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

# Combining Multiple Embeddings for Better Retrieval Outcomes

#### **How Qyver Represents Complex Data More Effectively**

Retrieving **high-quality results** from **LLM-powered vector searches** on **complex, embedded data** isn’t easy. The **traditional approach** looks like this:

1. **Embed all entity data** into a **single vector** using a model (e.g., Hugging Face, proprietary models).
2. **Run a vector search** to find the **top X nearest neighbors**. 
3. **Rerank results** using:

* Additional **contextual filters**
* Information **not captured in the embedding** (e.g., filtering out out-of-stock items)

This method has **serious limitations**:

* **Data loss:** A single embedding struggles to **capture multiple attributes**, reducing relevance.
* **Inefficient reranking:** Reranking with **cross-encoders** adds **latency**, slowing down production.
* **High computational cost:** Comparing all results **pairwise** to refine rankings is resource-intensive.

**Is there a way to achieve high-quality retrieval** without **data loss and latency overhead**? **Yes!**

***

### **A Smarter Approach: Multimodal Embeddings with Qyver**

Instead of embedding **all entity data into a single vector**, **Qyver Spaces** allow you to:

* **Embed each attribute separately** based on its modality.
* **Concatenate these embeddings** into a **multimodal vector**. 
* **Capture the full complexity** of data **without losing information**.

This results in:

* **Better quality retrieval**—more relevant, complete search results.
* **No need for expensive reranking**—eliminates computational overhead. 
* **Faster processing**—retrieval is **10x faster**, reducing latency from **hundreds** to **tens of milliseconds**.

#### **Example: Searching Multimodal Data Efficiently**

Consider a **simple dataset** where each paragraph has a **text body** and a **like count**:

* **Paragraph 1:** *"Glorious animals live in the wilderness."* → **likes: 10**
* **Paragraph 2:** *"Growing computation power enables advancements in AI."* → **likes: 75**

Using **Qyver’s Spaces**, we represent each attribute **individually**:

```python
@schema
class Paragraph:
    id: IdField
    body: String
    like_count: Integer
```

Instead of embedding everything into a **single vector**, we use **separate Spaces**:

```python
body_space = TextSimilaritySpace(
    text=paragraph.body, model="sentence-transformers/all-mpnet-base-v2"
)

like_space = NumberSpace(
    number=paragraph.like_count, min_value=0, max_value=100, mode=Mode.MAXIMUM
)
```

These Spaces are then **combined into a single index**:

```python
paragraph_index = Index([body_space, like_space])
```

***

### **Efficient Querying Without Reranking**

Once indexed, we **query for relevance** efficiently:

```python
query = (
    Query(paragraph_index)
    .find(paragraph)
    .similar(body_space.text, Param("query_text"))
)

result = app.query(
    query,
    query_text="What makes the AI industry go forward?",
)
```

**Expected Output:**

| body                                                  | like\_count | id          |
| ----------------------------------------------------- | ----------- | ----------- |
| Growing computation power enables advancements in AI. | 75          | paragraph-2 |
| Glorious animals live in the wilderness.              | 10          | paragraph-1 |

**Why is this better?**

1. **Structured & unstructured data are embedded separately** but stored in a **single searchable index**.
2. **No need for reranking**—**Qyver Spaces** ensure relevance is **captured upfront**.
3. ️**Faster & more accurate**—reduces processing overhead while improving **retrieval precision**.

***

### **Scaling Beyond Simple Use Cases**

Qyver **is designed for real-world complexity**, handling:

* **E-commerce recommendations** → *Blending product descriptions, user preferences, and purchase history.*
* **Search & discovery** → *Multimodal indexing for text, images, and metadata.*
* **LLM-powered applications** → *Enhancing RAG (Retrieval-Augmented Generation) with structured embeddings.*

***

### **Final Thoughts**

Instead of relying on **reranking, filtering, or expensive post-processing**, **Qyver optimizes embeddings upfront**—leading to **faster, higher-quality retrieval**.

**Want to try it?** Check out the notebook and see the power of **multimodal embeddings in action**.

**Like what we’re doing?** Give us a [star](https://github.com/qyverlabs/qyver)!


---

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