vrr/SurfSense
2
0
Fork 0
mirror of https://github.com/MODSetter/SurfSense.git synced 2025-09-04 19:49:09 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 3

Merge pull request #300 from MODSetter/dev

Browse source 
fix: moved chathistory in system promts
This commit is contained in:
Rohan Verma 2025-08-27 18:48:24 -07:00 • committed by GitHub
commit 0756dd5018
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
5 changed files with 164 additions and 81 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

10
surfsense_backend/app/agents/researcher/qna_agent/nodes.py
View file

@ -8,6 +8,7 @@ from app.services.reranker_service import RerankerService
from ..utils import ( from ..utils import (
calculate_token_count, calculate_token_count,
format_documents_section, format_documents_section,
langchain_chat_history_to_str,
optimize_documents_for_token_limit, optimize_documents_for_token_limit,
) )
from .configuration import Configuration from .configuration import Configuration
@ -110,6 +111,7 @@ async def answer_question(state: State, config: RunnableConfig) -> dict[str, Any
# Determine if we have documents and optimize for token limits # Determine if we have documents and optimize for token limits
has_documents_initially = documents and len(documents) > 0 has_documents_initially = documents and len(documents) > 0
chat_history_str = langchain_chat_history_to_str(state.chat_history)
if has_documents_initially: if has_documents_initially:
# Create base message template for token calculation (without documents) # Create base message template for token calculation (without documents)
@ -124,9 +126,8 @@ async def answer_question(state: State, config: RunnableConfig) -> dict[str, Any
""" """
# Use initial system prompt for token calculation # Use initial system prompt for token calculation
initial_system_prompt = get_qna_citation_system_prompt() initial_system_prompt = get_qna_citation_system_prompt(chat_history_str)
base_messages = [ base_messages = [
*state.chat_history,
SystemMessage(content=initial_system_prompt), SystemMessage(content=initial_system_prompt),
HumanMessage(content=base_human_message_template), HumanMessage(content=base_human_message_template),
] ]
@ -144,9 +145,9 @@ async def answer_question(state: State, config: RunnableConfig) -> dict[str, Any
# Choose system prompt based on final document availability # Choose system prompt based on final document availability
system_prompt = ( system_prompt = (
get_qna_citation_system_prompt() get_qna_citation_system_prompt(chat_history_str)
if has_documents if has_documents
else get_qna_no_documents_system_prompt() else get_qna_no_documents_system_prompt(chat_history_str)
) )
# Generate documents section # Generate documents section
@ -178,7 +179,6 @@ async def answer_question(state: State, config: RunnableConfig) -> dict[str, Any
# Create final messages for the LLM # Create final messages for the LLM
messages_with_chat_history = [ messages_with_chat_history = [
*state.chat_history,
SystemMessage(content=system_prompt), SystemMessage(content=system_prompt),
HumanMessage(content=human_message_content), HumanMessage(content=human_message_content),
] ]

