Semantic Search over Google Drive

This is the Semantic Search 101 example with exactly one thing swapped: instead of reading Markdown off your local disk, it reads documents straight from a Google Drive folder. Everything downstream — chunking, embedding, and storing the vectors in Postgres with pgvector — is identical, so this post spends its prose on the one piece that differs: the Drive connector.

The chunk-and-embed half is explained in full in the base walkthrough; read that first if you want the line-by-line tour. Here we focus on wiring Google Drive as the source.

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Flow overview

From a high level, these are the steps:

1. List documents under one or more Google Drive folders (recursively), exporting Docs/Sheets/Slides to text.
2. Split each file into overlapping chunks, then embed every chunk.
3. Store the chunks and their embeddings in Postgres (as target states).

You declare the transformation logic with native Python, without worrying about how updates propagate. Think: target_state = transformation(source_state).

Connect to Google Drive

The Drive source needs two things: a Google Cloud service-account JSON key with Drive access, and the folder id(s) to index. CocoIndex reads both from environment variables, so nothing is hardcoded.

from cocoindex.connectors import google_drive, postgres

credential_path = os.environ["GOOGLE_SERVICE_ACCOUNT_CREDENTIAL"]
root_folder_ids = [
    folder.strip()
    for folder in os.environ["GOOGLE_DRIVE_ROOT_FOLDER_IDS"].split(",")
    if folder.strip()
]

source = google_drive.GoogleDriveSource(
    service_account_credential_path=credential_path,
    root_folder_ids=root_folder_ids,
)

GoogleDriveSource walks each root folder recursively and yields one DriveFile per document. Native Google Docs, Sheets, and Slides are exported to text (Markdown, CSV, and plain text respectively); any other file is downloaded as-is. That’s the whole connector — from here on, a DriveFile behaves like any other FileLike, so await file.read_text() works just as it does for a local file.

Define the main function

app_main mounts the Postgres table, then fans out one processing component per Drive file with mount_each. The processing component is the same process_file from the base example — read the file, chunk it, embed each chunk, and declare_row a DocEmbedding per chunk.

@coco.fn
async def app_main() -> None:
    table = await postgres.mount_table_target(
        PG_DB,
        table_name=TABLE_NAME,
        table_schema=await postgres.TableSchema.from_class(
            DocEmbedding,
            primary_key=["id"],
        ),
        pg_schema_name=PG_SCHEMA_NAME,
    )

    source = google_drive.GoogleDriveSource(
        service_account_credential_path=credential_path,
        root_folder_ids=root_folder_ids,
    )

    await coco.mount_each(process_file, source.items(), table)

source.items() yields (key, file) pairs keyed by the file’s name path, which is exactly the shape mount_each expects — so the engine tracks each Drive file as its own component and updates them independently. mount_table_target creates and manages the Postgres table: schema, idempotent upserts, and orphan cleanup when a file disappears from the folder.

Setup

• A running Postgres with the pgvector extension:

  Copysh

  export POSTGRES_URL="postgres://cocoindex:cocoindex@localhost/cocoindex"

• A Google Cloud service account with Drive access, and the folder id(s) you want to index. Share the folders with the service account’s email, then:

  Copysh

  export GOOGLE_SERVICE_ACCOUNT_CREDENTIAL="/path/to/service-account.json"
  export GOOGLE_DRIVE_ROOT_FOLDER_IDS="folder_id_1,folder_id_2"

• Install CocoIndex with the Google Drive extra and the dependencies this example uses:

  Copysh

  pip install -U "cocoindex[postgres,sentence_transformers,google_drive]" asyncpg pgvector numpy python-dotenv

Run the pipeline

The Google Drive source does a one-shot catch-up (live mode isn’t supported), so build the index with a single cocoindex update:

cocoindex update main

Then search straight from the command line, reusing the same embedder from the indexing flow so indexing and querying stay consistent:

python main.py "what is self-attention?"

The most semantically similar chunks come back ranked — even when they share none of the words in your query.

Incremental updates

Just like the base example, CocoIndex does the minimum work to keep the index in sync. @coco.fn(memo=True) on process_file skips any Drive file whose content and the function’s code are both unchanged, and mount_table_target derives each row’s id from its chunk text, so only the rows that actually changed are upserted and rows whose source is gone are deleted.

• A document is added to the folder — only that file is chunked and embedded, and its rows are inserted.
• A document is edited — it is re-chunked; unchanged chunks keep their id and embedding, genuinely new chunks are embedded and inserted, and chunks that no longer exist are deleted.
• A document is removed from the folder — its rows are removed from the target automatically.

Because the Drive source is catch-up only, each cocoindex update main rescans the folders and applies exactly the difference.

Run it

The full, runnable example is in the CocoIndex repo: examples/gdrive_text_embedding. If you haven’t yet, read Semantic Search 101 for the line-by-line tour of chunking and embedding.

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