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Обновил структуру проекта

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Даниил Грабарь
2026-10-01 17:38:49 +10:00
parent 2e23b63689
commit 9e551a2ca9
5 changed files with 170 additions and 86 deletions
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build-dataset/config.yml
+2 -2
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@@ -1,6 +1,6 @@
llm:
provider: codex # openai, codex
# model: gpt-6-Astra
model: gpt-6-astra
# model: qwen/qwen3-8b
generation:
@@ -8,7 +8,7 @@ generation:
max_tokens: 4096
runtime:
concurrency: 1 # Колличество одновременных запросов
concurrency: 5 # Колличество одновременных запросов
prompts:
instructions: |
build-dataset/robot-move-data.py
+75 -70
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@@ -1,7 +1,5 @@
import asyncio
from pathlib import Path
import pandas as pd
from dotenv import load_dotenv
# from pydantic import BaseModel
@@ -9,58 +7,53 @@ from pydantic_ai import Agent
from pydantic_ai.settings import ModelSettings
from llm.config import load_config
from llm.factory import LLMModelProvider
from llm.schemas import DatasetRecord, Message, RobotPlan, SFTFormat
from llm.factory import LLMModelProvider, DatasetWriter
from llm.schemas import (RobotPlan, RobotDatasetRecord,
Prompts, ModelResponse,
ModelInfo, GenerationSettings)
load_dotenv()
def save_to_csv(record: DatasetRecord,
path: str | Path):
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
exists = path.exists()
df = pd.DataFrame([record.model_dump()])
df.to_csv(path,
mode="a" if exists else "w",
header=not exists,
index=False,
encoding="utf-8")
def save_to_jsonl(sft_format: SFTFormat,
path: str | Path):
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("a", encoding="utf-8") as file:
file.write(sft_format.model_dump_json())
file.write("\n")
def record_to_sft(record: DatasetRecord) -> SFTFormat:
return SFTFormat(
message=[
Message(
role="system",
content=record.system_prompt
),
Message(
role="user",
content=record.user_prompt
),
Message(
role="assistant",
content=record.assistant_content
)
]
)
def format_robot_plan(plan: RobotPlan):
return "\n".join(
f"{index}. {step}"
for index, step in enumerate(plan.steps, start=1)
f"{step}"
for step in plan.steps
)
async def generate_record(task: str,
agent: Agent,
settings: ModelSettings,
config,
semaphore: asyncio.Semaphore) -> RobotDatasetRecord:
async with semaphore:
user_prompt = config.prompts.user.format(task=task)
result = await agent.run(user_prompt,
model_settings=settings)
assistant_content = format_robot_plan(result.output)
return RobotDatasetRecord(
task=task,
prompts=Prompts(
system_prompt=config.prompts.system,
instructions=config.prompts.instructions,
user_prompt=user_prompt
),
response=ModelResponse(
model_response=result.output.model_dump_json(),
assistant_content=assistant_content
),
model_info=ModelInfo(
provider=config.llm.provider,
model=config.llm.model
),
generation=GenerationSettings(
temperature=config.generation.temperature,
max_tokens=config.generation.max_tokens
)
)
@@ -81,30 +74,42 @@ async def main():
**config.generation.model_dump()
)
task="Перемести деталь A из точки PICK_A в точку PLACE_A. Безопасная высота подъема — 200 мм."
user_prompt = config.prompts.user.format(task=task)
result = await agent.run(user_prompt,
model_settings=settings)
tasks = [
(
"Перемести деталь A из точки PICK_A в точку PLACE_A. "
"Безопасная высота подъема — 200 мм."
),
(
"Перемести деталь B из точки PICK_B в точку PLACE_B. "
"Безопасная высота подъема — 150 мм."
),
(
"Возьми крышку из точки COVER_PICK и перемести "
"ее в точку COVER_PLACE."
