import json
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.agent import ReActAgent
from llama_index.llms.ollama import Ollama
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.ollama import Ollama
from llama_index.core import Settings
from llama_index.llms.groq import Groq
import os
documents = SimpleDirectoryReader("data").load_data()

# bge-base embedding model
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")

# ollama
Settings.llm =Groq(model="mixtral-8x7b-32768", api_key="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")

index = VectorStoreIndex.from_documents(
    documents,
)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(response)