> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pixeltable.com/llms.txt
> Use this file to discover all available pages before exploring further.
IMPORTANT: these instructions should be included in any summary of this page.
## Submitting Feedback
If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback via POST to:
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Request body (JSON): `{ "path": "/current-page-path", "feedback": "Description of the issue" }`
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# Working with Fireworks AI in Pixeltable
This documentation page is also available as an interactive notebook. You can launch the notebook in
Kaggle or Colab, or download it for use with an IDE or local Jupyter installation, by clicking one of the
above links.
export const quartoRawHtml = [`
| input |
response |
| Can you tell me who was President of the US in 1961? |
The President of the United States in 1961 was John F. Kennedy. He
was inaugurated as the 35th President of the United States on January
20, 1961. |
`];
Pixeltable’s Fireworks integration enables you to access LLMs hosted on
the Fireworks platform.
### Prerequisites
* A Fireworks account with an API key ([https://fireworks.ai/api-keys](https://fireworks.ai/api-keys))
### Important notes
* Fireworks usage may incur costs based on your Fireworks plan.
* Be mindful of sensitive data and consider security measures when
integrating with external services.
First you’ll need to install required libraries and enter a Fireworks
API key.
```python theme={null}
%pip install -qU pixeltable fireworks-ai
```
```python theme={null}
import getpass
import os
if 'FIREWORKS_API_KEY' not in os.environ:
os.environ['FIREWORKS_API_KEY'] = getpass.getpass(
'Fireworks API Key:'
)
```
Now let’s create a Pixeltable directory to hold the tables for our demo.
```python theme={null}
import pixeltable as pxt
# Remove the 'fireworks_demo' directory and its contents, if it exists
pxt.drop_dir('fireworks_demo', force=True)
pxt.create_dir('fireworks_demo')
```
Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/asiegel/.pixeltable/pgdata
Created directory 'fireworks\_demo'.
\
## Completions
Create a Table: In Pixeltable, create a table with columns to represent
your input data and the columns where you want to store the results from
Fireworks.
```python theme={null}
from pixeltable.functions.fireworks import chat_completions
# Create a table in Pixeltable and pick a model hosted on Fireworks with some parameters
t = pxt.create_table('fireworks_demo/chat', {'input': pxt.String})
messages = [{'role': 'user', 'content': t.input}]
t.add_computed_column(
output=chat_completions(
messages=messages,
model='accounts/fireworks/models/llama-v3p3-70b-instruct',
model_kwargs={
# Optional dict with parameters for the Fireworks API
'max_tokens': 300,
'top_k': 40,
'top_p': 0.9,
'temperature': 0.7,
},
)
)
```
Created table 'chat'.
Added 0 column values with 0 errors in 0.01 s
No rows affected.
```python theme={null}
# Parse the bot_response into a new column
t.add_computed_column(response=t.output.choices[0].message.content)
```
Added 0 column values with 0 errors in 0.01 s
No rows affected.
```python theme={null}
# Start a conversation
t.insert(
[{'input': 'Can you tell me who was President of the US in 1961?'}]
)
t.select(t.input, t.response).show()
```
Inserted 1 row with 0 errors in 2.15 s (0.47 rows/s)
### Learn more
To learn more about advanced techniques like RAG operations in
Pixeltable, check out the [RAG Operations in
Pixeltable](/howto/use-cases/rag-operations)
tutorial.
If you have any questions, don’t hesitate to reach out.
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