mirror of
https://github.com/MODSetter/SurfSense.git
synced 2025-09-01 18:19:08 +00:00
feat: Document Selector in Chat.
- Still need improvements but lets use it first.
This commit is contained in:
parent
e8a19c496b
commit
d7bb31f894
12 changed files with 599 additions and 67 deletions
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@ -33,6 +33,7 @@ class Configuration:
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search_space_id: int
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search_mode: SearchMode
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research_mode: ResearchMode
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document_ids_to_add_in_context: List[int]
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@classmethod
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def from_runnable_config(
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@ -21,6 +21,237 @@ from app.utils.query_service import QueryService
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from langgraph.types import StreamWriter
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# Additional imports for document fetching
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from sqlalchemy.future import select
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from app.db import Document, SearchSpace
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async def fetch_documents_by_ids(
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document_ids: List[int],
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user_id: str,
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db_session: AsyncSession
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) -> tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
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"""
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Fetch documents by their IDs with ownership check using DOCUMENTS mode approach.
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This function ensures that only documents belonging to the user are fetched,
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providing security by checking ownership through SearchSpace association.
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Similar to SearchMode.DOCUMENTS, it fetches full documents and concatenates their chunks.
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Also creates source objects for UI display, grouped by document type.
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Args:
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document_ids: List of document IDs to fetch
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user_id: The user ID to check ownership
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db_session: The database session
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Returns:
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Tuple of (source_objects, document_chunks) - similar to ConnectorService pattern
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"""
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if not document_ids:
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return [], []
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try:
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# Query documents with ownership check
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result = await db_session.execute(
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select(Document)
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.join(SearchSpace)
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.filter(
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Document.id.in_(document_ids),
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SearchSpace.user_id == user_id
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)
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)
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documents = result.scalars().all()
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# Group documents by type for source object creation
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documents_by_type = {}
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formatted_documents = []
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for doc in documents:
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# Fetch associated chunks for this document (similar to DocumentHybridSearchRetriever)
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from app.db import Chunk
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chunks_query = select(Chunk).where(Chunk.document_id == doc.id).order_by(Chunk.id)
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chunks_result = await db_session.execute(chunks_query)
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chunks = chunks_result.scalars().all()
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# Concatenate chunks content (similar to SearchMode.DOCUMENTS approach)
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concatenated_chunks_content = " ".join([chunk.content for chunk in chunks]) if chunks else doc.content
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# Format to match connector service return format
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formatted_doc = {
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"chunk_id": f"user_doc_{doc.id}",
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"content": concatenated_chunks_content, # Use concatenated content like DOCUMENTS mode
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"score": 0.5, # High score since user explicitly selected these
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"document": {
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"id": doc.id,
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"title": doc.title,
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"document_type": doc.document_type.value if doc.document_type else "UNKNOWN",
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"metadata": doc.document_metadata or {},
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},
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"source": doc.document_type.value if doc.document_type else "UNKNOWN"
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}
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formatted_documents.append(formatted_doc)
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# Group by document type for source objects
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doc_type = doc.document_type.value if doc.document_type else "UNKNOWN"
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if doc_type not in documents_by_type:
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documents_by_type[doc_type] = []
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documents_by_type[doc_type].append(doc)
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# Create source objects for each document type (similar to ConnectorService)
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source_objects = []
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connector_id_counter = 100 # Start from 100 to avoid conflicts with regular connectors
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for doc_type, docs in documents_by_type.items():
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sources_list = []
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for doc in docs:
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metadata = doc.document_metadata or {}
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# Create type-specific source formatting (similar to ConnectorService)
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if doc_type == "LINEAR_CONNECTOR":
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# Extract Linear-specific metadata
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issue_identifier = metadata.get('issue_identifier', '')
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issue_title = metadata.get('issue_title', doc.title)
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issue_state = metadata.get('state', '')
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comment_count = metadata.get('comment_count', 0)
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# Create a more descriptive title for Linear issues
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title = f"Linear: {issue_identifier} - {issue_title}" if issue_identifier else f"Linear: {issue_title}"
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if issue_state:
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title += f" ({issue_state})"
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# Create description
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description = doc.content[:100] + "..." if len(doc.content) > 100 else doc.content
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if comment_count:
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description += f" | Comments: {comment_count}"
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# Create URL
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url = f"https://linear.app/issue/{issue_identifier}" if issue_identifier else ""
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elif doc_type == "SLACK_CONNECTOR":
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# Extract Slack-specific metadata
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channel_name = metadata.get('channel_name', 'Unknown Channel')
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channel_id = metadata.get('channel_id', '')
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message_date = metadata.get('start_date', '')
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title = f"Slack: {channel_name}"
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if message_date:
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title += f" ({message_date})"
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description = doc.content[:100] + "..." if len(doc.content) > 100 else doc.content
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url = f"https://slack.com/app_redirect?channel={channel_id}" if channel_id else ""
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elif doc_type == "NOTION_CONNECTOR":
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# Extract Notion-specific metadata
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page_title = metadata.get('page_title', doc.title)
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page_id = metadata.get('page_id', '')
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title = f"Notion: {page_title}"
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description = doc.content[:100] + "..." if len(doc.content) > 100 else doc.content
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url = f"https://notion.so/{page_id.replace('-', '')}" if page_id else ""
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elif doc_type == "GITHUB_CONNECTOR":
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title = f"GitHub: {doc.title}"
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description = metadata.get('description', doc.content[:100] + "..." if len(doc.content) > 100 else doc.content)
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url = metadata.get('url', '')
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elif doc_type == "YOUTUBE_VIDEO":
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# Extract YouTube-specific metadata
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video_title = metadata.get('video_title', doc.title)
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video_id = metadata.get('video_id', '')