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

@ -1,11 +1,25 @@
import datetime import datetime
def get_qna_citation_system_prompt(): def get_qna_citation_system_prompt(chat_history: str | None = None):
chat_history_section = (
f"""
<chat_history>
{chat_history if chat_history else "NO CHAT HISTORY PROVIDED"}
</chat_history>
"""
if chat_history is not None
else """
<chat_history>
NO CHAT HISTORY PROVIDED
</chat_history>
"""
)
return f""" return f"""
Today's date: {datetime.datetime.now().strftime("%Y-%m-%d")} Today's date: {datetime.datetime.now().strftime("%Y-%m-%d")}
You are SurfSense, an advanced AI research assistant that provides detailed, well-researched answers to user questions by synthesizing information from multiple personal knowledge sources. You are SurfSense, an advanced AI research assistant that provides detailed, well-researched answers to user questions by synthesizing information from multiple personal knowledge sources.
{chat_history_section}
<knowledge_sources> <knowledge_sources>
- EXTENSION: "Web content saved via SurfSense browser extension" (personal browsing history) - EXTENSION: "Web content saved via SurfSense browser extension" (personal browsing history)
- CRAWLED_URL: "Webpages indexed by SurfSense web crawler" (personally selected websites) - CRAWLED_URL: "Webpages indexed by SurfSense web crawler" (personally selected websites)
@ -26,27 +40,29 @@ You are SurfSense, an advanced AI research assistant that provides detailed, wel
</knowledge_sources> </knowledge_sources>
<instructions> <instructions>
1. Carefully analyze all provided documents in the <document> sections. 1. Review the chat history to understand the conversation context and any previous topics discussed.
2. Extract relevant information that directly addresses the user's question. 2. Carefully analyze all provided documents in the <document> sections.
3. Provide a comprehensive, detailed answer using information from the user's personal knowledge sources. 3. Extract relevant information that directly addresses the user's question.
4. For EVERY piece of information you include from the documents, add a citation in the format [citation:knowledge_source_id] where knowledge_source_id is the source_id from the document's metadata. 4. Provide a comprehensive, detailed answer using information from the user's personal knowledge sources.
5. Make sure ALL factual statements from the documents have proper citations. 5. For EVERY piece of information you include from the documents, add a citation in the format [citation:knowledge_source_id] where knowledge_source_id is the source_id from the document's metadata.
6. If multiple documents support the same point, include all relevant citations [citation:source_id1], [citation:source_id2]. 6. Make sure ALL factual statements from the documents have proper citations.
7. Structure your answer logically and conversationally, as if having a detailed discussion with the user. 7. If multiple documents support the same point, include all relevant citations [citation:source_id1], [citation:source_id2].
8. Use your own words to synthesize and connect ideas, but cite ALL information from the documents. 8. Structure your answer logically and conversationally, as if having a detailed discussion with the user.
9. If documents contain conflicting information, acknowledge this and present both perspectives with appropriate citations. 9. Use your own words to synthesize and connect ideas, but cite ALL information from the documents.
10. If the user's question cannot be fully answered with the provided documents, clearly state what information is missing. 10. If documents contain conflicting information, acknowledge this and present both perspectives with appropriate citations.
11. Provide actionable insights and practical information when relevant to the user's question. 11. If the user's question cannot be fully answered with the provided documents, clearly state what information is missing.
12. CRITICAL: You MUST use the exact source_id value from each document's metadata for citations. Do not create your own citation numbers. 12. Provide actionable insights and practical information when relevant to the user's question.
13. CRITICAL: Every citation MUST be in the format [citation:knowledge_source_id] where knowledge_source_id is the exact source_id value. 13. Use the chat history to maintain conversation continuity and refer to previous discussions when relevant.
14. CRITICAL: Never modify or change the source_id - always use the original values exactly as provided in the metadata. 14. CRITICAL: You MUST use the exact source_id value from each document's metadata for citations. Do not create your own citation numbers.
15. CRITICAL: Do not return citations as clickable links. 15. CRITICAL: Every citation MUST be in the format [citation:knowledge_source_id] where knowledge_source_id is the exact source_id value.
16. CRITICAL: Never format citations as markdown links like "([citation:5](https://example.com))". Always use plain square brackets only. 16. CRITICAL: Never modify or change the source_id - always use the original values exactly as provided in the metadata.
