Add quickstart notebook

.gitignore

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+.DS_Store

.pre-commit-config.yaml

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+repos:
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.7.0
+    hooks:
+      - id: nbqa-black
+        additional_dependencies: [black==23.3.0]
+      - id: nbqa-pyupgrade
+        additional_dependencies: [pyupgrade==2.7.3]
+      - id: nbqa-isort
+        additional_dependencies: [isort==5.12.0]

.python-version

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+mlexpert

00.quickstart-langchain.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": null,
+   "metadata": {
+    "id": "4OIZC5EaIYY_"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -Uqqq pip --progress-bar off\n",
+    "!pip install -qqq langchain==0.0.139 --progress-bar off\n",
+    "!pip install -qqq openai==0.27.4 --progress-bar off\n",
+    "!pip install -Uqqq watermark==2.3.1 --progress-bar off\n",
+    "!pip install -Uqqq chromadb==0.3.21 --progress-bar off\n",
+    "!pip install -Uqqq tiktoken==0.3.3 --progress-bar off"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%load_ext watermark"
+   ],
+   "metadata": {
+    "id": "qQ-YYVMCKbYf"
+   },
+   "execution_count": 2,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import os\n",
+    "import textwrap\n",
+    "\n",
+    "import chromadb\n",
+    "import langchain\n",
+    "import openai\n",
+    "from langchain.chains import RetrievalQA\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.document_loaders import WebBaseLoader\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.indexes import VectorstoreIndexCreator\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.vectorstores import Chroma"
+   ],
+   "metadata": {
+    "id": "htp4LNX0KOtb"
+   },
+   "execution_count": 3,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%watermark --iversions -v -m"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TYgj1UZTKl17",
+    "outputId": "01165430-c363-4e8d-cd6e-690a1e9363cc"
+   },
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Python implementation: CPython\n",
+      "Python version       : 3.9.16\n",
+      "IPython version      : 7.34.0\n",
+      "\n",
+      "Compiler    : GCC 9.4.0\n",
+      "OS          : Linux\n",
+      "Release     : 5.10.147+\n",
+      "Machine     : x86_64\n",
+      "Processor   : x86_64\n",
+      "CPU cores   : 2\n",
+      "Architecture: 64bit\n",
+      "\n",
+      "chromadb : 0.3.21\n",
+      "openai   : 0.27.4\n",
+      "langchain: 0.0.139\n",
+      "\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "def print_response(response: str):\n",
+    "    print(\"\\n\".join(textwrap.wrap(response, width=100)))"
+   ],
+   "metadata": {
+    "id": "STDwioZBj_pp"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "os.environ[\"OPENAI_API_KEY\"] = \"YOUR OPENAI KEY\""
+   ],
+   "metadata": {
+    "id": "p8f4eu1SKp4j"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "model = OpenAI(temperature=0)"
+   ],
+   "metadata": {
+    "id": "BA2qn63lLUc-"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print(\n",
+    "    model(\n",
+    "        \"You're Dwight K. Schrute from the Office. Suggest 5 places to visit in Scranton that are connected to the TV show.\"\n",
+    "    )\n",
+    ")"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1-rQ5ETGO0Q6",
+    "outputId": "a1a550a5-15b3-4fb6-ca8f-4247a696e1f1"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "\n",
+      "\n",
+      "1. The Dunder Mifflin Paper Company - Visit the office building where the show was filmed and take a tour of the set.\n",
+      "\n",
+      "2. Poor Richard's Pub - Enjoy a drink at the bar where the cast often hung out.\n",
+      "\n",
+      "3. Steamtown National Historic Site - Take a ride on the historic train that was featured in the show.\n",
+      "\n",
+      "4. The Scranton Cultural Center - Attend a show at the theater where the cast performed a play in the episode \"The Duel\".\n",
+      "\n",
+      "5. The Mall at Steamtown - Shop at the mall where the cast went on a shopping spree in the episode \"The Coup\".\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Q&A Over a Document"
+   ],
+   "metadata": {
+    "id": "lfA_kFae3ROU"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "loader = WebBaseLoader(\n",
+    "    \"https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm\"\n",
+    ")"
+   ],
+   "metadata": {
+    "id": "DlJWvzaSVGwk"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "documents = loader.load()\n",
+    "len(documents)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0k2sQmZCVKRF",
+    "outputId": "c46048ba-9ae2-4561-81fa-d53e5327ceb7"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "document = documents[0]\n",
+    "document.__dict__.keys()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "334_cQoO4cZP",
+    "outputId": "9c26612c-94d0-485d-c1a9-a24d89393339"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "dict_keys(['page_content', 'metadata'])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "document.page_content[:100]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "eHOdt3K_4x-E",
+    "outputId": "17c01652-b06d-473e-daff-18b74831c44e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "\"\\n\\n\\n\\n\\nTwitter's Recommendation Algorithm\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nEngineering\\n\\n\\n\\n\\n\\nBac\""
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "document.metadata"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "MxnizWE-4uzz",
