L5_basic_RAG.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d67a4729-cd2f-47e7-a4f6-f84a5677414f",
   "metadata": {},
   "source": [
    "# Basic RAG (Retrieval Augmented Generation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b880d1ed-3db0-45a1-807e-1b47e9ce1320",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "# ! pip install faiss-cpu \"mistralai>=0.1.2\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b350586c-f8ca-4013-8840-46460e4ba295",
   "metadata": {},
   "source": [
    "### Load API key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4b100be-c2cf-4e07-ba17-07eae31aea35",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "from helper import load_mistral_api_key\n",
    "api_key, dlai_endpoint = load_mistral_api_key(ret_key=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe8609d5-9f27-4202-b0be-36db34412998",
   "metadata": {},
   "source": [
    "### Get data\n",
    "\n",
    "- You can go to https://www.deeplearning.ai/the-batch/\n",
    "- Search for any article and copy its URL."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "983ce5f6-5eb1-4442-8e04-822bdbd701f4",
   "metadata": {},
   "source": [
    "### Parse the article with BeautifulSoup "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4c01740-72b4-482c-b61e-e272a734f01f",
   "metadata": {
    "height": 217
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How can AI help to fight climate change? A new report evaluates progress so far and explores options for the future.What’s new: The Innovation for Cool Earth Forum, a conference of climate researchers hosted by Japan, published a roadmap for the use of data science, computer vision, and AI-driven simulation to reduce greenhouse gas emissions. The roadmap evaluates existing approaches and suggests ways to scale them up.How it works: The roadmap identifies 6 “high-potential opportunities”: activities in which AI systems can make a significant difference based on the size of the opportunity, real-world results, and validated research. The authors emphasize the need for data, technical and scientific talent, computing power, funding, and leadership to take advantage of these opportunities.Monitoring emissions. AI systems analyze data from satellites, drones, and ground sensors to measure greenhouse gas emissions. The European Union uses them to measure methane emissions, environmental organizations gauge carbon monoxide emissions to help guide the carbon offset trading market, and consultancies like Kayrros identify large-scale sources of greenhouse gasses like landfills and oilfields. The authors recommend an impartial clearinghouse for climate-related data and wider access to satellite data.Energy. More than 30 percent of carbon emissions come from generating electricity. Simulations based on neural networks are helping to predict power generated by wind and solar plants and demand on electrical grids, which have proven to be difficult for other sorts of algorithms. AI systems also help to situate wind and solar plants and optimize grids. These approaches could scale up with more robust models, standards to evaluate performance, and security protocols.Manufacturing. An unnamed Brazilian steelmaker has used AI to measure the chemical composition of scrap metal to be reused batch by batch, allowing it to reduce carbon-intensive additives by 8 percent while improving overall quality. AI systems can analyze historical data to help factories use more recycled materials, cut waste, minimize energy use, and reduce downtime. Similarly, they can optimize supply chains to reduce emissions contributed by logistics. Agriculture. Farmers use AI-equipped sensors to simulate different crop rotations and weather events to forecast crop yield or loss. Armed with this data, food producers can cut waste and reduce carbon footprints. The authors cite lack of food-related datasets and investment in adapting farming practices as primary barriers to taking full advantage of AI in the food industry.Transportation. AI systems can reduce greenhouse-gas emissions by improving traffic flow, ameliorating congestion, and optimizing public transportation. Moreover, reinforcement learning can reduce the impact of electric vehicles on the power grid by optimizing their charging. More data, uniform standards, and AI talent are needed to realize this potential.Materials. Materials scientists use AI models to study traits of existing materials and design new ones. These techniques could accelerate development of more efficient batteries, solar cells, wind turbines, and transmission infrastructure. Better coordination between materials scientists and AI researchers would accelerate such benefits.Why it matters: AI has demonstrated its value in identifying sources of emissions, optimizing energy consumption, and developing and understanding materials. Scaling and extending this value in areas that generate the most greenhouse gasses — particularly energy generation, manufacturing, food production, and transportation — could make a significant dent in greenhouse gas emissions.We’re thinking: AI also has an important role to play in advancing the science of climate geoengineering, such as stratospheric aerosol injection (SAI), to cool down the planet. More research is needed to determine whether SAI is a good idea, but AI-enabled climate modeling will help answer this question.\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "\n",