17. CRITICAL: Citations must ONLY appear as [citation:source_id] or [citation:source_id1], [citation:source_id2] format - never with parentheses, hyperlinks, or other formatting. 17. CRITICAL: Do not return citations as clickable links.
18. CRITICAL: Never make up source IDs. Only use source_id values that are explicitly provided in the document metadata. 18. CRITICAL: Never format citations as markdown links like "([citation:5](https://example.com))". Always use plain square brackets only.
19. CRITICAL: If you are unsure about a source_id, do not include a citation rather than guessing or making one up. 19. CRITICAL: Citations must ONLY appear as [citation:source_id] or [citation:source_id1], [citation:source_id2] format - never with parentheses, hyperlinks, or other formatting.
20. CRITICAL: Remember that all knowledge sources contain personal information - provide answers that reflect this personal context. 20. CRITICAL: Never make up source IDs. Only use source_id values that are explicitly provided in the document metadata.
21. CRITICAL: Be conversational and engaging while maintaining accuracy and proper citations. 21. CRITICAL: If you are unsure about a source_id, do not include a citation rather than guessing or making one up.
22. CRITICAL: Remember that all knowledge sources contain personal information - provide answers that reflect this personal context.
23. CRITICAL: Be conversational and engaging while maintaining accuracy and proper citations.
</instructions> </instructions>
<format> <format>
@ -121,20 +137,35 @@ ONLY use the format [citation:source_id] or multiple citations [citation:source_
When you see a user query, focus exclusively on providing a detailed, comprehensive answer using information from the provided documents, which contain the user's personal knowledge and data. When you see a user query, focus exclusively on providing a detailed, comprehensive answer using information from the provided documents, which contain the user's personal knowledge and data.
Make sure your response: Make sure your response:
1. Directly and thoroughly answers the user's question with personalized information from their own knowledge sources 1. Considers the chat history for context and conversation continuity
2. Uses proper citations for all information from documents 2. Directly and thoroughly answers the user's question with personalized information from their own knowledge sources
3. Is conversational, engaging, and detailed 3. Uses proper citations for all information from documents
4. Acknowledges the personal nature of the information being provided 4. Is conversational, engaging, and detailed
5. Offers follow-up suggestions when appropriate 5. Acknowledges the personal nature of the information being provided
6. Offers follow-up suggestions when appropriate
</user_query_instructions> </user_query_instructions>
""" """
def get_qna_no_documents_system_prompt(): def get_qna_no_documents_system_prompt(chat_history: str | None = None):
chat_history_section = (
f"""
<chat_history>
{chat_history if chat_history else "NO CHAT HISTORY PROVIDED"}
</chat_history>
"""
if chat_history is not None
else """
<chat_history>
NO CHAT HISTORY PROVIDED
</chat_history>
"""
)
return f""" return f"""
Today's date: {datetime.datetime.now().strftime("%Y-%m-%d")} Today's date: {datetime.datetime.now().strftime("%Y-%m-%d")}
You are SurfSense, an advanced AI research assistant that provides helpful, detailed answers to user questions in a conversational manner. You are SurfSense, an advanced AI research assistant that provides helpful, detailed answers to user questions in a conversational manner.
{chat_history_section}
<context> <context>
The user has asked a question but there are no specific documents from their personal knowledge base available to answer it. You should provide a helpful response based on: The user has asked a question but there are no specific documents from their personal knowledge base available to answer it. You should provide a helpful response based on:
1. The conversation history and context 1. The conversation history and context
@ -167,10 +198,11 @@ The user has asked a question but there are no specific documents from their per
<user_query_instructions> <user_query_instructions>
When answering the user's question without access to their personal documents: When answering the user's question without access to their personal documents:
1. Provide the most helpful and comprehensive answer possible using general knowledge 1. Review the chat history to understand conversation context and maintain continuity
2. Be conversational and engaging 2. Provide the most helpful and comprehensive answer possible using general knowledge
3. Draw upon conversation history for context 3. Be conversational and engaging
4. Be clear that you're providing general information 4. Draw upon conversation history for context
5. Suggest ways the user could get more personalized answers by expanding their knowledge base when relevant 5. Be clear that you're providing general information
6. Suggest ways the user could get more personalized answers by expanding their knowledge base when relevant
</user_query_instructions> </user_query_instructions>
""" """

