mirror of
https://github.com/ggml-org/llama.cpp.git
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6777c544bd
* json: default additionalProperty to true * json: don't force additional props after normal properties! * json: allow space after enum/const * json: update pydantic example to set additionalProperties: false * json: prevent additional props to redefine a typed prop * port not_strings to python, add trailing space * fix not_strings & port to js+py * Update json-schema-to-grammar.cpp * fix _not_strings for substring overlaps * json: fix additionalProperties default, uncomment tests * json: add integ. test case for additionalProperties * json: nit: simplify condition * reformat grammar integ tests w/ R"""()""" strings where there's escapes * update # tokens in server test: consts can now have trailing space
80 lines
3.1 KiB
Python
80 lines
3.1 KiB
Python
# Usage:
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#! ./llama-server -m some-model.gguf &
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#! pip install pydantic
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#! python json-schema-pydantic-example.py
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from pydantic import BaseModel, Extra, TypeAdapter
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from annotated_types import MinLen
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from typing import Annotated, List, Optional
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import json, requests
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if True:
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def create_completion(*, response_model=None, endpoint="http://localhost:8080/v1/chat/completions", messages, **kwargs):
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'''
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Creates a chat completion using an OpenAI-compatible endpoint w/ JSON schema support
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(llama.cpp server, llama-cpp-python, Anyscale / Together...)
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The response_model param takes a type (+ supports Pydantic) and behaves just as w/ Instructor (see below)
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'''
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if response_model:
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type_adapter = TypeAdapter(response_model)
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schema = type_adapter.json_schema()
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messages = [{
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"role": "system",
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"content": f"You respond in JSON format with the following schema: {json.dumps(schema, indent=2)}"
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}] + messages
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response_format={"type": "json_object", "schema": schema}
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data = requests.post(endpoint, headers={"Content-Type": "application/json"},
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json=dict(messages=messages, response_format=response_format, **kwargs)).json()
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if 'error' in data:
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raise Exception(data['error']['message'])
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content = data["choices"][0]["message"]["content"]
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return type_adapter.validate_json(content) if type_adapter else content
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else:
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# This alternative branch uses Instructor + OpenAI client lib.
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# Instructor support streamed iterable responses, retry & more.
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# (see https://python.useinstructor.com/)
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#! pip install instructor openai
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import instructor, openai
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client = instructor.patch(
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openai.OpenAI(api_key="123", base_url="http://localhost:8080"),
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mode=instructor.Mode.JSON_SCHEMA)
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create_completion = client.chat.completions.create
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if __name__ == '__main__':
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class QAPair(BaseModel):
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class Config:
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extra = 'forbid' # triggers additionalProperties: false in the JSON schema
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question: str
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concise_answer: str
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justification: str
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stars: Annotated[int, Field(ge=1, le=5)]
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class PyramidalSummary(BaseModel):
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class Config:
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extra = 'forbid' # triggers additionalProperties: false in the JSON schema
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title: str
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summary: str
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question_answers: Annotated[List[QAPair], MinLen(2)]
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sub_sections: Optional[Annotated[List['PyramidalSummary'], MinLen(2)]]
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print("# Summary\n", create_completion(
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model="...",
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response_model=PyramidalSummary,
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messages=[{
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"role": "user",
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"content": f"""
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You are a highly efficient corporate document summarizer.
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Create a pyramidal summary of an imaginary internal document about our company processes
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(starting high-level, going down to each sub sections).
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Keep questions short, and answers even shorter (trivia / quizz style).
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"""
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}]))
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