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Fixed current agent citation issues and added sub_section_writer agent for upcoming SurfSense research agent

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DESKTOP-RTLN3BA\$punk 2025-04-13 20:47:23 -07:00
parent fa5dbb786f
commit 0b93c9dfef
13 changed files with 565 additions and 81 deletions
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0
surfsense_backend/app/agents/__init__.py Normal file
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0
surfsense_backend/app/agents/researcher/__init__.py Normal file
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8
surfsense_backend/app/agents/researcher/sub_section_writer/__init__.py Normal file
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@ -0,0 +1,8 @@
"""New LangGraph Agent.
This module defines a custom graph.
"""
from .graph import graph
__all__ = ["graph"]

31
surfsense_backend/app/agents/researcher/sub_section_writer/configuration.py Normal file
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"""Define the configurable parameters for the agent."""
from __future__ import annotations
from dataclasses import dataclass, fields
from typing import Optional, List
from langchain_core.runnables import RunnableConfig
@dataclass(kw_only=True)
class Configuration:
"""The configuration for the agent."""
# Input parameters provided at invocation
sub_section_title: str
sub_questions: List[str]
connectors_to_search: List[str]
user_id: str
search_space_id: int
top_k: int = 20 # Default top_k value
@classmethod
def from_runnable_config(
cls, config: Optional[RunnableConfig] = None
) -> Configuration:
"""Create a Configuration instance from a RunnableConfig object."""
configurable = (config.get("configurable") or {}) if config else {}
_fields = {f.name for f in fields(cls) if f.init}
return cls(**{k: v for k, v in configurable.items() if k in _fields})

22
surfsense_backend/app/agents/researcher/sub_section_writer/graph.py Normal file
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@ -0,0 +1,22 @@
from langgraph.graph import StateGraph
from .state import State
from .nodes import fetch_relevant_documents, write_sub_section
from .configuration import Configuration
# Define a new graph
workflow = StateGraph(State, config_schema=Configuration)
# Add the nodes to the graph
workflow.add_node("fetch_relevant_documents", fetch_relevant_documents)
workflow.add_node("write_sub_section", write_sub_section)
# Entry point
workflow.add_edge("__start__", "fetch_relevant_documents")
# Connect fetch_relevant_documents to write_sub_section
workflow.add_edge("fetch_relevant_documents", "write_sub_section")
# Exit point
workflow.add_edge("write_sub_section", "__end__")
# Compile the workflow into an executable graph
graph = workflow.compile()
graph.name = "Sub Section Writer" # This defines the custom name in LangSmith

