166 lines
10 KiB
Markdown
166 lines
10 KiB
Markdown
# Vector Database for Python Developers
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`vectordb` is a simple, user-friendly solution for Python developers looking to create their own vector database with CRUD support. Vector databases are a key component of the stack needed to use LLMs as they allow them to have access to context and memory. Many of the solutions out there require developers and users to use complex solutions that are often not needed. With `vectordb`, you can easily create your own vector database solution that can work locally and still be easily deployed and served with scalability features such as sharding and replication.
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Start with your solution as a local library and seamlessly transition into a served database with all the needed capability. No extra complexity than the needed one.
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`vectordb` is based on the local libraries wrapped inside [DocArray](https://github.com/docarray/docarray) and the scalability, reliability and servinc capabilities of [Jina](https://github.com/jina-ai/jina).
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In simple terms, one can think as [DocArray](https://github.com/docarray/docarray) being a the `Lucene` algorithmic logic for Vector Search powering the retrieval capabilities and [Jina](https://github.com/jina-ai/jina), the ElasticSearch making sure that the indexes are served and scaled for the clients, `vectordb` wraps these technologies to give a powerful and easy to use experience to
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use and develop vector databases.
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<!--(THIS CAN BE SHOWN WHEN CUSTOMIZATION IS ENABLED) `vectordb` allows you to start simple and work locally while allowing when needed to deploy and scale in a seamless manner. With the help of [DocArray](https://github.com/docarray/docarray) and [Jina](https://github.com/jina-ai/jina) `vectordb` allows developers to focus on the algorithmic part and tweak the core of the vector search with Python as they want while keeping it easy to scale and deploy the solution. -->
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<!--(THIS CAN BE SHOWN WHEN CUSTOMIZATION IS ENABLED) Stop wondering what exact algorithms do existing solutions apply, how do they apply filtering or how to map your schema to their solutions, with `vectordb` you as a Python developer can easily understand and control what is the vector search algorithm doing, giving you the full control if needed while supporting you for local setting and in more advanced and demanding scenarios in the cloud. -->
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## :muscle: Features
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- User-friendly interface: `vectordb` is designed with simplicity and ease of use in mind, making it accessible even for beginners.
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- Adapts to your needs: `vectordb` is designed to offer what you need without extra complexity, supporting the features needed at every step. From local, to serve, to the cloud in a seamless way.
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- CRUD support: `vectordb` support CRUD operations, index, search, update and delete.
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- Serve: Serve the databases to insert or search as a service with `gRPC` or `HTTP` protocol.
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- Scalable: With `vectordb`, you can deploy your database in the cloud and take advantage of powerful scalability features like sharding and replication. With this, you can easily improve the latency of your service by sharding your data, or improve the availability and throughput by allowing `vectordb` to offer replication.
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- Deploy to the cloud: If you need to deploy your service in the cloud, you can easily deploy in [Jina AI Cloud](). More deployment options will soon come.
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- Serverless capacity: `vectordb` can be deployed in the cloud in serverless mode, allowing you to save resources and have the data available only when needed.
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- Multiple ANN algorithms: `vectordb` contains different implementations of ANN algorithms. These are the ones offered so far, we plan to integrate more:
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- Exact NN Search: Implements Simple Nearest Neighbour Algorithm.
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- HNSWLib: Based on [HNSWLib](https://github.com/nmslib/hnswlib)
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<!--(THIS CAN BE SHOWN WHEN FILTER IS ENABLED)- Filter capacity: `vectordb` allows you to have filters on top of the ANN search. -->
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<!--(THIS CAN BE SHOWN WHEN FILTER IS ENABLED)- Customizable: `vectordb` can be easily extended to suit your specific needs or schemas, so you can build the database you want and for any input and output schema you want with the help of [DocArray](https://github.com/docarray/docarray).-->
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## 🏁 Getting Started
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To get started with Vector Database, simply follow these easy steps, in this example we are going to use `HNSWVecDB` as example:
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1. Install `vectordb`:
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```pip install vectordb```
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2. Define your Index Document schema using [DocArray](https://docs.docarray.org/user_guide/representing/first_step/):
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```python
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from docarray import BaseDoc
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from docarray.typing import NdArray
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class MyTextDoc(TextDoc):
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text: str = ''
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embedding: NdArray[768]
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```
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Make sure that the schema has a field `schema` as a `tensor` type with shape annotation as in the example.
