evelynmitchell
1 year ago
committed by
GitHub
commit
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# Swarm Ecosystem
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Welcome to the Swarm Ecosystem, a comprehensive suite of tools and frameworks designed to empower developers to orhestrate swarms of autonomous agents for a variety of applications. Dive into our ecosystem below:
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| Project | Description | Link |
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| ------- | ----------- | ---- |
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| **Swarms Framework** | A Python-based framework that enables the creation, deployment, and scaling of reliable swarms of autonomous agents aimed at automating complex workflows. | [Swarms Framework](https://github.com/kyegomez/swarms) |
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| **Swarms Cloud** | A cloud-based service offering Swarms-as-a-Service with guaranteed 100% uptime, cutting-edge performance, and enterprise-grade reliability for seamless scaling and management of swarms. | [Swarms Cloud](https://github.com/kyegomez/swarms-core) |
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| **Swarms Core** | Provides backend utilities focusing on concurrency, multi-threading, and advanced execution strategies, developed in Rust for maximum efficiency and performance. | [Swarms Core](https://github.com/kyegomez/swarms-core) |
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| **Swarm Foundation Models** | A dedicated repository for the creation, optimization, and training of groundbreaking swarming models. Features innovative models like PSO with transformers, ant colony optimizations, and more, aiming to surpass traditional architectures like Transformers and SSMs. Open for community contributions and ideas. | [Swarm Foundation Models](https://github.com/kyegomez/swarms-pytorch) |
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| **Swarm Platform** | The Swarms dashboard Platform | [Swarm Platform](https://swarms.world/) |
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| **Swarms JS** | Swarms Framework in JS. Orchestrate any agents and enable multi-agent collaboration between various agents! | [Swarm JS](https://github.com/kyegomez/swarms-js) |
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----
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## 🫶 Contributions:
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The easiest way to contribute is to pick any issue with the `good first issue` tag 💪. Read the Contributing guidelines [here](/CONTRIBUTING.md). Bug Report? [File here](https://github.com/swarms/gateway/issues) | Feature Request? [File here](https://github.com/swarms/gateway/issues)
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Swarms is an open-source project, and contributions are VERY welcome. If you want to contribute, you can create new features, fix bugs, or improve the infrastructure. Please refer to the [CONTRIBUTING.md](https://github.com/kyegomez/swarms/blob/master/CONTRIBUTING.md) and our [contributing board](https://github.com/users/kyegomez/projects/1) to participate in Roadmap discussions!
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<a href="https://github.com/kyegomez/swarms/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=kyegomez/swarms" />
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</a>
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<a href="https://github.com/kyegomez/swarms/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=kyegomez/swarms-cloud" />
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</a>
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<a href="https://github.com/kyegomez/swarms/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=kyegomez/swarms-platform" />
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</a>
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<a href="https://github.com/kyegomez/swarms/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=kyegomez/swarms-js" />
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</a>
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----
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## Community
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Join our growing community around the world, for real-time support, ideas, and discussions on Swarms 😊
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- View our official [Blog](https://swarms.apac.ai)
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- Chat live with us on [Discord](https://discord.gg/kS3rwKs3ZC)
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- Follow us on [Twitter](https://twitter.com/kyegomez)
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- Connect with us on [LinkedIn](https://www.linkedin.com/company/the-swarm-corporation)
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- Visit us on [YouTube](https://www.youtube.com/channel/UC9yXyitkbU_WSy7bd_41SqQ)
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- [Join the Swarms community on Discord!](https://discord.gg/AJazBmhKnr)
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- Join our Swarms Community Gathering every Thursday at 1pm NYC Time to unlock the potential of autonomous agents in automating your daily tasks [Sign up here](https://lu.ma/5p2jnc2v)
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---
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## Discovery Call
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Book a discovery call to learn how Swarms can lower your operating costs by 40% with swarms of autonomous agents in lightspeed. [Click here to book a time that works for you!](https://calendly.com/swarm-corp/30min?month=2023-11)
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## Accelerate Backlog
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Help us accelerate our backlog by supporting us financially! Note, we're an open source corporation and so all the revenue we generate is through donations at the moment ;)
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<a href="https://polar.sh/kyegomez"><img src="https://polar.sh/embed/fund-our-backlog.svg?org=kyegomez" /></a>
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---
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from swarms import Agent, Anthropic, tool
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# Tool
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@tool # Wrap the function with the tool decorator
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def search_api(query: str, max_results: int = 10):
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"""
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Search the web for the query and return the top `max_results` results.
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"""
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return f"Search API: {query} -> {max_results} results"
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## Initialize the workflow
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agent = Agent(
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agent_name="Youtube Transcript Generator",
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agent_description=(
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"Generate a transcript for a youtube video on what swarms"
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" are!"
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),
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llm=Anthropic(),
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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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tools=[search_api],
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)
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# Run the workflow on a task
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agent(
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"Generate a transcript for a youtube video on what swarms are!"
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" Output a <DONE> token when done."
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)
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@ -1,46 +0,0 @@
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import pytest
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def test_create_youtube_account():
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# Arrange
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# Act
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# Assert
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def test_install_video_editing_software():
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# Arrange
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# Act
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# Assert
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def test_write_script():
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# Arrange
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# Act
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# Assert
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def test_gather_footage():
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# Arrange
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# Act
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# Assert
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def test_edit_video():
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# Arrange
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# Act
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# Assert
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def test_export_video():
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# Arrange
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# Act
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# Assert
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def test_upload_video_to_youtube():
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# Arrange
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# Act
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# Assert
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def test_optimize_video_for_search():
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# Arrange
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# Act
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# Assert
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def test_share_video():
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# Arrange
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# Act
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# Assert
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@ -0,0 +1,26 @@
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"""
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Boss selects what agent to use
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B -> W1, W2, W3
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"""
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from typing import List, Optional
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from pydantic import BaseModel, Field
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from swarms.utils.json_utils import str_to_json
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class HierarchicalSwarm(BaseModel):
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class Config:
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arbitrary_types_allowed = True
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agents: Optional[List[str]] = Field(
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None, title="List of agents in the hierarchical swarm"
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)
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task: Optional[str] = Field(
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None, title="Task to be done by the agents"
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)
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all_agents = HierarchicalSwarm()
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agents_schema = HierarchicalSwarm.model_json_schema()
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agents_schema = str_to_json(agents_schema)
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print(agents_schema)
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@ -1,70 +0,0 @@
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import os
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import multion
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from dotenv import load_dotenv
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from swarms.models.base_llm import AbstractLLM
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# Load environment variables
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load_dotenv()
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# Muliton key
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MULTION_API_KEY = os.getenv("MULTION_API_KEY")
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class MultiOnAgent(AbstractLLM):
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"""
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Represents a multi-on agent that performs browsing tasks.
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Args:
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max_steps (int): The maximum number of steps to perform during browsing.
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starting_url (str): The starting URL for browsing.
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Attributes:
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max_steps (int): The maximum number of steps to perform during browsing.
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starting_url (str): The starting URL for browsing.
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"""
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def __init__(
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self,
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multion_api_key: str = MULTION_API_KEY,
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max_steps: int = 4,
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starting_url: str = "https://www.google.com",
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*args,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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self.multion_api_key = multion_api_key
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self.max_steps = max_steps
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self.starting_url = starting_url
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def run(self, task: str, *args, **kwargs):
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"""
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Runs a browsing task.
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Args:
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task (str): The task to perform during browsing.
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*args: Additional positional arguments.
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**kwargs: Additional keyword arguments.
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Returns:
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dict: The response from the browsing task.
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"""
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multion.login(
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use_api=True,
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multion_api_key=str(self.multion_api_key),
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*args,
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**kwargs,
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)
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response = multion.browse(
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{
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"cmd": task,
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"url": self.starting_url,
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"maxSteps": self.max_steps,
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},
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*args,
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**kwargs,
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)
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return response.result, response.status, response.lastUrl
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@ -1,575 +0,0 @@
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import contextlib
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import datetime
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import functools
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import importlib
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import re
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import warnings
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from importlib.metadata import version
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from typing import (
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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Set,
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Tuple,
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Union,
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)
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain.llms.base import LLM
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from langchain.schema.language_model import BaseLanguageModel
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from langchain.schema.output import GenerationChunk
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from langchain.schema.prompt import PromptValue
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from langchain.utils import get_from_dict_or_env
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from packaging.version import parse
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from pydantic import Field, SecretStr, root_validator
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from requests import HTTPError, Response
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def xor_args(*arg_groups: Tuple[str, ...]) -> Callable:
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"""Validate specified keyword args are mutually exclusive."""
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def decorator(func: Callable) -> Callable:
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@functools.wraps(func)
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def wrapper(*args: Any, **kwargs: Any) -> Any:
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"""Validate exactly one arg in each group is not None."""
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counts = [
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sum(
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1
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for arg in arg_group
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if kwargs.get(arg) is not None
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)
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for arg_group in arg_groups
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]
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invalid_groups = [
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i for i, count in enumerate(counts) if count != 1
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]
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if invalid_groups:
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invalid_group_names = [
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", ".join(arg_groups[i]) for i in invalid_groups
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]
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raise ValueError(
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"Exactly one argument in each of the following"
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" groups must be defined:"
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f" {', '.join(invalid_group_names)}"
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)
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return func(*args, **kwargs)
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return wrapper
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return decorator
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def raise_for_status_with_text(response: Response) -> None:
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"""Raise an error with the response text."""
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try:
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response.raise_for_status()
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except HTTPError as e:
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raise ValueError(response.text) from e
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@contextlib.contextmanager
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def mock_now(dt_value): # type: ignore
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"""Context manager for mocking out datetime.now() in unit tests.
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Example:
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with mock_now(datetime.datetime(2011, 2, 3, 10, 11)):
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assert datetime.datetime.now() == datetime.datetime(2011, 2, 3, 10, 11)
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"""
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class MockDateTime(datetime.datetime):
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"""Mock datetime.datetime.now() with a fixed datetime."""
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@classmethod
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def now(cls): # type: ignore
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# Create a copy of dt_value.
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return datetime.datetime(
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dt_value.year,
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dt_value.month,
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dt_value.day,
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dt_value.hour,
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dt_value.minute,
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dt_value.second,
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dt_value.microsecond,
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dt_value.tzinfo,
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)
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real_datetime = datetime.datetime
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datetime.datetime = MockDateTime
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try:
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yield datetime.datetime
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finally:
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datetime.datetime = real_datetime
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def guard_import(
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module_name: str,
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*,
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pip_name: Optional[str] = None,
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package: Optional[str] = None,
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) -> Any:
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"""Dynamically imports a module and raises a helpful exception if the module is not
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installed."""
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try:
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module = importlib.import_module(module_name, package)
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except ImportError:
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raise ImportError(
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f"Could not import {module_name} python package. Please"
|
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" install it with `pip install"
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f" {pip_name or module_name}`."
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)
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return module
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def check_package_version(
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package: str,
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lt_version: Optional[str] = None,
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lte_version: Optional[str] = None,
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gt_version: Optional[str] = None,
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gte_version: Optional[str] = None,
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) -> None:
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"""Check the version of a package."""
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imported_version = parse(version(package))
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if lt_version is not None and imported_version >= parse(
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lt_version
|
||||
):
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raise ValueError(
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f"Expected {package} version to be < {lt_version}."
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f" Received {imported_version}."
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)
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if lte_version is not None and imported_version > parse(
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lte_version
|
||||
):
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raise ValueError(
|
||||
f"Expected {package} version to be <= {lte_version}."
|
||||
f" Received {imported_version}."
|
||||
)
|
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if gt_version is not None and imported_version <= parse(
|
||||
gt_version
|
||||
):
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raise ValueError(
|
||||
f"Expected {package} version to be > {gt_version}."
|
||||
f" Received {imported_version}."
|
||||
)
|
||||
if gte_version is not None and imported_version < parse(
|
||||
gte_version
|
||||
):
|
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raise ValueError(
|
||||
f"Expected {package} version to be >= {gte_version}."
|
||||
f" Received {imported_version}."
|
||||
)
|
||||
|
||||
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def get_pydantic_field_names(pydantic_cls: Any) -> Set[str]:
|
||||
"""Get field names, including aliases, for a pydantic class.
|
||||
|
||||
Args:
|
||||
pydantic_cls: Pydantic class."""
|
||||
all_required_field_names = set()
|
||||
for field in pydantic_cls.__fields__.values():
|
||||
all_required_field_names.add(field.name)
|
||||
if field.has_alias:
|
||||
all_required_field_names.add(field.alias)
|
||||
return all_required_field_names
|
||||
|
||||
|
||||
def build_extra_kwargs(
|
||||
extra_kwargs: Dict[str, Any],
|
||||
values: Dict[str, Any],
|
||||
all_required_field_names: Set[str],
|
||||
) -> Dict[str, Any]:
|
||||
"""Build extra kwargs from values and extra_kwargs.
|
||||
|
||||
Args:
|
||||
extra_kwargs: Extra kwargs passed in by user.
|
||||
values: Values passed in by user.
|
||||
all_required_field_names: All required field names for the pydantic class.
|
||||
"""
|
||||
for field_name in list(values):
|
||||
if field_name in extra_kwargs:
|
||||
raise ValueError(f"Found {field_name} supplied twice.")
|
||||
if field_name not in all_required_field_names:
|
||||
warnings.warn(
|
||||
f"""WARNING! {field_name} is not default parameter.
|
||||
{field_name} was transferred to model_kwargs.
