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from enum import Enum, auto
from typing import List
from google import genai
from google.genai import types
from google.genai.types import *
import os
from dotenv import load_dotenv
import sys
from src.manager.agent_manager import AgentManager
from src.manager.budget_manager import BudgetManager
from src.manager.tool_manager import ToolManager
from src.manager.utils.suppress_outputs import suppress_output
import logging
import gradio as gr
from sentence_transformers import SentenceTransformer
import torch
from src.tools.default_tools.memory_manager import MemoryManager
from pathlib import Path
from google.genai.errors import APIError
import backoff
import mimetypes
import json
import traceback
logger = logging.getLogger(__name__)
handler = logging.StreamHandler(sys.stdout)
# handler.setLevel(logging.DEBUG)
logger.addHandler(handler)
class Mode(Enum):
ENABLE_AGENT_CREATION = auto()
ENABLE_LOCAL_AGENTS = auto()
ENABLE_CLOUD_AGENTS = auto()
ENABLE_TOOL_CREATION = auto()
ENABLE_TOOL_INVOCATION = auto()
ENABLE_RESOURCE_BUDGET = auto()
ENABLE_ECONOMY_BUDGET = auto()
ENABLE_MEMORY = auto()
def format_tool_response(response, indent=2):
return json.dumps(response, indent=indent, ensure_ascii=False)
class GeminiManager:
def __init__(self, system_prompt_file="./src/models/system6.prompt",
gemini_model="gemini-2.5-pro-exp-03-25",
modes: List[Mode] = []):
self.input_tokens = 0
self.output_tokens = 0
load_dotenv()
self.budget_manager = BudgetManager()
self.toolsLoader: ToolManager = ToolManager()
self.agentManager: AgentManager = AgentManager()
self.API_KEY = os.getenv("GEMINI_KEY")
self.client = genai.Client(api_key=self.API_KEY)
self.model_name = gemini_model
self.memory_manager = MemoryManager()
with open(system_prompt_file, 'r', encoding="utf8") as f:
self.system_prompt = f.read()
self.messages = []
self.set_modes(modes)
self.safety_settings = [
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_NONE",
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_NONE",
},
{
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"threshold": "BLOCK_NONE",
},
{
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
"threshold": "BLOCK_NONE",
},
]
def get_current_modes(self):
return [mode.name for mode in self.modes]
def set_modes(self, modes: List[Mode]):
self.modes = modes
self.budget_manager.set_resource_budget_status(
self.check_mode(Mode.ENABLE_RESOURCE_BUDGET))
self.budget_manager.set_expense_budget_status(
self.check_mode(Mode.ENABLE_ECONOMY_BUDGET))
self.toolsLoader.set_creation_mode(
self.check_mode(Mode.ENABLE_TOOL_CREATION))
self.toolsLoader.set_invocation_mode(
self.check_mode(Mode.ENABLE_TOOL_INVOCATION))
self.agentManager.set_creation_mode(
self.check_mode(Mode.ENABLE_AGENT_CREATION))
self.agentManager.set_local_invocation_mode(
self.check_mode(Mode.ENABLE_LOCAL_AGENTS))
self.agentManager.set_cloud_invocation_mode(
self.check_mode(Mode.ENABLE_CLOUD_AGENTS))
def check_mode(self, mode: Mode):
return mode in self.modes
@backoff.on_exception(backoff.expo,
APIError,
max_tries=3,
jitter=None)
def generate_response(self, messages):
tools = self.toolsLoader.getTools()
response = self.client.models.count_tokens(
model=self.model_name,
contents=messages,
)
self.budget_manager.add_to_expense_budget(
response.total_tokens * 0.10/1000000 # Assuming $0.10 per million tokens
)
self.input_tokens += response.total_tokens
return self.client.models.generate_content_stream(
model=self.model_name,
contents=messages,
config=types.GenerateContentConfig(
system_instruction=self.system_prompt,
temperature=0.2,
tools=tools,
safety_settings=self.safety_settings,
),
)
def handle_tool_calls(self, function_calls):
parts = []
i = 0
for function_call in function_calls:
title = ""
thinking = ""
toolResponse = None
logger.info(
f"Function Name: {function_call.name}, Arguments: {function_call.args}")
title = f"Invoking `{function_call.name}` with \n```json\n{format_tool_response(function_call.args)}\n```\n"
yield {
"role": "assistant",
"content": thinking,
"metadata": {
"title": title,
"id": i,
"status": "pending",
}
}
try:
self.input_tokens += len(repr(function_call).split())
toolResponse = self.toolsLoader.runTool(
function_call.name, function_call.args)
except Exception as e:
logger.warning(f"Error running tool: {e}")
toolResponse = {
"status": "error",
"message": f"Tool `{function_call.name}` failed to run.",
"output": str(e),
}
logger.debug(f"Tool Response: {toolResponse}")
thinking += f"Tool responded with \n```json\n{format_tool_response(toolResponse)}\n```\n"
yield {
"role": "assistant",
"content": thinking,
"metadata": {
"title": title,
"id": i,
"status": "done",
}
}
tool_content = types.Part.from_function_response(
name=function_call.name,
response={"result": toolResponse})
try:
if function_call.name == "ToolCreator" or function_call.name == "ToolDeletor":
self.toolsLoader.load_tools()
except Exception as e:
logger.info(
f"Error loading tools: {str(e)}. Deleting the tool.")
