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Merge pull request #174 from MODSetter/dev

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fix(backend): Fix rerank_documents node in sub_section_writer & qna_agent
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Rohan Verma 2025-07-03 14:19:14 -07:00 • committed by GitHub
commit 15a019ef76
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12 changed files with 2719 additions and 2120 deletions
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24
surfsense_backend/app/agents/researcher/graph.py
View file

@ -68,27 +68,3 @@ def build_graph():
# Compile the graph once when the module is loaded # Compile the graph once when the module is loaded
graph = build_graph() graph = build_graph()
# L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g==

62
surfsense_backend/app/agents/researcher/nodes.py
View file

@ -2,11 +2,10 @@ import asyncio
import json import json
from typing import Any, Dict, List from typing import Any, Dict, List
from app.db import async_session_maker
from app.utils.connector_service import ConnectorService from app.utils.connector_service import ConnectorService
from langchain_core.messages import HumanMessage, SystemMessage from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.runnables import RunnableConfig from langchain_core.runnables import RunnableConfig
from pydantic import BaseModel, Field
from sqlalchemy.ext.asyncio import AsyncSession from sqlalchemy.ext.asyncio import AsyncSession
from .configuration import Configuration, SearchMode from .configuration import Configuration, SearchMode
@ -15,6 +14,7 @@ from .state import State
from .sub_section_writer.graph import graph as sub_section_writer_graph from .sub_section_writer.graph import graph as sub_section_writer_graph
from .sub_section_writer.configuration import SubSectionType from .sub_section_writer.configuration import SubSectionType
from .qna_agent.graph import graph as qna_agent_graph from .qna_agent.graph import graph as qna_agent_graph
from .utils import AnswerOutline, get_connector_emoji, get_connector_friendly_name
from app.utils.query_service import QueryService from app.utils.query_service import QueryService
@ -24,7 +24,6 @@ from langgraph.types import StreamWriter
from sqlalchemy.future import select from sqlalchemy.future import select
from app.db import Document, SearchSpace from app.db import Document, SearchSpace
async def fetch_documents_by_ids( async def fetch_documents_by_ids(
document_ids: List[int], document_ids: List[int],
user_id: str, user_id: str,
@ -41,7 +40,7 @@ async def fetch_documents_by_ids(
Args: Args:
document_ids: List of document IDs to fetch document_ids: List of document IDs to fetch
user_id: The user ID to check ownership user_id: The user ID to check ownership
db_session: The database session eg L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g== db_session: The database session
Returns: Returns:
Tuple of (source_objects, document_chunks) - similar to ConnectorService pattern Tuple of (source_objects, document_chunks) - similar to ConnectorService pattern
@ -168,7 +167,7 @@ async def fetch_documents_by_ids(
url = f"https://www.youtube.com/watch?v={video_id}" if video_id else "" url = f"https://www.youtube.com/watch?v={video_id}" if video_id else ""
elif doc_type == "DISCORD_CONNECTOR": elif doc_type == "DISCORD_CONNECTOR":
# Extract Discord-specific metadata L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g== # Extract Discord-specific metadata
channel_name = metadata.get('channel_name', 'Unknown Channel') channel_name = metadata.get('channel_name', 'Unknown Channel')
channel_id = metadata.get('channel_id', '') channel_id = metadata.get('channel_id', '')
guild_id = metadata.get('guild_id', '') guild_id = metadata.get('guild_id', '')
@ -252,16 +251,6 @@ async def fetch_documents_by_ids(
return [], [] return [], []
class Section(BaseModel):
"""A section in the answer outline."""
section_id: int = Field(..., description="The zero-based index of the section")
section_title: str = Field(..., description="The title of the section")
questions: List[str] = Field(..., description="Questions to research for this section")
class AnswerOutline(BaseModel):
"""The complete answer outline with all sections."""
answer_outline: List[Section] = Field(..., description="List of sections in the answer outline")
async def write_answer_outline(state: State, config: RunnableConfig, writer: StreamWriter) -> Dict[str, Any]: async def write_answer_outline(state: State, config: RunnableConfig, writer: StreamWriter) -> Dict[str, Any]:
""" """
Create a structured answer outline based on the user query. Create a structured answer outline based on the user query.
@ -379,6 +368,7 @@ async def write_answer_outline(state: State, config: RunnableConfig, writer: Str
print(f"Raw response: {response.content}") print(f"Raw response: {response.content}")
raise raise
async def fetch_relevant_documents( async def fetch_relevant_documents(
research_questions: List[str], research_questions: List[str],
user_id: str, user_id: str,
@ -535,7 +525,7 @@ async def fetch_relevant_documents(
