Complete Technical Paper Content — Langchain Projects
1. Executive Abstract, Problem Formulation & Background
LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.
These lists include projects which release their software under open-source licenses and are related to artificial intelligence projects. These include software libraries, frameworks, platforms, and tools used for machine learning, deep learning, natural language processing, computer vision, reinforcement learning, artificial general intelligence, and more.
Auto-GPT — experimental open-source autonomous GPT-based agent OpenClaw — autonomous task-executing AI agent CrewAI — framework for building and orchestrating multi-agent AI systems LangChain — framework for building applications and agents powered by large language models Theia AI — framework for integrating AI capabilities and agents into integrated development environments (IDEs).
Codex CLI – AI coding agent and command-line interface developed by OpenAI. Cline – AI coding agent for IDEs and the command line. Command Code — CLI and desktop AI agent DeepSeek Coder – large language model designed for code generation and AI-assisted software development. goose – AI coding agent for the desktop and command line. Grok Build – SpaceXAI's terminal-based AI coding agent. Hermes Agent – AI coding agent for the command line and IDE extensions. Kilo Code — CLI and IDE extension developed by Anaconda OpenCode — CLI, desktop app, web app, and IDE extensions OpenHands — AI software development agent Oh My Pi – terminal-based AI coding agent developed as a fork of Pi. Pi – terminal-based AI coding agent. Warp – agentic development environment combining a terminal, integrated coding agent, and CLI. Zed – source-code editor with integrated AI-assisted development and agentic coding features.
EDLUT – event-driven neural network simulator for large-scale spiking networks Emergent – cognitive modeling platform implementing neural networks Encog – machine learning framework for Java and C# supporting neural networks JOONE – Java-based neural network framework with modular architecture for learning tasks Nengo – Python library for building and simulating large-scale neural systems Neuroph – lightweight Java framework for creating neural networks OpenNN – C++ library for designing, training, and deploying neural networks SNNS – Stuttgart Neural Network Simulator, supports feedforward and recurrent neural networks
ACT-R – cognitive architecture for modeling human cognition and developing cognitive agents. CLARION – Connectionist Learning with Adaptive Rule Induction On-line, hybrid connectionist/symbolic cognitive architecture. OpenCog – project that aims to build an open source artificial intelligence framework Soar – cognitive architecture for decision-making and learning in intelligent agents
AForge.NET – computer vision, artificial intelligence, and robotics library for the .NET framework Dlib – C++ library for computer vision and image processing OpenCV — library of programming functions mainly for real-time computer vision Point Cloud Library – library for point cloud processing Tesseract – optical character recognition
BigDL – distributed deep learning library for Apache Spark Caffe – deep learning framework focused on speed and modularity Deeplearning4j – Java library for deep learning algorithms on the Java virtual machine DeepSpeed – deep learning optimization library developed by Microsoft fastai – deep learning library built on top of PyTorch Fast Artificial Neural Network (FANN) – C library for feedforward neural networks Horovod – distributed deep learning framework for TensorFlow, Keras, and PyTorch Hugging Face Transformers — open-source deep learning library developed by Hugging Face Keras – Python library for artificial neural networks Microsoft Cognitive Toolkit – deep learning framework developed by Microsoft Research MXNet – deep learning framework for training and deploying deep neural networks Neuroph – object-oriented artificial neural network framework written in Java OpenNN – artificial neural network library written in C++ PlaidML – deep learning backend for neural networks and tensor computations PyTorch – deep learning framework developed by Meta AI PyTorch Lightning – high-level framework built on top of PyTorch for organizing and scaling deep learning models TensorFlow – end-to-end open-source platform for machine learning and deep learning developed by Google Brain Torch – scientific computing framework with support for machine learning and deep learning algorithms
Accord.NET — C# framework for machine learning, statistics, computer vision, and signal processing Apache Mahout — scalable machine learning library for big data built on Hadoop and Spark Apache SINGA — distributed deep learning and machine learning software Apache SystemDS — ML system for the end-to-end data science lifecycle CatBoost — machine learning library for gradient boosting on decision trees Caffe — Image classification and image segmentation Dlib — C++ machine learning and computer vision library ELKI — data mining and unsupervised learning software fastText — Word embeddings developed by Meta AI Flux — machine learning library for the Julia programming language Gensim — topic modelling and vector space model toolkit JAX — machine learning library for numerical computing and automatic differentiation H2O — machine learning and predictive analytics platform Infer.NET — probabilistic programming framework for Bayesian inference Jubatus — online machine learning and distributed computing framework KNIME — modular data pipelining LIBSVM — library for support vector machines LightGBM — machine learning framework for gradient boosting Massive Online Analysis — framework for machine learning and data mining on evolving data streams Microsoft Cognitive Toolkit — deep learning framework developed by Microsoft Research MindSpore — machine learning and deep learning framework ML.NET — machine learning framework for .NET mlpack — C++ header-only machine learning library MLX — machine learning framework for Apple silicon supporting model training, inference, and fine-tuning MXNet — deep learning framework Orange — machine learning, data mining, data visualization, and data analysis PyMC — Python framework for Bayesian modeling and probabilistic programming Scikit-learn — library built on top of NumPy, SciPy, and matplotlib Scikit-multiflow — Python framework for online and stream machine learning Shogun — C++ library for large-scale machine learning Spark MLlib — machine learning library for Apache Spark TensorFlow — machine learning and deep learning platform Theano — numerical computation library for machine learning Vowpal Wabbit — machine learning system for fast online learning Weka — collection of machine learning algorithms for data mining tasks XGBoost — machine learning library for gradient boosting
TPOT – tree-based pipeline optimization tool using genetic programming Neural Network Intelligence – Microsoft toolkit for hyperparameter tuning and neural architecture search MindsDB – AutoML platform that embeds machine learning into SQL databases and applications
Apertus – Swiss National AI Initiative LLM BERT – Google LLM BLOOM – BigScience multilingual LLM Cerebras-GPT – Cerebras Systems LLMs DeepSeek – R1 and V3 models Gemma 4 – Google LLM GLM-4.5 and later versions – Z.ai LLMs GPT-1, GPT-2, and GPT-OSS – OpenAI LLMs GPT-J, GPT-Neo, and GPT-NeoX – EleutherAI LLMs Hy – Tencent open-weight LLM Granite – IBM LLMs Grok-1 – xAI LLM Inkling – Thinking Machines Kimi (some versions) – Moonshot AI LLMs MiMo (some versions) – Xiaomi LLMs Mistral (some versions) Muse Glimmer — Meta Superintelligence Labs LLM Nemotron 3 – Nvidia open-weight models OLMo – Allen Institute for AI LLM Phi – Microsoft LLMs Qwen (some versions) – Alibaba Cloud LLMs Sarvam-M, Sarvam-105B and Sarvam-30B – Sarvam AI LLMs SmolLM – Hugging Face small language models (SLMs) Step-3.7Flash – StepFun LLM T5 – Google LLM XLNet – Google LLM
Apache OpenNLP Apertium – rule-based machine translation platform. ChatScript – natural language engine and dialog management system General Architecture for Text Engineering – information extraction Gensim – topic modeling and document similarity analysis library GloVe – unsupervised learning algorithm for obtaining vector representations of words Mallet – Java "Machine Learning for Language Toolkit" MontyLingua – libraries and programs for symbolic and statistical NLP for both Python and Java Moses – statistical machine translation engine to train statistical models of text from a source language to a target language NiuTrans – statistical machine translation NLTK – natural Language toolkit for symbolic and statistical NLP Probabilistic Action Cores – interpreter for natural-language instructions for robotic applications spaCy – Python library Spark NLP – text processing library for advanced NLP for Python, Java, and Scala. Word2vec – obtaining vector representations of words
2. Related Work, Literature Survey & Algorithmic Foundations
GOLOG – logic programming language, situation calculus, first-order logical language for reasoning about action and change.
AlphaTensor – DeepMind reinforcement-learning system for discovering efficient matrix multiplication algorithms
ConceptNet – multilingual commonsense knowledge graph used in natural-language processing and artificial-intelligence applications. DBpedia – project that extracts structured knowledge from Wikipedia using an open-source extraction framework.
