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fix: moved chathistory in system promts

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DESKTOP-RTLN3BA\$punk 2025-08-27 18:43:33 -07:00
parent a5bd1ebe4f
commit 4264c0248f
5 changed files with 164 additions and 81 deletions
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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 (
calculate_token_count,
format_documents_section,
langchain_chat_history_to_str,
optimize_documents_for_token_limit,
)
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
has_documents_initially = documents and len(documents) > 0
chat_history_str = langchain_chat_history_to_str(state.chat_history)
if has_documents_initially:
# 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
initial_system_prompt = get_qna_citation_system_prompt()
initial_system_prompt = get_qna_citation_system_prompt(chat_history_str)
base_messages = [
*state.chat_history,
SystemMessage(content=initial_system_prompt),
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
system_prompt = (
get_qna_citation_system_prompt()
get_qna_citation_system_prompt(chat_history_str)
if has_documents
else get_qna_no_documents_system_prompt()
else get_qna_no_documents_system_prompt(chat_history_str)
)
# Generate documents section
@ -178,7 +179,6 @@ async def answer_question(state: State, config: RunnableConfig) -> dict[str, Any
# Create final messages for the LLM
messages_with_chat_history = [
*state.chat_history,
SystemMessage(content=system_prompt),
HumanMessage(content=human_message_content),
]

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

@ -1,11 +1,25 @@
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"""
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.
{chat_history_section}
<knowledge_sources>
- EXTENSION: "Web content saved via SurfSense browser extension" (personal browsing history)
- 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>
<instructions>
1. Carefully analyze all provided documents in the <document> sections.
2. Extract relevant information that directly addresses the user's question.
3. Provide a comprehensive, detailed answer using information from the user's personal knowledge sources.
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.
5. Make sure ALL factual statements from the documents have proper citations.
6. If multiple documents support the same point, include all relevant citations [citation:source_id1], [citation:source_id2].
7. Structure your answer logically and conversationally, as if having a detailed discussion with the user.
8. Use your own words to synthesize and 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. If the user's question cannot be fully answered with the provided documents, clearly state what information is missing.
11. Provide actionable insights and practical information when relevant to the user's question.
12. CRITICAL: You MUST use the exact source_id value from each document's metadata for citations. Do not create your own citation numbers.
13. CRITICAL: Every citation MUST be in the format [citation:knowledge_source_id] where knowledge_source_id is the exact source_id value.
14. CRITICAL: Never modify or change the source_id - always use the original values exactly as provided in the metadata.
15. CRITICAL: Do not return citations as clickable links.
16. CRITICAL: Never format citations as markdown links like "([citation:5](https://example.com))". Always use plain square brackets only.
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.
18. CRITICAL: Never make up source IDs. Only use source_id values that are explicitly provided in the document metadata.
19. CRITICAL: If you are unsure about a source_id, do not include a citation rather than guessing or making one up.
20. CRITICAL: Remember that all knowledge sources contain personal information - provide answers that reflect this personal context.
21. CRITICAL: Be conversational and engaging while maintaining accuracy and proper citations.
1. Review the chat history to understand the conversation context and any previous topics discussed.
2. Carefully analyze all provided documents in the <document> sections.
3. Extract relevant information that directly addresses the user's question.
4. Provide a comprehensive, detailed answer using information from the user's personal knowledge sources.
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. Make sure ALL factual statements from the documents have proper citations.
7. If multiple documents support the same point, include all relevant citations [citation:source_id1], [citation:source_id2].
8. Structure your answer logically and conversationally, as if having a detailed discussion with the user.
9. Use your own words to synthesize and connect ideas, but cite ALL information from the documents.
10. If documents contain conflicting information, acknowledge this and present both perspectives with appropriate citations.
11. If the user's question cannot be fully answered with the provided documents, clearly state what information is missing.
12. Provide actionable insights and practical information when relevant to the user's question.
13. Use the chat history to maintain conversation continuity and refer to previous discussions when relevant.
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: Every citation MUST be in the format [citation:knowledge_source_id] where knowledge_source_id is the exact source_id value.
16. CRITICAL: Never modify or change the source_id - always use the original values exactly as provided in the metadata.
17. CRITICAL: Do not return citations as clickable links.
18. CRITICAL: Never format citations as markdown links like "([citation:5](https://example.com))". Always use plain square brackets only.
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: Never make up source IDs. Only use source_id values that are explicitly provided in the document metadata.
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>
<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.
Make sure your response:
1. Directly and thoroughly answers the user's question with personalized information from their own knowledge sources
2. Uses proper citations for all information from documents
3. Is conversational, engaging, and detailed
4. Acknowledges the personal nature of the information being provided
5. Offers follow-up suggestions when appropriate
1. Considers the chat history for context and conversation continuity
2. Directly and thoroughly answers the user's question with personalized information from their own knowledge sources
3. Uses proper citations for all information from documents
4. Is conversational, engaging, and detailed
5. Acknowledges the personal nature of the information being provided
6. Offers follow-up suggestions when appropriate
</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"""
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.
{chat_history_section}
<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:
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>
When answering the user's question without access to their personal documents:
1. Provide the most helpful and comprehensive answer possible using general knowledge
2. Be conversational and engaging
3. Draw upon conversation history for context
4. Be clear that you're providing general information
5. Suggest ways the user could get more personalized answers by expanding their knowledge base when relevant
1. Review the chat history to understand conversation context and maintain continuity
2. Provide the most helpful and comprehensive answer possible using general knowledge
3. Be conversational and engaging
4. Draw upon conversation history for context
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>
"""

