Kye
9 months ago
parent
01bfc71eb0
commit
8f91a7dd9b
@ -0,0 +1,134 @@
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from swarms import Agent, Anthropic, tool, ChromaDB
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import subprocess
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from pydantic import BaseModel
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# Initilaize the chromadb client
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chromadb = ChromaDB(
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metric="cosine",
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output="results",
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docs_folder="docs",
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)
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# Create a schema for the code revision tool
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class CodeRevisionSchema(BaseModel):
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code: str = None
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revision: str = None
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# iNitialize the schema
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tool_schema = CodeRevisionSchema(
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code="print('Hello, World!')",
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revision="print('What is 2+2')",
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)
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# Model
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llm = Anthropic(
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temperature=0.1,
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)
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# Tools
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@tool
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def terminal(
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code: str,
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):
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"""
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Run code in the terminal.
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Args:
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code (str): The code to run in the terminal.
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Returns:
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str: The output of the code.
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"""
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out = subprocess.run(
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code, shell=True, capture_output=True, text=True
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).stdout
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return str(out)
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@tool
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def browser(query: str):
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"""
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Search the query in the browser with the `browser` tool.
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Args:
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query (str): The query to search in the browser.
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Returns:
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str: The search results.
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"""
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import webbrowser
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url = f"https://www.google.com/search?q={query}"
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webbrowser.open(url)
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return f"Searching for {query} in the browser."
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@tool
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def create_file(file_path: str, content: str):
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"""
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Create a file using the file editor tool.
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Args:
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file_path (str): The path to the file.
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content (str): The content to write to the file.
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Returns:
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str: The result of the file creation operation.
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"""
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with open(file_path, "w") as file:
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file.write(content)
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return f"File {file_path} created successfully."
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@tool
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def file_editor(file_path: str, mode: str, content: str):
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"""
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Edit a file using the file editor tool.
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Args:
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file_path (str): The path to the file.
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mode (str): The mode to open the file in.
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content (str): The content to write to the file.
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Returns:
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str: The result of the file editing operation.
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"""
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with open(file_path, mode) as file:
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file.write(content)
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return f"File {file_path} edited successfully."
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# Agent
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agent = Agent(
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agent_name="Devin",
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system_prompt=(
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"Autonomous agent that can interact with humans and other"
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" agents. Be Helpful and Kind. Use the tools provided to"
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" assist the user. Return all code in markdown format."
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),
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llm=llm,
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max_loops="auto",
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autosave=True,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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interactive=True,
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tools=[terminal, browser, file_editor, create_file],
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long_term_memory=chromadb,
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output_type=tool_schema, # or dict, or str
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metadata_output_type="json",
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# List of schemas that the agent can handle
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list_tool_schemas=[tool_schema],
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function_calling_format_type="OpenAI",
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function_calling_type="json", # or soon yaml
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)
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# Run the agent
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out = agent.run("Create a new file for a plan to take over the world.")
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print(out)
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@ -0,0 +1,41 @@
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from pydantic import BaseModel, Field
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from typing import List
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class FunctionSchema(BaseModel):
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name: str = Field(
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...,
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title="Name",
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description="The name of the function.",
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)
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description: str = Field(
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...,
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title="Description",
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description="The description of the function.",
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)
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parameters: BaseModel = Field(
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...,
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title="Parameters",
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description="The parameters of the function.",
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)
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class OpenAIFunctionCallSchema(BaseModel):
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"""
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Represents the schema for an OpenAI function call.
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Attributes:
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type (str): The type of the function.
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function (List[FunctionSchema]): The function to call.
