Create a RAG system using OpenVINO and LangChain#

This Jupyter notebook can be launched after a local installation only.

Github

Retrieval-augmented generation (RAG) is a technique for augmenting LLM knowledge with additional, often private or real-time, data. LLMs can reason about wide-ranging topics, but their knowledge is limited to the public data up to a specific point in time that they were trained on. If you want to build AI applications that can reason about private data or data introduced after a model’s cutoff date, you need to augment the knowledge of the model with the specific information it needs. The process of bringing the appropriate information and inserting it into the model prompt is known as Retrieval Augmented Generation (RAG).

LangChain is a framework for developing applications powered by language models. It has a number of components specifically designed to help build RAG applications. In this tutorial, we’ll build a simple question-answering application over a text data source.

The tutorial consists of the following steps:

In this example, the customized RAG pipeline consists of following components in order, where embedding, rerank and LLM will be deployed with OpenVINO to optimize their inference performance.

RAG

RAG#

Table of contents:#

Prerequisites#

Install required dependencies

import os

os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false"

%pip install -Uq pip
%pip uninstall -q -y optimum optimum-intel
%pip install --pre -Uq openvino openvino-tokenizers[transformers] --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
%pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
"git+https://github.com/huggingface/optimum-intel.git"\
"git+https://github.com/openvinotoolkit/nncf.git"\
"datasets"\
"accelerate"\
"gradio"\
"onnx" "einops" "transformers_stream_generator" "tiktoken" "transformers>=4.38.1" "bitsandbytes" "faiss-cpu" "sentence_transformers" "langchain>=0.2.0" "langchain-community>=0.2.0" "langchainhub" "unstructured" "scikit-learn" "python-docx" "pypdf"
WARNING: Skipping openvino-dev as it is not installed.
Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.
import os
from pathlib import Path
import requests
import shutil
import io

# fetch model configuration

config_shared_path = Path("../../utils/llm_config.py")
config_dst_path = Path("llm_config.py")
text_example_en_path = Path("text_example_en.pdf")
text_example_cn_path = Path("text_example_cn.pdf")
text_example_en = "https://github.com/openvinotoolkit/openvino_notebooks/files/15039728/Platform.Brief_Intel.vPro.with.Intel.Core.Ultra_Final.pdf"
text_example_cn = "https://github.com/openvinotoolkit/openvino_notebooks/files/15039713/Platform.Brief_Intel.vPro.with.Intel.Core.Ultra_Final_CH.pdf"

if not config_dst_path.exists():
    if config_shared_path.exists():
        try:
            os.symlink(config_shared_path, config_dst_path)
        except Exception:
            shutil.copy(config_shared_path, config_dst_path)
    else:
        r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
        with open("llm_config.py", "w") as f:
            f.write(r.text)
elif not os.path.islink(config_dst_path):
    print("LLM config will be updated")
    if config_shared_path.exists():
        shutil.copy(config_shared_path, config_dst_path)
    else:
        r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
        with open("llm_config.py", "w") as f:
            f.write(r.text)


if not text_example_en_path.exists():
    r = requests.get(url=text_example_en)
    content = io.BytesIO(r.content)
    with open("text_example_en.pdf", "wb") as f:
        f.write(content.read())

if not text_example_cn_path.exists():
    r = requests.get(url=text_example_cn)
    content = io.BytesIO(r.content)
    with open("text_example_cn.pdf", "wb") as f:
        f.write(content.read())

Select model for inference#

The tutorial supports different models, you can select one from the provided options to compare the quality of open source LLM solutions.

Note: conversion of some models can require additional actions from user side and at least 64GB RAM for conversion.

The available embedding model options are:

BGE embedding is a general Embedding Model. The model is pre-trained using RetroMAE and trained on large-scale pair data using contrastive learning.

The available rerank model options are:

Reranker model with cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model.

You can also find available LLM model options in llm-chatbot notebook.

from pathlib import Path
import openvino as ov
import torch
import ipywidgets as widgets
from transformers import (
    TextIteratorStreamer,
    StoppingCriteria,
    StoppingCriteriaList,
)

Convert model and compress model weights#

The Weights Compression algorithm is aimed at compressing the weights of the models and can be used to optimize the model footprint and performance of large models where the size of weights is relatively larger than the size of activations, for example, Large Language Models (LLM). Compared to INT8 compression, INT4 compression improves performance even more, but introduces a minor drop in prediction quality.

