MMO_RAG_Docker.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This version uses Milvus through Docker Compose so you must have Docker installed to run this notebook (Milvus is spun up via `docker compose up -d` as shown in the block below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! pip install pymilvus milvus langchain sentence-transformers tiktoken octoai-sdk\n",
    "# docker-compose up -d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import LLMChain\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain_community.llms.octoai_endpoint import OctoAIEndpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "import os\n",
    "\n",
    "load_dotenv()\n",
    "os.environ[\"OCTOAI_TOKEN\"] = os.getenv(\"OCTOAI_API_TOKEN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = OctoAIEndpoint(\n",
    "    endpoint_url=\"https://text.octoai.run/v1/chat/completions\",\n",
    "    model_kwargs={\n",
    "        \"model\": \"mixtral-8x7b-instruct-fp16\",\n",
    "        \"max_tokens\": 128,\n",
    "        \"presence_penalty\": 0,\n",
    "        \"temperature\": 0.01,\n",
    "        \"top_p\": 0.9,\n",
    "        \"messages\": [\n",
    "            {\n",
    "                \"role\": \"system\",\n",
    "                \"content\": \"You are a helpful assistant. Keep your responses limited to one short paragraph if possible.\",\n",
    "            },\n",
    "        ],\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings import OctoAIEmbeddings\n",
    "from langchain_community.vectorstores import Milvus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OctoAIEmbeddings(endpoint_url=\"https://text.octoai.run/v1/embeddings\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.schema import Document\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = os.listdir(\"./data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Chicago.txt',\n",
       " 'Washington, D.C..txt',\n",
       " 'Cambridge, Massachusetts.txt',\n",
       " 'Houston.txt',\n",
       " 'Seattle.txt',\n",
       " 'Toronto.txt',\n",
       " 'San Francisco.txt',\n",
       " 'Boston.txt']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_texts = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for file in files:\n",
    "    with open(f\"./data/{file}\") as f:\n",
    "        file_text = f.read()\n",
    "    text_splitter = CharacterTextSplitter.from_tiktoken_encoder(\n",
    "        chunk_size=512, chunk_overlap=64, \n",
    "    )\n",
    "    texts = text_splitter.split_text(file_text)\n",
    "    for i, chunked_text in enumerate(texts):\n",
    "        file_texts.append(Document(page_content=chunked_text, \n",
    "                metadata={\"doc_title\": file.split(\".\")[0], \"chunk_num\": i}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Document(page_content=\"Chicago (  shih-KAH-goh, locally also  shih-KAW-goh; Miami-Illinois: Shikaakwa; Ojibwe: Zhigaagong) is the most populous city in the U.S. state of Illinois and the third-most populous in the United States after New York City and Los Angeles. With a population of 2,746,388 in the 2020 census, it is also the most populous city in the Midwest. As the seat of Cook County, the second-most populous county in the U.S., Chicago is the center of the Chicago metropolitan area.\\nLocated on the shore of Lake Michigan, Chicago was incorporated as a city in 1837 near a portage between the Great Lakes and the Mississippi River watershed. It grew rapidly in the mid-19th century. In 1871, the Great Chicago Fire destroyed several square miles and left more than 100,000 homeless, but Chicago's population continued to grow. Chicago made noted contributions to urban planning and architecture, such as the Chicago School, the development of the City Beautiful Movement, and the steel-framed skyscraper.Chicago is an international hub for finance, culture, commerce, industry, education, technology, telecommunications, and transportation. It has the largest and most diverse derivatives market in the world, generating 20% of all volume in commodities and financial futures alone. O'Hare International Airport is routinely ranked among the world's top six busiest airports by passenger traffic, and the region is also the nation's railroad hub. The Chicago area has one of the highest gross domestic products (GDP) in the world, generating $689 billion in 2018. Chicago's economy is diverse, with no single industry employing more than 14% of the workforce.Chicago is a major tourist destination. Chicago's culture has contributed much to the visual arts, literature, film, theater, comedy (especially improvisational comedy), food, dance, and music (particularly jazz, blues, soul, hip-hop, gospel, and electronic dance music, including house music). Chicago is home to the Chicago Symphony Orchestra and the Lyric Opera of Chicago. The Chicago area also hosts the University of Chicago, Northwestern University, and the University of Illinois Chicago, among other institutions of learning. Chicago has professional sports teams in each of the major professional leagues, including two Major League Baseball teams.\\n\\n\\n== Etymology and nicknames ==\", metadata={'doc_title': 'Chicago', 'chunk_num': 0})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_texts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector_store = Milvus.from_documents(\n",
    "    file_texts,\n",
    "    embedding=embeddings,\n",
    "    connection_args={\"host\": \"localhost\", \"port\": 19530},\n",
    "    collection_name=\"cities\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = vector_store.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"Answer the question based only on the following context:\n",
    "{context}\n",
    "\n",
    "Question: {question}\n",
    "\"\"\"\n",
    "prompt = PromptTemplate.from_template(template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "chain = (\n",
    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
    "    | prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" Seattle is a significant city with a 2022 population of 749,256 people, making it the most populous city in both the state of Washington and the Pacific Northwest region of North America. The Seattle metropolitan area's population is 4.02 million, making it the 15th-largest in the United States.\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke(\"How big is Seattle?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's make this a bit more fun and showcase the multilingual capabilities of Mixtal which really outshine other open source models\n",
    "\n",
    "# Our Vector DB is populated with entries from english text - even the embedding model we're using here, GTE-Large\n",
    "# works best on english text. However Mixtral has good mutlilingual capabilities in French, German, Spanish and Italian.\n",
    "# So what we'll do is ask the assistant to only answer in french in the system and user prompt. RAG here is performed based on \n",
    "# english text, but upon producing the user response, the Mixtral LLM will generate tokens in a different language here (french)\n",
    "french_llm = OctoAIEndpoint(\n",
    "    endpoint_url=\"https://text.octoai.run/v1/chat/completions\",\n",
    "    model_kwargs={\n",
    "        \"model\": \"mixtral-8x7b-instruct-fp16\",\n",
    "        \"max_tokens\": 128,\n",
    "        \"presence_penalty\": 0,\n",
    "        \"temperature\": 0.1,\n",
    "        \"top_p\": 0.9,\n",
    "        \"messages\": [\n",
    "            {\n",
    "                \"role\": \"system\",\n",
    "                \"content\": \"You are a helpful assistant who responds in French and not in English.\",\n",
    "            },\n",
    "        ],\n",
    "    },\n",
    ")\n",
    "\n",
    "french_template = \"\"\"Answer the question in French based only on the following context:\n",
    "{context}\n",
    "\n",
    "Question: {question}\n",
    "\"\"\"\n",
    "french_prompt = PromptTemplate.from_template(french_template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "french_chain = (\n",
    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
    "    | french_prompt\n",
    "    | french_llm\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fr_1 = french_chain.invoke(\"How big is the city of Seattle?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "pprint(fr_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fr_2 = french_chain.invoke(\"When should I visit Seattle?\")\n",
    "pprint(fr_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fr_3 = french_chain.invoke(\"What are some things to do Seattle?\")\n",
    "pprint(fr_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "\n",
    "template = \"\"\"Translate the entry into english {entry} \"\"\"\n",
    "prompt = PromptTemplate.from_template(template)\n",
    "\n",
    "print(llm_chain.invoke(fr_1)[\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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