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"""
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type: str = Field(
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"function",
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title="Type",
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description="The type of the function.",
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)
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function: List[FunctionSchema] = Field(
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...,
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title="Function",
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description="The function to call.",
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)
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@ -0,0 +1,547 @@
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import functools
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import inspect
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import json
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from logging import getLogger
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from typing import (
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Any,
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Callable,
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Dict,
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ForwardRef,
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List,
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Optional,
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Set,
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Tuple,
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Type,
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TypeVar,
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Union,
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get_args,
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)
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from pydantic import BaseModel, Field
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from pydantic.version import VERSION as PYDANTIC_VERSION
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from typing_extensions import Annotated, Literal, get_args, get_origin
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T = TypeVar("T")
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__all__ = (
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"JsonSchemaValue",
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"model_dump",
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"model_dump_json",
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"type2schema",
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"evaluate_forwardref",
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)
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PYDANTIC_V1 = PYDANTIC_VERSION.startswith("1.")
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logger = getLogger(__name__)
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if not PYDANTIC_V1:
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from pydantic import TypeAdapter
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from pydantic._internal._typing_extra import (
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eval_type_lenient as evaluate_forwardref,
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)
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from pydantic.json_schema import JsonSchemaValue
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def type2schema(t: Any) -> JsonSchemaValue:
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"""Convert a type to a JSON schema
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Args:
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t (Type): The type to convert
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Returns:
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JsonSchemaValue: The JSON schema
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"""
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return TypeAdapter(t).json_schema()
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def model_dump(model: BaseModel) -> Dict[str, Any]:
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"""Convert a pydantic model to a dict
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Args:
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model (BaseModel): The model to convert
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Returns:
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Dict[str, Any]: The dict representation of the model
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"""
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return model.model_dump()
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def model_dump_json(model: BaseModel) -> str:
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"""Convert a pydantic model to a JSON string
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Args:
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model (BaseModel): The model to convert
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Returns:
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str: The JSON string representation of the model
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"""
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return model.model_dump_json()
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# Remove this once we drop support for pydantic 1.x
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else: # pragma: no cover
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from pydantic import schema_of
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from pydantic.typing import (
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evaluate_forwardref as evaluate_forwardref, # type: ignore[no-redef]
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)
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JsonSchemaValue = Dict[str, Any] # type: ignore[misc]
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def type2schema(t: Any) -> JsonSchemaValue:
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"""Convert a type to a JSON schema
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Args:
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t (Type): The type to convert
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Returns:
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JsonSchemaValue: The JSON schema
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"""
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if PYDANTIC_V1:
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if t is None:
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return {"type": "null"}
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elif get_origin(t) is Union:
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return {"anyOf": [type2schema(tt) for tt in get_args(t)]}
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elif get_origin(t) in [Tuple, tuple]:
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prefixItems = [type2schema(tt) for tt in get_args(t)]
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return {
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"maxItems": len(prefixItems),
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"minItems": len(prefixItems),
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"prefixItems": prefixItems,
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"type": "array",
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}
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d = schema_of(t)
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if "title" in d:
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d.pop("title")
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if "description" in d:
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d.pop("description")
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return d
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def model_dump(model: BaseModel) -> Dict[str, Any]:
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"""Convert a pydantic model to a dict
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Args:
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model (BaseModel): The model to convert
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Returns:
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Dict[str, Any]: The dict representation of the model
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"""
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return model.dict()
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def model_dump_json(model: BaseModel) -> str:
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"""Convert a pydantic model to a JSON string
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Args:
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model (BaseModel): The model to convert
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Returns:
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str: The JSON string representation of the model
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"""
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return model.json()
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def get_typed_annotation(annotation: Any, globalns: Dict[str, Any]) -> Any:
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"""Get the type annotation of a parameter.
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Args:
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annotation: The annotation of the parameter
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globalns: The global namespace of the function
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Returns:
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The type annotation of the parameter
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"""
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if isinstance(annotation, str):
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annotation = ForwardRef(annotation)
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annotation = evaluate_forwardref(annotation, globalns, globalns)
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return annotation
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def get_typed_signature(call: Callable[..., Any]) -> inspect.Signature:
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"""Get the signature of a function with type annotations.
