Merge main to Llamaindex-chatui

surfsense_backend/app/agents/researcher/qna_agent/__init__.py

@@ -1,5 +1,4 @@
-"""QnA Agent.
-"""
+"""QnA Agent."""
 
 from .graph import graph
 

surfsense_backend/app/agents/researcher/qna_agent/configuration.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, fields
-from typing import Optional, List, Any
+from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 
@@ -15,13 +15,15 @@ class Configuration:
     # Configuration parameters for the Q&A agent
     user_query: str  # The user's question to answer
     reformulated_query: str  # The reformulated query
-    relevant_documents: List[Any]  # Documents provided directly to the agent for answering
+    relevant_documents: list[
+        Any
+    ]  # Documents provided directly to the agent for answering
     user_id: str  # User identifier
     search_space_id: int  # Search space identifier
 
     @classmethod
     def from_runnable_config(
-        cls, config: Optional[RunnableConfig] = None
+        cls, config: RunnableConfig | None = None
     ) -> Configuration:
         """Create a Configuration instance from a RunnableConfig object."""
         configurable = (config.get("configurable") or {}) if config else {}

surfsense_backend/app/agents/researcher/qna_agent/graph.py

@@ -1,7 +1,8 @@
 from langgraph.graph import StateGraph
-from .state import State
-from .nodes import rerank_documents, answer_question
+
 from .configuration import Configuration
+from .nodes import answer_question, rerank_documents
+from .state import State
 
 # Define a new graph
 workflow = StateGraph(State, config_schema=Configuration)

surfsense_backend/app/agents/researcher/qna_agent/nodes.py

@@ -1,18 +1,21 @@
-from app.services.reranker_service import RerankerService
-from .configuration import Configuration
-from langchain_core.runnables import RunnableConfig
-from .state import State
-from typing import Any, Dict
-from .prompts import get_qna_citation_system_prompt, get_qna_no_documents_system_prompt
+from typing import Any
+
 from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.runnables import RunnableConfig
+
+from app.services.reranker_service import RerankerService
+
 from ..utils import (
-    optimize_documents_for_token_limit,
     calculate_token_count,
     format_documents_section,
+    optimize_documents_for_token_limit,
 )
+from .configuration import Configuration
+from .prompts import get_qna_citation_system_prompt, get_qna_no_documents_system_prompt
+from .state import State
 
 
-async def rerank_documents(state: State, config: RunnableConfig) -> Dict[str, Any]:
+async def rerank_documents(state: State, config: RunnableConfig) -> dict[str, Any]:
     """
     Rerank the documents based on relevance to the user's question.
 
@@ -71,13 +74,13 @@ async def rerank_documents(state: State, config: RunnableConfig) -> Dict[str, An
                 f"Reranked {len(reranked_docs)} documents for Q&A query: {user_query}"
             )
         except Exception as e:
-            print(f"Error during reranking: {str(e)}")
+            print(f"Error during reranking: {e!s}")
             # Use original docs if reranking fails
 
     return {"reranked_documents": reranked_docs}
 
 
-async def answer_question(state: State, config: RunnableConfig) -> Dict[str, Any]:
+async def answer_question(state: State, config: RunnableConfig) -> dict[str, Any]:
     """
     Answer the user's question using the provided documents.
 
@@ -122,7 +125,8 @@ async def answer_question(state: State, config: RunnableConfig) -> Dict[str, Any
 
         # Use initial system prompt for token calculation
         initial_system_prompt = get_qna_citation_system_prompt()
-        base_messages = state.chat_history + [
+        base_messages = [
+            *state.chat_history,
             SystemMessage(content=initial_system_prompt),
             HumanMessage(content=base_human_message_template),
         ]
@@ -173,7 +177,8 @@ async def answer_question(state: State, config: RunnableConfig) -> Dict[str, Any
     """
 
     # Create final messages for the LLM
-    messages_with_chat_history = state.chat_history + [
+    messages_with_chat_history = [
+        *state.chat_history,
         SystemMessage(content=system_prompt),
         HumanMessage(content=human_message_content),
     ]

surfsense_backend/app/agents/researcher/qna_agent/prompts.py

@@ -15,7 +15,8 @@ You are SurfSense, an advanced AI research assistant that provides detailed, wel
 - YOUTUBE_VIDEO: "YouTube video transcripts and metadata" (personally saved videos)
 - GITHUB_CONNECTOR: "GitHub repository content and issues" (personal repositories and interactions)
 - LINEAR_CONNECTOR: "Linear project issues and discussions" (personal project management)
-- DISCORD_CONNECTOR: "Discord server messages and channels" (personal community interactions)
+- JIRA_CONNECTOR: "Jira project issues, tickets, and comments" (personal project tracking)
+- DISCORD_CONNECTOR: "Discord server conversations and shared content" (personal community communications)
 - TAVILY_API: "Tavily search API results" (personalized search results)
 - LINKUP_API: "Linkup search API results" (personalized search results)
 </knowledge_sources>
@@ -71,7 +72,7 @@ You are SurfSense, an advanced AI research assistant that provides detailed, wel
             Python's asyncio library provides tools for writing concurrent code using the async/await syntax. It's particularly useful for I/O-bound and high-level structured network code.
         </content>
     </document>
-    
+
     <document>
         <metadata>
             <source_id>12</source_id>

surfsense_backend/app/agents/researcher/qna_agent/state.py

@@ -3,14 +3,16 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import List, Optional, Any
+from typing import Any
+
 from sqlalchemy.ext.asyncio import AsyncSession
 
+
 @dataclass
 class State:
     """Defines the dynamic state for the Q&A agent during execution.
 
-    This state tracks the database session, chat history, and the outputs 
+    This state tracks the database session, chat history, and the outputs
     generated by the agent's nodes during question answering.
     See: https://langchain-ai.github.io/langgraph/concepts/low_level/#state
     for more information.
@@ -18,8 +20,8 @@ class State:
 
     # Runtime context
     db_session: AsyncSession
-    
-    chat_history: Optional[List[Any]] = field(default_factory=list)
+
+    chat_history: list[Any] | None = field(default_factory=list)
     # OUTPUT: Populated by agent nodes
-    reranked_documents: Optional[List[Any]] = None
-    final_answer: Optional[str] = None
+    reranked_documents: list[Any] | None = None
+    final_answer: str | None = None