> ## Documentation Index
> Fetch the complete documentation index at: https://morphik.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# How do I perform search over documents?

> Techniques for searching document collections efficiently

Effective document search relies on representing your data in a way that captures meaning. Common approaches include keyword search and vector similarity search.

With Morphik, you can ingest text, images, and other modalities. Use the `retrieve_docs` function for a simple vector similarity search or `query` to combine retrieval with language model generation:

```python theme={null}
from morphik import Morphik

db = Morphik()

# Retrieve top matching documents
docs = db.retrieve_docs(query="latest sales figures", k=3)

# Or generate an answer from the documents
answer = db.query("summarize the trends", k=3)
print(answer.text)
```

### Related questions

* **Q:** What is the difference between keyword and vector search?\
  **A:** Keyword search matches exact terms via an inverted index, while vector search compares dense embeddings to capture semantic similarity even when different words are used.

* **Q:** How can I limit search to a specific document category?\
  **A:** Pass a `filters` dictionary when calling `retrieve_docs` or `query`, e.g. `filters={"category": "finance"}`, to restrict results to documents with matching metadata.

* **Q:** When should I use `query` instead of `retrieve_docs`?\
  **A:** Use `query` when you need the language model to read the retrieved docs and generate a synthesized answer; use `retrieve_docs` when you only need the raw documents.