Former-commit-id: 4197920802
grit/923f7c6f-0958-480b-8748-ea6bbf1c2084
Kye
1 year ago
parent
7d375b72e3
commit
bab2835472
@ -0,0 +1,335 @@
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
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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.chat_models.base import (
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BaseChatModel,
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_agenerate_from_stream,
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_generate_from_stream,
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)
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from langchain.llms.cohere import BaseCohere
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from langchain.schema.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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ChatMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain.schema.output import ChatGeneration, ChatGenerationChunk, ChatResult
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def get_role(message: BaseMessage) -> str:
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"""Get the role of the message.
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Args:
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message: The message.
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Returns:
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The role of the message.
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Raises:
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ValueError: If the message is of an unknown type.
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"""
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if isinstance(message, ChatMessage) or isinstance(message, HumanMessage):
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return "User"
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elif isinstance(message, AIMessage):
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return "Chatbot"
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elif isinstance(message, SystemMessage):
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return "System"
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else:
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raise ValueError(f"Got unknown type {message}")
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def get_cohere_chat_request(
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messages: List[BaseMessage],
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*,
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connectors: Optional[List[Dict[str, str]]] = None,
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**kwargs: Any,
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) -> Dict[str, Any]:
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"""Get the request for the Cohere chat API.
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Args:
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messages: The messages.
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connectors: The connectors.
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**kwargs: The keyword arguments.
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Returns:
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The request for the Cohere chat API.
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"""
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documents = (
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None
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if "source_documents" not in kwargs
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else [
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{
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"snippet": doc.page_content,
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"id": doc.metadata.get("id") or f"doc-{str(i)}",
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}
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for i, doc in enumerate(kwargs["source_documents"])
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]
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)
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kwargs.pop("source_documents", None)
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maybe_connectors = connectors if documents is None else None
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# by enabling automatic prompt truncation, the probability of request failure is
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# reduced with minimal impact on response quality
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prompt_truncation = (
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"AUTO" if documents is not None or connectors is not None else None
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)
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return {
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"message": messages[0].content,
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"chat_history": [
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{"role": get_role(x), "message": x.content} for x in messages[1:]
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],
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"documents": documents,
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"connectors": maybe_connectors,
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"prompt_truncation": prompt_truncation,
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**kwargs,
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}
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class CohereChat(BaseChatModel, BaseCohere):
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"""`Cohere` chat large language models.
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To use, you should have the ``cohere`` python package installed, and the
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environment variable ``COHERE_API_KEY`` set with your API key, or pass
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it as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from swarms.models.cohere import CohereChat, HumanMessage
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chat = CohereChat(model="foo")
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result = chat([HumanMessage(content="Hello")])
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print(result.content)
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"""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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arbitrary_types_allowed = True
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "cohere-chat"
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling Cohere API."""
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return {
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"temperature": self.temperature,
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}
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model": self.model}, **self._default_params}
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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"""
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Stream the output
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Args:
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messages: The messages.
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stop: The stop tokens.
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run_manager: The callback manager.
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**kwargs: The keyword arguments.
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"""
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request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
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stream = self.client.chat(**request, stream=True)
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for data in stream:
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if data.event_type == "text-generation":
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delta = data.text
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yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
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if run_manager:
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run_manager.on_llm_new_token(delta)
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async def _astream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
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"""
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Stream generations from the model.
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Args:
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messages: The messages.
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stop: The stop tokens.
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run_manager: The callback manager.
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**kwargs: The keyword arguments.
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Yields:
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The generations.
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Examples:
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.. code-block:: python
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async for generation in model._astream(messages):
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print(generation.message.content)
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"""
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request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
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stream = await self.async_client.chat(**request, stream=True)
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async for data in stream:
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if data.event_type == "text-generation":
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delta = data.text
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yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
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if run_manager:
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await run_manager.on_llm_new_token(delta)
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def _get_generation_info(self, response: Any) -> Dict[str, Any]:
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"""Get the generation info from cohere API response."""
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return {
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"documents": response.documents,
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"citations": response.citations,
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"search_results": response.search_results,
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"search_queries": response.search_queries,
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"token_count": response.token_count,
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}
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def _run(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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"""
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Run the model.
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Args:
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messages: The messages.
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stop: The stop tokens.
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run_manager: The callback manager.
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**kwargs: The keyword arguments.
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Returns:
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The result.
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Examples:
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.. code-block:: python
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result = model._run(messages)
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print(result.content)
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"""
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if self.streaming:
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return _generate_from_stream(stream_iter)
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request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
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response = self.client.chat(**request)
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message = AIMessage(content=response.text)
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generation_info = None
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if hasattr(response, "documents"):
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generation_info = self._get_generation_info(response)
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return ChatResult(
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generations=[
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ChatGeneration(message=message, generation_info=generation_info)
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]
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)
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def __call__(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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"""
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__Call__ the model.
