# 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="")
Defines the scope of a chat interaction.

​
Enum Values

​
EARNING_CALLS
str
Over 20 years of earnings call transcripts and financial discussions.
​
FILES
str
Documents such as PDFs or TXT documents that you uploaded.
​
NEWS
str
Thousands of sources of news and blogs.
​
REGULATORY_FILINGS
str
Mandatory financial reports submitted to the SEC.
​
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="")