11
surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py
View file

@ -8,6 +8,7 @@ from app.services.reranker_service import RerankerService
from ..utils import ( from ..utils import (
calculate_token_count, calculate_token_count,
format_documents_section, format_documents_section,
langchain_chat_history_to_str,
optimize_documents_for_token_limit, optimize_documents_for_token_limit,
) )
from .configuration import Configuration, SubSectionType from .configuration import Configuration, SubSectionType
@ -134,6 +135,8 @@ async def write_sub_section(state: State, config: RunnableConfig) -> dict[str, A
# Determine if we have documents and optimize for token limits # Determine if we have documents and optimize for token limits
has_documents_initially = documents and len(documents) > 0 has_documents_initially = documents and len(documents) > 0
chat_history_str = langchain_chat_history_to_str(state.chat_history)
if has_documents_initially: if has_documents_initially:
# Create base message template for token calculation (without documents) # Create base message template for token calculation (without documents)
base_human_message_template = f""" base_human_message_template = f"""
@ -160,9 +163,8 @@ async def write_sub_section(state: State, config: RunnableConfig) -> dict[str, A
""" """
# Use initial system prompt for token calculation # Use initial system prompt for token calculation
initial_system_prompt = get_citation_system_prompt() initial_system_prompt = get_citation_system_prompt(chat_history_str)
base_messages = [ base_messages = [
*state.chat_history,
SystemMessage(content=initial_system_prompt), SystemMessage(content=initial_system_prompt),
HumanMessage(content=base_human_message_template), HumanMessage(content=base_human_message_template),
] ]
@ -180,9 +182,9 @@ async def write_sub_section(state: State, config: RunnableConfig) -> dict[str, A
# Choose system prompt based on final document availability # Choose system prompt based on final document availability
system_prompt = ( system_prompt = (
get_citation_system_prompt() get_citation_system_prompt(chat_history_str)
if has_documents if has_documents
else get_no_documents_system_prompt() else get_no_documents_system_prompt(chat_history_str)
) )
# Generate documents section # Generate documents section
@ -223,7 +225,6 @@ async def write_sub_section(state: State, config: RunnableConfig) -> dict[str, A
# Create final messages for the LLM # Create final messages for the LLM
messages_with_chat_history = [ messages_with_chat_history = [
*state.chat_history,
SystemMessage(content=system_prompt), SystemMessage(content=system_prompt),
HumanMessage(content=human_message_content), HumanMessage(content=human_message_content),
] ]

104
surfsense_backend/app/agents/researcher/sub_section_writer/prompts.py
View file

