diff --git a/README.md b/README.md index 8fa3eb5..c38c33c 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,8 @@ # Hyperledger QA PoC version 2 + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hyperledger-labs/aifaq/blob/main/src/hyperledger_aifaq_poc_v3.ipynb) + The scope of this Hyperledger Labs project is to support the users (users, developer, etc.) to their work, avoiding to wade through oceans of documents to find information they are looking for. We are implementing an open source conversational AI tool which replies to the questions related to specific context. This is a proof-of-concept software which allows to create a chatbot using Google Colab (or local notebook which requires GPU). Here the official Wiki page: [Hyperledger Labs aifaq](https://labs.hyperledger.org/labs/aifaq.html). Please, read also the [Antitrust Policy and the Code of Conduct](https://wiki.hyperledger.org/pages/viewpage.action?pageId=41587043). ## Background diff --git a/src/hyperledger_aifaq_poc_v3.ipynb b/src/hyperledger_aifaq_poc_v3.ipynb index 72521ca..cb57be8 100644 --- a/src/hyperledger_aifaq_poc_v3.ipynb +++ b/src/hyperledger_aifaq_poc_v3.ipynb @@ -1,1298 +1,609 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "LB2Q9pgs3y2k", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "0e0dccb8-aeaa-49e4-e810-686c03cfcc62" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.38.1)\n", - "Collecting peft\n", - " Downloading peft-0.9.0-py3-none-any.whl (190 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.9/190.9 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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" Attempting uninstall: importlib-metadata\n", - " Found existing installation: importlib-metadata 7.0.1\n", - " Uninstalling importlib-metadata-7.0.1:\n", - " Successfully uninstalled importlib-metadata-7.0.1\n", - "Successfully installed accelerate-0.27.2 aiofiles-23.2.1 asgiref-3.7.2 backoff-2.2.1 bcrypt-4.1.2 bitsandbytes-0.42.0 bs4-0.0.2 chroma-hnswlib-0.7.3 chromadb-0.4.24 colorama-0.4.6 coloredlogs-15.0.1 dataclasses-json-0.6.4 deprecated-1.2.14 fastapi-0.110.0 ffmpy-0.3.2 gradio-4.19.2 gradio-client-0.10.1 h11-0.14.0 httpcore-1.0.4 httptools-0.6.1 httpx-0.27.0 humanfriendly-10.0 importlib-metadata-6.11.0 jsonpatch-1.33 jsonpointer-2.4 kubernetes-29.0.0 langchain-0.1.9 langchain-community-0.0.24 langchain-core-0.1.28 langsmith-0.1.10 marshmallow-3.21.0 mmh3-4.1.0 monotonic-1.6 mypy-extensions-1.0.0 onnxruntime-1.17.1 opentelemetry-api-1.23.0 opentelemetry-exporter-otlp-proto-common-1.23.0 opentelemetry-exporter-otlp-proto-grpc-1.23.0 opentelemetry-instrumentation-0.44b0 opentelemetry-instrumentation-asgi-0.44b0 opentelemetry-instrumentation-fastapi-0.44b0 opentelemetry-proto-1.23.0 opentelemetry-sdk-1.23.0 opentelemetry-semantic-conventions-0.44b0 opentelemetry-util-http-0.44b0 orjson-3.9.15 overrides-7.7.0 peft-0.9.0 posthog-3.4.2 pulsar-client-3.4.0 pydub-0.25.1 pypika-0.48.9 python-dotenv-1.0.1 python-multipart-0.0.9 ruff-0.3.0 semantic-version-2.10.0 sentence-transformers-2.5.1 shellingham-1.5.4 starlette-0.36.3 tomlkit-0.12.0 typing-inspect-0.9.0 uvicorn-0.27.1 uvloop-0.19.0 watchfiles-0.21.0 websockets-11.0.3\n" - ] - } - ], + "cell_type": "markdown", "source": [ - "#install required packages\n", - "!pip install transformers peft accelerate bitsandbytes safetensors sentencepiece chromadb langchain sentence-transformers gradio bs4" - ] + "**Project setup**" + ], + "metadata": { + "id": "_DVY-5lzk2sR" + } }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { - "id": "As3Q9fo_Aska" + "id": "LB2Q9pgs3y2k" }, "outputs": [], "source": [ - "# fixing unicode error in google colab\n", - "import locale\n", - "locale.getpreferredencoding = lambda: \"UTF-8\"" + "# Install required packages\n", + "\n", + "!pip install transformers peft accelerate bitsandbytes safetensors sentencepiece chromadb langchain sentence-transformers gradio bs4" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "id": "b08wbHdz3y2m" }, "outputs": [], "source": [ - "# import dependencies\n", - "import torch\n", - "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline\n", + "# Import