Open-source artificial intelligence List of artificial intelligence algorithms List of artificial intelligence journals and List of artificial intelligence books List of artificial intelligence projects List of free and open-source software packages for artificial intelligence List of proprietary artificial intelligence software Open weights Common Crawl – nonprofit that crawls the web and freely provides its archives and datasets to the public under an MIT License Google Colab – an O-IDE Jupyter notebook environment with free access to GPUs and TPUs for machine learning and deep learning development Applications of artificial intelligence
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LangChain Practical Guide Welcome to the LangChain Crash Course repository! This repo contains all the code examples you'll need to follow along with the LangChain Master Class for Beginners video. By the end of this course, you'll know how to use LangChain to create your own AI agents, build RAG chatbots, and automate tasks with AI. What is LangChain? LangChain is a framework that simplifies building applications powered by large language models (LLMs). It provides a standardized interface for chains, prompt templates, tools, agents, and memories, making LLM-powered applications more modular and easier to develop. graph TD A[LangChain Framework] --> B[Models] A --> C[Prompt Templates] A --> D[Chains] A --> E[Agents] A --> F[Memory] A --> G[Tools] A --> H[Retrievers] B --> B1[OpenAI] B --> B2[Anthropic] B --> B3[Google] B --> B4[Ollama/Local] B --> B5[Hugging Face] E --> G E --> H Loading Course Outline Setup Environment : Configure your local environment to work with LangChain Chat Models : Learn to interact with different LLMs through a unified interface Prompt Templates : Master structured prompting techniques Chains : Combine models and prompts into reusable pipelines RAG (Retrieval-Augmented Generation) : Enhance LLM responses with external knowledge Agents & Tools : Create autonomous AI systems that can use tools to accomplish tasks Getting Started Prerequisites Python 3.11, 3.12, or 3.13 Poetry (Follow this Poetry installation tutorial to install Poetry on your system) (Optional) Ollama for local LLM inference ( Download Ollama ) Installation Clone the repository: git clone https://github.com/bibekgupta3333/langchain-practical-guide cd langchain-practical-guide Install dependencies using Poetry: poetry install Note: With Poetry 2.0+, the package-mode = false setting in pyproject.toml means --no-root is no longer needed. Set up your environment variables: Rename the .env.example file to .env and update the variables inside with your own values. Example: mv .env.example .env Activate the Poetry virtual environment: Poetry 2.0+ (Recommended): poetry env activate Or manually activate: source $( poetry env info --path ) /bin/activate Or run directly without activating: poetry run python 1_chat_models/1_chat_model_basic.py Run the code examples: python 1_chat_models/1_chat_model_basic.py Optional: Setting up Ollama for Local LLMs If you want to use local LLMs (free, private, offline): Install Ollama from ollama.ai Pull a model: # For chat models ollama pull llama3.2 ollama pull mistral ollama pull gemma2 # For embeddings ollama pull nomic-embed-text Verify Ollama is running: ollama list Use in your code: from langchain_ollama import ChatOllama model = ChatOllama ( model = "llama3.2" ) 🆕 Recent Updates - LangChain 1.0 Migration This repository has been updated to LangChain 1.0+ with several important changes: Major Updates LangChain 1.0+ : Upgraded from 0.3.x to 1.0.x with updated import paths Ollama Support : Added support for local LLM inference using Ollama LangGraph : Integrated for building stateful, multi-actor applications LangSmith : Added for observability and debugging Python 3.13 : Extended Python version support (3.11-3.13) Breaking Changes & Import Updates If you're upgrading from an older version, note these import path changes: # OLD (LangChain 0.3.x) from langchain . schema import AIMessage , HumanMessage from langchain . prompts import ChatPromptTemplate from langchain . text_splitter import CharacterTextSplitter from langchain . schema . runnable import RunnableLambda from langchain . pydantic_v1 import BaseModel # NEW (LangChain 1.0+) from langchain_core . messages import AIMessage , HumanMessage from langchain_core . prompts import ChatPromptTemplate from