# Bring your own vectors

If you already generate embeddings, store them directly. Create an index with a dense-vector field, upsert your vectors, and rank by similarity. Pinecone does no embedding on your behalf here.

## Prerequisites

- A Pinecone account and API key ([get one](https://app.pinecone.io)).
- **Python 3.10+**.
- The Pinecone Python SDK: `pip install --upgrade pinecone`.

## Upsert your vectors and search

::::steps
:::step{title="Set your API key"}
Set your API key as an environment variable so the SDK can authenticate:

```bash theme={null}
export PINECONE_API_KEY="YOUR_API_KEY"
```
:::

:::step{title="Create an index with a dense-vector field"}
Set `dimension` to match your embedding model's output, and pick a distance `metric` (`cosine`, `dotproduct`, or `euclidean`). Only the vector field goes in the schema; other fields like `text` are stored on the documents (non-schema fields are stored as metadata, capped at 40 KB per document).

```python theme={null}
import os, time
from pinecone import Pinecone, SchemaBuilder

pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])

schema = (
    SchemaBuilder()
      .add_dense_vector_field(name="embedding", dimension=1024, metric="cosine")
      .build()
)
if not pc.indexes.exists(name="byo-vectors"):
    pc.indexes.create(name="byo-vectors", schema=schema)

while not pc.indexes.describe(name="byo-vectors").status.ready:
    time.sleep(2)

index = pc.Index(name="byo-vectors")
```
:::

:::step{title="Upsert your vectors"}
Each document carries its `embedding` (a list of floats matching the schema's `dimension`) plus any fields you want to store. Replace the truncated vectors below with your real embeddings.

```python theme={null}
index.documents.upsert(
    namespace="__default__",
    documents=[
        # each `embedding` is a full list of 1024 floats (shown truncated)
        {"_id": "1", "embedding": [0.12, 0.04, ...], "text": "Refund requests must be submitted within 30 days."},
        {"_id": "2", "embedding": [0.08, 0.21, ...], "text": "Enterprise support responds within 4 hours."},
    ],
)

time.sleep(5)  # documents are indexed asynchronously, so wait a moment
```
:::

:::step{title="Search by vector similarity"}
Embed your query with the **same model** you used for the documents, then rank by the dense-vector field (`embedding`).

```python theme={null}
query_embedding = [0.10, 0.05, ...]  # embed your query text with the same model

resp = index.documents.search(
    namespace="__default__",
    top_k=3,
    score_by=[{"type": "dense_vector", "fields": ["embedding"], "values": query_embedding}],
    include_fields=["*"],
)

for m in resp.matches:
    print(m._id, m._score, getattr(m, "text", ""))
```
:::
::::

## Next steps

::::card-grid
:::card{title="Match keywords" href="/guides/index-data-search-full-text-search#schema-definition" icon="layer-group"}
Add a `full_text_search` field to your schema for keyword search, or combine it with your vectors for hybrid search.
:::

:::card{title="Data modeling" href="/guides/index-data-data-modeling" icon="table"}
How to design a schema for your workload
:::
::::

## Related pages

- [Pinecone quickstart](./get-started-quickstart.md)
- [Ingest your own files](./get-started-quickstart-ingest-files.md)
- [Try full-text search](./get-started-quickstart-full-text-search.md)
- [Pinecone Assistant: SDK quickstart](./get-started-assistant-quickstart-sdk-quickstart.md)
- [Pinecone Assistant: n8n quickstart](./get-started-assistant-quickstart-n8n-quickstart.md)

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