{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "2N8psBL6-wfJ"
},
"outputs": [],
"source": [
"# !cp -rf /content/drive/MyDrive/ML\\ Projects/Gemini-Pro/docs /content/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9AkjB4x3ybTb",
"outputId": "7b4fa13a-cb14-4e59-f5b7-c426a3fbea35"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Successfully installed PyYAML-6.0.1 SQLAlchemy-2.0.25 aiohttp-3.9.1 aiosignal-1.3.1 annotated-types-0.6.0 anyio-4.2.0 async-timeout-4.0.3 attrs-23.2.0 certifi-2023.11.17 charset-normalizer-3.3.2 dataclasses-json-0.6.3 frozenlist-1.4.1 greenlet-3.0.3 idna-3.6 jsonpatch-1.33 jsonpointer-2.4 langchain-0.1.0 langchain-community-0.0.12 langchain-core-0.1.10 langsmith-0.0.80 marshmallow-3.20.2 multidict-6.0.4 mypy-extensions-1.0.0 numpy-1.26.3 pydantic-2.5.3 pydantic-core-2.14.6 requests-2.31.0 sniffio-1.3.0 tenacity-8.2.3 typing-extensions-4.9.0 typing-inspect-0.9.0 urllib3-2.1.0 yarl-1.9.4\n",
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" Uninstalling websockets-12.0:\n",
" Successfully uninstalled websockets-12.0\n",
"Successfully installed aiofiles-23.2.1 altair-5.2.0 colorama-0.4.6 contourpy-1.2.0 cycler-0.12.1 ffmpy-0.3.1 fonttools-4.47.2 gradio-4.14.0 gradio-client-0.8.0 httpcore-1.0.2 httpx-0.26.0 jinja2-3.1.3 jsonschema-4.20.0 jsonschema-specifications-2023.12.1 kiwisolver-1.4.5 markdown-it-py-3.0.0 markupsafe-2.1.3 matplotlib-3.8.2 mdurl-0.1.2 orjson-3.9.10 pandas-2.1.4 pillow-10.2.0 pydub-0.25.1 pyparsing-3.1.1 python-multipart-0.0.6 pytz-2023.3.post1 referencing-0.32.1 rich-13.7.0 rpds-py-0.17.1 semantic-version-2.10.0 shellingham-1.5.4 tomlkit-0.12.0 toolz-0.12.0 tzdata-2023.4 websockets-11.0.3\n",
"Collecting faiss-cpu\n",
" Using cached faiss_cpu-1.7.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.6 MB)\n",
"Installing collected packages: faiss-cpu\n",
"Successfully installed faiss-cpu-1.7.4\n"
]
}
],
"source": [
"# !pip install langchain\n",
"# !pip install pypdf\n",
"# !pip install langchain_google_genai\n",
"# !pip install google-generativeai\n",
"# !pip install chromadb\n",
"# !pip install gradio\n",
"# !pip install faiss-cpu"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "ylbT549oymIl"
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"from langchain.document_loaders import (\n",
" PyPDFLoader,\n",
" TextLoader,\n",
" Docx2txtLoader\n",
")\n",
"\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"# from PyPDF2 import PdfReader\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain_google_genai import GoogleGenerativeAIEmbeddings\n",
"import google.generativeai as genai\n",
"from langchain.vectorstores import FAISS\n",
"from langchain_google_genai import ChatGoogleGenerativeAI\n",
"from langchain.chains.question_answering import load_qa_chain\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.memory import ConversationBufferMemory\n",
"from dotenv import load_dotenv\n",
"load_dotenv()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "65o268jqzN7O"
},
"outputs": [],
"source": [
"def extract_text(docs):\n",
" documents = []\n",
" files = os.listdir(docs)\n",
" \n",
" if len(files) == 0:\n",
" return \"Directory is empty\"\n",
"\n",
" base_dir = docs.split(\"/\")\n",
" base_dir = \"/\".join(base_dir)\n",
"\n",
" for file in files:\n",
" if file.endswith(\".pdf\"):\n",
" pdf_path=os.path.join(base_dir, file)\n",
" loader=PyPDFLoader(pdf_path)\n",
" documents.extend(loader.load())\n",
" elif file.endswith('.docx') or file.endswith('.doc'):\n",
" doc_path=os.path.join(base_dir, file)\n",
" loader=Docx2txtLoader(doc_path)\n",
" documents.extend(loader.load())\n",
" elif file.endswith('.txt'):\n",
" text_path=os.path.join(base_dir, file)\n",
" loader=TextLoader(text_path)\n",
" documents.extend(loader.load())\n",
" return documents"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "0gT5m9cD_cM7"
},
"outputs": [],
"source": [
"doc_dir = \"../docs\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "Svi3z1Rdzjbm"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['profile.txt', 'llm-research.pdf']\n",
"../docs\n"
]
}
],
"source": [
"documents = extract_text(doc_dir)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "CRe5WNKC0D88"
},
"outputs": [],
"source": [
"def get_text_chunks(text):\n",