langchain_text_splitters import CharacterTextSplitter from langchain_core . runnables import RunnableLambda from pydantic import BaseModel # Chain utilities moved to langchain_classic from langchain_classic . chains import create_history_aware_retriever , create_retrieval_chain from langchain_classic . chains . combine_documents import create_stuff_documents_chain New Models Supported Ollama : Local LLM inference (ChatOllama, OllamaEmbeddings) LangChain Ollama : Official LangChain integration for Ollama Hugging Face : Enhanced integration via langchain-huggingface Removed Dependencies langchain-google-firestore : Removed due to incompatibility with LangChain 1.0+ The example file 5_chat_model_save_message_history_firebase.py may need updates Updated Dependencies All dependencies have been updated to their latest compatible versions: langchain : 1.0.7 langchain-core : 1.0.5 langchain-community : 0.4.1 langchain-openai : 1.0.3 langchain-anthropic : 1.0.4 langchain-google-genai : 3.0.3 langgraph : 1.0.3 langsmith : 0.4.43 langchain-ollama : 1.0.0 langchain-huggingface : 1.0.1 Troubleshooting Common Migration Issues Import Error: "cannot import X from langchain.Y" Solution: Check the import path changes above. Most imports moved to langchain_core.* "Module langchain.pydantic_v1 not found" Solution: Use from pydantic import BaseModel, Field instead "Module langchain.schema not found" Solution: Use from langchain_core.messages import * instead Poetry install fails with dependency conflicts Solution: Delete poetry.lock and run poetry install again Or use poetry update to update all dependencies Examples using Google Firestore don't work Note: langchain-google-firestore is incompatible with LangChain 1.0+ Alternative: Use other memory providers or wait for an updated version Files Updated in This Migration All example files have been updated with the new import paths and Ollama integration: Chat Models (1_chat_models/): 1_chat_model_basic.py - Updated to use Ollama 2_chat_model_basic_conversation.py - Fixed import paths 4_chat_model_conversation_with_user.py - Updated to langchain_core.messages Prompt Templates (2_prompt_templates/): 2_prompt_template_with_chat_model.py - Updated to langchain_core.prompts and Ollama Chains (3_chains/): 1_chains_basics.py - Updated to Ollama 2_chains_under_the_hood.py - Updated to Ollama 3_chains_extended.py - Fixed langchain_core.runnables imports 4_chains_parallel.py - Updated to Ollama 5_chains_branching.py - Updated to Ollama RAG (4_rag/): 1a_rag_basics.py - Updated to langchain_text_splitters and OllamaEmbeddings 1b_rag_basics.py - Updated embeddings to Ollama 2a_rag_basics_metadata.py - Updated to Ollama embeddings 2b_rag_basics_metadata.py - Updated to Ollama embeddings 3_rag_text_splitting_deep_dive.py - Fixed text splitter imports 4_rag_embedding_deep_dive.py - Updated embeddings 5_rag_retriever_deep_dive.py - Updated retriever imports 6_rag_one_off_question.py - Updated to langchain_classic.chains 7_rag_conversational.py - Fixed chain imports to langchain_classic 8_rag_web_scrape_basic.py - Updated embeddings 8_rag_web_scrape_firecrawl.py - Updated to Ollama Agents & Tools (5_agents_and_tools/): 1_agent_and_tools_basics.py - Updated to langchain_classic.agents agent_deep_dive/1_agent_react_chat.py - Fixed agent imports agent_deep_dive/2_agent_react_docstore.py - Updated to langchain_classic tools_deep_dive/1_tool_constructor.py - Fixed pydantic imports tools_deep_dive/3_tool_base_tool.py - Updated tool imports Configuration: .env.example - Added Ollama configuration options pyproject.toml - Updated to LangChain 1.0+ dependencies poetry.lock - Regenerated with new dependency versions Repository Structure Here's a breakdown of the folders and what you'll find in each: 1. Chat Models graph LR A[Chat Models] --> B[Basic Usage] A --> C[Conversations] A --> D[Alternative Models] A --> E[User Interaction] A --> F[Message History Storage] A --> G[Advanced Usage] Loading 1_chat_model_basic.py : Introduction to calling LLMs 2_chat_model_basic_conversation.py : Managing multi-turn conversations 3_chat_model_alternatives.py : Exploring different providers (OpenAI, Anthropic, Google) 4_chat_model_conversation_with_user.py : Interactive chat applications 5_chat_model_save_message_history_firestore.py : Persisting conversations to databases advance/ : 6_chat_model_streaming.py : Implementing streaming responses for real-time interactions 7_chat_model_parameters.py : Exploring model parameters to control response generation 8_chat_model_multimodal.py : Working with multimodal models that can process text and images 9_chat_model_error_handling.py : Implementing robust error handling in LLM applications Learn how to interact with models like ChatGPT, Claude, and Gemini through a unified interface. 