From f50807396685710e4865ec3a1e5f85f9c3eb3151 Mon Sep 17 00:00:00 2001 From: Adithya Krishnan Date: Thu, 29 Feb 2024 05:13:50 +0100 Subject: [PATCH] Add: docs for duckdb vector store use (#11489) --- docs/examples/vector_stores/DuckDBDemo.ipynb | 374 +++++++++++++++++++ docs/module_guides/storing/vector_stores.md | 78 ++-- 2 files changed, 414 insertions(+), 38 deletions(-) create mode 100644 docs/examples/vector_stores/DuckDBDemo.ipynb diff --git a/docs/examples/vector_stores/DuckDBDemo.ipynb b/docs/examples/vector_stores/DuckDBDemo.ipynb new file mode 100644 index 0000000000000..4d0618c2f18c4 --- /dev/null +++ b/docs/examples/vector_stores/DuckDBDemo.ipynb @@ -0,0 +1,374 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DuckDB\n", + "\n", + ">[DuckDB](https://duckdb.org/docs/api/python/overview) is a fast in-process analytical database. DuckDB is under an MIT license.\n", + "\n", + "In this notebook we are going to show how to use DuckDB as a Vector store to be used in LlamaIndex.\n", + "\n", + "Install DuckDB with:\n", + "\n", + "```sh\n", + "pip install duckdb\n", + "```\n", + "\n", + "Make sure to use the latest DuckDB version (>= 0.10.0).\n", + "\n", + "You can run DuckDB in different modes depending on persistence:\n", + "- `in-memory` is the default mode, where the database is created in memory, you can force this to be use by setting `database_name = \":memory:\"` when initializing the vector store.\n", + "- `persistence` is set by using a name for a database and setting a persistence directory `database_name = \"my_vector_store.duckdb\"` where the database is persisted in the default `persist_dir` or to the one you set it to.\n", + "\n", + "With the vector store created, you can:\n", + "- `.add` \n", + "- `.get` \n", + "- `.update`\n", + "- `.upsert`\n", + "- `.delete`\n", + "- `.peek`\n", + "- `.query` to run a search. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic example\n", + "\n", + "In this basic example, we take the Paul Graham essay, split it into chunks, embed it using an open-source embedding model, load it into `DuckDBVectorStore`, and then query it.\n", + "\n", + "For the embedding model we will use OpenAI. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you're opening this Notebook on colab, you will probably need to install LlamaIndex ๐Ÿฆ™." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a DuckDB Index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install duckdb\n", + "!pip install llama-index-vector-stores-duckdb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n", + "from llama_index.vector_stores.duckdb import DuckDBVectorStore\n", + "from llama_index.core import StorageContext\n", + "\n", + "from IPython.display import Markdown, display" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup OpenAI API\n", + "import os\n", + "import openai\n", + "\n", + "openai.api_key = os.environ[\"OPENAI_API_KEY\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download and prepare the sample dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-02-16 19:38:34-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 75042 (73K) [text/plain]\n", + "Saving to: โ€˜data/paul_graham/paul_graham_essay.txtโ€™\n", + "\n", + "data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.06s \n", + "\n", + "2024-02-16 19:38:34 (1.24 MB/s) - โ€˜data/paul_graham/paul_graham_essay.txtโ€™ saved [75042/75042]\n", + "\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": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "documents = SimpleDirectoryReader(\"data/paul_graham/\").load_data()\n", + "\n", + "vector_store = DuckDBVectorStore()\n", + "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", + "\n", + "index = VectorStoreIndex.from_documents(\n", + " documents, storage_context=storage_context\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "The author mentions that before college, they worked on two main things outside of school: writing and programming. They wrote short stories and also tried writing programs on an IBM 1401 computer. They later got a microcomputer and started programming more extensively." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query_engine = index.as_query_engine()\n", + "response = query_engine.query(\"What did the author do growing up?