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channel_name = metadata.get('channel_name', '')
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title = video_title
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if channel_name:
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title += f" - {channel_name}"
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description = metadata.get('description', doc.content[:100] + "..." if len(doc.content) > 100 else doc.content)
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url = f"https://www.youtube.com/watch?v={video_id}" if video_id else ""
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elif doc_type == "DISCORD_CONNECTOR":
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# Extract Discord-specific metadata
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channel_name = metadata.get('channel_name', 'Unknown Channel')
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channel_id = metadata.get('channel_id', '')
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guild_id = metadata.get('guild_id', '')
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message_date = metadata.get('start_date', '')
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title = f"Discord: {channel_name}"
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if message_date:
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title += f" ({message_date})"
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description = doc.content[:100] + "..." if len(doc.content) > 100 else doc.content
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if guild_id and channel_id:
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url = f"https://discord.com/channels/{guild_id}/{channel_id}"
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elif channel_id:
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url = f"https://discord.com/channels/@me/{channel_id}"
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else:
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url = ""
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elif doc_type == "EXTENSION":
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# Extract Extension-specific metadata
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webpage_title = metadata.get('VisitedWebPageTitle', doc.title)
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webpage_url = metadata.get('VisitedWebPageURL', '')
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visit_date = metadata.get('VisitedWebPageDateWithTimeInISOString', '')
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title = webpage_title
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if visit_date:
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formatted_date = visit_date.split('T')[0] if 'T' in visit_date else visit_date
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title += f" (visited: {formatted_date})"
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description = doc.content[:100] + "..." if len(doc.content) > 100 else doc.content
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url = webpage_url
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elif doc_type == "CRAWLED_URL":
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title = doc.title
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description = metadata.get('og:description', metadata.get('ogDescription', doc.content[:100] + "..." if len(doc.content) > 100 else doc.content))
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url = metadata.get('url', '')
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else: # FILE and other types
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title = doc.title
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description = doc.content[:100] + "..." if len(doc.content) > 100 else doc.content
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url = metadata.get('url', '')
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# Create source entry
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source = {
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"id": doc.id,
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"title": title,
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"description": description,
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"url": url
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}
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sources_list.append(source)
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# Create source object for this document type
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friendly_type_names = {
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"LINEAR_CONNECTOR": "Linear Issues (Selected)",
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"SLACK_CONNECTOR": "Slack (Selected)",
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"NOTION_CONNECTOR": "Notion (Selected)",
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"GITHUB_CONNECTOR": "GitHub (Selected)",
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"YOUTUBE_VIDEO": "YouTube Videos (Selected)",
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"DISCORD_CONNECTOR": "Discord (Selected)",
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"EXTENSION": "Browser Extension (Selected)",
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"CRAWLED_URL": "Web Pages (Selected)",
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"FILE": "Files (Selected)"
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}
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source_object = {
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"id": connector_id_counter,
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"name": friendly_type_names.get(doc_type, f"{doc_type} (Selected)"),
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"type": f"USER_SELECTED_{doc_type}",
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"sources": sources_list,
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}
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source_objects.append(source_object)
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connector_id_counter += 1
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print(f"Fetched {len(formatted_documents)} user-selected documents (with concatenated chunks) from {len(document_ids)} requested IDs")
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print(f"Created {len(source_objects)} source objects for UI display")
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return source_objects, formatted_documents
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except Exception as e:
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print(f"Error fetching documents by IDs: {str(e)}")
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return [], []
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class Section(BaseModel):
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"""A section in the answer outline."""
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@ -150,7 +381,8 @@ async def fetch_relevant_documents(
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state: State = None,
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top_k: int = 10,
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connector_service: ConnectorService = None,
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search_mode: SearchMode = SearchMode.CHUNKS
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search_mode: SearchMode = SearchMode.CHUNKS,
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user_selected_sources: List[Dict[str, Any]] = None
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) -> List[Dict[str, Any]]:
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"""
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Fetch relevant documents for research questions using the provided connectors.
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@ -436,6 +668,21 @@ async def fetch_relevant_documents(
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deduplicated_sources = []
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seen_source_keys = set()
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# First add user-selected sources (if any)
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if user_selected_sources:
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for source_obj in user_selected_sources:
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source_id = source_obj.get('id')
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source_type = source_obj.get('type')
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if source_id and source_type:
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source_key = f"{source_type}_{source_id}"
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if source_key not in seen_source_keys:
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seen_source_keys.add(source_key)
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deduplicated_sources.append(source_obj)
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else:
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deduplicated_sources.append(source_obj)
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# Then add connector sources
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for source_obj in all_sources:
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# Use combination of source ID and type as a unique identifier
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# This ensures we don't accidentally deduplicate sources from different connectors
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@ -453,7 +700,9 @@ async def fetch_relevant_documents(
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# Stream info about deduplicated sources
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if streaming_service and writer:
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streaming_service.only_update_terminal(f"📚 Collected {len(deduplicated_sources)} unique sources across all connectors")
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user_source_count = len(user_selected_sources) if user_selected_sources else 0
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connector_source_count = len(deduplicated_sources) - user_source_count
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streaming_service.only_update_terminal(f"📚 Collected {len(deduplicated_sources)} total sources ({user_source_count} user-selected + {connector_source_count} from connectors)")
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writer({"yeild_value": streaming_service._format_annotations()})
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# After all sources are collected and deduplicated, stream them
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@ -576,8 +825,26 @@ async def process_sections(state: State, config: RunnableConfig, writer: StreamW
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TOP_K = 10
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relevant_documents = []
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user_selected_documents = []
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user_selected_sources = []
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async with async_session_maker() as db_session:
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try:
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# First, fetch user-selected documents if any
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if configuration.document_ids_to_add_in_context:
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streaming_service.only_update_terminal(f"📋 Including {len(configuration.document_ids_to_add_in_context)} user-selected documents...")
