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Build a chatbot API with conversation memory. Create contextual chatbot services. Architecture API Endpoint → Memory Store → LLM Implementation class ChatbotAPI: def __init__(self): self.memories = {} def chat(self, session_id, message): if session_id not in self.memories: self.memories[session_id] = [] self.memories[session_id].append({ “role”: “user”, “content”: message }) response = client.chat.completions.create( model=”gpt-4″, messages=self.memories[session_id] ) return response.choices[0].message.content Conclusion Memory … Read more

Generate embeddings for text at scale. Create text vectors for semantic search. Example response = client.embeddings.create( model=”text-embedding-3-small”, input=[“Hello world”, “How are you?”] ) embeddings = [e.embedding for e in response.data] Models ✅ text-embedding-3-small ✅ text-embedding-3-large Pricing $0.02/1M tokens (small) $0.13/1M tokens (large) Conclusion Embeddings enable semantic search!

Use vision models for image understanding. Process images with LLMs. GPT-4 Vision Example response = client.chat.completions.create( model=”gpt-4-vision-preview”, messages=[{ “role”: “user”, “content”: [ {“type”: “text”, “text”: “What’s in this image?”}, {“type”: “image_url”, “image_url”: {“url”: “image_url”}} ] }] ) Use Cases ✅ Image analysis ✅ Document OCR ✅ Visual Q&A Conclusion Vision APIs enable image understanding!

Use function calling for structured outputs. Make LLMs call your functions. Example tools = [{ “type”: “function”, “function”: { “name”: “get_weather”, “parameters”: { “type”: “object”, “properties”: {“location”: {“type”: “string”}} } } }] response = client.chat.completions.create( model=”gpt-4″, messages=[…], tools=tools ) Conclusion Function calling enables structured interactions!

Use async calls to improve performance. Make concurrent API requests efficiently. Async Example import asyncio from openai import AsyncOpenAI client = AsyncOpenAI() async def generate(prompt): return await client.chat.completions.create(…) async def main(): tasks = [generate(f”Task {i}”) for i in range(10)] results = await asyncio.gather(*tasks) Benefits ✅ Faster processing ✅ Better throughput ✅ Resource efficient Conclusion Async … Read more

Create your own AI-powered API. Build APIs that use LLMs for processing. Architecture FastAPI → AI Service → LLM Provider FastAPI Example from fastapi import FastAPI app = FastAPI() @app.post(“/generate”) async def generate_text(prompt: str): response = client.chat.completions.create(…) return {“text”: response.choices[0].message.content} Conclusion Build your own AI APIs easily!