These code snippets provide a short introduction to Jina's functionality and design framework. To run a snippet in a Jupyter Notebook, just click the "run" button next to the snippet.
First we look at basic CRUD operations. In Jina, CRUD corresponds to four functions: index (create), search (read), update, and delete. With Documents below as an example:
import numpy as np
from jina import Document
docs = [Document(id='🐲', embedding=np.array([0, 0]), tags={'guardian': 'Azure Dragon', 'position': 'East'}),
Document(id='🐦', embedding=np.array([1, 0]), tags={'guardian': 'Vermilion Bird', 'position': 'South'}),
Document(id='🐢', embedding=np.array([0, 1]), tags={'guardian': 'Black Tortoise', 'position': 'North'}),
Document(id='🐯', embedding=np.array([1, 1]), tags={'guardian': 'White Tiger', 'position': 'West'})]Let's build a Flow with a simple indexer:
from jina import Flow
f = Flow().add(uses='_index')Document and Flow are basic concepts in Jina, which will be explained later. _index is a built-in embedding + structured storage that you can use out of the box.
| Index |
save four docs (both embedding and structured info) into storage
with f:
f.index(docs, on_done=print) |
| Search |
retrieve top-3 neighbours of 🐲, this print 🐲🐦🐢 with score 0, 1, 1 respectively
with f:
f.search(docs[0], top_k=3, on_done=lambda x: print(x.docs[0].matches)){"id": "🐲", "tags": {"guardian": "Azure Dragon", "position": "East"}, "embedding": {"dense": {"buffer": "AAAAAAAAAAAAAAAAAAAAAA==", "shape": [2], "dtype": "<i8"}}, "score": {"opName": "NumpyIndexer", "refId": "🐲"}, "adjacency": 1}
{"id": "🐦", "tags": {"position": "South", "guardian": "Vermilion Bird"}, "embedding": {"dense": {"buffer": "AQAAAAAAAAAAAAAAAAAAAA==", "shape": [2], "dtype": "<i8"}}, "score": {"value": 1.0, "opName": "NumpyIndexer", "refId": "🐲"}, "adjacency": 1}
{"id": "🐢", "tags": {"guardian": "Black Tortoise", "position": "North"}, "embedding": {"dense": {"buffer": "AAAAAAAAAAABAAAAAAAAAA==", "shape": [2], "dtype": "<i8"}}, "score": {"value": 1.0, "opName": "NumpyIndexer", "refId": "🐲"}, "adjacency": 1} |
| Update |
update 🐲 embedding in the storage
docs[0].embedding = np.array([1, 1])
with f:
f.update(docs[0]) |
| Delete |
remove 🐦🐲 Documents from the storage
with f:
f.delete(['🐦', '🐲']) |
For further details about CRUD functionality, checkout docs.jina.ai.
Document is Jina's primitive data type. It can contain text, image, array, embedding, URI, and be accompanied by rich meta information. To construct a Document, you can use:
import numpy
from jina import Document
doc1 = Document(content=text_from_file, mime_type='text/x-python') # a text document contains python code
doc2 = Document(content=numpy.random.random([10, 10])) # a ndarray documentA Document can be recursed both vertically and horizontally to have nested Documents and matched Documents. To better see the Document's recursive structure, you can use .plot() function. If you are using JupyterLab/Notebook, all Document objects will be auto-rendered.
import numpy
from jina import Document
d0 = Document(id='🐲', embedding=np.array([0, 0]))
d1 = Document(id='🐦', embedding=np.array([1, 0]))
d2 = Document(id='🐢', embedding=np.array([0, 1]))
d3 = Document(id='🐯', embedding=np.array([1, 1]))
d0.chunks.append(d1)
d0.chunks[0].chunks.append(d2)
d0.matches.append(d3)
d0.plot() # simply `d0` on JupyterLab |
|
Click here to see more about MultimodalDocument
A MultimodalDocument is a document composed of multiple Document from different modalities (e.g. text, image, audio).
