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docs/Browser Use/index.md
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# Tutorial: Browser Use
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
**Browser Use** is a project that allows an *AI agent* to control a web browser and perform tasks automatically.
**Browser Use**<sup>[View Repo](https://github.com/browser-use/browser-use/tree/3076ba0e83f30b45971af58fe2aeff64472da812/browser_use)</sup> is a project that allows an *AI agent* to control a web browser and perform tasks automatically.
Think of it like an AI assistant that can browse websites, fill forms, click buttons, and extract information based on your instructions. It uses a Large Language Model (LLM) as its "brain" to decide what actions to take on a webpage to complete a given *task*. The project manages the browser session, understands the page structure (DOM), and communicates back and forth with the LLM.
**Source Repository:** [https://github.com/browser-use/browser-use/tree/3076ba0e83f30b45971af58fe2aeff64472da812/browser_use](https://github.com/browser-use/browser-use/tree/3076ba0e83f30b45971af58fe2aeff64472da812/browser_use)
```mermaid
flowchart TD
A0["Agent"]

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# Tutorial: Celery
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
Celery is a system for running **distributed tasks** *asynchronously*. You define *units of work* (Tasks) in your Python code. When you want a task to run, you send a message using a **message broker** (like RabbitMQ or Redis). One or more **Worker** processes are running in the background, listening for these messages. When a worker receives a message, it executes the corresponding task. Optionally, the task's result (or any error) can be stored in a **Result Backend** (like Redis or a database) so you can check its status or retrieve the output later. Celery helps manage this whole process, making it easier to handle background jobs, scheduled tasks, and complex workflows.
**Source Repository:** [https://github.com/celery/celery/tree/d1c35bbdf014f13f4ab698d75e3ea381a017b090/celery](https://github.com/celery/celery/tree/d1c35bbdf014f13f4ab698d75e3ea381a017b090/celery)
Celery<sup>[View Repo](https://github.com/celery/celery/tree/d1c35bbdf014f13f4ab698d75e3ea381a017b090/celery)</sup> is a system for running **distributed tasks** *asynchronously*. You define *units of work* (Tasks) in your Python code. When you want a task to run, you send a message using a **message broker** (like RabbitMQ or Redis). One or more **Worker** processes are running in the background, listening for these messages. When a worker receives a message, it executes the corresponding task. Optionally, the task's result (or any error) can be stored in a **Result Backend** (like Redis or a database) so you can check its status or retrieve the output later. Celery helps manage this whole process, making it easier to handle background jobs, scheduled tasks, and complex workflows.
```mermaid
flowchart TD

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# Tutorial: Click
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
Click is a Python library that makes creating **command-line interfaces (CLIs)** *easy and fun*.
Click<sup>[View Repo](https://github.com/pallets/click/tree/main/src/click)</sup> is a Python library that makes creating **command-line interfaces (CLIs)** *easy and fun*.
It uses simple Python **decorators** (`@click.command`, `@click.option`, etc.) to turn your functions into CLI commands with options and arguments.
Click handles parsing user input, generating help messages, validating data types, and managing the flow between commands, letting you focus on your application's logic.
It also provides tools for *terminal interactions* like prompting users and showing progress bars.
**Source Repository:** [https://github.com/pallets/click/tree/main/src/click](https://github.com/pallets/click/tree/main/src/click)
```mermaid
flowchart TD
A0["Context"]

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# Tutorial: Crawl4AI
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
`Crawl4AI` is a flexible Python library for *asynchronously crawling websites* and *extracting structured content*, specifically designed for **AI use cases**.
`Crawl4AI`<sup>[View Repo](https://github.com/unclecode/crawl4ai/tree/9c58e4ce2ee025debd3f36bf213330bd72b90e46/crawl4ai)</sup> is a flexible Python library for *asynchronously crawling websites* and *extracting structured content*, specifically designed for **AI use cases**.
You primarily interact with the `AsyncWebCrawler`, which acts as the main coordinator. You provide it with URLs and a `CrawlerRunConfig` detailing *how* to crawl (e.g., using specific strategies for fetching, scraping, filtering, and extraction).
It can handle single pages or multiple URLs concurrently using a `BaseDispatcher`, optionally crawl deeper by following links via `DeepCrawlStrategy`, manage `CacheMode`, and apply `RelevantContentFilter` before finally returning a `CrawlResult` containing all the gathered data.
**Source Repository:** [https://github.com/unclecode/crawl4ai/tree/9c58e4ce2ee025debd3f36bf213330bd72b90e46/crawl4ai](https://github.com/unclecode/crawl4ai/tree/9c58e4ce2ee025debd3f36bf213330bd72b90e46/crawl4ai)
```mermaid
flowchart TD
A0["AsyncWebCrawler"]

