Expert-level AI implementation, deployment, LLM integration, and production AI systems
Expert guidance for implementing AI systems, LLM integration, prompt engineering, and deploying production AI applications.
from openai import AsyncOpenAI
from anthropic import Anthropic
from typing import List, Dict, Optional
import asyncio
class LLMClient:
"""Unified LLM client with fallback"""
def __init__(self, primary: str = "openai", fallback: str = "anthropic"):
self.openai_client = AsyncOpenAI()
self.anthropic_client = Anthropic()
self.primary = primary
self.fallback = fallback
async def chat_completion(self, messages: List[Dict],
model: str = "gpt-4-turbo",
temperature: float = 0.7,
max_tokens: int = 1000) -> str:
"""Chat completion with fallback"""
try:
if self.primary == "openai":
response = await self.openai_client.chat.completions.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens
)
return response.choices[0].message.content
except Exception as e:
print(f"Primary provider failed: {e}, trying fallback")
if self.fallback == "anthropic":
response = self.anthropic_client.messages.create(
model="claude-3-5-sonnet-20241022",
messages=messages,
temperature=temperature,
max_tokens=max_tokens
)
return response.content[0].text
async def chat_completion_streaming(self, messages: List[Dict],
model: str = "gpt-4-turbo"):
"""Streaming chat completion"""
stream = await self.openai_client.chat.completions.create(
model=model,
messages=messages,
stream=True
)
async for chunk in stream:
if chunk.choices[0].delta.content:
yield chunk.choices[0].delta.content
async def function_calling(self, messages: List[Dict],
tools: List[Dict]) -> Dict:
"""Function calling with tools"""
response = await self.openai_client.chat.completions.create(
model="gpt-4-turbo",
messages=messages,
tools=tools,
tool_choice="auto"
)
message = response.choices[0].message
if message.tool_calls:
return {
"type": "function_call",
"function": message.tool_calls[0].function.name,
"arguments": message.tool_calls[0].function.arguments
}
else:
return {
"type": "message",
"content": message.content
}
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI
class RAGSystem:
"""Retrieval-Augmented Generation system"""
def __init__(self, persist_directory: str = "./chroma_db"):
self.embeddings = OpenAIEmbeddings()
self.vectorstore = None
self.persist_directory = persist_directory
self.llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)
def ingest_documents(self, documents: List[str]):
"""Ingest and index documents"""
# Split documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
chunks = text_splitter.create_documents(documents)
# Create vector store
self.vectorstore = Chroma.from_documents(
documents=chunks,
embedding=self.embeddings,
persist_directory=self.persist_directory
)
def query(self, question: str, k: int = 4) -> Dict:
"""Query with RAG"""
if not self.vectorstore:
raise ValueError("No documents ingested")
# Retrieve relevant documents
retriever = self.vectorstore.as_retriever(
search_kwargs={"k": k}
)
# Create QA chain
qa_chain = RetrievalQA.from_chain_type(
llm=self.llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True
)
# Get answer
result = qa_chain({"query": question})
return {
"answer": result["result"],
"sources": [doc.page_content for doc in result["source_documents"]]
}
def similarity_search(self, query: str, k: int = 4) -> List[Dict]:
"""Similarity search in vector database"""
results = self.vectorstore.similarity_search_with_score(query, k=k)
return [
{
"content": doc.page_content,
"score": score,
"metadata": doc.metadata
}
for doc, score in results
]
class PromptTemplate:
"""Advanced prompt templates"""
@staticmethod
def chain_of_thought(question: str) -> str:
"""Chain-of-thought prompting"""
return f"""Let's solve this step by step:
Question: {question}
Please think through this problem carefully:
1. First, identify what we need to find
2. Then, break down the problem into smaller steps
3. Solve each step
4. Finally, combine the results
Your step-by-step solution:"""
@staticmethod
def few_shot(task: str, examples: List[Dict], query: str) -> str:
"""Few-shot learning prompt"""
examples_text = "\n\n".join([
f"Input: {ex['input']}\nOutput: {ex['output']}"
for ex in examples
])
return f"""Task: {task}
Here are some examples:
{examples_text}
Now, please solve this:
Input: {query}
Output:"""
@staticmethod
def system_message(role: str, constraints: List[str],
format_instructions: str) -> str:
"""System message template"""
constraints_text = "\n".join([f"- {c}" for c in constraints])
return f"""You are a {role}.