Jina provides multiple ways to build a multimodal Document. For example, you can provide the modality names and the content in a dict:
from jina import MultimodalDocument
document = MultimodalDocument(modality_content_map={
'title': 'my holiday picture',
'description': 'the family having fun on the beach',
'image': PIL.Image.open('path/to/image.jpg')
})One can also compose a MultimodalDocument from multiple Document directly:
from jina.types import Document, MultimodalDocument
doc_title = Document(content='my holiday picture', modality='title')
doc_desc = Document(content='the family having fun on the beach', modality='description')
doc_img = Document(content=PIL.Image.open('path/to/image.jpg'), modality='image')
doc_img.tags['date'] = '10/08/2019'
document = MultimodalDocument(chunks=[doc_title, doc_description, doc_img])To extract fusion embeddings from different modalities Jina provides BaseMultiModalEncoder abstract class, which has a unique encode interface.
def encode(self, *data: 'numpy.ndarray', **kwargs) -> 'numpy.ndarray':
...MultimodalDriver provides data to the MultimodalDocument in the correct expected order. In this example below, image embedding is passed to the encoder as the first argument, and text as the second.
jtype: MyMultimodalEncoder
with:
positional_modality: ['image', 'text']
requests:
on:
[IndexRequest, SearchRequest]:
- jtype: MultiModalDriver {}Interested readers can refer to jina-ai/example: how to build a multimodal search engine for image retrieval using TIRG (Composing Text and Image for Image Retrieval) for the usage of MultimodalDriver and BaseMultiModalEncoder in practice.
Jina provides a high-level Flow API to simplify building CRUD workflows. To create a new Flow:
from jina import Flow
f = Flow().add()This creates a simple Flow with one Pod. You can chain multiple .add()s in a single Flow.
To visualize the Flow, simply chain it with .plot('my-flow.svg'). If you are using a Jupyter notebook, the Flow object will be displayed inline without plot.
Gateway is the entrypoint of the Flow.
Get the vibe? Now we're talking! Let's learn more about the basic concepts and features of Jina:
To use a Flow, open it via with context manager, like you would open a file in Python. Now let's create some empty Documents and index them:
from jina import Document
with Flow().add() as f:
f.index((Document() for _ in range(10)))Flow supports CRUD operations: index, search, update, delete. In addition, it also provides sugary syntax on ndarray, csv, ndjson and arbitrary files.
| Input |
Example of index/search
|
Explain |
numpy.ndarray
|
with f:
f.index_ndarray(numpy.random.random([4,2])) |
Input four |
| CSV |
with f, open('index.csv') as fp:
f.index_csv(fp, field_resolver={'pic_url': 'uri'}) |
Each line in |
JSON Lines/ndjson/LDJSON
|
with f, open('index.ndjson') as fp:
f.index_ndjson(fp, field_resolver={'question_id': 'id'}) |
Each line in |
| Files with wildcards |
with f:
f.index_files(['/tmp/*.mp4', '/tmp/*.pdf']) |
Each file captured is constructed as a |
Once a request is done, callback functions are fired. Jina Flow implements a Promise-like interface: You can add callback functions on_done, on_error, on_always to hook different events. In the example below, our Flow passes the message then prints the result when successful. If something goes wrong, it beeps. Finally, the result is written to output.txt.
def beep(*args):
# make a beep sound
import os
os.system('echo -n "\a";')
with Flow().add() as f, open('output.txt', 'w') as fp:
f.index(numpy.random.random([4, 5, 2]),
on_done=print, on_error=beep, on_always=lambda x: fp.write(x.json()))
To add logic to the Flow, use the uses parameter to attach a Pod with an Executor. uses accepts multiple value types including class name, Docker image, (inline) YAML or built-in shortcut.
f = (Flow().add(uses=MyBertEncoder) # the class of a Jina Executor
.add(uses='docker://jinahub/pod.encoder.dummy_mwu_encoder:0.0.6-0.9.3') # the image name
.add(uses='myencoder.yml') # YAML serialization of a Jina Executor
.add(uses='!WaveletTransformer | {freq: 20}') # inline YAML config
.add(uses='_pass') # built-in shortcut executor
.add(uses={'__cls': 'MyBertEncoder', 'with': {'param': 1.23}})) # dict config object with __cls keywordThe power of Jina lies in its decentralized architecture: Each add creates a new Pod, and these Pods can be run as a local thread/process, a remote process, inside a Docker container, or even inside a remote Docker container.