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writer({"yeild_value": streaming_service._format_annotations()})
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user_selected_sources, user_selected_documents = await fetch_documents_by_ids(
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document_ids=configuration.document_ids_to_add_in_context,
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user_id=configuration.user_id,
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db_session=db_session
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)
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if user_selected_documents:
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streaming_service.only_update_terminal(f"✅ Successfully added {len(user_selected_documents)} user-selected documents to context")
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writer({"yeild_value": streaming_service._format_annotations()})
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# Create connector service inside the db_session scope
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connector_service = ConnectorService(db_session, user_id=configuration.user_id)
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await connector_service.initialize_counter()
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@ -592,7 +859,8 @@ async def process_sections(state: State, config: RunnableConfig, writer: StreamW
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state=state,
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top_k=TOP_K,
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connector_service=connector_service,
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search_mode=configuration.search_mode
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search_mode=configuration.search_mode,
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user_selected_sources=user_selected_sources
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)
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except Exception as e:
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error_message = f"Error fetching relevant documents: {str(e)}"
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@ -603,8 +871,14 @@ async def process_sections(state: State, config: RunnableConfig, writer: StreamW
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# This allows the process to continue, but the report might lack information
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relevant_documents = []
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# Combine user-selected documents with connector-fetched documents
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all_documents = user_selected_documents + relevant_documents
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print(f"Fetched {len(relevant_documents)} relevant documents for all sections")
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streaming_service.only_update_terminal(f"✨ Starting to draft {len(answer_outline.answer_outline)} sections using {len(relevant_documents)} relevant document chunks")
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print(f"Added {len(user_selected_documents)} user-selected documents for all sections")
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print(f"Total documents for sections: {len(all_documents)}")
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streaming_service.only_update_terminal(f"✨ Starting to draft {len(answer_outline.answer_outline)} sections using {len(all_documents)} total document chunks ({len(user_selected_documents)} user-selected + {len(relevant_documents)} connector-found)")
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writer({"yeild_value": streaming_service._format_annotations()})
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# Create tasks to process each section in parallel with the same document set
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@ -635,7 +909,7 @@ async def process_sections(state: State, config: RunnableConfig, writer: StreamW
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user_query=configuration.user_query,
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user_id=configuration.user_id,
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search_space_id=configuration.search_space_id,
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relevant_documents=relevant_documents,
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relevant_documents=all_documents, # Use combined documents
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state=state,
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writer=writer,
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sub_section_type=sub_section_type,
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@ -875,8 +1149,26 @@ async def handle_qna_workflow(state: State, config: RunnableConfig, writer: Stre
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TOP_K = 15
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relevant_documents = []
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user_selected_documents = []
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user_selected_sources = []
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async with async_session_maker() as db_session:
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try:
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# First, fetch user-selected documents if any
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if configuration.document_ids_to_add_in_context:
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streaming_service.only_update_terminal(f"📋 Including {len(configuration.document_ids_to_add_in_context)} user-selected documents...")
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writer({"yeild_value": streaming_service._format_annotations()})
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user_selected_sources, user_selected_documents = await fetch_documents_by_ids(
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document_ids=configuration.document_ids_to_add_in_context,
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user_id=configuration.user_id,
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db_session=db_session
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)
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if user_selected_documents:
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streaming_service.only_update_terminal(f"✅ Successfully added {len(user_selected_documents)} user-selected documents to context")
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writer({"yeild_value": streaming_service._format_annotations()})
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# Create connector service inside the db_session scope
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connector_service = ConnectorService(db_session, user_id=configuration.user_id)
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await connector_service.initialize_counter()
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@ -894,7 +1186,8 @@ async def handle_qna_workflow(state: State, config: RunnableConfig, writer: Stre
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state=state,
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top_k=TOP_K,
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connector_service=connector_service,
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search_mode=configuration.search_mode
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search_mode=configuration.search_mode,
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user_selected_sources=user_selected_sources
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)
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except Exception as e:
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error_message = f"Error fetching relevant documents for QNA: {str(e)}"
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@ -904,15 +1197,21 @@ async def handle_qna_workflow(state: State, config: RunnableConfig, writer: Stre
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# Continue with empty documents - the QNA agent will handle this gracefully
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relevant_documents = []
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# Combine user-selected documents with connector-fetched documents
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all_documents = user_selected_documents + relevant_documents
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print(f"Fetched {len(relevant_documents)} relevant documents for QNA")
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streaming_service.only_update_terminal(f"🧠 Generating comprehensive answer using {len(relevant_documents)} relevant sources...")
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print(f"Added {len(user_selected_documents)} user-selected documents for QNA")
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print(f"Total documents for QNA: {len(all_documents)}")
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streaming_service.only_update_terminal(f"🧠 Generating comprehensive answer using {len(all_documents)} total sources ({len(user_selected_documents)} user-selected + {len(relevant_documents)} connector-found)...")