|
||||
Please confirm that {field_name} is what you intended."""
|
||||
)
|
||||
extra_kwargs[field_name] = values.pop(field_name)
|
||||
|
||||
invalid_model_kwargs = all_required_field_names.intersection(
|
||||
extra_kwargs.keys()
|
||||
)
|
||||
if invalid_model_kwargs:
|
||||
raise ValueError(
|
||||
f"Parameters {invalid_model_kwargs} should be specified"
|
||||
" explicitly. Instead they were passed in as part of"
|
||||
" `model_kwargs` parameter."
|
||||
)
|
||||
|
||||
return extra_kwargs
|
||||
|
||||
|
||||
def convert_to_secret_str(value: Union[SecretStr, str]) -> SecretStr:
|
||||
"""Convert a string to a SecretStr if needed."""
|
||||
if isinstance(value, SecretStr):
|
||||
return value
|
||||
return SecretStr(value)
|
||||
|
||||
|
||||
class _AnthropicCommon(BaseLanguageModel):
|
||||
client: Any = None #: :meta private:
|
||||
async_client: Any = None #: :meta private:
|
||||
model: str = Field(default="claude-2", alias="model_name")
|
||||
"""Model name to use."""
|
||||
|
||||
max_tokens_to_sample: int = Field(default=256, alias="max_tokens")
|
||||
"""Denotes the number of tokens to predict per generation."""
|
||||
|
||||
temperature: Optional[float] = None
|
||||
"""A non-negative float that tunes the degree of randomness in generation."""
|
||||
|
||||
top_k: Optional[int] = None
|
||||
"""Number of most likely tokens to consider at each step."""
|
||||
|
||||
top_p: Optional[float] = None
|
||||
"""Total probability mass of tokens to consider at each step."""
|
||||
|
||||
streaming: bool = False
|
||||
"""Whether to stream the results."""
|
||||
|
||||
default_request_timeout: Optional[float] = None
|
||||
"""Timeout for requests to Anthropic Completion API. Default is 600 seconds."""
|
||||
|
||||
anthropic_api_url: Optional[str] = None
|
||||
|
||||
anthropic_api_key: Optional[SecretStr] = None
|
||||
|
||||
HUMAN_PROMPT: Optional[str] = None
|
||||
AI_PROMPT: Optional[str] = None
|
||||
count_tokens: Optional[Callable[[str], int]] = None
|
||||
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
||||
|
||||
@root_validator(pre=True)
|
||||
def build_extra(cls, values: Dict) -> Dict:
|
||||
extra = values.get("model_kwargs", {})
|
||||
all_required_field_names = get_pydantic_field_names(cls)
|
||||
values["model_kwargs"] = build_extra_kwargs(
|
||||
extra, values, all_required_field_names
|
||||
)
|
||||
return values
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
values["anthropic_api_key"] = convert_to_secret_str(
|
||||
get_from_dict_or_env(
|
||||
values, "anthropic_api_key", "ANTHROPIC_API_KEY"
|
||||
)
|
||||
)
|
||||
# Get custom api url from environment.
|
||||
values["anthropic_api_url"] = get_from_dict_or_env(
|
||||
values,
|
||||
"anthropic_api_url",
|
||||
"ANTHROPIC_API_URL",
|
||||
default="https://api.anthropic.com",
|
||||
)
|
||||
|
||||
try:
|
||||
import anthropic
|
||||
|
||||
check_package_version("anthropic", gte_version="0.3")
|
||||
values["client"] = anthropic.Anthropic(
|
||||
base_url=values["anthropic_api_url"],
|
||||
api_key=values[
|
||||
"anthropic_api_key"
|
||||
].get_secret_value(),
|
||||
timeout=values["default_request_timeout"],
|
||||
)
|
||||
values["async_client"] = anthropic.AsyncAnthropic(
|
||||
base_url=values["anthropic_api_url"],
|
||||
api_key=values[
|
||||
"anthropic_api_key"
|
||||
].get_secret_value(),
|
||||
timeout=values["default_request_timeout"],
|
||||
)
|
||||
values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT
|
||||
values["AI_PROMPT"] = anthropic.AI_PROMPT
|
||||
values["count_tokens"] = values["client"].count_tokens
|
||||
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import anthropic python package. "
|
||||
"Please it install it with `pip install anthropic`."
|
||||
)
|
||||
return values
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Mapping[str, Any]:
|
||||
"""Get the default parameters for calling Anthropic API."""
|
||||
d = {
|
||||
"max_tokens_to_sample": self.max_tokens_to_sample,
|
||||
"model": self.model,
|
||||
}
|
||||
if self.temperature is not None:
|
||||
d["temperature"] = self.temperature
|
||||
if self.top_k is not None:
|
||||
d["top_k"] = self.top_k
|
||||
if self.top_p is not None:
|
||||
d["top_p"] = self.top_p
|
||||
return {**d, **self.model_kwargs}
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {**{}, **self._default_params}
|
||||
|
||||
def _get_anthropic_stop(
|
||||
self, stop: Optional[List[str]] = None
|
||||
) -> List[str]:
|
||||
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
|
||||
raise NameError(
|
||||
"Please ensure the anthropic package is loaded"
|
||||
)
|
||||
|
||||
if stop is None:
|
||||
stop = []
|
||||
|
||||
# Never want model to invent new turns of Human / Assistant dialog.
|
||||
stop.extend([self.HUMAN_PROMPT])
|
||||
|
||||
return stop
|
||||
|
||||
|
||||
class Anthropic(LLM, _AnthropicCommon):
|
||||
"""Anthropic large language models.
|
||||
|
||||
To use, you should have the ``anthropic`` python package installed, and the
|
||||
environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass
|
||||
it as a named parameter to the constructor.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
import anthropic
|
||||
from langchain.llms import Anthropic
|
||||
|
||||
model = Anthropic(model="<model_name>", anthropic_api_key="my-api-key")
|
||||
|
||||
# Simplest invocation, automatically wrapped with HUMAN_PROMPT
|
||||
# and AI_PROMPT.
|
||||
response = model("What are the biggest risks facing humanity?")
|
||||
|
||||
# Or if you want to use the chat mode, build a few-shot-prompt, or
|
||||
# put words in the Assistant's mouth, use HUMAN_PROMPT and AI_PROMPT:
|
||||
raw_prompt = "What are the biggest risks facing humanity?"
|
||||
prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}"
|
||||
response = model(prompt)
|
||||
"""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
allow_population_by_field_name = True
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@root_validator()
|
||||
def raise_warning(cls, values: Dict) -> Dict:
|
||||
"""Raise warning that this class is deprecated."""
|
||||
warnings.warn(
|
||||
"There may be an updated version of"
|
||||
f" {cls.__name__} available."
|
||||
)
|
||||
return values
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "anthropic-llm"
|
||||
|
||||
def _wrap_prompt(self, prompt: str) -> str:
|
||||
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
|
||||
raise NameError(
|
||||
"Please ensure the anthropic package is loaded"
|
||||
)
|
||||
|
||||
if prompt.startswith(self.HUMAN_PROMPT):
|
||||
return prompt # Already wrapped.
|
||||
|
||||
# Guard against common errors in specifying wrong number of newlines.
|
||||
corrected_prompt, n_subs = re.subn(
|
||||
r"^\n*Human:", self.HUMAN_PROMPT, prompt
|
||||
)
|
||||
if n_subs == 1:
|
||||
return corrected_prompt
|
||||
|
||||
# As a last resort, wrap the prompt ourselves to emulate instruct-style.
|
||||
return (
|
||||
f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here"
|
||||
" you go:\n"
|
||||
)
|
||||
|
||||
def _call(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
r"""Call out to Anthropic's completion endpoint.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
|
||||
Returns:
|
||||
The string generated by the model.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
prompt = "What are the biggest risks facing humanity?"
|
||||
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
|
||||
response = model(prompt)
|
||||
|
||||
"""
|
||||
if self.streaming:
|
||||
completion = ""
|
||||
for chunk in self._stream(
|
||||
prompt=prompt,
|
||||
stop=stop,
|
||||
run_manager=run_manager,
|
||||
**kwargs,
|
||||
):
|
||||
completion += chunk.text
|
||||
return completion
|
||||
|
||||
stop = self._get_anthropic_stop(stop)
|
||||
params = {**self._default_params, **kwargs}
|
||||
response = self.client.completions.create(
|
||||
prompt=self._wrap_prompt(prompt),
|
||||
stop_sequences=stop,
|
||||
**params,
|
||||
)
|
||||
return response.completion
|
||||
|
||||
def convert_prompt(self, prompt: PromptValue) -> str:
|
||||
return self._wrap_prompt(prompt.to_string())
|
||||
|
||||
async def _acall(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Call out to Anthropic's completion endpoint asynchronously."""
|
||||
if self.streaming:
|
||||
completion = ""
|
||||
async for chunk in self._astream(
|
||||
prompt=prompt,
|
||||
stop=stop,
|
||||
run_manager=run_manager,
|
||||
**kwargs,
|
||||
):
|
||||
completion += chunk.text
|
||||
return completion
|
||||
|
||||
stop = self._get_anthropic_stop(stop)
|
||||
params = {**self._default_params, **kwargs}
|
||||
|
||||
response = await self.async_client.completions.create(
|
||||
prompt=self._wrap_prompt(prompt),
|
||||
stop_sequences=stop,
|
||||
**params,
|
||||
)
|
||||
return response.completion
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[GenerationChunk]:
|
||||
r"""Call Anthropic completion_stream and return the resulting generator.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
Returns:
|
||||
A generator representing the stream of tokens from Anthropic.
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
prompt = "Write a poem about a stream."
|
||||
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
|
||||
generator = anthropic.stream(prompt)
|
||||
for token in generator:
|
||||
yield token
|
||||
"""
|
||||
stop = self._get_anthropic_stop(stop)
|
||||
params = {**self._default_params, **kwargs}
|
||||
|
||||
for token in self.client.completions.create(
|
||||
prompt=self._wrap_prompt(prompt),
|
||||
stop_sequences=stop,
|
||||
stream=True,
|
||||
**params,
|
||||
):
|
||||
chunk = GenerationChunk(text=token.completion)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[GenerationChunk]:
|
||||
r"""Call Anthropic completion_stream and return the resulting generator.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
Returns:
|
||||
A generator representing the stream of tokens from Anthropic.
|
||||
Example:
|
||||
.. code-block:: python
|
||||
prompt = "Write a poem about a stream."
|
||||
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
|
||||
generator = anthropic.stream(prompt)
|
||||
for token in generator:
|
||||
yield token
|
||||
"""
|
||||
stop = self._get_anthropic_stop(stop)
|
||||
params = {**self._default_params, **kwargs}
|
||||
|
||||
async for token in await self.async_client.completions.create(
|
||||
prompt=self._wrap_prompt(prompt),
|
||||
stop_sequences=stop,
|
||||
stream=True,
|
||||
**params,
|
||||
):
|
||||
chunk = GenerationChunk(text=token.completion)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(
|
||||
chunk.text, chunk=chunk
|
||||
)
|
||||
|
||||
def get_num_tokens(self, text: str) -> int:
|
||||
"""Calculate number of tokens."""
|
||||
if not self.count_tokens:
|
||||
raise NameError(
|
||||
"Please ensure the anthropic package is loaded"
|
||||
)
|
||||
return self.count_tokens(text)
|
||||
@ -1,223 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
from typing import Any, Callable, Mapping
|
||||
|
||||
import openai
|
||||
from langchain_core.pydantic_v1 import (
|
||||
Field,
|
||||
SecretStr,
|
||||
root_validator,
|
||||
)
|
||||
from langchain_core.utils import (
|
||||
convert_to_secret_str,
|
||||
get_from_dict_or_env,
|
||||
)
|
||||
from langchain_openai.llms.base import BaseOpenAI
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class AzureOpenAI(BaseOpenAI):
|
||||
"""Azure-specific OpenAI large language models.
|
||||
|
||||
To use, you should have the ``openai`` python package installed, and the
|
||||
environment variable ``OPENAI_API_KEY`` set with your API key.
|
||||
|
||||
Any parameters that are valid to be passed to the openai.create call can be passed
|
||||
in, even if not explicitly saved on this class.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from swarms import AzureOpenAI
|
||||
|
||||
openai = AzureOpenAI(model_name="gpt-3.5-turbo-instruct")
|
||||
"""
|
||||
|
||||
azure_endpoint: str | None = None
|
||||
"""Your Azure endpoint, including the resource.
|
||||
|
||||
Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
|
||||
|
||||
Example: `https://example-resource.azure.openai.com/`
|
||||
"""
|
||||
deployment_name: str | None = Field(
|
||||
default=None, alias="azure_deployment"
|
||||
)
|
||||
"""A model deployment.
|
||||
|
||||
If given sets the base client URL to include `/deployments/{azure_deployment}`.
|
||||
Note: this means you won't be able to use non-deployment endpoints.
|
||||
"""
|
||||
openai_api_version: str = Field(default="", alias="api_version")
|
||||
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
|
||||
openai_api_key: SecretStr | None = Field(
|
||||
default=None, alias="api_key"
|
||||
)
|
||||
"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
|
||||
azure_ad_token: SecretStr | None = None
|
||||
"""Your Azure Active Directory token.
|
||||
|
||||
Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
|
||||
|
||||
For more:
|
||||
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
|
||||
""" # noqa: E501
|
||||
azure_ad_token_provider: Callable[[], str] | None = None
|
||||
"""A function that returns an Azure Active Directory token.
