customer-service-chatbot/agents/customer_service.py

from agno.embedder.sentence_transformer import SentenceTransformerEmbedder
import os
import json
from agno.agent import Agent, RunResponse
from agno.knowledge.pdf import PDFKnowledgeBase, PDFReader
from agno.vectordb.pgvector import PgVector, SearchType
from agno.storage.agent.sqlite import SqliteAgentStorage
from agno.memory.v2 import Memory
from agno.memory.v2.db.sqlite import SqliteMemoryDb
from agno.models.groq import Groq
from dotenv import load_dotenv

load_dotenv()


def init_support_agents(reload_knowledge=False, recreate_knowledge=False):
    db_url = os.getenv("DOCKER_DB_URL")
    kb = PDFKnowledgeBase(
        path="tmp/knowledge-base/", reader=PDFReader(chunk=True),
        vector_db=PgVector(
            table_name="kb_documents", db_url=db_url, search_type=SearchType.hybrid,
            embedder=SentenceTransformerEmbedder(id="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"),
        ),
    )

    kb.load(recreate=recreate_knowledge)

    storage = SqliteAgentStorage(table_name="agent_sessions", db_file="tmp/agent_sessions.db")
    memory = Memory(db=SqliteMemoryDb(table_name="user_memories", db_file="tmp/user_memory.db"))

    context_agent = Agent(
        model=Groq(id="meta-llama/llama-4-maverick-17b-128e-instruct"),
        storage=storage,
        instructions=[
            """
            You are a structured context extractor.
            Given a user message, output a strict JSON object with the following fields:
            
            {
              "user_goal": string,              # what the user is trying to achieve
              "item_model": string | null,      # product or item model, if any
              "platform": string | null,        # device, system, or platform
              "emotional_state": string | null, # inferred emotion or sentiment
              "prior_steps": string | null      # steps already taken by the user
            }
            
            - Always return valid JSON only. No explanations, no markdown.
            - Use null for missing fields.
            - Be conservative in assumptions.
            """
        ],
    )

    knowledge_agent = Agent(
        model=Groq(id="meta-llama/llama-4-maverick-17b-128e-instruct"),
        knowledge=kb,
        search_knowledge=True,
        add_context=True,
        instructions=[
            """You are a knowledge retriever agent.
            Given a JSON context, search the knowledge base for the top 3 most relevant snippets.
            
            Output a plain text list like this:
            
            - "[Snippet 1 content]" (Document: <doc_id or title>)
            - "[Snippet 2 content]" (Document: <doc_id or title>)
            - "[Snippet 3 content]" (Document: <doc_id or title>)
            
            Guidelines:
            - Do not speculate or infer beyond retrieved content.
            - Prefer procedural or troubleshooting instructions if the user_goal implies an action.
            - Snippets should be self-contained and relevant to the user_goal, platform, or item_model.
            
            """
        ],
    )

    conversation_agent = Agent(
        model=Groq(id="meta-llama/llama-4-maverick-17b-128e-instruct"),
        storage=storage,
        instructions=[
            """
            You are a professional AI-powered customer support assistant. Your goal is to solve the user's issue directly, clearly, and thoroughly using the provided context and knowledge base.

            Guidelines:
            - Always provide **step-by-step help** whenever possible.
            - Use the **knowledge base snippets** to inform your reply. Rephrase them in user-friendly language.
            - Do **not** tell the user to go check a manual, a website, or external source unless the information is truly not available in the snippets.
            - If specific product details are missing from the context, ask for clarification — don’t assume.
            - End with a friendly question to continue support, like:
              - "Did that help resolve your issue?"
              - "Would you like me to guide you through that step?"
              - "Is there anything else you'd like to check?"
            
            Example structure:
            1. Brief recap of already tried actions, based on last messages. Max 2 row.
            2. Address the issue with specifics from KB
            3. Offer additional help
            
            Do NOT include markdown, citations, or code unless explicitly required.
            Do NOT be generic. You MUST be as specific as possible.
            
            If the already tried actions are more than 5 or 6, inform the user that you have informed the assistance about his problem, and they will contact him, then ask if he wants to continue in the meantime.
            
            All user messages are in ITALIAN. You must reply only in ITALIAN. Do not add text in english NEVER.
            Adding text in english is FORBIDDEN.
            """
        ],
    )

    return context_agent, knowledge_agent, conversation_agent


def get_customer_agents_response(user_message: str, user_id: str = "user_1", session_id: str = None, history=None):
    ctx_agent, kb_agent, convo_agent = init_support_agents()

    ctx_run: RunResponse = ctx_agent.run(user_message, user_id=user_id, session_id=session_id)
    ctx_text = ctx_run.content.strip()
    try:
        ctx = json.loads(ctx_text)
    except json.JSONDecodeError:
        ctx = {}

    kb_prompt = json.dumps(ctx)
    kb_run: RunResponse = kb_agent.run(kb_prompt, user_id=user_id, session_id=session_id)
    kb_text = kb_run.content.strip()

    history_lines = []
    if history:
        for msg in history[-4:]:
            role = "Utente" if msg["role"] == "user" else "Assistente"
            history_lines.append(f"{role}: {msg['content'].strip()}")
    history_str = "\n".join(history_lines)

    convo_prompt = f"""
        Sei un assistente virtuale professionale.
        
        Tieni conto del contesto, della cronologia della conversazione e degli snippet della knowledge base.
        
        CONTESTO ESTRATTO:
        {json.dumps(ctx, indent=2, ensure_ascii=False)}
        
        SNIPPET DELLA KNOWLEDGE BASE:
        {kb_text}
        
        STORIA DELLA CONVERSAZIONE:
        {history_str}
        
        NUOVA DOMANDA DELL'UTENTE:
        Utente: {user_message}
        
        Rispondi in modo utile e dettagliato. Non fare riferimento a fonti esterne. Rispondi solo in ITALIANO.
    """

    convo_run: RunResponse = convo_agent.run(convo_prompt, user_id=user_id, session_id=session_id)
    return convo_run.content.strip(), convo_run.session_id