
LangChain-Udemy-Course
This Repo contains the code for the Udemy Course LangChain Full Course - Master LLM Powered Applications
Stars: 80

LangChain-Udemy-Course is a comprehensive course directory focusing on LangChain, a framework for generative AI applications. The course covers various aspects such as OpenAI API usage, prompt templates, Chains exploration, callback functions, memory techniques, RAG implementation, autonomous agents, hybrid search, LangSmith utilization, microservice architecture, and LangChain Expression Language. Learners gain theoretical knowledge and practical insights to understand and apply LangChain effectively in generative AI scenarios.
README:
This Markdown file provides a concise overview of each directory in the LangChain course, detailing the key focus and content of each.
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01_OpenAI_API
- Basic usage of the OpenAI API for generative AI applications.
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02_LangChain_Inputs_and_Outputs
- Understanding the input and output mechanisms within LangChain.
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03_Prompt_Templates
- Templates and best practices for effective prompting for OpenAI models.
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04_Chains
- Detailed exploration of the Chains in LangChain with different use cases.
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05_Callbacks
- Utilizing callback functions in LangChain for dynamic responses and interactions.
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06_Memory
- Techniques and methods for implementing memory in generative AI models.
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07_OpenAI_Functions
- OpenAI Function Calling with the OpenAI API and LangChain.
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08_RAG
- Deep dive into Retrieval Augmented Generation (RAG) and its implementation in LangChain.
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09_Agents
- Building and managing Autonomous Agents within the LangChain framework.
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10_Hybrid_Search_and_Indexing_API
- Integration and use of Hybrid Search and the Indexing API for efficient data indexing.
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11_LangSmith
- Leveraging LangSmith for Tracing, Datasets, and Evaluation.
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12_MicroServiceArchitecture
- Understanding and applying microservice architecture in large language model (LLM) applications.
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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"
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