Chaining .add()s creates a sequential Flow. For parallelism, use the needs parameter:
f = (Flow().add(name='p1', needs='gateway')
.add(name='p2', needs='gateway')
.add(name='p3', needs='gateway')
.needs(['p1','p2', 'p3'], name='r1').plot())
p1, p2, p3 now subscribe to Gateway and conduct their work in parallel. The last .needs() blocks all Pods until they finish their work. Note: parallelism can also be performed inside a Pod using parallel:
f = (Flow().add(name='p1', needs='gateway')
.add(name='p2', needs='gateway')
.add(name='p3', parallel=3)
.needs(['p1','p3'], name='r1').plot())
A Flow does not have to be local-only: You can put any Pod to remote(s). In the example below, with the host keyword gpu-pod, is put to a remote machine for parallelization, whereas other Pods stay local. Extra file dependencies that need to be uploaded are specified via the upload_files keyword.
| 123.456.78.9 |
have docker installed
docker run --name=jinad --network=host -v /var/run/docker.sock:/var/run/docker.sock jinaai/jina:latest-daemon --port-expose 8000
to stop it
docker rm -f jinad |
| Local |
import numpy as np
from jina import Flow
f = (Flow()
.add()
.add(name='gpu_pod',
uses='mwu_encoder.yml',
host='123.456.78.9:8000',
parallel=2,
upload_files=['mwu_encoder.py'])
.add())
with f:
f.index_ndarray(np.random.random([10, 100]), output=print) |
We provide a demo server on cloud.jina.ai:8000, give the following snippet a try!
from jina import Flow
with Flow().add().add(host='cloud.jina.ai:8000') as f:
f.index(['hello', 'world'])
While synchronous from outside, Jina runs asynchronously under the hood: it manages the eventloop(s) for scheduling the jobs. If the user wants more control over the eventloop, then AsyncFlow can be used.
Unlike Flow, the CRUD of AsyncFlow accepts input and output functions as async generators. This is useful when your data sources involve other asynchronous libraries (e.g. motor for MongoDB):
from jina import AsyncFlow
async def input_function():
for _ in range(10):
yield Document()
await asyncio.sleep(0.1)
with AsyncFlow().add() as f:
async for resp in f.index(input_function):
print(resp)AsyncFlow is particularly useful when Jina is using as part of integration, where another heavy-lifting job is running concurrently:
async def run_async_flow_5s(): # WaitDriver pause 5s makes total roundtrip ~5s
with AsyncFlow().add(uses='- !WaitDriver {}') as f:
async for resp in f.index_ndarray(numpy.random.random([5, 4])):
print(resp)
async def heavylifting(): # total roundtrip takes ~5s
print('heavylifting other io-bound jobs, e.g. download, upload, file io')
await asyncio.sleep(5)
print('heavylifting done after 5s')
async def concurrent_main(): # about 5s; but some dispatch cost, can't be just 5s, usually at <7s
await asyncio.gather(run_async_flow_5s(), heavylifting())
if __name__ == '__main__':
asyncio.run(concurrent_main())AsyncFlow is very useful when using Jina inside a Jupyter Notebook. As Jupyter/ipython already manages an eventloop and thanks to autoawait, AsyncFlow can run out-of-the-box in Jupyter.
That's all you need to know for understanding the magic behind hello-world. Now let's dive deeper into it!
