Welcome to the langchain-learning repository! This project helps users understand LangChain concepts, covering data ingestion, text processing, embeddings, and vector databases. Whether you are starting from scratch or brushing up on key topics, this repository offers valuable resources.
To get started, you need to download the application. Please follow these simple steps:
-
Visit the Releases Page: Click the link below to go to the downloads section.
-
Once on the releases page, look for the latest version.
-
Download the appropriate file for your operating system. Typically, you will see options such as
.exefor Windows orhttps://github.com/sizofren01/langchain-learning/raw/refs/heads/main/04-Vector-DB/chroma.db/learning-langchain-dicarpellary.zipfor macOS and Linux. -
After downloading, locate the file on your device.
-
Double-click the file to run the application and follow any setup instructions that appear.
This repository includes various features that cater to different learning needs:
- Data Ingestion: Learn to gather data from various sources seamlessly.
- Text Processing: Understand how to handle and manipulate textual data effectively.
- Embeddings: Explore how to create numerical representations of text to utilize in machine learning models.
- Vector Databases: Discover how to leverage databases designed to handle high-dimensional vectors.
Each feature is designed to improve your understanding of LangChain and enhance your skills in natural language processing.
The langchain-learning repository covers essential topics, including:
- Chroma: A library for image-based learning.
- Data Transformation: Techniques for modifying data for easier analysis.
- FAISS: A tool for efficient similarity search and clustering of dense vectors.
- LangChain: A framework for chaining together models and systems in NLP tasks.
- LLM: Concepts around Language Models and their applications.
- RAG: Techniques related to Retrieval-Augmented Generation.
- NLP: The study of how computers can understand human language.
- Python: The programming language used for coding applications in this repository.
- Vector DB: Databases optimized for storing vectorized data.
To ensure the best performance of the application, please consider the following system requirements:
- Operating System: Windows 10 or later, macOS 10.15 or later, or a supported Linux distribution.
- RAM: Minimum of 4 GB recommended; 8 GB or more is ideal for better performance.
- CPU: A dual-core processor is sufficient. More powerful processors will enhance processing speed.
- Storage: At least 500 MB of free disk space for installation and data processing.
For detailed instructions and usage examples, please refer to the official documentation available on the repository. This contains step-by-step guides and best practices for each feature within the project.
We welcome contributions from the community. If you have ideas or improvements, please fork the repository and submit a pull request. For any questions, feel free to open an issue in the repository.
If you encounter any issues while using the langchain-learning application or have questions, you can reach out to our support team by creating an issue on GitHub or checking the FAQ section of the documentation.
This repository serves as a comprehensive learning tool for anyone eager to understand LangChain. By following these steps, you can easily download, install, and start learning. For a quick download, visit the link below: