FastAPI/Pydantic#

DocArray supports the pydantic data model via PydanticDocument and PydanticDocumentArray. Let’s take a look at what this means.

When you want to send or receive Document or DocumentArray objects via the REST API, you can use from_json()/to_json() to convert the Document or DocumentArray object into JSON. This has been introduced in the Serialization section.

This method, although quite intuitive to many data scientists, is not the modern way of building API services. Your engineer friends won’t be happy if you give them a service like this. The main problem is data validation.

Of course, you can include data validation in your service logic, but this can be difficult as you will need to check field by field and repeat things like isinstance(field, int), not to mention handling nested JSON.

Modern web frameworks validate the data before it enters the core logic. For example, FastAPI leverages pydantic to validate input and output data.

This chapter will introduce how to use DocArray’s pydantic support in a FastAPI service to build a modern API service. The fundamentals of FastAPI can be learned from its docs. I won’t repeat them here again.

Tip

Features introduced in this chapter require fastapi and pydantic as dependencies. Please run pip install "docarray[full]" to enable it.

JSON Schema#

You can get the JSON Schema (OpenAPI itself is based on JSON Schema) of a Document or DocumentArray using the get_json_schema() method.

from docarray import Document
Document.get_json_schema()
{
  "$ref": "#/definitions/PydanticDocument",
  "definitions": {
    "PydanticDocument": {
      "title": "PydanticDocument",
      "type": "object",
      "properties": {
        "id": {
          "title": "Id",
          "type": "string"
        },
from docarray import DocumentArray
DocumentArray.get_json_schema()
{
  "title": "DocumentArray Schema",
  "type": "array",
  "items": {
    "$ref": "#/definitions/PydanticDocument"
  },
  "definitions": {
    "PydanticDocument": {
      "title": "PydanticDocument",
      "type": "object",
      "properties": {
        "id": {
          "title": "Id",

When you give this schema to your engineer friends, they’ll be able to understand the data format you’re working with. These schemas also help them to easily integrate DocArray into any webservice.

Validate incoming Document and DocumentArray objects#

You can import PydanticDocument and PydanticDocumentArray pydantic data models, and use them to type hint your endpoint. This will enable the data validation.

from docarray.document.pydantic_model import PydanticDocument, PydanticDocumentArray
from fastapi import FastAPI

app = FastAPI()

@app.post('/single')
async def create_item(item: PydanticDocument):
    ...

@app.post('/multi')
async def create_array(items: PydanticDocumentArray):
    ...

Let’s now send some JSON:

from starlette.testclient import TestClient
client = TestClient(app)

response = client.post('/single', {'hello': 'world'})
print(response, response.text)
response = client.post('/single', {'id': [12, 23]})
print(response, response.text)
<Response [422]> {"detail":[{"loc":["body"],"msg":"value is not a valid dict","type":"type_error.dict"}]}
<Response [422]> {"detail":[{"loc":["body"],"msg":"value is not a valid dict","type":"type_error.dict"}]}

Both got rejected (422 error) as they are not valid.

Convert between pydantic model and DocArray objects#

PydanticDocument and PydanticDocumentArray are mainly for data validation. When you want to implement real logic, you need to convert them into a Document or DocumentArray. This can be easily achieved via the from_pydantic_model() method. When you are done with processing and want to send a PydanticDocument back, you can call to_pydantic_model().

In a nutshell, the whole procedure looks like the following:

../../_images/lifetime-pydantic.svg

Let’s see an example:

from docarray import Document, DocumentArray

@app.post('/single')
async def create_item(item: PydanticDocument):
    d = Document.from_pydantic_model(item)
    # now `d` is a Document object
    ...  # process `d` how ever you want
    return d.to_pydantic_model()
    

@app.post('/multi')
async def create_array(items: PydanticDocumentArray):
    da = DocumentArray.from_pydantic_model(items)
    # now `da` is a DocumentArray object
    ...  # process `da` how ever you want
    return da.to_pydantic_model()

Limit returned fields by response model#

Supporting the pydantic data model means much more than data validation. One useful pattern is to define a smaller data model and restrict the response to certain fields of the Document.

Imagine we have a DocumentArray with .embeddings on the server side, but we do not want to return them to the client for various reasons (for example, they may be meaningless to users, or too big to transfer). One can simply define the fields of interest via pydantic.BaseModel and then use it in response_model=.

from pydantic import BaseModel
from docarray import Document

class IdOnly(BaseModel):
    id: str

@app.get('/single', response_model=IdOnly)
async def get_item_no_embedding():
    d = Document(embedding=[1, 2, 3])
    return d.to_pydantic_model()

And you get:

<Response [200]> {'id': '065a5548756211ecaa8d1e008a366d49'}

Limit returned results recursively#

The same idea applies to DocumentArray as well. Say after .match(), you are only interested in .id—the parent .id and the .ids of all matches. You can declare a BaseModel as follows:

from typing import List, Optional
from pydantic import BaseModel

class IdAndMatch(BaseModel):
    id: str
    matches: Optional[List['IdAndMatch']]

Bind it to response_model:

@app.get('/get_match', response_model=List[IdAndMatch])
async def get_match_id_only():
    da = DocumentArray.empty(10)
    da.embeddings = np.random.random([len(da), 3])
    da.match(da)
    return da.to_pydantic_model()

Then you get a very nice result containing ids of matches (of potentially unlimited depth).

[{'id': 'ef82e4f4756411ecb2c01e008a366d49',
  'matches': [{'id': 'ef82e4f4756411ecb2c01e008a366d49', 'matches': None},
              {'id': 'ef82e6d4756411ecb2c01e008a366d49', 'matches': None},
              {'id': 'ef82e760756411ecb2c01e008a366d49', 'matches': None},
              {'id': 'ef82e7ec756411ecb2c01e008a366d49', 'matches': None},
              ...

If 'matches': None is annoying to you (they are here because you didn’t compute second-degree matches), you can further leverage FastAPI’s features and do:

@app.get('/get_match', response_model=List[IdMatch], response_model_exclude_none=True)
async def get_match_id_only():
    ...

Finally, you get a very clean result with ids and matches only:

[{'id': '3da6383e756511ecb7cb1e008a366d49',
  'matches': [{'id': '3da6383e756511ecb7cb1e008a366d49'},
              {'id': '3da63a14756511ecb7cb1e008a366d49'},
              {'id': '3da6392e756511ecb7cb1e008a366d49'},
              {'id': '3da63b72756511ecb7cb1e008a366d49'},
              {'id': '3da639ce756511ecb7cb1e008a366d49'},
              {'id': '3da63a5a756511ecb7cb1e008a366d49'},
              {'id': '3da63ae6756511ecb7cb1e008a366d49'},
              {'id': '3da63aa0756511ecb7cb1e008a366d49'},
              {'id': '3da63b2c756511ecb7cb1e008a366d49'},
              {'id': '3da63988756511ecb7cb1e008a366d49'}]},
 {'id': '3da6392e756511ecb7cb1e008a366d49',
  'matches': [{'id': '3da6392e756511ecb7cb1e008a366d49'},
              {'id': '3da639ce756511ecb7cb1e008a366d49'},
              ...

For more information about pydantic and FastAPI, we strongly recommend reading their documentation.