To view a call using the Python API, you can use the [`get_call`](../../reference/python-sdk/weave/trace/weave.trace.weave_client#method-get_call) method:
```python
import weave
# Initialize the client
client = weave.init("your-project-name")
# Get a specific call by its ID
call = client.get_call("call-uuid-here")
print(call)
```
### Customize rendered traces with `weave.Markdown`
Weave makes it easy to tailor how your traced operations are displayed without losing the original data.
With `weave.Markdown`, you can render inputs and outputs as clean, readable blocks of formatted content while preserving the underlying structured data for programmatic access.
Use `postprocess_inputs` and `postprocess_output` in your `@weave.op` decorator to enhance trace readability.
The following code sample uses postprocessors to render a call in Weave to with emojis and nicer formatting:
```python
import weave
def postprocess_inputs(query) -> weave.Markdown:
search_box = f"""
**Search Query:**
``+`
{query}
``+`
"""
return {"search_box": weave.Markdown(search_box),
"query": query}
def postprocess_output(docs) -> weave.Markdown:
formatted_docs = f"""
# {docs[0]["title"]}
{docs[0]["content"]}
[Read more]({docs[0]["url"]})
---
# {docs[1]["title"]}
{docs[1]["content"]}
[Read more]({docs[1]["url"]})
"""
return weave.Markdown(formatted_docs)
@weave.op(
postprocess_inputs=postprocess_inputs,
postprocess_output=postprocess_output,
)
def rag_step(query):
# example newspaper articles of the companies on the S&P 500
docs = [
{
"title": "OpenAI",
"content": "OpenAI is a company that makes AI models.",
"url": "https://www.openai.com",
},
{
"title": "Google",
"content": "Google is a company that makes search engines.",
"url": "https://www.google.com",
},
]
return docs
if __name__ == "__main__":
weave.init('markdown_renderers')
rag_step("Tell me about OpenAI")
```
