# Use the official Python image for Python 3.11
FROM python:3.11

# Set the working directory in the container
WORKDIR /app

# Copy the current directory contents into the container at /app
COPY . /app

# ARG FORCE_CMAKE=1

# ARG CMAKE_ARGS="-DLLAMA_CUBLAS=on"


# Install project dependencies

RUN CMAKE_ARGS="-DLLAMA_CUBLAS=on" python -m pip install -r requirements.txt

# Command to run the server
CMD ["python", "./server.py"]

client = weaviate.Client(
    url = "http://localhost:8080",  # Replace with your endpoint
)

class_obj = {
    "class": "testing",
    "vectorizer": "none",  # If set to "none" you must always provide vectors yourself. Could be any other "text2vec-*" also.
}
# client.schema.create_class(class_obj)
llm = my_llm.get_llm()
embed_model = embed_model = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-mpnet-base-v2"
    )
service_context = ServiceContext.from_defaults(
        llm=llm,
        embed_model=embed_model,
    )


# load the blogs in using the reader
doc = SimpleDirectoryReader(input_files=['./docs/Control.csv']).load_data()

# chunk up the blog posts into nodes
parser = SimpleNodeParser.from_defaults(chunk_size=1024, chunk_overlap=20)
nodes = parser.get_nodes_from_documents(doc)
print("-----LOGGING----- generated nodes")

# construct vector store
vector_store = WeaviateVectorStore(weaviate_client = client, index_name="Control", text_key="Control.csv",service_context = service_context)
print("-----LOGGING----- generated vector_store")
# setting up the storage for the embeddings
storage_context = StorageContext.from_defaults(vector_store = vector_store)
print("-----LOGGING----- generated storage_context")
# set up the index
index = VectorStoreIndex(nodes, storage_context = storage_context,)
print("-----LOGGING----- generated index")


query_engine = index.as_query_engine()
response = query_engine.query("What is control?")
print(response)