    "response = requests.get(\n",
    "    \"https://www.deeplearning.ai/the-batch/a-roadmap-explores-how-ai-can-detect-and-mitigate-greenhouse-gases/\"\n",
    ")\n",
    "html_doc = response.text\n",
    "soup = BeautifulSoup(html_doc, \"html.parser\")\n",
    "tag = soup.find(\"div\", re.compile(\"^prose--styled\"))\n",
    "text = tag.text\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "491cf036-7bc9-4e96-94ab-737872de531a",
   "metadata": {},
   "source": [
    "### Optionally, save the text into a text file\n",
    "- You can upload the text file into a chat interface in the next lesson.\n",
    "- To download this file to your own machine, click on the \"Jupyter\" logo to view the file directory.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fbfa8e2-08af-445b-8134-7395cc693b5b",
   "metadata": {
    "height": 64
   },
   "outputs": [],
   "source": [
    "file_name = \"AI_greenhouse_gas.txt\"\n",
    "with open(file_name, 'w') as file:\n",
    "    file.write(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aad1aa61-9e1c-46c8-ae5e-61855df440f9",
   "metadata": {},
   "source": [
    "### Chunking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8494655e-bd87-49de-8f1d-69ffbc1c256e",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "chunk_size = 512\n",
    "chunks = [text[i : i + chunk_size] for i in range(0, len(text), chunk_size)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c78c9936-0c1d-471c-b030-6c45639e7238",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chunks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e42e3f06-09d6-4186-be0b-6034b0c8330e",
   "metadata": {},
   "source": [
    "### Get embeddings of the chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e77d9805-7a53-4210-9f80-f4de52285588",
   "metadata": {
    "height": 149
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from mistralai.client import MistralClient\n",
    "\n",
    "\n",
    "def get_text_embedding(txt):\n",
    "    client = MistralClient(api_key=api_key, endpoint=dlai_endpoint)\n",
    "    embeddings_batch_response = client.embeddings(model=\"mistral-embed\", input=txt)\n",
    "    return embeddings_batch_response.data[0].embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "46503830-6ad5-493e-a629-152721e2d88e",
   "metadata": {
    "height": 64
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "text_embeddings = np.array([get_text_embedding(chunk) for chunk in chunks])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "55396758-c3f3-45b3-b6e7-d4912c0899f2",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.03274536,  0.04751587,  0.04489136, ..., -0.03289795,\n",
       "         0.02278137, -0.01459503],\n",
       "       [-0.03631592,  0.05548096,  0.03271484, ..., -0.03125   ,\n",
       "         0.01594543, -0.01722717],\n",
       "       [-0.04876709,  0.04779053,  0.05670166, ...,  0.0046463 ,\n",
       "         0.0184021 , -0.01251984],\n",
       "       ...,\n",
       "       [-0.02597046,  0.04049683,  0.03543091, ..., -0.01013184,\n",
       "        -0.00962067, -0.00917053],\n",
       "       [-0.03025818,  0.0541687 ,  0.06280518, ..., -0.00900269,\n",
       "        -0.00782776, -0.00432587],\n",
       "       [-0.02456665,  0.05093384,  0.04879761, ..., -0.0064888 ,\n",
       "         0.02600098, -0.01386261]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca875993-fe6d-42df-811e-a43891cd0350",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1024"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(text_embeddings[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cba33c7-9d1d-44d8-a01e-e30f16be1aac",
   "metadata": {},
   "source": [
    "### Store in a vector databsae\n",
    "- In this classroom, you'll use [Faiss](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0981a956-5f9c-4ea6-8fb3-a2cc9fe896d2",
   "metadata": {
    "height": 98
   },
   "outputs": [],
   "source": [
    "import faiss\n",
    "\n",
    "d = text_embeddings.shape[1]\n",
    "index = faiss.IndexFlatL2(d)\n",
    "index.add(text_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ee023ab-b26c-4df5-8a7b-7dd660bfad86",
   "metadata": {},
   "source": [
    "### Embed the user query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "894d9764-9da9-4629-8f2a-c9dcaf6ceb8d",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "question = \"What are the ways that AI can reduce emissions in Agriculture?\"\n",
    "question_embeddings = np.array([get_text_embedding(question)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9c4948cc-6d8b-449f-bc00-abb3591c7222",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.00073624,  0.04116821,  0.04318237, ..., -0.02453613,\n",
       "         0.01029968,  0.00930023]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question_embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15989e10-d0ec-41be-b6be-fa317565a926",
   "metadata": {},
   "source": [
    "### Search for chunks that are similar to the query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c930b378-7aac-434c-881b-ab69d3edb93d",
