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Documentation Index

Fetch the complete documentation index at: https://docs.bigdata.com/llms.txt

Use this file to discover all available pages before exploring further.

We are sunsetting our SDKs and will no longer add new features, security patches, bug fixes, or technical support for them. To access the latest capabilities and ongoing improvements, we encourage you to migrate to our RESTful API.SDK support will officially end on December 31, 2026. On this date, the underlying endpoints used by the SDKs and related documentation will be decommissioned.To avoid any disruption to your services, please ensure your migration is complete by that date.For migration assistance, please contact us at support@bigdata.com.
Defines the scope of a chat interaction.

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Enum Values

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EARNING_CALLS
str
Over 20 years of earnings call transcripts and financial discussions.
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FILES
str
Documents such as PDFs or TXT documents that you uploaded.
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NEWS
str
Thousands of sources of news and blogs.
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REGULATORY_FILINGS
str
Mandatory financial reports submitted to the SEC.
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FACTSET_TRANSCRIPTS
str
Over 20 years of earnings call transcripts and financial discussions.
# Import classes from the bigdata-client Python SDK        
from bigdata_client import Bigdata
from bigdata_client.models.chat import ChatScope

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

# Create a new chat
chat = bigdata.chat.new("Pfizer company analysis")

# First question using ChatScope
response = chat.ask("Evaluate the experience and reputation of the management team of Pfizer in 2024", streaming=True, scope=ChatScope.NEWS)

print(f"\nQuestion:\n - {response.question}")
print(f"\nAnswer:")
for streamingChatInteraction in response:
  print(streamingChatInteraction, end="")