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Copy file name to clipboardExpand all lines: docs/how-to-guides/query-api.md
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@@ -30,16 +30,15 @@ While you can [make direct HTTP requests](#making-direct-http-requests) to Logfi
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we provide Python clients to simplify the process of interacting with the API from Python.
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Logfire provides both synchronous and asynchronous clients.
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These clients are currently experimental, meaning we might introduce breaking changes in the future.
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To use these clients, you can import them from the `experimental` namespace:
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To use these clients, you can import them from the `query_client` namespace:
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```python
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from logfire.experimental.query_client import AsyncLogfireQueryClient, LogfireQueryClient
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from logfire.query_client import AsyncLogfireQueryClient, LogfireQueryClient
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```
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!!! note "Additional required dependencies"
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To use the query clients provided in `logfire.experimental.query_client`, you need to install `httpx`.
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To use the query clients provided in `logfire.query_client`, you need to install `httpx`.
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If you want to retrieve Arrow-format responses, you will also need to install `pyarrow`.
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@@ -57,7 +56,7 @@ Here's an example of how to use these clients:
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from io import StringIO
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import polars as pl
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from logfire.experimental.query_client import AsyncLogfireQueryClient
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from logfire.query_client import AsyncLogfireQueryClient
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async def main():
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from io import StringIO
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import polars as pl
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from logfire.experimental.query_client import LogfireQueryClient
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from logfire.query_client import LogfireQueryClient
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def main():
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-**`row_oriented`**: Only affects JSON responses. If set to `true`, the JSON response will be row-oriented; otherwise, it will be column-oriented.
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All query parameters besides `sql` are optional and can be used in any combination to tailor the API response to your needs.
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### Important Notes
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-**Experimental Feature**: The query clients are under the `experimental` namespace, indicating that the API may change in future versions.
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-**Environment Configuration**: Remember to securely store your read token in environment variables or a secure vault for production use.
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With read tokens, you have the flexibility to integrate Logfire into your workflow, whether using Python scripts, data analysis tools, or other systems.
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