| [](https://github.com/lancedb/vectordb-recipes/tree/main/examples/youtube_bot/main.py)|
-| [Langchain: Code Docs QA bot](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/) | | [](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/main.py) |
-| [AI Agents: Reducing Hallucination](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/) | | [](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/main.py)|
-| [Multimodal CLIP: DiffusionDB](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_clip/) | | [](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_clip/main.py) |
-| [Multimodal CLIP: Youtube videos](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_video_search/) | | [](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_video_search/main.py) |
-| [Movie Recommender](https://github.com/lancedb/vectordb-recipes/tree/main/examples/movie-recommender/) | | [](https://github.com/lancedb/vectordb-recipes/tree/main/examples/movie-recommender/main.py) |
-| [Audio Search](https://github.com/lancedb/vectordb-recipes/tree/main/examples/audio_search/) | | [](https://github.com/lancedb/vectordb-recipes/tree/main/examples/audio_search/main.py) |
-| [Multimodal Image + Text Search](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_search/) | | [](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_search/main.py) |
-| [Evaluating Prompts with Prompttools](https://github.com/lancedb/vectordb-recipes/tree/main/examples/prompttools-eval-prompts/) | | |
+You can find the latest examples in our [VectorDB Recipes](https://github.com/lancedb/vectordb-recipes) repository.
+
+**Introduction**
+
+Explore applied examples available as Colab notebooks or Python scripts to integrate into your applications. You can also checkout our blog posts related to the particular example for deeper understanding.
+
+| Explore | Description |
+|----------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [Build from Scratch with LanceDB π οΈπ](python_examples/build_from_scratch.md) | Start building your GenAI applications from the ground up using LanceDB's efficient vector-based document retrieval capabilities! Get started quickly with a solid foundation. |
+| [Multimodal Search with LanceDB π€ΉββοΈπ](python_examples/multimodal.md) | Combine text and image queries to find the most relevant results using LanceDBβs multimodal capabilities. Leverage the efficient vector-based similarity search. |
+| [RAG: Revolutionize Information Retrieval with LanceDB ππ§](python_examples/rag.md) | Build RAG (Retrieval-Augmented Generation) with LanceDB for efficient vector-based information retrieval and more accurate responses from AI. |
+| [Vector Search: Unlock Efficient Document Retrieval ππ](python_examples/vector_search.md) | Use LanceDB's vector search capabilities to perform efficient and accurate similarity searches, enabling rapid discovery and retrieval of relevant documents in Large datasets. |
+| [Chatbot Application with LanceDB π€](python_examples/chatbot.md) | Create chatbots that retrieves relevant context for coherent and context-aware replies, enhancing user experience through advanced conversational AI. |
+| [Evaluation: Assessing Text Performance with Precision ππ‘](python_examples/evaluations.md) | Develop evaluation applications that allows you to input reference and candidate texts to measure their performance across various metrics. |
+| [AI Agents: Intelligent Collaboration π€](python_examples/aiagent.md) | Enable AI agents to communicate and collaborate efficiently through dense vector representations, achieving shared goals seamlessly. |
+| [Recommender Systems: Personalized Discovery πΏπΊ](python_examples/recommendersystem.md) | Deliver personalized experiences by efficiently storing and querying item embeddings with LanceDB's powerful vector database capabilities. |
+| **Miscellaneous Examplesπ** | Find other unique examples and creative solutions using LanceDB, showcasing the flexibility and broad applicability of the platform. |
diff --git a/docs/src/notebooks/code_qa_bot.ipynb b/docs/src/notebooks/code_qa_bot.ipynb
deleted file mode 100644
index 5ff18a0c..00000000
--- a/docs/src/notebooks/code_qa_bot.ipynb
+++ /dev/null
@@ -1,378 +0,0 @@
-{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "13cb272e",
- "metadata": {},
- "source": [
- "# Code documentation Q&A bot example with LangChain\n",
- "\n",
- "This Q&A bot will allow you to query your own documentation easily using questions. We'll also demonstrate the use of LangChain and LanceDB using the OpenAI API. \n",
- "\n",
- "In this example we'll use Pandas 2.0 documentation, but, this could be replaced for your own docs as well\n",
- "\n",
- "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
- "\n",
- "Scripts - [](./examples/Code-Documentation-QA-Bot/main.py) [](./examples/Code-Documentation-QA-Bot/index.js)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "id": "66638d6c",
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install --quiet openai langchain\n",
- "!pip install --quiet -U lancedb"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d1cdcac3",
- "metadata": {},
- "source": [
- "First, let's get some setup out of the way. As we're using the OpenAI API, ensure that you've set your key (and organization if needed):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "id": "58ee1868",
- "metadata": {},
- "outputs": [],
- "source": [
- "from openai import OpenAI\n",
- "import os\n",
- "\n",
- "# Configuring the environment variable OPENAI_API_KEY\n",
- "if \"OPENAI_API_KEY\" not in os.environ:\n",
- " os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n",
- "client = OpenAI()\n",
- "assert len(client.models.list().data) > 0"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "34f524d3",
- "metadata": {},
- "source": [
- "# Loading in our code documentation, generating embeddings and storing our documents in LanceDB\n",
