Create an LLM-powered RAG system using OpenVINO

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

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 Markdown or CSV data source.

The tutorial consists of the following steps:

• Install prerequisites

• Download and convert the model from a public source using the OpenVINO integration with Hugging Face Optimum.

• Compress model weights to 4-bit or 8-bit data types using NNCF

• Create a RAG chain pipeline

• Run chat pipeline

Table of contents:

• Prerequisites

• Select model for inference

• login to huggingfacehub to get access to pretrained model

• Convert model

  • Convert LLM model

• Compress model weights

  • Weights Compression using Optimum Intel

  • Weights Compression using NNCF

  • Convert embedding model

• Select device for inference and model variant

  • Select device for embedding model inference

  • Select device for LLM model inference

• Load model

  • Load embedding model

  • Load LLM model

• Run QA over Document

Prerequisites

Install required dependencies

%pip uninstall -q -y openvino-dev openvino openvino-nightly
%pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
"git+https://github.com/huggingface/optimum-intel.git"\
"nncf>=2.8.0"\
"datasets"\
"accelerate"\
"openvino-nightly"\
"gradio"\
"onnx" "chromadb" "sentence_transformers" "langchain" "langchainhub" "transformers>=4.34.0" "unstructured" "scikit-learn" "python-docx" "pdfminer.six"

WARNING: Skipping openvino-dev as it is not installed.
WARNING: Skipping openvino as it is not installed.
Note: you may need to restart the kernel to use updated packages.

[notice] A new release of pip is available: 23.3.1 -> 23.3.2
[notice] To update, run: pip install --upgrade pip
Note: you may need to restart the kernel to use updated packages.

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:

• all-mpnet-base-v2(All) - This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. More details about model can be found in model card

• text2vec-large-chinese(Chinese) - This is a CoSENT model. It can be used for tasks like sentence embeddings, text matching or semantic search. More details about model can be found in model card

The available LLM model options are:

• tiny-llama-1b-chat - This is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-955k-2T. The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens with the adoption of the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. More details about model can be found in model card

• red-pajama-3b-chat - A 2.8B parameter pre-trained language model based on GPT-NEOX architecture. It was developed by Together Computer and leaders from the open-source AI community. The model is fine-tuned on OASST1 and Dolly2 datasets to enhance chatting ability. More details about model can be found in HuggingFace model card.

• llama-2-7b-chat - LLama 2 is the second generation of LLama models developed by Meta. Llama 2 is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. llama-2-7b-chat is 7 billions parameters version of LLama 2 finetuned and optimized for dialogue use case. More details about model can be found in the paper, repository and HuggingFace model card

  NOTE: run model with demo, you will need to accept license agreement. You must be a registered user in Hugging Face Hub. Please visit HuggingFace model card, carefully read terms of usage and click accept button. You will need to use an access token for the code below to run. For more information on access tokens, refer to this section of the documentation. You can login on Hugging Face Hub in notebook environment, using following code:

## login to huggingfacehub to get access to pretrained model

from huggingface_hub import notebook_login, whoami

try:
    whoami()
    print('Authorization token already provided')
except OSError:
    notebook_login()

• mpt-7b-chat - MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases (ALiBi). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT-7B-chat is a chatbot-like model for dialogue generation. It was built by finetuning MPT-7B on the ShareGPT-Vicuna, HC3, Alpaca, HH-RLHF, and Evol-Instruct datasets. More details about the model can be found in blog post, repository and HuggingFace model card.

• qwen-7b-chat - Qwen-7B is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. For more details about Qwen, please refer to the GitHub code repository.

• chatglm3-6b - ChatGLM3-6B is the latest open-source model in the ChatGLM series. While retaining many excellent features such as smooth dialogue and low deployment threshold from the previous two generations, ChatGLM3-6B employs a more diverse training dataset, more sufficient training steps, and a more reasonable training strategy. ChatGLM3-6B adopts a newly designed Prompt format, in addition to the normal multi-turn dialogue. You can find more details about model in the model card

• mistral-7b - The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. You can find more details about model in the model card, paper and release blog post.

• zephyr-7b-beta - Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-beta is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). You can find more details about model in technical report and HuggingFace model card.

• neural-chat-7b-v3-1 - Mistral-7b model fine-tuned using Intel Gaudi. The model fine-tuned on the open source dataset Open-Orca/SlimOrca and aligned with Direct Preference Optimization (DPO) algorithm. More details can be found in model card and blog post.