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writer({"yeild_value": streaming_service._format_annotations()})
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# Prepare configuration for the QNA agent
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qna_config = {
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"configurable": {
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"user_query": reformulated_query, # Use the reformulated query
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"relevant_documents": relevant_documents,
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"relevant_documents": all_documents, # Use combined documents
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"user_id": configuration.user_id,
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"search_space_id": configuration.search_space_id
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}
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@ -30,6 +30,7 @@ async def handle_chat_data(
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search_space_id = request.data.get('search_space_id')
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research_mode: str = request.data.get('research_mode')
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selected_connectors: List[str] = request.data.get('selected_connectors')
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document_ids_to_add_in_context: List[int] = request.data.get('document_ids_to_add_in_context')
|
||||
|
||||
search_mode_str = request.data.get('search_mode', "CHUNKS")
|
||||
|
||||
|
@ -71,7 +72,8 @@ async def handle_chat_data(
|
|||
research_mode,
|
||||
selected_connectors,
|
||||
langchain_chat_history,
|
||||
search_mode_str
|
||||
search_mode_str,
|
||||
document_ids_to_add_in_context
|
||||
))
|
||||
response.headers['x-vercel-ai-data-stream'] = 'v1'
|
||||
return response
|
||||
|
|
|
@ -17,7 +17,8 @@ async def stream_connector_search_results(
|
|||
research_mode: str,
|
||||
selected_connectors: List[str],
|
||||
langchain_chat_history: List[Any],
|
||||
search_mode_str: str
|
||||
search_mode_str: str,
|
||||
document_ids_to_add_in_context: List[int]
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
Stream connector search results to the client
|
||||
|
@ -62,7 +63,8 @@ async def stream_connector_search_results(
|
|||
"user_id": user_id_str,
|
||||
"search_space_id": search_space_id,
|
||||
"search_mode": search_mode,
|
||||
"research_mode": research_mode
|
||||
"research_mode": research_mode,
|
||||
"document_ids_to_add_in_context": document_ids_to_add_in_context
|
||||
}
|
||||
}
|
||||
# Initialize state with database session and streaming service
|
||||
|
|
|
@ -4,7 +4,7 @@ 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, Chunk, Document
|
||||
from app.db import SearchSourceConnector, SearchSourceConnectorType, Chunk, Document, SearchSpace
|
||||
from tavily import TavilyClient
|
||||
from linkup import LinkupClient
|
||||
from sqlalchemy import func
|
||||
|
@ -33,7 +33,8 @@ class ConnectorService:
|
|||
result = await self.session.execute(
|
||||
select(func.count(Chunk.id))
|
||||
.join(Document)
|
||||
.filter(Document.user_id == self.user_id)
|
||||
.join(SearchSpace)
|
||||
.filter(SearchSpace.user_id == self.user_id)
|
||||
)
|
||||
chunk_count = result.scalar() or 0
|
||||
self.source_id_counter = chunk_count + 1
|
||||
|
|
|
@ -72,7 +72,7 @@ export default function EditConnectorPage() {
|
|||
const [connector, setConnector] = useState<SearchSourceConnector | null>(null);
|
||||
const [isLoading, setIsLoading] = useState(true);
|
||||
const [isSubmitting, setIsSubmitting] = useState(false);
|
||||
console.log("connector", connector);
|
||||
// console.log("connector", connector);
|
||||
// Initialize the form
|
||||
const form = useForm<ApiConnectorFormValues>({
|
||||
resolver: zodResolver(apiConnectorFormSchema),
|
||||
|
|
|
@ -283,8 +283,8 @@ export default function DocumentsTable() {
|
|||
const searchSpaceId = Number(params.search_space_id);
|
||||
const { documents, loading, error, refreshDocuments, deleteDocument } = useDocuments(searchSpaceId);
|
||||
|
||||
console.log("Search Space ID:", searchSpaceId);
|
||||
console.log("Documents loaded:", documents?.length);
|
||||
// console.log("Search Space ID:", searchSpaceId);
|
||||
// console.log("Documents loaded:", documents?.length);
|
||||
|
||||
useEffect(() => {
|
||||
console.log("Delete document function available:", !!deleteDocument);
|
||||
|
@ -315,7 +315,7 @@ export default function DocumentsTable() {
|
|||
|
||||
const handleDeleteRows = async () => {
|
||||
const selectedRows = table.getSelectedRowModel().rows;
|
||||
console.log("Deleting selected rows:", selectedRows.length);
|
||||
// console.log("Deleting selected rows:", selectedRows.length);
|
||||
|
||||
if (selectedRows.length === 0) {
|
||||
toast.error("No rows selected");
