This module defines the paper download agent that connects to the arXiv API to fetch
paper details and PDFs. It is part of the Talk2Scholars project.
get_app(uniq_id, llm_model)
Initializes and returns the LangGraph application for the Talk2Scholars paper download agent.
Parameters:
| Name |
Type |
Description |
Default |
uniq_id
|
str
|
A unique identifier for tracking the current session.
|
required
|
llm_model
|
BaseChatModel
|
The language model to be used by the agent.
Defaults to ChatOpenAI(model="gpt-4o-mini", temperature=0.5).
|
required
|
Returns:
| Name | Type |
Description |
StateGraph |
|
A compiled LangGraph application that enables the paper download agent to
process user queries and retrieve arXiv papers.
|
Source code in aiagents4pharma/talk2scholars/agents/paper_download_agent.py
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85 | def get_app(uniq_id, llm_model: BaseChatModel):
"""
Initializes and returns the LangGraph application for the Talk2Scholars paper download agent.
Args:
uniq_id (str): A unique identifier for tracking the current session.
llm_model (BaseChatModel, optional): The language model to be used by the agent.
Defaults to ChatOpenAI(model="gpt-4o-mini", temperature=0.5).
Returns:
StateGraph: A compiled LangGraph application that enables the paper download agent to
process user queries and retrieve arXiv papers.
"""
# Load Hydra configuration
logger.info("Loading Hydra configuration for Talk2Scholars paper download agent")
with hydra.initialize(version_base=None, config_path="../configs"):
cfg = hydra.compose(
config_name="config",
overrides=["agents/talk2scholars/paper_download_agent=default"],
)
cfg = cfg.agents.talk2scholars.paper_download_agent
# Define tools properly
tools = ToolNode([download_arxiv_paper, query_results])
# Define the model
logger.info("Using OpenAI model %s", llm_model)
model = create_react_agent(
llm_model,
tools=tools,
state_schema=Talk2Scholars,
prompt=cfg.paper_download_agent,
checkpointer=MemorySaver(),
)
def paper_download_agent_node(state: Talk2Scholars) -> Dict[str, Any]:
"""
Processes the current state to fetch the arXiv paper.
"""
logger.info("Creating paper download agent node with thread_id: %s", uniq_id)
result = model.invoke(state, {"configurable": {"thread_id": uniq_id}})
return result
# Define new graph
workflow = StateGraph(Talk2Scholars)
# Adding node for paper download agent
workflow.add_node("paper_download_agent", paper_download_agent_node)
# Entering into the agent
workflow.add_edge(START, "paper_download_agent")
# Memory management for states between graph runs
checkpointer = MemorySaver()
# Compile the graph
app = workflow.compile(checkpointer=checkpointer, name="agent_paper_download")
# Logging the information and returning the app
logger.info("Compiled the graph")
return app
|