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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 |