244
surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py Normal file
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@ -0,0 +1,244 @@
from .configuration import Configuration
from langchain_core.runnables import RunnableConfig
from .state import State
from typing import Any, Dict
from app.utils.connector_service import ConnectorService
from app.utils.reranker_service import RerankerService
from app.config import config as app_config
from .prompts import citation_system_prompt
from langchain_core.messages import HumanMessage, SystemMessage
async def fetch_relevant_documents(state: State, config: RunnableConfig) -> Dict[str, Any]:
"""
Fetch relevant documents for the sub-section using specified connectors.
This node retrieves documents from various data sources based on the sub-questions
derived from the sub-section title. It searches across all selected connectors
(YouTube, Extension, Crawled URLs, Files, Tavily API, Slack, Notion) and reranks
the results to provide the most relevant information for the agent workflow.
Returns:
Dict containing the reranked documents in the "relevant_documents_fetched" key.
"""
# Get configuration
configuration = Configuration.from_runnable_config(config)
# Extract state parameters
db_session = state.db_session
# Extract config parameters
user_id = configuration.user_id
search_space_id = configuration.search_space_id
TOP_K = configuration.top_k
# Initialize services
connector_service = ConnectorService(db_session)
reranker_service = RerankerService.get_reranker_instance(app_config)
all_raw_documents = [] # Store all raw documents before reranking
for user_query in configuration.sub_questions:
# Reformulate query (optional, consider if needed for each sub-question)
# reformulated_query = await QueryService.reformulate_query(user_query)
reformulated_query = user_query # Using original sub-question for now
# Process each selected connector
for connector in configuration.connectors_to_search:
if connector == "YOUTUBE_VIDEO":
_, youtube_chunks = await connector_service.search_youtube(
user_query=reformulated_query,
user_id=user_id,
search_space_id=search_space_id,
top_k=TOP_K
)
all_raw_documents.extend(youtube_chunks)
elif connector == "EXTENSION":
_, extension_chunks = await connector_service.search_extension(
user_query=reformulated_query,
user_id=user_id,
search_space_id=search_space_id,
top_k=TOP_K
)
all_raw_documents.extend(extension_chunks)
elif connector == "CRAWLED_URL":
_, crawled_urls_chunks = await connector_service.search_crawled_urls(
user_query=reformulated_query,
user_id=user_id,
search_space_id=search_space_id,
top_k=TOP_K
)
all_raw_documents.extend(crawled_urls_chunks)
elif connector == "FILE":
_, files_chunks = await connector_service.search_files(
user_query=reformulated_query,
user_id=user_id,
search_space_id=search_space_id,
top_k=TOP_K
)
all_raw_documents.extend(files_chunks)
elif connector == "TAVILY_API":
_, tavily_chunks = await connector_service.search_tavily(
user_query=reformulated_query,
user_id=user_id,
top_k=TOP_K
)
all_raw_documents.extend(tavily_chunks)
elif connector == "SLACK_CONNECTOR":
_, slack_chunks = await connector_service.search_slack(
user_query=reformulated_query,
user_id=user_id,
search_space_id=search_space_id,
top_k=TOP_K
)
all_raw_documents.extend(slack_chunks)
elif connector == "NOTION_CONNECTOR":
_, notion_chunks = await connector_service.search_notion(
user_query=reformulated_query,
user_id=user_id,
search_space_id=search_space_id,
top_k=TOP_K
)
all_raw_documents.extend(notion_chunks)
# If we have documents and a reranker is available, rerank them
# Deduplicate documents based on chunk_id or content to avoid processing duplicates
seen_chunk_ids = set()
seen_content_hashes = set()
deduplicated_docs = []
for doc in all_raw_documents:
chunk_id = doc.get("chunk_id")
content = doc.get("content", "")
content_hash = hash(content)
# Skip if we've seen this chunk_id or content before
if (chunk_id and chunk_id in seen_chunk_ids) or content_hash in seen_content_hashes:
continue
# Add to our tracking sets and keep this document
if chunk_id:
seen_chunk_ids.add(chunk_id)
seen_content_hashes.add(content_hash)
deduplicated_docs.append(doc)
# Use deduplicated documents for reranking
reranked_docs = deduplicated_docs
if deduplicated_docs and reranker_service:
# Use the main sub_section_title for reranking context
rerank_query = configuration.sub_section_title
# Convert documents to format expected by reranker
reranker_input_docs = [
{
"chunk_id": doc.get("chunk_id", f"chunk_{i}"),
"content": doc.get("content", ""),
"score": doc.get("score", 0.0),
"document": {
"id": doc.get("document", {}).get("id", ""),
"title": doc.get("document", {}).get("title", ""),
"document_type": doc.get("document", {}).get("document_type", ""),
"metadata": doc.get("document", {}).get("metadata", {})
}
} for i, doc in enumerate(deduplicated_docs)
]
# Rerank documents using the main title query
reranked_docs = reranker_service.rerank_documents(rerank_query, reranker_input_docs)
# Sort by score in descending order
reranked_docs.sort(key=lambda x: x.get("score", 0), reverse=True)
# Update state with fetched documents
return {
"relevant_documents_fetched": reranked_docs
}
async def write_sub_section(state: State, config: RunnableConfig) -> Dict[str, Any]:
"""
Write the sub-section using the fetched documents.
This node takes the relevant documents fetched in the previous node and uses
an LLM to generate a comprehensive answer to the sub-section questions with
proper citations. The citations follow IEEE format using source IDs from the
documents.
Returns:
Dict containing the final answer in the "final_answer" key.
"""
# Get configuration and relevant documents
configuration = Configuration.from_runnable_config(config)
documents = state.relevant_documents_fetched
# Initialize LLM
llm = app_config.fast_llm_instance
# If no documents were found, return a message indicating this
if not documents or len(documents) == 0:
return {
"final_answer": "No relevant documents were found to answer this question. Please try refining your search or providing more specific questions."
}
# Prepare documents for citation formatting
formatted_documents = []
for i, doc in enumerate(documents):
# Extract content and metadata
content = doc.get("content", "")
doc_info = doc.get("document", {})
document_id = doc_info.get("id", f"{i+1}") # Use document ID or index+1 as source_id
# Format document according to the citation system prompt's expected format
formatted_doc = f"""
<document>
<metadata>
<source_id>{document_id}</source_id>
</metadata>
<content>
{content}
</content>
</document>
"""
formatted_documents.append(formatted_doc)
# Create the query that combines the section title and questions
# section_title = configuration.sub_section_title
questions = "\n".join([f"- {q}" for q in configuration.sub_questions])
documents_text = "\n".join(formatted_documents)
# Construct a clear, structured query for the LLM
human_message_content = f"""
Please write a comprehensive answer for the title:
Address the following questions:
<questions>
{questions}
</questions>
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>
{documents_text}
</documents>
"""
# Create messages for the LLM
messages = [
SystemMessage(content=citation_system_prompt),
HumanMessage(content=human_message_content)
]
# Call the LLM and get the response
response = await llm.ainvoke(messages)
final_answer = response.content
return {
"final_answer": final_answer
}