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Args:
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call: The function to get the signature for
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Returns:
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The signature of the function with type annotations
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"""
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signature = inspect.signature(call)
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globalns = getattr(call, "__globals__", {})
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typed_params = [
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inspect.Parameter(
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name=param.name,
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kind=param.kind,
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default=param.default,
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annotation=get_typed_annotation(param.annotation, globalns),
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)
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for param in signature.parameters.values()
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]
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typed_signature = inspect.Signature(typed_params)
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return typed_signature
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def get_typed_return_annotation(call: Callable[..., Any]) -> Any:
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"""Get the return annotation of a function.
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Args:
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call: The function to get the return annotation for
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Returns:
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The return annotation of the function
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"""
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signature = inspect.signature(call)
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annotation = signature.return_annotation
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if annotation is inspect.Signature.empty:
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return None
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globalns = getattr(call, "__globals__", {})
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return get_typed_annotation(annotation, globalns)
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def get_param_annotations(
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typed_signature: inspect.Signature,
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) -> Dict[str, Union[Annotated[Type[Any], str], Type[Any]]]:
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"""Get the type annotations of the parameters of a function
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Args:
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typed_signature: The signature of the function with type annotations
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Returns:
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A dictionary of the type annotations of the parameters of the function
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"""
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return {
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k: v.annotation
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for k, v in typed_signature.parameters.items()
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if v.annotation is not inspect.Signature.empty
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}
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class Parameters(BaseModel):
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"""Parameters of a function as defined by the OpenAI API"""
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type: Literal["object"] = "object"
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properties: Dict[str, JsonSchemaValue]
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required: List[str]
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class Function(BaseModel):
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"""A function as defined by the OpenAI API"""
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description: Annotated[
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str, Field(description="Description of the function")
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]
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name: Annotated[str, Field(description="Name of the function")]
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parameters: Annotated[
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Parameters, Field(description="Parameters of the function")
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]
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class ToolFunction(BaseModel):
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"""A function under tool as defined by the OpenAI API."""
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type: Literal["function"] = "function"
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function: Annotated[Function, Field(description="Function under tool")]
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def get_parameter_json_schema(
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k: str, v: Any, default_values: Dict[str, Any]
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) -> JsonSchemaValue:
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"""Get a JSON schema for a parameter as defined by the OpenAI API
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Args:
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k: The name of the parameter
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v: The type of the parameter
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default_values: The default values of the parameters of the function
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Returns:
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A Pydanitc model for the parameter
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"""
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def type2description(
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k: str, v: Union[Annotated[Type[Any], str], Type[Any]]
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) -> str:
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# handles Annotated
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if hasattr(v, "__metadata__"):
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retval = v.__metadata__[0]
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if isinstance(retval, str):
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return retval
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else:
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raise ValueError(
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f"Invalid description {retval} for parameter {k}, should be a string."
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)
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else:
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return k
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schema = type2schema(v)
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if k in default_values:
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dv = default_values[k]
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schema["default"] = dv
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schema["description"] = type2description(k, v)
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return schema
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def get_required_params(typed_signature: inspect.Signature) -> List[str]:
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"""Get the required parameters of a function
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Args:
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signature: The signature of the function as returned by inspect.signature
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Returns:
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A list of the required parameters of the function
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"""
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return [
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k
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for k, v in typed_signature.parameters.items()
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if v.default == inspect.Signature.empty
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]
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def get_default_values(
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typed_signature: inspect.Signature,
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) -> Dict[str, Any]:
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"""Get default values of parameters of a function
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Args:
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signature: The signature of the function as returned by inspect.signature
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Returns:
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A dictionary of the default values of the parameters of the function
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"""
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return {
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k: v.default
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for k, v in typed_signature.parameters.items()
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if v.default != inspect.Signature.empty
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}
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def get_parameters(
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required: List[str],
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param_annotations: Dict[
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str, Union[Annotated[Type[Any], str], Type[Any]]
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],
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default_values: Dict[str, Any],
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) -> Parameters:
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"""Get the parameters of a function as defined by the OpenAI API
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Args:
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required: The required parameters of the function
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hints: The type hints of the function as returned by typing.get_type_hints
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Returns:
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A Pydantic model for the parameters of the function
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"""
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return Parameters(
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properties={
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k: get_parameter_json_schema(k, v, default_values)
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for k, v in param_annotations.items()
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if v is not inspect.Signature.empty
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},
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required=required,
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)
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def get_missing_annotations(
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typed_signature: inspect.Signature, required: List[str]
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) -> Tuple[Set[str], Set[str]]:
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"""Get the missing annotations of a function
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Ignores the parameters with default values as they are not required to be annotated, but logs a warning.