yield {
"role": "assistant",
"content": f"Error loading tools: {str(e)}. Deleting the tool.\n",
"metadata": {
"title": "Trying to load the newly created tool",
"id": i,
"status": "done",
}
}
# delete the created tool
self.toolsLoader.delete_tool(
toolResponse['output']['tool_name'], toolResponse['output']['tool_file_path'])
tool_content = types.Part.from_function_response(
name=function_call.name,
response={"result": f"{function_call.name} with {function_call.args} doesn't follow the required format, please read the other tool implementations for reference." + str(e)})
parts.append(tool_content)
i += 1
self.output_tokens += len(repr(parts).split())
yield {
"role": "tool",
"content": repr(types.Content(
role='model' if self.model_name == "gemini-2.5-pro-exp-03-25" else 'tool',
parts=parts
))
}
def format_chat_history(self, messages=[]):
formatted_history = []
for message in messages:
# Skip thinking messages (messages with metadata)
if not ((message.get("role") == "assistant" and "metadata" in message
and message["metadata"] is not None)):
role = "model"
match message.get("role"):
case "user":
role = "user"
if isinstance(message["content"], tuple):
path = message["content"][0]
try:
image_bytes = open(path, "rb").read()
mime_type, _ = mimetypes.guess_type(path)
parts = [
types.Part.from_bytes(
data=image_bytes,
mime_type=mime_type
),
]
except Exception as e:
logger.error(f"Error uploading file: {e}")
parts = [types.Part.from_text(
text="Error uploading file: "+str(e))]
formatted_history.append(
types.Content(
role=role,
parts=parts
))
continue
else:
parts = [types.Part.from_text(
text=message.get("content", ""))]
case "memories":
role = "user"
parts = [types.Part.from_text(
text="Here are the relevant memories for the user's query: "+message.get("content", ""))]
case "tool":
role = "tool"
formatted_history.append(
eval(message.get("content", "")))
continue
case "function_call":
role = "model"
formatted_history.append(
eval(message.get("content", "")))
continue
case _:
role = "model"
content = message.get("content", "")
if content.strip() == "":
print("Empty message received: ", message)
continue
parts = [types.Part.from_text(
text=content)]
formatted_history.append(types.Content(
role=role,
parts=parts
))
return formatted_history
def get_k_memories(self, query, k=5, threshold=0.0):
raw_memories = MemoryManager().get_memories()
memories = []
for i in range(len(raw_memories)):
memories.append(raw_memories[i]['memory'])
if len(memories) == 0:
return []
top_k = min(k, len(memories))
# Semantic Retrieval with GPU
if torch.cuda.is_available():
device = 'cuda'
elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
device = 'mps'
else:
device = 'cpu'
model = SentenceTransformer('all-MiniLM-L6-v2', device=device)
doc_embeddings = model.encode(
memories, convert_to_tensor=True, device=device)
query_embedding = model.encode(
query, convert_to_tensor=True, device=device)
similarity_scores = model.similarity(
query_embedding, doc_embeddings)[0]
scores, indices = torch.topk(similarity_scores, k=top_k)
results = []
for score, idx in zip(scores, indices):
if score >= threshold:
results.append(raw_memories[idx.item()])
return results
def run(self, messages):
try:
if self.check_mode(Mode.ENABLE_MEMORY) and len(messages) > 0:
memories = self.get_k_memories(
messages[-1]['content'], k=5, threshold=0.1)
if len(memories) > 0:
messages.append({
"role": "memories",
"content": f"{memories}",
})
messages.append({
"role": "assistant",
"content": f"Memories: \n```json\n{format_tool_response(memories)}\n```\n",
"metadata": {"title": "Memories"}
})
yield messages
except Exception as e:
pass
yield from self.invoke_manager(messages)
print("Tokens used: Input: {}, Output: {}".format(
self.input_tokens, self.output_tokens))
def invoke_manager(self, messages):
chat_history = self.format_chat_history(messages)
logger.debug(f"Chat history: {chat_history}")
try:
response_stream = self.generate_response(chat_history)
full_text = "" # Accumulate the text from the stream
function_calls = []
function_call_requests = []
for chunk in response_stream:
if chunk.text:
full_text += chunk.text
if full_text.strip() != "":
yield messages + [{
"role": "assistant",
"content": full_text
}]
else:
print("Empty chunk received")
print(chunk)
for candidate in chunk.candidates:
if candidate.content and candidate.content.parts:
has_function_call = False
for part in candidate.content.parts:
if part.function_call:
has_function_call = True
function_calls.append(part.function_call)
if has_function_call:
function_call_requests.append({
"role": "function_call",
"content": repr(candidate.content),
})
if full_text.strip() != "":
messages.append({
"role": "assistant",
"content": full_text,
})
self.output_tokens += len(full_text.split())
self.budget_manager.add_to_expense_budget(
len(full_text.split()) * 0.40/1000000 # Assuming $0.40 per million tokens
)
if function_call_requests:
messages = messages + function_call_requests
yield messages
except Exception as e:
traceback.print_exc(file=sys.stdout)
print(messages)
print(chat_history)
messages.append({
"role": "assistant",
"content": f"Error generating response: {str(e)}",
"metadata": {
"title": "Error generating response",
"id": 0,
"status": "done"
}
})
logger.error(f"Error generating response{e}")
yield messages
return messages
# Check if any text was received
if len(full_text.strip()) == 0 and len(function_calls) == 0:
messages.append({
"role": "assistant",
"content": "No response from the model.",
"metadata": {"title": "No response from the model."}
})
if function_calls and len(function_calls) > 0:
for call in self.handle_tool_calls(function_calls):
yield messages + [call]
if (call.get("role") == "tool"
or (call.get("role") == "assistant" and call.get("metadata", {}).get("status") == "done")):
messages.append(call)
yield from self.invoke_manager(messages)
else:
yield messages
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