refactor: reorganize tools and enhance functionality; remove .env-example

fine-tuned some system prompt

.env-example

@@ -1,12 +0,0 @@
-# qBittorrent API Configuration
-QBIT_HOST=http://localhost:8080
-QBIT_USERNAME=admin
-QBIT_PASSWORD=password
-
-# OpenAI API Key (required for the LangChain agent)
-OPENAI_API_KEY=sk-proj-Rs93xxxxxxxxxxxxxxxxxxxxxUnStmeSHj_gUiEfbGzaFeZf0rgdaQzllQmvcMy6o-SywA
-
-# DuckDuckGo Search Configuration
-DUCKDUCKGO_ENABLED=true
-DUCKDUCKGO_MAX_RESULTS=5
-OMDB_API_KEY=3b6bc268

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
+cmNoIEdyb3VwMRUwEwYDVQQDEwxJU1JHIFJvb3QgWDEwHhcNMTUwNjA0MTEwNDM4
+WhcNMzUwNjA0MTEwNDM4WjBPMQswCQYDVQQGEwJVUzEpMCcGA1UEChMgSW50ZXJu
+ZXQgU2VjdXJpdHkgUmVzZWFyY2ggR3JvdXAxFTATBgNVBAMTDElTUkcgUm9vdCBY
+MTCCAiIwDQYJKoZIhvcNAQEBBQADggIPADCCAgoCggIBAK3oJHP0FDfzm54rVygc
+h77ct984kIxuPOZXoHj3dcKi/vVqbvYATyjb3miGbESTtrFj/RQSa78f0uoxmyF+
+0TM8ukj13Xnfs7j/EvEhmkvBioZxaUpmZmyPfjxwv60pIgbz5MDmgK7iS4+3mX6U
+A5/TR5d8mUgjU+g4rk8Kb4Mu0UlXjIB0ttov0DiNewNwIRt18jA8+o+u3dpjq+sW
+T8KOEUt+zwvo/7V3LvSye0rgTBIlDHCNAymg4VMk7BPZ7hm/ELNKjD+Jo2FR3qyH
+B5T0Y3HsLuJvW5iB4YlcNHlsdu87kGJ55tukmi8mxdAQ4Q7e2RCOFvu396j3x+UC
+B5iPNgiV5+I3lg02dZ77DnKxHZu8A/lJBdiB3QW0KtZB6awBdpUKD9jf1b0SHzUv
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+rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV
+HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq
+hkiG9w0BAQsFAAOCAgEAVR9YqbyyqFDQDLHYGmkgJykIrGF1XIpu+ILlaS/V9lZL
+ubhzEFnTIZd+50xx+7LSYK05qAvqFyFWhfFQDlnrzuBZ6brJFe+GnY+EgPbk6ZGQ
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

README.md

@@ -42,11 +42,17 @@ Or use the CLI interface by uncommenting the `cli_main()` line in `main.py`.
 
 ## Tools
 
-The agent includes several tools:
+The tools are organized into separate files for better modularity:
+
+### qBittorrent Tools (`tools/qbit.py`)
 - `get_downloads_list`: Get information about current downloads
 - `qbittorrent_search`: Search for torrents using qBittorrent's search functionality
 - `download_torrent`: Add a torrent to the download queue
-- `ForcedDuckDuckGoSearch`: Search for information about media content
+
+### Search Tools (`tools/search.py`)
+- `duckduckgo_search`: Search the web using DuckDuckGo
+- `media_info_search`: Find detailed information about movies, TV shows, or other media content
+- `movies_advice_search`: Get recommendations or advice about movies
 