|
||||
|
||||
Will be invoked on every request.
|
||||
"""
|
||||
openai_api_type: str = ""
|
||||
"""Legacy, for openai<1.0.0 support."""
|
||||
validate_base_url: bool = True
|
||||
"""For backwards compatibility. If legacy val openai_api_base is passed in, try to
|
||||
infer if it is a base_url or azure_endpoint and update accordingly.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def get_lc_namespace(cls) -> list[str]:
|
||||
"""Get the namespace of the langchain object."""
|
||||
return ["langchain", "llms", "openai"]
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: dict) -> dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
if values["n"] < 1:
|
||||
raise ValueError("n must be at least 1.")
|
||||
if values["streaming"] and values["n"] > 1:
|
||||
raise ValueError("Cannot stream results when n > 1.")
|
||||
if values["streaming"] and values["best_of"] > 1:
|
||||
raise ValueError(
|
||||
"Cannot stream results when best_of > 1."
|
||||
)
|
||||
|
||||
# Check OPENAI_KEY for backwards compatibility.
|
||||
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
|
||||
# other forms of azure credentials.
|
||||
openai_api_key = (
|
||||
values["openai_api_key"]
|
||||
or os.getenv("AZURE_OPENAI_API_KEY")
|
||||
or os.getenv("OPENAI_API_KEY")
|
||||
)
|
||||
values["openai_api_key"] = (
|
||||
convert_to_secret_str(openai_api_key)
|
||||
if openai_api_key
|
||||
else None
|
||||
)
|
||||
|
||||
values["azure_endpoint"] = values[
|
||||
"azure_endpoint"
|
||||
] or os.getenv("AZURE_OPENAI_ENDPOINT")
|
||||
azure_ad_token = values["azure_ad_token"] or os.getenv(
|
||||
"AZURE_OPENAI_AD_TOKEN"
|
||||
)
|
||||
values["azure_ad_token"] = (
|
||||
convert_to_secret_str(azure_ad_token)
|
||||
if azure_ad_token
|
||||
else None
|
||||
)
|
||||
values["openai_api_base"] = values[
|
||||
"openai_api_base"
|
||||
] or os.getenv("OPENAI_API_BASE")
|
||||
values["openai_proxy"] = get_from_dict_or_env(
|
||||
values,
|
||||
"openai_proxy",
|
||||
"OPENAI_PROXY",
|
||||
default="",
|
||||
)
|
||||
values["openai_organization"] = (
|
||||
values["openai_organization"]
|
||||
or os.getenv("OPENAI_ORG_ID")
|
||||
or os.getenv("OPENAI_ORGANIZATION")
|
||||
)
|
||||
values["openai_api_version"] = values[
|
||||
"openai_api_version"
|
||||
] or os.getenv("OPENAI_API_VERSION")
|
||||
values["openai_api_type"] = get_from_dict_or_env(
|
||||
values,
|
||||
"openai_api_type",
|
||||
"OPENAI_API_TYPE",
|
||||
default="azure",
|
||||
)
|
||||
# For backwards compatibility. Before openai v1, no distinction was made
|
||||
# between azure_endpoint and base_url (openai_api_base).
|
||||
openai_api_base = values["openai_api_base"]
|
||||
if openai_api_base and values["validate_base_url"]:
|
||||
if "/openai" not in openai_api_base:
|
||||
values["openai_api_base"] = (
|
||||
values["openai_api_base"].rstrip("/") + "/openai"
|
||||
)
|
||||
raise ValueError(
|
||||
"As of openai>=1.0.0, Azure endpoints should be"
|
||||
" specified via the `azure_endpoint` param not"
|
||||
" `openai_api_base` (or alias `base_url`)."
|
||||
)
|
||||
if values["deployment_name"]:
|
||||
raise ValueError(
|
||||
"As of openai>=1.0.0, if `deployment_name` (or"
|
||||
" alias `azure_deployment`) is specified then"
|
||||
" `openai_api_base` (or alias `base_url`) should"
|
||||
" not be. Instead use `deployment_name` (or alias"
|
||||
" `azure_deployment`) and `azure_endpoint`."
|
||||
)
|
||||
values["deployment_name"] = None
|
||||
client_params = {
|
||||
"api_version": values["openai_api_version"],
|
||||
"azure_endpoint": values["azure_endpoint"],
|
||||
"azure_deployment": values["deployment_name"],
|
||||
"api_key": (
|
||||
values["openai_api_key"].get_secret_value()
|
||||
if values["openai_api_key"]
|
||||
else None
|
||||
),
|
||||
"azure_ad_token": (
|
||||
values["azure_ad_token"].get_secret_value()
|
||||
if values["azure_ad_token"]
|
||||
else None
|
||||
),
|
||||
"azure_ad_token_provider": values[
|
||||
"azure_ad_token_provider"
|
||||
],
|
||||
"organization": values["openai_organization"],
|
||||
"base_url": values["openai_api_base"],
|
||||
"timeout": values["request_timeout"],
|
||||
"max_retries": values["max_retries"],
|
||||
"default_headers": values["default_headers"],
|
||||
"default_query": values["default_query"],
|
||||
"http_client": values["http_client"],
|
||||
}
|
||||
values["client"] = openai.AzureOpenAI(
|
||||
**client_params
|
||||
).completions
|
||||
values["async_client"] = openai.AsyncAzureOpenAI(
|
||||
**client_params
|
||||
).completions
|
||||
|
||||
return values
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
return {
|
||||
**{"deployment_name": self.deployment_name},
|
||||
**super()._identifying_params,
|
||||
}
|
||||
|
||||
@property
|
||||
def _invocation_params(self) -> dict[str, Any]:
|
||||
openai_params = {"model": self.deployment_name}
|
||||
return {**openai_params, **super()._invocation_params}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "azure"
|
||||
|
||||
@property
|
||||
def lc_attributes(self) -> dict[str, Any]:
|
||||
return {
|
||||
"openai_api_type": self.openai_api_type,
|
||||
"openai_api_version": self.openai_api_version,
|
||||
}
|
||||
@ -1,258 +0,0 @@
|
||||
import logging
|
||||
from typing import Any, Callable, Dict, List, Optional
|
||||
|
||||
from langchain.callbacks.manager import (
|
||||
AsyncCallbackManagerForLLMRun,
|
||||
CallbackManagerForLLMRun,
|
||||
)
|
||||
from langchain.llms.base import LLM
|
||||
from langchain.llms.utils import enforce_stop_tokens
|
||||
from langchain.load.serializable import Serializable
|
||||
from langchain.utils import get_from_dict_or_env
|
||||
from pydantic import Extra, Field, root_validator
|
||||
from tenacity import (
|
||||
before_sleep_log,
|
||||
retry,
|
||||
retry_if_exception_type,
|
||||
stop_after_attempt,
|
||||
wait_exponential,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _create_retry_decorator(llm) -> Callable[[Any], Any]:
|
||||
import cohere
|
||||
|
||||
min_seconds = 4
|
||||
max_seconds = 10
|
||||
# Wait 2^x * 1 second between each retry starting with
|
||||
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
|
||||
return retry(
|
||||
reraise=True,
|
||||
stop=stop_after_attempt(llm.max_retries),
|
||||
wait=wait_exponential(
|
||||
multiplier=1, min=min_seconds, max=max_seconds
|
||||
),
|
||||
retry=retry_if_exception_type(cohere.error.CohereError),
|
||||
before_sleep=before_sleep_log(logger, logging.WARNING),
|
||||
)
|
||||
|
||||
|
||||
def completion_with_retry(llm, **kwargs: Any) -> Any:
|
||||
"""Use tenacity to retry the completion call."""
|
||||
retry_decorator = _create_retry_decorator(llm)
|
||||
|
||||
@retry_decorator
|
||||
def _completion_with_retry(**kwargs: Any) -> Any:
|
||||
return llm.client.generate(**kwargs)
|
||||
|
||||
return _completion_with_retry(**kwargs)
|
||||
|
||||
|
||||
def acompletion_with_retry(llm, **kwargs: Any) -> Any:
|
||||
"""Use tenacity to retry the completion call."""
|
||||
retry_decorator = _create_retry_decorator(llm)
|
||||
|
||||
@retry_decorator
|
||||
async def _completion_with_retry(**kwargs: Any) -> Any:
|
||||
return await llm.async_client.generate(**kwargs)
|
||||
|
||||
return _completion_with_retry(**kwargs)
|
||||
|
||||
|
||||
class BaseCohere(Serializable):
|
||||
"""Base class for Cohere models."""
|
||||
|
||||
client: Any #: :meta private:
|
||||
async_client: Any #: :meta private:
|
||||
model: Optional[str] = Field(
|
||||
default=None, description="Model name to use."
|
||||
)
|
||||
"""Model name to use."""
|
||||
|
||||
temperature: float = 0.75
|
||||
"""A non-negative float that tunes the degree of randomness in generation."""
|
||||
|
||||
cohere_api_key: Optional[str] = None
|
||||
|
||||
stop: Optional[List[str]] = None
|
||||
|
||||
streaming: bool = Field(default=False)
|
||||
"""Whether to stream the results."""
|
||||
|
||||
user_agent: str = "langchain"
|
||||
"""Identifier for the application making the request."""
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
try:
|
||||
import cohere
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import cohere python package. "
|
||||
"Please install it with `pip install cohere`."
|
||||
)
|
||||
else:
|
||||
cohere_api_key = get_from_dict_or_env(
|
||||
values, "cohere_api_key", "COHERE_API_KEY"
|
||||
)
|
||||
client_name = values["user_agent"]
|
||||
values["client"] = cohere.Client(
|
||||
cohere_api_key, client_name=client_name
|
||||
)
|
||||
values["async_client"] = cohere.AsyncClient(
|
||||
cohere_api_key, client_name=client_name
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
class Cohere(LLM, BaseCohere):
|
||||
"""Cohere large language models.
|
||||
|
||||
To use, you should have the ``cohere`` python package installed, and the
|
||||
environment variable ``COHERE_API_KEY`` set with your API key, or pass
|
||||
it as a named parameter to the constructor.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.llms import Cohere
|
||||
|
||||
cohere = Cohere(model="gptd-instruct-tft", cohere_api_key="my-api-key")
|
||||
"""
|
||||
|
||||
max_tokens: int = 256
|
||||
"""Denotes the number of tokens to predict per generation."""
|
||||
|
||||
k: int = 0
|
||||
"""Number of most likely tokens to consider at each step."""
|
||||
|
||||
p: int = 1
|
||||
"""Total probability mass of tokens to consider at each step."""
|
||||
|
||||
frequency_penalty: float = 0.0
|
||||
"""Penalizes repeated tokens according to frequency. Between 0 and 1."""
|
||||
|
||||
presence_penalty: float = 0.0
|
||||
"""Penalizes repeated tokens. Between 0 and 1."""
|
||||
|
||||
truncate: Optional[str] = None
|
||||
"""Specify how the client handles inputs longer than the maximum token
|
||||
length: Truncate from START, END or NONE"""
|
||||
|
||||
max_retries: int = 10
|
||||
"""Maximum number of retries to make when generating."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling Cohere API."""
|
||||
return {
|
||||
"max_tokens": self.max_tokens,
|
||||
"temperature": self.temperature,
|
||||
"k": self.k,
|
||||
"p": self.p,
|
||||
"frequency_penalty": self.frequency_penalty,
|
||||
"presence_penalty": self.presence_penalty,
|
||||
"truncate": self.truncate,
|
||||
}
|
||||
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
return {"cohere_api_key": "COHERE_API_KEY"}
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Dict[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {**{"model": self.model}, **self._default_params}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "cohere"
|
||||
|
||||
def _invocation_params(
|
||||
self, stop: Optional[List[str]], **kwargs: Any
|
||||
) -> dict:
|
||||
params = self._default_params
|
||||
if self.stop is not None and stop is not None:
|
||||
raise ValueError(
|
||||
"`stop` found in both the input and default params."
|
||||
)
|
||||
elif self.stop is not None:
|
||||
params["stop_sequences"] = self.stop
|
||||
else:
|
||||
params["stop_sequences"] = stop
|
||||
return {**params, **kwargs}
|
||||
|
||||
def _process_response(
|
||||
self, response: Any, stop: Optional[List[str]]
|
||||
) -> str:
|
||||
text = response.generations[0].text
|
||||
# If stop tokens are provided, Cohere's endpoint returns them.
|
||||
# In order to make this consistent with other endpoints, we strip them.
|
||||
if stop:
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
||||
|
||||
def _call(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Call out to Cohere's generate endpoint.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
|
||||
Returns:
|
||||
The string generated by the model.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
response = cohere("Tell me a joke.")
|
||||
"""
|
||||
params = self._invocation_params(stop, **kwargs)
|
||||
response = completion_with_retry(
|
||||
self, model=self.model, prompt=prompt, **params
|
||||
)
|
||||
_stop = params.get("stop_sequences")
|
||||
return self._process_response(response, _stop)
|
||||
|
||||
async def _acall(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Async call out to Cohere's generate endpoint.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
|
||||
Returns:
|
||||
The string generated by the model.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
response = await cohere("Tell me a joke.")
