Client Construction

LLMClient.from_env() reads LLM_BASE_URL, LLM_API_KEY, LLM_MODEL, and CA_BUNDLE_PATH from the environment (.env file loaded automatically) and constructs the client.

from llm_framework.core import LLMClient

async with LLMClient.from_env() as client:
    ...

To use multiple endpoints in the same script, construct LLMClient directly:

local = LLMClient(base_url="http://localhost:1234/v1", api_key="nokey", model="qwen3-4b")
remote = LLMClient(base_url="https://api.example.com", api_key="sk-...", model="gpt-4o")

llm_framework.core.llm.LLMClient

LLMClient(base_url: str, api_key: str, model: str, timeout: float = 120.0, verify: str | bool = True)

Async HTTP client for OpenAI-compatible chat completion and embeddings endpoints.

Parameters:

Name	Type	Description	Default
base_url	str	Base URL of the OpenAI-compatible API endpoint.	required
api_key	str	API key; sent as both Bearer token and x-api-key for cross-provider compatibility.	required
model	str	Model identifier to use for all requests.	required
timeout	float	HTTP request timeout in seconds (default 120).	120.0
verify	str | bool	TLS verification — True for system CAs, False to skip, or a path to a CA bundle.	True

from_env classmethod

from_env() -> LLMClient

Construct from env vars; reads LLM_BASE_URL, LLM_API_KEY, LLM_MODEL, CA_BUNDLE_PATH.

chat_completions async

chat_completions(messages: list[dict[str, Any]], tools: list[dict[str, Any]] | None = None, temperature: float = 0.7, max_tokens: int = 1024, max_retries: int = 3, response_format: dict[str, Any] | None = None) -> dict[str, Any]

Send a chat completion request with exponential backoff on transient server errors.

Parameters:

Name	Type	Description	Default
messages	list[dict[str, Any]]	Conversation history in OpenAI message format.	required
tools	list[dict[str, Any]] | None	Optional list of JSON function schemas to expose to the model.	None
temperature	float	Sampling temperature (default 0.7); use 0.0 for deterministic output.	0.7
max_tokens	int	Maximum tokens in the response.	1024
max_retries	int	Retry attempts on 429/5xx errors with exponential backoff.	3
response_format	dict[str, Any] | None	Optional structured output schema (e.g. a json_schema object).	None

Returns:

Type	Description
dict[str, Any]	Raw OpenAI-compatible response dict with choices, usage, and model fields.

embeddings async

embeddings(input_texts: list[str], max_retries: int = 3) -> list[list[float]]

Return one embedding vector per input text.

Parameters:

Name	Type	Description	Default
input_texts	list[str]	List of strings to embed.	required
max_retries	int	Retry attempts on 429/5xx errors with exponential backoff.	3

Returns:

Type	Description
list[list[float]]	List of embedding vectors, one per input string, in the same order.