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models/models.py

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+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 config import config
+from langchain_openai import ChatOpenAI
+
+# 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)