diff --git a/surfsense_backend/app/agents/researcher/configuration.py b/surfsense_backend/app/agents/researcher/configuration.py index 8ba3849..0eb34b5 100644 --- a/surfsense_backend/app/agents/researcher/configuration.py +++ b/surfsense_backend/app/agents/researcher/configuration.py @@ -3,10 +3,16 @@ from __future__ import annotations from dataclasses import dataclass, fields +from enum import Enum from typing import Optional, List, Any from langchain_core.runnables import RunnableConfig +class SearchMode(Enum): + """Enum defining the type of search mode.""" + CHUNKS = "CHUNKS" + DOCUMENTS = "DOCUMENTS" + @dataclass(kw_only=True) class Configuration: @@ -18,6 +24,7 @@ class Configuration: connectors_to_search: List[str] user_id: str search_space_id: int + search_mode: SearchMode @classmethod diff --git a/surfsense_backend/app/agents/researcher/nodes.py b/surfsense_backend/app/agents/researcher/nodes.py index b0b81ae..644ddd9 100644 --- a/surfsense_backend/app/agents/researcher/nodes.py +++ b/surfsense_backend/app/agents/researcher/nodes.py @@ -10,7 +10,7 @@ from langchain_core.runnables import RunnableConfig from pydantic import BaseModel, Field from sqlalchemy.ext.asyncio import AsyncSession -from .configuration import Configuration +from .configuration import Configuration, SearchMode from .prompts import get_answer_outline_system_prompt from .state import State from .sub_section_writer.graph import graph as sub_section_writer_graph @@ -149,7 +149,8 @@ async def fetch_relevant_documents( writer: StreamWriter = None, state: State = None, top_k: int = 10, - connector_service: ConnectorService = None + connector_service: ConnectorService = None, + search_mode: SearchMode = SearchMode.CHUNKS ) -> List[Dict[str, Any]]: """ Fetch relevant documents for research questions using the provided connectors. @@ -213,7 +214,8 @@ async def fetch_relevant_documents( user_query=reformulated_query, user_id=user_id, search_space_id=search_space_id, - top_k=top_k + top_k=top_k, + search_mode=search_mode ) # Add to sources and raw documents @@ -231,7 +233,8 @@ async def fetch_relevant_documents( user_query=reformulated_query, user_id=user_id, search_space_id=search_space_id, - top_k=top_k + top_k=top_k, + search_mode=search_mode ) # Add to sources and raw documents @@ -249,7 +252,8 @@ async def fetch_relevant_documents( user_query=reformulated_query, user_id=user_id, search_space_id=search_space_id, - top_k=top_k + top_k=top_k, + search_mode=search_mode ) # Add to sources and raw documents @@ -267,7 +271,8 @@ async def fetch_relevant_documents( user_query=reformulated_query, user_id=user_id, search_space_id=search_space_id, - top_k=top_k + top_k=top_k, + search_mode=search_mode ) # Add to sources and raw documents @@ -286,7 +291,8 @@ async def fetch_relevant_documents( user_query=reformulated_query, user_id=user_id, search_space_id=search_space_id, - top_k=top_k + top_k=top_k, + search_mode=search_mode ) # Add to sources and raw documents @@ -304,7 +310,8 @@ async def fetch_relevant_documents( user_query=reformulated_query, user_id=user_id, search_space_id=search_space_id, - top_k=top_k + top_k=top_k, + search_mode=search_mode ) # Add to sources and raw documents @@ -322,7 +329,8 @@ async def fetch_relevant_documents( user_query=reformulated_query, user_id=user_id, search_space_id=search_space_id, - top_k=top_k + top_k=top_k, + search_mode=search_mode ) # Add to sources and raw documents @@ -340,7 +348,8 @@ async def fetch_relevant_documents( user_query=reformulated_query, user_id=user_id, search_space_id=search_space_id, - top_k=top_k + top_k=top_k, + search_mode=search_mode ) # Add to sources and raw documents @@ -558,7 +567,8 @@ async def process_sections(state: State, config: RunnableConfig, writer: StreamW writer=writer, state=state, top_k=TOP_K, - connector_service=connector_service + connector_service=connector_service, + search_mode=configuration.search_mode ) except Exception as e: error_message = f"Error fetching relevant documents: {str(e)}" diff --git a/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py b/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py index 765b619..5853283 100644 --- a/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py +++ b/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py @@ -141,6 +141,11 @@ async def write_sub_section(state: State, config: RunnableConfig) -> Dict[str, A # Construct a clear, structured query for the LLM human_message_content = f""" + Source material: + + {documents_text} + + Now user's