from llm_config import (
    SUPPORTED_EMBEDDING_MODELS,
    SUPPORTED_RERANK_MODELS,
    SUPPORTED_LLM_MODELS,
)

model_languages = list(SUPPORTED_LLM_MODELS)

model_language = widgets.Dropdown(
    options=model_languages,
    value=model_languages[0],
    description="Model Language:",
    disabled=False,
)

model_language
Dropdown(description='Model Language:', options=('English', 'Chinese', 'Japanese'), value='English')
llm_model_ids = [model_id for model_id, model_config in SUPPORTED_LLM_MODELS[model_language.value].items() if model_config.get("rag_prompt_template")]

llm_model_id = widgets.Dropdown(
    options=llm_model_ids,
    value=llm_model_ids[-1],
    description="Model:",
    disabled=False,
)

llm_model_id
Dropdown(description='Model:', index=9, options=('tiny-llama-1b-chat', 'gemma-2b-it', 'red-pajama-3b-chat', 'g…
llm_model_configuration = SUPPORTED_LLM_MODELS[model_language.value][llm_model_id.value]
print(f"Selected LLM model {llm_model_id.value}")
Selected LLM model neural-chat-7b-v3-1

Optimum Intel is the interface between the Transformers and Diffusers libraries and OpenVINO to accelerate end-to-end pipelines on Intel architectures. It provides ease-to-use cli interface for exporting models to OpenVINO Intermediate Representation (IR) format.

The command bellow demonstrates basic command for model export with optimum-cli

optimum-cli export openvino --model <model_id_or_path> --task <task> <out_dir>

where --model argument is model id from HuggingFace Hub or local directory with model (saved using .save_pretrained method), --task is one of supported task that exported model should solve. For LLMs it will be text-generation-with-past. If model initialization requires to use remote code, --trust-remote-code flag additionally should be passed.

LLM conversion and Weights Compression using Optimum-CLI#

You can also apply fp16, 8-bit or 4-bit weight compression on the Linear, Convolutional and Embedding layers when exporting your model with the CLI by setting --weight-format to respectively fp16, int8 or int4. This type of optimization allows to reduce the memory footprint and inference latency. By default the quantization scheme for int8/int4 will be asymmetric, to make it symmetric you can add --sym.

For INT4 quantization you can also specify the following arguments :

  • The --group-size parameter will define the group size to use for quantization, -1 it will results in per-column quantization.

  • The --ratio parameter controls the ratio between 4-bit and 8-bit quantization. If set to 0.9, it means that 90% of the layers will be quantized to int4 while 10% will be quantized to int8.

Smaller group_size and ratio values usually improve accuracy at the sacrifice of the model size and inference latency.

Note: There may be no speedup for INT4/INT8 compressed models on dGPU.

from IPython.display import Markdown, display

prepare_int4_model = widgets.Checkbox(
    value=True,
    description="Prepare INT4 model",
    disabled=False,
)
prepare_int8_model = widgets.Checkbox(
    value=False,
    description="Prepare INT8 model",
    disabled=False,
)
prepare_fp16_model = widgets.Checkbox(
    value=False,
    description="Prepare FP16 model",
    disabled=False,
)

display(prepare_int4_model)
display(prepare_int8_model)
display(prepare_fp16_model)
Checkbox(value=True, description='Prepare INT4 model')
Checkbox(value=False, description='Prepare INT8 model')
Checkbox(value=False, description='Prepare FP16 model')
pt_model_id = llm_model_configuration["model_id"]
pt_model_name = llm_model_id.value.split("-")[0]
fp16_model_dir = Path(llm_model_id.value) / "FP16"
int8_model_dir = Path(llm_model_id.value) / "INT8_compressed_weights"
int4_model_dir = Path(llm_model_id.value) / "INT4_compressed_weights"


def convert_to_fp16():
    if (fp16_model_dir / "openvino_model.xml").exists():
        return
    remote_code = llm_model_configuration.get("remote_code", False)
    export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format fp16".format(pt_model_id)
    if remote_code:
        export_command_base += " --trust-remote-code"
    export_command = export_command_base + " " + str(fp16_model_dir)
    display(Markdown("**Export command:**"))
    display(Markdown(f"`{export_command}`"))
    ! $export_command


def convert_to_int8():
    if (int8_model_dir / "openvino_model.xml").exists():
        return
    int8_model_dir.mkdir(parents=True, exist_ok=True)
    remote_code = llm_model_configuration.get("remote_code", False)
    export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int8".format(pt_model_id)
    if remote_code:
        export_command_base += " --trust-remote-code"
    export_command = export_command_base + " " + str(int8_model_dir)
    display(Markdown("**Export command:**"))
    display(Markdown(f"`{export_command}`"))
    ! $export_command


def convert_to_int4():
    compression_configs = {
        "zephyr-7b-beta": {
            "sym": True,
            "group_size": 64,
            "ratio": 0.6,
        },
        "mistral-7b": {
            "sym": True,
            "group_size": 64,
            "ratio": 0.6,
        },
        "minicpm-2b-dpo": {
            "sym": True,
            "group_size": 64,
            "ratio": 0.6,
        },
        "gemma-2b-it": {
            "sym": True,
            "group_size": 64,
            "ratio": 0.6,
        },
        "notus-7b-v1": {
            "sym": True,
            "group_size": 64,
            "ratio": 0.6,
        },
        "neural-chat-7b-v3-1": {
            "sym": True,
            "group_size": 64,
            "ratio": 0.6,
        },
        "llama-2-chat-7b": {
            "sym": True,
            "group_size": 128,
            "ratio": 0.8,
        },
        "llama-3-8b-instruct": {
            "sym": True,
            "group_size": 128,
            "ratio": 0.8,
        },
        "gemma-7b-it": {
            "sym": True,
            "group_size": 128,
            "ratio": 0.8,
        },
        "chatglm2-6b": {
            "sym": True,
            "group_size": 128,
            "ratio": 0.72,
        },
        "qwen-7b-chat": {"sym": True, "group_size": 128, "ratio": 0.6},
        "red-pajama-3b-chat": {
            "sym": False,
            "group_size": 128,
            "ratio": 0.5,
        },
        "default": {
            "sym": False,
            "group_size": 128,
            "ratio": 0.8,
        },
    }

    model_compression_params = compression_configs.get(llm_model_id.value, compression_configs["default"])
    if (int4_model_dir / "openvino_model.xml").exists():
        return
    remote_code = llm_model_configuration.get("remote_code", False)
    export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int4".format(pt_model_id)
    int4_compression_args = " --group-size {} --ratio {}".format(model_compression_params["group_size"], model_compression_params["ratio"])
    if model_compression_params["sym"]:
        int4_compression_args += " --sym"
    export_command_base += int4_compression_args
    if remote_code:
        export_command_base += " --trust-remote-code"
    export_command = export_command_base + " " + str(int4_model_dir)
    display(Markdown("**Export command:**"))
    display(Markdown(f"`{export_command}`"))
    ! $export_command


if prepare_fp16_model.value:
    convert_to_fp16()
if prepare_int8_model.value:
    convert_to_int8()
if prepare_int4_model.value:
    convert_to_int4()

Let’s compare model size for different compression types

fp16_weights = fp16_model_dir / "openvino_model.bin"
int8_weights = int8_model_dir / "openvino_model.bin"
int4_weights = int4_model_dir / "openvino_model.bin"

if fp16_weights.exists():
    print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
for precision, compressed_weights in zip([8, 4], [int8_weights, int4_weights]):
    if compressed_weights.exists():
        print(f"Size of model with INT{precision} compressed weights is {compressed_weights.stat().st_size / 1024 / 1024:.2f} MB")
    if compressed_weights.exists() and fp16_weights.exists():
        print(f"Compression rate for INT{precision} model: {fp16_weights.stat().st_size / compressed_weights.stat().st_size:.3f}")
Size of model with INT4 compressed weights is 5069.90 MB

Convert embedding model using Optimum-CLI#

Since some embedding models can only support limited languages, we can filter them out according the LLM you selected.

embedding_model_id = list(SUPPORTED_EMBEDDING_MODELS[model_language.value])

embedding_model_id = widgets.Dropdown(
    options=embedding_model_id,
    value=embedding_model_id[0],
    description="Embedding Model:",
    disabled=False,
)

embedding_model_id
Dropdown(description='Embedding Model:', options=('bge-small-en-v1.5', 'bge-large-en-v1.5'), value='bge-small-…
embedding_model_configuration = SUPPORTED_EMBEDDING_MODELS[model_language.value][embedding_model_id.value]
print(f"Selected {embedding_model_id.value} model")
Selected bge-small-en-v1.5 model

OpenVINO embedding model and tokenizer can be exported by feature-extraction task with optimum-cli.

export_command_base = "optimum-cli export openvino --model {} --task feature-extraction".format(embedding_model_configuration["model_id"])
export_command = export_command_base + " " + str(embedding_model_id.value)

if not Path(embedding_model_id.value).exists():
    ! $export_command

Convert rerank model using Optimum-CLI#

rerank_model_id = list(SUPPORTED_RERANK_MODELS)

rerank_model_id = widgets.Dropdown(
    options=rerank_model_id,
    value=rerank_model_id[0],
    description="Rerank Model:",
    disabled=False,
)

rerank_model_id
Dropdown(description='Rerank Model:', options=('bge-reranker-large', 'bge-reranker-base'), value='bge-reranker…
rerank_model_configuration = SUPPORTED_RERANK_MODELS[rerank_model_id.value]
print(f"Selected {rerank_model_id.value} model")
Selected bge-reranker-large model

Since rerank model is sort of sentence classification task, its OpenVINO IR and tokenizer can be exported by text-classification task with optimum-cli.

export_command_base = "optimum-cli export openvino --model {} --task text-classification".format(rerank_model_configuration["model_id"])
export_command = export_command_base + " " + str(rerank_model_id.value)

if not Path(rerank_model_id.value).exists():
    ! $export_command

Select device for inference and model variant#

Note: There may be no speedup for INT4/INT8 compressed models on dGPU.

Select device for embedding model inference#

core = ov.Core()

support_devices = core.available_devices
if "NPU" in support_devices:
    support_devices.remove("NPU")

embedding_device = widgets.Dropdown(
    options=support_devices + ["AUTO"],
    value="CPU",
    description="Device:",
    disabled=False,
)

embedding_device
Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU')
print(f"Embedding model will be loaded to {embedding_device.value} device for text embedding")
Embedding model will be loaded to CPU device for text embedding

Select device for rerank model inference#

rerank_device = widgets.Dropdown(
    options=support_devices + ["AUTO"],
    value="CPU",
    description="Device:",
    disabled=False,
)

rerank_device
Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU')
print(f"Rerenk model will be loaded to {rerank_device.value} device for text reranking")
Rerenk model will be loaded to CPU device for text reranking

Select device for LLM model inference#

llm_device = widgets.Dropdown(
    options=support_devices + ["AUTO"],
    value="CPU",
    description="Device:",
    disabled=False,
)

llm_device
Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU')
print(f"LLM model will be loaded to {llm_device.value} device for response generation")
LLM model will be loaded to CPU device for response generation

Load models#

Load embedding model#

Now a Hugging Face embedding model can be supported by OpenVINO through OpenVINOEmbeddings and OpenVINOBgeEmbeddingsclasses of LangChain.

from langchain_community.embeddings import OpenVINOBgeEmbeddings

embedding_model_name = embedding_model_id.value
embedding_model_kwargs = {"device": embedding_device.value}
encode_kwargs = {
    "mean_pooling": embedding_model_configuration["mean_pooling"],
    "normalize_embeddings": embedding_model_configuration["normalize_embeddings"],
}

embedding = OpenVINOBgeEmbeddings(
    model_name_or_path=embedding_model_name,
    model_kwargs=embedding_model_kwargs,
    encode_kwargs=encode_kwargs,
)

text = "This is a test document."
embedding_result = embedding.embed_query(text)
embedding_result[:3]
INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
2024-05-24 00:13:06.057342: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2024-05-24 00:13:06.061389: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2024-05-24 00:13:06.108453: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-05-24 00:13:06.108490: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-05-24 00:13:06.108542: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-05-24 00:13:06.120406: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-05-24 00:13:06.938926: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Compiling the model to CPU ...
[-0.04208654910326004, 0.06681869924068451, 0.007916687056422234]

Load rerank model#

Now a Hugging Face embedding model can be supported by OpenVINO through OpenVINOReranker class of LangChain.

Note: Rerank can be skipped in RAG.

from langchain_community.document_compressors.openvino_rerank import OpenVINOReranker

rerank_model_name = rerank_model_id.value
rerank_model_kwargs = {"device": rerank_device.value}
rerank_top_n = 2

reranker = OpenVINOReranker(
    model_name_or_path=rerank_model_name,
    model_kwargs=rerank_model_kwargs,
    top_n=rerank_top_n,
)
Compiling the model to CPU ...

Load LLM model#

OpenVINO models can be run locally through the HuggingFacePipeline class. To deploy a model with OpenVINO, you can specify the backend="openvino" parameter to trigger OpenVINO as backend inference framework.

available_models = []
if int4_model_dir.exists():
    available_models.append("INT4")
if int8_model_dir.exists():
    available_models.append("INT8")
if fp16_model_dir.exists():
    available_models.append("FP16")

model_to_run = widgets.Dropdown(
    options=available_models,
    value=available_models[0],
    description="Model to run:",
    disabled=False,
)

model_to_run
Dropdown(description='Model to run:', options=('INT4',), value='INT4')

OpenVINO models can be run locally through the HuggingFacePipeline class in LangChain. To deploy a model with OpenVINO, you can specify the backend="openvino" parameter to trigger OpenVINO as backend inference framework.

from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline

if model_to_run.value == "INT4":
    model_dir = int4_model_dir
elif model_to_run.value == "INT8":
    model_dir = int8_model_dir
else:
    model_dir = fp16_model_dir
print(f"Loading model from {model_dir}")

ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}

# On a GPU device a model is executed in FP16 precision. For red-pajama-3b-chat model there known accuracy
# issues caused by this, which we avoid by setting precision hint to "f32".
if llm_model_id.value == "red-pajama-3b-chat" and "GPU" in core.available_devices and llm_device.value in ["GPU", "AUTO"]:
    ov_config["INFERENCE_PRECISION_HINT"] = "f32"

llm = HuggingFacePipeline.from_model_id(
    model_id=str(model_dir),
    task="text-generation",
    backend="openvino",
    model_kwargs={
        "device": llm_device.value,
        "ov_config": ov_config,
        "trust_remote_code": True,
    },
    pipeline_kwargs={"max_new_tokens": 2},
)

llm.invoke("2 + 2 =")
The argument trust_remote_code is to be used along with export=True. It will be ignored.
Loading model from neural-chat-7b-v3-1/INT4_compressed_weights
Compiling the model to CPU ...
'2 + 2 = 4'

Run QA over Document#

Now, when model created, we can setup Chatbot interface using Gradio.

A typical RAG application has two main components:

  • Indexing: a pipeline for ingesting data from a source and indexing it. This usually happen offline.

  • Retrieval and generation: the actual RAG chain, which takes the user query at run time and retrieves the relevant data from the index, then passes that to the model.

The most common full sequence from raw data to answer looks like:

Indexing

  1. Load: First we need to load our data. We’ll use DocumentLoaders for this.

  2. Split: Text splitters break large Documents into smaller chunks. This is useful both for indexing data and for passing it in to a model, since large chunks are harder to search over and won’t in a model’s finite context window.

  3. Store: We need somewhere to store and index our splits, so that they can later be searched over. This is often done using a VectorStore and Embeddings model.

Indexing pipeline

Indexing pipeline#

Retrieval and generation

  1. Retrieve: Given a user input, relevant splits are retrieved from storage using a Retriever.

  2. Generate: A LLM produces an answer using a prompt that includes the question and the retrieved data.

Retrieval and generation pipeline

Retrieval and generation pipeline#

import re
from typing import List
from langchain.text_splitter import (
    CharacterTextSplitter,
    RecursiveCharacterTextSplitter,
    MarkdownTextSplitter,
)
from langchain.document_loaders import (
    CSVLoader,
    EverNoteLoader,
    PyPDFLoader,
    TextLoader,
    UnstructuredEPubLoader,
    UnstructuredHTMLLoader,
    UnstructuredMarkdownLoader,
    UnstructuredODTLoader,
    UnstructuredPowerPointLoader,
    UnstructuredWordDocumentLoader,
)


class ChineseTextSplitter(CharacterTextSplitter):
    def __init__(self, pdf: bool = False, **kwargs):
        super().__init__(**kwargs)
        self.pdf = pdf

    def split_text(self, text: str) -> List[str]:
        if self.pdf:
            text = re.sub(r"\n{3,}", "\n", text)
            text = text.replace("\n\n", "")
        sent_sep_pattern = re.compile('([﹒﹔﹖﹗.。!?]["’”」』]{0,2}|(?=["‘“「『]{1,2}|$))')
        sent_list = []
        for ele in sent_sep_pattern.split(text):
            if sent_sep_pattern.match(ele) and sent_list:
                sent_list[-1] += ele
            elif ele:
                sent_list.append(ele)
        return sent_list


TEXT_SPLITERS = {
    "Character": CharacterTextSplitter,
    "RecursiveCharacter": RecursiveCharacterTextSplitter,
    "Markdown": MarkdownTextSplitter,
    "Chinese": ChineseTextSplitter,
}


LOADERS = {
    ".csv": (CSVLoader, {}),
    ".doc": (UnstructuredWordDocumentLoader, {}),
    ".docx": (UnstructuredWordDocumentLoader, {}),
    ".enex": (EverNoteLoader, {}),
    ".epub": (UnstructuredEPubLoader, {}),
    ".html": (UnstructuredHTMLLoader, {}),
    ".md": (UnstructuredMarkdownLoader, {}),
    ".odt": (UnstructuredODTLoader, {}),
    ".pdf": (PyPDFLoader, {}),
    ".ppt": (UnstructuredPowerPointLoader, {}),
    ".pptx": (UnstructuredPowerPointLoader, {}),
    ".txt": (TextLoader, {"encoding": "utf8"}),
}

chinese_examples = [
    ["英特尔®酷睿™ Ultra处理器可以降低多少功耗?"],
    ["相比英特尔之前的移动处理器产品,英特尔®酷睿™ Ultra处理器的AI推理性能提升了多少?"],
    ["英特尔博锐® Enterprise系统提供哪些功能?"],
]

english_examples = [
    ["How much power consumption can Intel® Core™ Ultra Processors help save?"],
    ["Compared to Intel’s previous mobile processor, what is the advantage of Intel® Core™ Ultra Processors for Artificial Intelligence?"],
    ["What can Intel vPro® Enterprise systems offer?"],
]

if model_language.value == "English":
    text_example_path = "text_example_en.pdf"
else:
    text_example_path = "text_example_cn.pdf"

examples = chinese_examples if (model_language.value == "Chinese") else english_examples

