This Markdown file provides a concise overview of each directory in the LangChain course, detailing the key focus and content of each.
-
01_OpenAI_API- Basic usage of the OpenAI API for generative AI applications.
-
02_LangChain_Inputs_and_Outputs- Understanding the input and output mechanisms within LangChain.
-
03_Prompt_Templates- Templates and best practices for effective prompting for OpenAI models.
-
04_Chains- Detailed exploration of the Chains in LangChain with different use cases.
-
05_Callbacks- Utilizing callback functions in LangChain for dynamic responses and interactions.
-
06_Memory- Techniques and methods for implementing memory in generative AI models.
-
07_OpenAI_Functions- OpenAI Function Calling with the OpenAI API and LangChain.
-
08_RAG- Deep dive into Retrieval Augmented Generation (RAG) and its implementation in LangChain.
-
09_Agents- Building and managing Autonomous Agents within the LangChain framework.
-
10_Hybrid_Search_and_Indexing_API- Integration and use of Hybrid Search and the Indexing API for efficient data indexing.
-
11_LangSmith- Leveraging LangSmith for Tracing, Datasets, and Evaluation.
-
12_MicroServiceArchitecture- Understanding and applying microservice architecture in large language model (LLM) applications.
-
13_LangChain_ExpressionLanguage- Exploring the LangChain Expression Language with the Runnable Interface.
Each directory is structured to provide learners with theoretical knowledge and practical insights, enabling a comprehensive understanding of LangChain and its applications in the field of generative AI.
Clone the repository: LangChain Udemy Course
Please rename the .env.example to .env and provide your OpenAI API Key.
Linux: find . -name "*.ipynb" -exec jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace {} \;
Windows: for /r %i in (*.ipynb) do jupyter nbconvert --to notebook --ClearOutputPreprocessor.enabled=True --inplace "%i"