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 5]]\n"
     ]
    }
   ],
   "source": [
    "D, I = index.search(question_embeddings, k=2)\n",
    "print(I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "73aab584-1dbf-4532-b41e-0403eeeeb567",
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['data to help factories use more recycled materials, cut waste, minimize energy use, and reduce downtime. Similarly, they can optimize supply chains to reduce emissions contributed by logistics.\\xa0Agriculture.\\xa0Farmers use AI-equipped sensors to simulate different crop rotations and weather events to forecast crop yield or loss. Armed with this data, food producers can cut waste and reduce carbon footprints. The authors cite lack of food-related datasets and investment in adapting farming practices as primary b', 'arriers to taking full advantage of AI in the food industry.Transportation.\\xa0AI systems can reduce greenhouse-gas emissions by improving traffic flow, ameliorating congestion, and optimizing public transportation. Moreover, reinforcement learning can reduce the impact of electric vehicles on the power grid by optimizing their charging. More data, uniform standards, and AI talent are needed to realize this potential.Materials.\\xa0Materials scientists use AI models to study traits of existing materials and design']\n"
     ]
    }
   ],
   "source": [
    "retrieved_chunk = [chunks[i] for i in I.tolist()[0]]\n",
    "print(retrieved_chunk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da042a53-4564-4057-9a60-9b57dffff6a1",
   "metadata": {
    "height": 166
   },
   "outputs": [],
   "source": [
    "prompt = f\"\"\"\n",
    "Context information is below.\n",
    "---------------------\n",
    "{retrieved_chunk}\n",
    "---------------------\n",
    "Given the context information and not prior knowledge, answer the query.\n",
    "Query: {question}\n",
    "Answer:\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "94e7661e-51e2-4148-a44c-f262e7e85d56",
   "metadata": {
    "height": 268
   },
   "outputs": [],
   "source": [
    "from mistralai.models.chat_completion import ChatMessage\n",
    "\n",
    "\n",
    "def mistral(user_message, model=\"mistral-small-latest\", is_json=False):\n",
    "    client = MistralClient(api_key=api_key, endpoint=dlai_endpoint)\n",
    "    messages = [ChatMessage(role=\"user\", content=user_message)]\n",
    "\n",
    "    if is_json:\n",
    "        chat_response = client.chat(\n",
    "            model=model, messages=messages, response_format={\"type\": \"json_object\"}\n",
    "        )\n",
    "    else:\n",
    "        chat_response = client.chat(model=model, messages=messages)\n",
    "\n",
    "    return chat_response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "46a964d3-0dea-422a-83e6-342a4ab6906b",
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In agriculture, AI can help reduce emissions by enabling farmers to use AI-equipped sensors to simulate different crop rotations and weather events. This simulation can forecast crop yield or loss, allowing food producers to cut waste and reduce their carbon footprints. However, the authors mention that lack of food-related datasets and investment in adapting farming practices are primary barriers to fully utilizing AI in the food industry.\n"
     ]
    }
   ],
   "source": [
    "response = mistral(prompt)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9ce2ffa-c9bf-48a4-a6f3-6727da6a882d",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a653b9c2-d6e7-42f5-88e9-d5dcd376e61e",
   "metadata": {},
   "source": [
    "## RAG + Function calling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f41aac3a-20b4-4e33-ac58-f245577141f8",
   "metadata": {
    "height": 455
   },
   "outputs": [],
   "source": [
    "def qa_with_context(text, question, chunk_size=512):\n",
    "    # split document into chunks\n",
    "    chunks = [text[i : i + chunk_size] for i in range(0, len(text), chunk_size)]\n",
    "    # load into a vector database\n",
    "    text_embeddings = np.array([get_text_embedding(chunk) for chunk in chunks])\n",
    "    d = text_embeddings.shape[1]\n",
    "    index = faiss.IndexFlatL2(d)\n",
    "    index.add(text_embeddings)\n",
    "    # create embeddings for a question\n",
    "    question_embeddings = np.array([get_text_embedding(question)])\n",
    "    # retrieve similar chunks from the vector database\n",
    "    D, I = index.search(question_embeddings, k=2)\n",
    "    retrieved_chunk = [chunks[i] for i in I.tolist()[0]]\n",
    "    # generate response based on the retrieve relevant text chunks\n",
    "\n",
    "    prompt = f\"\"\"\n",
    "    Context information is below.\n",
    "    ---------------------\n",
    "    {retrieved_chunk}\n",
    "    ---------------------\n",
    "    Given the context information and not prior knowledge, answer the query.\n",
    "    Query: {question}\n",
    "    Answer:\n",
    "    \"\"\"\n",
    "    response = mistral(prompt)\n",
