pipeline.py

import os
import getpass
import spacy
import pandas as pd
from typing import Optional, List, Dict, Any
import subprocess
from langchain.llms.base import LLM
from langchain.docstore.document import Document
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from smolagents import CodeAgent, DuckDuckGoSearchTool, ManagedAgent, LiteLLMModel
from pydantic import BaseModel, Field, ValidationError, validator
from mistralai import Mistral
from langchain.prompts import PromptTemplate

# Import chains and tools
from classification_chain import get_classification_chain
from cleaner_chain import get_cleaner_chain
from refusal_chain import get_refusal_chain
from tailor_chain import get_tailor_chain
from prompts import classification_prompt, refusal_prompt, tailor_prompt

# Initialize Mistral API client
mistral_api_key = os.environ.get("MISTRAL_API_KEY")
client = Mistral(api_key=mistral_api_key)

# Initialize LiteLLM model for web search
pydantic_agent = LiteLLMModel(model_id="gemini/gemini-pro", api_key=os.environ.get("GEMINI_API_KEY"))

# Pydantic models for validation and type safety
class QueryInput(BaseModel):
    query: str = Field(..., min_length=1, description="The input query string")
    
    @validator('query')
    def check_query_is_string(cls, v):
        if not isinstance(v, str):
            raise ValueError("Query must be a valid string")
        if v.strip() == "":
            raise ValueError("Query cannot be empty or just whitespace")
        return v.strip()

class ModerationResult(BaseModel):
    is_safe: bool = Field(..., description="Whether the content is safe")
    categories: Dict[str, bool] = Field(default_factory=dict, description="Detected content categories")
    original_text: str = Field(..., description="The original input text")

# Load spaCy model for NER
def install_spacy_model():
    try:
        spacy.load("en_core_web_sm")
        print("spaCy model 'en_core_web_sm' is already installed.")
    except OSError:
        print("Downloading spaCy model 'en_core_web_sm'...")
        subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"], check=True)
        print("spaCy model 'en_core_web_sm' downloaded successfully.")

install_spacy_model()
nlp = spacy.load("en_core_web_sm")

def extract_main_topic(query: str) -> str:
    try:
        query_input = QueryInput(query=query)
        doc = nlp(query_input.query)
        main_topic = None
        
        for ent in doc.ents:
            if ent.label_ in ["ORG", "PRODUCT", "PERSON", "GPE", "TIME"]:
                main_topic = ent.text
                break
        
        if not main_topic:
            for token in doc:
                if token.pos_ in ["NOUN", "PROPN"]:
                    main_topic = token.text
                    break
        
        return main_topic if main_topic else "this topic"
    except Exception as e:
        print(f"Error extracting main topic: {e}")
        return "this topic"

def moderate_text(query: str) -> ModerationResult:
    try:
        query_input = QueryInput(query=query)
        
        response = client.classifiers.moderate_chat(
            model="mistral-moderation-latest",
            inputs=[{"role": "user", "content": query_input.query}]
        )
        
        is_safe = True
        categories = {}
        
        if hasattr(response, 'results') and response.results:
            categories = {
                "violence": response.results[0].categories.get("violence_and_threats", False),
                "hate": response.results[0].categories.get("hate_and_discrimination", False),
                "dangerous": response.results[0].categories.get("dangerous_and_criminal_content", False),
                "selfharm": response.results[0].categories.get("selfharm", False)
            }
            is_safe = not any(categories.values())
        
        return ModerationResult(
            is_safe=is_safe,
            categories=categories,
            original_text=query_input.query
        )
    except ValidationError as e:
        raise ValueError(f"Input validation failed: {str(e)}")
    except Exception as e:
        raise RuntimeError(f"Moderation failed: {str(e)}")

def classify_query(query: str) -> str:
    try:
        query_input = QueryInput(query=query)
        
        wellness_keywords = ["box breathing", "meditation", "yoga", "mindfulness", "breathing exercises"]
        if any(keyword in query_input.query.lower() for keyword in wellness_keywords):
            return "Wellness"
        
        class_result = classification_chain.invoke({"query": query_input.query})
        classification = class_result.get("text", "").strip()
        
