Vechord¶

Turn PostgreSQL into your search engine in a Pythonic way.

Installation¶

pip install vechord

The related Docker images can be found in VectorChord Suite.

DockerHub: tensorchord/vchord-suite:pg17-20250620
GitHub Packages: ghcr.io/tensorchord/vchord-suite:pg17-20250620

Examples¶

simple.py: for people that are familiar with specialized vector database APIs
beir.py: the most flexible way to use the library (loading, indexing, querying and evaluation)
web.py: build a web application with from the defined tables and pipeline
essay.py: extract the content from Paul Graham’s essays and evaluate the search results from LLM generated queries
contextual.py: contextual retrieval example with local PDF
anthropic.py: contextual retrieval with the Anthropic’s Tutorial example
hybrid.py: hybrid search that rerank the results from vector search with keyword search
graph.py: graph-like entity-relation retrieval

User Guide¶

For more details, check our API reference and User Guide.

Define the table¶

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

# use 3072 dimension vector
DenseVector = Vector[3072]

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
    vector: DenseVector  # this comes with a default vector index
    text: str

Inject with decorator¶

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/", tables=[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
async def add_document(url: str) -> Document:  # the return type is `Document`
    async with httpx.AsyncClient() as client:
        resp = await 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
async def add_chunk(uid: int, text: str) -> list[Chunk]:  # the return type is `list[Chunk]`
    chunks = text.split("\n")
    return [Chunk(doc_id=uid, vector=await emb.vectorize_chunk(t), text=t) for t in chunks]

async def main():
    async with vr, emb:  # handle the connection with context manager
        await add_document("https://paulgraham.com/best.html")  # add arguments as usual
        await add_chunk()  # omit the arguments since the `input` is will be loaded from the `Document` table
        await vr.insert(Document(text="hello world"))  # insert manually
        print(await vr.select_by(Document.partial_init()))  # select all the columns from table `Document`

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

Transaction¶

To guarantee the data consistency, users can use the VechordRegistry.run method to run multiple functions in a transaction.

In this transaction, all the functions will only load the data from the database that is inserted in the current transaction. So users can focus on the data processing part without worrying about which part of data has not been processed yet.

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

Search¶

print(await vr.search_by_vector(Chunk, await emb.vectorize_query("startup")))

Customized Index Configuration¶

from vechord.spec import VectorIndex

class Chunk(Table, kw_only=True):
    uid: Optional[PrimaryKeyAutoIncrease] = None
    vector: Annotated[DenseVector, VectorIndex(distance="cos", lists=128)]
    text: str

Access the underlying database cursor directly¶

await vr.client.get_cursor().execute("SET vchordrq.probes = 100;")

HTTP Service¶

This creates a WSGI application that can be served by any WSGI server.

Open the OpenAPI Endpoint to check the API documentation.

import uvicorn

uvicorn.run(create_web_app(vr))

Development¶

docker run --rm -d --name vdb -e POSTGRES_PASSWORD=postgres -p 5432:5432 ghcr.io/tensorchord/vchord-suite:pg17-20250620
envd up
# inside the envd env, sync all the dependencies
make sync
# format the code
make format

Indices¶

Index