 ## License
 

main.py

@@ -1,41 +1,44 @@
 import os
 from langchain.agents import Tool, initialize_agent, AgentType
 from dotenv import load_dotenv
-from tools import DownloadListTool, QBitSearchTool, DownloadTorrentTool
+from tools.qbit import QbitDownloadListTool, QbitSearchTool, QbitDownloadTorrentTool
-from langchain_community.tools import DuckDuckGoSearchRun
+from tools.search import MediaInfoSearchTool, MoviesAdviceSearchTool
 from langchain.memory import ConversationBufferMemory
 from langchain.chat_models import init_chat_model
 import gradio as gr
+import datetime
 # Load environment variables
 load_dotenv()
 
 def create_agent():
     # Initialize the language model
-
+    #llm = init_chat_model("gpt-4.1-mini", model_provider="openai")
-
+    llm = init_chat_model("qwen2.5-coder:14b", model_provider="ollama", temperature=0)
-    llm = init_chat_model("gpt-4o-mini", model_provider="openai")
+    # Create system message with current time and other info
+    current_time = datetime.datetime.now()
 
     # Initialize memory
-    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    memory = ConversationBufferMemory(
-    
+        memory_key="chat_history", 
-    # Initialize search tool
+        return_messages=True,
-    search_tool = DuckDuckGoSearchRun()
+        human_prefix="User", 
-    
+        ai_prefix="Assistant"
-    # Function to force DuckDuckGo for specific search types
+    )
-    def forced_duckduckgo_search(query: str) -> str:
+    memory.chat_memory.add_message(f"SYSTEM: today is {current_time.strftime('%Y-%m-%d')}")
-        """Use DuckDuckGo to search for specific information."""
+    memory.chat_memory.add_message(f"SYSTEM:from now on when User ask for movie or tv series suggestion reply with a numbered markdown list with a brief description of each title")
-        return search_tool.run(query)
+    memory.chat_memory.add_message(f"SYSTEM:from now on when list torrents show seeds number and MAGNET LINK (USING A MARKDOWN LINK WITH TEXT 'Magnet link 🧲')")
-    
+    memory.chat_memory.add_message(f"SYSTEM:from now on, when show downloads list show a clean and nice markdown format with name and the most important information, \
+                                   also add near it an emoji of progress of the download to represent how complete it is and set a coloured bullet emoji after status of torrent status, for example blue for uploading, green for downloading, \
+                                   red for error, yellow for paused, and grey for completed")
+    memory.chat_memory.add_message(f"SYSTEM: from now on, when user ask for downolad NEVER start a qbittorrent download if user hasn't viewed the list of torrents first, \
+                                   and choosed one of them")
     # Initialize tools
     tools = [
-        DownloadListTool(),
+        QbitDownloadListTool(),
-        QBitSearchTool(),
+        QbitSearchTool(),
-        DownloadTorrentTool(),
+        QbitDownloadTorrentTool(),
-        Tool(
+        MoviesAdviceSearchTool(),
-            name="ForcedDuckDuckGoSearch",
+        MediaInfoSearchTool()
-            func=forced_duckduckgo_search,
-            description="Use this tool when you need to find specific information about movies, TV shows. Input should be a search query including the keyword 'imdb'.",
-        )
     ]
     
     # Initialize the agent with memory
@@ -65,20 +68,23 @@ def main():
     print("Starting qBittorrent AI Agent...")
     
     # Create Gradio interface
-    with gr.Blocks(title="qBittorrent AI Agent") as interface:
+    with gr.Blocks(title="qbit-agent") as interface:
-        gr.Markdown("# qBittorrent AI Agent")
+        gr.Markdown("# qbit-agent")
-        gr.Markdown("Ask questions about downloads, search for content, or get recommendations.")
+        gr.Markdown("### Made by Matteo with hate and piracy 💀")
+        gr.Markdown("Ask about downloads, search for content (and torrent), or get recommendations.")
         
         chatbot = gr.ChatInterface(
             process_query,
             examples=["Find me the latest sci-fi movies", 
                      "What are the top TV shows from 2023?",
-                     "Download Interstellar in 1080p"],
+                     "Download Interstellar in 1080p",
-            title="qBittorrent Assistant"
+                     "Show me my current downloads",
+                     "What is The Matrix",
+                     "Get me a list of horror movies"],
         )
     
