HasAPI - Modern Python Framework for AI-Native APIs & UIs

🎯 Why HasAPI?

📦 Installation

# Core framework only
pip install hasapi
# With AI support (LLM, RAG, Embeddings)
pip install hasapi[ai]
# With all features
pip install hasapi[all]

🏁 Quick Start

Minimal API

from hasapi import HasAPI, JSONResponse
app = HasAPI(title="My API", version="1.0.0")
@app.get("/")
async def root(request):
 return JSONResponse({"message": "Hello from HasAPI!"})
if __name__ == "__main__":
 import uvicorn
 uvicorn.run(app, host="0.0.0.0", port=8000)

AI Chatbot

import os
from hasapi import HasAPI, JSONResponse
from hasapi.ai import LLM, ConversationManager
llm = LLM(provider="openai", api_key=os.getenv("OPENAI_API_KEY"))
conversation_manager = ConversationManager()
app = HasAPI(title="AI Chatbot")
@app.post("/chat/{conversation_id}")
async def chat(request, conversation_id: str):
 body = await request.json()
 message = body.get("message", "")
 
 conversation = conversation_manager.get_or_create_conversation(conversation_id)
 conversation.add_message("user", message)
 
 messages = [{"role": "system", "content": "You are a helpful AI assistant."}]
 messages.extend(conversation.get_context())
 
 result = await llm.chat(messages, temperature=0.7)
 conversation.add_message("assistant", result["content"])
 
 return JSONResponse({"response": result["content"]})

RAG System

import os
from hasapi import HasAPI, JSONResponse
from hasapi.ai import LLM, RAG, Embeddings
from hasapi.ai.vectors import InMemoryVectorStore
llm = LLM("openai", api_key=os.getenv("OPENAI_API_KEY"))
embeddings = Embeddings("openai", api_key=os.getenv("OPENAI_API_KEY"))
vector_store = InMemoryVectorStore(dimension=embeddings.get_dimension())
rag = RAG(embeddings=embeddings, llm=llm, vector_store=vector_store)
app = HasAPI(title="RAG Knowledge Base")
@app.post("/documents")
async def upload_document(request):
 body = await request.json()
 doc_ids = await rag.add_texts([body.get("text", "")])
 return JSONResponse({"id": doc_ids[0]})
@app.post("/chat")
async def rag_chat(request):
 body = await request.json()
 result = await rag.answer(body.get("message", ""), top_k=3)
 return JSONResponse({"answer": result["answer"], "sources": result["sources"]})

🎨 UI Components

from hasapi import HasAPI
from hasapi.ui import UI, Textbox, Text
from hasapi.templates import default_layout, TemplateResponse
def analyze_sentiment(text):
 positive = ["good", "great", "awesome", "love", "happy"]
 negative = ["bad", "terrible", "hate", "sad", "awful"]
 text_lower = text.lower()
 pos = sum(1 for w in positive if w in text_lower)
 neg = sum(1 for w in negative if w in text_lower)
 if pos > neg: return "😊 Positive"
 elif neg > pos: return "😢 Negative"
 return "😐 Neutral"
app = HasAPI(title="Sentiment Analysis")
sentiment_ui = UI(
 fn=analyze_sentiment,
 inputs=Textbox(label="Enter text"),
 outputs=Text(label="Sentiment"),
 title="📝 Sentiment Analysis"
)
@app.get("/")
async def sentiment_page(request):
 layout = default_layout(sentiment_ui.title)
 return TemplateResponse(
 template_string=layout.wrap(sentiment_ui._render_template()),
 title=sentiment_ui.title,
 custom_js=sentiment_ui._get_javascript()
 )
sentiment_ui._setup_api_endpoint(app)

🤖 AI Features

LLM Support

from hasapi.ai import LLM
llm = LLM("openai", api_key="sk-...")
# or: LLM("claude", api_key="sk-ant-...")
# or: LLM("openai", api_key="...", base_url="https://api.groq.com/v1")
response = await llm.chat([
 {"role": "system", "content": "You are helpful"},
 {"role": "user", "content": "Hello!"}
])

RAG

from hasapi.ai import RAG, Embeddings, LLM
rag = RAG(
 embeddings=Embeddings("openai", api_key="..."),
 llm=LLM("openai", api_key="...")
)
await rag.add_texts(["Document 1", "Document 2"])
result = await rag.answer("What is in the documents?")

🔧 Middleware

from hasapi.middleware import CORSMiddleware, JWTAuthMiddleware
app.middleware(CORSMiddleware(allow_origins=["*"]))
app.middleware(JWTAuthMiddleware(secret_key="your-secret"))

📚 Examples

🔗 API Documentation

📄 License