# 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="")