query is: {user_query} @@ -158,11 +163,6 @@ async def write_sub_section(state: State, config: RunnableConfig) -> Dict[str, A {questions_text} - - Use the provided documents as your source material and cite them properly using the IEEE citation format [X] where X is the source_id. - - {documents_text} - """ # Create messages for the LLM diff --git a/surfsense_backend/app/agents/researcher/sub_section_writer/prompts.py b/surfsense_backend/app/agents/researcher/sub_section_writer/prompts.py index 18a91eb..48345c9 100644 --- a/surfsense_backend/app/agents/researcher/sub_section_writer/prompts.py +++ b/surfsense_backend/app/agents/researcher/sub_section_writer/prompts.py @@ -25,6 +25,8 @@ You are a research assistant tasked with analyzing documents and providing compr 16. CRITICAL: Citations must ONLY appear as [X] or [X], [Y], [Z] format - never with parentheses, hyperlinks, or other formatting. 17. CRITICAL: Never make up citation numbers. Only use source_id values that are explicitly provided in the document metadata. 18. CRITICAL: If you are unsure about a source_id, do not include a citation rather than guessing or making one up. +19. CRITICAL: Focus only on answering the user's query. Any guiding questions provided are for your thinking process only and should not be mentioned in your response. +20. CRITICAL: Ensure your response aligns with the provided sub-section title and section position. @@ -37,6 +39,8 @@ You are a research assistant tasked with analyzing documents and providing compr - NEVER create your own citation numbering system - use the exact source_id values from the documents. - NEVER format citations as clickable links or as markdown links like "([1](https://example.com))". Always use plain square brackets only. - NEVER make up citation numbers if you are unsure about the source_id. It is better to omit the citation than to guess. +- NEVER include or mention the guiding questions in your response. They are only to help guide your thinking. +- ALWAYS focus on answering the user's query directly from the information in the documents. @@ -84,4 +88,21 @@ ONLY use plain square brackets [1] or multiple citations [1], [2], [3] Note that the citation numbers match exactly with the source_id values (1, 13, and 21) and are not renumbered sequentially. Citations follow IEEE style with square brackets and appear at the end of sentences. + + +When you see a user query like: + + Give all linear issues. + + +Focus exclusively on answering this query using information from the provided documents. + +If guiding questions are provided in a section, use them only to guide your thinking process. Do not mention or list these questions in your response. + +Make sure your response: +1. Directly answers the user's query +2. Fits the provided sub-section title and section position +3. Uses proper citations for all information from documents +4. Is well-structured and professional in tone + """ \ No newline at end of file diff --git a/surfsense_backend/app/retriver/documents_hybrid_search.py b/surfsense_backend/app/retriver/documents_hybrid_search.py index 060c3b1..2163635 100644 --- a/surfsense_backend/app/retriver/documents_hybrid_search.py +++ b/surfsense_backend/app/retriver/documents_hybrid_search.py @@ -113,8 +113,6 @@ class DocumentHybridSearchRetriever: search_space_id: Optional search space ID to filter results document_type: Optional document type to filter results (e.g., "FILE", "CRAWLED_URL") - Returns: - List of dictionaries containing document data and relevance scores """ from sqlalchemy import select, func, text from sqlalchemy.orm import joinedload @@ -224,10 +222,22 @@ class DocumentHybridSearchRetriever: # Convert to serializable dictionaries serialized_results = [] for document, score in documents_with_scores: + # Fetch associated chunks for this document + from sqlalchemy import select + from app.db import Chunk + + chunks_query = select(Chunk).where(Chunk.document_id == document.id).order_by(Chunk.id) + chunks_result = await self.db_session.execute(chunks_query) + chunks = chunks_result.scalars().all() + + # Concatenate chunks content + concatenated_chunks_content = " ".join([chunk.content for chunk in chunks]) if chunks else document.content + serialized_results.append({ "document_id": document.id, "title": document.title, "content": document.content, + "chunks_content": concatenated_chunks_content, "document_type": document.document_type.value if hasattr(document, 'document_type') else None, "metadata": document.document_metadata, "score": float(score), # Ensure