We can build a RAG pipeline of LangChain through create_retrieval_chain, which will help to create a chain to connect RAG components including:

from langchain.prompts import PromptTemplate
from langchain_community.vectorstores import FAISS
from langchain.chains.retrieval import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.docstore.document import Document
from langchain.retrievers import ContextualCompressionRetriever
from threading import Thread
import gradio as gr

stop_tokens = llm_model_configuration.get("stop_tokens")
rag_prompt_template = llm_model_configuration["rag_prompt_template"]


class StopOnTokens(StoppingCriteria):
    def __init__(self, token_ids):
        self.token_ids = token_ids

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        for stop_id in self.token_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False


if stop_tokens is not None:
    if isinstance(stop_tokens[0], str):
        stop_tokens = llm.pipeline.tokenizer.convert_tokens_to_ids(stop_tokens)

    stop_tokens = [StopOnTokens(stop_tokens)]


def load_single_document(file_path: str) -> List[Document]:
    """
    helper for loading a single document

    Params:
      file_path: document path
    Returns:
      documents loaded

    """
    ext = "." + file_path.rsplit(".", 1)[-1]
    if ext in LOADERS:
        loader_class, loader_args = LOADERS[ext]
        loader = loader_class(file_path, **loader_args)
        return loader.load()

    raise ValueError(f"File does not exist '{ext}'")


def default_partial_text_processor(partial_text: str, new_text: str):
    """
    helper for updating partially generated answer, used by default

    Params:
      partial_text: text buffer for storing previosly generated text
      new_text: text update for the current step
    Returns:
      updated text string

    """
    partial_text += new_text
    return partial_text


text_processor = llm_model_configuration.get("partial_text_processor", default_partial_text_processor)


def create_vectordb(docs, spliter_name, chunk_size, chunk_overlap, vector_search_top_k, vector_search_top_n, run_rerank, search_method, score_threshold):
    """
    Initialize a vector database

    Params:
      doc: orignal documents provided by user
      chunk_size:  size of a single sentence chunk
      chunk_overlap: overlap size between 2 chunks
      vector_search_top_k: Vector search top k

    """
    documents = []
    for doc in docs:
        documents.extend(load_single_document(doc.name))

    text_splitter = TEXT_SPLITERS[spliter_name](chunk_size=chunk_size, chunk_overlap=chunk_overlap)

    texts = text_splitter.split_documents(documents)

    global db
    db = FAISS.from_documents(texts, embedding)

    global retriever
    if search_method == "similarity_score_threshold":
        search_kwargs = {"k": vector_search_top_k, "score_threshold": score_threshold}
    else:
        search_kwargs = {"k": vector_search_top_k}
    retriever = db.as_retriever(search_kwargs=search_kwargs, search_type=search_method)
    if run_rerank:
        reranker.top_n = vector_search_top_n
        retriever = ContextualCompressionRetriever(base_compressor=reranker, base_retriever=retriever)
    prompt = PromptTemplate.from_template(rag_prompt_template)

    global combine_docs_chain
    combine_docs_chain = create_stuff_documents_chain(llm, prompt)

    global rag_chain
    rag_chain = create_retrieval_chain(retriever, combine_docs_chain)

    return "Vector database is Ready"


def update_retriever(vector_search_top_k, vector_rerank_top_n, run_rerank, search_method, score_threshold):
    """
    Update retriever

    Params:
      vector_search_top_k: size of searching results
      vector_rerank_top_n:  size of rerank results
      run_rerank: whether run rerank step
      search_method: search method used by vector store

    """
    global retriever
    global db
    global rag_chain
    global combine_docs_chain

    if search_method == "similarity_score_threshold":
        search_kwargs = {"k": vector_search_top_k, "score_threshold": score_threshold}
    else:
        search_kwargs = {"k": vector_search_top_k}
    retriever = db.as_retriever(search_kwargs=search_kwargs, search_type=search_method)
    if run_rerank:
        retriever = ContextualCompressionRetriever(base_compressor=reranker, base_retriever=retriever)
        reranker.top_n = vector_rerank_top_n
    rag_chain = create_retrieval_chain(retriever, combine_docs_chain)


def user(message, history):
    """
    callback function for updating user messages in interface on submit button click