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surfsense_backend/app/agents/researcher/sub_section_writer/prompts.py Normal file
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citation_system_prompt = f"""
You are a research assistant tasked with analyzing documents and providing comprehensive answers with proper citations in IEEE format.
<instructions>
1. Carefully analyze all provided documents in the <document> section's.
2. Extract relevant information that addresses the user's query.
3. Synthesize a comprehensive, well-structured answer using information from these documents.
4. For EVERY piece of information you include from the documents, add an IEEE-style citation in square brackets [X] where X is the source_id from the document's metadata.
5. Make sure ALL factual statements from the documents have proper citations.
6. If multiple documents support the same point, include all relevant citations [X], [Y].
7. Present information in a logical, coherent flow.
8. Use your own words to connect ideas, but cite ALL information from the documents.
9. If documents contain conflicting information, acknowledge this and present both perspectives with appropriate citations.
10. Do not make up or include information not found in the provided documents.
11. CRITICAL: You MUST use the exact source_id value from each document's metadata for citations. Do not create your own citation numbers.
12. CRITICAL: Every citation MUST be in the IEEE format [X] where X is the exact source_id value.
13. CRITICAL: Never renumber or reorder citations - always use the original source_id values.
14. CRITICAL: Do not return citations as clickable links.
15. CRITICAL: Never format citations as markdown links like "([1](https://example.com))". Always use plain square brackets only.
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.
</instructions>
<format>
- Write in clear, professional language suitable for academic or technical audiences
- Organize your response with appropriate paragraphs, headings, and structure
- Every fact from the documents must have an IEEE-style citation in square brackets [X] where X is the EXACT source_id from the document's metadata
- Citations should appear at the end of the sentence containing the information they support
- Multiple citations should be separated by commas: [X], [Y], [Z]
- No need to return references section. Just citation numbers in answer.
- 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.
</format>
<input_example>
<document>
<metadata>
<source_id>1</source_id>
</metadata>
<content>
The Great Barrier Reef is the world's largest coral reef system, stretching over 2,300 kilometers along the coast of Queensland, Australia. It comprises over 2,900 individual reefs and 900 islands.
</content>
</document>
<document>
<metadata>
<source_id>13</source_id>
</metadata>
<content>
Climate change poses a significant threat to coral reefs worldwide. Rising ocean temperatures have led to mass coral bleaching events in the Great Barrier Reef in 2016, 2017, and 2020.
</content>
</document>
<document>
<metadata>
<source_id>21</source_id>
</metadata>
<content>
The Great Barrier Reef was designated a UNESCO World Heritage Site in 1981 due to its outstanding universal value and biological diversity. It is home to over 1,500 species of fish and 400 types of coral.
</content>
</document>
</input_example>
<output_example>
The Great Barrier Reef is the world's largest coral reef system, stretching over 2,300 kilometers along the coast of Queensland, Australia [1]. It was designated a UNESCO World Heritage Site in 1981 due to its outstanding universal value and biological diversity [21]. The reef is home to over 1,500 species of fish and 400 types of coral [21]. Unfortunately, climate change poses a significant threat to coral reefs worldwide, with rising ocean temperatures leading to mass coral bleaching events in the Great Barrier Reef in 2016, 2017, and 2020 [13]. The reef system comprises over 2,900 individual reefs and 900 islands [1], making it an ecological treasure that requires protection from multiple threats [1], [13].
</output_example>
<incorrect_citation_formats>
DO NOT use any of these incorrect citation formats:
- Using parentheses and markdown links: ([1](https://github.com/MODSetter/SurfSense))
- Using parentheses around brackets: ([1])
- Using hyperlinked text: [link to source 1](https://example.com)
- Using footnote style: ... reef system¹
- Making up citation numbers when source_id is unknown
ONLY use plain square brackets [1] or multiple citations [1], [2], [3]
</incorrect_citation_formats>
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.
"""