score is a Python float diff --git a/surfsense_backend/app/routes/chats_routes.py b/surfsense_backend/app/routes/chats_routes.py index 62c7e8a..9a2aa79 100644 --- a/surfsense_backend/app/routes/chats_routes.py +++ b/surfsense_backend/app/routes/chats_routes.py @@ -11,6 +11,8 @@ from sqlalchemy.exc import IntegrityError, OperationalError from sqlalchemy.ext.asyncio import AsyncSession from sqlalchemy.future import select from langchain.schema import HumanMessage, AIMessage + + router = APIRouter() @router.post("/chat") @@ -28,6 +30,8 @@ async def handle_chat_data( search_space_id = request.data.get('search_space_id') research_mode: str = request.data.get('research_mode') selected_connectors: List[str] = request.data.get('selected_connectors') + + search_mode_str = request.data.get('search_mode', "CHUNKS") # Convert search_space_id to integer if it's a string if search_space_id and isinstance(search_space_id, str): @@ -66,7 +70,8 @@ async def handle_chat_data( session, research_mode, selected_connectors, - langchain_chat_history + langchain_chat_history, + search_mode_str )) response.headers['x-vercel-ai-data-stream'] = 'v1' return response diff --git a/surfsense_backend/app/tasks/stream_connector_search_results.py b/surfsense_backend/app/tasks/stream_connector_search_results.py index 2f3b50a..aa5f401 100644 --- a/surfsense_backend/app/tasks/stream_connector_search_results.py +++ b/surfsense_backend/app/tasks/stream_connector_search_results.py @@ -6,6 +6,8 @@ from app.agents.researcher.state import State from app.utils.streaming_service import StreamingService from sqlalchemy.ext.asyncio import AsyncSession +from app.agents.researcher.configuration import SearchMode + async def stream_connector_search_results( user_query: str, @@ -14,7 +16,8 @@ async def stream_connector_search_results( session: AsyncSession, research_mode: str, selected_connectors: List[str], - langchain_chat_history: List[Any] + langchain_chat_history: List[Any], + search_mode_str: str ) -> AsyncGenerator[str, None]: """ Stream connector search results to the client @@ -41,6 +44,11 @@ async def stream_connector_search_results( # Convert UUID to string if needed user_id_str = str(user_id) if isinstance(user_id, UUID) else user_id + if search_mode_str == "CHUNKS": + search_mode = SearchMode.CHUNKS + elif search_mode_str == "DOCUMENTS": + search_mode = SearchMode.DOCUMENTS + # Sample configuration config = { "configurable": { @@ -48,7 +56,8 @@ async def stream_connector_search_results( "num_sections": NUM_SECTIONS, "connectors_to_search": selected_connectors, "user_id": user_id_str, - "search_space_id": search_space_id + "search_space_id": search_space_id, + "search_mode": search_mode } } # Initialize state with database session and streaming service diff --git a/surfsense_backend/app/utils/connector_service.py b/surfsense_backend/app/utils/connector_service.py index c7ad692..49c3b08 100644 --- a/surfsense_backend/app/utils/connector_service.py +++ b/surfsense_backend/app/utils/connector_service.py @@ -4,32 +4,47 @@ import asyncio from sqlalchemy.ext.asyncio import AsyncSession from sqlalchemy.future import select from app.retriver.chunks_hybrid_search import ChucksHybridSearchRetriever +from app.retriver.documents_hybrid_search import DocumentHybridSearchRetriever from app.db import SearchSourceConnector, SearchSourceConnectorType from tavily import TavilyClient from linkup import LinkupClient +from app.agents.researcher.configuration import SearchMode + class ConnectorService: def __init__(self, session: AsyncSession): self.session = session - self.retriever = ChucksHybridSearchRetriever(session) + self.chunk_retriever = ChucksHybridSearchRetriever(session) + self.document_retriever = DocumentHybridSearchRetriever(session) self.source_id_counter = 1 self.counter_lock = asyncio.Lock() # Lock to protect counter in multithreaded environments - async def search_crawled_urls(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20) -> tuple: + async def search_crawled_urls(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20, search_mode: SearchMode = SearchMode.CHUNKS) -> tuple: """ Search for crawled URLs and return both the source information and langchain documents Returns: tuple: (sources_info, langchain_documents) """ - crawled_urls_chunks = await self.retriever.hybrid_search( - query_text=user_query, - top_k=top_k, - user_id=user_id, - search_space_id=search_space_id, - document_type="CRAWLED_URL" - ) + if search_mode == SearchMode.CHUNKS: + crawled_urls_chunks = await self.chunk_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="CRAWLED_URL" + ) + elif search_mode == SearchMode.DOCUMENTS: + crawled_urls_chunks = await self.document_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="CRAWLED_URL" + ) + # Transform document retriever results to match expected format + crawled_urls_chunks = self._transform_document_results(crawled_urls_chunks) # Early return if no results if not crawled_urls_chunks: @@ -71,20 +86,31 @@ class ConnectorService: return result_object, crawled_urls_chunks - async def search_files(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20) -> tuple: + async def search_files(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20, search_mode: SearchMode = SearchMode.CHUNKS) -> tuple: """ Search for files and return both the source information and langchain documents Returns: tuple: (sources_info, langchain_documents) """ - files_chunks = await self.retriever.hybrid_search( - query_text=user_query, - top_k=top_k, - user_id=user_id, - search_space_id=search_space_id, - document_type="FILE" - ) + if search_mode == SearchMode.CHUNKS: + files_chunks = await self.chunk_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="FILE" + ) + elif search_mode == SearchMode.DOCUMENTS: + files_chunks = await self.document_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="FILE" + ) + # Transform document retriever results to match expected format + files_chunks = self._transform_document_results(files_chunks) # Early return if no results if not files_chunks: @@ -126,6 +152,31 @@ class ConnectorService: return result_object, files_chunks + def _transform_document_results(self, document_results: List[Dict]) -> List[Dict]: + """ + Transform results from document_retriever.hybrid_search() to match the format + expected by the processing code. + + Args: + document_results: Results from document_retriever.hybrid_search() + + Returns: + List of transformed results in the format expected by the processing code + """ + transformed_results = [] + for doc in document_results: + transformed_results.append({ + 'document': { + 'id': doc.get('document_id'), + 'title': doc.get('title', 'Untitled Document'), + 'document_type': doc.get('document_type'), + 'metadata': doc.get('metadata', {}), + }, + 'content': doc.get('chunks_content', doc.get('content', '')), + 'score': doc.get('score', 0.0) + }) + return transformed_results + async def get_connector_by_type(self, user_id: str, connector_type: SearchSourceConnectorType) -> Optional[SearchSourceConnector]: """ Get a connector by type for a specific user @@ -249,20 +300,31 @@ class ConnectorService: "sources": [], }, [] - async def search_slack(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20) -> tuple: + async def search_slack(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20, search_mode: SearchMode = SearchMode.CHUNKS) -> tuple: """ Search for slack and return both the source information and langchain documents Returns: tuple: (sources_info, langchain_documents) """ - slack_chunks = await self.retriever.hybrid_search( - query_text=user_query, - top_k=top_k, - user_id=user_id, - search_space_id=search_space_id, - document_type="SLACK_CONNECTOR" - ) + if search_mode == SearchMode.CHUNKS: + slack_chunks = await self.chunk_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="SLACK_CONNECTOR" + ) + elif search_mode == SearchMode.DOCUMENTS: + slack_chunks = await self.document_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="SLACK_CONNECTOR" + ) + # Transform document retriever results to match expected format + slack_chunks = self._transform_document_results(slack_chunks) # Early return if no results if not slack_chunks: @@ -323,7 +385,7 @@ class ConnectorService: return result_object, slack_chunks - async def search_notion(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20) -> tuple: + async def search_notion(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20, search_mode: SearchMode = SearchMode.CHUNKS) -> tuple: """ Search for Notion pages and return both the source information and langchain documents @@ -336,14 +398,25 @@ class ConnectorService: Returns: tuple: (sources_info, langchain_documents) """ - notion_chunks = await self.retriever.hybrid_search( - query_text=user_query, - top_k=top_k, - user_id=user_id, - search_space_id=search_space_id, - document_type="NOTION_CONNECTOR" - ) - + if search_mode == SearchMode.CHUNKS: + notion_chunks = await self.chunk_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="NOTION_CONNECTOR" + ) + elif