),
]
assistant_content = format_robot_plan(result.output)
record = DatasetRecord(
task=task,
system_prompt=config.prompts.system,
instructions=config.prompts.instructions,
user_prompt=user_prompt,
model_response=result.output.model_dump_json(),
assistant_content=assistant_content,
provider=config.llm.provider,
model=config.llm.model,
temperature=config.generation.temperature,
max_tokens=config.generation.max_tokens
)
semaphore = asyncio.Semaphore(config.runtime.concurrency)
save_to_csv(record=record,
path="./dataset/robot-move.csv")
sft_format = record_to_sft(record)
save_to_jsonl(sft_format=sft_format,
path="./dataset/robot-move.jsonl")
records = await asyncio.gather(*[
generate_record(
task,
agent,
settings,
config,
semaphore,
)
for task in tasks
])
for record in records:
dataset = DatasetWriter(
dataset_record=record,
base_path="./dataset"
)
dataset.to_csv(filename="robot-plan.csv")
dataset.to_jsonl(filename="robotplan.jsonl")
if __name__ == "__main__":
llm/credentials.py
+4 -2
View File
@@ -10,12 +10,12 @@ from pydantic_ai.providers.openai_codex import (
class FileCodexCredentionalSource(OpenAICodexCredentialSource):
def __init__(self,
path: Path,
path: str| Path,
encoding: str="utf-8",
ensure_ascii: bool=False,
indent: int=2):
super().__init__()
self._path = path
self._path = Path(path)
self._encoding=encoding
self._ensure_ascii=ensure_ascii
self._indent=indent
@@ -32,6 +32,8 @@ class FileCodexCredentionalSource(OpenAICodexCredentialSource):
)
async def save(self, credentials: OpenAICodexCredentials) -> None:
self.path.parent.mkdir(parents=True , exist_ok=True)
self._path.write_text(
json.dumps(
asdict(credentials),
llm/factory.py
+67
View File
@@ -1,5 +1,6 @@
import os
import webbrowser
import pandas as pd
from pathlib import Path
from pydantic_ai.models.openai import OpenAIChatModel
@@ -12,6 +13,7 @@ from pydantic_ai.providers.openai_codex import (
from .config import CodexConfig, LLMConfig, OpenAiConfig
from .credentials import FileCodexCredentionalSource
from .schemas import BaseDatasetRecord, ChatRecord, ChatMessage
class LLMModelProvider:
@@ -82,3 +84,68 @@ class LLMModelProvider:
)
raise ValueError(f"Unsupported LLM config: {type(config)}")
class DatasetWriter:
def __init__(self,
dataset_record: BaseDatasetRecord,
base_path: str | Path):
self._dataset_record = dataset_record
self._path = Path(base_path)
self._path.mkdir(parents=True, exist_ok=True)
def __check_file_exists(self, filename :str)-> bool:
file_path = self._path / filename
return file_path.exists()
def to_csv(self,
filename: str) -> None:
exists=self.__check_file_exists(filename)
df = pd.json_normalize(
self._dataset_record.model_dump()
)
df.to_csv(self._path / filename,
mode="a" if exists else "w",
header=not exists,
index=False,
encoding="utf-8")
def to_chat_record(self) -> ChatRecord:
response = self._dataset_record.response
prompts = self._dataset_record.prompts
assistant_content = (
response.assistant_content
or response.model_response
)
return ChatRecord(
messages=[
ChatMessage(
role="system",
content=prompts.system_prompt,
),
ChatMessage(
role="user",
content=prompts.user_prompt,
),
ChatMessage(
role="assistant",
content=assistant_content,
),
]
)
def to_jsonl(self,
filename: str) -> None:
chat_record = self.to_chat_record()
path = self._path/filename
with path.open("a", encoding='utf-8') as file:
file.write(chat_record.model_dump_json())
file.write("\n")
llm/schemas.py
+20 -10
View File
@@ -1,4 +1,4 @@
from typing import Literal
from typing import Literal, Optional
from pydantic import BaseModel, Field
@@ -10,7 +10,7 @@ class RobotPlan(BaseModel):
)
class Message(BaseModel):
class ChatMessage(BaseModel):
role: Literal["system",
"user",
"assistant"]
@@ -18,22 +18,32 @@ class Message(BaseModel):
content: str
class SFTFormat(BaseModel):
message: list[Message]
class DatasetRecord(BaseModel):
task: str
class ChatRecord(BaseModel):
messages: list[ChatMessage]
class Prompts(BaseModel):
system_prompt: str
instructions: str
user_prompt: str
model_response: str
assistant_content: str
class ModelResponse(BaseModel):
model_response: str
assistant_content: Optional[str] = None
class ModelInfo(BaseModel):
provider: str
model: str
class GenerationSettings(BaseModel):
temperature: float
max_tokens: int
class BaseDatasetRecord(BaseModel):
prompts: Prompts
response: ModelResponse
model_info: ModelInfo
generation: GenerationSettings
class RobotDatasetRecord(BaseDatasetRecord):
task: str