wehub-resource-sync
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664 lines
18 KiB
Plaintext
664 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "bccd47fc",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/vector_stores/postgres.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "db0855d0",
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"metadata": {},
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"source": [
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"# Gel Vector Store\n",
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"[Gel](https://www.geldata.com/) is an open-source PostgreSQL data layer optimized for fast development to production cycle. It comes with a high-level strictly typed graph-like data model, composable hierarchical query language, full SQL support, migrations, Auth and AI modules."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "e4f33fc9",
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"metadata": {},
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"source": [
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"If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d2fc9c18",
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install gel llama-index-vector-stores-gel"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "712daea5",
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install llama-index"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c2d1c538",
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"metadata": {},
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"outputs": [],
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"source": [
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"# import logging\n",
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"# import sys\n",
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"\n",
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"# Uncomment to see debug logs\n",
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"# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n",
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"# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
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"\n",
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"from llama_index.core import SimpleDirectoryReader, StorageContext\n",
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"from llama_index.core import VectorStoreIndex\n",
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"from llama_index.vector_stores.gel import GelVectorStore\n",
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"import textwrap\n",
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"import openai"
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]
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},
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{
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"cell_type": "markdown",
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"id": "26c71b6d",
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"metadata": {},
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"source": [
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"### Setup OpenAI\n",
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"The first step is to configure the openai key. It will be used to created embeddings for the documents loaded into the index"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "67b86621",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"<your key>\"\n",
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"openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "eecf4bd5",
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"metadata": {},
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"source": [
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"Download Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6df9fa89",
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"metadata": {},
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"outputs": [],
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"source": [
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"!mkdir -p 'data/paul_graham/'\n",
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"!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'"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
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"metadata": {},
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"source": [
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"### Loading documents\n",
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"Load the documents stored in the `data/paul_graham/` using the SimpleDirectoryReader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c154dd4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"documents = SimpleDirectoryReader(\"./data/paul_graham\").load_data()\n",
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"print(\"Document ID:\", documents[0].doc_id)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7bd24f0a",
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"metadata": {},
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"source": [
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"### Create the Database\n",
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"\n",
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"In order to use Gel as a backend for your vectorstore, you're going to need a working Gel instance.\n",
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"Fortunately, it doesn't have to involve Docker containers or anything complicated, unless you want to!\n",
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"\n",
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"To set up a local instance, run:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "00a7a5e3",
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"metadata": {},
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"outputs": [],
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"source": [
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"! gel project init --non-interactive"
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]
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},
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{
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"cell_type": "markdown",
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"id": "009a5484",
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"metadata": {},
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"source": [
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"If you are using [Gel Cloud](cloud.geldata.com) (and you should!), add one more argument to that command:\n",
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"\n",
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"```bash\n",
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"gel project init --server-instance <org-name>/<instance-name>\n",
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"```\n",
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"\n",
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"For a comprehensive list of ways to run Gel, take a look at [Running Gel](https://docs.geldata.com/reference/running) section of the reference docs.\n",
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"\n",
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"### Set up the schema\n",
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"\n",
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"[Gel schema](https://docs.geldata.com/reference/datamodel) is an explicit high-level description of your application's data model. \n",
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"Aside from enabling you to define exactly how your data is going to be laid out, it drives Gel's many powerful features such as links, access policies, functions, triggers, constraints, indexes, and more.\n",
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"\n",
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"The LlamaIndex's `GelVectorStore` expects the following layout for the schema:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8f1bdf33",
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"metadata": {},
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"outputs": [],
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"source": [
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"schema_content = \"\"\"\n",
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"using extension pgvector;\n",
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" \n",
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"module default {\n",
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" scalar type EmbeddingVector extending ext::pgvector::vector<1536>;\n",
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"\n",
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" type Record {\n",
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" required collection: str;\n",
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" text: str;\n",
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" embedding: EmbeddingVector; \n",
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" external_id: str {\n",
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" constraint exclusive;\n",
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" };\n",
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" metadata: json;\n",
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"\n",
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" index ext::pgvector::hnsw_cosine(m := 16, ef_construction := 128)\n",
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" on (.embedding)\n",
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" } \n",
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"}\n",
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"\"\"\".strip()\n",
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"\n",
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"with open(\"dbschema/default.gel\", \"w\") as f:\n",
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" f.write(schema_content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "958bdd1e",
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"metadata": {},
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"source": [
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"In order to apply schema changes to the database, run the migration using the Gel's [migration tool](https://docs.geldata.com/reference/datamodel/migrations):"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fa92518f",
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"metadata": {},
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"outputs": [],
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"source": [
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"! gel migration create --non-interactive\n",
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"! gel migrate"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3665aad3",
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"metadata": {},
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"source": [
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"From this point onward, `GelVectorStore` can be used as a drop-in replacement for any other vectorstore available in LlamaIndex."