Constraints:
{constraints_text}
Output Format:
{format_instructions}
Remember to follow these guidelines strictly."""
from typing import Callable
import json
class Tool:
"""Tool that agents can use"""
def __init__(self, name: str, description: str, function: Callable):
self.name = name
self.description = description
self.function = function
def to_openai_function(self) -> Dict:
"""Convert to OpenAI function format"""
return {
"type": "function",
"function": {
"name": self.name,
"description": self.description,
"parameters": self.get_parameters()
}
}
class AIAgent:
"""AI agent with tools"""
def __init__(self, llm_client: LLMClient, tools: List[Tool]):
self.llm = llm_client
self.tools = {tool.name: tool for tool in tools}
self.conversation_history = []
async def run(self, user_input: str, max_iterations: int = 10) -> str:
"""Run agent with tool use"""
self.conversation_history.append({
"role": "user",
"content": user_input
})
for i in range(max_iterations):
# Get LLM response with function calling
response = await self.llm.function_calling(
messages=self.conversation_history,
tools=[tool.to_openai_function() for tool in self.tools.values()]
)
if response["type"] == "message":
# Agent is done
return response["content"]
# Execute tool
tool_name = response["function"]
arguments = json.loads(response["arguments"])
tool_result = await self.execute_tool(tool_name, arguments)
# Add tool result to conversation
self.conversation_history.append({
"role": "function",
"name": tool_name,
"content": str(tool_result)
})
return "Max iterations reached"
async def execute_tool(self, tool_name: str, arguments: Dict) -> any:
"""Execute a tool"""
if tool_name not in self.tools:
raise ValueError(f"Tool {tool_name} not found")
tool = self.tools[tool_name]
return await tool.function(**arguments)
from fastapi import FastAPI, HTTPException, Depends
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from circuitbreaker import circuit
import asyncio
app = FastAPI()
class ChatRequest(BaseModel):
messages: List[Dict]
model: str = "gpt-4-turbo"
stream: bool = False
class RateLimiter:
"""Rate limiter for API"""
def __init__(self, max_requests: int, window_seconds: int):
self.max_requests = max_requests
self.window_seconds = window_seconds
self.requests = {}
async def check_limit(self, user_id: str) -> bool:
"""Check if user is within rate limit"""
import time
now = time.time()
if user_id not in self.requests:
self.requests[user_id] = []
# Remove old requests
self.requests[user_id] = [
req_time for req_time in self.requests[user_id]
if now - req_time < self.window_seconds
]
if len(self.requests[user_id]) >= self.max_requests:
return False
self.requests[user_id].append(now)
return True
rate_limiter = RateLimiter(max_requests=100, window_seconds=60)
llm_client = LLMClient()
@circuit(failure_threshold=5, recovery_timeout=60)
async def call_llm(messages: List[Dict]) -> str:
"""LLM call with circuit breaker"""
return await llm_client.chat_completion(messages)
@app.post("/chat")
async def chat(request: ChatRequest, user_id: str = Depends(get_user_id)):
"""Chat endpoint with rate limiting"""
# Check rate limit
if not await rate_limiter.check_limit(user_id):
raise HTTPException(status_code=429, detail="Rate limit exceeded")
try:
if request.stream:
async def generate():
async for chunk in llm_client.chat_completion_streaming(request.messages):
yield chunk
return StreamingResponse(generate(), media_type="text/event-stream")
else:
response = await call_llm(request.messages)
return {"response": response}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
❌ No error handling or fallbacks ❌ Exposing raw LLM outputs without validation ❌ No rate limiting or cost controls ❌ Storing API keys in code ❌ No monitoring or logging ❌ Ignoring token limits ❌ No testing of prompts