|
||||
"""
|
||||
params = self._invocation_params(stop, **kwargs)
|
||||
response = await acompletion_with_retry(
|
||||
self, model=self.model, prompt=prompt, **params
|
||||
)
|
||||
_stop = params.get("stop_sequences")
|
||||
return self._process_response(response, _stop)
|
||||
@ -1 +0,0 @@
|
||||
# Base implementation for the diffusers library
|
||||
@ -1,114 +0,0 @@
|
||||
import tempfile
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, Union
|
||||
|
||||
from langchain.utils import get_from_dict_or_env
|
||||
from pydantic import model_validator
|
||||
|
||||
from swarms.tools.tool import BaseTool
|
||||
|
||||
|
||||
def _import_elevenlabs() -> Any:
|
||||
try:
|
||||
import elevenlabs
|
||||
except ImportError as e:
|
||||
raise ImportError(
|
||||
"Cannot import elevenlabs, please install `pip install"
|
||||
" elevenlabs`."
|
||||
) from e
|
||||
return elevenlabs
|
||||
|
||||
|
||||
class ElevenLabsModel(str, Enum):
|
||||
"""Models available for Eleven Labs Text2Speech."""
|
||||
|
||||
MULTI_LINGUAL = "eleven_multilingual_v1"
|
||||
MONO_LINGUAL = "eleven_monolingual_v1"
|
||||
|
||||
|
||||
class ElevenLabsText2SpeechTool(BaseTool):
|
||||
"""Tool that queries the Eleven Labs Text2Speech API.
|
||||
|
||||
In order to set this up, follow instructions at:
|
||||
https://docs.elevenlabs.io/welcome/introduction
|
||||
|
||||
Attributes:
|
||||
model (ElevenLabsModel): The model to use for text to speech.
|
||||
Defaults to ElevenLabsModel.MULTI_LINGUAL.
|
||||
name (str): The name of the tool. Defaults to "eleven_labs_text2speech".
|
||||
description (str): The description of the tool.
|
||||
Defaults to "A wrapper around Eleven Labs Text2Speech. Useful for when you need to convert text to speech. It supports multiple languages, including English, German, Polish, Spanish, Italian, French, Portuguese, and Hindi."
|
||||
|
||||
|
||||
Usage:
|
||||
>>> from swarms.models import ElevenLabsText2SpeechTool
|
||||
>>> stt = ElevenLabsText2SpeechTool()
|
||||
>>> speech_file = stt.run("Hello world!")
|
||||
>>> stt.play(speech_file)
|
||||
>>> stt.stream_speech("Hello world!")
|
||||
|
||||
"""
|
||||
|
||||
model: Union[ElevenLabsModel, str] = ElevenLabsModel.MULTI_LINGUAL
|
||||
|
||||
name: str = "eleven_labs_text2speech"
|
||||
description: str = (
|
||||
"A wrapper around Eleven Labs Text2Speech. Useful for when"
|
||||
" you need to convert text to speech. It supports multiple"
|
||||
" languages, including English, German, Polish, Spanish,"
|
||||
" Italian, French, Portuguese, and Hindi. "
|
||||
)
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key exists in environment."""
|
||||
_ = get_from_dict_or_env(
|
||||
values, "eleven_api_key", "ELEVEN_API_KEY"
|
||||
)
|
||||
|
||||
return values
|
||||
|
||||
def _run(
|
||||
self,
|
||||
task: str,
|
||||
) -> str:
|
||||
"""Use the tool."""
|
||||
elevenlabs = _import_elevenlabs()
|
||||
try:
|
||||
speech = elevenlabs.generate(text=task, model=self.model)
|
||||
with tempfile.NamedTemporaryFile(
|
||||
mode="bx", suffix=".wav", delete=False
|
||||
) as f:
|
||||
f.write(speech)
|
||||
return f.name
|
||||
except Exception as e:
|
||||
raise RuntimeError(
|
||||
f"Error while running ElevenLabsText2SpeechTool: {e}"
|
||||
)
|
||||
|
||||
def play(self, speech_file: str) -> None:
|
||||
"""Play the text as speech."""
|
||||
elevenlabs = _import_elevenlabs()
|
||||
with open(speech_file, mode="rb") as f:
|
||||
speech = f.read()
|
||||
|
||||
elevenlabs.play(speech)
|
||||
|
||||
def stream_speech(self, query: str) -> None:
|
||||
"""Stream the text as speech as it is generated.
|
||||
Play the text in your speakers."""
|
||||
elevenlabs = _import_elevenlabs()
|
||||
speech_stream = elevenlabs.generate(
|
||||
text=query, model=self.model, stream=True
|
||||
)
|
||||
elevenlabs.stream(speech_stream)
|
||||
|
||||
def save(self, speech_file: str, path: str) -> None:
|
||||
"""Save the speech file to a path."""
|
||||
raise NotImplementedError(
|
||||
"Saving not implemented for this tool."
|
||||
)
|
||||
|
||||
def __str__(self):
|
||||
return "ElevenLabsText2SpeechTool"
|
||||
@ -1 +0,0 @@
|
||||
""""""
|
||||
@ -1,82 +0,0 @@
|
||||
import inspect
|
||||
import pkgutil
|
||||
|
||||
|
||||
class ModelRegistry:
|
||||
"""
|
||||
A registry for storing and querying models.
|
||||
|
||||
Attributes:
|
||||
models (dict): A dictionary of model names and corresponding model classes.
|
||||
|
||||
Methods:
|
||||
__init__(): Initializes the ModelRegistry object and retrieves all available models.
|
||||
_get_all_models(): Retrieves all available models from the models package.
|
||||
query(text): Queries the models based on the given text and returns a dictionary of matching models.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.models = self._get_all_models()
|
||||
|
||||
def _get_all_models(self):
|
||||
"""
|
||||
Retrieves all available models from the models package.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary of model names and corresponding model classes.
|
||||
"""
|
||||
models = {}
|
||||
for importer, modname, ispkg in pkgutil.iter_modules(
|
||||
models.__path__
|
||||
):
|
||||
module = importer.find_module(modname).load_module(
|
||||
modname
|
||||
)
|
||||
for name, obj in inspect.getmembers(module):
|
||||
if inspect.isclass(obj):
|
||||
models[name] = obj
|
||||
return models
|
||||
|
||||
def query(self, text):
|
||||
"""
|
||||
Queries the models based on the given text and returns a dictionary of matching models.
|
||||
|
||||
Args:
|
||||
text (str): The text to search for in the model names.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary of matching model names and corresponding model classes.
|
||||
"""
|
||||
return {
|
||||
name: model
|
||||
for name, model in self.models.items()
|
||||
if text in name
|
||||
}
|
||||
|
||||
def run_model(
|
||||
self, model_name: str, task: str, img: str, *args, **kwargs
|
||||
):
|
||||
"""
|
||||
Runs the specified model for the given task and image.
|
||||
|
||||
Args:
|
||||
model_name (str): The name of the model to run.
|
||||
task (str): The task to perform using the model.
|
||||
img (str): The image to process.
|
||||
*args: Additional positional arguments to pass to the model's run method.
|
||||
**kwargs: Additional keyword arguments to pass to the model's run method.
|
||||
|
||||
Returns:
|
||||
The result of running the model.
|
||||
|
||||
Raises:
|
||||
ValueError: If the specified model is not found in the model registry.
|
||||
"""
|
||||
if model_name not in self.models:
|
||||
raise ValueError(f"Model {model_name} not found")
|
||||
|
||||
# Get the model
|
||||
model = self.models[model_name]
|
||||
|
||||
# Run the model
|
||||
return model.run(task, img, *args, **kwargs)
|
||||
@ -1,83 +0,0 @@
|
||||
from typing import Optional
|
||||
|
||||
from modelscope import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
from swarms.models.base_llm import AbstractLLM
|
||||
|
||||
|
||||
class ModelScopeAutoModel(AbstractLLM):
|
||||
"""
|
||||
ModelScopeAutoModel is a class that represents a model for generating text using the ModelScope framework.
|
||||
|
||||
Args:
|
||||
model_name (str): The name or path of the pre-trained model.
|
||||
tokenizer_name (str, optional): The name or path of the tokenizer to use. Defaults to None.
|
||||
device (str, optional): The device to use for model inference. Defaults to "cuda".
|
||||
device_map (str, optional): The device mapping for multi-GPU setups. Defaults to "auto".
|
||||
max_new_tokens (int, optional): The maximum number of new tokens to generate. Defaults to 500.
|
||||
skip_special_tokens (bool, optional): Whether to skip special tokens during decoding. Defaults to True.
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Attributes:
|
||||
tokenizer (AutoTokenizer): The tokenizer used for tokenizing input text.
|
||||
model (AutoModelForCausalLM): The pre-trained model for generating text.
|
||||
|
||||
Methods:
|
||||
run(task, *args, **kwargs): Generates text based on the given task.
|
||||
|
||||
Examples:
|
||||
>>> from swarms.models import ModelScopeAutoModel
|
||||
>>> mp = ModelScopeAutoModel(
|
||||
... model_name="gpt2",
|
||||
... )
|
||||
>>> mp.run("Generate a 10,000 word blog on health and wellness.")
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_name: str,
|
||||
tokenizer_name: Optional[str] = None,
|
||||
device: str = "cuda",
|
||||
device_map: str = "auto",
|
||||
max_new_tokens: int = 500,
|
||||
skip_special_tokens: bool = True,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.model_name = model_name
|
||||
self.tokenizer_name = tokenizer_name
|
||||
self.device = device
|
||||
self.device_map = device_map
|
||||
self.max_new_tokens = max_new_tokens
|
||||
self.skip_special_tokens = skip_special_tokens
|
||||
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(
|
||||
self.tokenizer_name
|
||||
)
|
||||
self.model = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_name, device_map=device_map * args, **kwargs
|
||||
)
|
||||
|
||||
def run(self, task: str, *args, **kwargs):
|
||||
"""
|
||||
Run the model on the given task.
|
||||
|
||||
Parameters:
|
||||
task (str): The input task to be processed.
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The generated output from the model.
|
||||
"""
|
||||
text = self.tokenizer(task, return_tensors="pt")
|
||||
|
||||
outputs = self.model.generate(
|
||||
**text, max_new_tokens=self.max_new_tokens, **kwargs
|
||||
)
|
||||
|
||||
return self.tokenizer.decode(
|
||||
outputs[0], skip_special_tokens=self.skip_special_tokens
|
||||
)
|
||||
@ -1,58 +0,0 @@
|
||||
from modelscope.pipelines import pipeline
|
||||
|
||||
from swarms.models.base_llm import AbstractLLM
|
||||
|
||||
|
||||
class ModelScopePipeline(AbstractLLM):
|
||||
"""
|
||||
A class representing a ModelScope pipeline.
|
||||
|
||||
Args:
|
||||
type_task (str): The type of task for the pipeline.
|
||||
model_name (str): The name of the model for the pipeline.
|
||||
*args: Variable length argument list.
|
||||
**kwargs: Arbitrary keyword arguments.
|
||||
|
||||
Attributes:
|
||||
type_task (str): The type of task for the pipeline.
|
||||
model_name (str): The name of the model for the pipeline.
|
||||
model: The pipeline model.
|
||||
|
||||
Methods:
|
||||
run: Runs the pipeline for a given task.
|
||||
|
||||
Examples:
|
||||
>>> from swarms.models import ModelScopePipeline
|
||||
>>> mp = ModelScopePipeline(
|
||||
... type_task="text-generation",
|
||||
... model_name="gpt2",
|
||||
... )
|
||||
>>> mp.run("Generate a 10,000 word blog on health and wellness.")
|
||||
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, type_task: str, model_name: str, *args, **kwargs
|
||||
):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.type_task = type_task
|
||||
self.model_name = model_name
|
||||
|
||||
self.model = pipeline(
|
||||
self.type_task, model=self.model_name, *args, **kwargs
|
||||
)
|
||||
|
||||
def run(self, task: str, *args, **kwargs):
|
||||
"""
|
||||
Runs the pipeline for a given task.
|
||||
|
||||
Args:
|
||||
task (str): The task to be performed by the pipeline.
|
||||
*args: Variable length argument list.
|
||||
**kwargs: Arbitrary keyword arguments.
|
||||
|
||||
Returns:
|
||||
The result of running the pipeline on the given task.
|
||||
|
||||
"""
|
||||
return self.model(task, *args, **kwargs)
|
||||
@ -1,262 +0,0 @@
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
|
||||
import openai
|
||||
import requests
|
||||
from pydantic import BaseModel, validator
|
||||
from tenacity import (
|
||||
retry,
|
||||
stop_after_attempt,
|
||||
wait_random_exponential,
|
||||
)
|
||||
from termcolor import colored
|
||||
|
||||
|
||||
class FunctionSpecification(BaseModel):
|
||||
"""
|
||||
Defines the specification for a function including its parameters and metadata.
|
||||
|
||||
Attributes:
|
||||
-----------
|
||||
name: str
|
||||
The name of the function.
|
||||
description: str
|
||||
A brief description of what the function does.
|
||||
parameters: Dict[str, Any]
|
||||
The parameters required by the function, with their details.
|
||||
required: Optional[List[str]]
|
||||
List of required parameter names.
|
||||
|
||||
Methods:
|
||||
--------
|
||||
validate_params(params: Dict[str, Any]) -> None:
|
||||
Validates the parameters against the function's specification.
|
||||
|
||||
|
||||
|
||||
Example:
|
||||
|
||||
# Example Usage
|
||||
def get_current_weather(location: str, format: str) -> str:
|
||||
``'
|
||||
Example function to get current weather.
|
||||
|
||||
Args:
|
||||
location (str): The city and state, e.g. San Francisco, CA.