|
||||
|
@ -324,14 +324,14 @@ export default function DocumentsTable() {
|
|||
|
||||
// Create an array of promises for each delete operation
|
||||
const deletePromises = selectedRows.map(row => {
|
||||
console.log("Deleting row with ID:", row.original.id);
|
||||
// console.log("Deleting row with ID:", row.original.id);
|
||||
return deleteDocument(row.original.id);
|
||||
});
|
||||
|
||||
try {
|
||||
// Execute all delete operations
|
||||
const results = await Promise.all(deletePromises);
|
||||
console.log("Delete results:", results);
|
||||
// console.log("Delete results:", results);
|
||||
|
||||
// Check if all deletions were successful
|
||||
const allSuccessful = results.every(result => result === true);
|
||||
|
|
|
@ -15,8 +15,14 @@ import {
|
|||
Database,
|
||||
SendHorizontal,
|
||||
FileText,
|
||||
Grid3x3
|
||||
Grid3x3,
|
||||
File,
|
||||
Globe,
|
||||
Webhook,
|
||||
FolderOpen,
|
||||
Upload
|
||||
} from 'lucide-react';
|
||||
import { IconBrandDiscord, IconBrandGithub, IconBrandNotion, IconBrandSlack, IconBrandYoutube, IconLayoutKanban } from "@tabler/icons-react";
|
||||
import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card';
|
||||
import { Button } from '@/components/ui/button';
|
||||
import { Input } from '@/components/ui/input';
|
||||
|
@ -47,6 +53,7 @@ import {
|
|||
import { MarkdownViewer } from '@/components/markdown-viewer';
|
||||
import { Logo } from '@/components/Logo';
|
||||
import { useSearchSourceConnectors } from '@/hooks';
|
||||
import { useDocuments } from '@/hooks/use-documents';
|
||||
|
||||
interface SourceItem {
|
||||
id: number;
|
||||
|
@ -63,6 +70,31 @@ interface ConnectorSource {
|
|||
sources: SourceItem[];
|
||||
}
|
||||
|
||||
type DocumentType = "EXTENSION" | "CRAWLED_URL" | "SLACK_CONNECTOR" | "NOTION_CONNECTOR" | "FILE" | "YOUTUBE_VIDEO" | "GITHUB_CONNECTOR" | "LINEAR_CONNECTOR" | "DISCORD_CONNECTOR";
|
||||
|
||||
interface Document {
|
||||
id: number;
|
||||
title: string;
|
||||
document_type: DocumentType;
|
||||
document_metadata: any;
|
||||
content: string;
|
||||
created_at: string;
|
||||
search_space_id: number;
|
||||
}
|
||||
|
||||
// Document type icons mapping
|
||||
const documentTypeIcons = {
|
||||
EXTENSION: Webhook,
|
||||
CRAWLED_URL: Globe,
|
||||
SLACK_CONNECTOR: IconBrandSlack,
|
||||
NOTION_CONNECTOR: IconBrandNotion,
|
||||
FILE: File,
|
||||
YOUTUBE_VIDEO: IconBrandYoutube,
|
||||
GITHUB_CONNECTOR: IconBrandGithub,
|
||||
LINEAR_CONNECTOR: IconLayoutKanban,
|
||||
DISCORD_CONNECTOR: IconBrandDiscord,
|
||||
} as const;
|
||||
|
||||
/**
|
||||
* Button that displays selected connectors and opens connector selection dialog
|
||||
*/
|
||||
|
@ -78,6 +110,41 @@ const ConnectorButton = ({ selectedConnectors, onClick }: { selectedConnectors:
|
|||
);
|
||||
};
|
||||
|
||||
/**
|
||||
* Button that displays selected documents count and opens document selection dialog
|
||||
*/
|
||||
const DocumentSelectorButton = ({
|
||||
selectedDocuments,
|
||||
onClick,
|
||||
documentsCount
|
||||
}: {
|
||||
selectedDocuments: number[],
|
||||
onClick: () => void,
|
||||
documentsCount: number
|
||||
}) => {
|
||||
return (
|
||||
<div className="relative">
|
||||
<Button
|
||||
variant="outline"
|
||||
onClick={onClick}
|
||||
className="h-8 px-2 text-xs font-medium transition-colors border-border bg-background hover:bg-muted/50"
|
||||
>
|
||||
<FolderOpen className="h-3 w-3" />
|
||||
</Button>
|
||||
{selectedDocuments.length > 0 && (
|
||||
<span className="absolute -top-1 -right-1 h-4 w-4 rounded-full bg-primary text-primary-foreground text-xs font-medium flex items-center justify-center leading-none">
|
||||
{selectedDocuments.length > 99 ? '99+' : selectedDocuments.length}
|
||||
</span>
|
||||
)}
|
||||
{selectedDocuments.length === 0 && (
|
||||
<span className="absolute -top-1 -right-1 h-4 w-4 rounded-full bg-muted text-muted-foreground text-xs font-medium flex items-center justify-center leading-none">
|
||||
0
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
// Create a wrapper component for the sources dialog content
|
||||
const SourcesDialogContent = ({
|
||||
connector,
|
||||
|
@ -245,7 +312,7 @@ const ChatPage = () => {
|
|||
const [sourceFilter, setSourceFilter] = useState("");
|
||||
const tabsListRef = useRef<HTMLDivElement>(null);
|
||||
const [terminalExpanded, setTerminalExpanded] = useState(false);
|
||||
const [selectedConnectors, setSelectedConnectors] = useState<string[]>(["CRAWLED_URL"]);
|
||||
const [selectedConnectors, setSelectedConnectors] = useState<string[]>([]);
|
||||
const [searchMode, setSearchMode] = useState<'DOCUMENTS' | 'CHUNKS'>('DOCUMENTS');
|
||||
const [researchMode, setResearchMode] = useState<ResearchMode>("QNA");
|
||||
const [currentTime, setCurrentTime] = useState<string>('');
|
||||
|
@ -256,6 +323,11 @@ const ChatPage = () => {
|
|||
const INITIAL_SOURCES_DISPLAY = 3;
