Try RAG with LlamaIndex

FROM nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04

# Set bash as the default shell
ENV SHELL=/bin/bash

# Build with some basic utilities
RUN apt update \
	&& apt install -y \
        wget \
        bzip2 \
        git \
        git-lfs \
        curl \
        unzip \
        file \
        xz-utils \
        sudo \
        python3 \
        python3-pip && \
        apt-get autoremove -y && \
        apt-get clean && \
        rm -rf /usr/local/src/*

# alias python='python3'
RUN ln -s /usr/bin/python3 /usr/bin/python

RUN pip install --upgrade pip setuptools \
	&& pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 \
	--index-url https://download.pytorch.org/whl/cu121 \
	&& pip install jupyterlab matplotlib pandas scikit-learn ipywidgets \
	&& pip install transformers accelerate sentencepiece einops \
	&& pip install langchain bitsandbytes protobuf \
	&& pip install auto-gptq optimum \
	&& pip install pypdf sentence-transformers \
	&& pip install llama-index-embeddings-huggingface llama-index-llms-openai \
	&& pip install llama-index-llms-llama-cpp \
	&& pip install llama-index

# Install llama-cpp-python[server] with cuBLAS on
RUN CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 \
        pip install llama-cpp-python[server] --force-reinstall --no-cache-dir

# Create a working directory
WORKDIR /workdir

# Port number in container side
EXPOSE 8888

ENTRYPOINT ["jupyter-lab", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root", "--NotebookApp.token=''"]

CMD ["--notebook-dir=/workdir"]

llama_cpp_python                        0.2.75
llama-index                             0.10.38
llama-index-agent-openai                0.2.5
llama-index-cli                         0.1.12
llama-index-core                        0.10.38.post2
llama-index-embeddings-huggingface      0.2.0
llama-index-embeddings-openai           0.1.10
llama-index-indices-managed-llama-cloud 0.1.6
llama-index-legacy                      0.9.48
llama-index-llms-llama-cpp              0.1.3
llama-index-llms-openai                 0.1.20
llama-index-multi-modal-llms-openai     0.1.6
llama-index-program-openai              0.1.6
llama-index-question-gen-openai         0.1.3
llama-index-readers-file                0.1.22
llama-index-readers-llama-parse         0.1.4

torch                                   2.2.2+cu121
torchaudio                              2.2.2+cu121
torchvision                             0.17.2+cu121

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
Cell In[1], line 5
      2 import os
      3 import sys
----> 5 from llama_index import (
      6     LLMPredictor,
      7     PromptTemplate,
      8     ServiceContext,
      9     SimpleDirectoryReader,
     10     VectorStoreIndex,
     11 )
     12 from llama_index.callbacks import CallbackManager, LlamaDebugHandler
     13 from llama_index.embeddings import HuggingFaceEmbedding

ImportError: cannot import name 'LLMPredictor' from 'llama_index' (unknown location)

import logging
import os
import sys

"""
from llama_index import (
    LLMPredictor,
    PromptTemplate,
    ServiceContext,
    SimpleDirectoryReader,
    VectorStoreIndex,
)

from llama_index.callbacks import CallbackManager, LlamaDebugHandler
from llama_index.embeddings import HuggingFaceEmbedding
from llama_index.llms import LlamaCPP
"""
from llama_index.legacy import LLMPredictor
from llama_index.core.prompts import PromptTemplate
from llama_index.core import ServiceContext, SimpleDirectoryReader, VectorStoreIndex

from llama_index.core.callbacks import CallbackManager, LlamaDebugHandler
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.llama_cpp import LlamaCPP

# ログレベルの設定
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG, force=True)

model_path = f"../20240421_llamacpp/ELYZA-japanese-Llama-2-7b-fast-instruct-q4_K_M.gguf"

    model_kwargs={"n_ctx": 4096, "n_gpu_layers": -1},

EMBEDDING_DEVICE = "cuda"

    chunk_size=512,

# ドキュメントの読み込み
documents = SimpleDirectoryReader("data").load_data()

target_url = "https://ja.wikipedia.org/wiki/R%E9%81%8E%E7%A8%8B"
# NOTE: the html_to_text=True option requires html2text to be installed
documents = SimpleWebPageReader(html_to_text=True).load_data(
    [target_url]
)

## Question: what is the mechanism of the r-process in neutron star mergers?
(omitted).
## Answer:
 The r-process can occur as a result of neutron star mergers.

Neutron stars are very dense, and in them nuclei with unstable neutron capture are produced. This produced nucleus with unstable neutron capture repeatedly beta-decays and incorporates neutrons into nuclei such as nickel56, resulting in a process called the r-process.

## Question: explain the co-evolution of galaxies and black holes
(omitted)
## Answer:
 Information not in the context information will not be included in the answer.

Also, the answer to this question is "I don't know" because there is no information that the release of energy from neutron star mergers ultimately results in a black hole.