|
||||
format (str): The temperature unit, e.g. celsius or fahrenheit.
|
||||
|
||||
Returns:
|
||||
str: Weather information.
|
||||
'''
|
||||
# Implementation goes here
|
||||
return "Sunny, 23°C"
|
||||
|
||||
|
||||
weather_function_spec = FunctionSpecification(
|
||||
name="get_current_weather",
|
||||
description="Get the current weather",
|
||||
parameters={
|
||||
"location": {"type": "string", "description": "The city and state"},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit",
|
||||
},
|
||||
},
|
||||
required=["location", "format"],
|
||||
)
|
||||
|
||||
# Validating parameters for the function
|
||||
params = {"location": "San Francisco, CA", "format": "celsius"}
|
||||
weather_function_spec.validate_params(params)
|
||||
|
||||
# Calling the function
|
||||
print(get_current_weather(**params))
|
||||
"""
|
||||
|
||||
name: str
|
||||
description: str
|
||||
parameters: Dict[str, Any]
|
||||
required: Optional[List[str]] = None
|
||||
|
||||
@validator("parameters")
|
||||
def check_parameters(cls, params):
|
||||
if not isinstance(params, dict):
|
||||
raise ValueError("Parameters must be a dictionary.")
|
||||
return params
|
||||
|
||||
def validate_params(self, params: Dict[str, Any]) -> None:
|
||||
"""
|
||||
Validates the parameters against the function's specification.
|
||||
|
||||
Args:
|
||||
params (Dict[str, Any]): The parameters to validate.
|
||||
|
||||
Raises:
|
||||
ValueError: If any required parameter is missing or if any parameter is invalid.
|
||||
"""
|
||||
for key, value in params.items():
|
||||
if key in self.parameters:
|
||||
self.parameters[key]
|
||||
# Perform specific validation based on param_spec
|
||||
# This can include type checking, range validation, etc.
|
||||
else:
|
||||
raise ValueError(f"Unexpected parameter: {key}")
|
||||
|
||||
for req_param in self.required or []:
|
||||
if req_param not in params:
|
||||
raise ValueError(
|
||||
f"Missing required parameter: {req_param}"
|
||||
)
|
||||
|
||||
|
||||
class OpenAIFunctionCaller:
|
||||
def __init__(
|
||||
self,
|
||||
openai_api_key: str,
|
||||
model: str = "text-davinci-003",
|
||||
max_tokens: int = 3000,
|
||||
temperature: float = 0.5,
|
||||
top_p: float = 1.0,
|
||||
n: int = 1,
|
||||
stream: bool = False,
|
||||
stop: Optional[str] = None,
|
||||
echo: bool = False,
|
||||
frequency_penalty: float = 0.0,
|
||||
presence_penalty: float = 0.0,
|
||||
logprobs: Optional[int] = None,
|
||||
best_of: int = 1,
|
||||
logit_bias: Dict[str, float] = None,
|
||||
user: str = None,
|
||||
messages: List[Dict] = None,
|
||||
timeout_sec: Union[float, None] = None,
|
||||
):
|
||||
self.openai_api_key = openai_api_key
|
||||
self.model = model
|
||||
self.max_tokens = max_tokens
|
||||
self.temperature = temperature
|
||||
self.top_p = top_p
|
||||
self.n = n
|
||||
self.stream = stream
|
||||
self.stop = stop
|
||||
self.echo = echo
|
||||
self.frequency_penalty = frequency_penalty
|
||||
self.presence_penalty = presence_penalty
|
||||
self.logprobs = logprobs
|
||||
self.best_of = best_of
|
||||
self.logit_bias = logit_bias
|
||||
self.user = user
|
||||
self.messages = messages if messages is not None else []
|
||||
self.timeout_sec = timeout_sec
|
||||
|
||||
def add_message(self, role: str, content: str):
|
||||
self.messages.append({"role": role, "content": content})
|
||||
|
||||
@retry(
|
||||
wait=wait_random_exponential(multiplier=1, max=40),
|
||||
stop=stop_after_attempt(3),
|
||||
)
|
||||
def chat_completion_request(
|
||||
self,
|
||||
messages,
|
||||
tools=None,
|
||||
tool_choice=None,
|
||||
):
|
||||
headers = {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": "Bearer " + openai.api_key,
|
||||
}
|
||||
json_data = {"model": self.model, "messages": messages}
|
||||
if tools is not None:
|
||||
json_data.update({"tools": tools})
|
||||
if tool_choice is not None:
|
||||
json_data.update({"tool_choice": tool_choice})
|
||||
try:
|
||||
response = requests.post(
|
||||
"https://api.openai.com/v1/chat/completions",
|
||||
headers=headers,
|
||||
json=json_data,
|
||||
)
|
||||
return response
|
||||
except Exception as e:
|
||||
print("Unable to generate ChatCompletion response")
|
||||
print(f"Exception: {e}")
|
||||
return e
|
||||
|
||||
def pretty_print_conversation(self, messages):
|
||||
role_to_color = {
|
||||
"system": "red",
|
||||
"user": "green",
|
||||
"assistant": "blue",
|
||||
"tool": "magenta",
|
||||
}
|
||||
|
||||
for message in messages:
|
||||
if message["role"] == "system":
|
||||
print(
|
||||
colored(
|
||||
f"system: {message['content']}\n",
|
||||
role_to_color[message["role"]],
|
||||
)
|
||||
)
|
||||
elif message["role"] == "user":
|
||||
print(
|
||||
colored(
|
||||
f"user: {message['content']}\n",
|
||||
role_to_color[message["role"]],
|
||||
)
|
||||
)
|
||||
elif message["role"] == "assistant" and message.get(
|
||||
"function_call"
|
||||
):
|
||||
print(
|
||||
colored(
|
||||
f"assistant: {message['function_call']}\n",
|
||||
role_to_color[message["role"]],
|
||||
)
|
||||
)
|
||||
elif message["role"] == "assistant" and not message.get(
|
||||
"function_call"
|
||||
):
|
||||
print(
|
||||
colored(
|
||||
f"assistant: {message['content']}\n",
|
||||
role_to_color[message["role"]],
|
||||
)
|
||||
)
|
||||
elif message["role"] == "tool":
|
||||
print(
|
||||
colored(
|
||||
(
|
||||
f"function ({message['name']}):"
|
||||
f" {message['content']}\n"
|
||||
),
|
||||
role_to_color[message["role"]],
|
||||
)
|
||||
)
|
||||
|
||||
def call(self, task: str, *args, **kwargs) -> Dict:
|
||||
return openai.Completion.create(
|
||||
engine=self.model,
|
||||
prompt=task,
|
||||
max_tokens=self.max_tokens,
|
||||
temperature=self.temperature,
|
||||
top_p=self.top_p,
|
||||
n=self.n,
|
||||
stream=self.stream,
|
||||
stop=self.stop,
|
||||
echo=self.echo,
|
||||
frequency_penalty=self.frequency_penalty,
|
||||
presence_penalty=self.presence_penalty,
|
||||
logprobs=self.logprobs,
|
||||
best_of=self.best_of,
|
||||
logit_bias=self.logit_bias,
|
||||
user=self.user,
|
||||
messages=self.messages,
|
||||
timeout_sec=self.timeout_sec,
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def run(self, task: str, *args, **kwargs) -> str:
|
||||
response = self.call(task, *args, **kwargs)
|
||||
return response["choices"][0]["text"].strip()
|
||||
File diff suppressed because it is too large
Load Diff
@ -1 +0,0 @@
|
||||
"""Phi by Microsoft written by Kye"""
|
||||
@ -0,0 +1,48 @@
|
||||
from langchain_community.chat_models.azure_openai import (
|
||||
AzureChatOpenAI,
|
||||
)
|
||||
from langchain_community.chat_models.openai import (
|
||||
ChatOpenAI as OpenAIChat,
|
||||
)
|
||||
from langchain_community.llms import (
|
||||
Anthropic,
|
||||
Cohere,
|
||||
MosaicML,
|
||||
OpenAI,
|
||||
Replicate,
|
||||
)
|
||||
|
||||
|
||||
class AnthropicChat(Anthropic):
|
||||
def __call__(self, *args, **kwargs):
|
||||
return self.invoke(*args, **kwargs)
|
||||
|
||||
|
||||
class CohereChat(Cohere):
|
||||
def __call__(self, *args, **kwargs):
|
||||
return self.invoke(*args, **kwargs)
|
||||
|
||||
|
||||
class MosaicMLChat(MosaicML):
|
||||
def __call__(self, *args, **kwargs):
|
||||
return self.invoke(*args, **kwargs)
|
||||
|
||||
|
||||
class OpenAILLM(OpenAI):
|
||||
def __call__(self, *args, **kwargs):
|
||||
return self.invoke(*args, **kwargs)
|
||||
|
||||
|
||||
class ReplicateLLM(Replicate):
|
||||
def __call__(self, *args, **kwargs):
|
||||
return self.invoke(*args, **kwargs)
|
||||
|
||||
|
||||
class AzureOpenAILLM(AzureChatOpenAI):
|
||||
def __call__(self, *args, **kwargs):
|
||||
return self.invoke(*args, **kwargs)
|
||||
|
||||
|
||||
class OpenAIChatLLM(OpenAIChat):
|
||||
def __call__(self, *args, **kwargs):
|
||||
return self.invoke(*args, **kwargs)
|
||||
@ -1,44 +0,0 @@
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
from swarms.models.fire_function import FireFunctionCaller
|
||||
|
||||
|
||||
def test_fire_function_caller_run(mocker):
|
||||
# Create mock model and tokenizer
|
||||
model = MagicMock()
|
||||
tokenizer = MagicMock()
|
||||
mocker.patch.object(FireFunctionCaller, "model", model)
|
||||
mocker.patch.object(FireFunctionCaller, "tokenizer", tokenizer)
|
||||
|
||||
# Create mock task and arguments
|
||||
task = "Add 2 and 3"
|
||||
args = (2, 3)
|
||||
kwargs = {}
|
||||
|
||||
# Create mock generated_ids and decoded output
|
||||
generated_ids = [1, 2, 3]
|
||||
decoded_output = "5"
|
||||
model.generate.return_value = generated_ids
|
||||
tokenizer.batch_decode.return_value = [decoded_output]
|
||||
|
||||
# Create FireFunctionCaller instance
|
||||
fire_function_caller = FireFunctionCaller()
|
||||
|
||||
# Run the function
|
||||
fire_function_caller.run(task, *args, **kwargs)
|
||||
|
||||
# Assert model.generate was called with the correct inputs
|
||||
model.generate.assert_called_once_with(
|
||||
tokenizer.apply_chat_template.return_value,
|
||||
max_new_tokens=fire_function_caller.max_tokens,
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Assert tokenizer.batch_decode was called with the correct inputs
|
||||
tokenizer.batch_decode.assert_called_once_with(generated_ids)
|
||||
|
||||
# Assert the decoded output is printed
|
||||
assert decoded_output in mocker.patch.object(
|
||||
print, "call_args_list"
|
||||
)
|
||||
@ -1,97 +0,0 @@
|
||||
import torch
|
||||
|
||||
from swarms.models.base_llm import AbstractLLM
|
||||
|
||||
if torch.cuda.is_available() or torch.cuda.device_count() > 0:
|
||||
# Download vllm with pip
|
||||
try:
|
||||
from vllm import LLM, SamplingParams
|
||||
except ImportError as error:
|
||||
print(f"[ERROR] [vLLM] {error}")
|
||||
raise error
|
||||
else:
|
||||
from swarms.models.huggingface import HuggingfaceLLM as LLM
|
||||
|
||||
SamplingParams = None
|
||||
|
||||
|
||||
class vLLM(AbstractLLM):
|
||||
"""vLLM model
|
||||
|
||||
|
||||
Args:
|
||||
model_name (str, optional): _description_. Defaults to "facebook/opt-13b".
|
||||
tensor_parallel_size (int, optional): _description_. Defaults to 4.
|
||||
trust_remote_code (bool, optional): _description_. Defaults to False.
|
||||
revision (str, optional): _description_. Defaults to None.
|
||||
temperature (float, optional): _description_. Defaults to 0.5.
|
||||
top_p (float, optional): _description_. Defaults to 0.95.
|
||||
*args: _description_.
|
||||
**kwargs: _description_.
|
||||
|
||||
Methods:
|
||||
run: run the vLLM model
|
||||
|
||||
Raises:
|
||||
error: _description_
|
||||
|
||||
Examples:
|
||||
>>> from swarms.models.vllm import vLLM
|
||||
>>> vllm = vLLM()
|
||||
>>> vllm.run("Hello world!")
|
||||
|
||||
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_name: str = "facebook/opt-13b",
|
||||
tensor_parallel_size: int = 4,
|
||||
trust_remote_code: bool = False,
|
||||
revision: str = None,
|
||||
temperature: float = 0.5,
|
||||
top_p: float = 0.95,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.model_name = model_name
|
||||
self.tensor_parallel_size = tensor_parallel_size
|
||||
self.trust_remote_code = trust_remote_code
|
||||
self.revision = revision
|
||||
self.top_p = top_p
|
||||
|
||||
# LLM model
|
||||
self.llm = LLM(
|
||||
model_name=self.model_name,
|
||||
tensor_parallel_size=self.tensor_parallel_size,
|
||||
trust_remote_code=self.trust_remote_code,
|
||||
revision=self.revision,
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Sampling parameters
|
||||
self.sampling_params = SamplingParams(
|
||||
temperature=temperature, top_p=top_p, *args, **kwargs
|
||||
)
|
||||
|
||||
def run(self, task: str = None, *args, **kwargs):
|
||||
"""Run the vLLM model
|
||||
|
||||
Args:
|
||||
task (str, optional): _description_. Defaults to None.
