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

# 🔍 search

`.search()` enables you to uncover the most pertinent context by performing a semantic search across your data sources based on a given query. Refer to the function signature below:

### Parameters

<ParamField path="query" type="str">
  Question
</ParamField>

<ParamField path="num_documents" type="int" optional>
  Number of relevant documents to fetch. Defaults to `3`
</ParamField>

### Returns

<ResponseField name="answer" type="dict">
  Return list of dictionaries that contain the relevant chunk and their source information.
</ResponseField>

## Usage

Refer to the following example on how to use the search api:

```python Code example
from embedchain import App

# Initialize app
app = App()

# Add data source
app.add("https://www.forbes.com/profile/elon-musk")

# Get relevant context using semantic search
context = app.search("What is the net worth of Elon?", num_documents=2)
print(context)
# Context:
# [
#     {
#         'context': 'Elon Musk PROFILEElon MuskCEO, Tesla$221.9BReal Time Net Worth ...',
#         'metadata': {
#             'source': 'https://www.forbes.com/profile/elon-musk',
#             'document_id': 'some_document_id',
#             'score': 0.404,
#         }
#     },
#     {
#         'context': 'company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH ...',
#         'metadata': {
#             'source': 'https://www.forbes.com/profile/elon-musk',
#             'document_id': 'some_document_id',
#             'score': 0.435,
#         }
#     }
# ]
```