• notus-7b-v1 - Notus is a collection of fine-tuned models using Direct Preference Optimization (DPO). and related RLHF techniques. This model is the first version, fine-tuned with DPO over zephyr-7b-sft. Following a data-first approach, the only difference between Notus-7B-v1 and Zephyr-7B-beta is the preference dataset used for dDPO. Proposed approach for dataset creation helps to effectively fine-tune Notus-7b that surpasses Zephyr-7B-beta and Claude 2 on AlpacaEval. More details about model can be found in model card.

from pathlib import Path
from optimum.intel import OVQuantizer
from optimum.intel.openvino import OVModelForCausalLM
import openvino as ov
import torch
import nncf
import logging
import shutil
import gc
import ipywidgets as widgets
from transformers import (
    AutoModelForCausalLM,
    AutoModel,
    AutoTokenizer,
    AutoConfig,
    TextIteratorStreamer,
    pipeline,
    StoppingCriteria,
    StoppingCriteriaList,
)

INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino

2023-12-25 07:58:21.310297: 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.
2023-12-25 07:58:21.312367: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-12-25 07:58:21.337757: 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
2023-12-25 07:58:21.337778: 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
2023-12-25 07:58:21.337798: 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
2023-12-25 07:58:21.343045: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-12-25 07:58:21.343941: 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 AVX512_BF16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-12-25 07:58:21.912373: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

Convert model

Convert LLM model

from config import SUPPORTED_EMBEDDING_MODELS, SUPPORTED_LLM_MODELS

llm_model_id = list(SUPPORTED_LLM_MODELS)

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

llm_model_id

Dropdown(description='LLM Model:', options=('tiny-llama-1b-chat', 'red-pajama-3b-chat', 'llama-2-chat-7b', 'mp…

llm_model_configuration = SUPPORTED_LLM_MODELS[llm_model_id.value]
print(f"Selected LLM model {llm_model_id.value}")

Selected LLM model chatglm3-6b

Optimum Intel can be used to load optimized models from the Hugging Face Hub and create pipelines to run an inference with OpenVINO Runtime using Hugging Face APIs. The Optimum Inference models are API compatible with Hugging Face Transformers models. This means we just need to replace AutoModelForXxx class with the corresponding OVModelForXxx class.

Below is an example of the RedPajama model

-from transformers import AutoModelForCausalLM
+from optimum.intel.openvino import OVModelForCausalLM
from transformers import AutoTokenizer, pipeline

model_id = "togethercomputer/RedPajama-INCITE-Chat-3B-v1"
-model = AutoModelForCausalLM.from_pretrained(model_id)
+model = OVModelForCausalLM.from_pretrained(model_id, export=True)

Model class initialization starts with calling from_pretrained method. When downloading and converting Transformers model, the parameter export=True should be added. We can save the converted model for the next usage with the save_pretrained method. Tokenizer class and pipelines API are compatible with Optimum models.

To optimize the generation process and use memory more efficiently, the use_cache=True option is enabled. Since the output side is auto-regressive, an output token hidden state remains the same once computed for every further generation step. Therefore, recomputing it every time you want to generate a new token seems wasteful. With the cache, the model saves the hidden state once it has been computed. The model only computes the one for the most recently generated output token at each time step, re-using the saved ones for hidden tokens. This reduces the generation complexity from \(O(n^3)\) to \(O(n^2)\) for a transformer model. More details about how it works can be found in this article. With this option, the model gets the previous step’s hidden states (cached attention keys and values) as input and additionally provides hidden states for the current step as output. It means for all next iterations, it is enough to provide only a new token obtained from the previous step and cached key values to get the next token prediction.

In our case, MPT, Qwen and ChatGLM model currently is not covered by Optimum Intel, we will convert it manually and create wrapper compatible with Optimum Intel.

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.

Weights Compression using Optimum Intel

To enable weights compression via NNCF for models supported by Optimum Intel OVQuantizer class should be used for OVModelForCausalLM model. OVQuantizer.quantize(save_directory=save_dir, weights_only=True) enables weights compression. We will consider how to do it on RedPajama, LLAMA and Zephyr examples.

NOTE: Weights Compression using Optimum Intel currently supports only INT8 compression. We will apply INT4 compression for these model using NNCF API described below.

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

Weights Compression using NNCF

You also can perform weights compression for OpenVINO models using NNCF directly. nncf.compress_weights function accepts OpenVINO model instance and compresses its weights for Linear and Embedding layers. We will consider this variant based on MPT model.