|
||||
|
||||
Raises:
|
||||
error: _description_
|
||||
|
||||
Returns:
|
||||
_type_: _description_
|
||||
"""
|
||||
try:
|
||||
return self.llm.generate(
|
||||
task, self.sampling_params, *args, **kwargs
|
||||
)
|
||||
except Exception as error:
|
||||
print(f"[ERROR] [vLLM] [run] {error}")
|
||||
raise error
|
||||
@ -0,0 +1,20 @@
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
import sentry_sdk
|
||||
|
||||
load_dotenv()
|
||||
|
||||
os.environ["USE_TELEMETRY"] = "True"
|
||||
|
||||
use_telementry = os.getenv("USE_TELEMETRY")
|
||||
|
||||
def activate_sentry():
|
||||
if use_telementry == "True":
|
||||
sentry_sdk.init(
|
||||
dsn="https://5d72dd59551c02f78391d2ea5872ddd4@o4504578305490944.ingest.us.sentry.io/4506951704444928",
|
||||
traces_sample_rate=1.0,
|
||||
profiles_sample_rate=1.0,
|
||||
enable_tracing=True,
|
||||
debug = True,
|
||||
)
|
||||
|
||||
@ -1,36 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
|
||||
from cohere import Client
|
||||
|
||||
|
||||
@dataclass
|
||||
class CohereTokenizer:
|
||||
"""
|
||||
A tokenizer class for Cohere models.
|
||||
"""
|
||||
|
||||
model: str
|
||||
client: Client
|
||||
DEFAULT_MODEL: str = "command"
|
||||
DEFAULT_MAX_TOKENS: int = 2048
|
||||
max_tokens: int = DEFAULT_MAX_TOKENS
|
||||
|
||||
def count_tokens(self, text: str | list) -> int:
|
||||
"""
|
||||
Count the number of tokens in the given text.
|
||||
|
||||
Args:
|
||||
text (str | list): The input text to tokenize.
|
||||
|
||||
Returns:
|
||||
int: The number of tokens in the text.
|
||||
|
||||
Raises:
|
||||
ValueError: If the input text is not a string.
|
||||
"""
|
||||
if isinstance(text, str):
|
||||
return len(self.client.tokenize(text=text).tokens)
|
||||
else:
|
||||
raise ValueError("Text must be a string.")
|
||||
@ -1,953 +1,6 @@
|
||||
"""Base implementation for tools or skills."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import inspect
|
||||
import warnings
|
||||
from abc import abstractmethod
|
||||
from functools import partial
|
||||
from inspect import signature
|
||||
from typing import Any, Awaitable, Callable, Dict, Union
|
||||
|
||||
from langchain.callbacks.base import BaseCallbackManager
|
||||
from langchain.callbacks.manager import (
|
||||
AsyncCallbackManager,
|
||||
AsyncCallbackManagerForToolRun,
|
||||
CallbackManager,
|
||||
CallbackManagerForToolRun,
|
||||
Callbacks,
|
||||
)
|
||||
from langchain.load.serializable import Serializable
|
||||
from langchain.schema.runnable import (
|
||||
Runnable,
|
||||
RunnableConfig,
|
||||
RunnableSerializable,
|
||||
)
|
||||
from pydantic import (
|
||||
BaseModel,
|
||||
Extra,
|
||||
Field,
|
||||
create_model,
|
||||
root_validator,
|
||||
validate_arguments,
|
||||
)
|
||||
|
||||
|
||||
class SchemaAnnotationError(TypeError):
|
||||
"""Raised when 'args_schema' is missing or has an incorrect type annotation."""
|
||||
|
||||
|
||||
def _create_subset_model(
|
||||
name: str, model: BaseModel, field_names: list
|
||||
) -> type[BaseModel]:
|
||||
"""Create a pydantic model with only a subset of model's fields."""
|
||||
fields = {}
|
||||
for field_name in field_names:
|
||||
field = model.__fields__[field_name]
|
||||
fields[field_name] = (field.outer_type_, field.field_info)
|
||||
return create_model(name, **fields) # type: ignore
|
||||
|
||||
|
||||
def _get_filtered_args(
|
||||
inferred_model: type[BaseModel],
|
||||
func: Callable,
|
||||
) -> dict:
|
||||
"""Get the arguments from a function's signature."""
|
||||
schema = inferred_model.schema()["properties"]
|
||||
valid_keys = signature(func).parameters
|
||||
return {
|
||||
k: schema[k]
|
||||
for k in valid_keys
|
||||
if k not in ("run_manager", "callbacks")
|
||||
}
|
||||
|
||||
|
||||
class _SchemaConfig:
|
||||
"""Configuration for the pydantic model."""
|
||||
|
||||
extra: Any = Extra.forbid
|
||||
arbitrary_types_allowed: bool = True
|
||||
|
||||
|
||||
def create_schema_from_function(
|
||||
model_name: str,
|
||||
func: Callable,
|
||||
) -> type[BaseModel]:
|
||||
"""Create a pydantic schema from a function's signature.
|
||||
Args:
|
||||
model_name: Name to assign to the generated pydandic schema
|
||||
func: Function to generate the schema from
|
||||
Returns:
|
||||
A pydantic model with the same arguments as the function
|
||||
"""
|
||||
# https://docs.pydantic.dev/latest/usage/validation_decorator/
|
||||
validated = validate_arguments(func, config=_SchemaConfig) # type: ignore
|
||||
inferred_model = validated.model # type: ignore
|
||||
if "run_manager" in inferred_model.__fields__:
|
||||
del inferred_model.__fields__["run_manager"]
|
||||
if "callbacks" in inferred_model.__fields__:
|
||||
del inferred_model.__fields__["callbacks"]
|
||||
# Pydantic adds placeholder virtual fields we need to strip
|
||||
valid_properties = _get_filtered_args(inferred_model, func)
|
||||
return _create_subset_model(
|
||||
f"{model_name}Schema", inferred_model, list(valid_properties)
|
||||
)
|
||||
|
||||
|
||||
class ToolException(Exception):
|
||||
"""An optional exception that tool throws when execution error occurs.
|
||||
|
||||
When this exception is thrown, the agent will not stop working,
|
||||
but will handle the exception according to the handle_tool_error
|
||||
variable of the tool, and the processing result will be returned
|
||||
to the agent as observation, and printed in red on the console.
|
||||
"""
|
||||
|
||||
|
||||
class BaseTool(RunnableSerializable[Union[str, Dict], Any]):
|
||||
"""Interface swarms tools must implement."""
|
||||
|
||||
def __init_subclass__(cls, **kwargs: Any) -> None:
|
||||
"""Create the definition of the new tool class."""
|
||||
super().__init_subclass__(**kwargs)
|
||||
|
||||
args_schema_type = cls.__annotations__.get(
|
||||
"args_schema", None
|
||||
)
|
||||
|
||||
if args_schema_type is not None:
|
||||
if (
|
||||
args_schema_type is None
|
||||
or args_schema_type == BaseModel
|
||||
):
|
||||
# Throw errors for common mis-annotations.
|
||||
# TODO: Use get_args / get_origin and fully
|
||||
# specify valid annotations.
|
||||
typehint_mandate = """
|
||||
class ChildTool(BaseTool):
|
||||
...
|
||||
args_schema: Type[BaseModel] = SchemaClass
|
||||
..."""
|
||||
name = cls.__name__
|
||||
raise SchemaAnnotationError(
|
||||
f"Tool definition for {name} must include valid"
|
||||
" type annotations for argument 'args_schema' to"
|
||||
" behave as expected.\nExpected annotation of"
|
||||
" 'Type[BaseModel]' but got"
|
||||
f" '{args_schema_type}'.\nExpected class looks"
|
||||
f" like:\n{typehint_mandate}"
|
||||
)
|
||||
|
||||
name: str
|
||||
"""The unique name of the tool that clearly communicates its purpose."""
|
||||
description: str
|
||||
"""Used to tell the model how/when/why to use the tool.
|
||||
|
||||
You can provide few-shot examples as a part of the description.
|
||||
"""
|
||||
args_schema: type[BaseModel] | None = None
|
||||
"""Pydantic model class to validate and parse the tool's input arguments."""
|
||||
return_direct: bool = False
|
||||
"""Whether to return the tool's output directly. Setting this to True means
|
||||
|
||||
that after the tool is called, the AgentExecutor will stop looping.
|
||||
"""
|
||||
verbose: bool = False
|
||||
"""Whether to log the tool's progress."""
|
||||
|
||||
callbacks: Callbacks = Field(default=None, exclude=True)
|
||||
"""Callbacks to be called during tool execution."""
|
||||
callback_manager: BaseCallbackManager | None = Field(
|
||||
default=None, exclude=True
|
||||
)
|
||||
"""Deprecated. Please use callbacks instead."""
|
||||
tags: list[str] | None = None
|
||||
"""Optional list of tags associated with the tool. Defaults to None
|
||||
These tags will be associated with each call to this tool,
|
||||
and passed as arguments to the handlers defined in `callbacks`.
|
||||
You can use these to eg identify a specific instance of a tool with its use case.
|
||||
"""
|
||||
metadata: dict[str, Any] | None = None
|
||||
"""Optional metadata associated with the tool. Defaults to None
|
||||
This metadata will be associated with each call to this tool,
|
||||
and passed as arguments to the handlers defined in `callbacks`.
|
||||
You can use these to eg identify a specific instance of a tool with its use case.
|
||||
"""
|
||||
|
||||
handle_tool_error: (
|
||||
bool | str | Callable[[ToolException], str] | None
|
||||
) = False
|
||||
"""Handle the content of the ToolException thrown."""
|
||||
|
||||
class Config(Serializable.Config):
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@property
|
||||
def is_single_input(self) -> bool:
|
||||
"""Whether the tool only accepts a single input."""
|
||||
keys = {k for k in self.args if k != "kwargs"}
|
||||
return len(keys) == 1
|
||||
|
||||
@property
|
||||
def args(self) -> dict:
|
||||
if self.args_schema is not None:
|
||||
return self.args_schema.schema()["properties"]
|
||||
else:
|
||||
schema = create_schema_from_function(self.name, self._run)
|
||||
return schema.schema()["properties"]
|
||||
|
||||
# --- Runnable ---
|
||||
|
||||
@property
|
||||
def input_schema(self) -> type[BaseModel]:
|
||||
"""The tool's input schema."""
|
||||
if self.args_schema is not None:
|
||||
return self.args_schema
|
||||
else:
|
||||
return create_schema_from_function(self.name, self._run)
|
||||
|
||||
def invoke(
|
||||
self,
|
||||
input: str | dict,
|
||||
config: RunnableConfig | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
config = config or {}
|
||||
return self.run(
|
||||
input,
|
||||
callbacks=config.get("callbacks"),
|
||||
tags=config.get("tags"),
|
||||
metadata=config.get("metadata"),
|
||||
run_name=config.get("run_name"),
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
async def ainvoke(
|
||||
self,
|
||||
input: str | dict,
|
||||
config: RunnableConfig | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
config = config or {}
|
||||
return await self.arun(
|
||||
input,
|
||||
callbacks=config.get("callbacks"),
|
||||
tags=config.get("tags"),
|
||||
metadata=config.get("metadata"),
|
||||
run_name=config.get("run_name"),
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# --- Tool ---
|
||||
|
||||
def _parse_input(
|
||||
self,
|
||||
tool_input: str | dict,
|
||||
) -> str | dict[str, Any]:
|
||||
"""Convert tool input to pydantic model."""
|
||||
input_args = self.args_schema
|
||||
if isinstance(tool_input, str):
|
||||
if input_args is not None:
|
||||
key_ = next(iter(input_args.__fields__.keys()))
|
||||
input_args.validate({key_: tool_input})
|
||||
return tool_input
|
||||
else:
|
||||
if input_args is not None:
|
||||
result = input_args.parse_obj(tool_input)
|
||||
return {
|
||||
k: v
|
||||
for k, v in result.dict().items()
|
||||
if k in tool_input
|
||||
}
|
||||
return tool_input
|
||||
|
||||
@root_validator(skip_on_failure=True)
|
||||
def raise_deprecation(cls, values: dict) -> dict:
|
||||
"""Raise deprecation warning if callback_manager is used."""
|
||||
if values.get("callback_manager") is not None:
|
||||
warnings.warn(
|
||||
(
|
||||
"callback_manager is deprecated. Please use"
|
||||
" callbacks instead."
|
||||
),
|
||||
DeprecationWarning,
|
||||
)
|
||||
values["callbacks"] = values.pop("callback_manager", None)
|
||||
return values
|
||||
|
||||
@abstractmethod
|
||||
def _run(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""Use the tool.
|
||||
|
||||
Add run_manager: Optional[CallbackManagerForToolRun] = None
|
||||
to child implementations to enable tracing,
|
||||
"""
|
||||
|
||||
async def _arun(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""Use the tool asynchronously.
