import chromadb
from langchain_openai import AzureOpenAIEmbeddings, AzureChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain.prompts import ChatPromptTemplate
from llama_index.core import Settings
from groq import Groq
from mem0 import MemoryClient
from langchain_openai import ChatOpenAI
import sys
import os
# Add config to path
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'config'))
from config import config
# Initialize embedding function for Chroma
embedding_function = chromadb.utils.embedding_functions.OpenAIEmbeddingFunction(
    api_base=config.OPENAI_API_BASE,
    api_key=config.API_KEY,
    model_name=config.EMBEDDING_MODEL
)

# Initialize OpenAI Embeddings
embedding_model = OpenAIEmbeddings(
    openai_api_base=config.OPENAI_API_BASE,
    openai_api_key=config.API_KEY,
    model=config.EMBEDDING_MODEL
)

# Initialize Chat OpenAI model
llm =ChatOpenAI(
    openai_api_base=config.OPENAI_API_BASE,
    openai_api_key=config.API_KEY,
    model=config.CHAT_MODEL,
    streaming=False
)

# Set LlamaIndex settings
Settings.llm = llm
Settings.embedding = embedding_model

# Initialize vector store
vector_store = Chroma(
    collection_name=config.COLLECTION_NAME,
    persist_directory=config.PERSIST_DIRECTORY,
    embedding_function=embedding_model
)

# Create retriever
retriever = vector_store.as_retriever(
    search_type='similarity',
    search_kwargs={'k': config.RETRIEVAL_K}
)

# Initialize Groq client for Llama Guard
llama_guard_client = Groq(api_key=config.GROQ_API_KEY)

# Initialize Memory client
memory_client = MemoryClient(api_key=config.MEM0_API_KEY)