gpt-client-lite 1.0.0

Minimal OpenAI GPT client for Python - chat, embeddings, functions, streaming, no heavy deps

gpt-client-lite

Minimal OpenAI GPT client for Python — chat, embeddings, function calling, and streaming with zero heavy dependencies.

Built entirely on the Python standard library (urllib, json, dataclasses). Works on Python 3.9 and above.

Install

pip install gpt-client-lite

Quick start

from gpt_client_lite import GPTClient

client = GPTClient()              # reads OPENAI_API_KEY from environment
# client = GPTClient("sk-...")   # or pass the key directly

---

Chat

Single-turn prompt

reply = client.chat.simple("What is the capital of Japan?")
print(reply)  # "Tokyo"

With a system prompt

reply = client.chat.simple(
    "Tell me a joke.",
    system="You are a stand-up comedian who only tells puns.",
)

Full response object

from gpt_client_lite import Message

resp = client.chat.complete(
    [Message.user("Explain black holes in one sentence.")],
    model="gpt-4o",
    temperature=0.7,
    max_tokens=120,
)

print(resp.text)          # assistant reply
print(resp.usage)         # Usage(prompt=..., completion=..., total=...)
print(resp.finish_reason) # "stop"

JSON mode

data = client.chat.json("List 5 world capitals as a JSON array of strings.")
# data is already a parsed Python object: ["Tokyo", "Paris", ...]

Summarise / translate helpers

short = client.chat.summarise(long_article, max_words=80)
es    = client.chat.translate("Good morning, have a great day!", "Spanish")

Multi-turn conversation

conv = client.chat.conversation(system="You are a helpful travel guide.")

print(conv.say("I'm visiting Tokyo next month."))
print(conv.say("What neighbourhood should I stay in?"))
print(conv.say("What's the food like there?"))

# Inspect history
for msg in conv.history:
    print(f"[{msg.role}] {msg.content[:60]}")

conv.clear()  # reset (keeps system message by default)

---

Streaming

# Print tokens to stdout as they arrive
client.streaming.simple("Write a haiku about the ocean").print_stream()

# Iterate manually
for chunk in client.streaming.simple("Count from 1 to 5."):
    print(chunk.text, end="", flush=True)

# Callback style
full_text = client.streaming.simple("Tell me a story").on_token(
    lambda token: print(token, end="", flush=True)
)

# Collect the whole text at once (buffers internally)
text = client.streaming.simple("Explain recursion.").text

Streaming from a messages list

from gpt_client_lite import Message

msgs = [
    Message.system("You are a Python expert."),
    Message.user("Show me a quick sort implementation."),
]

stream = client.streaming.chat(msgs, temperature=0.2)
stream.print_stream()

---

Embeddings

# Embed a single text
vec = client.embeddings.embed("The quick brown fox")
print(len(vec))  # 1536 for text-embedding-3-small

# Embed multiple texts (one API call)
vecs = client.embeddings.embed_batch(["cat", "dog", "car", "truck"])

# Semantic similarity (0 – 1)
score = client.embeddings.similarity("puppy", "dog")
print(f"{score:.3f}")  # ~0.92

# Find most similar
results = client.embeddings.most_similar(
    "machine learning",
    ["deep learning", "pasta cooking", "neural networks", "astronomy"],
    top_k=2,
)
for text, score in results:
    print(f"{score:.3f}  {text}")

# Rank documents by relevance
ranked = client.embeddings.rank("Python async programming", documents)
for idx, text, score in ranked:
    print(f"[{idx}] {score:.3f}  {text[:60]}")

# Cluster texts (pure-Python k-means, no numpy)
groups = client.embeddings.cluster(sentences, n_clusters=3)

---

Function / Tool Calling

import json
from gpt_client_lite import Message

# Register functions — schema is inferred from type hints + docstring
@client.functions.register
def get_weather(location: str, unit: str = "celsius") -> str:
    """Get the current weather for a location.

    Args:
        location: City name or address.
        unit: Temperature unit — celsius or fahrenheit.
    """
    # Your real implementation here
    return json.dumps({"location": location, "temperature": 22, "unit": unit})


@client.functions.register
def search_web(query: str, max_results: int = 5) -> str:
    """Search the web and return a summary.

