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from openai import OpenAI
from opik import track
from smolagents import Tool
from second_brain_online.config import settings
class HuggingFaceEndpointSummarizerTool(Tool):
name = "huggingface_summarizer"
description = """Use this tool to summarize a piece of text. Especially useful when you need to summarize a document."""
inputs = {
"text": {
"type": "string",
"description": """The text to summarize.""",
}
}
output_type = "string"
SYSTEM_PROMPT = """
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
You are a helpful assistant specialized in summarizing documents. Generate a concise TL;DR summary in markdown format having a maximum of 512 characters of the key findings from the provided documents, highlighting the most significant insights
### Input:
{content}
### Response:
"""
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
assert settings.HUGGINGFACE_ACCESS_TOKEN is not None, (
"HUGGINGFACE_ACCESS_TOKEN is required to use the dedicated endpoint. Add it to the .env file."
)
assert settings.HUGGINGFACE_DEDICATED_ENDPOINT is not None, (
"HUGGINGFACE_DEDICATED_ENDPOINT is required to use the dedicated endpoint. Add it to the .env file."
)
self.__client = OpenAI(
base_url=settings.HUGGINGFACE_DEDICATED_ENDPOINT,
api_key=settings.HUGGINGFACE_ACCESS_TOKEN,
)
@track
def forward(self, text: str) -> str:
result = self.__client.chat.completions.create(
model="tgi",
messages=[
{
"role": "user",
"content": self.SYSTEM_PROMPT.format(content=text),
},
],
)
return result.choices[0].message.content
class OpenAISummarizerTool(Tool):
name = "answer_with_sources"
description = """Use this tool to generate the complete final answer to the user's question based on search results.
After retrieving documents with mongodb_vector_search_retriever, use this tool to synthesize a comprehensive answer with a Sources section.
CRITICAL: This tool's output is the complete answer - after getting results from this tool, you MUST call the built-in final_answer tool and pass this output EXACTLY as-is without any modifications."""
inputs = {
"search_results": {
"type": "string",
"description": """The complete search results from mongodb_vector_search_retriever to analyze and synthesize into an answer. Pass the ENTIRE output from the retriever tool.""",
}
}
output_type = "string"
SYSTEM_PROMPT = """Based on the search results below, create a comprehensive answer to the user's question.
{content}
Create a two-part response:
1. **ANSWER** (with inline citations):
- Focus on the core issues, concerns, or highlights identified
- DO NOT mention specific customer names or personal identifiers
- Group related insights by topic with bullet points
- Be concise and general, highlighting the problem/concern rather than individuals
- Add INLINE CITATIONS at the end of each point using format: [Doc X]
- Number each unique document sequentially (Doc 1, Doc 2, etc.)
Example:
• Organizations are planning phone number porting transitions, but custom porting is expensive (~$1,000) and should be done in bulk [Doc 1]
• Questions about additional license requirements for integrations ($45 per user) [Doc 1]
• Ringtone volume issues in embedded Salesforce app [Doc 2]
2. **📚 Sources** (at the end):
- List ONLY UNIQUE documents (de-duplicate by Document ID)
- Number each unique source to match the inline citations (Doc 1, Doc 2, etc.)
- Format URLs as markdown links: [View Chat](url) or [View Recording](url)
For EACH unique document, use this EXACT structure with proper spacing and NO bold/italic formatting:
Doc X: [Title (Date)]
Source: [Type] | Document ID: [ID] | [Hyperlinked URL if available] | [User ID if available]
Summary: [One-line summary of the conversation]
Key Findings:
- [Type/Impact] Finding text here
- [Type/Impact] Finding text here
Example:
Doc 1: JustCall Checkin (2025-10-07)
Source: Justcall Meeting Recordings | Document ID: 4f6f9cee4f
Summary: Discussion about phone number porting timeline and costs
Key Findings:
- [Technical Issue/High] Custom porting is expensive at $1,000 per request
- [Feature Request/Medium] Need bulk porting option to reduce costs
Doc 2: Intercom Conversation (2025-10-05)
Source: Intercom Chats | Document ID: 7a6678783fea06d | [View Chat](https://app.intercom.com/...) | User ID: 432830
Summary: Customer requesting billing discount due to service interruption
Key Findings:
- [Pricing Concern/High] Request for discount due to porting delays
- [Policy Gap/Medium] No current policy for inactivity-based discounts
Provide a focused answer with inline citations followed by the well-formatted Sources section with conversation insights."""
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.__client = OpenAI(
base_url="https://api.openai.com/v1",
api_key=settings.OPENAI_API_KEY,
)
def forward(self, search_results: str) -> str:
"""Generate final answer with sources based on search results.
Args:
search_results: The complete search results to analyze (includes the original query)
Returns:
Complete answer with Sources section
"""
result = self.__client.chat.completions.create(
model=settings.OPENAI_MODEL_ID,
messages=[
{
"role": "system",
"content": "You are an expert analyst. Answer the user's question based on the search results provided. Create a comprehensive answer with a Sources section."
},
{
"role": "user",
"content": self.SYSTEM_PROMPT.format(content=search_results),
},
],
temperature=0.0, # Deterministic output
max_tokens=1500, # Reduced for faster response
timeout=45.0, # Reduced timeout
)
return result.choices[0].message.content
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