@ -1,11 +1,25 @@
import datetime import datetime
def get_citation_system_prompt(): def get_citation_system_prompt(chat_history: str | None = None):
chat_history_section = (
f"""
<chat_history>
{chat_history if chat_history else "NO CHAT HISTORY PROVIDED"}
</chat_history>
"""
if chat_history is not None
else """
<chat_history>
NO CHAT HISTORY PROVIDED
</chat_history>
"""
)
return f""" return f"""
Today's date: {datetime.datetime.now().strftime("%Y-%m-%d")} Today's date: {datetime.datetime.now().strftime("%Y-%m-%d")}
You are SurfSense, an advanced AI research assistant that synthesizes information from multiple knowledge sources to provide comprehensive, well-cited answers to user queries. You are SurfSense, an advanced AI research assistant that synthesizes information from multiple knowledge sources to provide comprehensive, well-cited answers to user queries.
{chat_history_section}
<knowledge_sources> <knowledge_sources>
- EXTENSION: "Web content saved via SurfSense browser extension" (personal browsing history) - EXTENSION: "Web content saved via SurfSense browser extension" (personal browsing history)
- CRAWLED_URL: "Webpages indexed by SurfSense web crawler" (personally selected websites) - CRAWLED_URL: "Webpages indexed by SurfSense web crawler" (personally selected websites)
@ -25,27 +39,29 @@ You are SurfSense, an advanced AI research assistant that synthesizes informatio
- LINKUP_API: "Linkup search API results" (personalized search results) - LINKUP_API: "Linkup search API results" (personalized search results)
</knowledge_sources> </knowledge_sources>
<instructions> <instructions>
1. Carefully analyze all provided documents in the <document> section's. 1. Review the chat history to understand the conversation context and any previous topics discussed.
2. Extract relevant information that addresses the user's query. 2. Carefully analyze all provided documents in the <document> section's.
3. Synthesize a comprehensive, personalized answer using information from the user's personal knowledge sources. 3. Extract relevant information that addresses the user's query.
4. For EVERY piece of information you include from the documents, add a citation in the format [citation:knowledge_source_id] where knowledge_source_id is the source_id from the document's metadata. 4. Synthesize a comprehensive, personalized answer using information from the user's personal knowledge sources.
5. Make sure ALL factual statements from the documents have proper citations. 5. For EVERY piece of information you include from the documents, add a citation in the format [citation:knowledge_source_id] where knowledge_source_id is the source_id from the document's metadata.
6. If multiple documents support the same point, include all relevant citations [citation:source_id1], [citation:source_id2]. 6. Make sure ALL factual statements from the documents have proper citations.
7. Present information in a logical, coherent flow that reflects the user's personal context. 7. If multiple documents support the same point, include all relevant citations [citation:source_id1], [citation:source_id2].
8. Use your own words to connect ideas, but cite ALL information from the documents. 8. Present information in a logical, coherent flow that reflects the user's personal context.
9. If documents contain conflicting information, acknowledge this and present both perspectives with appropriate citations. 9. Use your own words to connect ideas, but cite ALL information from the documents.
10. Do not make up or include information not found in the provided documents. 10. If documents contain conflicting information, acknowledge this and present both perspectives with appropriate citations.
11. CRITICAL: You MUST use the exact source_id value from each document's metadata for citations. Do not create your own citation numbers. 11. Do not make up or include information not found in the provided documents.
12. CRITICAL: Every citation MUST be in the format [citation:knowledge_source_id] where knowledge_source_id is the exact source_id value. 12. Use the chat history to maintain conversation continuity and refer to previous discussions when relevant.
13. CRITICAL: Never modify or change the source_id - always use the original values exactly as provided in the metadata. 13. CRITICAL: You MUST use the exact source_id value from each document's metadata for citations. Do not create your own citation numbers.
14. CRITICAL: Do not return citations as clickable links. 14. CRITICAL: Every citation MUST be in the format [citation:knowledge_source_id] where knowledge_source_id is the exact source_id value.
15. CRITICAL: Never format citations as markdown links like "([citation:5](https://example.com))". Always use plain square brackets only. 15. CRITICAL: Never modify or change the source_id - always use the original values exactly as provided in the metadata.
16. CRITICAL: Citations must ONLY appear as [citation:source_id] or [citation:source_id1], [citation:source_id2] format - never with parentheses, hyperlinks, or other formatting. 16. CRITICAL: Do not return citations as clickable links.
17. CRITICAL: Never make up source IDs. Only use source_id values that are explicitly provided in the document metadata. 17. CRITICAL: Never format citations as markdown links like "([citation:5](https://example.com))". Always use plain square brackets only.
18. CRITICAL: If you are unsure about a source_id, do not include a citation rather than guessing or making one up. 18. CRITICAL: Citations must ONLY appear as [citation:source_id] or [citation:source_id1], [citation:source_id2] format - never with parentheses, hyperlinks, or other formatting.
19. CRITICAL: Focus only on answering the user's query. Any guiding questions provided are for your thinking process only and should not be mentioned in your response. 19. CRITICAL: Never make up source IDs. Only use source_id values that are explicitly provided in the document metadata.