     # Launch the interface
-    interface.launch(share=False)
+    interface.launch(share=True)
 
 def cli_main():
     print("Starting qBittorrent AI Agent in CLI mode...")

requirements.txt

@@ -4,3 +4,4 @@ requests>=2.28.2
 python-dotenv>=1.0.0
 gradio>=3.0.0
 langchain_community>=0.0.1
+langchain-openai

test.py

@@ -0,0 +1,80 @@
+import gradio as gr
+from langchain_community.llms import Ollama
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.schema import HumanMessage, AIMessage, SystemMessage
+from typing import List, Dict, Any
+from langchain.chat_models import init_chat_model
+
+# Initialize Ollama model with streaming capability
+ollama_model_name = "gemma3"  # Change to your preferred model
+llm = init_chat_model("qwen2.5-coder:14b", model_provider="ollama", temperature=0,
+    streaming=True,  # Enable streaming
+)
+
+# Store conversation history
+conversation_history = []
+
+def add_message_to_history(role: str, content: str):
+    """Add a message to the conversation history."""
+    if role == "human":
+        conversation_history.append(HumanMessage(content=content))
+    elif role == "ai":
+        conversation_history.append(AIMessage(content=content))
+    elif role == "system":
+        conversation_history.append(SystemMessage(content=content))
+    return conversation_history
+
+# Initialize with a system message
+add_message_to_history("system", "You are a helpful, friendly AI assistant.")
+
+def stream_response(message: str, history: List[List[str]]):
+    """Process user message and stream the response."""
+    # Add user message to history
+    add_message_to_history("human", message)
+    
+    # Create a generator to stream responses
+    response = ""
+    for chunk in llm.stream([m for m in conversation_history]):
+        # Extract content from AIMessageChunk
+        if hasattr(chunk, 'content'):
+            chunk_content = chunk.content
+        else:
+            chunk_content = str(chunk)
+            
+        response += chunk_content
+        yield response
+    
+    # Add AI response to history when complete
+    add_message_to_history("ai", response)
+
+# Create Gradio interface with streaming
+with gr.Blocks() as demo:
+    gr.Markdown("# Ollama Chatbot with Streaming")
+    
+    chatbot = gr.Chatbot(height=500)
+    msg = gr.Textbox(placeholder="Type your message here...", container=False)
+    clear = gr.Button("Clear Chat")
+    
+    def user(message, history):
+        # Return immediately for the user message
+        return "", history + [[message, None]]
+    
+    def bot(history):
+        # Process the last user message
+        user_message = history[-1][0]
+        history[-1][1] = ""  # Initialize bot's response
+        
+        for response in stream_response(user_message, history):
+            history[-1][1] = response
+            yield history
+    
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+if __name__ == "__main__":
+    # Launch the Gradio interface
+    demo.queue()
+    demo.launch(share=False)  # Set share=True to create a public link

tools/qbit.py

@@ -3,8 +3,9 @@ from langchain.tools.base import BaseTool
 from langchain.callbacks.manager import CallbackManagerForToolRun
 import requests
 from typing import Optional
+from langchain_community.tools import DuckDuckGoSearchRun
 
-class DownloadListTool(BaseTool):
+class QbitDownloadListTool(BaseTool):
     name: str = "get_downloads_list"
     description: str = '''Useful for getting a list of current downloads from the qBittorrent API and
     information about them. The response will include the name, size, and status of each download.
@@ -56,11 +57,11 @@ class DownloadListTool(BaseTool):
         except Exception as e:
             return f"Error getting downloads list: {str(e)}"
 
-class QBitSearchTool(BaseTool):
+class QbitSearchTool(BaseTool):
     name: str = "qbittorrent_search"
     description: str = '''Useful for searching torrents using qBittorrent's search functionality. 
     Input should be a search query for content the user wants to find. 
-    The tool will return a list of matching torrents with their details including magnet links.
+    The tool will return a list of matching torrents ordered by the number of seeders (highest first).
     '''
     
     def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
@@ -83,7 +84,7 @@ class QBitSearchTool(BaseTool):
             