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# Tutorial: CrewAI
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
**CrewAI** is a framework for orchestrating *autonomous AI agents*.
**CrewAI**<sup>[View Repo](https://github.com/crewAIInc/crewAI/tree/e723e5ca3fb7e4cb890c4befda47746aedbd7408/src/crewai)</sup> is a framework for orchestrating *autonomous AI agents*.
Think of it like building a specialized team (a **Crew**) where each member (**Agent**) has a role, goal, and tools.
You assign **Tasks** to Agents, defining what needs to be done. The **Crew** manages how these Agents collaborate, following a specific **Process** (like sequential steps).
Agents use their "brain" (an **LLM**) and can utilize **Tools** (like web search) and access shared **Memory** or external **Knowledge** bases to complete their tasks effectively.
**Source Repository:** [https://github.com/crewAIInc/crewAI/tree/e723e5ca3fb7e4cb890c4befda47746aedbd7408/src/crewai](https://github.com/crewAIInc/crewAI/tree/e723e5ca3fb7e4cb890c4befda47746aedbd7408/src/crewai)
```mermaid
flowchart TD
A0["Agent"]

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# Tutorial: DSPy
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
DSPy helps you build and optimize *programs* that use **Language Models (LMs)** and **Retrieval Models (RMs)**.
DSPy<sup>[View Repo](https://github.com/stanfordnlp/dspy/tree/7cdfe988e6404289b896d946d957f17bb4d9129b/dspy)</sup> helps you build and optimize *programs* that use **Language Models (LMs)** and **Retrieval Models (RMs)**.
Think of it like composing Lego bricks (**Modules**) where each brick performs a specific task (like generating text or retrieving information).
**Signatures** define what each Module does (its inputs and outputs), and **Teleprompters** automatically tune these modules (like optimizing prompts or examples) to get the best performance on your data.
**Source Repository:** [https://github.com/stanfordnlp/dspy/tree/7cdfe988e6404289b896d946d957f17bb4d9129b/dspy](https://github.com/stanfordnlp/dspy/tree/7cdfe988e6404289b896d946d957f17bb4d9129b/dspy)
```mermaid
flowchart TD
A0["Module / Program"]

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docs/FastAPI/index.md
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# Tutorial: FastAPI
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
FastAPI is a modern, *high-performance* web framework for building APIs with Python.
FastAPI<sup>[View Repo](https://github.com/fastapi/fastapi/tree/628c34e0cae200564d191c95d7edea78c88c4b5e/fastapi)</sup> is a modern, *high-performance* web framework for building APIs with Python.
It's designed to be **easy to use**, fast to code, and ready for production.
Key features include **automatic data validation** (using Pydantic), **dependency injection**, and **automatic interactive API documentation** (OpenAPI and Swagger UI).
**Source Repository:** [https://github.com/fastapi/fastapi/tree/628c34e0cae200564d191c95d7edea78c88c4b5e/fastapi](https://github.com/fastapi/fastapi/tree/628c34e0cae200564d191c95d7edea78c88c4b5e/fastapi)
```mermaid
flowchart TD
A0["FastAPI Application & Routing"]

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# Tutorial: Flask
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
Flask is a lightweight **web framework** for Python.
Flask<sup>[View Repo](https://github.com/pallets/flask/tree/ab8149664182b662453a563161aa89013c806dc9/src/flask)</sup> is a lightweight **web framework** for Python.
It helps you build web applications by handling incoming *web requests* and sending back *responses*.
Flask provides tools for **routing** URLs to your Python functions, managing *request data*, creating *responses*, and using *templates* to generate HTML.
**Source Repository:** [https://github.com/pallets/flask/tree/ab8149664182b662453a563161aa89013c806dc9/src/flask](https://github.com/pallets/flask/tree/ab8149664182b662453a563161aa89013c806dc9/src/flask)
```mermaid
flowchart TD
A0["0: Application Object (Flask)"]