    "    return response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ddb4467f-0db8-4247-8150-8746a4630432",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[4, 5]]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7b1bcc8d-b957-4167-b1e9-1353a6301762",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I.tolist()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f23d8ef9-36d4-4912-8303-d2fe3860d7c6",
   "metadata": {
    "height": 64
   },
   "outputs": [],
   "source": [
    "import functools\n",
    "\n",
    "names_to_functions = {\"qa_with_context\": functools.partial(qa_with_context, text=text)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cae3717b-37e6-40b3-93b1-cfd023b59079",
   "metadata": {
    "height": 336
   },
   "outputs": [],
   "source": [
    "tools = [\n",
    "    {\n",
    "        \"type\": \"function\",\n",
    "        \"function\": {\n",
    "            \"name\": \"qa_with_context\",\n",
    "            \"description\": \"Answer user question by retrieving relevant context\",\n",
    "            \"parameters\": {\n",
    "                \"type\": \"object\",\n",
    "                \"properties\": {\n",
    "                    \"question\": {\n",
    "                        \"type\": \"string\",\n",
    "                        \"description\": \"user question\",\n",
    "                    }\n",
    "                },\n",
    "                \"required\": [\"question\"],\n",
    "            },\n",
    "        },\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2e442fa-5cca-4eb1-9c3f-24276fe4f75c",
   "metadata": {
    "height": 251
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatCompletionResponse(id='ad456f7a2848489ab0328b2e55018824', object='chat.completion', created=1715940183, model='mistral-large-latest', choices=[ChatCompletionResponseChoice(index=0, message=ChatMessage(role='assistant', content='', name=None, tool_calls=[ToolCall(id='5adWMr1FR', type=<ToolType.function: 'function'>, function=FunctionCall(name='qa_with_context', arguments='{\"question\": \"What are the ways AI can mitigate climate change in transportation?\"}'))]), finish_reason=<FinishReason.tool_calls: 'tool_calls'>)], usage=UsageInfo(prompt_tokens=92, total_tokens=126, completion_tokens=34))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"\"\"\n",
    "What are the ways AI can mitigate climate change in transportation?\n",
    "\"\"\"\n",
    "\n",
    "client = MistralClient(api_key=api_key, endpoint=dlai_endpoint)\n",
    "\n",
    "response = client.chat(\n",
    "    model=\"mistral-large-latest\",\n",
    "    messages=[ChatMessage(role=\"user\", content=question)],\n",
    "    tools=tools,\n",
    "    tool_choice=\"any\",\n",
    ")\n",
    "\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4d349dd7-0138-4857-9bcb-69ed151cb1b8",
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FunctionCall(name='qa_with_context', arguments='{\"question\": \"What are the ways AI can mitigate climate change in transportation?\"}')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tool_function = response.choices[0].message.tool_calls[0].function\n",
    "tool_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca751c08-e6e7-46a4-8e4c-a30407853cfc",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'qa_with_context'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tool_function.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "08910b72-2aaa-4393-a35a-5ed2671b8faf",
   "metadata": {
    "height": 81
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'question': 'What are the ways AI can mitigate climate change in transportation?'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "args = json.loads(tool_function.arguments)\n",
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "409f6a67-2787-424e-8b8d-92fc9b66bdf9",
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The context information does not explicitly mention specific ways AI can mitigate climate change in transportation. However, it does discuss six \"high-potential opportunities\" where AI can be used to reduce greenhouse gas emissions. These opportunities include sectors such as manufacturing, food production, and possibly transportation, although the details are not provided. To answer your question more accurately, I would need more specific information about how AI is being applied in the transportation sector.'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function_result = names_to_functions[tool_function.name](**args)\n",
    "function_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f2d1982-a899-4ad5-a5de-2a33d46cd311",
   "metadata": {},
   "source": [
    "## More about RAG\n",
    "To learn about more advanced chunking and retrieval methods, you can check out:\n",
    "- [Advanced Retrieval for AI with Chroma](https://learn.deeplearning.ai/courses/advanced-retrieval-for-ai/lesson/1/introduction)\n",
    "  - Sentence window retrieval\n",
    "  - Auto-merge retrieval\n",
    "- [Building and Evaluating Advanced RAG Applications](https://learn.deeplearning.ai/courses/building-evaluating-advanced-rag)\n",
    "  - Query Expansion\n",
    "  - Cross-encoder reranking\n",
    "  - Training and utilizing Embedding Adapters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9932106-163e-45f4-85db-d6b373cf5bbd",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e3845a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}