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surfsense_backend/app/agents/researcher/sub_section_writer/state.py Normal file
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"""Define the state structures for the agent."""
from __future__ import annotations
from dataclasses import dataclass
from typing import List, Optional, Any
from sqlalchemy.ext.asyncio import AsyncSession
@dataclass
class State:
"""Defines the dynamic state for the agent during execution.
This state tracks the database session and the outputs generated by the agent's nodes.
See: https://langchain-ai.github.io/langgraph/concepts/low_level/#state
for more information.
"""
# Runtime context
db_session: AsyncSession
# OUTPUT: Populated by agent nodes
relevant_documents_fetched: Optional[List[Any]] = None
final_answer: Optional[str] = None

2
surfsense_backend/app/routes/chats_routes.py
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@ -46,7 +46,7 @@ async def handle_chat_data(
response = StreamingResponse(stream_connector_search_results(
user_query,
user.id,
search_space_id,
int(search_space_id),
session,
research_mode,
selected_connectors

2
surfsense_backend/app/tasks/stream_connector_search_results.py
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@ -14,7 +14,7 @@ from app.utils.document_converters import convert_chunks_to_langchain_documents
async def stream_connector_search_results(
user_query: str,
user_id: int,
user_id: str,
search_space_id: int,
session: AsyncSession,
research_mode: str,