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# Tutorial: LangGraph
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
LangGraph helps you build complex **stateful applications**, like chatbots or agents, using a *graph-based approach*.
LangGraph<sup>[View Repo](https://github.com/langchain-ai/langgraph/tree/55f922cf2f3e63600ed8f0d0cd1262a75a991fdc/libs/langgraph/langgraph)</sup> helps you build complex **stateful applications**, like chatbots or agents, using a *graph-based approach*.
You define your application's logic as a series of steps (**Nodes**) connected by transitions (**Edges**) in a **Graph**.
The system manages the application's *shared state* using **Channels** and executes the graph step-by-step with its **Pregel engine**, handling things like branching, interruptions, and saving progress (**Checkpointing**).
**Source Repository:** [https://github.com/langchain-ai/langgraph/tree/55f922cf2f3e63600ed8f0d0cd1262a75a991fdc/libs/langgraph/langgraph](https://github.com/langchain-ai/langgraph/tree/55f922cf2f3e63600ed8f0d0cd1262a75a991fdc/libs/langgraph/langgraph)
```mermaid
flowchart TD
A0["Pregel Execution Engine"]

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docs/LevelDB/index.md
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# Tutorial: LevelDB
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
LevelDB is a fast *key-value storage library* written at Google.
LevelDB<sup>[View Repo](https://github.com/google/leveldb/tree/main/db)</sup> is a fast *key-value storage library* written at Google.
Think of it like a simple database where you store pieces of data (values) associated with unique names (keys).
It's designed to be **very fast** for both writing new data and reading existing data, and it reliably stores everything on **disk**.
It uses a *log-structured merge-tree (LSM-tree)* design to achieve high write performance and manages data in sorted files (*SSTables*) across different levels for efficient reads and space management.
**Source Repository:** [https://github.com/google/leveldb/tree/main/db](https://github.com/google/leveldb/tree/main/db)
```mermaid
flowchart TD
A0["DBImpl"]

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# Tutorial: MCP Python SDK
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
The **MCP Python SDK** helps developers build applications (clients and servers) that talk to each other using the *Model Context Protocol (MCP)* specification.
The **MCP Python SDK**<sup>[View Repo](https://github.com/modelcontextprotocol/python-sdk/tree/d788424caa43599de38cee2f70233282d83e3a34/src/mcp)</sup> helps developers build applications (clients and servers) that talk to each other using the *Model Context Protocol (MCP)* specification.
It simplifies communication by handling the low-level details like standard **message formats** (Abstraction 0), connection **sessions** (Abstraction 1), and different ways to send/receive data (**transports**, Abstraction 2).
It also provides a high-level framework, **`FastMCP`** (Abstraction 3), making it easy to create servers that expose **tools** (Abstraction 5), **resources** (Abstraction 4), and **prompts** (Abstraction 6) to clients.
The SDK includes **command-line tools** (Abstraction 8) for running and managing these servers.
**Source Repository:** [https://github.com/modelcontextprotocol/python-sdk/tree/d788424caa43599de38cee2f70233282d83e3a34/src/mcp](https://github.com/modelcontextprotocol/python-sdk/tree/d788424caa43599de38cee2f70233282d83e3a34/src/mcp)
```mermaid
flowchart TD
A0["MCP Protocol Types"]

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docs/NumPy Core/index.md
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# Tutorial: NumPy Core
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
NumPy provides the powerful **ndarray** object, a *multi-dimensional grid* optimized for numerical computations on large datasets. It uses **dtypes** (data type objects) to precisely define the *kind of data* (like integers or floating-point numbers) stored within an array, ensuring memory efficiency and enabling optimized low-level operations. NumPy also features **ufuncs** (universal functions), which are functions like `add` or `sin` designed to operate *element-wise* on entire arrays very quickly, leveraging compiled code. Together, these components form the foundation for high-performance scientific computing in Python.
**Source Repository:** [https://github.com/numpy/numpy/tree/3b377854e8b1a55f15bda6f1166fe9954828231b/numpy/_core](https://github.com/numpy/numpy/tree/3b377854e8b1a55f15bda6f1166fe9954828231b/numpy/_core)
NumPy Core<sup>[View Repo](https://github.com/numpy/numpy/tree/3b377854e8b1a55f15bda6f1166fe9954828231b/numpy/_core)</sup> provides the powerful **ndarray** object, a *multi-dimensional grid* optimized for numerical computations on large datasets. It uses **dtypes** (data type objects) to precisely define the *kind of data* (like integers or floating-point numbers) stored within an array, ensuring memory efficiency and enabling optimized low-level operations. NumPy also features **ufuncs** (universal functions), which are functions like `add` or `sin` designed to operate *element-wise* on entire arrays very quickly, leveraging compiled code. Together, these components form the foundation for high-performance scientific computing in Python.
```mermaid
flowchart TD