NOTE: This tutorial involves conversion model for FP16 and INT4/INT8 weights compression scenarios. It may be memory and time-consuming in the first run. You can manually control the compression precision below.

from IPython.display import 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')

from converter import converters

nncf.set_log_level(logging.ERROR)

pt_model_id = llm_model_configuration["model_id"]
pt_model_name = llm_model_id.value.split("-")[0]
model_type = AutoConfig.from_pretrained(pt_model_id, trust_remote_code=True).model_type
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
    if not llm_model_configuration["remote"]:
        ov_model = OVModelForCausalLM.from_pretrained(
            pt_model_id, export=True, compile=False, load_in_8bit=False
        )
        ov_model.half()
        ov_model.save_pretrained(fp16_model_dir)
        del ov_model
    else:
        model_kwargs = {}
        if "revision" in llm_model_configuration:
            model_kwargs["revision"] = llm_model_configuration["revision"]
        model = AutoModelForCausalLM.from_pretrained(
            llm_model_configuration["model_id"],
            torch_dtype=torch.float32,
            trust_remote_code=True,
            **model_kwargs
        )
        converters[pt_model_name](model, fp16_model_dir)
        del model
    gc.collect()


def convert_to_int8():
    if (int8_model_dir / "openvino_model.xml").exists():
        return
    int8_model_dir.mkdir(parents=True, exist_ok=True)
    if not llm_model_configuration["remote"]:
        if fp16_model_dir.exists():
            ov_model = OVModelForCausalLM.from_pretrained(fp16_model_dir, compile=False, load_in_8bit=False)
        else:
            ov_model = OVModelForCausalLM.from_pretrained(
                pt_model_id, export=True, compile=False
            )
            ov_model.half()
        quantizer = OVQuantizer.from_pretrained(ov_model)
        quantizer.quantize(save_directory=int8_model_dir, weights_only=True)
        del quantizer
        del ov_model
    else:
        convert_to_fp16()
        ov_model = ov.Core().read_model(fp16_model_dir / "openvino_model.xml")
        shutil.copy(fp16_model_dir / "config.json", int8_model_dir / "config.json")
        configuration_file = fp16_model_dir / f"configuration_{model_type}.py"
        if configuration_file.exists():
            shutil.copy(
                configuration_file, int8_model_dir / f"configuration_{model_type}.py"
            )
        compressed_model = nncf.compress_weights(ov_model)
        ov.save_model(compressed_model, int8_model_dir / "openvino_model.xml")
        del ov_model
        del compressed_model
    gc.collect()


def convert_to_int4():
    compression_configs = {
        "zephyr-7b-beta": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 64,
            "ratio": 0.6,
        },
        "mistral-7b": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 64,
            "ratio": 0.6,
        },
        "notus-7b-v1": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 64,
            "ratio": 0.6,
        },"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1",
        "neural-chat-7b-v3-1": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 64,
            "ratio": 0.6,
        },
        "llama-2-chat-7b": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 128,
            "ratio": 0.8,
        },
        "chatglm2-6b": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 128,
            "ratio": 0.72
        },
        "qwen-7b-chat": {
            "mode": nncf.CompressWeightsMode.INT4_SYM,
            "group_size": 128,
            "ratio": 0.6
        },
        'red-pajama-3b-chat': {
            "mode": nncf.CompressWeightsMode.INT4_ASYM,
            "group_size": 128,
            "ratio": 0.5,
        },
        "default": {
            "mode": nncf.CompressWeightsMode.INT4_ASYM,
            "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
    int4_model_dir.mkdir(parents=True, exist_ok=True)
    if not llm_model_configuration["remote"]:
        if not fp16_model_dir.exists():
            model = OVModelForCausalLM.from_pretrained(
                pt_model_id, export=True, compile=False, load_in_8bit=False
            ).half()
            model.config.save_pretrained(int4_model_dir)
            ov_model = model._original_model
            del model
            gc.collect()
        else:
            ov_model = ov.Core().read_model(fp16_model_dir / "openvino_model.xml")
            shutil.copy(fp16_model_dir / "config.json", int4_model_dir / "config.json")

    else:
        convert_to_fp16()
        ov_model = ov.Core().read_model(fp16_model_dir / "openvino_model.xml")
        shutil.copy(fp16_model_dir / "config.json", int4_model_dir / "config.json")
        configuration_file = fp16_model_dir / f"configuration_{model_type}.py"
        if configuration_file.exists():
            shutil.copy(
                configuration_file, int4_model_dir / f"configuration_{model_type}.py"
            )
    compressed_model = nncf.compress_weights(ov_model, **model_compression_params)
    ov.save_model(compressed_model, int4_model_dir / "openvino_model.xml")
    del ov_model
    del compressed_model
    gc.collect()


