# Import classes from the bigdata-client Python SDK        
from bigdata_client import Bigdata
from bigdata_client.models.chat import MarkdownLinkFormatter
formatter = MarkdownLinkFormatter()

# Log in to Bigdata
bigdata = Bigdata("YOUR_USERNAME", "YOUR_PASSWORD")

# Create a new chat with the Markdown formatter
chat = bigdata.chat.new("Pfizer company analysis", formatter=formatter)

# First question
response = chat.ask("Evaluate the experience and reputation of the management team of Pfizer in 2024?", streaming=True)
print(f"\nQuestion:\n - {response.question}")
print(f"\nAnswer:")
for streamingChatInteraction in response:
    print(streamingChatInteraction, end="")
Formatter for inline attributions in chat messages that uses Markdown links.

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Methods

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__init__(headline_length=None, skip_empty_urls=True)

Initialize the MarkdownLinkInlineAttributionFormatter. Parameters:
​
headline_length
int
The maximum length of the headline to be displayed in the link.
​
skip_empty_urls
bool
Whether to skip sources with empty URLs.

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format(index, source)

Format an inline attribution as a Markdown link. Parameters:
​
index
int
required
The index of the attribution within the list of attributions.
​
source
ChatSource
required
The inline attribution to format: ChatSource
Returns: A string representing the formatted attribution as a Markdown link. 
# Import classes from the bigdata-client Python SDK        
from bigdata_client import Bigdata
from bigdata_client.models.chat import MarkdownLinkFormatter
formatter = MarkdownLinkFormatter()

# Log in to Bigdata
bigdata = Bigdata("YOUR_USERNAME", "YOUR_PASSWORD")

# Create a new chat with the Markdown formatter
chat = bigdata.chat.new("Pfizer company analysis", formatter=formatter)

# First question
response = chat.ask("Evaluate the experience and reputation of the management team of Pfizer in 2024?", streaming=True)
print(f"\nQuestion:\n - {response.question}")
print(f"\nAnswer:")
for streamingChatInteraction in response:
    print(streamingChatInteraction, end="")