|
||||
|
||||
Add run_manager: Optional[AsyncCallbackManagerForToolRun] = None
|
||||
to child implementations to enable tracing,
|
||||
"""
|
||||
return await asyncio.get_running_loop().run_in_executor(
|
||||
None,
|
||||
partial(self._run, **kwargs),
|
||||
*args,
|
||||
)
|
||||
|
||||
def _to_args_and_kwargs(
|
||||
self, tool_input: str | dict
|
||||
) -> tuple[tuple, dict]:
|
||||
# For backwards compatibility, if run_input is a string,
|
||||
# pass as a positional argument.
|
||||
if isinstance(tool_input, str):
|
||||
return (tool_input,), {}
|
||||
else:
|
||||
return (), tool_input
|
||||
|
||||
def run(
|
||||
self,
|
||||
tool_input: str | dict,
|
||||
verbose: bool | None = None,
|
||||
start_color: str | None = "green",
|
||||
color: str | None = "green",
|
||||
callbacks: Callbacks = None,
|
||||
*,
|
||||
tags: list[str] | None = None,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
run_name: str | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""Run the tool."""
|
||||
parsed_input = self._parse_input(tool_input)
|
||||
if not self.verbose and verbose is not None:
|
||||
verbose_ = verbose
|
||||
else:
|
||||
verbose_ = self.verbose
|
||||
callback_manager = CallbackManager.configure(
|
||||
callbacks,
|
||||
self.callbacks,
|
||||
verbose_,
|
||||
tags,
|
||||
self.tags,
|
||||
metadata,
|
||||
self.metadata,
|
||||
)
|
||||
# TODO: maybe also pass through run_manager is _run supports kwargs
|
||||
new_arg_supported = signature(self._run).parameters.get(
|
||||
"run_manager"
|
||||
)
|
||||
run_manager = callback_manager.on_tool_start(
|
||||
{"name": self.name, "description": self.description},
|
||||
(
|
||||
tool_input
|
||||
if isinstance(tool_input, str)
|
||||
else str(tool_input)
|
||||
),
|
||||
color=start_color,
|
||||
name=run_name,
|
||||
**kwargs,
|
||||
)
|
||||
try:
|
||||
tool_args, tool_kwargs = self._to_args_and_kwargs(
|
||||
parsed_input
|
||||
)
|
||||
observation = (
|
||||
self._run(
|
||||
*tool_args, run_manager=run_manager, **tool_kwargs
|
||||
)
|
||||
if new_arg_supported
|
||||
else self._run(*tool_args, **tool_kwargs)
|
||||
)
|
||||
except ToolException as e:
|
||||
if not self.handle_tool_error:
|
||||
run_manager.on_tool_error(e)
|
||||
raise e
|
||||
elif isinstance(self.handle_tool_error, bool):
|
||||
if e.args:
|
||||
observation = e.args[0]
|
||||
else:
|
||||
observation = "Tool execution error"
|
||||
elif isinstance(self.handle_tool_error, str):
|
||||
observation = self.handle_tool_error
|
||||
elif callable(self.handle_tool_error):
|
||||
observation = self.handle_tool_error(e)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Got unexpected type of `handle_tool_error`."
|
||||
" Expected bool, str or callable. Received:"
|
||||
f" {self.handle_tool_error}"
|
||||
)
|
||||
run_manager.on_tool_end(
|
||||
str(observation),
|
||||
color="red",
|
||||
name=self.name,
|
||||
**kwargs,
|
||||
)
|
||||
return observation
|
||||
except (Exception, KeyboardInterrupt) as e:
|
||||
run_manager.on_tool_error(e)
|
||||
raise e
|
||||
else:
|
||||
run_manager.on_tool_end(
|
||||
str(observation),
|
||||
color=color,
|
||||
name=self.name,
|
||||
**kwargs,
|
||||
)
|
||||
return observation
|
||||
|
||||
async def arun(
|
||||
self,
|
||||
tool_input: str | dict,
|
||||
verbose: bool | None = None,
|
||||
start_color: str | None = "green",
|
||||
color: str | None = "green",
|
||||
callbacks: Callbacks = None,
|
||||
*,
|
||||
tags: list[str] | None = None,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
run_name: str | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""Run the tool asynchronously."""
|
||||
parsed_input = self._parse_input(tool_input)
|
||||
if not self.verbose and verbose is not None:
|
||||
verbose_ = verbose
|
||||
else:
|
||||
verbose_ = self.verbose
|
||||
callback_manager = AsyncCallbackManager.configure(
|
||||
callbacks,
|
||||
self.callbacks,
|
||||
verbose_,
|
||||
tags,
|
||||
self.tags,
|
||||
metadata,
|
||||
self.metadata,
|
||||
)
|
||||
new_arg_supported = signature(self._arun).parameters.get(
|
||||
"run_manager"
|
||||
)
|
||||
run_manager = await callback_manager.on_tool_start(
|
||||
{"name": self.name, "description": self.description},
|
||||
(
|
||||
tool_input
|
||||
if isinstance(tool_input, str)
|
||||
else str(tool_input)
|
||||
),
|
||||
color=start_color,
|
||||
name=run_name,
|
||||
**kwargs,
|
||||
)
|
||||
try:
|
||||
# We then call the tool on the tool input to get an observation
|
||||
tool_args, tool_kwargs = self._to_args_and_kwargs(
|
||||
parsed_input
|
||||
)
|
||||
observation = (
|
||||
await self._arun(
|
||||
*tool_args, run_manager=run_manager, **tool_kwargs
|
||||
)
|
||||
if new_arg_supported
|
||||
else await self._arun(*tool_args, **tool_kwargs)
|
||||
)
|
||||
except ToolException as e:
|
||||
if not self.handle_tool_error:
|
||||
await run_manager.on_tool_error(e)
|
||||
raise e
|
||||
elif isinstance(self.handle_tool_error, bool):
|
||||
if e.args:
|
||||
observation = e.args[0]
|
||||
else:
|
||||
observation = "Tool execution error"
|
||||
elif isinstance(self.handle_tool_error, str):
|
||||
observation = self.handle_tool_error
|
||||
elif callable(self.handle_tool_error):
|
||||
observation = self.handle_tool_error(e)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Got unexpected type of `handle_tool_error`."
|
||||
" Expected bool, str or callable. Received:"
|
||||
f" {self.handle_tool_error}"
|
||||
)
|
||||
await run_manager.on_tool_end(
|
||||
str(observation),
|
||||
color="red",
|
||||
name=self.name,
|
||||
**kwargs,
|
||||
)
|
||||
return observation
|
||||
except (Exception, KeyboardInterrupt) as e:
|
||||
await run_manager.on_tool_error(e)
|
||||
raise e
|
||||
else:
|
||||
await run_manager.on_tool_end(
|
||||
str(observation),
|
||||
color=color,
|
||||
name=self.name,
|
||||
**kwargs,
|
||||
)
|
||||
return observation
|
||||
|
||||
def __call__(
|
||||
self, tool_input: str, callbacks: Callbacks = None
|
||||
) -> str:
|
||||
"""Make tool callable."""
|
||||
return self.run(tool_input, callbacks=callbacks)
|
||||
|
||||
|
||||
class Tool(BaseTool):
|
||||
"""Tool that takes in function or coroutine directly."""
|
||||
|
||||
description: str = ""
|
||||
func: Callable[..., str] | None
|
||||
"""The function to run when the tool is called."""
|
||||
coroutine: Callable[..., Awaitable[str]] | None = None
|
||||
"""The asynchronous version of the function."""
|
||||
|
||||
# --- Runnable ---
|
||||
async def ainvoke(
|
||||
self,
|
||||
input: str | dict,
|
||||
config: RunnableConfig | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
if not self.coroutine:
|
||||
# If the tool does not implement async, fall back to default implementation
|
||||
return await asyncio.get_running_loop().run_in_executor(
|
||||
None, partial(self.invoke, input, config, **kwargs)
|
||||
)
|
||||
|
||||
return await super().ainvoke(input, config, **kwargs)
|
||||
|
||||
# --- Tool ---
|
||||
|
||||
@property
|
||||
def args(self) -> dict:
|
||||
"""The tool's input arguments."""
|
||||
if self.args_schema is not None:
|
||||
return self.args_schema.schema()["properties"]
|
||||
# For backwards compatibility, if the function signature is ambiguous,
|
||||
# assume it takes a single string input.
|
||||
return {"tool_input": {"type": "string"}}
|
||||
|
||||
def _to_args_and_kwargs(
|
||||
self, tool_input: str | dict
|
||||
) -> tuple[tuple, dict]:
|
||||
"""Convert tool input to pydantic model."""
|
||||
args, kwargs = super()._to_args_and_kwargs(tool_input)
|
||||
# For backwards compatibility. The tool must be run with a single input
|
||||
all_args = list(args) + list(kwargs.values())
|
||||
if len(all_args) != 1:
|
||||
raise ToolException(
|
||||
"Too many arguments to single-input tool"
|
||||
f" {self.name}. Args: {all_args}"
|
||||
)
|
||||
return tuple(all_args), {}
|
||||
|
||||
def _run(
|
||||
self,
|
||||
*args: Any,
|
||||
run_manager: CallbackManagerForToolRun | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""Use the tool."""
|
||||
if self.func:
|
||||
new_argument_supported = signature(
|
||||
self.func
|
||||
).parameters.get("callbacks")
|
||||
return (
|
||||
self.func(
|
||||
*args,
|
||||
callbacks=(
|
||||
run_manager.get_child()
|
||||
if run_manager
|
||||
else None
|
||||
),
|
||||
**kwargs,
|
||||
)
|
||||
if new_argument_supported
|
||||
else self.func(*args, **kwargs)
|
||||
)
|
||||
raise NotImplementedError("Tool does not support sync")
|
||||
|
||||
async def _arun(
|
||||
self,
|
||||
*args: Any,
|
||||
run_manager: AsyncCallbackManagerForToolRun | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""Use the tool asynchronously."""
|
||||
if self.coroutine:
|
||||
new_argument_supported = signature(
|
||||
self.coroutine
|
||||
).parameters.get("callbacks")
|
||||
return (
|
||||
await self.coroutine(
|
||||
*args,
|
||||
callbacks=(
|
||||
run_manager.get_child()
|
||||
if run_manager
|
||||
else None
|
||||
),
|
||||
**kwargs,
|
||||
)
|
||||
if new_argument_supported
|
||||
else await self.coroutine(*args, **kwargs)
|
||||
)
|
||||
else:
|
||||
return await asyncio.get_running_loop().run_in_executor(
|
||||
None,
|
||||
partial(self._run, run_manager=run_manager, **kwargs),
|
||||
*args,
|
||||
)
|
||||
|
||||
# TODO: this is for backwards compatibility, remove in future
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
func: Callable | None,
|
||||
description: str,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Initialize tool."""
|
||||
super().__init__(
|
||||
name=name, func=func, description=description, **kwargs
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_function(
|
||||
cls,
|
||||
func: Callable | None,
|
||||
name: str, # We keep these required to support backwards compatibility
|
||||
description: str,
|
||||
return_direct: bool = False,
|
||||
args_schema: type[BaseModel] | None = None,
|
||||
coroutine: (Callable[..., Awaitable[Any]])
|
||||
| None = None, # This is last for compatibility, but should be after func
|
||||
**kwargs: Any,
|
||||
) -> Tool:
|
||||
"""Initialize tool from a function."""
|
||||
if func is None and coroutine is None:
|
||||
raise ValueError(
|
||||
"Function and/or coroutine must be provided"
|
||||
)
|
||||
return cls(
|
||||
name=name,
|
||||
func=func,
|
||||
coroutine=coroutine,
|
||||
description=description,
|
||||
return_direct=return_direct,
|
||||
args_schema=args_schema,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
class StructuredTool(BaseTool):
|
||||
"""Tool that can operate on any number of inputs."""
|
||||
|
||||
description: str = ""
|
||||
args_schema: type[BaseModel] = Field(
|
||||
..., description="The tool schema."
|
||||
)
|
||||
"""The input arguments' schema."""
|
||||
func: Callable[..., Any] | None
|
||||
"""The function to run when the tool is called."""
|
||||
coroutine: Callable[..., Awaitable[Any]] | None = None
|
||||
"""The asynchronous version of the function."""
|
||||
|
||||
# --- Runnable ---
|
||||
async def ainvoke(
|
||||
self,
|
||||
input: str | dict,
|
||||
config: RunnableConfig | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
if not self.coroutine:
|
||||
# If the tool does not implement async, fall back to default implementation
|
||||
return await asyncio.get_running_loop().run_in_executor(
|
||||
None, partial(self.invoke, input, config, **kwargs)
|
||||
)
|
||||
|
||||
return await super().ainvoke(input, config, **kwargs)
|
||||
|
||||
# --- Tool ---
|
||||
|
||||
@property
|
||||
def args(self) -> dict:
|
||||
"""The tool's input arguments."""
|
||||
return self.args_schema.schema()["properties"]
|
||||
|
||||
def _run(
|
||||
self,
|
||||
*args: Any,
|
||||
run_manager: CallbackManagerForToolRun | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""Use the tool."""
|
||||
if self.func:
|
||||
new_argument_supported = signature(
|
||||
self.func
|
||||
).parameters.get("callbacks")
|
||||
return (
|
||||
self.func(
|
||||
*args,
|
||||
callbacks=(
|
||||
run_manager.get_child()
|
||||
if run_manager
|
||||
else None
|
||||
),
|
||||
**kwargs,
|
||||
)
|
||||
if new_argument_supported
|
||||
else self.func(*args, **kwargs)
|
||||
)
|
||||
raise NotImplementedError("Tool does not support sync")
|
||||
|
||||
async def _arun(
|
||||
self,
|
||||
*args: Any,
|
||||
run_manager: AsyncCallbackManagerForToolRun | None = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Use the tool asynchronously."""