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 FP16 model is 11909.69 MB
Size of model with INT4 compressed weights is 3890.41 MB
Compression rate for INT4 model: 3.061

Convert embedding model

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)

if "qwen" not in llm_model_id.value and "chatglm" not in llm_model_id.value:
    embedding_model_id = [x for x in embedding_model_id if "chinese" not in x]

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=('all-mpnet-base-v2', 'text2vec-large-chinese'), value='all-m…

embedding_model_configuration = SUPPORTED_EMBEDDING_MODELS[embedding_model_id.value]
print(f"Selected {embedding_model_id.value} model")

Selected all-mpnet-base-v2 model

embedding_model_dir = Path(embedding_model_id.value)

if not (embedding_model_dir / "openvino_model.xml").exists():
    model = AutoModel.from_pretrained(embedding_model_configuration["model_id"])
    converters[embedding_model_id.value](model, embedding_model_dir)
    tokenizer = AutoTokenizer.from_pretrained(embedding_model_configuration["model_id"])
    tokenizer.save_pretrained(embedding_model_dir)
    del model

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()
embedding_device = widgets.Dropdown(
    options=core.available_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 response generation")

Embedding model will be loaded to CPU device for response generation

Select device for LLM model inference

llm_device = widgets.Dropdown(
    options=core.available_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 model

Load embedding model

Wrapper around a text embedding model for LangChain, used for converting text to embeddings.

from ov_embedding_model import OVEmbeddings

embedding = OVEmbeddings.from_model_id(
    embedding_model_dir,
    do_norm=embedding_model_configuration["do_norm"],
    ov_config={
        "device_name": embedding_device.value,
        "config": {"PERFORMANCE_HINT": "THROUGHPUT"},
    },
    model_kwargs={
        "model_max_length": 512,
    },
)

Load LLM model

The cell below create OVMPTModel, OVQWENModel and OVCHATGLM2Model wrapper based on OVModelForCausalLM model.

from ov_llm_model import model_classes

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', 'FP16'), value='INT4')

from langchain.llms 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"

model_name = llm_model_configuration["model_id"]
stop_tokens = llm_model_configuration.get("stop_tokens")
class_key = llm_model_id.value.split("-")[0]
tok = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

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 = tok.convert_tokens_to_ids(stop_tokens)

    stop_tokens = [StopOnTokens(stop_tokens)]

model_class = (
    OVModelForCausalLM
    if not llm_model_configuration["remote"]
    else model_classes[class_key]
)
ov_model = model_class.from_pretrained(
    model_dir,
    device=llm_device.value,
    ov_config=ov_config,
    config=AutoConfig.from_pretrained(model_dir, trust_remote_code=True),
    trust_remote_code=True,
)

Loading model from chatglm3-6b/INT4_compressed_weights

The argument trust_remote_code is to be used along with export=True. It will be ignored.
Compiling the model to CPU ...

Wrapper around a LLM/chat model for LangChain, used for generating the response text. An OpenVINO compiled model can be run locally through the HuggingFacePipeline class.

streamer = TextIteratorStreamer(
    tok, timeout=30.0, skip_prompt=True, skip_special_tokens=True
)
generate_kwargs = dict(
    model=ov_model,
    tokenizer=tok,
    max_new_tokens=256,
    streamer=streamer,
    # temperature=1,
    # do_sample=True,
    # top_p=0.8,
    # top_k=20,
    # repetition_penalty=1.1,
)
if stop_tokens is not None:
    generate_kwargs["stopping_criteria"] = StoppingCriteriaList(stop_tokens)

pipe = pipeline("text-generation", **generate_kwargs)
llm = HuggingFacePipeline(pipeline=pipe)

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

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

from typing import List
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter, MarkdownTextSplitter
from langchain.document_loaders import (
    CSVLoader,
    EverNoteLoader,
    PDFMinerLoader,
    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": (PDFMinerLoader, {}),
    ".ppt": (UnstructuredPowerPointLoader, {}),
    ".pptx": (UnstructuredPowerPointLoader, {}),
    ".txt": (TextLoader, {"encoding": "utf8"}),
}

from langchain.prompts import PromptTemplate
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.docstore.document import Document
from threading import Event, Thread
import gradio as gr
import re
from uuid import uuid4


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 build_chain(docs, spliter_name, chunk_size, chunk_overlap, vector_search_top_k):
    """
    Initialize a QA chain

    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)

    db = Chroma.from_documents(texts, embedding)
    retriever = db.as_retriever(search_kwargs={"k": vector_search_top_k})

    global rag_chain
    prompt = PromptTemplate.from_template(llm_model_configuration["prompt_template"])
    chain_type_kwargs = {"prompt": prompt}
    rag_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        chain_type_kwargs=chain_type_kwargs,
    )

    return "Retriever is Ready"


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, conversation_id):
    """
    callback function for running chatbot on submit button click

    Params:
      history: conversation history.
      conversation_id: unique conversation identifier.

