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Merge pull request #128 from sushruth2003/adaptive-batch
Better auto batching for resolve LLM calls
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| Original file line number | Diff line number | Diff line change |
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| from typing import Any, Dict, List, Optional, Union | ||
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| from pydantic import BaseModel, Field, field_validator | ||
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| # from ..operations import map | ||
| # MapOp = map.MapOperation.schema | ||
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| class ToolFunction(BaseModel): | ||
| name: str | ||
| description: str | ||
| parameters: Dict[str, Any] | ||
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| class Tool(BaseModel): | ||
| code: str | ||
| function: ToolFunction | ||
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| class ParsingTool(BaseModel): | ||
| """ | ||
| Represents a parsing tool used for custom data parsing in the pipeline. | ||
| Attributes: | ||
| name (str): The name of the parsing tool. This should be unique within the pipeline configuration. | ||
| function_code (str): The Python code defining the parsing function. This code will be executed | ||
| to parse the input data according to the specified logic. It should return a list of strings, where each string is its own document. | ||
| Example: | ||
| ```yaml | ||
| parsing_tools: | ||
| - name: ocr_parser | ||
| function_code: | | ||
| import pytesseract | ||
| from pdf2image import convert_from_path | ||
| def ocr_parser(filename: str) -> List[str]: | ||
| images = convert_from_path(filename) | ||
| text = "" | ||
| for image in images: | ||
| text += pytesseract.image_to_string(image) | ||
| return [text] | ||
| ``` | ||
| """ | ||
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| name: str | ||
| function_code: str | ||
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| class PipelineStep(BaseModel): | ||
| """ | ||
| Represents a step in the pipeline. | ||
| Attributes: | ||
| name (str): The name of the step. | ||
| operations (List[Union[Dict[str, Any], str]]): A list of operations to be applied in this step. | ||
| Each operation can be either a string (the name of the operation) or a dictionary | ||
| (for more complex configurations). | ||
| input (Optional[str]): The input for this step. It can be either the name of a dataset | ||
| or the name of a previous step. If not provided, the step will use the output | ||
| of the previous step as its input. | ||
| Example: | ||
| ```python | ||
| # Simple step with a single operation | ||
| process_step = PipelineStep( | ||
| name="process_step", | ||
| input="my_dataset", | ||
| operations=["process"] | ||
| ) | ||
| # Step with multiple operations | ||
| summarize_step = PipelineStep( | ||
| name="summarize_step", | ||
| input="process_step", | ||
| operations=["summarize"] | ||
| ) | ||
| # Step with a more complex operation configuration | ||
| custom_step = PipelineStep( | ||
| name="custom_step", | ||
| input="previous_step", | ||
| operations=[ | ||
| { | ||
| "custom_operation": { | ||
| "model": "gpt-4", | ||
| "prompt": "Perform a custom analysis on the following text:" | ||
| } | ||
| } | ||
| ] | ||
| ) | ||
| ``` | ||
| These examples show different ways to configure pipeline steps, from simple | ||
| single-operation steps to more complex configurations with custom parameters. | ||
| """ | ||
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| name: str | ||
| operations: List[Union[Dict[str, Any], str]] | ||
| input: Optional[str] = None | ||
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| class PipelineOutput(BaseModel): | ||
| """ | ||
| Represents the output configuration for a pipeline. | ||
| Attributes: | ||
| type (str): The type of output. This could be 'file', 'database', etc. | ||
| path (str): The path where the output will be stored. This could be a file path, | ||
| database connection string, etc., depending on the type. | ||
| intermediate_dir (Optional[str]): The directory to store intermediate results, | ||
| if applicable. Defaults to None. | ||
| Example: | ||
| ```python | ||
| output = PipelineOutput( | ||
| type="file", | ||
| path="/path/to/output.json", | ||
| intermediate_dir="/path/to/intermediate/results" | ||
| ) | ||
| ``` | ||
| """ | ||
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| type: str | ||
| path: str | ||
| intermediate_dir: Optional[str] = None | ||
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| class PipelineSpec(BaseModel): | ||
| steps: list[PipelineStep] | ||
| output: PipelineOutput |
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