Create functions.py

functions.py

@@ -0,0 +1,193 @@
+from exa_py import Exa
+from groq import Groq
+import os
+
+# Declare the exa search API
+exa = Exa(api_key=os.getenv("EXA_API_KEY"))
+
+# Define your API Model and key
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+utilized_model = "llama3-70b-8192"
+
+# Options for highlights from Exa search
+highlights_options = {
+    "num_sentences": 7,  # Length of highlights
+    "highlights_per_url": 1,  # Get the best highlight for each URL
+}
+
+def call_llm(prompt):
+    """
+    Call the Exa and Groq APIs to generate content using the provided prompt.
+    """
+    # Perform search with Exa
+    search_response = exa.search_and_contents(query=prompt, highlights=highlights_options, num_results=3, use_autoprompt=True)
+    info = [sr.highlights[0] for sr in search_response.results]
+    
+    # System prompt for Groq LLM
+    system_prompt = "You are an academic PhD proposal generator. Read the provided contexts and generate a well-structured research proposal based on them."
+    user_prompt = f"Sources: {info}\nResearch Prompt: {prompt}"
+    
+    # Call the Groq model
+    completion = client.chat.completions.create(
+        model=utilized_model,
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt},
+        ]
+    )
+    
+    # Return the generated content
+    return completion.choices[0].message.content
+
+
+# Functions to generate different sections of the research proposal
+
+def generate_executive_summary(data):
+    """
+    Generate an executive summary based on the user's input data.
+    """
+    prompt = f"""
+    Generate a concise executive summary for a PhD research proposal with the following details:
+    
+    Research Topic: {data.get("research_topic")}
+    Research Question: {data.get("research_question")}
+    Objectives: {data.get("objectives")}
+    Methodology: {data.get("methodology")}
+    Contribution: {data.get("contribution")}
+    Literature Gap: {data.get("literature_gap")}
+    """
+    return call_llm(prompt)
+
+
+def generate_literature_review_outline(data):
+    """
+    Generate a literature review outline based on the user's input data.
+    """
+    prompt = f"""
+    Generate a structured outline for the literature review of a PhD thesis on the following topic:
+    
+    Research Topic: {data.get("research_topic")}
+    Key Authors: {data.get("key_authors")}
+    Recent Developments: {data.get("recent_developments")}
+    Gaps in Literature: {data.get("literature_gap")}
+    """
+    return call_llm(prompt)
+
+
+def generate_methodology_section(data):
+    """
+    Generate a methodology section for a PhD proposal.
+    """
+    prompt = f"""
+    Write a detailed research methodology section for a PhD proposal based on the following:
+    
+    Research Topic: {data.get("research_topic")}
+    Data Collection Methods: {data.get("data_collection")}
+    Data Analysis Methods: {data.get("data_analysis")}
+    Justification: {data.get("justification")}
+    """
+    return call_llm(prompt)
+
+
+def generate_research_objectives(data):
+    """
+    Generate research objectives using the SMART framework.
+    """
+    prompt = f"""
+    Generate a detailed list of short-term and long-term research objectives for the following PhD thesis topic:
+    
+    Research Topic: {data.get("research_topic")}
+    Objectives: {data.get("objectives")}
+    
+    The objectives should follow the SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound).
+    """
+    return call_llm(prompt)
+
+
+def generate_hypotheses(data):
+    """
+    Generate hypotheses for the research.
+    """
+    prompt = f"""
+    Generate research hypotheses based on the following topic:
+    
+    Research Topic: {data.get("research_topic")}
+    Research Question: {data.get("research_question")}
+    """
+    return call_llm(prompt)
+
+
+def generate_contribution_statement(data):
+    """
+    Generate a contribution statement for a PhD proposal.
+    """
+    prompt = f"""
+    Generate a statement of contribution for the following PhD research proposal:
+    
+    Research Topic: {data.get("research_topic")}
+    Contribution to the Field: {data.get("contribution")}
+    
+    The statement should highlight how the research will address existing gaps and advance knowledge in the field.
+    """
+    return call_llm(prompt)
+
+
+def generate_research_timeline(data):
+    """
+    Generate a detailed research timeline for a PhD thesis.
+    """
+    prompt = f"""
+    Generate a detailed research timeline for completing a PhD thesis on the following topic:
+    
+    Research Topic: {data.get("research_topic")}
+    Total Timeframe: {data.get("total_timeframe")}
+    
+    The timeline should break down tasks into manageable phases (e.g., literature review, data collection, analysis) with deadlines.
+    """
+    return call_llm(prompt)
+
+
+def generate_proposal_introduction(data):
+    """
+    Generate a proposal introduction based on the user's input data.
+    """
+    prompt = f"""
+    Write an engaging introduction for a PhD proposal on the following research topic:
+    
+    Research Topic: {data.get("research_topic")}
+    Research Problem: {data.get("research_problem")}
+    
+    The introduction should provide background, introduce the problem, and explain the significance of the research.
+    """
+    return call_llm(prompt)
+
+
+def generate_limitations_section(data):
+    """
+    Generate a section on research limitations.
+    """
+    prompt = f"""
+    Generate a section describing the potential limitations and challenges of the following research:
+    
+    Research Topic: {data.get("research_topic")}
+    Methodology: {data.get("methodology")}
+    
+    The limitations should address possible obstacles and suggest ways to mitigate them.
+    """
+    return call_llm(prompt)
+
+
+def generate_future_work_section(data):
+    """
+    Generate a future work section for a research proposal.
+    """
+    prompt = f"""
+    Generate a section on future work based on the following research:
+    
+    Research Topic: {data.get("research_topic")}
+    Contribution: {data.get("contribution")}
+    
+    The future work section should suggest further areas for research that could build upon the findings.
+    """
+    return call_llm(prompt)
+