search_mode=search_mode search_mode=search_mode
) )
# Add to sources and raw documents L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g== # Add to sources and raw documents
if source_object: if source_object:
all_sources.append(source_object) all_sources.append(source_object)
all_raw_documents.extend(slack_chunks) all_raw_documents.extend(slack_chunks)
@ -746,37 +736,6 @@ async def fetch_relevant_documents(
# Return deduplicated documents # Return deduplicated documents
return deduplicated_docs return deduplicated_docs
def get_connector_emoji(connector_name: str) -> str:
"""Get an appropriate emoji for a connector type."""
connector_emojis = {
"YOUTUBE_VIDEO": "📹",
"EXTENSION": "🧩",
"CRAWLED_URL": "🌐",
"FILE": "📄",
"SLACK_CONNECTOR": "💬",
"NOTION_CONNECTOR": "📘",
"GITHUB_CONNECTOR": "🐙",
"LINEAR_CONNECTOR": "📊",
"TAVILY_API": "🔍",
"LINKUP_API": "🔗"
}
return connector_emojis.get(connector_name, "🔎")
def get_connector_friendly_name(connector_name: str) -> str:
"""Convert technical connector IDs to user-friendly names."""
connector_friendly_names = {
"YOUTUBE_VIDEO": "YouTube",
"EXTENSION": "Browser Extension",
"CRAWLED_URL": "Web Pages",
"FILE": "Files",
"SLACK_CONNECTOR": "Slack",
"NOTION_CONNECTOR": "Notion",
"GITHUB_CONNECTOR": "GitHub",
"LINEAR_CONNECTOR": "Linear",
"TAVILY_API": "Tavily Search",
"LINKUP_API": "Linkup Search"
}
return connector_friendly_names.get(connector_name, connector_name)
async def process_sections(state: State, config: RunnableConfig, writer: StreamWriter) -> Dict[str, Any]: async def process_sections(state: State, config: RunnableConfig, writer: StreamWriter) -> Dict[str, Any]:
""" """
@ -787,7 +746,7 @@ async def process_sections(state: State, config: RunnableConfig, writer: StreamW
using the sub_section_writer graph with the shared document pool. using the sub_section_writer graph with the shared document pool.
Returns: Returns:
Dict containing the final written report in the "final_written_report" key L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g==s. Dict containing the final written report in the "final_written_report" key.
""" """
# Get configuration and answer outline from state # Get configuration and answer outline from state
configuration = Configuration.from_runnable_config(config) configuration = Configuration.from_runnable_config(config)
@ -969,6 +928,7 @@ async def process_sections(state: State, config: RunnableConfig, writer: StreamW
"final_written_report": final_written_report "final_written_report": final_written_report
} }
async def process_section_with_documents( async def process_section_with_documents(
section_id: int, section_id: int,
section_title: str, section_title: str,
@ -1080,7 +1040,7 @@ async def process_section_with_documents(
complete_answer.extend(content_lines) complete_answer.extend(content_lines)
complete_answer.append("") # Empty line after content complete_answer.append("") # Empty line after content
# Update answer in UI in real-time L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g== # Update answer in UI in real-time
state.streaming_service.only_update_answer(complete_answer) state.streaming_service.only_update_answer(complete_answer)
writer({"yeild_value": state.streaming_service._format_annotations()}) writer({"yeild_value": state.streaming_service._format_annotations()})
@ -1106,7 +1066,6 @@ async def process_section_with_documents(
return f"Error processing section: {section_title}. Details: {str(e)}" return f"Error processing section: {section_title}. Details: {str(e)}"
async def reformulate_user_query(state: State, config: RunnableConfig, writer: StreamWriter) -> Dict[str, Any]: async def reformulate_user_query(state: State, config: RunnableConfig, writer: StreamWriter) -> Dict[str, Any]:
""" """
Reforms the user query based on the chat history. Reforms the user query based on the chat history.
@ -1124,6 +1083,7 @@ async def reformulate_user_query(state: State, config: RunnableConfig, writer: S
"reformulated_query": reformulated_query "reformulated_query": reformulated_query
} }
async def handle_qna_workflow(state: State, config: RunnableConfig, writer: StreamWriter) -> Dict[str, Any]: async def handle_qna_workflow(state: State, config: RunnableConfig, writer: StreamWriter) -> Dict[str, Any]:
""" """
Handle the QNA research workflow. Handle the QNA research workflow.
@ -1201,7 +1161,7 @@ async def handle_qna_workflow(state: State, config: RunnableConfig, writer: Stre
# Continue with empty documents - the QNA agent will handle this gracefully # Continue with empty documents - the QNA agent will handle this gracefully
relevant_documents = [] relevant_documents = []
# Combine user-selected documents with connector-fetched documents L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g== # Combine user-selected documents with connector-fetched documents
all_documents = user_selected_documents + relevant_documents all_documents = user_selected_documents + relevant_documents
print(f"Fetched {len(relevant_documents)} relevant documents for QNA") print(f"Fetched {len(relevant_documents)} relevant documents for QNA")

