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feat: added configurable LLM's

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DESKTOP-RTLN3BA\$punk 2025-06-09 15:50:15 -07:00
parent d0e9fdf810
commit a85f7920a9
36 changed files with 3415 additions and 293 deletions
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14
surfsense_backend/app/agents/researcher/qna_agent/nodes.py
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@ -85,14 +85,20 @@ async def answer_question(state: State, config: RunnableConfig) -> Dict[str, Any
Returns:
Dict containing the final answer in the "final_answer" key.
"""
from app.utils.llm_service import get_user_fast_llm
# Get configuration and relevant documents from configuration
configuration = Configuration.from_runnable_config(config)
documents = state.reranked_documents
user_query = configuration.user_query
user_id = configuration.user_id
# Initialize LLM
llm = app_config.fast_llm_instance
# Get user's fast LLM
llm = await get_user_fast_llm(state.db_session, user_id)
if not llm:
error_message = f"No fast LLM configured for user {user_id}"
print(error_message)
raise RuntimeError(error_message)
# Determine if we have documents and optimize for token limits
has_documents_initially = documents and len(documents) > 0
@ -118,7 +124,7 @@ async def answer_question(state: State, config: RunnableConfig) -> Dict[str, Any
# Optimize documents to fit within token limits
optimized_documents, has_optimized_documents = optimize_documents_for_token_limit(
documents, base_messages, app_config.FAST_LLM
documents, base_messages, llm.model
)
# Update state based on optimization result
@ -161,7 +167,7 @@ async def answer_question(state: State, config: RunnableConfig) -> Dict[str, Any
]
# Log final token count
total_tokens = calculate_token_count(messages_with_chat_history, app_config.FAST_LLM)
total_tokens = calculate_token_count(messages_with_chat_history, llm.model)
print(f"Final token count: {total_tokens}")