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Args:
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typed_signature: The signature of the function with type annotations
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required: The required parameters of the function
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Returns:
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A set of the missing annotations of the function
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"""
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all_missing = {
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k
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for k, v in typed_signature.parameters.items()
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if v.annotation is inspect.Signature.empty
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}
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missing = all_missing.intersection(set(required))
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unannotated_with_default = all_missing.difference(missing)
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return missing, unannotated_with_default
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def get_openai_function_schema(
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function: Callable[..., Any],
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*,
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name: Optional[str] = None,
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description: str,
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) -> Dict[str, Any]:
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"""Get a JSON schema for a function as defined by the OpenAI API
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Args:
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f: The function to get the JSON schema for
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name: The name of the function
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description: The description of the function
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Returns:
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A JSON schema for the function
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Raises:
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TypeError: If the function is not annotated
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Examples:
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```python
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def f(a: Annotated[str, "Parameter a"], b: int = 2, c: Annotated[float, "Parameter c"] = 0.1) -> None:
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pass
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get_function_schema(f, description="function f")
|
||||
|
||||
# {'type': 'function',
|
||||
# 'function': {'description': 'function f',
|
||||
# 'name': 'f',
|
||||
# 'parameters': {'type': 'object',
|
||||
# 'properties': {'a': {'type': 'str', 'description': 'Parameter a'},
|
||||
# 'b': {'type': 'int', 'description': 'b'},
|
||||
# 'c': {'type': 'float', 'description': 'Parameter c'}},
|
||||
# 'required': ['a']}}}
|
||||
```
|
||||
|
||||
"""
|
||||
typed_signature = get_typed_signature(function)
|
||||
required = get_required_params(typed_signature)
|
||||
default_values = get_default_values(typed_signature)
|
||||
param_annotations = get_param_annotations(typed_signature)
|
||||
return_annotation = get_typed_return_annotation(function)
|
||||
missing, unannotated_with_default = get_missing_annotations(
|
||||
typed_signature, required
|
||||
)
|
||||
|
||||
if return_annotation is None:
|
||||
logger.warning(
|
||||
f"The return type of the function '{function.__name__}' is not annotated. Although annotating it is "
|
||||
+ "optional, the function should return either a string, a subclass of 'pydantic.BaseModel'."
|
||||
)
|
||||
|
||||
if unannotated_with_default != set():
|
||||
unannotated_with_default_s = [
|
||||
f"'{k}'" for k in sorted(unannotated_with_default)
|
||||
]
|
||||
logger.warning(
|
||||
f"The following parameters of the function '{function.__name__}' with default values are not annotated: "
|
||||
+ f"{', '.join(unannotated_with_default_s)}."