search_mode == SearchMode.DOCUMENTS: + notion_chunks = await self.document_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="NOTION_CONNECTOR" + ) + # Transform document retriever results to match expected format + notion_chunks = self._transform_document_results(notion_chunks) + # Early return if no results if not notion_chunks: return { @@ -405,7 +478,7 @@ class ConnectorService: return result_object, notion_chunks - async def search_extension(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20) -> tuple: + async def search_extension(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20, search_mode: SearchMode = SearchMode.CHUNKS) -> tuple: """ Search for extension data and return both the source information and langchain documents @@ -418,14 +491,25 @@ class ConnectorService: Returns: tuple: (sources_info, langchain_documents) """ - extension_chunks = await self.retriever.hybrid_search( - query_text=user_query, - top_k=top_k, - user_id=user_id, - search_space_id=search_space_id, - document_type="EXTENSION" - ) - + if search_mode == SearchMode.CHUNKS: + extension_chunks = await self.chunk_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="EXTENSION" + ) + elif search_mode == SearchMode.DOCUMENTS: + extension_chunks = await self.document_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="EXTENSION" + ) + # Transform document retriever results to match expected format + extension_chunks = self._transform_document_results(extension_chunks) + # Early return if no results if not extension_chunks: return { @@ -505,7 +589,7 @@ class ConnectorService: return result_object, extension_chunks - async def search_youtube(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20) -> tuple: + async def search_youtube(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20, search_mode: SearchMode = SearchMode.CHUNKS) -> tuple: """ Search for YouTube videos and return both the source information and langchain documents @@ -518,13 +602,24 @@ class ConnectorService: Returns: tuple: (sources_info, langchain_documents) """ - youtube_chunks = await self.retriever.hybrid_search( - query_text=user_query, - top_k=top_k, - user_id=user_id, - search_space_id=search_space_id, - document_type="YOUTUBE_VIDEO" - ) + if search_mode == SearchMode.CHUNKS: + youtube_chunks = await self.chunk_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="YOUTUBE_VIDEO" + ) + elif search_mode == SearchMode.DOCUMENTS: + youtube_chunks = await self.document_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="YOUTUBE_VIDEO" + ) + # Transform document retriever results to match expected format + youtube_chunks = self._transform_document_results(youtube_chunks) # Early return if no results if not youtube_chunks: @@ -587,20 +682,31 @@ class ConnectorService: return result_object, youtube_chunks - async def search_github(self, user_query: str, user_id: int, search_space_id: int, top_k: int = 20) -> tuple: + async def search_github(self, user_query: str, user_id: int, search_space_id: int, top_k: int = 20, search_mode: SearchMode = SearchMode.CHUNKS) -> tuple: """ Search for GitHub documents and return both the source information and langchain documents Returns: tuple: (sources_info, langchain_documents) """ - github_chunks = await self.retriever.hybrid_search( - query_text=user_query, - top_k=top_k, - user_id=user_id, - search_space_id=search_space_id, - document_type="GITHUB_CONNECTOR" - ) + if search_mode == SearchMode.CHUNKS: + github_chunks = await self.chunk_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="GITHUB_CONNECTOR" + ) + elif search_mode == SearchMode.DOCUMENTS: + github_chunks = await self.document_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="GITHUB_CONNECTOR" + ) + # Transform document retriever results to match expected format + github_chunks = self._transform_document_results(github_chunks) # Early return if no results if not github_chunks: @@ -643,7 +749,7 @@ class ConnectorService: return result_object, github_chunks - async def search_linear(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20) -> tuple: + async def search_linear(self, user_query: str, user_id: str, search_space_id: int, top_k: int = 20, search_mode: SearchMode = SearchMode.CHUNKS) -> tuple: """ Search for Linear issues and comments and return both the source information and langchain documents @@ -656,14 +762,25 @@ class ConnectorService: Returns: tuple: (sources_info, langchain_documents) """ - linear_chunks = await