             # Start a search
             start_search_url = f"{QBIT_HOST}/api/v2/search/start"
-            search_data = {"pattern": query, "plugins": "all", "category": "all"}
+            search_data = {"pattern": query, "plugins": "all", "category": "all", "limit": 5, "sort": "seeders", "order": "desc"}
             
             search_response = session.post(start_search_url, data=search_data)
             
@@ -97,7 +98,7 @@ class QBitSearchTool(BaseTool):
             
             # Wait for results (simple implementation, can be improved)
             import time
-            max_wait = 10  # seconds
+            max_wait = 5  # seconds
             wait_time = 0
             step = 1
             
@@ -119,7 +120,7 @@ class QBitSearchTool(BaseTool):
             
             # Get search results
             results_url = f"{QBIT_HOST}/api/v2/search/results"
-            results_params = {"id": search_id, "limit": 10}  # Limiting to top 10 results
+            results_params = {"id": search_id, "limit": 5}  # Increased limit to find more seeders
             
             results_response = session.get(results_url, params=results_params)
             
@@ -134,17 +135,21 @@ class QBitSearchTool(BaseTool):
             stop_params = {"id": search_id}
             session.post(stop_url, params=stop_params)
         
+            print(results)
+            # Limit to top 10 results after sorting
+            results = results[:10]
+            
             # Format the response
             if not results:
                 return f"No results found for '{query}'."
             
-            response = f"Search results for '{query}':\n\n"
+            response = f"Search results for '{query}' (sorted by seeders):\n\n"
             
             for i, result in enumerate(results, 1):
                 name = result.get("fileName", "Unknown")
                 size = result.get("fileSize", "Unknown")
-                seeds = result.get("seeders", 0)
+                seeds = result.get("nbSeeders", 0)
-                leech = result.get("leechers", 0)
+                leech = result.get("nbLeechers", 0)
                 magnet = result.get("fileUrl", "")
                 
                 # Convert size to human-readable format
@@ -166,7 +171,7 @@ class QBitSearchTool(BaseTool):
         except Exception as e:
             return f"Error searching torrents: {str(e)}"
 
-class DownloadTorrentTool(BaseTool):
+class QbitDownloadTorrentTool(BaseTool):
     name: str = "download_torrent"
     description: str = '''Useful for starting a new torrent download in qBittorrent. 
     Input should be a magnet link or a torrent URL that the user wants to download.

tools/search.py

@@ -0,0 +1,36 @@
+import os
+from langchain.tools.base import BaseTool
+from langchain.callbacks.manager import CallbackManagerForToolRun
+import requests
+from typing import Optional
+from langchain_community.tools import DuckDuckGoSearchRun
+
+
+class MediaInfoSearchTool(BaseTool):
+    name: str = "duckduckgo_search"
+    description: str = '''Useful for searching the web using DuckDuckGo for information about \
+        movies and TV shows, actors and  directors. To be used only on imdb.com adding relative keyword imdb to query to filter results.
+    Input should be a search query, and the tool will return relevant results.'''
+
+    def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
+        """Perform a DuckDuckGo search."""
+        try:
+            search_tool = DuckDuckGoSearchRun()
+            return search_tool.run(query)
+        except Exception as e:
+            return f"Error performing DuckDuckGo search: {str(e)}"
+
+class MoviesAdviceSearchTool(BaseTool):
+    name: str = "movies_advice_search"
+    description: str = '''Useful for searching the web using DuckDuckGo for movie recommendations and similar content to a given title or plot.
+    prefer searching on one (on your preference) known trustworthy sites. add relative keyword (like "reddit" for reddit.com for example) to query to filter results only on that site.
+    Input should be a search query, and the tool will return relevant results.'''
+
+    def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
+        """Perform a DuckDuckGo search."""
+        try:
+            search_tool = DuckDuckGoSearchRun()
+            search_results = search_tool.run(query)
+            return search_results
+        except Exception as e:
+            return f"Error performing DuckDuckGo search: {str(e)}"