I hope someone can help me with llamaindex. I try to play with it, and I am at the beginning. I have created a program for indexing a PDF document (741 pages). This program is in main.py.

And then I try to query the document PDF, which is indexed in a JSON file. The program is query1.py.

main.py created successfully index in JSON files.
Once I execute query1.py, and the CPU is 350% and execution is endless.

Someone, could tell me if something is wrong in my program or if optimizations should be done? I am novice so surely I don’t use correctly llamaindex.

Screenshots :

https://i.sstatic.net/jyeGImRF.png
https://i.sstatic.net/VZVj0Kth.png
https://i.sstatic.net/3G9Hox3l.png

main.py

```
from llama_index.core import SimpleDirectoryReader
from llama_index.core import GPTVectorStoreIndex
from sentence_transformers import SentenceTransformer
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core import Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding

class LocalEmbeddingModel(BaseEmbedding):
  def __init__(self):
    self.model = SentenceTransformer('all-MiniLM-L6-v2')

  def embed(self, texts):
    return self.model.encode(texts)

# Initialiser le modèle d'embeddings local
#embed_model = LocalEmbeddingModel()

# Configurer LlamaIndex avec les paramètres appropriés
#settings = Settings(embed_model=embed_model)
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

#service_context = ServiceContext.from_defaults(embed_model=None)

# Charger les documents à partir d'un répertoire
documents = SimpleDirectoryReader('./mesdoc').load_data()

# Créer un index à partir des documents
index = GPTVectorStoreIndex(documents)

# Sauvegarder l'index pour une utilisation future
#index.save_to_disk('index.json')
index.storage_context.persist(persist_dir="./mesdoc")
```

query1.py

```
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.core import Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import ServiceContext

# Configurer LlamaIndex avec les paramètres appropriés
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")


# Fonction pour poser des questions
def ask_question(question):
  # Recharger l'index depuis le répertoire de persistance
  # Load documents and build index
  documents = SimpleDirectoryReader("./mesdoc").load_data()
  index = VectorStoreIndex.from_documents(documents)
  #index = GPTVectorStoreIndex.load_from_storage(persist_dir="./",)

  # Poser la question et obtenir une réponse
  query_engine = index.as_query_engine()
  response = query_engine.query(question)
  return response

# Exemple de question
question = "What is tasklist ?"
answer = ask_question(question)
print(f"Réponse : {answer}")
```