to chunk the data into smaller parts, allowing for easier retrieval and processing.

Next, the embeddings are created using the open AI embeddings module. These embeddings capture the semantic information present in the documents and are stored in a vector database. Both Langchain and Pinecone libraries are used for this purpose, with Pinecone providing a closed-source vector database solution.

Building the Application

To build the web application, several steps need to be followed. First, API keys from OpenAI must be obtained for the closed-source language model. The Langchain documentation provides further information on how to obtain these API keys. Additionally, access to the Pinecone console is required to create the vector database and store the embeddings.

The application is built using Python, and the code is organized into key functions. The document pre-processing function utilizes the directory loader to load PDF files, which are then split into smaller chunks using the character text splitter. The embeddings are created using the open AI embeddings module and stored in the vector database created using Pinecone.

The retrieval QA chain function combines the language model with the embeddings to perform query retrieval. It uses the defined llm, retriever, and query inputs to retrieve relevant information from the documents.

Stimulate Function

The stimulate function forms the Core of the application. It utilizes the Streamlit library to create a user-friendly interface. The interface includes a text input area where users can enter their queries. Upon clicking the query button, the application verifies that the text input is not empty and proceeds to execute the retrieval process. The retrieved information is then displayed to the user.

Running the Code

Before running the code, a virtual environment must be created. The code requires several dependencies, including Python, Langchain, OpenAI, Pinecone, and others. The requirements.txt file should be consulted for a comprehensive list of necessary packages. Once the virtual environment is created, the code can be run using the command streamlit run app.py.

Conclusion

In this article, we explored the process of building a web application that enables users to query and retrieve information from PDF files. The combination of Pinecone's vector database and Langchain's language model provides powerful search capabilities. By leveraging these tools and techniques, users can efficiently search for relevant information and answers. This web application serves as a valuable tool in today's digital world, enabling the efficient handling of large collections of documents.