srx-lib-llm 0.1.5

LLM helpers for SRX services: ChatOpenAI wrapper, tool base and Tavily tool

srx-lib-llm

LLM helpers for SRX services built on LangChain.

What it includes:

• responses_chat(prompt, cache=False): simple text chat via OpenAI Responses API
• Tool strategy base and registry
• Tavily search tool strategy
• Structured output helpers: build Pydantic model from JSON Schema and generate structured outputs via LLM
• Request models, e.g. DynamicStructuredOutputRequest

Designed to work with official OpenAI only.

Install

PyPI (public):

• pip install srx-lib-llm

uv (pyproject):

[project]
dependencies = ["srx-lib-llm>=0.1.0"]

Usage

from srx_lib_llm import responses_chat
text = await responses_chat("Hello there", cache=True)

Structured output from JSON Schema:

from srx_lib_llm import StructuredOutputGenerator, build_model_from_schema, preprocess_json_schema

json_schema = {
  "type": "object",
  "properties": {
    "title": {"type": "string"},
    "score": {"type": "number"}
  },
  "required": ["title"]
}

gen = StructuredOutputGenerator()
model = build_model_from_schema("MyOutput", preprocess_json_schema(json_schema))
result = await gen.generate_from_model("Give me a title and score", model)
print(result.model_dump())

Back-compat helpers and request models:

from srx_lib_llm import create_dynamic_schema, DynamicStructuredOutputRequest

schema_model = create_dynamic_schema("MyOutput", json_schema)
payload = DynamicStructuredOutputRequest(text="...", json_schema=json_schema)

Tools:

from srx_lib_llm.tools import ToolStrategyBase, register_strategy, get_strategies
from srx_lib_llm.tools.tavily import TavilyToolStrategy

register_strategy(TavilyToolStrategy())
strategies = get_strategies()

Environment Variables

• OPENAI_API_KEY (required)
• OPENAI_MODEL (optional, default: gpt-4.1-nano)
• TAVILY_API_KEY (optional, for the Tavily tool)

Release

Tag vX.Y.Z to publish to GitHub Packages via Actions.

License

Proprietary © SRX