hammad-python

# **hammad-python** > **Harmoniously Accelerated Micro-Modules (for) Application Development** --- `hammad-python` is an ecosystem of **wrappers** with the sole purpose of defining opinionated resources that I myself use in my projects and day to day work when building with Python. The ecosystem is split into **4** main sub-packages: - `hammad-python-core` : Contains "stdlib" like resources such as converters, types, caching, etc. All of these resources can easily be implemented from scratch, and are built soley for convenience. - `hammad-python-data` : Contains a `Collection` class / system that implements a simple vector / non vector database system. - `hammad-python-genai` : Contains a collection of patterns for using **Generative AI** models as well as implementing concepts like *Agentic Reasoning*. - `hammad-python-http` : HTTP / API related resources and utilities. Contains resources related to *MCP, HTTP, OpenAPI & GraphQL Servers*. --- ## Installation You can install any of the sub-packages, or the main package through `pip` or `uv`. ```bash pip install hammad-python ``` --- ## Quickstart ### Easily Use Language Models With Various Tools & Patterns ```python from ham.genai import Agent, LanguageModel from ham.http import SearchClient client = SearchClient() def search_web(query: str) -> str: return client.search(query) # create your agent # agents have rich functionality, and use `litellm` & `instructor` under the hood agent = Agent( instructions = "You are a helpful assistant who can search the web", tools = [search_web] ) print(agent.run("What is the weather in Tokyo?")) ``` ```bash >>> AgentResponse: city='Tokyo' temperature=25.0 description='Clear sky' >>> Model: openai/gpt-4o-mini >>> Steps: 2 >>> Output Type: Response >>> Total Tool Calls: 1 ``` ### Vectorized & Searchable Collections ```python from ham.data import Collection collection = Collection(vector=True) collection.add("Rocks") collection.add("Cucumber") collection.add("Dragon") collection.add("Apple") # query the collection for result in collection.query("Fruit"): print(result.item) ``` ```bash >>> Apple >>> Cucumber >>> Dragon >>> Rocks ``` ### HTTP & API Resources ```python from ham.http import function_server # instantly launch a server by decorating a function @function_server(path="/some-endpoint", auto_start=True) def some_endpoint(name : str) -> str: return f"Hello, {name}!" ``` Now in a separate file: ```python import requests print( requests.post( "http://0.0.0.0:8000/some-endpoint", json = {"name": "John"} ).json() ) ``` ```bash >>> {'result' : 'Hello, John!'} ```