GitHub - jina-ai/jina: Cloud-native neural search framework for 𝙖𝙣𝙮 kind of data

Build Your First Jina App

Document, Executor, and Flow are the three fundamental concepts in Jina.

Leveraging these three components, we want to build an app that finds lines from a code snippet that are most similar to the query.

Preliminaries: character embedding, pooling, Euclidean distance

Copy-paste the minimum example below and run it:

import numpy as np
from jina import Document, DocumentArray, Executor, Flow, requests

class CharEmbed(Executor):  # a simple character embedding with mean-pooling
    offset = 32  # letter `a`
    dim = 127 - offset + 1  # last pos reserved for `UNK`
    char_embd = np.eye(dim) * 1  # one-hot embedding for all chars

    @requests
    def foo(self, docs: DocumentArray, **kwargs):
        for d in docs:
            r_emb = [ord(c) - self.offset if self.offset <= ord(c) <= 127 else (self.dim - 1) for c in d.text]
            d.embedding = self.char_embd[r_emb, :].mean(axis=0)  # average pooling

class Indexer(Executor):
    _docs = DocumentArray()  # for storing all documents in memory

    @requests(on='/index')
    def foo(self, docs: DocumentArray, **kwargs):
        self._docs.extend(docs)  # extend stored `docs`

    @requests(on='/search')
    def bar(self, docs: DocumentArray, **kwargs):
         docs.match(self._docs, metric='euclidean', limit=20)

f = Flow(port_expose=12345, protocol='http', cors=True).add(uses=CharEmbed, parallel=2).add(uses=Indexer)  # build a Flow, with 2 parallel CharEmbed, tho unnecessary
with f:
    f.post('/index', (Document(text=t.strip()) for t in open(__file__) if t.strip()))  # index all lines of _this_ file
    f.block()  # block for listening request