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# Tutorial: OpenManus
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
OpenManus is a framework for building autonomous *AI agents*.
OpenManus<sup>[View Repo](https://github.com/mannaandpoem/OpenManus/tree/f616c5d43d02d93ccc6e55f11666726d6645fdc2)</sup> is a framework for building autonomous *AI agents*.
Think of it like a digital assistant that can perform tasks. It uses a central **brain** (an `LLM` like GPT-4) to understand requests and decide what to do next.
Agents can use various **tools** (like searching the web or writing code) to interact with the world or perform specific actions. Some complex tasks might involve a **flow** that coordinates multiple agents.
It keeps track of the conversation using `Memory` and ensures secure code execution using a `DockerSandbox`.
The system is flexible, allowing new tools to be added, even dynamically through the `MCP` protocol.
**Source Repository:** [https://github.com/mannaandpoem/OpenManus/tree/f616c5d43d02d93ccc6e55f11666726d6645fdc2](https://github.com/mannaandpoem/OpenManus/tree/f616c5d43d02d93ccc6e55f11666726d6645fdc2)
```mermaid
flowchart TD
A0["BaseAgent"]

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# Tutorial: Pydantic Core
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
Pydantic Core provides the fundamental machinery for **data validation**, **parsing**, and **serialization** in Pydantic. It takes Python *type hints* and uses them to define how data should be structured and processed. Users typically interact with it by defining classes that inherit from `BaseModel`, which automatically gets validation and serialization capabilities based on its annotated fields. Pydantic Core ensures data conforms to the defined types and allows converting between Python objects and formats like JSON efficiently, leveraging Rust for performance.
**Source Repository:** [https://github.com/pydantic/pydantic/tree/6c38dc93f40a47f4d1350adca9ec0d72502e223f/pydantic](https://github.com/pydantic/pydantic/tree/6c38dc93f40a47f4d1350adca9ec0d72502e223f/pydantic)
Pydantic Core<sup>[View Repo](https://github.com/pydantic/pydantic/tree/6c38dc93f40a47f4d1350adca9ec0d72502e223f/pydantic)</sup> provides the fundamental machinery for **data validation**, **parsing**, and **serialization** in Pydantic. It takes Python *type hints* and uses them to define how data should be structured and processed. Users typically interact with it by defining classes that inherit from `BaseModel`, which automatically gets validation and serialization capabilities based on its annotated fields. Pydantic Core ensures data conforms to the defined types and allows converting between Python objects and formats like JSON efficiently, leveraging Rust for performance.
```mermaid
flowchart TD

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docs/Requests/index.md
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# Tutorial: Requests
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
Requests is a Python library that makes sending *HTTP requests* incredibly simple.
Requests<sup>[View Repo](https://github.com/psf/requests/tree/0e322af87745eff34caffe4df68456ebc20d9068/src/requests)</sup> is a Python library that makes sending *HTTP requests* incredibly simple.
Instead of dealing with complex details, you can use straightforward functions (like `requests.get()`) or **Session objects** to interact with web services.
It automatically handles things like *cookies*, *redirects*, *authentication*, and connection pooling, returning easy-to-use **Response objects** with all the server's data.
**Source Repository:** [https://github.com/psf/requests/tree/0e322af87745eff34caffe4df68456ebc20d9068/src/requests](https://github.com/psf/requests/tree/0e322af87745eff34caffe4df68456ebc20d9068/src/requests)
```mermaid
flowchart TD
A0["Request & Response Models"]

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docs/SmolaAgents/index.md
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# Tutorial: SmolaAgents
> This tutorial is AI-generated! To learn more: https://github.com/The-Pocket/Tutorial-Codebase-Knowledge
> This tutorial is AI-generated! To learn more, check out [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
`SmolaAgents` is a project for building *autonomous agents* that can solve complex tasks.
`SmolaAgents`<sup>[View Repo](https://github.com/huggingface/smolagents/tree/076cca5e8a130d3fa2ff990ad630231b49767745/src/smolagents)</sup> is a project for building *autonomous agents* that can solve complex tasks.
The core component is the **MultiStepAgent**, which acts like a project manager. It uses a **Model Interface** to talk to language models (LLMs), employs **Tools** (like web search or code execution) to interact with the world or perform actions, and keeps track of its progress and conversation history using **AgentMemory**.
For agents that write and run Python code (`CodeAgent`), a **PythonExecutor** provides a safe environment. **PromptTemplates** help structure the instructions given to the LLM, while **AgentType** handles different data formats like images or audio. Finally, **AgentLogger & Monitor** provides logging and tracking for debugging and analysis.
**Source Repository:** [https://github.com/huggingface/smolagents/tree/076cca5e8a130d3fa2ff990ad630231b49767745/src/smolagents](https://github.com/huggingface/smolagents/tree/076cca5e8a130d3fa2ff990ad630231b49767745/src/smolagents)
```mermaid
flowchart TD
A0["MultiStepAgent"]