|
||||
|
||||
const { search_space_id, chat_id } = useParams();
|
||||
|
||||
// Document selection state
|
||||
const [selectedDocuments, setSelectedDocuments] = useState<number[]>([]);
|
||||
const [documentFilter, setDocumentFilter] = useState("");
|
||||
const { documents, loading: isLoadingDocuments, error: documentsError } = useDocuments(Number(search_space_id));
|
||||
|
||||
// Function to scroll terminal to bottom
|
||||
const scrollTerminalToBottom = () => {
|
||||
|
@ -342,6 +414,13 @@ const ChatPage = () => {
|
|||
background-color: rgba(155, 155, 155, 0.5);
|
||||
border-radius: 20px;
|
||||
}
|
||||
/* Line clamp utility */
|
||||
.line-clamp-2 {
|
||||
display: -webkit-box;
|
||||
-webkit-line-clamp: 2;
|
||||
-webkit-box-orient: vertical;
|
||||
overflow: hidden;
|
||||
}
|
||||
`;
|
||||
document.head.appendChild(style);
|
||||
|
||||
|
@ -362,7 +441,8 @@ const ChatPage = () => {
|
|||
search_space_id: search_space_id,
|
||||
selected_connectors: selectedConnectors,
|
||||
research_mode: researchMode,
|
||||
search_mode: searchMode
|
||||
search_mode: searchMode,
|
||||
document_ids_to_add_in_context: selectedDocuments
|
||||
}
|
||||
},
|
||||
onError: (error) => {
|
||||
|
@ -377,7 +457,7 @@ const ChatPage = () => {
|
|||
try {
|
||||
if (!token) return; // Wait for token to be set
|
||||
|
||||
console.log('Fetching chat details for chat ID:', chat_id);
|
||||
// console.log('Fetching chat details for chat ID:', chat_id);
|
||||
|
||||
const response = await fetch(`${process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL}/api/v1/chats/${Number(chat_id)}`, {
|
||||
method: 'GET',
|
||||
|
@ -392,7 +472,7 @@ const ChatPage = () => {
|
|||
}
|
||||
|
||||
const chatData = await response.json();
|
||||
console.log('Chat details fetched:', chatData);
|
||||
// console.log('Chat details fetched:', chatData);
|
||||
|
||||
// Set research mode from chat data
|
||||
if (chatData.type) {
|
||||
|
@ -442,7 +522,7 @@ const ChatPage = () => {
|
|||
const title = userMessages[0].content;
|
||||
|
||||
|
||||
console.log('Updating chat with title:', title);
|
||||
// console.log('Updating chat with title:', title);
|
||||
|
||||
// Update the chat
|
||||
const response = await fetch(`${process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL}/api/v1/chats/${Number(chat_id)}`, {
|
||||
|
@ -464,7 +544,7 @@ const ChatPage = () => {
|
|||
throw new Error(`Failed to update chat: ${response.statusText}`);
|
||||
}
|
||||
|
||||
console.log('Chat updated successfully');
|
||||
// console.log('Chat updated successfully');
|
||||
}
|
||||
} catch (err) {
|
||||
console.error('Error updating chat:', err);
|
||||
|
@ -519,10 +599,9 @@ const ChatPage = () => {
|
|||
|
||||
if (!input.trim() || status !== 'ready') return;
|
||||
|
||||
// You can add additional logic here if needed
|
||||
// For example, validation for selected connectors
|
||||
if (selectedConnectors.length === 0) {
|
||||
alert("Please select at least one connector");
|
||||
// Validation: require at least one connector OR at least one document
|
||||
if (selectedConnectors.length === 0 && selectedDocuments.length === 0) {
|
||||
alert("Please select at least one connector or document");
|
||||
return;
|
||||
}
|
||||
|
||||
|
@ -988,17 +1067,162 @@ const ChatPage = () => {
|
|||
</Button>
|
||||
</form>
|
||||
<div className="flex items-center justify-between px-2 py-2 mt-3">
|
||||
<div className="flex items-center space-x-3">
|
||||
{/* Connector Selection Dialog */}
|
||||
<Dialog>
|
||||
<DialogTrigger asChild>
|
||||
<div className="h-8">
|
||||
<ConnectorButton
|
||||
selectedConnectors={selectedConnectors}
|
||||
onClick={() => { }}
|
||||
/>
|
||||
<div className="flex items-center gap-2 flex-wrap">
|
||||
{/* Document Selection Dialog */}
|
||||
<Dialog>
|
||||
<DialogTrigger asChild>
|
||||
<DocumentSelectorButton
|
||||
selectedDocuments={selectedDocuments}
|
||||
onClick={() => { }}
|
||||
documentsCount={documents?.length || 0}
|
||||
/>
|
||||
</DialogTrigger>
|
||||
<DialogContent className="sm:max-w-2xl max-h-[80vh] overflow-y-auto">
|
||||
<DialogHeader>
|
||||
<DialogTitle className="flex items-center justify-between">
|
||||
<span>Select Documents</span>
|
||||
<Button
|
||||
variant="outline"
|
||||
size="sm"
|
||||
onClick={() => window.open(`/dashboard/${search_space_id}/documents/upload`, '_blank')}
|
||||
className="h-8"
|
||||
>
|
||||
<Upload className="h-3 w-3 mr-1.5" />
|
||||
Upload
|
||||
</Button>
|
||||
</DialogTitle>
|
||||
<DialogDescription>
|
||||
Choose documents to include in your research context
|
||||
</DialogDescription>
|
||||
</DialogHeader>
|
||||
|
||||
{/* Document Search */}
|
||||
<div className="relative my-4">
|
||||
<Search className="absolute left-2 top-1/2 transform -translate-y-1/2 h-4 w-4 text-gray-400 dark:text-gray-500" />
|
||||
<Input
|
||||
placeholder="Search documents..."