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]
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},
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{
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"cell_type": "markdown",
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"id": "c0232fd1",
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"metadata": {},
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"source": [
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"### Create the index"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8731da62",
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"metadata": {},
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"outputs": [],
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"source": [
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"vector_store = GelVectorStore()\n",
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"\n",
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"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
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"index = VectorStoreIndex.from_documents(\n",
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" documents, storage_context=storage_context, show_progress=True\n",
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")\n",
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"query_engine = index.as_query_engine()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8ee4473a-094f-4d0a-a825-e1213db07240",
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"metadata": {},
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"source": [
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"### Query the index\n",
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"We can now ask questions using our index."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0a2bcc07",
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"metadata": {},
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"outputs": [],
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"source": [
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"response = query_engine.query(\"What did the author do?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8cf55bf7",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(textwrap.fill(str(response), 100))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
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"metadata": {},
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"outputs": [],
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"source": [
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"response = query_engine.query(\"What happened in the mid 1980s?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fdf5287f",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(textwrap.fill(str(response), 100))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2e5e8083",
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"metadata": {},
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"source": [
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"### Metadata filters\n",
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"\n",
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"GelVectorStore supports storing metadata in nodes, and filtering based on that metadata during the retrieval step."
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]
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},
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{
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"cell_type": "markdown",
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"id": "2d0ad3fc",
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"metadata": {},
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"source": [
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"#### Download git commits dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "63e90a89",
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"metadata": {},
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"outputs": [],
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"source": [
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"!mkdir -p 'data/git_commits/'\n",
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"!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/csv/commit_history.csv' -O 'data/git_commits/commit_history.csv'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fef41f44",
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"metadata": {},
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"outputs": [],
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"source": [
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"import csv\n",
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"\n",
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"with open(\"data/git_commits/commit_history.csv\", \"r\") as f:\n",
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" commits = list(csv.DictReader(f))\n",
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"\n",
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"print(commits[0])\n",
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"print(len(commits))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3b0d9f47",
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"metadata": {},
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"source": [
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"#### Add nodes with custom metadata"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3920109b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create TextNode for each of the first 100 commits\n",
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"from llama_index.core.schema import TextNode\n",
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"from datetime import datetime\n",
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"import re\n",
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"\n",
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"nodes = []\n",
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"dates = set()\n",
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"authors = set()\n",
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"for commit in commits[:100]:\n",
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" author_email = commit[\"author\"].split(\"<\")[1][:-1]\n",
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" commit_date = datetime.strptime(\n",
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" commit[\"date\"], \"%a %b %d %H:%M:%S %Y %z\"\n",
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" ).strftime(\"%Y-%m-%d\")\n",