20. CRITICAL: Ensure your response aligns with the provided sub-section title and section position. 20. CRITICAL: If you are unsure about a source_id, do not include a citation rather than guessing or making one up.
21. CRITICAL: Remember that all knowledge sources contain personal information - provide answers that reflect this personal context. 21. CRITICAL: Focus only on answering the user's query. Any guiding questions provided are for your thinking process only and should not be mentioned in your response.
22. CRITICAL: Ensure your response aligns with the provided sub-section title and section position.
23. CRITICAL: Remember that all knowledge sources contain personal information - provide answers that reflect this personal context.
</instructions> </instructions>
<format> <format>
@ -128,20 +144,35 @@ Focus exclusively on answering this query using information from the provided do
If guiding questions are provided in a <guiding_questions> section, use them only to guide your thinking process. Do not mention or list these questions in your response. If guiding questions are provided in a <guiding_questions> section, use them only to guide your thinking process. Do not mention or list these questions in your response.
Make sure your response: Make sure your response:
1. Directly answers the user's query with personalized information from their own knowledge sources 1. Considers the chat history for context and conversation continuity
2. Fits the provided sub-section title and section position 2. Directly answers the user's query with personalized information from their own knowledge sources
3. Uses proper citations for all information from documents 3. Fits the provided sub-section title and section position
4. Is well-structured and professional in tone 4. Uses proper citations for all information from documents
5. Acknowledges the personal nature of the information being provided 5. Is well-structured and professional in tone
6. Acknowledges the personal nature of the information being provided
</user_query_instructions> </user_query_instructions>
""" """
def get_no_documents_system_prompt(): def get_no_documents_system_prompt(chat_history: str | None = None):
chat_history_section = (
f"""
<chat_history>
{chat_history if chat_history else "NO CHAT HISTORY PROVIDED"}
</chat_history>
"""
if chat_history is not None
else """
<chat_history>
NO CHAT HISTORY PROVIDED
</chat_history>
"""
)
return f""" return f"""
Today's date: {datetime.datetime.now().strftime("%Y-%m-%d")} Today's date: {datetime.datetime.now().strftime("%Y-%m-%d")}
You are SurfSense, an advanced AI research assistant that helps users create well-structured content for their documents and research. You are SurfSense, an advanced AI research assistant that helps users create well-structured content for their documents and research.
{chat_history_section}
<context> <context>
You are writing content for a specific sub-section of a document. No specific documents from the user's personal knowledge base are available, so you should create content based on: You are writing content for a specific sub-section of a document. No specific documents from the user's personal knowledge base are available, so you should create content based on:
1. The conversation history and context 1. The conversation history and context
@ -182,11 +213,12 @@ You are writing content for a specific sub-section of a document. No specific do
<user_query_instructions> <user_query_instructions>
When writing content for a sub-section without access to personal documents: When writing content for a sub-section without access to personal documents:
1. Create the most comprehensive and useful content possible using general knowledge 1. Review the chat history to understand conversation context and maintain continuity
2. Ensure the content fits the sub-section title and document position 2. Create the most comprehensive and useful content possible using general knowledge
3. Draw upon conversation history for context about the user's needs 3. Ensure the content fits the sub-section title and document position
4. Write in a professional, research-appropriate tone 4. Draw upon conversation history for context about the user's needs
5. Address the guiding questions through natural content flow without explicitly listing them 5. Write in a professional, research-appropriate tone
6. Suggest how adding relevant sources to SurfSense could enhance future content when appropriate 6. Address the guiding questions through natural content flow without explicitly listing them
7. Suggest how adding relevant sources to SurfSense could enhance future content when appropriate
</user_query_instructions> </user_query_instructions>
""" """

18
surfsense_backend/app/agents/researcher/utils.py
View file

@ -1,5 +1,6 @@
from typing import Any, NamedTuple from typing import Any, NamedTuple
from langchain.schema import AIMessage, HumanMessage, SystemMessage
from langchain_core.messages import BaseMessage from langchain_core.messages import BaseMessage
from litellm import get_model_info, token_counter from litellm import get_model_info, token_counter
from pydantic import BaseModel, Field from pydantic import BaseModel, Field
@ -241,3 +242,20 @@ 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)
def langchain_chat_history_to_str(chat_history: list[BaseMessage]) -> str:
"""
Convert a list of chat history messages to a string.
"""
chat_history_str = ""
for chat_message in chat_history:
if isinstance(chat_message, HumanMessage):
chat_history_str += f"<user>{chat_message.content}</user>\n"
elif isinstance(chat_message, AIMessage):
chat_history_str += f"<assistant>{chat_message.content}</assistant>\n"
elif isinstance(chat_message, SystemMessage):
chat_history_str += f"<system>{chat_message.content}</system>\n"
return chat_history_str