    """
    stream_complete = Event()

    def infer(question):
        rag_chain.run(question)
        stream_complete.set()

    t1 = Thread(target=infer, args=(history[-1][0],))
    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 get_uuid():
    """
    universal unique identifier for thread
    """
    return str(uuid4())


with gr.Blocks(
    theme=gr.themes.Soft(),
    css=".disclaimer {font-variant-caps: all-small-caps;}",
) as demo:
    conversation_id = gr.State(get_uuid)
    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="Load text files",
                file_count="multiple",
                file_types=[
                    ".csv",
                    ".doc",
                    ".docx",
                    ".enex",
                    ".epub",
                    ".html",
                    ".md",
                    ".odt",
                    ".pdf",
                    ".ppt",
                    ".pptx",
                    ".txt",
                ],
            )
            load_docs = gr.Button("Build Retriever")
            retriever_argument = gr.Accordion("Retriever Configuration", open=False)
            with retriever_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=1000,
                    minimum=100,
                    maximum=2000,
                    step=50,
                    interactive=True,
                    info="Size of sentence chunk",
                )

                chunk_overlap = gr.Slider(
                    label="Chunk overlap",
                    value=200,
                    minimum=0,
                    maximum=400,
                    step=10,
                    interactive=True,
                    info=("Overlap between 2 chunks"),
                )

                vector_search_top_k = gr.Slider(
                    1,
                    10,
                    value=6,
                    step=1,
                    label="Vector search top k",
                    interactive=True,
                )
            langchain_status = gr.Textbox(
                label="Status", value="Retriever is Not ready", interactive=False
            )
        with gr.Column(scale=4):
            chatbot = gr.Chatbot(height=600)
            with gr.Row():
                with gr.Column():
                    msg = gr.Textbox(
                        label="Chat Message Box",
                        placeholder="Chat Message Box",
                        show_label=False,
                        container=False,
                    )
                with gr.Column():
                    with gr.Row():
                        submit = gr.Button("Submit")
                        clear = gr.Button("Clear")
    load_docs.click(
        build_chain,
        inputs=[docs, spliter, chunk_size, chunk_overlap, vector_search_top_k],
        outputs=[langchain_status],
        queue=False,
    )
    submit_event = msg.submit(
        user, [msg, chatbot], [msg, chatbot], queue=False, trigger_mode="once"
    ).then(bot, [chatbot, conversation_id], chatbot, queue=True)
    submit_click_event = submit.click(
        user, [msg, chatbot], [msg, chatbot], queue=False, trigger_mode="once"
    ).then(bot, [chatbot, conversation_id], chatbot, queue=True)
    clear.click(lambda: None, None, chatbot, queue=False)

demo.queue(max_size=2)
# 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()

Running on local URL:  http://10.3.233.70:4888

To create a public link, set share=True in launch().

/home/ethan/intel/openvino_notebooks/openvino_env/lib/python3.10/site-packages/optimum/intel/openvino/modeling_decoder.py:388: FutureWarning: shared_memory is deprecated and will be removed in 2024.0. Value of shared_memory is going to override share_inputs value. Please use only share_inputs explicitly.
  self.request.start_async(inputs, shared_memory=True)
/home/ethan/intel/openvino_notebooks/openvino_env/lib/python3.10/site-packages/optimum/intel/openvino/modeling_decoder.py:388: FutureWarning: shared_memory is deprecated and will be removed in 2024.0. Value of shared_memory is going to override share_inputs value. Please use only share_inputs explicitly.
  self.request.start_async(inputs, shared_memory=True)
/home/ethan/intel/openvino_notebooks/openvino_env/lib/python3.10/site-packages/optimum/intel/openvino/modeling_decoder.py:388: FutureWarning: shared_memory is deprecated and will be removed in 2024.0. Value of shared_memory is going to override share_inputs value. Please use only share_inputs explicitly.
  self.request.start_async(inputs, shared_memory=True)

# please run this cell for stopping gradio interface
demo.close()
del rag_chain

Closing server running on port: 4888