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| # | |
| # SPDX-FileCopyrightText: Hadad <[email protected]> | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # | |
| import json # Import JSON module for encoding and decoding JSON data | |
| from src.tools.deep_search import SearchTools # Import SearchTools class for deep search functionality | |
| # Asynchronous handler for deep search command | |
| async def deep_search_integration( | |
| input, # User input containing the /dp command and query | |
| new_history, # Conversation history in message format | |
| session_id, # Session ID for conversation context | |
| selected_model, # Selected AI model for generation | |
| jarvis, # AI backend function for generating responses | |
| mode, # Mode for AI response generation | |
| temperature, # Temperature parameter for AI | |
| top_k, # Top-k parameter for AI | |
| min_p, # Min-p parameter for AI | |
| top_p, # Top-p parameter for AI | |
| repetition_penalty # Repetition penalty for AI | |
| ): | |
| # Instantiate SearchTools class to enable deep search capabilities when requested by user | |
| search_tools = SearchTools() # Create SearchTools instance | |
| # Extract the search query after the '/dp' command prefix and strip whitespace | |
| search_query = input[3:].strip() # Get search query after /dp | |
| # If no search query is provided after the command, yield empty and exit early | |
| if not search_query: # Check if search query is empty | |
| yield [] # Yield empty list for missing search query | |
| return # Exit function | |
| try: # Try block for deep search | |
| # Perform an asynchronous deep search using SearchTools with the given query | |
| search_results = await search_tools.search(search_query) # Perform deep search | |
| # Serialize the search query and results (limited to first 5000 characters) into JSON string | |
| search_content = json.dumps({ | |
| "query": search_query, # Search query | |
| "search_results": search_results[:5000] # Search results limited to 5000 characters | |
| }) | |
| # Construct conversation history including deep search results and detailed instructions for summarization | |
| search_instructions = ( | |
| new_history | |
| + [ | |
| { | |
| "role": "system", | |
| "content": ( | |
| "Deep search results for query: '" + search_query + "':\n\n\n" + search_content + "\n\n\n" | |
| "Please analyze these search results and provide a comprehensive summary of the information.\n" | |
| "Identify the most relevant information related to the query.\n" | |
| "Format your response in a clear, structured way with appropriate headings and bullet points if needed.\n" | |
| "If the search results don't provide sufficient information, acknowledge this limitation.\n" | |
| "Please provide links or URLs from each of your search results.\n\n\n" | |
| "Use the same language as the previous user input or user request.\n" | |
| "For example, if the previous user input or user request is in Indonesian, explain in Indonesian.\n" | |
| "If it is in English, explain in English. This also applies to other languages.\n\n\n" | |
| ) | |
| } | |
| ] | |
| ) | |
| # Use async generator to process the deep search results and generate a summary response | |
| async for search_response in jarvis( | |
| session_id=session_id, # Session ID | |
| model=selected_model, # Selected model | |
| history=search_instructions, # Updated history with search results | |
| user_message=input, # User input | |
| mode=mode, # Mode for AI response | |
| temperature=temperature, # temperature parameter | |
| top_k=top_k, # top_k parameter | |
| min_p=min_p, # min_p parameter | |
| top_p=top_p, # top_p parameter | |
| repetition_penalty=repetition_penalty # repetition_penalty parameter | |
| ): | |
| yield [{"role": "tool", "content": search_response}] # Yield search summary in tool role | |
| return # Exit after handling deep search | |
| except Exception as e: # Exception handling for deep search failure | |
| # If deep search fails, let AI generate a contextual error message | |
| generation_failed = ( | |
| new_history | |
| + [ | |
| { | |
| "role": "system", | |
| "content": ( | |
| "Deep search failed for the user's query: '" + search_query + "'\n\n\n" | |
| "Please explain to the user that the search operation failed and suggest they try again later.\n" | |
| "Be helpful and empathetic in your response. You can also suggest alternative approaches or workarounds.\n\n\n" | |
| "Use the same language as the previous user input or user request.\n" | |
| "For example, if the previous user input or user request is in Indonesian, explain in Indonesian.\n" | |
| "If it is in English, explain in English. This also applies to other languages.\n\n\n" | |
| ) | |
| } | |
| ] | |
| ) | |
| # Use AI to generate a contextual error message | |
| async for error_response in jarvis( | |
| session_id=session_id, # Session ID | |
| model=selected_model, # Selected model | |
| history=generation_failed, # History with error context | |
| user_message=input, # User input | |
| mode="/no_think", # Use non-reasoning mode for error handling | |
| temperature=0.7, # Fixed temperature for more consistent error messages | |
| top_k=20, # Limit token sampling | |
| min_p=0, # Minimum probability threshold | |
| top_p=0.8, # Nucleus sampling threshold | |
| repetition_penalty=1 # No repetition penalty | |
| ): | |
| yield [{"role": "tool", "content": error_response}] # Yield error response in tool role | |
| return # Exit after error handling |