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---
title: '💬 chat'
---

`chat()` method allows you to chat over your data sources using a user-friendly chat API. You can find the signature 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="session_id" type="str" optional>
    Session ID of the chat. This can be used to maintain chat history of different user sessions. Default value: `default`
</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
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.chat("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
from embedchain import App

# Initialize app
app = App()

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

# Chat on your data using `.chat()`
answer = app.chat("What is the net worth of Elon?")
print(answer)
# Answer: The net worth of Elon Musk is $221.9 billion.
```

### With session id

If you want to maintain chat sessions for different users, you can simply pass the `session_id` keyword argument. See the example below:

```python With session id
from embedchain import App

app = App()
app.add("https://www.forbes.com/profile/elon-musk")

# Chat on your data using `.chat()`
app.chat("What is the net worth of Elon Musk?", session_id="user1")
# 'The net worth of Elon Musk is $250.8 billion.'
app.chat("What is the net worth of Bill Gates?", session_id="user2")
# "I don't know the current net worth of Bill Gates."
app.chat("What was my last question", session_id="user1")
# 'Your last question was "What is the net worth of Elon Musk?"'
```

### With custom context window

If you want to customize the context window that you want to use during chat (default context window is 3 document chunks), you can do using the following code snippet:

```python with custom chunks size
from embedchain import App
from embedchain.config import BaseLlmConfig

app = App()
app.add("https://www.forbes.com/profile/elon-musk")

query_config = BaseLlmConfig(number_documents=5)
app.chat("What is the net worth of Elon Musk?", config=query_config)
```