    Args:
        query: Search query string.
        max_results: Maximum number of results to return.
    """
    return f"Top {max_results} results for: {query}"


# Single round
messages = [Message.user("What's the weather like in Paris?")]
tools    = client.functions.build_tools()
resp     = client.chat.with_tools(messages, tools=tools)

if resp.has_tool_calls:
    # Execute and get ready-to-send result messages
    tool_results = client.functions.execute_all_tool_calls(resp.tool_calls)

    messages.append(resp.message.to_dict())
    messages.extend(tool_results)

    final = client.chat.complete(messages)
    print(final.text)

Agentic loop (auto-executes until the model stops calling tools)

messages = [Message.user("Search for Python news and summarise the weather in London.")]

final = client.functions.run_agentic_loop(
    messages=[m.to_dict() for m in messages],
    chat_api=client.chat,
    max_rounds=5,
)
print(final.text)

---

Configuration

client = GPTClient(
    api_key="sk-...",          # or OPENAI_API_KEY env var
    model="gpt-4o",            # default model for chat + streaming
    timeout=30,                # seconds (default 60)
    max_retries=5,             # exponential backoff retries (default 3)
    organization="org-...",    # optional OpenAI org ID
    project="proj-...",        # optional project ID
    base_url="https://...",    # override for proxies / compatible APIs
    extra_headers={"X-Custom": "value"},
)

# Change the default model after construction
client.set_model("gpt-4o-mini")

---

Error handling

from gpt_client_lite import (
    AuthenticationError,
    RateLimitError,
    ContextLengthExceededError,
    InvalidRequestError,
    APIError,
    GPTClientError,
)

try:
    reply = client.chat.simple("Hello!")
except AuthenticationError:
    print("Invalid API key.")
except RateLimitError as e:
    print(f"Rate limited. Retry after: {e.retry_after}s")
except ContextLengthExceededError:
    print("Prompt too long for this model.")
except APIError as e:
    print(f"Server error {e.status_code}: {e}")
except GPTClientError as e:
    print(f"Unexpected error: {e}")

---

API Reference

GPTClient

Parameter	Type	Default	Description
api_key	str	$OPENAI_API_KEY	API key
model	str	"gpt-4o-mini"	Default model
timeout	int	60	Request timeout in seconds
max_retries	int	3	Retry attempts for transient errors
organization	str	None	OpenAI organization ID
project	str	None	OpenAI project ID
base_url	str	OpenAI API	Override base URL
extra_headers	dict	None	Additional HTTP headers

client.chat

Method	Returns	Description
.simple(prompt, system, **kw)	str	One-shot prompt
.complete(messages, **kw)	ChatResponse	Full completions call
.json(prompt, system, **kw)	Any	JSON-mode, auto-parsed
.summarise(text, max_words)	str	Summarise text
.translate(text, language)	str	Translate text
.with_tools(messages, tools)	ChatResponse	Tool-calling request
.conversation(system, **kw)	Conversation	Multi-turn session

client.streaming

Method	Returns	Description
.simple(prompt, system, **kw)	StreamResult	Stream a prompt
.chat(messages, **kw)	StreamResult	Stream a messages list
.complete_to_string(msgs, on_token)	str	Stream + collect

StreamResult

Method / Property	Description
iter_text()	Iterator over text fragments
collect() / .text	Buffer and return full text
print_stream()	Write to stdout, return full text
on_token(callback)	Call callback(token) per fragment
to_message()	Return buffered result as Message

client.embeddings

Method	Returns	Description
.embed(text)	List[float]	Single embedding vector
.embed_batch(texts)	List[List[float]]	Batch embedding
.similarity(a, b)	float	Semantic similarity 0–1
.most_similar(query, candidates, top_k)	List[(str, float)]	Nearest texts
.rank(query, documents)	List[(int, str, float)]	Ranked with original index
.cluster(texts, n_clusters)	List[List[str]]	K-means clustering

client.functions

Method	Description
@.register	Decorator to register a function
.build_tools(names?)	List of FunctionDefinition for the API
.execute_tool_call(tc)	Execute one tool call dict
.execute_all_tool_calls(tcs)	Execute all, return result messages
.run_agentic_loop(messages, chat_api)	Auto loop until no more tool calls

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License

MIT © Vladyslav Zaiets