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---
title: '🔍 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>
<ParamField path="where" type="dict" optional>
Key value pair for metadata filtering.
</ParamField>
<ParamField path="raw_filter" type="dict" optional>
Pass raw filter query based on your vector database.
Currently, `raw_filter` param is only supported for Pinecone vector database.
</ParamField>
### Returns
<ResponseField name="answer" type="dict">
Return list of dictionaries that contain the relevant chunk and their source information.
</ResponseField>
## Usage
### Basic
Refer to the following example on how to use the search api:
```python Code example
from embedchain import App
app = App()
app.add("https://www.forbes.com/profile/elon-musk")
context = app.search("What is the net worth of Elon?", num_documents=2)
print(context)
```
### Advanced
#### Metadata filtering using `where` params
Here is an advanced example of `search()` API with metadata filtering on pinecone database:
```python
import os
from embedchain import App
os.environ["PINECONE_API_KEY"] = "xxx"
config = {
"vectordb": {
"provider": "pinecone",
"config": {
"metric": "dotproduct",
"vector_dimension": 1536,
"index_name": "ec-test",
"serverless_config": {"cloud": "aws", "region": "us-west-2"},
},
}
}
app = App.from_config(config=config)
app.add("https://www.forbes.com/profile/bill-gates", metadata={"type": "forbes", "person": "gates"})
app.add("https://en.wikipedia.org/wiki/Bill_Gates", metadata={"type": "wiki", "person": "gates"})
results = app.search("What is the net worth of Bill Gates?", where={"person": "gates"})
print("Num of search results: ", len(results))
```
#### Metadata filtering using `raw_filter` params
Following is an example of metadata filtering by passing the raw filter query that pinecone vector database follows:
```python
import os
from embedchain import App
os.environ["PINECONE_API_KEY"] = "xxx"
config = {
"vectordb": {
"provider": "pinecone",
"config": {
"metric": "dotproduct",
"vector_dimension": 1536,
"index_name": "ec-test",
"serverless_config": {"cloud": "aws", "region": "us-west-2"},
},
}
}
app = App.from_config(config=config)
app.add("https://www.forbes.com/profile/bill-gates", metadata={"year": 2022, "person": "gates"})
app.add("https://en.wikipedia.org/wiki/Bill_Gates", metadata={"year": 2024, "person": "gates"})
print("Filter with person: gates and year > 2023")
raw_filter = {"$and": [{"person": "gates"}, {"year": {"$gt": 2023}}]}
results = app.search("What is the net worth of Bill Gates?", raw_filter=raw_filter)
print("Num of search results: ", len(results))
```
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