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" commit_text = commit[\"change summary\"]\n",
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" if commit[\"change details\"]:\n",
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" commit_text += \"\\n\\n\" + commit[\"change details\"]\n",
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" fixes = re.findall(r\"#(\\d+)\", commit_text, re.IGNORECASE)\n",
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" nodes.append(\n",
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" TextNode(\n",
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" text=commit_text,\n",
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" metadata={\n",
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" \"commit_date\": commit_date,\n",
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" \"author\": author_email,\n",
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" \"fixes\": fixes,\n",
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" },\n",
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" )\n",
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" )\n",
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" dates.add(commit_date)\n",
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" authors.add(author_email)\n",
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"\n",
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"print(nodes[0])\n",
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"print(min(dates), \"to\", max(dates))\n",
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"print(authors)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a638f76a",
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"metadata": {},
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"outputs": [],
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"source": [
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"vector_store = GelVectorStore()\n",
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"\n",
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"index = VectorStoreIndex.from_vector_store(vector_store=vector_store)\n",
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"index.insert_nodes(nodes)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "15f7cf45",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(index.as_query_engine().query(\"How did Lakshmi fix the segfault?\"))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7ab03ed4",
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"metadata": {},
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"source": [
|
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"#### Apply metadata filters\n",
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"\n",
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"Now we can filter by commit author or by date when retrieving nodes."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aa6212e7",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"from llama_index.core.vector_stores.types import (\n",
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" MetadataFilter,\n",
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" MetadataFilters,\n",
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")\n",
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"\n",
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"filters = MetadataFilters(\n",
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" filters=[\n",
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" MetadataFilter(key=\"author\", value=\"mats@timescale.com\"),\n",
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" MetadataFilter(key=\"author\", value=\"sven@timescale.com\"),\n",
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" ],\n",
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" condition=\"or\",\n",
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")\n",
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"\n",
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"retriever = index.as_retriever(\n",
|
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" similarity_top_k=10,\n",
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" filters=filters,\n",
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")\n",
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"\n",
|
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"retrieved_nodes = retriever.retrieve(\"What is this software project about?\")\n",
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"\n",
|
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"for node in retrieved_nodes:\n",
|
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" print(node.node.metadata)"
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]
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},
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{
|
|
"cell_type": "code",
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|
"execution_count": null,
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|
"id": "67c19ec6",
|
|
"metadata": {},
|
|
"outputs": [],
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|
"source": [
|
|
"filters = MetadataFilters(\n",
|
|
" filters=[\n",
|
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" MetadataFilter(key=\"commit_date\", value=\"2023-08-15\", operator=\">=\"),\n",
|
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" MetadataFilter(key=\"commit_date\", value=\"2023-08-25\", operator=\"<=\"),\n",
|
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" ],\n",
|
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" condition=\"and\",\n",
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")\n",
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"\n",
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"retriever = index.as_retriever(\n",
|
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" similarity_top_k=10,\n",
|
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" filters=filters,\n",
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")\n",
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"\n",
|
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"retrieved_nodes = retriever.retrieve(\"What is this software project about?\")\n",
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"\n",
|
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"for node in retrieved_nodes:\n",
|
|
" print(node.node.metadata)"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4f6e9cdf",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Apply nested filters\n",
|
|
"\n",
|
|
"In the above examples, we combined multiple filters using AND or OR. We can also combine multiple sets of filters."