- "\n",
- "We're going to use the power of LangChain to help us create our Q&A bot. It comes with several APIs that can make our development much easier as well as a LanceDB integration for vectorstore."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "b55d22f1",
- "metadata": {},
- "outputs": [],
- "source": [
- "import lancedb\n",
- "import re\n",
- "import pickle\n",
- "import requests\n",
- "import zipfile\n",
- "from pathlib import Path\n",
- "\n",
- "from langchain.document_loaders import UnstructuredHTMLLoader\n",
- "from langchain.embeddings import OpenAIEmbeddings\n",
- "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
- "from langchain.vectorstores import LanceDB\n",
- "from langchain.llms import OpenAI\n",
- "from langchain.chains import RetrievalQA"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "56cc6d50",
- "metadata": {},
- "source": [
- "To make this easier, we've downloaded Pandas documentation and stored the raw HTML files for you to download. We'll download them and then use LangChain's HTML document readers to parse them and store them in LanceDB as a vector store, along with relevant metadata."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7da77e75",
- "metadata": {},
- "outputs": [],
- "source": [
- "pandas_docs = requests.get(\"https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip\")\n",
- "with open('/tmp/pandas.documentation.zip', 'wb') as f:\n",
- " f.write(pandas_docs.content)\n",
- "\n",
- "file = zipfile.ZipFile(\"/tmp/pandas.documentation.zip\")\n",
- "file.extractall(path=\"/tmp/pandas_docs\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "ae42496c",
- "metadata": {},
- "source": [
- "We'll create a simple helper function that can help to extract metadata, so we can use this downstream when we're wanting to query with filters. In this case, we want to keep the lineage of the uri or path for each document that we process:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "d171d062",
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_document_title(document):\n",
- " m = str(document.metadata[\"source\"])\n",
- " title = re.findall(\"pandas.documentation(.*).html\", m)\n",
- " if title[0] is not None:\n",
- " return(title[0])\n",
- " return ''"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "130162ad",
- "metadata": {},
- "source": [
- "# Pre-processing and loading the documentation\n",
- "\n",
- "Next, let's pre-process and load the documentation. To make sure we don't need to do this repeatedly if we were updating code, we're caching it using pickle so we can retrieve it again (this could take a few minutes to run the first time you do it). We'll also add some more metadata to the docs here such as the title and version of the code:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "33bfe7d8",
- "metadata": {},
- "outputs": [],
- "source": [
- "docs_path = Path(\"docs.pkl\")\n",
- "docs = []\n",
- "\n",
- "if not docs_path.exists():\n",
- " for p in Path(\"/tmp/pandas_docs/pandas.documentation\").rglob(\"*.html\"):\n",
- " print(p)\n",
- " if p.is_dir():\n",
- " continue\n",
- " loader = UnstructuredHTMLLoader(p)\n",
- " raw_document = loader.load()\n",
- " \n",
- " m = {}\n",
- " m[\"title\"] = get_document_title(raw_document[0])\n",
- " m[\"version\"] = \"2.0rc0\"\n",
- " raw_document[0].metadata = raw_document[0].metadata | m\n",
- " raw_document[0].metadata[\"source\"] = str(raw_document[0].metadata[\"source\"])\n",
- " docs = docs + raw_document\n",
- "\n",
- " with docs_path.open(\"wb\") as fh:\n",
- " pickle.dump(docs, fh)\n",
- "else:\n",
- " with docs_path.open(\"rb\") as fh:\n",
- " docs = pickle.load(fh)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "c3852dd3",
- "metadata": {},
- "source": [
- "# Generating embeddings from our docs\n",
- "\n",
- "Now that we have our raw documents loaded, we need to pre-process them to generate embeddings:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "id": "82230563",
- "metadata": {},
- "outputs": [],
- "source": [
- "text_splitter = RecursiveCharacterTextSplitter(\n",
- " chunk_size=1000,\n",
- " chunk_overlap=200,\n",
- ")\n",
- "documents = text_splitter.split_documents(docs)\n",
- "embeddings = OpenAIEmbeddings()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "43e68215",
- "metadata": {},
- "source": [
- "# Storing and querying with LanceDB\n",
- "\n",
- "Let's connect to LanceDB so we can store our documents. We'll create a Table to store them in:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "id": "74780a58",
- "metadata": {},
- "outputs": [],
- "source": [
- "db = lancedb.connect('/tmp/lancedb')\n",
- "table = db.create_table(\"pandas_docs\", data=[\n",
- " {\"vector\": embeddings.embed_query(\"Hello World\"), \"text\": \"Hello World\", \"id\": \"1\"}\n",
- "], mode=\"overwrite\")\n",
- "docsearch = LanceDB.from_documents(documents, embeddings, connection=table)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "3cb1dc5d",
- "metadata": {},
- "source": [
- "Now let's create our RetrievalQA chain using the LanceDB vector store:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "id": "6a5891ad",
- "metadata": {},
- "outputs": [],
- "source": [
- "qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever())"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "28d93b85",
- "metadata": {},
- "source": [
- "And that's it! We're all set up. The next step is to run some queries, let's try a few:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "id": "70d88316",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "' The major differences in pandas 2.0 include installing optional dependencies with pip extras, the ability to use any numpy numeric dtype in an Index, and enhancements, notable bug fixes, backwards incompatible API changes, deprecations, and performance improvements.'"