\")\n", + "display(Markdown(f\"{response}\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Persisting to disk example\n", + "\n", + "Extending the previous example, if you want to save to disk, simply initialize the DuckDBVectorStore by specifying a database name and persist directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save to disk\n", + "documents = SimpleDirectoryReader(\"data/paul_graham/\").load_data()\n", + "\n", + "vector_store = DuckDBVectorStore(\"pg.duckdb\", persist_dir=\"./persist/\")\n", + "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", + "\n", + "index = VectorStoreIndex.from_documents(\n", + " documents, storage_context=storage_context\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load from disk\n", + "vector_store = DuckDBVectorStore.from_local(\"./persist/pg.duckdb\")\n", + "index = VectorStoreIndex.from_vector_store(vector_store)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "The author mentions that before college, they worked on two main things outside of school: writing and programming. They wrote short stories and also tried writing programs on an IBM 1401 computer. They later got a microcomputer and started programming more extensively." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Query Data\n", + "query_engine = index.as_query_engine()\n", + "response = query_engine.query(\"What did the author do growing up?\")\n", + "display(Markdown(f\"{response}\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metadata filter example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is possible to narrow down the search space by filter with metadata. Below is an example to show that in practice. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.schema import TextNode\n", + "\n", + "nodes = [\n", + " TextNode(\n", + " **{\n", + " \"text\": \"The Shawshank Redemption\",\n", + " \"metadata\": {\n", + " \"author\": \"Stephen King\",\n", + " \"theme\": \"Friendship\",\n", + " \"year\": 1994,\n", + " \"ref_doc_id\": \"doc_1\",\n", + " },\n", + " }\n", + " ),\n", + " TextNode(\n", + " **{\n", + " \"text\": \"The Godfather\",\n", + " \"metadata\": {\n", + " \"director\": \"Francis Ford Coppola\",\n", + " \"theme\": \"Mafia\",\n", + " \"year\": 1972,\n", + " \"ref_doc_id\": \"doc_1\",\n", + " },\n", + " }\n", + " ),\n", + " TextNode(\n", + " **{\n", + " \"text\": \"Inception\",\n", + " \"metadata\": {\n", + " \"director\": \"Christopher Nolan\",\n", + " \"theme\": \"Sci-fi\",\n", + " \"year\": 2010,\n", + " \"ref_doc_id\": \"doc_2\",\n", + " },\n", + " }\n", + " ),\n", + "]\n", + "\n", + "vector_store = DuckDBVectorStore()\n", + "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", + "index = VectorStoreIndex(nodes, storage_context=storage_context)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define the metadata filters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n", + "\n", + "filters = MetadataFilters(\n", + " filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the index as a retriever to use the metadatafilter option. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[NodeWithScore(node=TextNode(id_='736a1279-4ebd-496e-87b5-925197646477', embedding=[-0.006784645840525627, -0.021635770797729492, -0.015731574967503548, -0.03265434503555298, -0.005616107489913702, 0.025351788848638535, -0.0057811918668448925, 0.0027044713497161865, -0.01623653806746006, -0.023759208619594574, 0.027164479717612267, 0.017932699993252754, 0.029028963297605515, 0.003991158679127693, -0.0009047273779287934, 0.010973258875310421, 0.027164479717612267, -0.012844215147197247, 0.006972389295697212, -0.011148054152727127, 0.003528274828568101, 0.007736308965831995, -0.031022923067212105, -0.013996569439768791, 0.0012567456578835845, 0.004988139029592276, 0.010571876540780067, -0.024290068075060844, 0.019123896956443787, -0.02119554579257965, 0.014022464863955975, -0.023098871111869812, -0.009050510823726654, 0.001241370104253292, 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-0.02596033550798893], metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia', 'year': 1972, 'ref_doc_id': 'None', '_node_content': '{\"id_\": \"736a1279-4ebd-496e-87b5-925197646477\", \"embedding\": null, \"metadata\": {\"director\": \"Francis Ford Coppola\", \"theme\": \"Mafia\", \"year\": 1972, \"ref_doc_id\": \"doc_1\"}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.7543986421543848)]" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "retriever = index.as_retriever(filters=filters)\n", + "retriever.retrieve(\"What is inception about?\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llama-index-dev", + "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": 2 +} diff --git a/docs/module_guides/storing/vector_stores.md b/docs/module_guides/storing/vector_stores.md index c00873909697f..70e8f8e24b969 100644 --- a/docs/module_guides/storing/vector_stores.md +++ b/docs/module_guides/storing/vector_stores.md @@ -13,44 +13,45 @@ They can be persisted to (and loaded from) disk by calling `vector_store.persist LlamaIndex supports over 20 different vector store options. We are actively adding more integrations and improving feature coverage for each. -| Vector Store | Type | Metadata Filtering | Hybrid Search | Delete | Store Documents | Async | -| ------------------------ | ------------------- | ------------------ | ------------- | ------ | --------------- | ----- | -| Apache Cassandraยฎ | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | -| Astra DB | cloud | โœ“ | | โœ“ | โœ“ | | -| Azure Cognitive Search | cloud | | โœ“ | โœ“ | โœ“ | | -| Azure CosmosDB MongoDB | cloud | | | โœ“ | โœ“ | | -| ChatGPT Retrieval Plugin | aggregator | | | โœ“ | โœ“ | | -| Chroma | self-hosted | โœ“ | | โœ“ | โœ“ | | -| DashVector | cloud | โœ“ | โœ“ | โœ“ | โœ“ | | -| Deeplake | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | -| DocArray | aggregator | โœ“ | | โœ“ | โœ“ | | -| DynamoDB | cloud | | | โœ“ | | | -| Elasticsearch | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | -| FAISS | in-memory | | | | | | -| txtai | in-memory | | | | | | -| Jaguar | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | | -| LanceDB | cloud | โœ“ | | โœ“ | โœ“ | | -| Lantern | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | -| Metal | cloud | โœ“ | | โœ“ | โœ“ | | -| MongoDB Atlas | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | -| MyScale | cloud | โœ“ | โœ“ | โœ“ | โœ“ | | -| Milvus / Zilliz | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | -| Neo4jVector | self-hosted / cloud | | | โœ“ | โœ“ | | -| OpenSearch | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | -| Pinecone | cloud | โœ“ | โœ“ | โœ“ | โœ“ | | -| Postgres | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | -| pgvecto.rs | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | | -| Qdrant | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | -| Redis | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | -| Simple | in-memory | โœ“ | | โœ“ | | | -| SingleStore | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | -| Supabase | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | -| Tair | cloud | โœ“ | | โœ“ | โœ“ | | -| TencentVectorDB | cloud | โœ“ | โœ“ | โœ“ | โœ“ | | -| Timescale | | โœ“ | | โœ“ | โœ“ | โœ“ | -| Typesense | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | -| Upstash | cloud | | | | โœ“ | | -| Weaviate | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | | +| Vector Store | Type | Metadata Filtering | Hybrid Search | Delete | Store Documents | Async | +| ------------------------ | ----------------------- | ------------------ | ------------- | ------ | --------------- | ----- | +| Apache Cassandraยฎ | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | +| Astra DB | cloud | โœ“ | | โœ“ | โœ“ | | +| Azure Cognitive Search | cloud | | โœ“ | โœ“ | โœ“ | | +| Azure CosmosDB MongoDB | cloud | | | โœ“ | โœ“ | | +| ChatGPT Retrieval Plugin | aggregator | | | โœ“ | โœ“ | | +| Chroma | self-hosted | โœ“ | | โœ“ | โœ“ | | +| DashVector | cloud | โœ“ | โœ“ | โœ“ | โœ“ | | +| Deeplake | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | +| DocArray | aggregator | โœ“ | | โœ“ | โœ“ | | +| DuckDB | in-memory / self-hosted | โœ“ | | โœ“ | โœ“ | | +| DynamoDB | cloud | | | โœ“ | | | +| Elasticsearch | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| FAISS | in-memory | | | | | | +| txtai | in-memory | | | | | | +| Jaguar | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | | +| LanceDB | cloud | โœ“ | | โœ“ | โœ“ | | +| Lantern | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| Metal | cloud | โœ“ | | โœ“ | โœ“ | | +| MongoDB Atlas | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | +| MyScale | cloud | โœ“ | โœ“ | โœ“ | โœ“ | | +| Milvus / Zilliz | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | +| Neo4jVector | self-hosted / cloud | | | โœ“ | โœ“ | | +| OpenSearch | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| Pinecone | cloud | โœ“ | โœ“ | โœ“ | โœ“ | | +| Postgres | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| pgvecto.rs | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | | +| Qdrant | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | โœ“ | +| Redis | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | +| Simple | in-memory | โœ“ | | โœ“ | | | +| SingleStore | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | +| Supabase | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | +| Tair | cloud | โœ“ | | โœ“ | โœ“ | | +| TencentVectorDB | cloud | โœ“ | โœ“ | โœ“ | โœ“ | | +| Timescale | | โœ“ | | โœ“ | โœ“ | โœ“ | +| Typesense | self-hosted / cloud | โœ“ | | โœ“ | โœ“ | | +| Upstash | cloud | | | | โœ“ | | +| Weaviate | self-hosted / cloud | โœ“ | โœ“ | โœ“ | โœ“ | | For more details, see [Vector Store Integrations](/community/integrations/vector_stores.md). @@ -70,6 +71,7 @@ maxdepth: 1 /examples/vector_stores/DeepLakeIndexDemo.ipynb /examples/vector_stores/DocArrayHnswIndexDemo.ipynb /examples/vector_stores/DocArrayInMemoryIndexDemo.ipynb +/examples/vector_stores/DuckDBDemo.ipynb /examples/vector_stores/EpsillaIndexDemo.ipynb /examples/vector_stores/JaguarIndexDemo.ipynb /examples/vector_stores/LanceDBIndexDemo.ipynb