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 (
calculate_token_count,
format_documents_section,
langchain_chat_history_to_str,
optimize_documents_for_token_limit,
)
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
has_documents_initially = documents and len(documents) > 0
chat_history_str = langchain_chat_history_to_str(state.chat_history)
if has_documents_initially:
# Create base message template for token calculation (without documents)
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
initial_system_prompt = get_citation_system_prompt()
initial_system_prompt = get_citation_system_prompt(chat_history_str)
base_messages = [
*state.chat_history,
SystemMessage(content=initial_system_prompt),
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
system_prompt = (
get_citation_system_prompt()
get_citation_system_prompt(chat_history_str)
if has_documents
else get_no_documents_system_prompt()
else get_no_documents_system_prompt(chat_history_str)
)
# 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
messages_with_chat_history = [
*state.chat_history,
SystemMessage(content=system_prompt),
HumanMessage(content=human_message_content),
]

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

@ -1,11 +1,25 @@
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"""
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.
{chat_history_section}
<knowledge_sources>
- EXTENSION: "Web content saved via SurfSense browser extension" (personal browsing history)
- 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)
</knowledge_sources>
<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, personalized answer using information from the user's personal knowledge sources.
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.
5. Make sure ALL factual statements from the documents have proper citations.
6. If multiple documents support the same point, include all relevant citations [citation:source_id1], [citation:source_id2].
7. Present information in a logical, coherent flow that reflects the user's personal context.
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 format [citation:knowledge_source_id] where knowledge_source_id is the exact source_id value.
13. CRITICAL: Never modify or change the source_id - always use the original values exactly as provided in the metadata.
14. CRITICAL: Do not return citations as clickable links.
15. CRITICAL: Never format citations as markdown links like "([citation:5](https://example.com))". Always use plain square brackets only.
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.
17. CRITICAL: Never make up source IDs. Only use source_id values that are explicitly provided in the document metadata.
18. CRITICAL: If you are unsure about a source_id, do not include a citation rather than guessing or making one up.
19. CRITICAL: Focus only on answering the user's query. Any guiding questions provided are for your thinking process only and should not be mentioned in your response.
20. CRITICAL: Ensure your response aligns with the provided sub-section title and section position.
21. CRITICAL: Remember that all knowledge sources contain personal information - provide answers that reflect this personal context.
1. Review the chat history to understand the conversation context and any previous topics discussed.
2. Carefully analyze all provided documents in the <document> section's.
3. Extract relevant information that addresses the user's query.
4. Synthesize a comprehensive, personalized answer using information from the user's personal knowledge sources.
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. Make sure ALL factual statements from the documents have proper citations.
7. If multiple documents support the same point, include all relevant citations [citation:source_id1], [citation:source_id2].
8. Present information in a logical, coherent flow that reflects the user's personal context.
9. Use your own words to connect ideas, but cite ALL information from the documents.
10. If documents contain conflicting information, acknowledge this and present both perspectives with appropriate citations.
11. Do not make up or include information not found in the provided documents.
12. Use the chat history to maintain conversation continuity and refer to previous discussions when relevant.
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: Every citation MUST be in the format [citation:knowledge_source_id] where knowledge_source_id is the exact source_id value.
15. CRITICAL: Never modify or change the source_id - always use the original values exactly as provided in the metadata.
16. CRITICAL: Do not return citations as clickable links.
17. CRITICAL: Never format citations as markdown links like "([citation:5](https://example.com))". Always use plain square brackets only.
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: Never make up source IDs. Only use source_id values that are explicitly provided in the document metadata.
20. CRITICAL: If you are unsure about a source_id, do not include a citation rather than guessing or making one up.
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>
<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.
Make sure your response:
1. Directly answers the user's query with personalized information from their own knowledge sources
2. Fits the provided sub-section title and section position
3. Uses proper citations for all information from documents
4. Is well-structured and professional in tone
5. Acknowledges the personal nature of the information being provided
1. Considers the chat history for context and conversation continuity
2. Directly answers the user's query with personalized information from their own knowledge sources
3. Fits the provided sub-section title and section position
4. Uses proper citations for all information from documents
5. Is well-structured and professional in tone
6. Acknowledges the personal nature of the information being provided
</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"""
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.
{chat_history_section}
<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:
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>
When writing content for a sub-section without access to personal documents:
1. Create the most comprehensive and useful content possible using general knowledge
2. Ensure the content fits the sub-section title and document position
3. Draw upon conversation history for context about the user's needs
4. Write in a professional, research-appropriate tone
5. Address the guiding questions through natural content flow without explicitly listing them
6. Suggest how adding relevant sources to SurfSense could enhance future content when appropriate
1. Review the chat history to understand conversation context and maintain continuity
2. Create the most comprehensive and useful content possible using general knowledge
3. Ensure the content fits the sub-section title and document position
4. Draw upon conversation history for context about the user's needs
5. Write in a professional, research-appropriate tone
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>
"""

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

@ -1,5 +1,6 @@
from typing import Any, NamedTuple
from langchain.schema import AIMessage, HumanMessage, SystemMessage
from langchain_core.messages import BaseMessage
from litellm import get_model_info, token_counter
from pydantic import BaseModel, Field
@ -241,3 +242,20 @@ def calculate_token_count(messages: list[BaseMessage], model_name: str) -> int:
model = model_name
messages_dict = convert_langchain_messages_to_dict(messages)
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