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September 3, 2024 01:15
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| import dataclasses | |
| import openai | |
| import os | |
| import re | |
| import yaml | |
| from pydantic import BaseModel, ValidationError | |
| from typing import Optional | |
| @dataclasses.dataclass | |
| class Message: | |
| role: str | |
| content: str | |
| def llm_call(model: str, messages: list[Message]): | |
| client = openai.OpenAI(api_key=os.environ["TOGETHER_API_KEY"], base_url="https://api.together.xyz/v1") | |
| res = client.chat.completions.create(model=model, messages=[dataclasses.asdict(x) for x in messages], temperature=0.8, max_tokens=4096) | |
| return res.choices[0].message.content | |
| def generate_response_prompt(model: type(BaseModel)) -> str: | |
| TAB = " " | |
| ret = "Respond in YAML format, following the below Pydantic model:\n```python\n" | |
| ret += f"from pydantic import BaseModel, Field\n\nclass {model.__name__}(BaseModel):\n" | |
| yaml_example = "" | |
| for attr, prop in model.model_json_schema()["properties"].items(): | |
| yaml_example += f"\n{attr}: ..." | |
| if prop["type"] == "array": ret += f"{TAB}{attr}: list[{prop['items']['type']}] = Field(desc='{prop['desc']}', max_length={prop['maxItems']})\n" | |
| else: ret += f"{TAB}{attr}: {prop['type']} = Field(desc='{prop['desc']}')\n" | |
| ret += f"```\n\nYAML should be enclosed in triple backticks like\n```yaml{yaml_example}\n```\n" | |
| return ret | |
| def parse_llm_response(model: type[BaseModel], llm_res: str): | |
| ptrn = re.compile(r"```yaml(.*?)```", re.DOTALL) | |
| match = re.search(ptrn, llm_res) | |
| if match: | |
| try: | |
| res_dict = yaml.safe_load(match.group(1)) | |
| if isinstance(res_dict, dict): ret = model.model_validate(res_dict) | |
| elif isinstance(res_dict, list): ret = [model.model_validate(x) for x in res_dict] | |
| else: raise Exception(f"Shouldn't have reached here. Expected type of dict or list, but got {type(res_dict)}") | |
| return ret | |
| except ValidationError as e: | |
| print("Pydantic Validation Error:", e) | |
| print(llm_res) | |
| except yaml.YAMLError as e: | |
| print("YAML parsing error:", e) | |
| print(llm_res) | |
| except Exception as e: | |
| print("Error:", e) | |
| print(llm_res) | |
| else: | |
| print("Couldn't find yaml tags\nResponse:") | |
| print(llm_res) | |
| return None | |
| def run(model: str, messages: list[Message], max_retries: int, response_model: Optional[type(BaseModel)] = None): | |
| messages[0].content += f"\n---\n\n{generate_response_prompt(response_model)}---\n" | |
| while max_retries: | |
| res = llm_call(model, messages) | |
| ret = parse_llm_response(response_model, res) | |
| if ret is None: max_retries -= 1 | |
| else: return ret |
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Usage Example:
Output: