agentic_implementation/re_act.py

# orchestrator.py

import os
import json
import re
from typing import Any, Dict, Tuple, Optional
from datetime import datetime

from dotenv import load_dotenv
from openai import OpenAI

from schemas import Plan, PlanStep, FetchEmailsParams
from tools import TOOL_MAPPING

# Load .env and initialize OpenAI client
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
    raise RuntimeError("Missing OPENAI_API_KEY in environment")
client = OpenAI(api_key=api_key)

# File paths for name mapping
NAME_MAPPING_FILE = "name_mapping.json"

# === Hard-coded list of available actions ===
SYSTEM_PLAN_PROMPT = """
You are an email assistant agent. You have access to the following actions:

  • fetch_emails - fetch emails based on sender and date criteria (includes date extraction)
  • show_email - display specific email content
  • analyze_emails - analyze email patterns or content
  • draft_reply - create a reply to an email
  • send_reply - send a drafted reply
  • done - complete the task

When the user gives you a query, output _only_ valid JSON of this form:

{
  "plan": [
    "fetch_emails",
    ...,
    "done"
  ]
}

Rules:
- Use "fetch_emails" when you need to retrieve emails (it automatically handles date extraction)
- The final entry _must_ be "done"
- If no tool is needed, return `{"plan":["done"]}`

Example: For "show me emails from dev today" → ["fetch_emails", "done"]
"""

SYSTEM_VALIDATOR_TEMPLATE = """
You are a plan validator.  
Context (results so far):  
{context}

Next action:  
{action}

Reply _only_ with JSON:
{{
  "should_execute": <true|false>,
  "parameters": <null or a JSON object with this action's parameters>
}}
"""


def _load_name_mapping() -> Dict[str, str]:
    """Load name to email mapping from JSON file"""
    if not os.path.exists(NAME_MAPPING_FILE):
        return {}
    try:
        with open(NAME_MAPPING_FILE, "r") as f:
            return json.load(f)
    except (json.JSONDecodeError, IOError):
        return {}


def _save_name_mapping(mapping: Dict[str, str]):
    """Save name to email mapping to JSON file"""
    with open(NAME_MAPPING_FILE, "w") as f:
        json.dump(mapping, f, indent=2)


def store_name_email_mapping(name: str, email: str):
    """Store new name to email mapping"""
    name_mapping = _load_name_mapping()
    name_mapping[name.lower().strip()] = email.lower().strip()
    _save_name_mapping(name_mapping)


def extract_sender_info(query: str) -> Dict:
    """
    Extract sender information from user query using LLM
    """
    system_prompt = """
You are an email query parser that extracts sender information.

Given a user query, extract the sender intent - the person/entity they want emails from.
This could be:
- A person's name (e.g., "dev", "john smith", "dev agarwal")
- A company/service (e.g., "amazon", "google", "linkedin")
- An email address (e.g., "john@company.com")

Examples:
- "emails from dev agarwal last week" → "dev agarwal"
- "show amazon emails from last month" → "amazon"  
- "emails from john@company.com yesterday" → "john@company.com"
- "get messages from sarah" → "sarah"

Return ONLY valid JSON:
{
  "sender_intent": "extracted name, company, or email"
}
"""
    
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        temperature=0.0,
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": query}
        ],
    )
    
    result = json.loads(response.choices[0].message.content)
    return result


def resolve_sender_email(sender_intent: str) -> Tuple[Optional[str], bool]:
    """
    Resolve sender intent to actual email address
    Returns: (email_address, needs_user_input)
    """
    # Check if it's already an email address
    if "@" in sender_intent:
        return sender_intent.lower(), False
    
    # Load name mapping
    name_mapping = _load_name_mapping()
    
    # Normalize the intent (lowercase for comparison)
    normalized_intent = sender_intent.lower().strip()
    
    # Check direct match
    if normalized_intent in name_mapping:
        return name_mapping[normalized_intent], False
    
    # Check partial matches (fuzzy matching)
    for name, email in name_mapping.items():
        if normalized_intent in name.lower() or name.lower() in normalized_intent:
            return email, False
    
    # No match found
    return None, True


def get_plan_from_llm(user_query: str) -> Plan:
    """
    Ask the LLM which actions to run, in order. No parameters here.
    """
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        temperature=0.0,
        messages=[
            {"role": "system", "content": SYSTEM_PLAN_PROMPT},
            {"role": "user",   "content": user_query},
        ],
    )

    plan_json = json.loads(response.choices[0].message.content)
    steps = [PlanStep(action=a) for a in plan_json["plan"]]
    return Plan(plan=steps)


def think(
    step: PlanStep,
    context: Dict[str, Any],
    user_query: str
) -> Tuple[bool, Optional[PlanStep], Optional[str]]:
    """
    Fill in parameters or skip based on the action:
     - fetch_emails: extract sender and pass the raw query for date extraction
     - others: ask the LLM validator for params
    
    Returns: (should_execute, updated_step, user_prompt_if_needed)
    """
    # 1) fetch_emails → extract sender and pass query for internal date extraction
    if step.action == "fetch_emails":
        # Extract sender using LLM
        sender_info = extract_sender_info(user_query)
        sender_intent = sender_info.get("sender_intent", "")
        
        if not sender_intent:
            return False, None, None
        
        # Resolve sender to email address
        email_address, needs_input = resolve_sender_email(sender_intent)
        
        if needs_input:
            # Need user input for email address
            prompt_msg = f"I don't have an email address for '{sender_intent}'. Please provide the email address:"
            return False, None, prompt_msg
        
        params = FetchEmailsParams(
            email=email_address,
            query=user_query  # Pass the full query for date extraction
        )
        return True, PlanStep(action="fetch_emails", parameters=params), None

    # 2) everything else → validate & supply params via LLM
    prompt = SYSTEM_VALIDATOR_TEMPLATE.format(
        context=json.dumps(context, indent=2),
        action=step.action,
    )
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        temperature=0.0,
        messages=[
            {"role": "system",  "content": "Validate or supply parameters for this action."},
            {"role": "user",    "content": prompt},
        ],
    )
    verdict = json.loads(response.choices[0].message.content)
    if not verdict.get("should_execute", False):
        return False, None, None

    return True, PlanStep(
        action=step.action,
        parameters=verdict.get("parameters")
    ), None


def act(step: PlanStep) -> Any:
    """
    Dispatch to the actual implementation in tools.py.
    """
    fn = TOOL_MAPPING.get(step.action)
    if fn is None:
        raise ValueError(f"Unknown action '{step.action}'")

    kwargs = step.parameters.model_dump() if step.parameters else {}
    return fn(**kwargs)