langChain QuestionMyDoc ChatBot

QuestionMyDoc_Manual_Version.ipynb

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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "76BpiP5vMhpG"
+ },
+ "outputs": [],
+ "source": [
+ "# !pip install openai langchain python-dotenv -q"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# !pip install chromadb==0.3.22 tiktoken -q"
+ ],
+ "metadata": {
+ "id": "ASD5ljxgNNbs"
+ },
+ "execution_count": 24,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# !pip install chromadb -U"
+ ],
+ "metadata": {
+ "id": "8IWdv5UgNP6c"
+ },
+ "execution_count": 25,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# !pip install gradio"
+ ],
+ "metadata": {
+ "id": "DliXsYaZOtAH"
+ },
+ "execution_count": 26,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+ "from langchain.vectorstores import Chroma\n",
+ "from langchain.text_splitter import CharacterTextSplitter\n",
+ "from langchain.chains.question_answering import load_qa_chain\n",
+ "from langchain.llms import OpenAI\n",
+ "import os\n"
+ ],
+ "metadata": {
+ "id": "jGEXeboZNAb9"
+ },
+ "execution_count": 27,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "with open(\"/content/Data_Engineering.txt\") as f:\n",
+ " hitchhikersguide = f.read()"
+ ],
+ "metadata": {
+ "id": "h4QnGIJYNjeM"
+ },
+ "execution_count": 28,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separator = \"\\n\")\n",
+ "texts = text_splitter.split_text(hitchhikersguide)\n",
+ "print(f\"Final lenght: {len(texts)}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "RmfWIfclN4DP",
+ "outputId": "58e3ffcf-b56a-4120-bcd9-718396bfa49c"
+ },
+ "execution_count": 29,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Final lenght: 1\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "### Setting up the OpenAI env\n",
+ "\n",
+ "!echo OPENAI_API_KEY=\"\" > .env"
+ ],
+ "metadata": {
+ "id": "4Y4-ZTsZONsZ"
+ },
+ "execution_count": 30,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "import openai\n",
+ "from dotenv import load_dotenv\n",
+ "\n",
+ "load_dotenv(\".env\")\n",
+ "\n",
+ "openai.api_key = os.environ.get(\"OPENAI_API_KEY\")"
+ ],
+ "metadata": {
+ "id": "PPYw5waOOT0D"
+ },
+ "execution_count": 31,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "embeddings = OpenAIEmbeddings()"
+ ],
+ "metadata": {
+ "id": "pj-lRr3UODGm"
+ },
+ "execution_count": 32,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))]).as_retriever()"
+ ],
+ "metadata": {
+ "id": "DcDeDj9HOFgI"
+ },
+ "execution_count": 33,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Creating the Chain Model\n",
+ "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")"
+ ],
+ "metadata": {
+ "id": "7Sh5PEFoOcF9"
+ },
+ "execution_count": 34,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def make_inference(query):\n",
+ " docs = docsearch.get_relevant_documents(query)\n",
+ " return(chain.run(input_documents=docs, question=query))"
+ ],
+ "metadata": {
+ "id": "meb-lvSsOgsM"
+ },
+ "execution_count": 35,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import gradio\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " # make a gradio interface\n",
+ " import gradio as gr\n",
+ "\n",
+ " gr.Interface(\n",
+ " make_inference,\n",
+ " [\n",
+ " gr.inputs.Textbox(lines=2, label=\"Query\"),\n",
+ " ],\n",
+ " gr.outputs.Textbox(label=\"Response\"),\n",
+ " title=\"🗣️TalkToMyDoc📄\",\n",
+ " description=\"🗣️TalkToMyDoc📄 is a tool that allows you to ask questions about a document. In this case - Hitch Hitchhiker's Guide to the Galaxy.\",\n",
+ " ).launch()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 781
+ },
+ "id": "-btP40G1OkgI",
+ "outputId": "062d6b92-d8c2-4256-deef-023bb9b0292a"
+ },
+ "execution_count": 36,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "<ipython-input-36-636b02531079>:10: GradioDeprecationWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
+ " gr.inputs.Textbox(lines=2, label=\"Query\"),\n",
+ "<ipython-input-36-636b02531079>:10: GradioDeprecationWarning: `optional` parameter is deprecated, and it has no effect\n",
+ " gr.inputs.Textbox(lines=2, label=\"Query\"),\n",
+ "<ipython-input-36-636b02531079>:10: GradioDeprecationWarning: `numeric` parameter is deprecated, and it has no effect\n",
+ " gr.inputs.Textbox(lines=2, label=\"Query\"),\n",
+ "<ipython-input-36-636b02531079>:12: GradioDeprecationWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
+ " gr.outputs.Textbox(label=\"Response\"),\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
+ "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
+ "\n",
+ "To create a public link, set `share=True` in `launch()`.\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ],
+ "application/javascript": [
+ "(async (port, path, width, height, cache, element) => {\n",
+ " if (!google.colab.kernel.accessAllowed && !cache) {\n",
+ " return;\n",
+ " }\n",
+ " element.appendChild(document.createTextNode(''));\n",
+ " const url = await google.colab.kernel.proxyPort(port, {cache});\n",
+ "\n",
+ " const external_link = document.createElement('div');\n",
+ " external_link.innerHTML = `\n",
+ " <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
+ " Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
+ " https://localhost:${port}${path}\n",
+ " </a>\n",
+ " </div>\n",
+ " `;\n",
+ " element.appendChild(external_link);\n",
+ "\n",
+ " const iframe = document.createElement('iframe');\n",
+ " iframe.src = new URL(path, url).toString();\n",
+ " iframe.height = height;\n",
+ " iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
+ " iframe.width = width;\n",
+ " iframe.style.border = 0;\n",
+ " element.appendChild(iframe);\n",
+ " })(7861, \"/\", \"100%\", 500, false, window.element)"
+ ]
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "fqFPXldYOm0X"
+ },
+ "execution_count": 36,
+ "outputs": []
+ }
+ ]
+}

README.md

@@ -1,10 +1,10 @@
 ---
-title: LangChain Testing Version2
-emoji: 📉
-colorFrom: green
-colorTo: yellow
+title: TalkToMyDoc Hitch Hikers Guide
+emoji: 🐠
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 3.41.2
+sdk_version: 3.27.0
 app_file: app.py
 pinned: false
 license: openrail

app.py

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+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+import os
+
+with open("guide1.txt") as f:
+ hitchhikersguide = f.read()
+
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separator = "\n")
+texts = text_splitter.split_text(hitchhikersguide)
+
+embeddings = OpenAIEmbeddings()
+
+docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()
+
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
+
+def make_inference(query):
+ docs = docsearch.get_relevant_documents(query)
+ return(chain.run(input_documents=docs, question=query))
+
+if __name__ == "__main__":
+ # make a gradio interface
+ import gradio as gr
+
+ gr.Interface(
+ make_inference,
+ [
+ gr.inputs.Textbox(lines=2, label="Query"),
+ ],
+ gr.outputs.Textbox(label="Response"),
+ title="🗣️TalkToMyDoc📄",
+ description="🗣️TalkToMyDoc📄 is a tool that allows you to ask questions about a document. In this case - Hitch Hitchhiker's Guide to the Galaxy.",
+ ).launch()

requirements.txt

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+langchain
+openai
+tiktoken
+chromadb