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Args:
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messages: The messages.
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stop: The stop tokens.
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run_manager: The callback manager.
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**kwargs: The keyword arguments.
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Returns:
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The result.
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"""
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if self.streaming:
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return _generate_from_stream(stream_iter)
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request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
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response = self.client.chat(**request)
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message = AIMessage(content=response.text)
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generation_info = None
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if hasattr(response, "documents"):
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generation_info = self._get_generation_info(response)
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return ChatResult(
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generations=[
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ChatGeneration(message=message, generation_info=generation_info)
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]
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)
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async def _arun(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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"""
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Asynchronously run the model.
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Args:
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messages: The messages.
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stop: The stop tokens.
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run_manager: The callback manager.
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**kwargs: The keyword arguments.
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Returns:
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The result.
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Examples:
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.. code-block:: python
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result = await model._arun(messages)
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print(result.content)
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"""
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if self.streaming:
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stream_iter = self._astream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return await _agenerate_from_stream(stream_iter)
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request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
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response = self.client.chat(**request, stream=False)
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message = AIMessage(content=response.text)
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generation_info = None
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if hasattr(response, "documents"):
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generation_info = self._get_generation_info(response)
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return ChatResult(
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generations=[
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ChatGeneration(message=message, generation_info=generation_info)
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]
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)
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def get_num_tokens(self, text: str) -> int:
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"""Calculate number of tokens."""
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return len(self.client.tokenize(text).tokens)
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@ -1,144 +0,0 @@
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import pytest
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from unittest.mock import Mock, patch
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from swarms.agents.agents import (
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AgentNodeInitializer,
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AgentNode,
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agent,
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) # replace with actual import
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# For initializing AgentNodeInitializer in multiple tests
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@pytest.fixture
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def mock_agent_node_initializer():
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with patch("swarms.agents.agents.ChatOpenAI") as mock_llm, patch(
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"swarms.agents.agents.AutoGPT"
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) as mock_agent:
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initializer = AgentNodeInitializer(
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model_type="openai",
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model_id="test",
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openai_api_key="test_key",
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temperature=0.5,
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)
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initializer.llm = mock_llm
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initializer.tools = [Mock(spec=BaseTool)]
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initializer.vectorstore = Mock()
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initializer.agent = mock_agent
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return initializer
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# Test initialize_llm method of AgentNodeInitializer class
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@pytest.mark.parametrize("model_type", ["openai", "huggingface", "invalid"])
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def test_agent_node_initializer_initialize_llm(model_type, mock_agent_node_initializer):
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with patch("swarms.agents.agents.ChatOpenAI") as mock_openai, patch(
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"swarms.agents.agents.HuggingFaceLLM"
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) as mock_huggingface:
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if model_type == "invalid":
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with pytest.raises(ValueError):
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mock_agent_node_initializer.initialize_llm(
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model_type, "model_id", "openai_api_key", 0.5
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)
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else:
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mock_agent_node_initializer.initialize_llm(
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model_type, "model_id", "openai_api_key", 0.5
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)
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if model_type == "openai":
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mock_openai.assert_called_once()
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elif model_type == "huggingface":
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mock_huggingface.assert_called_once()
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# Test add_tool method of AgentNodeInitializer class
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def test_agent_node_initializer_add_tool(mock_agent_node_initializer):
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with patch("swarms.agents.agents.BaseTool") as mock_base_tool:
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mock_agent_node_initializer.add_tool(mock_base_tool)
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assert mock_base_tool in mock_agent_node_initializer.tools
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# Test run method of AgentNodeInitializer class
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@pytest.mark.parametrize("prompt", ["valid prompt", ""])
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def test_agent_node_initializer_run(prompt, mock_agent_node_initializer):
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if prompt == "":
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with pytest.raises(ValueError):
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mock_agent_node_initializer.run(prompt)
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else:
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assert mock_agent_node_initializer.run(prompt) == "Task completed by AgentNode"
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# For initializing AgentNode in multiple tests
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@pytest.fixture
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def mock_agent_node():
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with patch("swarms.agents.agents.ChatOpenAI") as mock_llm, patch(
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"swarms.agents.agents.AgentNodeInitializer"
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) as mock_agent_node_initializer:
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mock_agent_node = AgentNode("test_key")
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mock_agent_node.llm_class = mock_llm
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mock_agent_node.vectorstore = Mock()
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mock_agent_node_initializer.llm = mock_llm
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return mock_agent_node
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# Test initialize_llm method of AgentNode class
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@pytest.mark.parametrize("llm_class", ["openai", "huggingface"])
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def test_agent_node_initialize_llm(llm_class, mock_agent_node):
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with patch("swarms.agents.agents.ChatOpenAI") as mock_openai, patch(
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"swarms.agents.agents.HuggingFaceLLM"
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) as mock_huggingface:
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mock_agent_node.initialize_llm(llm_class)
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if llm_class == "openai":
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mock_openai.assert_called_once()
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elif llm_class == "huggingface":
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mock_huggingface.assert_called_once()
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# Test initialize_tools method of AgentNode class
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def test_agent_node_initialize_tools(mock_agent_node):
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with patch("swarms.agents.agents.DuckDuckGoSearchRun") as mock_ddg, patch(
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"swarms.agents.agents.WriteFileTool"
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) as mock_write_file, patch(
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"swarms.agents.agents.ReadFileTool"
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) as mock_read_file, patch(
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"swarms.agents.agents.process_csv"
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) as mock_process_csv, patch(
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"swarms.agents.agents.WebpageQATool"
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) as mock_webpage_qa:
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mock_agent_node.initialize_tools("openai")
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assert mock_ddg.called
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assert mock_write_file.called
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assert mock_read_file.called
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assert mock_process_csv.called
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assert mock_webpage_qa.called
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# Test create_agent method of AgentNode class
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def test_agent_node_create_agent(mock_agent_node):
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with patch.object(mock_agent_node, "initialize_llm"), patch.object(
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mock_agent_node, "initialize_tools"
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), patch.object(mock_agent_node, "initialize_vectorstore"), patch(
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"swarms.agents.agents.AgentNodeInitializer"
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) as mock_agent_node_initializer:
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mock_agent_node.create_agent()
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mock_agent_node_initializer.assert_called_once()
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mock_agent_node_initializer.return_value.create_agent.assert_called_once()
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# Test agent function
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@pytest.mark.parametrize(
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"openai_api_key,objective",
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[("valid_key", "valid_objective"), ("", "valid_objective"), ("valid_key", "")],
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)
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def test_agent(openai_api_key, objective):
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if openai_api_key == "" or objective == "":
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with pytest.raises(ValueError):
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agent(openai_api_key, objective)
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else:
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with patch(
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"swarms.agents.agents.AgentNodeInitializer"
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) as mock_agent_node_initializer:
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mock_agent_node = (
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mock_agent_node_initializer.return_value.create_agent.return_value
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)
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mock_agent_node.run.return_value = "Agent output"
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result = agent(openai_api_key, objective)
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assert result == "Agent output"
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@ -1,59 +0,0 @@
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import unittest
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from unittest.mock import patch, Mock
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from apps.discord import (
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Bot,
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) # Replace 'Bot' with the name of the file containing your bot's code.
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class TestBot(unittest.TestCase):
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def setUp(self):
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self.llm_mock = Mock()
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self.agent_mock = Mock()
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self.bot = Bot(agent=self.agent_mock, llm=self.llm_mock)
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@patch("Bot.load_dotenv") # Mocking the `load_dotenv` function call.
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def test_initialization(self, mock_load_dotenv):
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self.assertIsNotNone(self.bot.bot)
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self.assertEqual(self.bot.agent, self.agent_mock)
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self.assertEqual(self.bot.llm, self.llm_mock)
|
||||
mock_load_dotenv.assert_called_once()
|
||||
|
||||
@patch("Bot.commands.bot")
|
||||
def test_greet(self, mock_bot):
|
||||
ctx_mock = Mock()
|
||||
ctx_mock.author.name = "TestUser"
|
||||
self.bot.bot.clear()
|
||||
self.bot.bot.greet(ctx_mock)
|
||||
ctx_mock.send.assert_called_with("hello, TestUser!")
|
||||
|
||||
# Similarly, you can add tests for other commands.
|
||||
|
||||
@patch("Bot.commands.bot")
|
||||
def test_help_me(self, mock_bot):
|
||||
ctx_mock = Mock()
|
||||
self.bot.bot.clear()
|
||||
self.bot.bot.help_me(ctx_mock)
|
||||
# Verify the help text was sent. You can check for a substring to make it shorter.
|
||||
ctx_mock.send.assert_called()
|
||||
|
||||
@patch("Bot.commands.bot")
|
||||
def test_on_command_error(self, mock_bot):
|
||||
ctx_mock = Mock()
|
||||
error_mock = Mock()
|
||||
error_mock.__class__.__name__ = "CommandNotFound"
|
||||
self.bot.bot.clear()
|
||||
self.bot.bot.on_command_error(ctx_mock, error_mock)
|
||||
ctx_mock.send.assert_called_with("that command does not exist!")
|
||||
|
||||
def test_add_command(self):
|
||||
def sample_function(*args):
|
||||
return "Test Response"
|
||||
|
||||
self.bot.add_command("test_command", sample_function)
|
||||
# Here, you can further test by triggering the command and checking the response.
|
||||
|
||||
# You can add more tests for other commands and functionalities.
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue