EARNING CALL TRANSCRIPTS LOADER

pip install llama-index-readers-earnings-call-transcript

This loader fetches the earning call transcripts of US based companies from the website discountingcashflows.com. It is not available for commercial purposes

Install the required dependencies

pip install -r requirements.txt

The Earning call transcripts takes in three arguments

• Year
• Ticker symbol
• Quarter name from the list ["Q1","Q2","Q3","Q4"]

Usage

from llama_index.readers.earnings_call_transcript import EarningsCallTranscript

loader = EarningsCallTranscript(2023, "AAPL", "Q3")
docs = loader.load_data()

The metadata of the transcripts are the following

• ticker
• quarter
• date_time
• speakers_list

Examples

Llama Index

from llama_index.core import VectorStoreIndex, download_loader

from llama_index.readers.earnings_call_transcript import EarningsCallTranscript

loader = EarningsCallTranscript(2023, "AAPL", "Q3")
docs = loader.load_data()

index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

response = query_engine.query(
    "What was discussed about Generative AI?",
)
print(response)

Langchain

from langchain.agents import Tool
from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI

from llama_index.readers.earnings_call_transcript import EarningsCallTranscript

loader = EarningsCallTranscript(2023, "AAPL", "Q3")
docs = loader.load_data()

tools = [
    Tool(
        name="LlamaIndex",
        func=lambda q: str(index.as_query_engine().query(q)),
        description="useful for questions about investor transcripts calls for a company. The input to this tool should be a complete english sentence.",
        return_direct=True,
    ),
]
llm = ChatOpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent="conversational-react-description")
agent.run("What was discussed about Generative AI?")