2. Prompt Templates graph TD A[Prompt Templates] --> B[Template Creation] A --> C[Variable Substitution] A --> D[Integration with Chat Models] A --> E[Few-shot Examples] Loading 1_prompt_template_basic.py : Creating reusable prompt structures 2_prompt_template_with_chat_model.py : Combining templates with chat models Understand how to systematically design prompts with variables that can be filled in at runtime. 3. Chains graph LR A[Chains] --> B[Sequential Processing] A --> C[Internal Mechanics] A --> D[Advanced Patterns] A --> E[Parallel Execution] A --> F[Conditional Branching] B --> C C --> D D --> E E --> F Loading 1_chains_basics.py : Fundamentals of chaining operations 2_chains_under_the_hood.py : Understanding the internal mechanisms 3_chains_extended.py : Advanced chain patterns 4_chains_parallel.py : Running multiple operations simultaneously 5_chains_branching.py : Creating chains with decision points Learn how to compose complex workflows by connecting models, prompts, and other components in sequence. 4. RAG (Retrieval-Augmented Generation) flowchart TD A[Document Loading] --> B[Text Splitting] B --> C[Embedding Generation] C --> D[Vector Storage] D --> E[Similarity Search] E --> F[Context Augmentation] F --> G[LLM Response] H[User Query] --> E Loading 1a_rag_basics.py & 1b_rag_basics.py : Core RAG concepts and implementation 2a_rag_basics_metadata.py & 2b_rag_basics_metadata.py : Using metadata to enhance retrieval 3_rag_text_splitting_deep_dive.py : Advanced document chunking strategies 4_rag_embedding_deep_dive.py : Understanding vector embeddings 5_rag_retriever_deep_dive.py : Fine-tuning retrieval mechanisms 6_rag_one_off_question.py : Single-query RAG implementation 7_rag_conversational.py : Chat systems with context retention 8_rag_web_scrape_firecrawl.py & 8_rag_web_scrape.py : Building knowledge bases from web content Explore how to enhance LLM responses by incorporating relevant information from external documents, websites, or databases. 5. Agents & Tools graph TD A[Agent] --> B[Reasoning] A --> C[Tool Selection] A --> D[Action Execution] A --> E[Result Analysis] B --> C C --> D D --> E E --> B F[Available Tools] --> C Loading 1_agent_and_tools_basics.py : Introduction to autonomous AI systems agent_deep_dive/ : 1_agent_react_chat.py : ReAct pattern for reasoning with chat models 2_react_docstore.py : Agents that can search document collections tools_deep_dive/ : 1_tool_constructor.py : Creating tools with the constructor approach 2_tool_decorator.py : Using decorators to define tools 3_tool_base_tool.py : Building tools by extending the BaseTool class Learn how to create systems that can reason about what actions to take, use tools to gather information or perform tasks, and make decisions based on outcomes. How to Use This Repository Watch the Video: Start by watching the LangChain Master Class for Beginners video on YouTube at 2X speed for a high-level overview. Run the Code Examples: Follow along with the code examples provided in this repository. Each section in the video corresponds to a folder in this repo. Experiment and Adapt: Modify the examples to explore different parameters, models, or use cases relevant to your projects. Learning Path graph LR A[Basics: Chat Models] --> B[Prompt Engineering] B --> C[Building Chains] C --> D[RAG Implementation] D --> E[Agents & Tools] E --> F[Complete Applications] Loading For optimal learning, follow the numbered folders in sequence, as each builds upon concepts introduced in the previous sections. Comprehensive Documentation Each script in this repository contains detailed comments explaining the purpose and functionality of the code. This will help you understand the flow and logic behind each example. FAQ Q: What is LangChain? A: LangChain is a framework designed to simplify the process of building applications that utilize language models. It provides abstractions for working with LLMs, prompts, chains, agents, memory systems, and tools. Q: How do I set up my environment? A: Follow the instructions in the "Getting Started" section above. Ensure you have Python 