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

# ❓ query

`.query()` method empowers developers to ask questions and receive relevant answers through a user-friendly query API. Function signature is given below:

### Parameters

<ParamField path="input_query" type="str">
  Question to ask
</ParamField>

<ParamField path="config" type="BaseLlmConfig" optional>
  Configure different llm settings such as prompt, temprature, number\_documents etc.
</ParamField>

<ParamField path="dry_run" type="bool" optional>
  The purpose is to test the prompt structure without actually running LLM inference. Defaults to `False`
</ParamField>

<ParamField path="where" type="dict" optional>
  A dictionary of key-value pairs to filter the chunks from the vector database. Defaults to `None`
</ParamField>

<ParamField path="citations" type="bool" optional>
  Return citations along with the LLM answer. Defaults to `False`
</ParamField>

### Returns

<ResponseField name="answer" type="str | tuple">
  If `citations=False`, return a stringified answer to the question asked. <br />
  If `citations=True`, returns a tuple with answer and citations respectively.
</ResponseField>

## Usage

### With citations

If you want to get the answer to question and return both answer and citations, use the following code snippet:

```python With Citations theme={null}
from embedchain import App

# Initialize app
app = App()

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

# Get relevant answer for your query
answer, sources = app.query("What is the net worth of Elon?", citations=True)
print(answer)
# Answer: The net worth of Elon Musk is $221.9 billion.

print(sources)
# [
#    (
#        'Elon Musk PROFILEElon MuskCEO, Tesla$247.1B$2.3B (0.96%)Real Time Net Worthas of 12/7/23 ...',
#        {
#           'url': 'https://www.forbes.com/profile/elon-musk', 
#           'score': 0.89,
#           ...
#        }
#    ),
#    (
#        '74% of the company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH BY YEARForbes ...',
#        {
#           'url': 'https://www.forbes.com/profile/elon-musk', 
#           'score': 0.81,
#           ...
#        }
#    ),
#    (
#        'founded in 2002, is worth nearly $150 billion after a $750 million tender offer in June 2023 ...',
#        {
#           'url': 'https://www.forbes.com/profile/elon-musk', 
#           'score': 0.73,
#           ...
#        }
#    )
# ]
```

<Note>
  When `citations=True`, note that the returned `sources` are a list of tuples where each tuple has two elements (in the following order):

  1. source chunk
  2. dictionary with metadata about the source chunk
     * `url`: url of the source
     * `doc_id`: document id (used for book keeping purposes)
     * `score`: score of the source chunk with respect to the question
     * other metadata you might have added at the time of adding the source
</Note>

### Without citations

If you just want to return answers and don't want to return citations, you can use the following example:

```python Without Citations theme={null}
from embedchain import App

# Initialize app
app = App()

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

# Get relevant answer for your query
answer = app.query("What is the net worth of Elon?")
print(answer)
# Answer: The net worth of Elon Musk is $221.9 billion.
```