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3. Use any of the pre-built databases with the document schema (InMemoryExactNNVectorDB or HNSWLibDB):
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```python
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from vectordb import InMemoryExactNNVectorDB, HNSWLibDB
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db = InMemoryExactNNVectorDB[MyTextDoc](workspace='./workspace_path')
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db.index(inputs=DocList[MyTextDoc]([MyTextDoc(text=f'index {i}', embedding=np.random.rand(128)) for i in range(1000)]))
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results = db.search(inputs=DocList[MyTextDoc]([MyTextDoc(text='query', embedding=np.random.rand(128)]), limit=10)
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```
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Each result will contain the matches under the `.matches` attribute as a `DocList[MyTextDoc]`
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4. Serve the database as a service with any of these protocols: `gRPC`, `HTTP` and `Webscoket`.
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```python
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with InMemoryExactNNVectorDB[MyTextDoc].serve(workspace='./hnwslib_path', protocol='grpc', port=12345, replicas=1, shards=1) as service:
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service.index(inputs=DocList[TextDoc]([TextDoc(text=f'index {i}', embedding=np.random.rand(128)) for i in range(1000)]))
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service.block()
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```
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5. Interact with the database through a client in a similar way as previously:
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```python
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from vectordb import Client
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c = Client[MyTextDoc](address='grpc://0.0.0.0:12345')
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results = c.search(inputs=DocList[TextDoc]([TextDoc(text='query', embedding=np.random.rand(128)]), limit=10)
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```
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## CRUD API:
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When using `vectordb` as a library or accesing it from a client to a served instance, the Python objects share the exact same API
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to provide `index`, `search`, `update` and `delete` capability:
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- `index`: Index gets as input the `DocList` to index.
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- `search`: Search gets as input the `DocList` of batched queries or a single `BaseDoc` as single query. It returns a single or multiple results where each query has `matches` and `scores` attributes sorted by `relevance`.
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- `delete`: Delete gets as input the `DocList` of documents to delete from the index. The `delete` operation will only care for the `id` attribute, so you need to keep track of the `indexed` `IDs` if you want to delete documents.
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- `update`: Delete gets as input the `DocList` of documents to update in the index. The `update` operation will update the `indexed` document with the same Index with the attributes and payload from the input documents.
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## :rocket: Serve and scale your own Database, add replication and sharding
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### Serving:
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In order to enable your `vectordb` served so that it can be accessed from a Client, you can give the following parameters:
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- protocol: The protocol to be used for serving, it can be `gRPC`, `HTTP`, `websocket` or any combination of them provided as a list. Defaults to `gRPC`
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- port: The port where the service will be accessible, it can be a list of one port for each protocol provided. Default to 8081
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- workspace: The workspace is the path used by the VectorDB to hold and persist required data. Defaults to '.' (current directory)
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### Scalability
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When serving or deploying your Vector Databases you can set 2 scaling parameters and `vectordb`:
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- Shards: The number of shards in which the data will be split. This will allow for better latency. `vectordb` will make sure that Documents are indexed in only one of the shards, while search request will be sent to all the shards and `vectordb` will make sure to merge the results from all shards.
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- Replicas: The number of replicas of the same DB that must exist. The given replication factor will be shared by all the `shards`. `vectordb` uses [RAFT](https://raft.github.io/) algorithm to ensure that the index is in sync between all the replicas of each shard. With this, `vectordb` increases the availability of the service and allows for better search throughput as multiple replicas can respond in parallel to more search requests while allowing CRUD operations.
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** When deployed to JCloud, the number of replicas will be set to 1. We are working to enable replication in the cloud
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## :cloud: Deploy it to the cloud
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`vectordb` allows you to deploy your solution to the cloud easily.
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1. First, you need to get a [Jina AI Cloud](https://cloud.jina.ai/) account
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2. Login to your Jina AI Cloud account using the `jc` command line:
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```jc login```
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3. Deploy:
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```python
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HNSWLibDB[MyTextDoc].deploy(config={'data_path'= './hnswlib_path'}, replicas=1, shards=1)
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```
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You can then list and delete your deployed DBs with `jc`:
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```jc list <>```
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```jc delete <>```
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## 🛣️ Roadmap
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We have big plans for the future of Vector Database! Here are some of the features we have in the works:
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- Further configuration of ANN algorithms.
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- More ANN search algorithms: We want to support more ANN search algorithms.
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- Filter capacity: We want to support filtering for our offered ANN Search solutions.
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- Customizable: We want to make it easy for users to customize the behavior for their specific needs in an easy way for Python developers.
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- Serverless capacity: We're working on adding serverless capacity to `vectordb` in the cloud. We currenly allow to scale between 0 and 1 replica, we aim to offer from 0 to N.
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- More deploying options: We want to enable deploying `vectordb` on different clouds with more options
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If you need any help with `vectordb`, or you are interested on using it and have some requests to make it fit your own need. don't hesitate to reach out to us. You can join our [Slack community](https://jina.ai/slack) and chat with us and other community members.
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## Contributing
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We welcome contributions from the community! If you have an idea for a new feature or improvement, please let us know. We're always looking for ways to make `vectordb` better for our users.
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