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query = \"What are the major differences in pandas 2.0?\"\n",
- "qa.run(query)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "id": "85a0397c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "' 2.0.0rc0'"
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query = \"What's the current version of pandas?\"\n",
- "qa.run(query)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "id": "923f86c6",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "' Optional dependencies can be installed with pip install \"pandas[all]\" or \"pandas[performance]\". This will install all recommended performance dependencies such as numexpr, bottleneck and numba.'"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query = \"How do I make use of installing optional dependencies?\"\n",
- "qa.run(query)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "id": "02082f83",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "\" \\n\\nPandas 2.0 includes a number of API breaking changes, such as increased minimum versions for dependencies, the use of os.linesep for DataFrame.to_csv's line_terminator, and reorganization of the library. See the release notes for a full list of changes.\""
- ]
- },
- "execution_count": 53,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query = \"What are the backwards incompatible API changes in Pandas 2.0?\"\n",
- "qa.run(query)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "75cea547",
- "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": "3.10.11"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/src/notebooks/multimodal_search.ipynb b/docs/src/notebooks/multimodal_search.ipynb
deleted file mode 100644
index ddbda8d7..00000000
--- a/docs/src/notebooks/multimodal_search.ipynb
+++ /dev/null
@@ -1,297 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- " | [](./examples/multimodal_clip/main.py) |"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.1.2\u001B[0m\n",
- "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n",
- "\n",
- "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.1.2\u001B[0m\n",
- "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n"
- ]
- }
- ],
- "source": [
- "!pip install --quiet -U lancedb\n",
- "!pip install --quiet gradio transformers torch torchvision"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import io\n",
- "\n",
- "import PIL\n",
- "import duckdb\n",
- "import lancedb"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## First run setup: Download data and pre-process"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "### Get dataset\n",
- "\n",
- "!wget https://eto-public.s3.us-west-2.amazonaws.com/datasets/diffusiondb_lance.tar.gz\n",
- "!tar -xvf diffusiondb_lance.tar.gz\n",
- "!mv diffusiondb_test rawdata.lance\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "\n",
- "\n",
- "Scripts - [](./examples/youtube_bot/main.py) [](./examples/youtube_bot/index.js)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "48547ddb",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.1\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
- "\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.1\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
- ]
- }
- ],
- "source": [
- "!pip install --quiet openai datasets\n",
- "!pip install --quiet -U lancedb"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "22e570f4",
- "metadata": {},
- "source": [
- "## Download the data\n",
- "\n",
- "For this dataset we're using the HuggingFace dataset `jamescalam/youtube-transcriptions`.\n",
- "\n",
- "From the [website](https://huggingface.co/datasets/jamescalam/youtube-transcriptions):\n",
- "\n",
- "```\n",
- "The YouTube transcriptions dataset contains technical tutorials (currently from James Briggs, Daniel Bourke, and AI Coffee Break) transcribed using OpenAI's Whisper (large). Each row represents roughly a sentence-length chunk of text alongside the video URL and timestamp.\n",
- "```\n",
- "\n",
- "We'll use the training split with 700 videos and 208619 sentences"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "a8987fcb",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Found cached dataset json (/Users/changshe/.cache/huggingface/datasets/jamescalam___json/jamescalam--youtube-transcriptions-08d889f6a5386b9b/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Dataset({\n",
- " features: ['title', 'published', 'url', 'video_id', 'channel_id', 'id', 'text', 'start', 'end'],\n",
- " num_rows: 208619\n",
- "})"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from datasets import load_dataset\n",
- "\n",
- "data = load_dataset('jamescalam/youtube-transcriptions', split='train')\n",
- "data"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "5ac2b6a3",
- "metadata": {},
- "source": [
- "## Prepare context\n",
- "\n",
- "Each item in the dataset contains just a short chunk of text. We'll need to merge a bunch of these chunks together on a rolling basis. For this demo, we'll merge 20 rows and step over 4 rows at a time. LanceDB offers chaining support so you can write declarative, readable and parameterized queries. Here we serialize to Pandas as well:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
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