# EARNING CALL TRANSCRIPTS LOADER
This loader fetches the earning call transcripts of US based companies from the website [discountingcashflows.com](https://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
```python
from llama_index import download_loader
IMDBReviewsloader = download_loader("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
```python
from llama_index import download_loader
from llama_index import VectorStoreIndex, download_loader
EarningsCallTranscript = download_loader("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
```python
from llama_index import download_loader
from langchain.agents import Tool
from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
EarningsCallTranscript = download_loader("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?")
```
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"description": "# EARNING CALL TRANSCRIPTS LOADER\n\nThis loader fetches the earning call transcripts of US based companies from the website [discountingcashflows.com](https://discountingcashflows.com/). It is not available for commercial purposes\n\nInstall the required dependencies\n\n```\npip install -r requirements.txt\n```\n\nThe Earning call transcripts takes in three arguments\n\n- Year\n- Ticker symbol\n- Quarter name from the list [\"Q1\",\"Q2\",\"Q3\",\"Q4\"]\n\n## Usage\n\n```python\nfrom llama_index import download_loader\n\nIMDBReviewsloader = download_loader(\"EarningsCallTranscript\")\n\nloader = EarningsCallTranscript(2023, \"AAPL\", \"Q3\")\ndocs = loader.load_data()\n```\n\nThe metadata of the transcripts are the following\n\n- ticker\n- quarter\n- date_time\n- speakers_list\n\n## Examples\n\n#### Llama Index\n\n```python\nfrom llama_index import download_loader\nfrom llama_index import VectorStoreIndex, download_loader\n\nEarningsCallTranscript = download_loader(\"EarningsCallTranscript\")\n\nloader = EarningsCallTranscript(2023, \"AAPL\", \"Q3\")\ndocs = loader.load_data()\n\nindex = VectorStoreIndex.from_documents(documents)\nquery_engine = index.as_query_engine()\n\nresponse = query_engine.query(\n \"What was discussed about Generative AI?\",\n)\nprint(response)\n```\n\n#### Langchain\n\n```python\nfrom llama_index import download_loader\nfrom langchain.agents import Tool\nfrom langchain.agents import initialize_agent\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.llms import OpenAI\n\nEarningsCallTranscript = download_loader(\"EarningsCallTranscript\")\n\nloader = EarningsCallTranscript(2023, \"AAPL\", \"Q3\")\ndocs = loader.load_data()\n\ntools = [\n Tool(\n name=\"LlamaIndex\",\n func=lambda q: str(index.as_query_engine().query(q)),\n description=\"useful for questions about investor transcripts calls for a company. The input to this tool should be a complete english sentence.\",\n return_direct=True,\n ),\n]\nllm = ChatOpenAI(temperature=0)\nagent = initialize_agent(tools, llm, agent=\"conversational-react-description\")\nagent.run(\"What was discussed about Generative AI?\")\n```\n",
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