chat_with_multiple_website.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BL6o2vsI_w1D"
   },
   "source": [
    "# Chat with Multiple Websites\n",
    "\n",
    "### Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TmF3lrdfLjZq",
    "outputId": "2ddd5b21-de6f-43bd-c8bc-951ba19ce5e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Building wheels for collected packages: sentence-transformers, chroma-hnswlib, llama-index-sl, transformers3, pypika, pdfminer\n",
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      "  Building wheel for pdfminer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pdfminer: filename=pdfminer-20191125-py3-none-any.whl size=6140061 sha256=ffb95eef612cdc662dd0d04eaffe23a6fffaf9316c1b0bc52060ec1c417456b0\n",
      "  Stored in directory: /root/.cache/pip/wheels/4e/c1/68/f7bd0a8f514661f76b5cbe3b5f76e0033d79f1296012cbbf72\n",
      "Successfully built sentence-transformers chroma-hnswlib llama-index-sl transformers3 pypika pdfminer\n",
      "Installing collected packages: transformers3, sentencepiece, pypika, monotonic, websockets, validators, uvloop, urllib3, safetensors, retrying, python-dotenv, pypdf, pycryptodome, pulsar-client, overrides, mypy-extensions, marshmallow, mako, jsonpointer, humanfriendly, httptools, html2text, h11, deprecated, chroma-hnswlib, backoff, watchfiles, uvicorn, typing-inspect, starlette, pdfminer, jsonpatch, coloredlogs, tiktoken, posthog, openai, onnxruntime, llama-index-sl, langsmith, huggingface-hub, gpt4all, fastapi, dataclasses-json, authlib, weaviate-client, tokenizers, langchain, atlassian-python-api, transformers, llama-index, chromadb, llama-hub, sentence-transformers, genai_stack\n",
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      "    Found existing installation: urllib3 2.0.6\n",
      "    Uninstalling urllib3-2.0.6:\n",
      "      Successfully uninstalled urllib3-2.0.6\n",
      "Successfully installed atlassian-python-api-3.41.2 authlib-1.2.1 backoff-2.2.1 chroma-hnswlib-0.7.2 chromadb-0.4.5 coloredlogs-15.0.1 dataclasses-json-0.5.14 deprecated-1.2.14 fastapi-0.99.1 genai_stack-0.2.5 gpt4all-1.0.12 h11-0.14.0 html2text-2020.1.16 httptools-0.6.1 huggingface-hub-0.17.3 humanfriendly-10.0 jsonpatch-1.33 jsonpointer-2.4 langchain-0.0.315 langsmith-0.0.44 llama-hub-0.0.34 llama-index-0.8.45.post1 llama-index-sl-0.5.3.1 mako-1.2.4 marshmallow-3.20.1 monotonic-1.6 mypy-extensions-1.0.0 onnxruntime-1.16.1 openai-0.28.1 overrides-7.4.0 pdfminer-20191125 posthog-3.0.2 pulsar-client-3.3.0 pycryptodome-3.19.0 pypdf-3.14.0 pypika-0.48.9 python-dotenv-1.0.0 retrying-1.3.4 safetensors-0.4.0 sentence-transformers-2.2.2 sentencepiece-0.1.99 starlette-0.27.0 tiktoken-0.5.1 tokenizers-0.14.1 transformers-4.34.0 transformers3-0.0.0a1 typing-inspect-0.9.0 urllib3-1.26.17 uvicorn-0.23.0 uvloop-0.18.0 validators-0.22.0 watchfiles-0.21.0 weaviate-client-3.24.2 websockets-11.0.3\n"
     ]
    }
   ],
   "source": [
    "!pip install genai_stack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QeIrtUI1_2s0"
   },
   "source": [
    "## Setup your API Key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cs6UPxk4sIGg"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S2vfFbmssZfW",
    "outputId": "afd599e4-fa4d-4381-94b1-1385fa18a26c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter OpenAI API Key:··········\n"
     ]
    }
   ],
   "source": [
    "api_key = getpass(\"Enter OpenAI API Key:\")\n",
    "os.environ['OPENAI_API_KEY'] = api_key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9ahC5bx7Dfl_"
   },
   "source": [
    "## Import required modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vEfjWMuVPpCY"
   },
   "outputs": [],
   "source": [
    "from genai_stack.stack.stack import Stack\n",
    "from genai_stack.etl.langchain import LangchainETL\n",
    "from genai_stack.embedding.langchain import LangchainEmbedding\n",
    "from genai_stack.vectordb.chromadb import ChromaDB\n",
    "from genai_stack.prompt_engine.engine import PromptEngine\n",
    "from genai_stack.model.gpt3_5 import OpenAIGpt35Model\n",
    "from genai_stack.retriever.langchain import LangChainRetriever\n",
    "from genai_stack.memory.langchain import ConversationBufferMemory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p7TVObv1DiLg"
   },
   "source": [
    "## ETL -  \"Extract, Transform, and Load.\"\n",
    "\n",