" text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)\n",
" chunks = text_splitter.split_documents(text)\n",
" return chunks\n",
"\n",
"def save_in_faiss(text_chunks, save=False):\n",
" embeddings = GoogleGenerativeAIEmbeddings(model = \"models/embedding-001\")\n",
" vector_store = FAISS.from_documents(text_chunks, embedding=embeddings)\n",
" if not save:\n",
" vector_store.save_local(\"faiss_index\")\n",
" return vector_store"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def process_files(docs):\n",
" documents = extract_text(docs)\n",
" text_chunks = get_text_chunks(documents)\n",
" vector_store = save_in_faiss(text_chunks)\n",
" return vector_store"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"llm = model = ChatGoogleGenerativeAI(model=\"gemini-pro\",temperature=0.7)\n",
"\n",
"template = \"\"\"You are a chatbot having a conversation with a human.\n",
"\n",
"Given the following extracted parts of a long document and a question, create a final answer.\n",
"\n",
"{context}\n",
"\n",
"{chat_history}\n",
"Human: {human_input}\n",
"Chatbot:\"\"\"\n",
"\n",
"prompt = PromptTemplate(\n",
" input_variables=[\"chat_history\", \"human_input\", \"context\"], template=template\n",
")\n",
"memory = ConversationBufferMemory(memory_key=\"chat_history\", input_key=\"human_input\")\n",
"chain = load_qa_chain(\n",
" llm=llm, chain_type=\"stuff\", memory=memory, prompt=prompt\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def build_qa_chain(llm=llm, prompt=prompt, memory=memory):\n",
" chain = load_qa_chain(\n",
" llm=llm, chain_type=\"stuff\", memory=memory, prompt=prompt\n",
")\n",
" return chain\n",
"\n",
"def build_agent(query, db):\n",
" query = \"is vasim plumber\"\n",
" docs = db.similarity_search(query)\n",
" response = chain({\"input_documents\": docs, \"human_input\": query}, return_only_outputs=True)\n",
" return response['output_text']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['profile.txt', 'llm-research.pdf']\n",
"../docs\n"
]
}
],
"source": [
"db = process_files(doc_dir)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output_text': 'I do not have any information about Vasim being a plumber.'}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"is vasim plumber\"\n",
"docs = db.similarity_search(query)\n",
"chain({\"input_documents\": docs, \"human_input\": query}, return_only_outputs=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def answer_query(message, history):\n",
" docs = db.similarity_search(message)\n",
" response = chain({\"input_documents\": docs, \"human_input\": message}, return_only_outputs=True)\n",
" return response['output_text']"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"colab": {
"background_save": true
},
"id": "a8tNUutJB9EA"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chat interface is cool.\n",
"Running on local URL: http://127.0.0.1:7875\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Gradio App\n",
"import gradio as gr\n",
"\n",
"title = \"\"\n",
"description = f\"Chat with any docs\"\n",
"\n",
"# def answer_query(message, history):\n",
"# docs = db.similarity_search(message)\n",
"# message = agent(\n",
"# {\"input_documents\":docs, \"question\": message}\n",
"# ,return_only_outputs=True)\n",
"# return message['output_text']\n",
"\n",
"\n",
"chatbot = gr.Chatbot(label=\"ExploreText\")\n",
"\n",
"with gr.Blocks(\n",
" title=\"ExploreText\",\n",
" ) as textbot:\n",
"\n",
" gr.Markdown(\"# Welcome to ExploreDoc Web App\")\n",
" \n",
" with gr.Accordion(\"Upload a file here\", open=False):\n",
" file_output = gr.File(scale=1)\n",
" upload_button = gr.UploadButton(\"Click to Upload a File\", file_types=[\"txt\",\"doc\",\"pdf\"])\n",
" upload_button.upload(process_files, upload_button, file_output)\n",
" # gr.Info(\"Click on Chat with PDF tab\")\n",
"\n",
" # with gr.Row(\"Chat with Text\"):\n",
" gr.ChatInterface(fn=answer_query, chatbot=chatbot, submit_btn=\"Ask\", undo_btn=None, retry_btn=None, clear_btn=None)\n",
" print(\"Chat interface is cool.\")\n",
" gr.Markdown(\" Developed by Mohammed Vasim | AI Engineer & Computer Vision Engineer @ ZestIoT. \")\n",
" \n",
"\n",
"if __name__ == \"__main__\":\n",
" textbot.queue().launch()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}