Python Foundations for AI — Quick Reference
JSON handling
python
import json
data = json.loads(response_text) # string -> Python dict/list
text = json.dumps(data, indent=2) # Python object -> pretty string
value = data.get("key", "default") # safe access, no KeyError
Basic API call pattern
python
import os
from anthropic import Anthropic
client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=500,
messages=[{"role": "user", "content": "Your prompt here"}],
)
print(response.content[0].text)
Streaming
python
with client.messages.stream(
model="claude-sonnet-4-6", max_tokens=1000,
messages=[{"role": "user", "content": prompt}],
) as stream:
for text in stream.text_stream:
print(text, end="", flush=True) # flush=True matters, see Troubleshooting
Retry with exponential backoff
python
import time
def call_with_retry(fn, max_retries=3):
for attempt in range(max_retries):
try:
return fn()
except Exception:
if attempt == max_retries - 1:
raise
time.sleep(2 ** attempt)
Cosine similarity (embeddings)
python
import numpy as np
def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
Async concurrent calls
python
import asyncio
async def call_many(prompts: list[str]) -> list[str]:
return await asyncio.gather(*[call_one(p) for p in prompts])
Common error codes, quick reference
|---|---|
| `401` | Missing/invalid API key |
|---|
429 | Rate limited (requests or tokens per minute) |
KeyError on response | Accessing a field that's conditionally present, or an error-response shape |
nan from cosine similarity | Zero vector — usually a silently-failed embedding upstream |
| Async code hangs | Missing await, or blocking sync call inside async function |
Terminology quick reference
|---|---|
| Embedding | A vector representation of text, used for semantic similarity |
|---|
| RAG | Retrieval-Augmented Generation — retrieve relevant context, then generate an answer using it |
| Agent loop | Model calls a tool, gets a result, continues — repeated until a final answer |
| Context window | The maximum tokens (input + output) a model call can handle |
| Structured output | Constraining model output to a specific schema, instead of free text |
| Prompt caching | Reusing billed context across calls instead of re-paying for repeated content |