|
||||
if self.coroutine:
|
||||
new_argument_supported = signature(
|
||||
self.coroutine
|
||||
).parameters.get("callbacks")
|
||||
return (
|
||||
await self.coroutine(
|
||||
*args,
|
||||
callbacks=(
|
||||
run_manager.get_child()
|
||||
if run_manager
|
||||
else None
|
||||
),
|
||||
**kwargs,
|
||||
)
|
||||
if new_argument_supported
|
||||
else await self.coroutine(*args, **kwargs)
|
||||
)
|
||||
return await asyncio.get_running_loop().run_in_executor(
|
||||
None,
|
||||
partial(self._run, run_manager=run_manager, **kwargs),
|
||||
*args,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_function(
|
||||
cls,
|
||||
func: Callable | None = None,
|
||||
coroutine: Callable[..., Awaitable[Any]] | None = None,
|
||||
name: str | None = None,
|
||||
description: str | None = None,
|
||||
return_direct: bool = False,
|
||||
args_schema: type[BaseModel] | None = None,
|
||||
infer_schema: bool = True,
|
||||
**kwargs: Any,
|
||||
) -> StructuredTool:
|
||||
"""Create tool from a given function.
|
||||
|
||||
A classmethod that helps to create a tool from a function.
|
||||
|
||||
Args:
|
||||
func: The function from which to create a tool
|
||||
coroutine: The async function from which to create a tool
|
||||
name: The name of the tool. Defaults to the function name
|
||||
description: The description of the tool. Defaults to the function docstring
|
||||
return_direct: Whether to return the result directly or as a callback
|
||||
args_schema: The schema of the tool's input arguments
|
||||
infer_schema: Whether to infer the schema from the function's signature
|
||||
**kwargs: Additional arguments to pass to the tool
|
||||
|
||||
Returns:
|
||||
The tool
|
||||
|
||||
Examples:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def add(a: int, b: int) -> int:
|
||||
\"\"\"Add two numbers\"\"\"
|
||||
return a + b
|
||||
tool = StructuredTool.from_function(add)
|
||||
tool.run(1, 2) # 3
|
||||
"""
|
||||
|
||||
if func is not None:
|
||||
source_function = func
|
||||
elif coroutine is not None:
|
||||
source_function = coroutine
|
||||
else:
|
||||
raise ValueError(
|
||||
"Function and/or coroutine must be provided"
|
||||
)
|
||||
name = name or source_function.__name__
|
||||
description = description or source_function.__doc__
|
||||
if description is None:
|
||||
raise ValueError(
|
||||
"Function must have a docstring if description not"
|
||||
" provided."
|
||||
)
|
||||
|
||||
# Description example:
|
||||
# search_api(query: str) - Searches the API for the query.
|
||||
sig = signature(source_function)
|
||||
description = f"{name}{sig} - {description.strip()}"
|
||||
_args_schema = args_schema
|
||||
if _args_schema is None and infer_schema:
|
||||
_args_schema = create_schema_from_function(
|
||||
f"{name}Schema", source_function
|
||||
)
|
||||
return cls(
|
||||
name=name,
|
||||
func=func,
|
||||
coroutine=coroutine,
|
||||
args_schema=_args_schema,
|
||||
description=description,
|
||||
return_direct=return_direct,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
def tool(
|
||||
*args: str | Callable | Runnable,
|
||||
return_direct: bool = False,
|
||||
args_schema: type[BaseModel] | None = None,
|
||||
infer_schema: bool = True,
|
||||
) -> Callable:
|
||||
"""Make tools out of functions, can be used with or without arguments.
|
||||
|
||||
Args:
|
||||
*args: The arguments to the tool.
|
||||
return_direct: Whether to return directly from the tool rather
|
||||
than continuing the agent loop.
|
||||
args_schema: optional argument schema for user to specify
|
||||
infer_schema: Whether to infer the schema of the arguments from
|
||||
the function's signature. This also makes the resultant tool
|
||||
accept a dictionary input to its `run()` function.
|
||||
|
||||
Requires:
|
||||
- Function must be of type (str) -> str
|
||||
- Function must have a docstring
|
||||
|
||||
Examples:
|
||||
.. code-block:: python
|
||||
|
||||
@tool
|
||||
def search_api(query: str) -> str:
|
||||
# Searches the API for the query.
|
||||
return
|
||||
|
||||
|
||||
@tool("search", return_direct=True)
|
||||
def search_api(query: str) -> str:
|
||||
# Searches the API for the query.
|
||||
return
|
||||
"""
|
||||
|
||||
def _make_with_name(tool_name: str) -> Callable:
|
||||
def _make_tool(dec_func: Callable | Runnable) -> BaseTool:
|
||||
if isinstance(dec_func, Runnable):
|
||||
runnable = dec_func
|
||||
|
||||
if (
|
||||
runnable.input_schema.schema().get("type")
|
||||
!= "object"
|
||||
):
|
||||
raise ValueError(
|
||||
"Runnable must have an object schema."
|
||||
)
|
||||
|
||||
async def ainvoke_wrapper(
|
||||
callbacks: Callbacks | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
return await runnable.ainvoke(
|
||||
kwargs, {"callbacks": callbacks}
|
||||
)
|
||||
|
||||
def invoke_wrapper(
|
||||
callbacks: Callbacks | None = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
return runnable.invoke(
|
||||
kwargs, {"callbacks": callbacks}
|
||||
)
|
||||
|
||||
coroutine = ainvoke_wrapper
|
||||
func = invoke_wrapper
|
||||
schema: type[BaseModel] | None = runnable.input_schema
|
||||
description = repr(runnable)
|
||||
elif inspect.iscoroutinefunction(dec_func):
|
||||
coroutine = dec_func
|
||||
func = None
|
||||
schema = args_schema
|
||||
description = None
|
||||
else:
|
||||
coroutine = None
|
||||
func = dec_func
|
||||
schema = args_schema
|
||||
description = None
|
||||
|
||||
if infer_schema or args_schema is not None:
|
||||
return StructuredTool.from_function(
|
||||
func,
|
||||
coroutine,
|
||||
name=tool_name,
|
||||
description=description,
|
||||
return_direct=return_direct,
|
||||
args_schema=schema,
|
||||
infer_schema=infer_schema,
|
||||
)
|
||||
# If someone doesn't want a schema applied, we must treat it as
|
||||
# a simple string->string function
|
||||
if func.__doc__ is None:
|
||||
raise ValueError(
|
||||
"Function must have a docstring if description"
|
||||
" not provided and infer_schema is False."
|
||||
)
|
||||
return Tool(
|
||||
name=tool_name,
|
||||
func=func,
|
||||
description=f"{tool_name} tool",
|
||||
return_direct=return_direct,
|
||||
coroutine=coroutine,
|
||||
)
|
||||
|
||||
return _make_tool
|
||||
|
||||
if (
|
||||
len(args) == 2
|
||||
and isinstance(args[0], str)
|
||||
and isinstance(args[1], Runnable)
|
||||
):
|
||||
return _make_with_name(args[0])(args[1])
|
||||
elif len(args) == 1 and isinstance(args[0], str):
|
||||
# if the argument is a string, then we use the string as the tool name
|
||||
# Example usage: @tool("search", return_direct=True)
|
||||
return _make_with_name(args[0])
|
||||
elif len(args) == 1 and callable(args[0]):
|
||||
# if the argument is a function, then we use the function name as the tool name
|
||||
# Example usage: @tool
|
||||
return _make_with_name(args[0].__name__)(args[0])
|
||||
elif len(args) == 0:
|
||||
# if there are no arguments, then we use the function name as the tool name
|
||||
# Example usage: @tool(return_direct=True)
|
||||
def _partial(func: Callable[[str], str]) -> BaseTool:
|
||||
return _make_with_name(func.__name__)(func)
|
||||
|
||||
return _partial
|
||||
else:
|
||||
raise ValueError("Too many arguments for tool decorator")
|
||||
from langchain.tools import (
|
||||
BaseTool,
|
||||
Tool,
|
||||
StructuredTool,
|
||||
tool,
|
||||
) # noqa F401
|
||||
|
||||
@ -0,0 +1,65 @@
|
||||
from typing import Any, List, Union
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from swarms.tools.tool import BaseTool
|
||||
from swarms.utils.loguru_logger import logger
|
||||
|
||||
|
||||
class OmniTool(BaseModel):
|
||||
"""
|
||||
A class representing an OmniTool.
|
||||
|
||||
Attributes:
|
||||
tools (Union[List[BaseTool], List[BaseModel], List[Any]]): A list of tools.
|
||||
verbose (bool): A flag indicating whether to enable verbose mode.
|
||||
|
||||
Methods:
|
||||
transform_models_to_tools(): Transforms models to tools.
|
||||
__call__(*args, **kwargs): Calls the tools.
|
||||
|
||||
"""
|
||||
|
||||
tools: Union[List[BaseTool], List[BaseModel], List[Any]]
|
||||
verbose: bool = False
|
||||
|
||||
def transform_models_to_tools(self):
|
||||
"""
|
||||
Transforms models to tools.
|
||||
"""
|
||||
for i, tool in enumerate(self.tools):
|
||||
if isinstance(tool, BaseModel):
|
||||
tool_json = tool.model_dump_json()
|
||||
# Assuming BaseTool has a method to load from json
|
||||
self.tools[i] = BaseTool.load_from_json(tool_json)
|
||||
|
||||
def __call__(self, *args, **kwargs):
|
||||
"""
|
||||
Calls the tools.
|
||||
|
||||
Args:
|
||||
*args: Variable length argument list.
|
||||
**kwargs: Arbitrary keyword arguments.
|
||||
|
||||
Returns:
|
||||
Tuple: A tuple containing the arguments and keyword arguments.
|
||||
|
||||
"""
|
||||
try:
|
||||
self.transform_models_to_tools()
|
||||
logger.info(f"Number of tools: {len(self.tools)}")
|
||||
try:
|
||||
for tool in self.tools:
|
||||
logger.info(f"Running tool: {tool}")
|
||||
tool(*args, **kwargs)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Error occurred while running tools: {e}"
|
||||
)
|
||||
return args, kwargs
|
||||
|
||||
except Exception as error:
|
||||
logger.error(
|
||||
f"Error occurred while running tools: {error}"
|
||||
)
|
||||
return args, kwargs
|
||||
@ -1,93 +0,0 @@
|
||||
from typing import Dict, List, Optional, Union
|
||||
|
||||
|
||||
class AbstractWorker:
|
||||
"""(In preview) An abstract class for AI worker.
|
||||
|
||||
An worker can communicate with other workers and perform actions.
|
||||
Different workers can differ in what actions they perform in the `receive` method.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
name (str): name of the worker.
|
||||
"""
|
||||
# a dictionary of conversations, default value is list
|
||||
self._name = name
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
"""Get the name of the worker."""
|
||||
return self._name
|
||||
|
||||
def run(self, task: str):
|
||||
"""Run the worker agent once"""
|
||||
|
||||
def send(
|
||||
self,
|
||||
message: Union[Dict, str],
|
||||
recipient, # add AbstractWorker
|
||||
request_reply: Optional[bool] = None,
|
||||
):
|
||||
"""(Abstract method) Send a message to another worker."""
|
||||
|
||||
async def a_send(
|
||||
self,
|
||||
message: Union[Dict, str],
|
||||
recipient, # add AbstractWorker
|
||||
request_reply: Optional[bool] = None,
|
||||
):
|
||||
"""(Aabstract async method) Send a message to another worker."""
|
||||
|
||||
def receive(
|
||||
self,
|
||||
message: Union[Dict, str],
|
||||
sender, # add AbstractWorker
|
||||
request_reply: Optional[bool] = None,
|
||||
):
|
||||
"""(Abstract method) Receive a message from another worker."""
|
||||
|
||||
async def a_receive(
|
||||
self,
|
||||
message: Union[Dict, str],
|
||||
sender, # add AbstractWorker
|
||||
request_reply: Optional[bool] = None,
|
||||
):
|
||||
"""(Abstract async method) Receive a message from another worker."""
|
||||
|
||||
def reset(self):
|
||||
"""(Abstract method) Reset the worker."""
|
||||
|
||||
def generate_reply(
|
||||
self,
|
||||
messages: Optional[List[Dict]] = None,
|
||||
sender=None, # Optional["AbstractWorker"] = None,
|
||||
**kwargs,
|
||||
) -> Union[str, Dict, None]:
|
||||
"""(Abstract method) Generate a reply based on the received messages.
|
||||
|
||||
Args:
|
||||
messages (list[dict]): a list of messages received.
|
||||
sender: sender of an Agent instance.
|
||||
Returns:
|
||||
str or dict or None: the generated reply. If None, no reply is generated.
|
||||
"""
|
||||
|
||||
async def a_generate_reply(
|
||||
self,
|
||||
messages: Optional[List[Dict]] = None,
|
||||
sender=None, # Optional["AbstractWorker"] = None,
|
||||
**kwargs,
|
||||
) -> Union[str, Dict, None]:
|
||||
"""(Abstract async method) Generate a reply based on the received messages.
|
||||
|
||||
Args:
|
||||
messages (list[dict]): a list of messages received.
|
||||
sender: sender of an Agent instance.
|
||||
Returns:
|
||||
str or dict or None: the generated reply. If None, no reply is generated.
|
||||
"""
|
||||
Loading…
Reference in new issue