Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix: Shuttle AI Model tool #150

Merged
merged 1 commit into from
Aug 20, 2024
Merged

Fix: Shuttle AI Model tool #150

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
122 changes: 69 additions & 53 deletions swarmauri/standard/llms/concrete/ShuttleAIToolModel.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,14 +1,17 @@
import json
import logging
from typing import List, Literal, Dict, Any
import requests
import requests
from swarmauri.core.typing import SubclassUnion

from swarmauri.standard.messages.base.MessageBase import MessageBase
from swarmauri.standard.messages.concrete.AgentMessage import AgentMessage
from swarmauri.standard.messages.concrete.FunctionMessage import FunctionMessage
from swarmauri.standard.llms.base.LLMBase import LLMBase
from swarmauri.standard.schema_converters.concrete.ShuttleAISchemaConverter import ShuttleAISchemaConverter
from swarmauri.standard.schema_converters.concrete.ShuttleAISchemaConverter import (
ShuttleAISchemaConverter,
)


class ShuttleAIToolModel(LLMBase):
api_key: str
Expand All @@ -24,87 +27,100 @@ class ShuttleAIToolModel(LLMBase):
"wizardlm-2-8x22b",
"mistral-7b-instruct-v0.2",
"gemini-1.5-pro-latest",
"gemini-1.0-pro-latest"
"gemini-1.0-pro-latest",
]
name: str = "shuttle-2-turbo"
type: Literal['ShuttleAIToolModel'] = 'ShuttleAIToolModel'
type: Literal["ShuttleAIToolModel"] = "ShuttleAIToolModel"

def _schema_convert_tools(self, tools) -> List[Dict[str, Any]]:
return [ShuttleAISchemaConverter().convert(tools[tool]) for tool in tools]

def _format_messages(self, messages: List[SubclassUnion[MessageBase]]) -> List[Dict[str, str]]:
message_properties = ['content', 'role', 'name', 'tool_call_id', 'tool_calls']
formatted_messages = [message.model_dump(include=message_properties, exclude_none=True) for message in messages]
def _format_messages(
self, messages: List[SubclassUnion[MessageBase]]
) -> List[Dict[str, str]]:
message_properties = ["content", "role", "name", "tool_call_id", "tool_calls"]
formatted_messages = [
message.model_dump(include=message_properties, exclude_none=True)
for message in messages
]
return formatted_messages

def predict(self,
conversation,
toolkit=None,
tool_choice=None,
temperature=0.7,
max_tokens=1024,
top_p=1.0,
internet=True,
raw=False,
image=None,
citations=True,
tone='precise'):

def predict(
self,
conversation,
toolkit=None,
tool_choice=None,
temperature=0.7,
max_tokens=1024,
top_p=1.0,
internet=True,
raw=False,
image=None,
citations=True,
tone="precise",
):
formatted_messages = self._format_messages(conversation.history)

if toolkit and not tool_choice:
tool_choice = "auto"

url = "https://api.shuttleai.app/v1/chat/completions"
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}

formatted_messages = self._format_messages(conversation.history)
payload = {
"model": self.name,
"messages": formatted_messages,
"max_tokens": max_tokens,
"temperature": temperature,
"top_p": top_p,
"internet": internet,
"raw": raw,
"image": image,
"tool_choice": tool_choice,
formatted_messages = self._format_messages(conversation.history)

payload = {
"model": self.name,
"messages": formatted_messages,
"max_tokens": max_tokens,
"temperature": temperature,
"top_p": top_p,
"internet": internet,
# "raw": raw,
# "image": image,
"tool_choice": tool_choice,
"tools": self._schema_convert_tools(toolkit.tools),
}
}

if self.name in ['gpt-4-bing', 'gpt-4-turbo-bing']:
payload['tone'] = tone
payload['citations'] = citations
if self.name in ["gpt-4-bing", "gpt-4-turbo-bing"]:
payload["tone"] = tone
payload["citations"] = citations

agent_response = requests.request("POST", url, json=payload, headers=headers)
logging.info(agent_response.json())
agent_response = requests.request("POST", url, json=payload, headers=headers)
logging.info(agent_response.json())

try:
messages = [formatted_messages[-1], agent_response.json()['choices'][0]['message']['content']]
except Exception as error:
logging.warn(error)
tool_calls = agent_response.json()['choices'][0]['message'].get('tool_calls', None)
try:
messages = [
formatted_messages[-1],
agent_response.json()["choices"][0]["message"]["content"],
]
except Exception as error:
logging.warn(error)
tool_calls = agent_response.json()["choices"][0]["message"].get(
"tool_calls", None
)
if tool_calls:
for tool_call in tool_calls:
func_name = tool_call['function']['name']
func_name = tool_call["function"]["name"]
func_call = toolkit.get_tool_by_name(func_name)
func_args = json.loads(tool_call['function']['arguments'])
func_args = json.loads(tool_call["function"]["arguments"])
func_result = func_call(**func_args)
messages.append(
{
"tool_call_id": tool_call.id,
"tool_call_id": tool_call["id"],
"role": "tool",
"name": func_name,
"content": func_result,
}
)
logging.info(f'messages: {messages}')
logging.info(f"messages: {messages}")
logging.info(f"agent_response: {agent_response.json()}")
agent_message = AgentMessage(content=agent_response.json()['choices'][0]['message']['content'])
agent_message = AgentMessage(
content=agent_response.json()["choices"][0]["message"]["content"]
)
conversation.add_message(agent_message)
logging.info(f"conversation: {conversation}")
return conversation
return conversation