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import mimetypes
import base64
import yaml
from typing import TypedDict, Annotated
from dotenv import load_dotenv
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage
from langgraph.prebuilt import ToolNode
from langgraph.graph import START, StateGraph
from langgraph.prebuilt import tools_condition
from langchain_core.messages.utils import (
trim_messages,
count_tokens_approximately
)
# Import our custom tools from their modules
from tools import webpage_reader_tool, python_repl_tool, transcribe_youtube_video_tool, wikipedia_query_tool, web_search_tool, read_excel_csv, arxiv_query_tool
load_dotenv()
class FinalAgent:
def __init__(self, model_type="GOOGLE", system_prompt_path="system_prompt.yaml", use_memory=False):
"""
Args: model_type "GOOGLE" or "HUGGINGFACE" or "OLLAMA"
"""
with open(system_prompt_path, 'r') as stream:
prompt_templates = yaml.safe_load(stream)
self.model_type = model_type
if model_type == "HUGGINGFACE":
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
# Initialize the Hugging Face model
# Generate the chat interface, including the tools
llm = HuggingFaceEndpoint(
repo_id="Qwen/Qwen2.5-Coder-32B-Instruct"
)
chat = ChatHuggingFace(llm=llm, verbose=True)
elif model_type == "OLLAMA":
from langchain_ollama import ChatOllama
chat = ChatOllama(model = "qwen3:8b")
elif model_type == "GOOGLE":
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.rate_limiters import InMemoryRateLimiter
rate_limiter = InMemoryRateLimiter(
# Max allowed rate per free API: 10 requests per minute, but we use 6 to avoid hitting the limit on subsquent answers.
requests_per_second=6/60,
# Wake up every 100 ms to check whether allowed to make a request,
check_every_n_seconds=0.1,
max_bucket_size=10, # Controls the maximum burst size.
)
chat = ChatGoogleGenerativeAI(model="gemini-2.5-flash", rate_limiter=rate_limiter)
else:
raise ValueError(f'Model provider can be only one between GOOGLE, OLLAMA or HUGGINGFACE, received {model_type}')
tools = [webpage_reader_tool,
transcribe_youtube_video_tool,
web_search_tool,
wikipedia_query_tool,
arxiv_query_tool,
read_excel_csv,
python_repl_tool,]
chat_with_tools = chat.bind_tools(tools)
class AgentState(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
def assistant(state: AgentState):
messages = trim_messages(
state["messages"],
strategy="last",
token_counter=count_tokens_approximately,
max_tokens=1e6 if self.model_type == "GOOGLE" else 126000,
start_on="human",
end_on=("human", "tool"),
)
return {
"messages": [chat_with_tools.invoke([SystemMessage(content=prompt_templates['system_prompt'])] + messages)],
}
builder = StateGraph(AgentState)
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
builder.add_edge(START, "assistant")
builder.add_conditional_edges("assistant", tools_condition)
builder.add_edge("tools", "assistant")
if use_memory:
checkpointer = InMemorySaver()
self.agent = builder.compile(checkpointer=checkpointer)
else:
checkpointer = None
self.agent = builder.compile()
print("FinalAgent initialized.")
def clear_memory(self, thread_id: str) -> None:
""" Clear the memory for a given thread_id. """
memory = self.agent.checkpointer
if memory is None:
return
try:
# If it's an InMemorySaver (which MemorySaver is an alias for),
# we can directly clear the storage and writes
if hasattr(memory, 'storage') and hasattr(memory, 'writes'):
# Clear all checkpoints for this thread_id (all namespaces)
memory.storage.pop(thread_id, None)
# Clear all writes for this thread_id (for all namespaces)
keys_to_remove = [key for key in memory.writes.keys() if key[0] == thread_id]
for key in keys_to_remove:
memory.writes.pop(key, None)
print(f"Memory cleared for thread_id: {thread_id}")
return
except Exception as e:
print(f"Error clearing InMemorySaver storage for thread_id {thread_id}: {e}")
def __call__(self, question: str, attached_file: dict, recursion_limit=9) -> str:
print(f"Agent received question (first 100 chars): {question[:100]}...")
if attached_file['name'] != "" and attached_file['content'] is not None:
mime_type, _ = mimetypes.guess_type(attached_file['name'])
if mime_type.startswith("image/") or mime_type.startswith("audio/") or mime_type.startswith("video/"):
# Image file - convert to base64
encoded_file = base64.b64encode(attached_file['content']).decode('utf-8')
#
if self.model_type == "GOOGLE":
question = [{"type": "text", "text": question},
{"type": "image" if mime_type.startswith("image/") else "media",
"source_type": "base64",
"data": encoded_file,
"mime_type": mime_type,},
]
else:
question = f"{question}\n\nAttached file extension:{attached_file['name'].split('.')[-1]} - Attached file base64 encoded: \n{encoded_file}"
elif mime_type.startswith("text/"):
# Text-based file (like .py, .txt, .json)
question = f"{question}\n\nAttached file extension:{attached_file['name'].split('.')[-1]} - Attached file content: \n{attached_file['content'].decode('utf-8')}"
else:
encoded_file = base64.b64encode(attached_file['content']).decode('utf-8')
print(f"Unsupported file {attached_file['name']} type: {mime_type}. Only images, audio, video, and text files are supported.")
question = f"{question}\n\nAttached file extension: {attached_file['name'].split('.')[-1]}. File path: {attached_file['path']} - Attached file base64 encoded:\n{encoded_file}"
if recursion_limit>0:
agent_reply = self.agent.invoke({"messages": [HumanMessage(content=question)]}, {"recursion_limit": recursion_limit})
else:
agent_reply = self.agent.invoke({"messages": [HumanMessage(content=question)]})
return str(agent_reply['messages'][-1].content) |