first node is working!

README.md

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+
+## HypePack
+
+A Python package for developing your own hype session with AI to motivate you to work on your goals.

hype_pack/hype_agent.py

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-from utils.state import State
-from utils.tools import read_pdf
+from hype_pack.utils.state import InterviewState
+from hype_pack.utils.nodes import build_reference_material_node
+from hype_pack.utils.state import InitialInput
 
-def upload_documents(pdf_path: str, personal_history_text: str, aspiring_position_text: str) -> State:
-    """
-    Uploads documents and updates the state with focus points.
-    
-    Args:
-    - pdf_path (str): Path to the PDF file.
-    - personal_history_text (str): Free text input for personal history.
-    - aspiring_position_text (str): Free text input for aspiring position.
-    
-    Returns:
-    - State: Updated state with focus points.
-    """
-    # Read PDF and create a new state instance
-    pdf_text = read_pdf(pdf_path)
-    
-    # Create a new State instance with the gathered data
-    state = State(
-        personal_history_focus_points=pdf_text + personal_history_text,
-        aspiring_position_focus_points=aspiring_position_text
-    )
-    
-    # Here you would add logic to generate focus points from the text
-    # For now, we just return the state
-    return state
+def hype_agent(initial_input: InitialInput) -> InterviewState:
+    interview_state = InterviewState(user_inital_input=initial_input)
+    interview_state = build_reference_material_node(interview_state)
+    return interview_state
+
+if __name__ == "__main__":
+    initial_input = InitialInput(
+        resume_text=(
+            "John Doe\n"
+            "Experienced data scientist with expertise in machine learning, data analysis, and business intelligence.\n"
+        ),
+        personal_text="Seeking to transition into a leadership role within the AI/ML domain.",
+        job_text=(
+            "Role: Senior Data Scientist\n"
+            "Responsibilities:\n"
+            "- Develop machine learning models to enhance product recommendations.\n"
+            "- Lead data science team to design experiments and analyze data.\n"
+            "- Communicate findings to stakeholders using dashboards and reports.\n"
+            "Requirements:\n"
+            "- 8+ years experience in data science or related fields.\n"
+            "- Proficiency in Python, SQL, and visualization tools like Power BI or Tableau.\n"
+            "- Experience with leadership and team management."
+            )
+        )
+    hype_agent(initial_input)

hype_pack/tests/test_reference_material_node.py

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+from hype_pack.utils.state import InterviewState, InitialInput, ReferenceMaterial
+from hype_pack.utils.nodes import build_reference_material_node  # Import the function to be tested
+
+# Define the initial input for the interview
+initial_input = InitialInput(
+    resume_text=(
+        "John Doe\n"
+        "Experienced data scientist with expertise in machine learning, data analysis, and business intelligence.\n"
+        "10+ years experience in Python, SQL, and Tableau. Developed predictive models improving operational efficiency by 25%.\n"
+        "Education: MSc in Data Science from XYZ University."
+    ),
+    personal_text="Seeking to transition into a leadership role within the AI/ML domain.",
+    job_text=(
+        "Role: Senior Data Scientist\n"
+        "Responsibilities:\n"
+        "- Develop machine learning models to enhance product recommendations.\n"
+        "- Lead data science team to design experiments and analyze data.\n"
+        "- Communicate findings to stakeholders using dashboards and reports.\n"
+        "Requirements:\n"
+        "- 8+ years experience in data science or related fields.\n"
+        "- Proficiency in Python, SQL, and visualization tools like Power BI or Tableau.\n"
+        "- Experience with leadership and team management."
+    )
+)
+
+# Create an empty reference material object
+empty_reference_material = ReferenceMaterial()
+
+# Create the interview state with the provided initial input
+interview_state = InterviewState(
+    user_inital_input=initial_input,
+    reference_material=empty_reference_material
+)
+
+# Now this `interview_state` can be used to test the function.
+updated_state = build_reference_material_node(interview_state)
+
+# Output the updated state for inspection
+print(updated_state.reference_material)
+
+# Add assertions to verify the expected behavior
+assert updated_state.reference_material is not None, "Reference material should not be None"
+# Add more specific assertions based on expected structure and content of reference_material

hype_pack/utils/nodes.py

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+from langchain_openai import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from hype_pack.utils.state import InterviewState, ReferenceMaterial
+
+def build_reference_material_node(interview_state: InterviewState) -> InterviewState:
+    """
+    Analyzes candidate background and job requirements to generate structured reference material.
+    """
+    print(f"Start State: {interview_state}")
+    print(f"\n\n")
+    
+    # Initialize the LLM with structured output
+    llm = ChatOpenAI(
+        model="gpt-4o-mini",
+        temperature=0.1
+    ).with_structured_output(ReferenceMaterial)
+
+    # Create a simple prompt
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", "You are an expert career analyst. Analyze the provided information to generate a structured analysis."),
+        ("human", """
+        Resume: {resume}
+        Personal Info: {personal}
+        Job Description: {job}
+        """)
+    ])
+
+    # Get structured output directly
+    reference_material = llm.invoke(prompt.format_messages(
+        resume=interview_state.user_inital_input.resume_text,
+        personal=interview_state.user_inital_input.personal_text or "",
+        job=interview_state.user_inital_input.job_text or ""
+    ))
+    print(reference_material)
+    # Convert ReferenceMaterial instance to a dictionary if needed
+    if isinstance(reference_material, ReferenceMaterial):
+        reference_material = reference_material.model_dump()
+    
+    # Update state with reference material only
+    interview_state.reference_material = reference_material
+    print(f"EndState: {interview_state.reference_material}")
+
+    return interview_state

hype_pack/utils/state.py

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+from pydantic import BaseModel, Field
+from typing import List, Optional
+
+class ReferenceMaterial(BaseModel):
+    """Structured analysis of candidate background and target position."""
+    
+    personal_history_summary: str = Field(
+        description="Summary of candidate's career background and key achievements"
+    )
+    
+    aspiring_position_summary: str = Field(
+        description="Overview of the target role and its key requirements"
+    )
+    
+    personal_focus_points: List[str] = Field(
+        default_factory=list,
+        description="Key points highlighting candidate's relevant experiences and skills"
+    )
+    
+    aspiring_position_focus_points: List[str] = Field(
+        default_factory=list,
+        description="Essential requirements and expectations of the target role"
+    )
+
+class QAPair(BaseModel):
+    """Individual question-answer interaction during the interview process."""
+    
+    question_id: int = Field(description="Unique identifier for the Q&A pair")
+    question: str = Field(description="The question asked")
+    answer: Optional[str] = Field(default=None, description="The generated or provided answer")
+    
+class InitialInput(BaseModel):
+    """Raw input from the user."""
+    resume_text: str = Field(default="", description="Raw text extracted from the resume")
+    personal_text: Optional[str] = Field(default=None, description="Additional personal information provided by the user")
+    job_text: Optional[str] = Field(default=None, description="Job description or position-related text")
+
+class InterviewState(BaseModel):
+    """Current state of the interview process."""
+    user_inital_input: InitialInput
+    reference_material: Optional[ReferenceMaterial] = None
+    qa_history: List[QAPair] = Field(default_factory=list)
+    message_history: List[str] = Field(default_factory=list)  # Consider using specific message types if applicable
+    transcript: str = Field(default="", description="Generated podcast transcript as a single string")