self.retriever.hybrid_search( - query_text=user_query, - top_k=top_k, - user_id=user_id, - search_space_id=search_space_id, - document_type="LINEAR_CONNECTOR" - ) - + if search_mode == SearchMode.CHUNKS: + linear_chunks = await self.chunk_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="LINEAR_CONNECTOR" + ) + elif search_mode == SearchMode.DOCUMENTS: + linear_chunks = await self.document_retriever.hybrid_search( + query_text=user_query, + top_k=top_k, + user_id=user_id, + search_space_id=search_space_id, + document_type="LINEAR_CONNECTOR" + ) + # Transform document retriever results to match expected format + linear_chunks = self._transform_document_results(linear_chunks) + # Early return if no results if not linear_chunks: return { diff --git a/surfsense_web/app/dashboard/[search_space_id]/researcher/[chat_id]/page.tsx b/surfsense_web/app/dashboard/[search_space_id]/researcher/[chat_id]/page.tsx index bc58e8c..78239e2 100644 --- a/surfsense_web/app/dashboard/[search_space_id]/researcher/[chat_id]/page.tsx +++ b/surfsense_web/app/dashboard/[search_space_id]/researcher/[chat_id]/page.tsx @@ -13,7 +13,9 @@ import { ArrowDown, CircleUser, Database, - SendHorizontal + SendHorizontal, + FileText, + Grid3x3 } from 'lucide-react'; import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card'; import { Button } from '@/components/ui/button'; @@ -248,6 +250,7 @@ const ChatPage = () => { const tabsListRef = useRef(null); const [terminalExpanded, setTerminalExpanded] = useState(false); const [selectedConnectors, setSelectedConnectors] = useState(["CRAWLED_URL"]); + const [searchMode, setSearchMode] = useState<'DOCUMENTS' | 'CHUNKS'>('DOCUMENTS'); const [researchMode, setResearchMode] = useState("GENERAL"); const [currentTime, setCurrentTime] = useState(''); const [currentDate, setCurrentDate] = useState(''); @@ -362,7 +365,8 @@ const ChatPage = () => { data: { search_space_id: search_space_id, selected_connectors: selectedConnectors, - research_mode: researchMode + research_mode: researchMode, + search_mode: searchMode } }, onError: (error) => { @@ -557,11 +561,6 @@ const ChatPage = () => { } }, [terminalExpanded]); - // Get total sources count for a connector type - const getSourcesCount = (connectorType: string) => { - return getSourcesCountUtil(getMessageConnectorSources(messages[messages.length - 1]), connectorType); - }; - // Function to check scroll position and update indicators const updateScrollIndicators = () => { updateScrollIndicatorsUtil(tabsListRef as React.RefObject, setCanScrollLeft, setCanScrollRight); @@ -587,23 +586,6 @@ const ChatPage = () => { // Use the scroll to bottom hook useScrollToBottom(messagesEndRef as React.RefObject, [messages]); - // Function to get sources for the main view - const getMainViewSources = (connector: any) => { - return getMainViewSourcesUtil(connector, INITIAL_SOURCES_DISPLAY); - }; - - // Function to get filtered sources for the dialog with null check - const getFilteredSourcesWithCheck = (connector: any, sourceFilter: string) => { - if (!connector?.sources) return []; - return getFilteredSourcesUtil(connector, sourceFilter); - }; - - // Function to get paginated dialog sources with null check - const getPaginatedDialogSourcesWithCheck = (connector: any, sourceFilter: string, expandedSources: boolean, sourcesPage: number, sourcesPerPage: number) => { - if (!connector?.sources) return []; - return getPaginatedDialogSourcesUtil(connector, sourceFilter, expandedSources, sourcesPage, sourcesPerPage); - }; - // Function to get a citation source by ID const getCitationSource = React.useCallback((citationId: number, messageIndex?: number): Source | null => { if (!messages || messages.length === 0) return null; @@ -995,15 +977,17 @@ const ChatPage = () => { Send -
-
+
+
{/* Connector Selection Dialog */} - { }} - /> +
+ { }} + /> +
@@ -1070,12 +1054,40 @@ const ChatPage = () => { + {/* Search Mode Control */} +
+ + +
+ {/* Research Mode Segmented Control */} - - value={researchMode} - onChange={setResearchMode} - options={researcherOptions} - /> +
+ + value={researchMode} + onChange={setResearchMode} + options={researcherOptions} + /> +
diff --git a/surfsense_web/components/chat/ConnectorComponents.tsx b/surfsense_web/components/chat/ConnectorComponents.tsx index e52348c..163d5bf 100644 --- a/surfsense_web/components/chat/ConnectorComponents.tsx +++ b/surfsense_web/components/chat/ConnectorComponents.tsx @@ -147,7 +147,7 @@ export const ConnectorButton = ({ selectedConnectors, onClick, connectorSources return (