    Params:
      message: current message
      history: conversation history
    Returns:
      None
    """
    # Append the user's message to the conversation history
    return "", history + [[message, ""]]


def bot(history, temperature, top_p, top_k, repetition_penalty, hide_full_prompt, do_rag):
    """
    callback function for running chatbot on submit button click

    Params:
      history: conversation history
      temperature:  parameter for control the level of creativity in AI-generated text.
                    By adjusting the `temperature`, you can influence the AI model's probability distribution, making the text more focused or diverse.
      top_p: parameter for control the range of tokens considered by the AI model based on their cumulative probability.
      top_k: parameter for control the range of tokens considered by the AI model based on their cumulative probability, selecting number of tokens with highest probability.
      repetition_penalty: parameter for penalizing tokens based on how frequently they occur in the text.
      hide_full_prompt: whether to show searching results in promopt.
      do_rag: whether do RAG when generating texts.

    """
    streamer = TextIteratorStreamer(
        llm.pipeline.tokenizer,
        timeout=60.0,
        skip_prompt=hide_full_prompt,
        skip_special_tokens=True,
    )
    llm.pipeline._forward_params = dict(
        max_new_tokens=512,
        temperature=temperature,
        do_sample=temperature > 0.0,
        top_p=top_p,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        streamer=streamer,
    )
    if stop_tokens is not None:
        llm.pipeline._forward_params["stopping_criteria"] = StoppingCriteriaList(stop_tokens)

    if do_rag:
        t1 = Thread(target=rag_chain.invoke, args=({"input": history[-1][0]},))
    else:
        input_text = rag_prompt_template.format(input=history[-1][0], context="")
        t1 = Thread(target=llm.invoke, args=(input_text,))
    t1.start()

    # Initialize an empty string to store the generated text
    partial_text = ""
    for new_text in streamer:
        partial_text = text_processor(partial_text, new_text)
        history[-1][1] = partial_text
        yield history


def request_cancel():
    llm.pipeline.model.request.cancel()


with gr.Blocks(
    theme=gr.themes.Soft(),
    css=".disclaimer {font-variant-caps: all-small-caps;}",
) as demo:
    gr.Markdown("""<h1><center>QA over Document</center></h1>""")
    gr.Markdown(f"""<center>Powered by OpenVINO and {llm_model_id.value} </center>""")
    with gr.Row():
        with gr.Column(scale=1):
            docs = gr.File(
                label="Step 1: Load text files",
                value=[text_example_path],
                file_count="multiple",
                file_types=[
                    ".csv",
                    ".doc",
                    ".docx",
                    ".enex",
                    ".epub",
                    ".html",
                    ".md",
                    ".odt",
                    ".pdf",
                    ".ppt",
                    ".pptx",
                    ".txt",
                ],
            )
            load_docs = gr.Button("Step 2: Build Vector Store")
            db_argument = gr.Accordion("Vector Store Configuration", open=False)
            with db_argument:
                spliter = gr.Dropdown(
                    ["Character", "RecursiveCharacter", "Markdown", "Chinese"],
                    value="RecursiveCharacter",
                    label="Text Spliter",
                    info="Method used to splite the documents",
                    multiselect=False,
                )

                chunk_size = gr.Slider(
                    label="Chunk size",
                    value=400,
                    minimum=50,
                    maximum=2000,
                    step=50,
                    interactive=True,
                    info="Size of sentence chunk",
                )

                chunk_overlap = gr.Slider(
                    label="Chunk overlap",
                    value=50,
                    minimum=0,
                    maximum=400,
                    step=10,
                    interactive=True,
                    info=("Overlap between 2 chunks"),
                )