        return classification if classification != "" else "OutOfScope"
    except ValidationError as e:
        raise ValueError(f"Classification input validation failed: {str(e)}")
    except Exception as e:
        raise RuntimeError(f"Classification failed: {str(e)}")

def build_or_load_vectorstore(csv_path: str, store_dir: str) -> FAISS:
    try:
        if os.path.exists(store_dir):
            print(f"Loading existing FAISS store from '{store_dir}'")
            embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/multi-qa-mpnet-base-dot-v1")
            return FAISS.load_local(store_dir, embeddings)
        
        print(f"Building new FAISS store from CSV: {csv_path}")
        df = pd.read_csv(csv_path)
        df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
        df.columns = df.columns.str.strip()
        
        docs = [
            Document(page_content=str(row["Answers"]), metadata={"question": str(row["Question"])})
            for _, row in df.iterrows()
        ]
        
        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/multi-qa-mpnet-base-dot-v1")
        vectorstore = FAISS.from_documents(docs, embedding=embeddings)
        vectorstore.save_local(store_dir)
        return vectorstore
        
    except Exception as e:
        raise RuntimeError(f"Error building/loading vector store: {str(e)}")

def build_rag_chain(llm_model: LiteLLMModel, vectorstore: FAISS) -> RetrievalQA:
    class GeminiLangChainLLM(LLM):
        def _call(self, prompt: str, stop: Optional[list] = None, **kwargs) -> str:
            messages = [{"role": "user", "content": prompt}]
            return llm_model(messages, stop_sequences=stop)

        @property
        def _llm_type(self) -> str:
            return "custom_gemini"
    
    try:
        retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 3})
        gemini_as_llm = GeminiLangChainLLM()
        return RetrievalQA.from_chain_type(
            llm=gemini_as_llm,
            chain_type="stuff",
            retriever=retriever,
            return_source_documents=True
        )
    except Exception as e:
        raise RuntimeError(f"Error building RAG chain: {str(e)}")

def run_pipeline(query: str) -> str:
    try:
        query = sanitize_message(query)
        
        moderation_result = moderate_text(query)
        if not moderation_result.is_safe:
            return "Sorry, this query contains harmful or inappropriate content."

        classification = classify_query(moderation_result.original_text)

        if classification == "OutOfScope":
            refusal_text = refusal_chain.run({"topic": "this topic"})
            return tailor_chain.run({"response": refusal_text}).strip()

        if classification == "Wellness":
            rag_result = wellness_rag_chain({"query": moderation_result.original_text})
            csv_answer = rag_result["result"].strip()
            web_answer = "" if csv_answer else do_web_search(moderation_result.original_text)
            final_merged = merge_responses(csv_answer, web_answer)
            return tailor_chain.run({"response": final_merged}).strip()

        if classification == "Brand":
            rag_result = brand_rag_chain({"query": moderation_result.original_text})
            csv_answer = rag_result["result"].strip()
            final_merged = merge_responses(csv_answer, "")
            return tailor_chain.run({"response": final_merged}).strip()

        refusal_text = refusal_chain.run({"topic": "this topic"})
        return tailor_chain.run({"response": refusal_text}).strip()

# Initialize chains and vectorstores
try:
    classification_chain = get_classification_chain()
    refusal_chain = get_refusal_chain()
    tailor_chain = get_tailor_chain()
    cleaner_chain = get_cleaner_chain()

    wellness_csv = "AIChatbot.csv"
    brand_csv = "BrandAI.csv"
    wellness_store_dir = "faiss_wellness_store"
    brand_store_dir = "faiss_brand_store"

    wellness_vectorstore = build_or_load_vectorstore(wellness_csv, wellness_store_dir)
    brand_vectorstore = build_or_load_vectorstore(brand_csv, brand_store_dir)

    gemini_llm = LiteLLMModel(model_id="gemini/gemini-pro", api_key=os.environ.get("GEMINI_API_KEY"))
    wellness_rag_chain = build_rag_chain(gemini_llm, wellness_vectorstore)
    brand_rag_chain = build_rag_chain(gemini_llm, brand_vectorstore)
    
    print("Pipeline initialized successfully!")
except Exception as e:
    print(f"Error initializing pipeline: {str(e)}")

def run_with_chain(query: str) -> str:
    return run_pipeline(query)