> For the complete documentation index, see [llms.txt](https://docs.projectbit.ca/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.projectbit.ca/documentation/embeddings/qdrant-fastembed-embedder.md).

# Qdrant FastEmbed Embedder

The `FastEmbedEmbedder` class is used to embed text data into vectors using the [FastEmbed](https://qdrant.github.io/fastembed/).

### [​](https://docs.phidata.com/embedder/qdrant_fastembed#usage)Usage <a href="#usage" id="usage"></a>

cookbook/embedders/qdrant\_fastembed.py

```python
from bitca.agent import AgentKnowledge
from bitca.vectordb.pgvector import PgVector
from bitca.embedder.fastembed import FastEmbedEmbedder

embeddings = FastEmbedEmbedder().get_embedding("The quick brown fox jumps over the lazy dog.")

# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")

# Example usage:
knowledge_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="qdrant_embeddings",
        embedder=FastEmbedEmbedder(),
    ),
    num_documents=2,
)
```

### [​](https://docs.phidata.com/embedder/qdrant_fastembed#params)Params <a href="#params" id="params"></a>

| Parameter    | Type  | Default                  | Description                                    |
| ------------ | ----- | ------------------------ | ---------------------------------------------- |
| `dimensions` | `int` | -                        | The dimensionality of the generated embeddings |
| `model`      | `str` | `BAAI/bge-small-en-v1.5` | The name of the qdrant\_fastembed model to use |