3
surfsense_backend/app/agents/researcher/prompts.py
View file

@ -1,6 +1,3 @@
"""
File Hash: L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g==
"""
import datetime import datetime

102
surfsense_backend/app/agents/researcher/qna_agent/graph.py
View file

@ -18,105 +18,3 @@ workflow.add_edge("answer_question", "__end__")
# Compile the workflow into an executable graph # Compile the workflow into an executable graph
graph = workflow.compile() graph = workflow.compile()
graph.name = "SurfSense QnA Agent" # This defines the custom name in LangSmith graph.name = "SurfSense QnA Agent" # This defines the custom name in LangSmith
# L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g==

4
surfsense_backend/app/agents/researcher/qna_agent/nodes.py
View file

@ -1,8 +1,8 @@
from app.utils.reranker_service import RerankerService
from .configuration import Configuration from .configuration import Configuration
from langchain_core.runnables import RunnableConfig from langchain_core.runnables import RunnableConfig
from .state import State from .state import State
from typing import Any, Dict from typing import Any, Dict
from app.config import config as app_config
from .prompts import get_qna_citation_system_prompt, get_qna_no_documents_system_prompt from .prompts import get_qna_citation_system_prompt, get_qna_no_documents_system_prompt
from langchain_core.messages import HumanMessage, SystemMessage from langchain_core.messages import HumanMessage, SystemMessage
from ..utils import ( from ..utils import (
@ -35,7 +35,7 @@ async def rerank_documents(state: State, config: RunnableConfig) -> Dict[str, An
} }
# Get reranker service from app config # Get reranker service from app config
reranker_service = getattr(app_config, "reranker_service", None) reranker_service = RerankerService.get_reranker_instance()
# Use documents as is if no reranker service is available # Use documents as is if no reranker service is available
reranked_docs = documents reranked_docs = documents

4
surfsense_backend/app/agents/researcher/qna_agent/state.py
View file

@ -1,6 +1,4 @@
"""Define the state structures for the agent. """Define the state structures for the agent."""
File Hash: L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g==
"""
from __future__ import annotations from __future__ import annotations

4
surfsense_backend/app/agents/researcher/state.py
View file

@ -1,6 +1,4 @@
"""Define the state structures for the agent. """Define the state structures for the agent."""
File Hash: L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g==
"""
from __future__ import annotations from __future__ import annotations

6
surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py
View file

@ -2,7 +2,7 @@ from .configuration import Configuration
from langchain_core.runnables import RunnableConfig from langchain_core.runnables import RunnableConfig
from .state import State from .state import State
from typing import Any, Dict from typing import Any, Dict
from app.config import config as app_config from app.utils.reranker_service import RerankerService
from .prompts import get_citation_system_prompt, get_no_documents_system_prompt from .prompts import get_citation_system_prompt, get_no_documents_system_prompt
from langchain_core.messages import HumanMessage, SystemMessage from langchain_core.messages import HumanMessage, SystemMessage
from .configuration import SubSectionType from .configuration import SubSectionType
@ -35,7 +35,7 @@ async def rerank_documents(state: State, config: RunnableConfig) -> Dict[str, An
} }
# Get reranker service from app config # Get reranker service from app config
reranker_service = getattr(app_config, "reranker_service", None) reranker_service = RerankerService.get_reranker_instance()
# Use documents as is if no reranker service is available # Use documents as is if no reranker service is available
reranked_docs = documents reranked_docs = documents
@ -211,7 +211,7 @@ async def write_sub_section(state: State, config: RunnableConfig) -> Dict[str, A
HumanMessage(content=human_message_content) HumanMessage(content=human_message_content)
] ]
# Log final token count L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g== # Log final token count
total_tokens = calculate_token_count(messages_with_chat_history, llm.model) total_tokens = calculate_token_count(messages_with_chat_history, llm.model)
print(f"Final token count: {total_tokens}") print(f"Final token count: {total_tokens}")

4
surfsense_backend/app/agents/researcher/sub_section_writer/state.py
View file

@ -1,6 +1,4 @@
"""Define the state structures for the agent. """Define the state structures for the agent."""
File Hash: L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g==
"""
from __future__ import annotations from __future__ import annotations