+    "outputId": "0de4c185-06f6-4e93-c88f-4fc14e3a225a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "{'source': 'https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm'}"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "index = VectorstoreIndexCreator().from_loaders([loader])"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dNk8kGhDa3GR",
+    "outputId": "0f14832b-ec0e-4263-96e1-752fabf757c2"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "WARNING:chromadb:Using embedded DuckDB without persistence: data will be transient\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "query = \"\"\"\n",
+    "You're Dwight K. Schrute from the Office.\n",
+    "Explain the Twitter recommendation algorithm in 5 sentences using analogies from the Office.\n",
+    "\"\"\"\n",
+    "print_response(index.query(query))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "b-9FzImFchcH",
+    "outputId": "064fa564-1d4c-4e4f-d589-6f86d1de61aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      " The Twitter recommendation algorithm is like Dwight K. Schrute's job at Dunder Mifflin. It takes\n",
+      "the 500 million Tweets posted daily and distills them down to a handful of top Tweets that show up\n",
+      "on your timeline, just like Dwight distills the vast amount of paper at Dunder Mifflin into a few\n",
+      "important documents. The algorithm uses a set of core models and features to extract latent\n",
+      "information from Tweet, user, and engagement data, just like Dwight uses his keen eye to spot the\n",
+      "important details in the documents. It then uses a logistic regression model to rank the Tweets,\n",
+      "similar to how Dwight ranks the documents in order of importance. Finally, it traverses the graph of\n",
+      "engagements and follows to answer questions about what Tweets and Users are similar to your\n",
+      "interests, just like Dwight uses his knowledge of the office to answer questions about the people\n",
+      "and documents in the office.\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Using a Prompt Template"
+   ],
+   "metadata": {
+    "id": "8sZR4jBD3eGs"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "template = \"\"\"You're Dwight K. Schrute from the Office.\n",
+    "\n",
+    "{context}\n",
+    "\n",
+    "Answer with analogies from the Office to the question and the way Dwight speaks.\n",
+    "\n",
+    "Question: {question}\n",
+    "Answer:\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"context\", \"question\"])\n",
+    "print(\n",
+    "    prompt.format(\n",
+    "        context=\"Paper sells are declining 10% year over year.\",\n",
+    "        question=\"How to sell paper?\",\n",
+    "    )\n",
+    ")"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "oRptJsAvPNbF",
+    "outputId": "7e632e2b-5a8c-46c2-cf36-59fba0a45cea"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "You're Dwight K. Schrute from the Office.\n",
+      "\n",
+      "Paper sells are declining 10% year over year.\n",
+      "\n",
+      "Answer with analogies from the Office to the question and the way Dwight speaks.\n",
+      "\n",
+      "Question: How to sell paper?\n",
+      "Answer:\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "embeddings = OpenAIEmbeddings()\n",
+    "db = Chroma.from_documents(documents, embeddings)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rXFpoKGbkvSn",
+    "outputId": "9e0c4f83-44c3-4e5c-c35b-a3a2a45738d2"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "WARNING:chromadb:Using embedded DuckDB without persistence: data will be transient\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "chain_type_kwargs = {\"prompt\": prompt}\n",
+    "chain = RetrievalQA.from_chain_type(\n",
+    "    llm=ChatOpenAI(temperature=0),\n",
+    "    chain_type=\"stuff\",\n",
+    "    retriever=db.as_retriever(search_kwargs={\"k\": 1}),\n",
+    "    chain_type_kwargs=chain_type_kwargs,\n",
+    ")"
+   ],
+   "metadata": {
+    "id": "3TjhORB7xEUp"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "query = \"Explain the Twitter recommendation algorithm in 5 sentences\"\n",
+    "response = chain.run(query)"
+   ],
+   "metadata": {
+    "id": "sfbQY893xbdY"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print_response(response)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "rT_8hNbP6mN1",
+    "outputId": "2be2e3f0-8a31-4565-869d-4eb668b3a2bb"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Well, Twitter's got this fancy algorithm that picks out the best tweets from the millions of tweets\n",
+      "posted every day. It's like Michael Scott trying to pick the best Dundie award winners from all the\n",
+      "employees. They use a bunch of models and features to figure out what you might like, like how\n",
+      "Dwight uses his knowledge of his coworkers to predict their behavior. Then they rank the tweets\n",
+      "using a big neural network, kind of like how Jim ranks his pranks on Dwight. Finally, they filter\n",
+      "out any tweets you don't want to see, like how Angela filters out any fun from the office. And\n",
+      "voila, you've got your personalized Twitter timeline.\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## References\n",
+    "\n",
+    "- [Twitter's Recommendation Algorithm](https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm)"
+   ],
+   "metadata": {
+    "id": "FpikEA-s8RMw"
+   }
+  }
+ ]
+}

LICENSE

@@ -1,21 +1,201 @@
-MIT License
-
-Copyright (c) 2023 Venelin Valkov
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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README.md

@@ -0,0 +1,3 @@
+# Get SH\*T Done with Prompt Engineering and Langchain
+
+Welcome to the guide on Prompt Engineering!