3.11 installed, install Poetry, clone the repository, install dependencies, rename the .env.example file to .env , and activate the Poetry shell. Q: Which LLM provider should I use? A: The examples work with multiple providers: OpenAI (GPT-4, GPT-3.5): Best for general-purpose tasks Anthropic (Claude): Excellent for reasoning and analysis Google (Gemini): Strong multimodal capabilities Ollama (Local): Free, private, offline-capable (recommended: llama3, mistral, gemma2) Choose based on your needs for cost, capabilities, privacy, and whether you need local/offline inference. Q: I am getting an error when running the examples. What should I do? A: Ensure all dependencies are installed correctly and your environment variables are set up properly. If the issue persists, seek help in the Skool community or open an issue on GitHub. Q: Can I contribute to this repository? A: Yes! Contributions are welcome. Please open an issue or submit a pull request with your changes. Q: Where can I find more information about LangChain? A: Check out the official LangChain documentation and join the Skool community for additional resources and support. License This project is licensed under the MIT License. References Click to view references LangChain Documentation - Official documentation LangChain GitHub Repository - Source code and examples OpenAI API Documentation - API reference for OpenAI models Anthropic API Documentation - API reference for Claude models Google AI Documentation - Documentation for Google AI services Brandon Hancock's GitHub - Gen AI course creator
Welcome to the LangChain Crash Course repository! This repo contains all the code examples you'll need to follow along with the LangChain Master Class for Beginners video. By the end of this course, you'll know how to use LangChain to create your own AI agents, build RAG chatbots, and automate tasks with AI.
3. Dataset Ingestion, Preprocessing & Stochastic Augmentations
LangChain is a framework that simplifies building applications powered by large language models (LLMs). It provides a standardized interface for chains, prompt templates, tools, agents, and memories, making LLM-powered applications more modular and easier to develop.
graph TD A[LangChain Framework] --> B[Models] A --> C[Prompt Templates] A --> D[Chains] A --> E[Agents] A --> F[Memory] A --> G[Tools] A --> H[Retrievers] B --> B1[OpenAI] B --> B2[Anthropic] B --> B3[Google] B --> B4[Ollama/Local] B --> B5[Hugging Face] E --> G E --> H
git clone https://github.com/bibekgupta3333/langchain-practical-guide cd langchain-practical-guide
Note: With Poetry 2.0+, the package-mode = false setting in pyproject.toml means --no-root is no longer needed.
# For chat models ollama pull llama3.2 ollama pull mistral ollama pull gemma2 # For embeddings ollama pull nomic-embed-text
# OLD (LangChain 0.3.x) from langchain . schema import AIMessage , HumanMessage from langchain . prompts import ChatPromptTemplate from langchain . text_splitter import CharacterTextSplitter from langchain . schema . runnable import RunnableLambda from langchain . pydantic_v1 import BaseModel # NEW (LangChain 1.0+) from langchain_core . messages import AIMessage , HumanMessage from langchain_core . prompts import ChatPromptTemplate from langchain_text_splitters import CharacterTextSplitter from langchain_core . runnables import RunnableLambda from pydantic import BaseModel # Chain utilities moved to langchain_classic from langchain_classic . chains import create_history_aware_retriever , create_retrieval_chain from langchain_classic . chains . combine_documents import create_stuff_documents_chain
graph LR A[Chat Models] --> B[Basic Usage] A --> C[Conversations] A --> D[Alternative Models] A --> E[User Interaction] A --> F[Message History Storage] A --> G[Advanced Usage]
Learn how to interact with models like ChatGPT, Claude, and Gemini through a unified interface.
graph TD A[Prompt Templates] --> B[Template Creation] A --> C[Variable Substitution] A --> D[Integration with Chat Models] A --> E[Few-shot Examples]
Understand how to systematically design prompts with variables that can be filled in at runtime.
graph LR A[Chains] --> B[Sequential Processing] A --> C[Internal Mechanics] A --> D[Advanced Patterns] A --> E[Parallel Execution] A --> F[Conditional Branching] B --> C C --> D D --> E E --> F
Learn how to compose complex workflows by connecting models, prompts, and other components in sequence.
