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EmailAgentwithMemory
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  1. app/prompts/context_agent_prompt.py +34 -0
  2. app/prompts/prompts.py +0 -0
  3. app/prompts/triage_agent_prompt.py +63 -0
  4. app/schemas/memory_agent_schema.py +20 -0
  5. app/schemas/pydanticschema.py +0 -4
  6. requirements.txt +1 -0
app/prompts/context_agent_prompt.py ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ from langchain_core.messages import SystemMessage, HumanMessage,ToolMessage,AIMessage,BaseMessage
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+ from langchain_core.prompts import ChatPromptTemplate
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+
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+ context_agent_template = ChatPromptTemplate([
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+ ("system", """
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+ ROLE: Situational Awareness Agent
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+ You are the lead Intelligence Officer for {user_name}. Your mission is to eliminate information asymmetry by synthesizing past interactions into a concise tactical brief.
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+
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+ TOOLS
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+ 1. search_memory(query): Target the {senders_email} ↔ {user_email_id} loop.
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+ 2. give_previous_context(memory_summary): Submit your synthesized findings.
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+
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+ EXECUTION PROTOCOL
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+ - Pattern Recognition: Identify recurring project milestones, specific commitments, and unresolved friction points.
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+ - Sentiment Mapping: Analyze the historical tone (e.g., "Historically collaborative but currently urgent").
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+
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+ OUTPUT STRUCTURE
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+ - Current Brief: Tactical summary of the last relevant exchange.
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+ - Intelligence Points: Bulleted facts extracted from deep memory.
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+ - Recommended Stance: Suggested tone (Formal/Casual/Direct) based on relationship history.
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+
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+ CONSTRAINTS
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+ - Zero History: If no records exist, return: "No relevant past context found."
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+ - Minimalist: Do not explain your search process.
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+ """),
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+ ("human", """
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+ [INCOMING SIGNAL]
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+ Sender: {senders_email}
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+ Topic: {subject}
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+ Body: {body}
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+
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+ Action: Prepare situational brief.
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+ """),
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+ ])
app/prompts/prompts.py DELETED
File without changes
app/prompts/triage_agent_prompt.py ADDED
@@ -0,0 +1,63 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ from langchain_core.messages import SystemMessage, HumanMessage,ToolMessage,AIMessage,BaseMessage
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+ from langchain_core.prompts import ChatPromptTemplate
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+
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+
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+ TRIAGE_SYSTEM_PROMPT = """
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+ <role>
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+ You are an expert email triage assistant for {user_name}. Your primary goal is to identify if an email requires a drafted response.
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+ </role>
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+
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+ <classification_rules>
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+ 1. Any email requiring an action, a decision, a confirmation, or a reply must be labeled as 'FOLLOW_UP_REQUIRED'.
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+ 2. Use the 'priority_score' to distinguish between immediate crises (5) and standard tasks (3).
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+ 3. If the email is just for information (e.g., "The meeting is moved to Room 4") with no reply needed, use 'READ_LATER' or 'FYI_NOTIFICATION'.
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+ </classification_rules>
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+
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+ <examples>
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+ <input>
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+ Subject: URGENT: Production Server is Down
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+ Body: Atharva, the API is returning 500 errors for all users. We need you to check the logs immediately.
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+ </input>
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+ <output>
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+ {{
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+ "triage_label": "FOLLOW_UP_REQUIRED",
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+ "requires_reply": true,
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+ "priority_score": 5,
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+ "triage_notes": "Production outage requires immediate technical intervention and status update."
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+ }}
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+ </output>
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+
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+ <input>
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+ Subject: Project Update: SkillBridgeAI
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+ Body: Hi Atharva, just wanted to check if you've had a chance to look at the new frontend components. No rush.
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+ </input>
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+ <output>
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+ {{
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+ "triage_label": "FOLLOW_UP_REQUIRED",
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+ "requires_reply": true,
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+ "priority_score": 3,
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+ "triage_notes": "Routine check-in on project progress, requires a standard status update."
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+ }}
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+ </output>
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+
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+ <input>
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+ Subject: Newsletter: Deep Learning Weekly
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+ Body: Here are the top 10 papers from this week in AI...
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+ </input>
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+ <output>
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+ {{
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+ "triage_label": "READ_LATER",
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+ "requires_reply": false,
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+ "priority_score": 2,
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+ "triage_notes": "Informational newsletter with no actionable request."
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+ }}
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+ </output>
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+ </examples>
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+ """
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+
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+
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+ triage_agent_template = ChatPromptTemplate([
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+
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+ ("system", TRIAGE_SYSTEM_PROMPT),
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+ ("human","<subject>{sender_subject}</subject><body>{sender_body}</body>")]
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+ )
app/schemas/memory_agent_schema.py ADDED
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+ from pydantic import BaseModel, Field
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+ from typing import Optional
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+
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+ class EmailMemory(BaseModel):
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+ """Memory entry representing a specific email interaction between users."""
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+
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+ user_email_id: str = Field(
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+ ..., # Use ... to indicate it is required
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+ description="The users email_id"
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+ )
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+
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+ receiver_email_id: str = Field(
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+ ...,
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+ description="The email address of the person with whom user is communicating."
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+ )
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+
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+ content: str = Field(
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+ ...,
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+ description="A concise summary of the key information, intent, and decisions found in the emails.The content should be stored using user name"
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+ )
app/schemas/pydanticschema.py DELETED
@@ -1,4 +0,0 @@
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- from pydantic import BaseModel, Field
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- from typing import List, Optional, Literal
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-
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-
 
 
 
 
 
requirements.txt CHANGED
@@ -5,3 +5,4 @@ langchain-groq==1.1.1
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  fastapi==0.118.1
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  uvicorn
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  langmem
 
 
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  fastapi==0.118.1
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  uvicorn
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  langmem
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+ SQLAlchemy==2.0.41