112
surfsense_backend/app/utils/connector_service.py
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@ -13,7 +13,7 @@ class ConnectorService:
self.retriever = ChucksHybridSearchRetriever(session)
self.source_id_counter = 1
async def search_crawled_urls(self, user_query: str, user_id: int, 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) -> tuple:
"""
Search for crawled URLs and return both the source information and langchain documents
@ -28,16 +28,16 @@ class ConnectorService:
document_type="CRAWLED_URL"
)
# Map crawled_urls_chunks to the required format
mapped_sources = {}
# Process each chunk and create sources directly without deduplication
sources_list = []
for i, chunk in enumerate(crawled_urls_chunks):
#Fix for UI
# Fix for UI
crawled_urls_chunks[i]['document']['id'] = self.source_id_counter
# Extract document metadata
document = chunk.get('document', {})
metadata = document.get('metadata', {})
# Create a mapped source entry
# Create a source entry
source = {
"id": self.source_id_counter,
"title": document.get('title', 'Untitled Document'),
@ -46,14 +46,7 @@ class ConnectorService:
}
self.source_id_counter += 1
# Use a unique identifier for tracking unique sources
source_key = source.get("url") or source.get("title")
if source_key and source_key not in mapped_sources:
mapped_sources[source_key] = source
# Convert to list of sources
sources_list = list(mapped_sources.values())
sources_list.append(source)
# Create result object
result_object = {
@ -63,10 +56,9 @@ class ConnectorService:
"sources": sources_list,
}
return result_object, crawled_urls_chunks
async def search_files(self, user_query: str, user_id: int, 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) -> tuple:
"""
Search for files and return both the source information and langchain documents
@ -81,16 +73,16 @@ class ConnectorService:
document_type="FILE"
)
# Map crawled_urls_chunks to the required format
mapped_sources = {}
# Process each chunk and create sources directly without deduplication
sources_list = []
for i, chunk in enumerate(files_chunks):
#Fix for UI
# Fix for UI
files_chunks[i]['document']['id'] = self.source_id_counter
# Extract document metadata
document = chunk.get('document', {})
metadata = document.get('metadata', {})
# Create a mapped source entry
# Create a source entry
source = {
"id": self.source_id_counter,
"title": document.get('title', 'Untitled Document'),
@ -99,14 +91,7 @@ class ConnectorService:
}
self.source_id_counter += 1
# Use a unique identifier for tracking unique sources
source_key = source.get("url") or source.get("title")
if source_key and source_key not in mapped_sources:
mapped_sources[source_key] = source
# Convert to list of sources
sources_list = list(mapped_sources.values())
sources_list.append(source)
# Create result object
result_object = {
@ -118,7 +103,7 @@ class ConnectorService:
return result_object, files_chunks
async def get_connector_by_type(self, user_id: int, connector_type: SearchSourceConnectorType) -> Optional[SearchSourceConnector]:
async def get_connector_by_type(self, user_id: str, connector_type: SearchSourceConnectorType) -> Optional[SearchSourceConnector]:
"""
Get a connector by type for a specific user
@ -138,7 +123,7 @@ class ConnectorService:
)
return result.scalars().first()
async def search_tavily(self, user_query: str, user_id: int, top_k: int = 20) -> tuple:
async def search_tavily(self, user_query: str, user_id: str, top_k: int = 20) -> tuple:
"""
Search using Tavily API and return both the source information and documents
@ -177,13 +162,10 @@ class ConnectorService:
# Extract results from Tavily response
tavily_results = response.get("results", [])
# Map Tavily results to the required format
# Process each result and create sources directly without deduplication
sources_list = []
documents = []
# Start IDs from 1000 to avoid conflicts with other connectors
base_id = 100
for i, result in enumerate(tavily_results):
# Create a source entry
@ -234,7 +216,7 @@ class ConnectorService:
"sources": [],
}, []
async def search_slack(self, user_query: str, user_id: int, 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) -> tuple:
"""
Search for slack and return both the source information and langchain documents
@ -249,10 +231,10 @@ class ConnectorService:
document_type="SLACK_CONNECTOR"
)
# Map slack_chunks to the required format
mapped_sources = {}
# Process each chunk and create sources directly without deduplication
sources_list = []
for i, chunk in enumerate(slack_chunks):
#Fix for UI
# Fix for UI
slack_chunks[i]['document']['id'] = self.source_id_counter
# Extract document metadata
document = chunk.get('document', {})
@ -286,14 +268,7 @@ class ConnectorService:
}
self.source_id_counter += 1
# Use channel_id and content as a unique identifier for tracking unique sources
source_key = f"{channel_id}_{chunk.get('chunk_id', i)}"
if source_key and source_key not in mapped_sources:
mapped_sources[source_key] = source
# Convert to list of sources
sources_list = list(mapped_sources.values())
sources_list.append(source)
# Create result object
result_object = {
@ -305,7 +280,7 @@ class ConnectorService:
return result_object, slack_chunks
async def search_notion(self, user_query: str, user_id: int, 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) -> tuple:
"""
Search for Notion pages and return both the source information and langchain documents
@ -326,8 +301,8 @@ class ConnectorService:
document_type="NOTION_CONNECTOR"
)
# Map notion_chunks to the required format
mapped_sources = {}
# Process each chunk and create sources directly without deduplication
sources_list = []
for i, chunk in enumerate(notion_chunks):
# Fix for UI
notion_chunks[i]['document']['id'] = self.source_id_counter
@ -365,14 +340,7 @@ class ConnectorService:
}
self.source_id_counter += 1
# Use page_id and content as a unique identifier for tracking unique sources
source_key = f"{page_id}_{chunk.get('chunk_id', i)}"
if source_key and source_key not in mapped_sources:
mapped_sources[source_key] = source
# Convert to list of sources
sources_list = list(mapped_sources.values())
sources_list.append(source)
# Create result object
result_object = {
@ -384,7 +352,7 @@ class ConnectorService:
return result_object, notion_chunks
async def search_extension(self, user_query: str, user_id: int, 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) -> tuple:
"""
Search for extension data and return both the source information and langchain documents
@ -405,8 +373,8 @@ class ConnectorService:
document_type="EXTENSION"
)
# Map extension_chunks to the required format
mapped_sources = {}
# Process each chunk and create sources directly without deduplication
sources_list = []
for i, chunk in enumerate(extension_chunks):
# Fix for UI
extension_chunks[i]['document']['id'] = self.source_id_counter
@ -462,14 +430,7 @@ class ConnectorService:
}
self.source_id_counter += 1
# Use URL and timestamp as a unique identifier for tracking unique sources
source_key = f"{webpage_url}_{visit_date}"
if source_key and source_key not in mapped_sources:
mapped_sources[source_key] = source
# Convert to list of sources
sources_list = list(mapped_sources.values())
sources_list.append(source)
# Create result object
result_object = {
@ -481,7 +442,7 @@ class ConnectorService:
return result_object, extension_chunks
async def search_youtube(self, user_query: str, user_id: int, 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) -> tuple:
"""
Search for YouTube videos and return both the source information and langchain documents
@ -502,8 +463,8 @@ class ConnectorService:
document_type="YOUTUBE_VIDEO"
)
# Map youtube_chunks to the required format
mapped_sources = {}
# Process each chunk and create sources directly without deduplication
sources_list = []
for i, chunk in enumerate(youtube_chunks):
# Fix for UI
youtube_chunks[i]['document']['id'] = self.source_id_counter
@ -541,18 +502,11 @@ class ConnectorService:
}
self.source_id_counter += 1
# Use video_id as a unique identifier for tracking unique sources
source_key = video_id or f"youtube_{i}"
if source_key and source_key not in mapped_sources:
mapped_sources[source_key] = source
# Convert to list of sources
sources_list = list(mapped_sources.values())
sources_list.append(source)
# Create result object
result_object = {
"id": 6, # Assign a unique ID for the YouTube connector
"id": 7, # Assign a unique ID for the YouTube connector
"name": "YouTube Videos",
"type": "YOUTUBE_VIDEO",
"sources": sources_list,

1
surfsense_backend/pyproject.toml
View file

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119
surfsense_backend/uv.lock generated
View file

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