4. Neural Network Backbone, Layer Specifications & Loss Formulations
flowchart TD A[Document Loading] --> B[Text Splitting] B --> C[Embedding Generation] C --> D[Vector Storage] D --> E[Similarity Search] E --> F[Context Augmentation] F --> G[LLM Response] H[User Query] --> E
Explore how to enhance LLM responses by incorporating relevant information from external documents, websites, or databases.
graph TD A[Agent] --> B[Reasoning] A --> C[Tool Selection] A --> D[Action Execution] A --> E[Result Analysis] B --> C C --> D D --> E E --> B F[Available Tools] --> C
Learn how to create systems that can reason about what actions to take, use tools to gather information or perform tasks, and make decisions based on outcomes.
Watch the Video: Start by watching the LangChain Master Class for Beginners video on YouTube at 2X speed for a high-level overview.
Run the Code Examples: Follow along with the code examples provided in this repository. Each section in the video corresponds to a folder in this repo.
Experiment and Adapt: Modify the examples to explore different parameters, models, or use cases relevant to your projects.
graph LR A[Basics: Chat Models] --> B[Prompt Engineering] B --> C[Building Chains] C --> D[RAG Implementation] D --> E[Agents & Tools] E --> F[Complete Applications]
For optimal learning, follow the numbered folders in sequence, as each builds upon concepts introduced in the previous sections.
Each script in this repository contains detailed comments explaining the purpose and functionality of the code. This will help you understand the flow and logic behind each example.
Q: What is LangChain? A: LangChain is a framework designed to simplify the process of building applications that utilize language models. It provides abstractions for working with LLMs, prompts, chains, agents, memory systems, and tools.
Q: How do I set up my environment? A: Follow the instructions in the "Getting Started" section above. Ensure you have Python 3.11 installed, install Poetry, clone the repository, install dependencies, rename the .env.example file to .env , and activate the Poetry shell.
5. Training Protocol, Convergence Behavior & Experimental Evaluation
Choose based on your needs for cost, capabilities, privacy, and whether you need local/offline inference.
Q: I am getting an error when running the examples. What should I do? A: Ensure all dependencies are installed correctly and your environment variables are set up properly. If the issue persists, seek help in the Skool community or open an issue on GitHub.
Q: Can I contribute to this repository? A: Yes! Contributions are welcome. Please open an issue or submit a pull request with your changes.
Q: Where can I find more information about LangChain? A: Check out the official LangChain documentation and join the Skool community for additional resources and support.
A comprehensive, hands-on tutorial repository for learning and mastering LangChain - the powerful framework for building applications with Large Language Models (LLMs). This codebase provides a structured learning path with practical examples covering everything from basic chat models to advanced AI agents, organized in a progressive curriculum.
LangChain is a framework that makes it easier to build applications using large language models (LLMs) by connecting them with data, tools and APIs. It helps developers move beyond simple text generation and create intelligent workflows.
This section introduces LangChain and explains its purpose, core features, and main modules for building LLM-powered applications.
Here you’ll explore the essential parts of LangChain which plays a important role in building efficient, context-aware AI applications.
Prompts are the foundation of LLM interactions. In this section, you’ll learn about prompt templates and how to parse outputs effectively.
Chains helps connect multiple steps into structured workflows, while agents make decisions to build task-oriented, multi-step AI applications.
This section explains how to store and retrieve information using embeddings, indexing, vector databases and RAG.
LangChain offers ecosystem tools like LangGraph for workflows, LangSmith for debugging and LCEL for easier development.
Model Figures & Evaluation Curves (4 Images)
Deep Learning Execution Pipeline
(Preprocessing)
(Feature Extraction)
(Self-Attention)
(Loss Optimization)