DuckDB
Source: llama_index

# DuckDB>[DuckDB](https://duckdb.org/docs/api/python/overview) is a fast in-process analytical database. DuckDB is under an MIT license.In this notebook we are going to show how to use DuckDB as a Vector store to be used in LlamaIndex.Install DuckDB with:```shpip install duckdb```Make sure to use the latest DuckDB version (>= 0.10.0).You can run DuckDB in different modes depending on persistence:- `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.- `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.With the vector store created, you can:- `.add` - `.get` - `.update`- `.upsert`- `.delete`- `.peek`- `.query` to run a search. ## Basic exampleIn 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.For the embedding model we will use OpenAI. If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.```python!pip install llama-index```### Creating a DuckDB Index```python!pip install duckdb!pip install llama-index-vector-stores-duckdb``````pythonfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReaderfrom llama_index.vector_stores.duckdb import DuckDBVectorStorefrom llama_index.core import StorageContextfrom IPython.display import Markdown, display``````python# Setup OpenAI APIimport osimport openaiopenai.api_key = os.environ["OPENAI_API_KEY"]```Download and prepare the sample dataset```python!mkdir -p 'data/paul_graham/'!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'```    --2024-02-16 19:38:34--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt    Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...    Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.    HTTP request sent, awaiting response... 200 OK    Length: 75042 (73K) [text/plain]    Saving to: ‘data/paul_graham/paul_graham_essay.txt’        data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.06s           2024-02-16 19:38:34 (1.24 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]    ```pythondocuments = SimpleDirectoryReader("data/paul_graham/").load_data()vector_store = DuckDBVectorStore()storage_context = StorageContext.from_defaults(vector_store=vector_store)index = VectorStoreIndex.from_documents(    documents, storage_context=storage_context)``````pythonquery_engine = index.as_query_engine()response = query_engine.query("What did the author do growing up?")display(Markdown(f"<b>{response}</b>"))```<b>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.</b>## Persisting to disk exampleExtending the previous example, if you want to save to disk, simply initialize the DuckDBVectorStore by specifying a database name and persist directory.```python# Save to diskdocuments = SimpleDirectoryReader("data/paul_graham/").load_data()vector_store = DuckDBVectorStore("pg.duckdb", persist_dir="./persist/")storage_context = StorageContext.from_defaults(vector_store=vector_store)index = VectorStoreIndex.from_documents(    documents, storage_context=storage_context)``````python# Load from diskvector_store = DuckDBVectorStore.from_local("./persist/pg.duckdb")index = VectorStoreIndex.from_vector_store(vector_store)``````python# Query Dataquery_engine = index.as_query_engine()response = query_engine.query("What did the author do growing up?")display(Markdown(f"<b>{response}</b>"))```<b>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.</b>## Metadata filter exampleIt is possible to narrow down the search space by filter with metadata. Below is an example to show that in practice. ```pythonfrom llama_index.core.schema import TextNodenodes = [    TextNode(        **{            "text": "The Shawshank Redemption",            "metadata": {                "author": "Stephen King",                "theme": "Friendship",                "year": 1994,                "ref_doc_id": "doc_1",            },        }    ),    TextNode(        **{            "text": "The Godfather",            "metadata": {                "director": "Francis Ford Coppola",                "theme": "Mafia",                "year": 1972,                "ref_doc_id": "doc_1",            },        }    ),    TextNode(        **{            "text": "Inception",            "metadata": {                "director": "Christopher Nolan",                "theme": "Sci-fi",                "year": 2010,                "ref_doc_id": "doc_2",            },        }    ),]vector_store = DuckDBVectorStore()storage_context = StorageContext.from_defaults(vector_store=vector_store)index = VectorStoreIndex(nodes, storage_context=storage_context)```Define the metadata filters.```pythonfrom llama_index.core.vector_stores import ExactMatchFilter, MetadataFiltersfilters = MetadataFilters(    filters=[ExactMatchFilter(key="theme", value="Mafia")])```Use the index as a retriever to use the metadatafilter option. ```pythonretriever = index.as_retriever(filters=filters)retriever.retrieve("What is inception about?")```    [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, 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