Make a folder of raw documents searchable. The flow is: extract text, chunk it, embed the chunks, upsert, then search. Your coding agent can run this end to end (see the [Quickstart hub](/guides/get-started-quickstart)), or follow the steps.

:::callout{intent="note"}
This path uses [Pinecone Inference](/guides/core-concepts-key-terms#pinecone-inference), Pinecone's hosted embedding service, to turn your text into vectors. If you already generate your own embeddings, skip this and go to [Bring your own vectors](/guides/get-started-quickstart-bring-your-own-vectors) instead.
:::

## Prerequisites

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

## Ingest your own files 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="Extract text from your files"}
Convert each file (PDF, DOCX, HTML, and so on) to plain text using a parser of your choice. This step happens outside Pinecone. The result should be a list of records, each with an `id` and `text`, like the `docs` list below. It runs as-is, so you can complete the quickstart first and swap in your own extracted text after.

:::callout{intent="note"}
If you're embedding images instead of text, skip this step and the chunking step. Embed your images directly with a multimodal embedding model, then upsert the resulting vectors following [Bring your own vectors](/guides/get-started-quickstart-bring-your-own-vectors).
:::

```python theme={null}
docs = [
    {"id": "handbook-1", "text": "Refund requests must be submitted within 30 days of purchase."},
    {"id": "handbook-2", "text": "Enterprise customers get support with a 4-hour response time."},
    # ...extracted from your files
]
```
::::

:::step{title="Chunk the text"}
Split your text into smaller pieces so each fits your embedding model's input limit. The function below is a simple length-based split you can run as-is. For smarter approaches (by sentence, token, or document structure), see [chunking strategies](https://www.pinecone.io/learn/chunking-strategies/).

```python theme={null}
def chunk(text, size=500):
    return [text[i:i + size] for i in range(0, len(text), size)]

chunks = [
    {"id": f"{d['id']}#{i}", "text": part}
    for d in docs
    for i, part in enumerate(chunk(d["text"]))
]
```
:::

:::step{title="Create an index with a dense-vector field"}
Set `dimension` to match your embedding model. `llama-text-embed-v2` outputs 1024 dimensions. Only the vector field goes in the schema; other fields 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="my-files"):
    pc.indexes.create(name="my-files", schema=schema)

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

index = pc.Index(name="my-files")
```
:::

:::step{title="Embed the chunks and upsert"}
Use [Pinecone Inference](/guides/core-concepts-key-terms#pinecone-inference) to embed the chunks (in batches of 96, this model's per-call limit), then `upsert` the vectors alongside the text. For a large dataset, use `batch_upsert` or [Import](/guides/index-data-import-data) instead.

```python theme={null}
# llama-text-embed-v2 accepts up to 96 inputs per call, so embed in batches.
embeddings = []
for i in range(0, len(chunks), 96):
    resp = pc.inference.embed(
        model="llama-text-embed-v2",
        inputs=[c["text"] for c in chunks[i:i + 96]],
        parameters={"input_type": "passage"},
    )
    embeddings.extend(resp)

index.documents.upsert(
    namespace="__default__",
    documents=[
        {"_id": c["id"], "embedding": e['values'], "text": c["text"]}
        for c, e in zip(chunks, embeddings)
    ],
)

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

:::step{title="Search your documents"}
To search, embed the query with the same model you used for the documents, then rank documents by vector similarity.

```python theme={null}
q = pc.inference.embed(
    model="llama-text-embed-v2",
    inputs=["what is the refund policy?"],
    parameters={"input_type": "query"},
)

resp = index.documents.search(
    namespace="__default__",
    top_k=3,
    score_by=[{"type": "dense_vector", "fields": ["embedding"], "values": q[0]['values']}],
    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="Bulk import" href="/guides/index-data-import-data" icon="database"}
Load large document sets efficiently
:::
::::

## Related pages

- [Pinecone quickstart](./get-started-quickstart.md)
- [Bring your own vectors](./get-started-quickstart-bring-your-own-vectors.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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