required libraries\n", "\n", "import os\n", + "import locale\n", + "import subprocess\n", + "import torch\n", + "from transformers import (\n", + " AutoModelForCausalLM,\n", + " AutoTokenizer,\n", + " BitsAndBytesConfig,\n", + " pipeline,\n", + ")\n", "import gradio as gr\n", - "from google.colab import drive\n", - "\n", - "import chromadb\n", "from langchain.llms import HuggingFacePipeline\n", - "from langchain.document_loaders import TextLoader\n", "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain.embeddings import HuggingFaceEmbeddings\n", "from langchain.vectorstores import Chroma\n", - "from langchain import HuggingFacePipeline\n", - "from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader\n", - "from langchain_community.document_loaders import GitHubIssuesLoader ###\n", - "from langchain_community.document_loaders.merge import MergedDataLoader ###\n", - "from langchain_community.vectorstores.utils import filter_complex_metadata\n", "from langchain.chains import ConversationalRetrievalChain\n", - "from langchain.memory import ConversationBufferMemory" + "from langchain.memory import ConversationBufferMemory\n", + "from langchain_text_splitters import MarkdownHeaderTextSplitter\n", + "\n", + "# LCEL related imports\n", + "# from langchain.schema.runnable import RunnablePassthrough\n", + "# from langchain.schema.output_parser import StrOutputParser\n", + "# from langchain.prompts import ChatPromptTemplate\n", + "\n", + "# fixing unicode error in google colab\n", + "locale.getpreferredencoding = lambda: \"UTF-8\"" ] }, + { + "cell_type": "markdown", + "source": [ + "**Functions**" + ], + "metadata": { + "id": "EyIfsXiwmgKw" + } + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "id": "kKkVcgAv8frU" }, "outputs": [], "source": [ - "# specify model huggingface mode name\n", - "model_name = \"anakin87/zephyr-7b-alpha-sharded\"\n", - "\n", - "# function for loading 4-bit quantized model\n", + "# Function to load a 4-bit quantized model\n", "def load_quantized_model(model_name: str):\n", - " \"\"\"\n", - " :param model_name: Name or path of the model to be loaded.\n", - " :return: Loaded quantized model.\n", - " \"\"\"\n", " bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_use_double_quant=True,\n", " bnb_4bit_quant_type=\"nf4\",\n", - " bnb_4bit_compute_dtype=torch.bfloat16\n", + " bnb_4bit_compute_dtype=torch.bfloat16,\n", " )\n", - "\n", " model = AutoModelForCausalLM.from_pretrained(\n", - " model_name,\n", - " torch_dtype=torch.bfloat16,\n", - " quantization_config=bnb_config\n", + " model_name, torch_dtype=torch.bfloat16, quantization_config=bnb_config\n", + " )\n", + " return model\n", + "\n", + "\n", + "# Function to initialize tokenizer\n", + "def initialize_tokenizer(model_name: str):\n", + " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + " tokenizer.bos_token_id = 1\n", + " return tokenizer\n", + "\n", + "\n", + "# Function to clone a repository\n", + "def clone_repo(repo_name, target_dir):\n", + " repo_url = f\"https://github.com/{repo_name}.git\"\n", + " try:\n", + " subprocess.check_output([\"git\", \"clone\", repo_url, target_dir])\n", + " print(f\"Success! Repository {repo_name} cloned into {target_dir}\")\n", + " return True\n", + " except subprocess.CalledProcessError as e:\n", + " print(f\"Fail... {str(e)}\")\n", + " return False\n", + "\n", + "\n", + "# Function to get a list of files with specific extensions\n", + "def get_file_list(target_dir, ext_whitelist):\n", + " filtered_files = []\n", + " for root, dirs, files in os.walk(target_dir):\n", + " for file in files:\n", + " if any(file.endswith(ext) for ext in ext_whitelist):\n", + " filtered_files.append(os.path.join(root, file))\n", + " return filtered_files\n", + "\n", + "\n", + "# Function to create documents from files\n", + "def create_documents(files):\n", + " headers_to_split_on = [(\"#\", \"Header 1\"), (\"##\", \"Header 2\"), (\"###\", \"Header 3\")]\n", + " markdown_splitter = MarkdownHeaderTextSplitter(\n", + " headers_to_split_on=headers_to_split_on, strip_headers=False\n", + " )\n", + " text_splitter = RecursiveCharacterTextSplitter(chunk_size=6000, chunk_overlap=600)\n", + " documents = []\n", + " for f in files:\n", + " with open(f, \"r\", encoding=\"utf-8\") as file:\n", + " text = file.read()\n", + " md_splits = markdown_splitter.split_text(text)\n", + " docs = text_splitter.split_documents(md_splits)\n", + " documents.extend(docs)\n", + " return documents\n", + "\n", + "\n", + "# Function to create a retriever\n", + "def create_retriever(documents):\n", + " embeddings = HuggingFaceEmbeddings(\n", + " model_name=\"sentence-transformers/all-mpnet-base-v2\",\n", + " model_kwargs={\"device\": \"cuda\"},\n", + " )\n", + " vectordb = Chroma.from_documents(\n", + " documents=documents, embedding=embeddings, persist_directory=\"chroma_db\"\n", " )\n", - " return model" + " return vectordb.as_retriever()\n", + "\n", + "\n", + "# Define the conversation function\n", + "def create_conversation(query: str, chat_history: list) -> tuple:\n", + " try:\n", + " memory = ConversationBufferMemory(\n", + " memory_key=\"chat_history\", return_messages=False\n", + " )\n", + " qa_chain = ConversationalRetrievalChain.from_llm(\n", + " llm=llm, retriever=retriever, memory=memory, get_chat_history=lambda h: h\n", + " )\n", + " result = qa_chain({\"question\": query, \"chat_history\": chat_history})\n", + " chat_history.append((query, result[\"answer\"]))\n", + " return \"\", chat_history\n", + " except Exception as e:\n", + " chat_history.append((query, str(e)))\n", + " return \"\", chat_history" ] }, + { + "cell_type": "markdown", + "source": [ + "**Setup model**" + ], + "metadata": { + "id": "-SNm-mr5mrNF" + } + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { - "id": "2C0DVleS8iwj" + "id": "EQP-tdh88qiv" }, "outputs": [], "source": [ - "# function for initializing tokenizer\n", - "def initialize_tokenizer(model_name: str):\n", - " \"\"\"\n", - " Initialize the tokenizer with the specified model_name.\n", + "# Load model and initialize tokenizer\n", + "model_name = \"anakin87/zephyr-7b-alpha-sharded\"\n", + "model = load_quantized_model(model_name)\n", + "tokenizer = initialize_tokenizer(model_name)\n", "\n", - " :param model_name: Name or path of the model for tokenizer initialization.\n", - " :return: Initialized tokenizer.\n", - " \"\"\"\n", - " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", - " tokenizer.bos_token_id = 1 # Set beginning of sentence token id\n", - " return tokenizer" + "# Configure pipeline\n", + "my_pipeline = pipeline(\n", + " \"text-generation\",\n", + " model=model,\n", + " tokenizer=tokenizer,\n", + " use_cache=True,\n", + " device_map=\"auto\",\n", + " max_length=2048,\n", + " do_sample=True,\n", + " top_k=5,\n", + " num_return_sequences=1,\n", + " eos_token_id=tokenizer.eos_token_id,\n", + " pad_token_id=tokenizer.eos_token_id,\n", + ")\n", + "\n", + "# Specify the LLM and prepare documents and retriever\n", + "llm = HuggingFacePipeline(pipeline=my_pipeline)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Set target repository and file extensions**" + ], + "metadata": { + "id": "grIuRzTirKNZ" + } + }, + { + "cell_type": "code", + "source": [ + "repo_name = \"hyperledger/iroha\"\n", + "ext_whitelist = [\".md\", \".rst\"]" + ], + "metadata": { + "id": "ZU1s04jskoAL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Clone repo, filter files, create documents and retreiver**" + ], + "metadata": { + "id": "g-IIQKm_rvuW" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iRrUu0oMF2QX" + }, + "outputs": [], + "source": [ + "target_dir = repo_name.split(\"/\")[-1]\n", + "\n", + "clone_repo(repo_name, target_dir)\n", + "files = get_file_list(target_dir, ext_whitelist)\n", + "documents = create_documents(files)\n", + "retriever = create_retriever(documents)" ] }, { "cell_type": "code", - "execution_count": 6, + "source": [ + "# Langchain LCEL implementation - but no memory!\n", + "\n", + "# template = \"\"\"Answer the question based only on the following context:\n", + "# {context}\n", + "\n", + "# Question: {question}\n", + "# \"\"\"\n", + "\n", + "# prompt = ChatPromptTemplate.from_template(template)\n", + "# chain = (\n", + "# {\"context\": retriever, \"question\": RunnablePassthrough()}\n", + "# | prompt\n", + "# | llm\n", + "# | StrOutputParser()\n", + "# )\n", + "# chain.invoke(\"How do I restart an Iroha node?