Learn how to talk to your code using Large Language Models (LLM) and Langchain


Learn how to talk to your code using Large Language Models (LLM) and Langchain

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Learn how to talk to your code using Large Language Models (LLM) and Langchain

Being able to understand the intricacies of your code when you are writing it is a major part of learning a new programming language. Thanks to the explosion in AI technology, it is now possible for you to talk to your code, ask it questions when you become stuck, and allow it to aid you in the best way possible by providing options and even writing suggestions for you.

The ability to interact with code repositories using Large Language Models (LLM) and Langchain has become a fascinating development.  Prompt Engineering has recently released a guide that delves into this subject, demonstrating how to create a Q&A system for a GitHub code repository using Langchain and LLM. This system allows users to chat with the Langchain GitHub repository, but the same code example can be used to build a chat system for any code base.

The architecture used in this system is akin to those used for interacting with documents such as PDF files. However, there is a key difference when it comes to context splitting while working with source code. Unlike document splitting in traditional documents, the strategy used for code involves separating each top-level function in a file into its own document. This approach allows for a more granular and context-specific interaction with the code.

Talk to your code using LLM and Langchain

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To implement this system, several packages are required. These include OpenAI LLM, Tick Token, Vector Store, and Chroma DB, all of which are integrated using Langchain. The .EnV package is also utilized to load the OpenAI API key from the .EnV file.

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The code example used in the guide is a Langchain repository on GitHub. This repository is downloaded and cloned onto the local system for further use. A generic loader is then employed to load all the documents or Python files from the repository. The Recursive Character Text Splitter is used to split these documents into chunks or sub-documents, facilitating a more detailed interaction with the code.

To perform retrieval Q&A on the code base, embeddings are computed using OpenAI’s embeddings and stored in the Chroma Vector Store. A retrieve object is then created based on the Vector DB to perform a vector search on the embeddings. The type of search used is Maximum Marginal Relevance (MMR), which ensures that the retrieved documents or chunks are not from the same Python file. This approach enhances the diversity of the search results, providing a more comprehensive understanding of the code.

The conversational retrieval chain is defined using Chat OpenAI, Conversation Summary Memory, and the Conversational Retrieval Chain. This system is capable of answering questions based on the code base and can remember previous conversations due to the memory component. This feature allows for a more interactive and engaging experience when interacting with the code.

Interestingly, the same can be achieved using an open-source model. Specifically, the Code Llama, a 13B model, can be used to create a similar system. This flexibility demonstrates the versatility of Large Language Models and Langchain in creating interactive systems for code repositories.

The ability to talk to your code using Large Language Models and Langchain is a significant advancement in the field of technology. It not only enhances the interaction with code repositories but also provides a more comprehensive understanding of the code. Whether you’re using the OpenAI LLM or an open-source model like Code Llama, the possibilities are endless. The guide and video by the Prompt Engineering YouTube channel provide a valuable resource for anyone interested in exploring this fascinating area.

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What is a Large Language Model?

A Large Language Model (LLM) is a machine learning model trained on a vast corpus of text data to perform tasks related to natural language understanding and generation. These models, which are often based on architectures like the Transformer, are designed to handle a variety of NLP tasks, such as translation, summarization, question-answering, and more.

LLMs are characterized by their scale, both in terms of the data they are trained on and the size of the model itself. For example, models like GPT-3 or GPT-4 consist of tens or hundreds of billions of parameters. This large number of parameters enables the model to capture more complex relationships in the data and produce more nuanced outputs.

The model’s architecture and training process involve learning to predict the next token (e.g., word or subword) in a sequence given the preceding tokens. This is done by optimizing a loss function during training, where the model’s predictions are compared to the actual tokens in the training data.

LLMs have broad applications, ranging from automating customer service inquiries to aiding in scientific research. However, they also come with challenges, such as the potential to generate misleading or incorrect information, or to perpetuate biases present in the training data.

What is Langchain?

LangChain is an innovative framework aimed at facilitating the development of applications that leverage the power of language models. Unlike traditional setups that interact with language models solely through APIs, LangChain emphasizes a more integrated approach. It encourages applications to be data-aware by connecting language models to various data sources and to be agentic by enabling these models to interact with their environment.

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The framework offers two primary value propositions. First, it provides a set of modular components essential for working with language models, complete with a collection of implementations for these abstractions. These components are designed for ease of use, whether or not developers choose to utilize the entire LangChain framework.

Second, it introduces the concept of “Use-Case Specific Chains,” which are higher-level interfaces assembled from these components to address specific use cases. These chains are fully customizable, allowing developers to tailor the framework to their specific needs. The framework’s documentation is structured to cover these two core aspects, offering language-agnostic guidance while also providing language-specific resources for more tailored application development. For more information jump over to the official documents.

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