96
surfsense_backend/app/agents/researcher/utils.py
View file

@ -1,7 +1,18 @@
from typing import List, Dict, Any, Tuple, NamedTuple from typing import List, Dict, Any, Tuple, NamedTuple
from langchain_core.messages import BaseMessage from langchain_core.messages import BaseMessage
from pydantic import BaseModel, Field
from litellm import token_counter, get_model_info from litellm import token_counter, get_model_info
class Section(BaseModel):
"""A section in the answer outline."""
section_id: int = Field(..., description="The zero-based index of the section")
section_title: str = Field(..., description="The title of the section")
questions: List[str] = Field(..., description="Questions to research for this section")
class AnswerOutline(BaseModel):
"""The complete answer outline with all sections."""
answer_outline: List[Section] = Field(..., description="List of sections in the answer outline")
class DocumentTokenInfo(NamedTuple): class DocumentTokenInfo(NamedTuple):
"""Information about a document and its token cost.""" """Information about a document and its token cost."""
@ -11,6 +22,40 @@ class DocumentTokenInfo(NamedTuple):
token_count: int token_count: int
def get_connector_emoji(connector_name: str) -> str:
"""Get an appropriate emoji for a connector type."""
connector_emojis = {
"YOUTUBE_VIDEO": "📹",
"EXTENSION": "🧩",
"CRAWLED_URL": "🌐",
"FILE": "📄",
"SLACK_CONNECTOR": "💬",
"NOTION_CONNECTOR": "📘",
"GITHUB_CONNECTOR": "🐙",
"LINEAR_CONNECTOR": "📊",
"TAVILY_API": "🔍",
"LINKUP_API": "🔗"
}
return connector_emojis.get(connector_name, "🔎")
def get_connector_friendly_name(connector_name: str) -> str:
"""Convert technical connector IDs to user-friendly names."""
connector_friendly_names = {
"YOUTUBE_VIDEO": "YouTube",
"EXTENSION": "Browser Extension",
"CRAWLED_URL": "Web Pages",
"FILE": "Files",
"SLACK_CONNECTOR": "Slack",
"NOTION_CONNECTOR": "Notion",
"GITHUB_CONNECTOR": "GitHub",
"LINEAR_CONNECTOR": "Linear",
"TAVILY_API": "Tavily Search",
"LINKUP_API": "Linkup Search"
}
return connector_friendly_names.get(connector_name, connector_name)
def convert_langchain_messages_to_dict(messages: List[BaseMessage]) -> List[Dict[str, str]]: def convert_langchain_messages_to_dict(messages: List[BaseMessage]) -> List[Dict[str, str]]:
"""Convert LangChain messages to format expected by token_counter.""" """Convert LangChain messages to format expected by token_counter."""
role_mapping = { role_mapping = {
@ -82,9 +127,6 @@ def calculate_document_token_costs(documents: List[Dict[str, Any]], model: str)
)) ))
return document_token_info return document_token_info
"""
File Hash: L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g==
"""
def find_optimal_documents_with_binary_search( def find_optimal_documents_with_binary_search(
@ -185,51 +227,3 @@ def calculate_token_count(messages: List[BaseMessage], model_name: str) -> int:
model = model_name model = model_name
messages_dict = convert_langchain_messages_to_dict(messages) messages_dict = convert_langchain_messages_to_dict(messages)
return token_counter(messages=messages_dict, model=model) return token_counter(messages=messages_dict, model=model)
"""
File Hash: L0o55JzTBlCYJNCRYbbxt8mxqRs5kPm6QO8NzVqEZtzqWtG0EklbHuQ3I5ZBdSy8n+EqrdQxcp+R3Yc57NIm79iNS2sxt4tVMSTLeAT6qpMS2SbBER4hRiLaH5BKpXBJoCRPoFMYpDf6pdIokZyJz/EQWQZj531TfLcBfFkxJuWEqvinKhvWJPjApBd1RldixOj57mNXybHN8WFe+FnayhYQhptesoFAVXAk1WuV2URSqXxs5/00Eo8osC55gsye6LXTYzieyUKxurLKw+uy3g==
"""

14
surfsense_backend/app/utils/reranker_service.py
View file

@ -80,16 +80,16 @@ class RerankerService:
return documents return documents
@staticmethod @staticmethod
def get_reranker_instance(config=None) -> Optional['RerankerService']: def get_reranker_instance() -> Optional['RerankerService']:
""" """
Get a reranker service instance based on configuration Get a reranker service instance from the global configuration.
Args:
config: Configuration object that may contain a reranker_instance
Returns: Returns:
Optional[RerankerService]: A reranker service instance or None Optional[RerankerService]: A reranker service instance if configured, None otherwise
""" """
if config and hasattr(config, 'reranker_instance') and config.reranker_instance: from app.config import config
if hasattr(config, 'reranker_instance') and config.reranker_instance:
return RerankerService(config.reranker_instance) return RerankerService(config.reranker_instance)
return None return None

4512
surfsense_backend/uv.lock generated
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