Let's first build a naive image encoder that embeds images into vectors using an orthogonal projection. To do this, we simply inherit from BaseImageEncoder: a base class from the jina.executors.encoders module. We then override its __init__() and encode() methods.
import numpy as np
from jina.executors.encoders import BaseImageEncoder
class MyEncoder(BaseImageEncoder):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
np.random.seed(1337)
H = np.random.rand(784, 64)
u, s, vh = np.linalg.svd(H, full_matrices=False)
self.oth_mat = u @ vh
def encode(self, data: 'np.ndarray', *args, **kwargs):
return (data.reshape([-1, 784]) / 255) @ self.oth_matJina provides a family of Executor classes, which summarize frequently-used algorithmic components in neural search. This family consists of encoders, indexers, crafters, evaluators, and classifiers, each with a well-designed interface. You can find the list of all 107 built-in executors here. If they don't meet your needs, inheriting from one of them is the easiest way to bootstrap your own Executor. Simply use our Jina Hub CLI:
pip install jina[hub] && jina hub newLet's test our encoder in the Flow with some synthetic data:
def validate(req):
assert len(req.docs) == 100
assert NdArray(req.docs[0].embedding).value.shape == (64,)
f = Flow().add(uses='MyEncoder')
with f:
f.index_ndarray(numpy.random.random([100, 28, 28]), on_done=validate)All good! Now our validate function confirms that all one hundred 28x28 synthetic images have been embedded into 100x64 vectors.
By setting a larger input, you can play with batch_size and parallel:
f = Flow().add(uses='MyEncoder', parallel=10)
with f:
f.index_ndarray(numpy.random.random([60000, 28, 28]), batch_size=1024)Now we need to add an indexer to store all the embeddings and the image for later retrieval. Jina provides a simple numpy-powered vector indexer NumpyIndexer, and a key-value indexer BinaryPbIndexer. We can combine them in a single YAML file:
jtype: CompoundIndexer
components:
- jtype: NumpyIndexer
with:
index_filename: vec.gz
- jtype: BinaryPbIndexer
with:
index_filename: chunk.gz
metas:
workspace: ./jtype:defines the class name of the structure;with:defines arguments for initializing this class object.
💡 Config your IDE to enable autocompletion on YAML
Essentially, the above YAML config is equivalent to the following Python code:
from jina.executors.indexers.vector import NumpyIndexer
from jina.executors.indexers.keyvalue import BinaryPbIndexer
from jina.executors.indexers import CompoundIndexer
a = NumpyIndexer(index_filename='vec.gz')
b = BinaryPbIndexer(index_filename='vec.gz')
c = CompoundIndexer()
c.components = lambda: [a, b]Now let's add our indexer YAML file to the Flow with .add(uses=). Let's also add two shards to the indexer to improve its scalability:
f = Flow().add(uses='MyEncoder', parallel=2).add(uses='myindexer.yml', shards=2).plot()
When you have many arguments, constructing a Flow in Python can get cumbersome. In that case, you can simply move all arguments into one flow.yml:
jtype: Flow
version: '1.0'
pods:
- name: encode
uses: MyEncoder
parallel: 2
- name:index
uses: myindexer.yml
shards: 2And then load it in Python:
f = Flow.load_config('flow.yml')Querying a Flow is similar to what we did with indexing. Simply load the query Flow and switch from f.index to f.search. Say you want to retrieve the top 50 documents that are similar to your query and then plot them in HTML:
f = Flow.load_config('flows/query.yml')
with f:
f.search_ndarray(numpy.random.random([10, 28, 28]), shuffle=True, on_done=plot_in_html, top_k=50)To compute precision recall on the retrieved result, you can add _eval_pr, a built-in evaluator for computing precision & recall.
f = (Flow().add(...)
.add(uses='_eval_pr'))You can construct an iterator of query and groundtruth pairs and feed to the flow f, via:
from jina import Document
def query_generator():
for _ in range(10):
q = Document()
# now construct expect matches as groundtruth
gt = Document(q, copy=True) # make sure 'gt' is identical to 'q'
gt.matches.append(...)
yield q, gt
f.search(query_iterator, ...)In practice, the query Flow and the client (i.e. data sender) are often physically separated. Moreover, the client may prefer to use a REST API rather than gRPC when querying. You can set port_expose to a public port and turn on REST support with restful=True:
f = Flow(port_expose=45678, restful=True)
with f:
f.block()That is the essence behind jina hello fashion. It is merely a taste of what Jina can do. We’re really excited to see what you do with Jina! You can easily create a Jina project from templates with one terminal command:
pip install jina[hub] && jina hub new --type appThis creates a Python entrypoint, YAML configs and a Dockerfile. You can start from there.