|
||||
)
|
||||
|
||||
if missing != set():
|
||||
missing_s = [f"'{k}'" for k in sorted(missing)]
|
||||
raise TypeError(
|
||||
f"All parameters of the function '{function.__name__}' without default values must be annotated. "
|
||||
+ f"The annotations are missing for the following parameters: {', '.join(missing_s)}"
|
||||
)
|
||||
|
||||
fname = name if name else function.__name__
|
||||
|
||||
parameters = get_parameters(
|
||||
required, param_annotations, default_values=default_values
|
||||
)
|
||||
|
||||
function = ToolFunction(
|
||||
function=Function(
|
||||
description=description,
|
||||
name=fname,
|
||||
parameters=parameters,
|
||||
)
|
||||
)
|
||||
|
||||
return model_dump(function)
|
||||
|
||||
def test(a: int = 1, b: int = 2):
|
||||
return a + b
|
||||
|
||||
#
|
||||
def get_load_param_if_needed_function(
|
||||
t: Any,
|
||||
) -> Optional[Callable[[Dict[str, Any], Type[BaseModel]], BaseModel]]:
|
||||
"""Get a function to load a parameter if it is a Pydantic model
|
||||
|
||||
Args:
|
||||
t: The type annotation of the parameter
|
||||
|
||||
Returns:
|
||||
A function to load the parameter if it is a Pydantic model, otherwise None
|
||||
|
||||
"""
|
||||
if get_origin(t) is Annotated:
|
||||
return get_load_param_if_needed_function(get_args(t)[0])
|
||||
|
||||
def load_base_model(
|
||||
v: Dict[str, Any], t: Type[BaseModel]
|
||||
) -> BaseModel:
|
||||
return t(**v)
|
||||
|
||||
return (
|
||||
load_base_model
|
||||
if isinstance(t, type) and issubclass(t, BaseModel)
|
||||
else None
|
||||
)
|
||||
|
||||
|
||||
def load_basemodels_if_needed(
|
||||
func: Callable[..., Any]
|
||||
) -> Callable[..., Any]:
|
||||
"""A decorator to load the parameters of a function if they are Pydantic models
|
||||
|
||||
Args:
|
||||
func: The function with annotated parameters
|
||||
|
||||
Returns:
|
||||
A function that loads the parameters before calling the original function
|
||||
|
||||
"""
|
||||
# get the type annotations of the parameters
|
||||
typed_signature = get_typed_signature(func)
|
||||
param_annotations = get_param_annotations(typed_signature)
|
||||
|
||||
# get functions for loading BaseModels when needed based on the type annotations
|
||||
kwargs_mapping_with_nones = {
|
||||
k: get_load_param_if_needed_function(t)
|
||||
for k, t in param_annotations.items()
|
||||
}
|
||||
|
||||
# remove the None values
|
||||
kwargs_mapping = {
|
||||
k: f for k, f in kwargs_mapping_with_nones.items() if f is not None
|
||||
}
|
||||
|
||||
# a function that loads the parameters before calling the original function
|
||||
@functools.wraps(func)
|
||||
def _load_parameters_if_needed(*args: Any, **kwargs: Any) -> Any:
|
||||
# load the BaseModels if needed
|
||||
for k, f in kwargs_mapping.items():
|
||||
kwargs[k] = f(kwargs[k], param_annotations[k])
|
||||
|
||||
# call the original function
|
||||
return func(*args, **kwargs)
|
||||
|
||||
@functools.wraps(func)
|
||||
async def _a_load_parameters_if_needed(
|
||||
*args: Any, **kwargs: Any
|
||||
) -> Any:
|
||||
# load the BaseModels if needed
|
||||
for k, f in kwargs_mapping.items():
|
||||
kwargs[k] = f(kwargs[k], param_annotations[k])
|
||||
|
||||
# call the original function
|
||||
return await func(*args, **kwargs)
|
||||
|
||||
if inspect.iscoroutinefunction(func):
|
||||
return _a_load_parameters_if_needed
|
||||
else:
|
||||
return _load_parameters_if_needed
|
||||
|
||||
|
||||
def serialize_to_str(x: Any) -> str:
|
||||
if isinstance(x, str):
|
||||
return x
|
||||
elif isinstance(x, BaseModel):
|
||||
return model_dump_json(x)
|
||||
else:
|
||||
return json.dumps(x)
|
||||
Loading…
Reference in new issue