\")" + ], + "metadata": { + "id": "XyVVkATEXCFD" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Run Gradio**" + ], + "metadata": { + "id": "AGmPta4Wr8bY" + } + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { + "id": "dETfjAAAjuaU", "colab": { "base_uri": "https://localhost:8080/", - "height": 670, - "referenced_widgets": [ - "28236dfb592846bba5443bf6bfb41650", - "ce7b138a42ae49e7b5b3a5376e11d7cb", - 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"You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "config.json: 0%| | 0.00/638 [00:00" ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "3263a8fa93144d4f9febbbb054d1f6a8" - } + "text/html": [ + "
" + ] }, "metadata": {} }, { - "output_type": "display_data", + "output_type": "execute_result", "data": { - "text/plain": [ - "model-00007-of-00008.safetensors: 0%| | 0.00/1.98G [00:00 tuple:\n", - " try:\n", - "\n", - " memory = ConversationBufferMemory(\n", - " memory_key='chat_history',\n", - " return_messages=False\n", - " )\n", - " qa_chain = ConversationalRetrievalChain.from_llm(\n", - " llm=llm,\n", - " retriever=retriever,\n", - " memory=memory,\n", - " get_chat_history=lambda h: h,\n", - " )\n", - "\n", - " result = qa_chain({'question': query, 'chat_history': chat_history})\n", - " chat_history.append((query, result['answer']))\n", - " return '', chat_history\n", - "\n", - "\n", - " except Exception as e:\n", - " chat_history.append((query, e))\n", - " return '', chat_history" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "dETfjAAAjuaU", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 626 - }, - "outputId": "abacc373-cdad-4421-bd43-25a61765d7b5" - }, - "outputs": [ + "id": "BUpWmnmF7ooq", + "outputId": "361e7556-c084-4c76-aee8-f21c803eb8ff" + }, + "execution_count": null, + "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n", - "\n", - "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", - "Running on public URL: https://b23e5830f7f2d5e9c4.gradio.live\n", - "\n", - "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n" + "Collecting pigar\n", + " Downloading pigar-2.1.3-py3-none-any.whl (29.5 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.5/29.5 MB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: click>=8.1 in /usr/local/lib/python3.10/dist-packages (from pigar) (8.1.7)\n", + "Requirement already satisfied: nbformat>=5.7 in /usr/local/lib/python3.10/dist-packages (from pigar) (5.9.2)\n", + "Requirement already satisfied: aiohttp>=3.9 in /usr/local/lib/python3.10/dist-packages (from pigar) (3.9.3)\n", + "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.9->pigar) (1.3.1)\n", + "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.9->pigar) (23.2.0)\n", + "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.9->pigar) (1.4.1)\n", + "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.9->pigar) (6.0.5)\n", + "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.9->pigar) (1.9.4)\n", + "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp>=3.9->pigar) (4.0.3)\n", + "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.7->pigar) (2.19.1)\n", + "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.7->pigar) (4.19.2)\n", + "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.7->pigar) (5.7.1)\n", + "Requirement already satisfied: traitlets>=5.1 in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.7->pigar) (5.7.1)\n", + "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.7->pigar) (2023.12.1)\n", + "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.7->pigar) (0.33.0)\n", + "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.7->pigar) (0.18.0)\n", + "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.10/dist-packages (from yarl<2.0,>=1.0->aiohttp>=3.9->pigar) (3.6)\n", + "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core->nbformat>=5.7->pigar) (4.2.0)\n", + "Installing collected packages: pigar\n", + "Successfully installed pigar-2.1.3\n" ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
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