|
||||
className="pl-8 pr-4"
|
||||
value={documentFilter}
|
||||
onChange={(e) => setDocumentFilter(e.target.value)}
|
||||
/>
|
||||
{documentFilter && (
|
||||
<Button
|
||||
variant="ghost"
|
||||
size="icon"
|
||||
className="absolute right-2 top-1/2 transform -translate-y-1/2 h-4 w-4"
|
||||
onClick={() => setDocumentFilter("")}
|
||||
>
|
||||
<X className="h-3 w-3" />
|
||||
</Button>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* Document List */}
|
||||
<div className="space-y-2 max-h-96 overflow-y-auto">
|
||||
{isLoadingDocuments ? (
|
||||
<div className="flex justify-center py-8">
|
||||
<Loader2 className="h-6 w-6 animate-spin text-primary" />
|
||||
</div>
|
||||
) : documentsError ? (
|
||||
<div className="text-center py-8 text-destructive">
|
||||
<p>Error loading documents</p>
|
||||
</div>
|
||||
) : (
|
||||
(() => {
|
||||
const filteredDocuments = documents?.filter(doc =>
|
||||
doc.title.toLowerCase().includes(documentFilter.toLowerCase())
|
||||
) || [];
|
||||
|
||||
if (filteredDocuments.length === 0) {
|
||||
return (
|
||||
<div className="text-center py-8 text-muted-foreground">
|
||||
<FolderOpen className="h-8 w-8 mx-auto mb-2 opacity-50" />
|
||||
<p>{documentFilter ? `No documents found matching "${documentFilter}"` : 'No documents available'}</p>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
return filteredDocuments.map((document) => {
|
||||
const Icon = documentTypeIcons[document.document_type];
|
||||
const isSelected = selectedDocuments.includes(document.id);
|
||||
|
||||
return (
|
||||
<div
|
||||
key={document.id}
|
||||
className={`flex items-start gap-3 p-3 rounded-md border cursor-pointer transition-colors ${
|
||||
isSelected
|
||||
? 'border-primary bg-primary/10'
|
||||
: 'border-border hover:border-primary/50 hover:bg-muted'
|
||||
}`}
|
||||
onClick={() => {
|
||||
setSelectedDocuments(prev =>
|
||||
isSelected
|
||||
? prev.filter(id => id !== document.id)
|
||||
: [...prev, document.id]
|
||||
);
|
||||
}}
|
||||
>
|
||||
<div className="flex-shrink-0 w-6 h-6 flex items-center justify-center mt-0.5">
|
||||
<Icon size={16} className="text-muted-foreground" />
|
||||
</div>
|
||||
<div className="flex-1 min-w-0">
|
||||
<h3 className="font-medium text-sm truncate">{document.title}</h3>
|
||||
<p className="text-xs text-muted-foreground mt-1">
|
||||
{document.document_type.replace(/_/g, ' ').toLowerCase()}
|
||||
{' • '}
|
||||
{new Date(document.created_at).toLocaleDateString()}
|
||||
</p>
|
||||
<p className="text-xs text-muted-foreground mt-1 line-clamp-2">
|
||||
{document.content.substring(0, 150)}...
|
||||
</p>
|
||||
</div>
|
||||
{isSelected && (
|
||||
<div className="flex-shrink-0">
|
||||
<Check className="h-4 w-4 text-primary" />
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
});
|
||||
})()
|
||||
)}
|
||||
</div>
|
||||
|
||||
<DialogFooter className="flex justify-between items-center">
|
||||
<div className="text-sm text-muted-foreground">
|
||||
{selectedDocuments.length} document{selectedDocuments.length !== 1 ? 's' : ''} selected
|
||||
</div>
|
||||
</DialogTrigger>
|
||||
<div className="flex gap-2">
|
||||
<Button
|
||||
variant="outline"
|
||||
onClick={() => setSelectedDocuments([])}
|
||||
>
|
||||
Clear All
|
||||
</Button>
|
||||
<Button
|
||||
onClick={() => {
|
||||
const filteredDocuments = documents?.filter(doc =>
|
||||
doc.title.toLowerCase().includes(documentFilter.toLowerCase())
|
||||
) || [];
|
||||
const allFilteredIds = filteredDocuments.map(doc => doc.id);
|
||||
setSelectedDocuments(allFilteredIds);
|
||||
}}
|
||||
>
|
||||
Select All Filtered
|
||||
</Button>
|
||||
</div>
|
||||
</DialogFooter>
|
||||
</DialogContent>
|
||||
</Dialog>
|
||||
|
||||
{/* Connector Selection Dialog */}
|
||||
<Dialog>
|
||||
<DialogTrigger asChild>
|
||||
<ConnectorButton
|
||||
selectedConnectors={selectedConnectors}
|
||||
onClick={() => { }}
|
||||
/>
|
||||
</DialogTrigger>
|
||||
<DialogContent className="sm:max-w-md">
|
||||
<DialogHeader>
|
||||
<DialogTitle>Select Connectors</DialogTitle>
|
||||
|
@ -1065,33 +1289,31 @@ const ChatPage = () => {
|
|||
</Dialog>
|
||||
|
||||
{/* Search Mode Control */}
|
||||
<div className="flex items-center p-0.5 rounded-md border border-border bg-muted/20 h-8">
|
||||
<button
|
||||
<div className="flex gap-1">
|
||||
<Button
|
||||
variant={searchMode === 'DOCUMENTS' ? 'default' : 'outline'}
|
||||