            langchain_status = gr.Textbox(
                label="Vector Store Status",
                value="Vector Store is Not ready",
                interactive=False,
            )
            do_rag = gr.Checkbox(
                value=True,
                label="RAG is ON",
                interactive=True,
                info="Whether to do RAG for generation",
            )
            with gr.Accordion("Generation Configuration", open=False):
                with gr.Row():
                    with gr.Column():
                        with gr.Row():
                            temperature = gr.Slider(
                                label="Temperature",
                                value=0.1,
                                minimum=0.0,
                                maximum=1.0,
                                step=0.1,
                                interactive=True,
                                info="Higher values produce more diverse outputs",
                            )
                    with gr.Column():
                        with gr.Row():
                            top_p = gr.Slider(
                                label="Top-p (nucleus sampling)",
                                value=1.0,
                                minimum=0.0,
                                maximum=1,
                                step=0.01,
                                interactive=True,
                                info=(
                                    "Sample from the smallest possible set of tokens whose cumulative probability "
                                    "exceeds top_p. Set to 1 to disable and sample from all tokens."
                                ),
                            )
                    with gr.Column():
                        with gr.Row():
                            top_k = gr.Slider(
                                label="Top-k",
                                value=50,
                                minimum=0.0,
                                maximum=200,
                                step=1,
                                interactive=True,
                                info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
                            )
                    with gr.Column():
                        with gr.Row():
                            repetition_penalty = gr.Slider(
                                label="Repetition Penalty",
                                value=1.1,
                                minimum=1.0,
                                maximum=2.0,
                                step=0.1,
                                interactive=True,
                                info="Penalize repetition — 1.0 to disable.",
                            )
        with gr.Column(scale=4):
            chatbot = gr.Chatbot(
                height=600,
                label="Step 3: Input Query",
            )
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        msg = gr.Textbox(
                            label="QA Message Box",
                            placeholder="Chat Message Box",
                            show_label=False,
                            container=False,
                        )
                with gr.Column():
                    with gr.Row():
                        submit = gr.Button("Submit")
                        stop = gr.Button("Stop")
                        clear = gr.Button("Clear")
            gr.Examples(examples, inputs=msg, label="Click on any example and press the 'Submit' button")
            retriever_argument = gr.Accordion("Retriever Configuration", open=True)
            with retriever_argument:
                with gr.Row():
                    with gr.Row():
                        do_rerank = gr.Checkbox(
                            value=True,
                            label="Rerank searching result",
                            interactive=True,
                        )
                        hide_context = gr.Checkbox(
                            value=True,
                            label="Hide searching result in prompt",
                            interactive=True,
                        )
                    with gr.Row():
                        search_method = gr.Dropdown(
                            ["similarity_score_threshold", "similarity", "mmr"],
                            value="similarity_score_threshold",
                            label="Searching Method",
                            info="Method used to search vector store",
                            multiselect=False,
                            interactive=True,
                        )
                    with gr.Row():
                        score_threshold = gr.Slider(
                            0.01,
                            0.99,
                            value=0.5,
                            step=0.01,
                            label="Similarity Threshold",
                            info="Only working for 'similarity score threshold' method",
                            interactive=True,
                        )
                    with gr.Row():
                        vector_rerank_top_n = gr.Slider(
                            1,
                            10,
                            value=2,
                            step=1,
                            label="Rerank top n",
                            info="Number of rerank results",
                            interactive=True,
                        )
                    with gr.Row():
                        vector_search_top_k = gr.Slider(
                            1,
                            50,
                            value=10,
                            step=1,
                            label="Search top k",
                            info="Number of searching results, must >= Rerank top n",
                            interactive=True,
                        )
    load_docs.click(
        create_vectordb,
        inputs=[docs, spliter, chunk_size, chunk_overlap, vector_search_top_k, vector_rerank_top_n, do_rerank, search_method, score_threshold],
        outputs=[langchain_status],
        queue=False,
    )
    submit_event = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot,
        [chatbot, temperature, top_p, top_k, repetition_penalty, hide_context, do_rag],
        chatbot,
        queue=True,
    )
    submit_click_event = submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot,
        [chatbot, temperature, top_p, top_k, repetition_penalty, hide_context, do_rag],
        chatbot,
        queue=True,
    )
    stop.click(
        fn=request_cancel,
        inputs=None,
        outputs=None,
        cancels=[submit_event, submit_click_event],
        queue=False,
    )
    clear.click(lambda: None, None, chatbot, queue=False)
    vector_search_top_k.release(
        update_retriever,
        [vector_search_top_k, vector_rerank_top_n, do_rerank, search_method, score_threshold],
    )
    vector_rerank_top_n.release(
        update_retriever,
        [vector_search_top_k, vector_rerank_top_n, do_rerank, search_method, score_threshold],
    )
    do_rerank.change(
        update_retriever,
        [vector_search_top_k, vector_rerank_top_n, do_rerank, search_method, score_threshold],
    )
    search_method.change(
        update_retriever,
        [vector_search_top_k, vector_rerank_top_n, do_rerank, search_method, score_threshold],
    )
    score_threshold.change(
        update_retriever,
        [vector_search_top_k, vector_rerank_top_n, do_rerank, search_method, score_threshold],
    )


demo.queue()
# if you are launching remotely, specify server_name and server_port
#  demo.launch(server_name='your server name', server_port='server port in int')
# if you have any issue to launch on your platform, you can pass share=True to launch method:
# demo.launch(share=True)
# it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
demo.launch()
# please run this cell for stopping gradio interface
demo.close()