    "- Add your data here. Check documentation for the required loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FxQ9zTuasoLK"
   },
   "outputs": [],
   "source": [
    "etl = LangchainETL.from_kwargs(name=\"WebBaseLoader\",\n",
    "                               fields={\"web_path\": [\n",
    "                                \"https://aiplanet.com\",\n",
    "                                \"https://aimarketplace.co\"\n",
    "                               ]\n",
    "                        }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FS87zjIwDvne"
   },
   "source": [
    "## Create Embeddings to store in VectorDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-gZdeZpDs8-6"
   },
   "outputs": [],
   "source": [
    "config = {\n",
    "    \"model_name\": \"sentence-transformers/all-mpnet-base-v2\",\n",
    "    \"model_kwargs\": {\"device\": \"cpu\"},\n",
    "    \"encode_kwargs\": {\"normalize_embeddings\": False},\n",
    "}\n",
    "embedding = LangchainEmbedding.from_kwargs(name=\"HuggingFaceEmbeddings\", fields=config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mh1appk9D09D"
   },
   "source": [
    "## Define the VectorDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "e1qTWu6Vs9ua"
   },
   "outputs": [],
   "source": [
    "chromadb = ChromaDB.from_kwargs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YEccaQgvD3ln"
   },
   "source": [
    "## Define your LLM - Large Language Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dwLeBUTQtBFL"
   },
   "outputs": [],
   "source": [
    "llm = OpenAIGpt35Model.from_kwargs(parameters={\"openai_api_key\": api_key})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P6duBcfjD7pF"
   },
   "source": [
    "## Add Retrieval and Stack all the components"
   ]
  },
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      "text/plain": [
       "Downloading (…)a8e1d/.gitattributes:   0%|          | 0.00/1.18k [00:00<?, ?B/s]"
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       "<genai_stack.stack.stack.Stack at 0x7d4cd511eb30>"
      ]
     },
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     "metadata": {},
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   ],
   "source": [
    "prompt_engine = PromptEngine.from_kwargs(should_validate=False)\n",
    "retriever = LangChainRetriever.from_kwargs()\n",
    "memory = ConversationBufferMemory.from_kwargs()\n",
    "Stack(\n",
    "    etl=etl,\n",
    "    embedding=embedding,\n",
    "    vectordb=chromadb,\n",
    "    model=llm,\n",
    "    prompt_engine=prompt_engine,\n",
    "    retriever=retriever,\n",
    "    memory=memory\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dEHtQQPkD_ng"
   },
   "source": [
    "## Run your ETL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R9AU8SSltKaX"
   },
   "outputs": [],
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QrUamZzWtPa_"
   },
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   "source": [
    "prompt1 = \"Why choose models from AI Marketplace?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MdLkaSeDtUbg"
   },
   "outputs": [],
   "source": [
    "response = retriever.retrieve(prompt1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Chde1cIDtXVm",
    "outputId": "63343353-983b-455f-b3ee-1dab2e816686"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models from AI Marketplace are a good choice because they are ready-to-use and can accelerate innovation. They simplify integration across diverse applications and bring ideas to life. Additionally, AI Marketplace offers a large collection of models for different industries and use cases, allowing users to explore diverse AI models. The marketplace also provides cost-saving solutions with pre-built models, eliminating the need for a data science team, and offering flexible scaling and pay-as-you-go pricing. Finally, AI Marketplace offers business-focused models that can optimize operations and drive growth.\n"
     ]
    }
   ],
   "source": [
    "print(response['output'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3DLv-rWOtfm3"
   },
   "outputs": [],
   "source": [
    "prompt2 = \"What is the total community members count at AI Planet?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SalOePZetsXQ"
   },
   "outputs": [],
   "source": [
    "response = retriever.retrieve(prompt2)"
   ]
  }
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