File size: 9,722 Bytes
434b328 2f85c93 434b328 8875451 9d9d55a 434b328 980918c 434b328 6900003 2f85c93 6900003 434b328 6900003 434b328 9d9d55a 434b328 60b4d0f 434b328 d648fe6 434b328 980918c d648fe6 980918c 434b328 60b4d0f 434b328 60b4d0f 434b328 980918c 60b4d0f d648fe6 60b4d0f 434b328 980918c 434b328 60b4d0f 434b328 60b4d0f 434b328 980918c 434b328 60b4d0f fde43e7 60b4d0f fde43e7 60b4d0f 434b328 980918c 434b328 980918c 434b328 60b4d0f 980918c 434b328 980918c 434b328 980918c 434b328 9d9d55a 434b328 980918c 434b328 980918c 434b328 980918c 434b328 60b4d0f 9d9d55a 980918c 344c9c4 9d9d55a 434b328 980918c 434b328 d648fe6 8875451 434b328 980918c 434b328 980918c 434b328 d648fe6 980918c 434b328 980918c 434b328 980918c 434b328 60b4d0f 9d9d55a 8875451 d648fe6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 |
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.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
logger = logging.getLogger(__name__)
handler = logging.StreamHandler(sys.stdout)
# handler.setLevel(logging.DEBUG)
logger.addHandler(handler)
class GeminiManager:
def __init__(self, toolsLoader: ToolManager = None,
system_prompt_file="./src/models/system3.prompt",
gemini_model="gemini-2.5-pro-exp-03-25",
local_only=False, allow_tool_creation=True,
cloud_only=False, use_economy=True):
load_dotenv()
self.toolsLoader: ToolManager = toolsLoader
if not toolsLoader:
self.toolsLoader: ToolManager = ToolManager()
self.local_only = local_only
self.allow_tool_creation = allow_tool_creation
self.cloud_only = cloud_only
self.use_economy = use_economy
self.API_KEY = os.getenv("GEMINI_KEY")
self.client = genai.Client(api_key=self.API_KEY)
self.toolsLoader.load_tools()
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 = []
def generate_response(self, messages):
tools = self.toolsLoader.getTools()
return self.client.models.generate_content(
model=self.model_name,
contents=messages,
config=types.GenerateContentConfig(
system_instruction=self.system_prompt,
temperature=0.2,
tools=tools,
),
)
def handle_tool_calls(self, response):
parts = []
i = 0
for function_call in response.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 `{function_call.args}`\n"
yield {
"role": "assistant",
"content": thinking,
"metadata": {
"title": title,
"id": i,
"status": "pending",
}
}
try:
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{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:
self.toolsLoader.load_tools()
except Exception as e:
logger.info(f"Error loading tools: {e}. Deleting the tool.")
yield {
"role": "assistant",
"content": f"Error loading tools: {e}. Deleting the tool.\n",
"metadata": {
"title": "Trying to load the newly created tool",
}
}
# 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
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):
role = "model"
parts = [types.Part.from_text(text=message.get("content", ""))]
match message.get("role"):
case "user":
role = "user"
case "memories":
role = "user"
parts = [types.Part.from_text(text="User memories: "+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"
formatted_history.append(types.Content(
role=role,
parts=parts
))
return formatted_history
def get_k_memories(self, query, k=5, threshold=0.0):
memories = MemoryManager().get_memories()
if len(memories) == 0:
return []
top_k = min(k, len(memories))
# Semantic Retrieval with GPU
device = 'cuda' if torch.cuda.is_available() else '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):
print(memories[idx], f"(Score: {score:.4f})")
if score >= threshold:
results.append(memories[idx])
return results
def run(self, messages):
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: {memories}",
"metadata": {"title": "Memories"}
})
yield messages
yield from self.invoke_manager(messages)
def invoke_manager(self, messages):
chat_history = self.format_chat_history(messages)
logger.debug(f"Chat history: {chat_history}")
try:
response = suppress_output(self.generate_response)(chat_history)
except Exception as e:
logger.debug(f"Error generating response: {e}")
messages.append({
"role": "assistant",
"content": f"Error generating response: {e}"
})
logger.error(f"Error generating response: {e}")
yield messages
return
logger.debug(f"Response: {response}")
if (not response.text and not response.function_calls):
messages.append({
"role": "assistant",
"content": "No response from the model.",
"metadata": {"title": "No response from the model."}
})
# Attach the llm response to the messages
if response.text is not None and response.text != "":
messages.append({
"role": "assistant",
"content": response.text
})
yield messages
# Attach the function call response to the messages
if response.candidates[0].content and response.candidates[0].content.parts:
# messages.append(response.candidates[0].content)
messages.append({
"role": "function_call",
"content": repr(response.candidates[0].content),
})
# Invoke the function calls if any and attach the response to the messages
if response.function_calls:
for call in self.handle_tool_calls(response):
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)
return
yield messages
|