推出 Vechord：以 Pythonic 的方式将 PostgreSQL 打造成你的搜索引擎

from typing import Annotated, Optional
from vechord.spec import Table, Vector, PrimaryKeyAutoIncrease, ForeignKey, Keyword

# use 768 dimension vector
DenseVector = Vector[768]

class Document(Table, kw_only=True):
    uid: Optional[PrimaryKeyAutoIncrease] = None  # auto-increase id, no need to set
    link: str = ""
    text: str

class Chunk(Table, kw_only=True)
    uid: Optional[PrimaryKeyAutoIncrease] = None
    doc_id: Annotated[int, ForeignKey[Document.uid]]  # reference to `Document.uid` on DELETE CASCADE
    vec: DenseVector  # this comes with a default vector index
    keyword: Keyword  # this comes with a default tokenizer and text index
    text: str

import httpx
from vechord.registry import VechordRegistry
from vechord.extract import SimpleExtractor
from vechord.embedding import GeminiDenseEmbedding

vr = VechordRegistry(namespace="test", url="postgresql://postgres:postgres@127.0.0.1:5432/")
# ensure the table and index are created if not exists
vr.register([Document, Chunk])
extractor = SimpleExtractor()
emb = GeminiDenseEmbedding()

@vr.inject(output=Document)  # dump to the `Document` table
# function parameters are free to define since `inject(input=...)` is not set
def add_document(url: str) -> Document:  # the return type is `Document`
    with httpx.Client() as client:
        resp = client.get(url)
        text = extractor.extract_html(resp.text)
        return Document(link=url, text=text)

@vr.inject(input=Document, output=Chunk)  # load from the `Document` table and dump to the `Chunk` table
# function parameters are the attributes of the `Document` table, only defined attributes
# will be loaded from the `Document` table
def add_chunk(uid: int, text: str) -> list[Chunk]:  # the return type is `list[Chunk]`
    chunks = text.split("\n")
    return [Chunk(doc_id=uid, vec=emb.vectorize_chunk(t), keyword=Keyword(t), text=t) for t in chunks]

if __name__ == "__main__":
    add_document("https://paulgraham.com/best.html")  # add arguments as usual
    add_chunk()  # omit the arguments since the `input` is will be loaded from the `Document` table
    vr.insert(Document(text="hello world"))  # insert manually
    print(vr.select_by(Document.partial_init()))  # select all the columns from table `Document`

pipeline = vr.create_pipeline([add_document, add_chunk])
pipeline.run("https://paulgraham.com/best.html")  # only accept the arguments for the first function

from vechord.rerank import CohereReranker

reranker = CohereReranker()
text = vr.search_by_vector(Chunk, emb.vectorize_query("startup"))
vec = vr.search_by_keyword(Chunk, "startup")
chunks = list({chunk.uid: chunk for chunk in text_retrieves + vec_retrievse}.values())
indices = reranker.rerank(query, [chunk.text for chunk in chunks])
print([chunks[i] for i in indices[:topk]])