|
|
]
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|
},
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|
{
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|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "94f20be7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"filters = MetadataFilters(\n",
|
|
" filters=[\n",
|
|
" MetadataFilters(\n",
|
|
" filters=[\n",
|
|
" MetadataFilter(\n",
|
|
" key=\"commit_date\", value=\"2023-08-01\", operator=\">=\"\n",
|
|
" ),\n",
|
|
" MetadataFilter(\n",
|
|
" key=\"commit_date\", value=\"2023-08-15\", operator=\"<=\"\n",
|
|
" ),\n",
|
|
" ],\n",
|
|
" condition=\"and\",\n",
|
|
" ),\n",
|
|
" MetadataFilters(\n",
|
|
" filters=[\n",
|
|
" MetadataFilter(key=\"author\", value=\"mats@timescale.com\"),\n",
|
|
" MetadataFilter(key=\"author\", value=\"sven@timescale.com\"),\n",
|
|
" ],\n",
|
|
" condition=\"or\",\n",
|
|
" ),\n",
|
|
" ],\n",
|
|
" condition=\"and\",\n",
|
|
")\n",
|
|
"\n",
|
|
"retriever = index.as_retriever(\n",
|
|
" similarity_top_k=10,\n",
|
|
" filters=filters,\n",
|
|
")\n",
|
|
"\n",
|
|
"retrieved_nodes = retriever.retrieve(\"What is this software project about?\")\n",
|
|
"\n",
|
|
"for node in retrieved_nodes:\n",
|
|
" print(node.node.metadata)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "737692ce",
|
|
"metadata": {},
|
|
"source": [
|
|
"The above can be simplified by using the IN operator. `GelVectorStore` supports `in`, `nin`, and `contains` for comparing an element with a list."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "85faf8b3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"filters = MetadataFilters(\n",
|
|
" filters=[\n",
|
|
" MetadataFilter(key=\"commit_date\", value=\"2023-08-01\", operator=\">=\"),\n",
|
|
" MetadataFilter(key=\"commit_date\", value=\"2023-08-15\", operator=\"<=\"),\n",
|
|
" MetadataFilter(\n",
|
|
" key=\"author\",\n",
|
|
" value=[\"mats@timescale.com\", \"sven@timescale.com\"],\n",
|
|
" operator=\"in\",\n",
|
|
" ),\n",
|
|
" ],\n",
|
|
" condition=\"and\",\n",
|
|
")\n",
|
|
"\n",
|
|
"retriever = index.as_retriever(\n",
|
|
" similarity_top_k=10,\n",
|
|
" filters=filters,\n",
|
|
")\n",
|
|
"\n",
|
|
"retrieved_nodes = retriever.retrieve(\"What is this software project about?\")\n",
|
|
"\n",
|
|
"for node in retrieved_nodes:\n",
|
|
" print(node.node.metadata)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "1ab9c333",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Same thing, with NOT IN\n",
|
|
"filters = MetadataFilters(\n",
|
|
" filters=[\n",
|
|
" MetadataFilter(key=\"commit_date\", value=\"2023-08-01\", operator=\">=\"),\n",
|
|
" MetadataFilter(key=\"commit_date\", value=\"2023-08-15\", operator=\"<=\"),\n",
|
|
" MetadataFilter(\n",
|
|
" key=\"author\",\n",
|
|
" value=[\"mats@timescale.com\", \"sven@timescale.com\"],\n",
|
|
" operator=\"nin\",\n",
|
|
" ),\n",
|
|
" ],\n",
|
|
" condition=\"and\",\n",
|
|
")\n",
|
|
"\n",
|
|
"retriever = index.as_retriever(\n",
|
|
" similarity_top_k=10,\n",
|
|
" filters=filters,\n",
|
|
")\n",
|
|
"\n",
|
|
"retrieved_nodes = retriever.retrieve(\"What is this software project about?\")\n",
|
|
"\n",
|
|
"for node in retrieved_nodes:\n",
|
|
" print(node.node.metadata)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "a46764cf",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# CONTAINS\n",
|
|
"filters = MetadataFilters(\n",
|
|
" filters=[\n",
|
|
" MetadataFilter(key=\"fixes\", value=\"5680\", operator=\"contains\"),\n",
|
|
" ]\n",
|
|
")\n",
|
|
"\n",
|
|
"retriever = index.as_retriever(\n",
|
|
" similarity_top_k=10,\n",
|
|
" filters=filters,\n",
|
|
")\n",
|
|
"\n",
|
|
"retrieved_nodes = retriever.retrieve(\"How did these commits fix the issue?\")\n",
|
|
"for node in retrieved_nodes:\n",
|
|
" print(node.node.metadata)"
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|