tketool.llm

OpenAI-compatible large-model access, structured-output prompts, embeddings, memory, tools, and the current scheduler.

pip install tketool.llm

Chat Completions

import os

from tketool.llm import OpenAI_Complete_Model

llm = OpenAI_Complete_Model(
    model_name="gpt-4o-mini",
    apitoken=os.environ["OPENAI_API_KEY"],
    base_url="https://api.openai.com/v1",
    call_dict={"temperature": 0.2},
)

text = llm("用三句话解释向量检索", return_detail=False)
print(text)

Responses API

import os

from tketool.llm import OpenAI_Response_Model

llm = OpenAI_Response_Model(
    model_name="gpt-5-mini",
    apitoken=os.environ["OPENAI_API_KEY"],
    base_url="https://api.openai.com/v1",
    call_dict={"max_output_tokens": 300},
)

text, detail = llm("给出一个最小 RAG 流程", return_detail=True)
print(text)
print(detail)

Both transports accept a complete messages list and return the same detail shape. OpenAI-compatible gateways are supported by changing base_url and model_name.

messages = [
    {"role": "system", "content": "回答要简洁。"},
    {"role": "user", "content": "什么是结构化输出？"},
]

answer = llm("", return_detail=False, messages=messages)

New code should import from tketool.llm. The existing tketool.lmc path is kept as a compatibility API during migration.

Local Hugging Face embeddings are optional:

pip install "tketool.llm[local-embeddings]"

The optional memory extra requires Python 3.11 or newer because its external backend does. The legacy agent/context implementation was removed because it depended on the retired scheduler; use scheduler2 for current agent flows.