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ClickHouse as a vector store (#10583)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,303 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "e0c2f11f", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/vector_stores/ClickHouseIndexDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "307804a3-c02b-4a57-ac0d-172c30ddc851", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# ClickHouse Vector Store\n", | ||
| "In this notebook we are going to show a quick demo of using the ClickHouseVectorStore." | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "c12f55a9", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "c1edec46", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!pip install llama-index\n", | ||
| "!pip install clickhouse_connect" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Creating a ClickHouse Client" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "d48af8e1", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import logging\n", | ||
| "import sys\n", | ||
| "\n", | ||
| "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", | ||
| "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "50ad978c", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from os import environ\n", | ||
| "import clickhouse_connect\n", | ||
| "\n", | ||
| "environ[\"OPENAI_API_KEY\"] = \"sk-*\"\n", | ||
| "\n", | ||
| "# initialize client\n", | ||
| "client = clickhouse_connect.get_client(\n", | ||
| " host=\"localhost\",\n", | ||
| " port=8123,\n", | ||
| " username=\"default\",\n", | ||
| " password=\"\",\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "8ee4473a-094f-4d0a-a825-e1213db07240", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Load documents, build and store the VectorStoreIndex with ClickHouseVectorStore\n", | ||
| "\n", | ||
| "Here we will use a set of Paul Graham essays to provide the text to turn into embeddings, store in a ``ClickHouseVectorStore`` and query to find context for our LLM QnA loop." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "0a2bcc07", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n", | ||
| "from llama_index.vector_stores.clickhouse import ClickHouseVectorStore" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "68cbd239-880e-41a3-98d8-dbb3fab55431", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Document ID: d03ac7db-8dae-4199-bc38-445dec51a534\n", | ||
| "Number of Documents: 1\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# load documents\n", | ||
| "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n", | ||
| "print(\"Document ID:\", documents[0].doc_id)\n", | ||
| "print(\"Number of Documents: \", len(documents))" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "b6afe88c", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Download Data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "0d09a78f", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "--2024-02-13 10:08:31-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt\r\n", | ||
| "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.110.133, ...\r\n", | ||
| "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\r\n", | ||
| "HTTP request sent, awaiting response... 200 OK\r\n", | ||
| "Length: 75042 (73K) [text/plain]\r\n", | ||
| "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\r\n", | ||
| "\r\n", | ||
| "data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.003s \r\n", | ||
| "\r\n", | ||
| "2024-02-13 10:08:31 (23.9 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\r\n", | ||
| "\r\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "!mkdir -p 'data/paul_graham/'\n", | ||
| "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "4fe3dc84", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "You can process your files individually using [SimpleDirectoryReader](/examples/data_connectors/simple_directory_reader.ipynb):" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "4febd54a", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "data/paul_graham/paul_graham_essay.txt\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "loader = SimpleDirectoryReader(\"./data/paul_graham/\")\n", | ||
| "documents = loader.load_data()\n", | ||
| "for file in loader.input_files:\n", | ||
| " print(file)\n", | ||
| " # Here is where you would do any preprocessing" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "ba1558b3", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# initialize with metadata filter and store indexes\n", | ||
| "from llama_index.core import StorageContext\n", | ||
| "\n", | ||
| "for document in documents:\n", | ||
| " document.metadata = {\"user_id\": \"123\", \"favorite_color\": \"blue\"}\n", | ||
| "vector_store = ClickHouseVectorStore(clickhouse_client=client)\n", | ||
| "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", | ||
| "index = VectorStoreIndex.from_documents(\n", | ||
| " documents, storage_context=storage_context\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "04304299-fc3e-40a0-8600-f50c3292767e", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Query Index\n", | ||
| "\n", | ||
| "Now ClickHouse vector store supports filter search and hybrid search\n", | ||
| "\n", | ||
| "You can learn more about [query_engine](/module_guides/deploying/query_engine/root.md) and [retriever](/module_guides/querying/retriever/root.md)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "35369eda", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "The author learned several things during their time at Interleaf, including the importance of having\n", | ||
| "technology companies run by product people rather than sales people, the drawbacks of having too\n", | ||
| "many people edit code, the value of corridor conversations over planned meetings, the challenges of\n", | ||
| "dealing with big bureaucratic customers, and the importance of being the \"entry level\" option in a\n", | ||
| "market.\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import textwrap\n", | ||
| "\n", | ||
| "from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n", | ||
| "\n", | ||
| "# set Logging to DEBUG for more detailed outputs\n", | ||
| "query_engine = index.as_query_engine(\n", | ||
| " filters=MetadataFilters(\n", | ||
| " filters=[\n", | ||
| " ExactMatchFilter(key=\"user_id\", value=\"123\"),\n", | ||
| " ]\n", | ||
| " ),\n", | ||
| " similarity_top_k=2,\n", | ||
| " vector_store_query_mode=\"hybrid\",\n", | ||
| ")\n", | ||
| "response = query_engine.query(\"What did the author learn?\")\n", | ||
| "print(textwrap.fill(str(response), 100))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "a732d16f0a29f8ab", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Clear All Indexes" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "552c203fd054d771", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "for document in documents:\n", | ||
| " index.delete_ref_doc(document.doc_id)" | ||
| ] | ||
| } | ||
| ], | ||
| "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" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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