Semantic search over Markdown, stored in Turbopuffer.
The Semantic Search 101 pipeline pointed at Turbopuffer — a managed, serverless vector store, so there's no database to run yourself.
Walk, chunk, embed locally, upsert — incrementally — in plain async Python.
This is [Semantic Search 101](https://cocoindex.io/docs/examples/text-embedding/) with one thing swapped: instead of storing the vectors in Postgres with pgvector, we write them to a [Turbopuffer](https://turbopuffer.com/) namespace — a managed, serverless vector store, so there's no database to run yourself. The chunking and embedding are identical; only the target changes. You declare the transformation in native Python and your own types — `target_state = transformation(source_state)` — and the heavy lifting (incremental processing, change tracking, managed targets) runs in a Rust engine underneath, so editing one file re-embeds one file, not the whole folder.
## How it works
Turbopuffer is a cloud service, so the shared resource is an `AsyncTurbopuffer` client (keyed off `TURBOPUFFER_API_KEY`) rather than a database pool. A Turbopuffer row is an `id`, a `vector`, and an open bag of `attributes` — the filename, text, and offsets ride along as attributes while the embedding is the indexed vector. Read it in [`main.py`](main.py):
```python
@coco.fn
async def process_chunk(chunk, filename, id_gen, target: turbopuffer.NamespaceTarget) -> None:
embedding_vec = await coco.use_context(EMBEDDER).embed(chunk.text)
target.declare_row(
turbopuffer.Row(
id=str(await id_gen.next_id(chunk.text)), # stable id derived from chunk text
vector=embedding_vec,
attributes={"filename": str(filename), "chunk_start": chunk.start.char_offset,
"chunk_end": chunk.end.char_offset, "text": chunk.text},
)
)
@coco.fn
async def app_main(sourcedir: pathlib.Path) -> None:
target_namespace = await turbopuffer.mount_namespace_target(
TPUF_DB, namespace_name=TPUF_NAMESPACE,
schema=await turbopuffer.NamespaceSchema.create(vectors=turbopuffer.VectorDef(schema=EMBEDDER)),
)
files = localfs.walk_dir(sourcedir, recursive=True,
path_matcher=PatternFilePathMatcher(included_patterns=["**/*.md"]), live=True)
await coco.mount_each(process_file, files.items(), target_namespace)
```
`target.declare_row` declares the row as a target state; CocoIndex handles upserting and deleting it to match. The namespace's dimension comes straight from the embedder, so it always matches what you write.
📘 Full Tutorial →
Step-by-step walkthrough with the Turbopuffer client, the namespace target, the row schema, and the incremental story.
## Why it's worth a star ⭐
- **No database to run.** Turbopuffer is managed and serverless — bring an API key and the namespace is created and managed for you.
- **Managed namespace target.** A single `mount_namespace_target` handles schema, idempotent upserts, and orphan cleanup when a file disappears.
- **No hardcoded dimensions.** The namespace's vector size comes from `VectorDef(schema=EMBEDDER)`, so swapping the model carries the schema along.
- **Incremental by default.** `@coco.fn(memo=True)` skips files whose content and code are unchanged; each row's `id` is derived from its chunk text, so only changed rows are upserted and vanished ones are deleted.
- **Same flow, different store.** The chunk-and-embed half is identical to the Postgres version; the query reuses the *same* local `all-MiniLM-L6-v2` embedder and asks Turbopuffer for the nearest vectors with `rank_by=("vector", "ANN", ...)`.
## Run it
**1. Get a Turbopuffer API key** — a free key from [turbopuffer.com](https://turbopuffer.com/).
**2. Configure & install:**
```sh
cp .env.example .env # set TURBOPUFFER_API_KEY=tpuf_... (TURBOPUFFER_REGION defaults to gcp-us-central1)
pip install -e .
```
**3. Build the index** — the example ships a `markdown_files/` folder of sample docs:
```sh
cocoindex update main # catch-up: scan, sync, exit
cocoindex update -L main # live: keep watching for file changes
```
**4. Search** — embeds your query with the *same* model and asks Turbopuffer for the nearest vectors:
```sh
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.
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