size="sm"
|
||||
onClick={() => setSearchMode('DOCUMENTS')}
|
||||
className={`flex h-full items-center justify-center gap-1 px-2 rounded text-xs font-medium transition-colors flex-1 whitespace-nowrap overflow-hidden ${
|
||||
searchMode === 'DOCUMENTS'
|
||||
? 'bg-primary text-primary-foreground shadow-sm'
|
||||
: 'text-muted-foreground hover:text-foreground hover:bg-muted/50'
|
||||
}`}
|
||||
className="h-8 px-3 text-xs"
|
||||
title="Search full documents"
|
||||
>
|
||||
<FileText className="h-3 w-3 flex-shrink-0 mr-1" />
|
||||
<span>Full Document</span>
|
||||
</button>
|
||||
<button
|
||||
<FileText className="h-3 w-3 mr-1.5" />
|
||||
<span className="hidden sm:inline">Full</span>
|
||||
</Button>
|
||||
<Button
|
||||
variant={searchMode === 'CHUNKS' ? 'default' : 'outline'}
|
||||
size="sm"
|
||||
onClick={() => setSearchMode('CHUNKS')}
|
||||
className={`flex h-full items-center justify-center gap-1 px-2 rounded text-xs font-medium transition-colors flex-1 whitespace-nowrap overflow-hidden ${
|
||||
searchMode === 'CHUNKS'
|
||||
? 'bg-primary text-primary-foreground shadow-sm'
|
||||
: 'text-muted-foreground hover:text-foreground hover:bg-muted/50'
|
||||
}`}
|
||||
className="h-8 px-3 text-xs"
|
||||
title="Search document chunks"
|
||||
>
|
||||
<Grid3x3 className="h-3 w-3 flex-shrink-0 mr-1" />
|
||||
<span>Document Chunks</span>
|
||||
</button>
|
||||
<Grid3x3 className="h-3 w-3 mr-1.5" />
|
||||
<span className="hidden sm:inline">Chunks</span>
|
||||
</Button>
|
||||
</div>
|
||||
|
||||
{/* Research Mode Control */}
|
||||
<div className="h-8">
|
||||
<div className="h-8 min-w-0 overflow-hidden">
|
||||
<ResearchModeControl
|
||||
value={researchMode}
|
||||
onChange={setResearchMode}
|
||||
|
|
|
@ -35,7 +35,7 @@ const TokenHandler = ({
|
|||
try {
|
||||
// Store token in localStorage
|
||||
localStorage.setItem(storageKey, token);
|
||||
console.log(`Token stored in localStorage with key: ${storageKey}`);
|
||||
// console.log(`Token stored in localStorage with key: ${storageKey}`);
|
||||
|
||||
// Redirect to specified path
|
||||
router.push(redirectPath);
|
||||
|
|
|
@ -120,8 +120,13 @@ export function AppSidebarProvider({
|
|||
// Use the API client instead of direct fetch - filter by current search space ID
|
||||
const chats: Chat[] = await apiClient.get<Chat[]>(`api/v1/chats/?limit=5&skip=0&search_space_id=${searchSpaceId}`);
|
||||
|
||||
// Sort chats by created_at in descending order (newest first)
|
||||
const sortedChats = chats.sort((a, b) =>
|
||||
new Date(b.created_at).getTime() - new Date(a.created_at).getTime()
|
||||
);
|
||||
// console.log("sortedChats", sortedChats);
|
||||
// Transform API response to the format expected by AppSidebar
|
||||
const formattedChats = chats.map(chat => ({
|
||||
const formattedChats = sortedChats.map(chat => ({
|
||||
name: chat.title || `Chat ${chat.id}`, // Fallback if title is empty
|
||||
url: `/dashboard/${chat.search_space_id}/researcher/${chat.id}`,
|
||||
icon: 'MessageCircleMore',
|
||||
|
|
|
@ -227,7 +227,7 @@ export function AppSidebar({
|
|||
</SidebarHeader>
|
||||
<SidebarContent>
|
||||
<NavMain items={processedNavMain} />
|
||||
{processedRecentChats.length > 0 && <NavProjects projects={processedRecentChats} />}
|
||||
{processedRecentChats.length > 0 && <NavProjects chats={processedRecentChats} />}
|
||||
<NavSecondary items={processedNavSecondary} className="mt-auto" />
|
||||
</SidebarContent>
|
||||
<SidebarFooter>
|
||||
|
|
|
@ -43,9 +43,9 @@ interface ChatAction {
|
|||
}
|
||||
|
||||
export function NavProjects({
|
||||
projects,
|
||||
chats,
|
||||
}: {
|
||||
projects: {
|
||||
chats: {
|
||||
name: string
|
||||
url: string
|
||||
icon: LucideIcon
|
||||
|
@ -57,13 +57,13 @@ export function NavProjects({
|
|||
const { isMobile } = useSidebar()
|
||||
const router = useRouter()
|
||||
|
||||
const searchSpaceId = projects[0]?.search_space_id || ""
|
||||
const searchSpaceId = chats[0]?.search_space_id || ""
|
||||
|
||||
return (
|
||||
<SidebarGroup className="group-data-[collapsible=icon]:hidden">
|
||||
<SidebarGroupLabel>Recent Chats</SidebarGroupLabel>
|
||||
<SidebarMenu>
|
||||
{projects.map((item, index) => (
|
||||
{chats.map((item, index) => (
|
||||
<SidebarMenuItem key={item.id ? `chat-${item.id}` : `chat-${item.name}-${index}`}>
|
||||
<SidebarMenuButton>
|
||||
<item.icon />
|
||||
|
|
Loading…
Add table
Reference in a new issue