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modules/quiz/quiz_helper.py

@@ -0,0 +1,130 @@
+import random
+from typing import List, Optional, Literal
+from pydantic import BaseModel, Field
+from config import SanatanConfig
+from sanatan_assistant import allowedScriptureTitles, query, allowedCollections
+from openai import OpenAI
+
+client = OpenAI()
+
+
+class Question(BaseModel):
+    preferred_language : str = Field(description="User's preferred language. Respond in this language")
+    question: str = Field(
+        description="The generated question to be asked to the user"
+    )
+    scripture: allowedScriptureTitles = Field(
+        description="The scripture title this question is sourced from (e.g., Bhagavad Gita, Divya Prabandham)"
+    )
+    native_lyrics: str = Field(
+        description="The lyrics in its native/original script (e.g., Sanskrit/Tamil). Sanitize this if there are garbled characters."
+    )
+    transliteration: str = Field(
+        description="The transliterated text of the verse in English/the user's preferred language"
+    )
+    translation: str = Field(
+        description="The translated meaning of the verse in English/the user's preferred language"
+    )
+    detailed_explanation: Optional[str] = Field(
+        default=None,
+        description="A detailed explanation of the verse or context, if available in the context"
+    )
+    word_by_word_meaning: Optional[str] = Field(
+        default=None,
+        description="Word-by-word meaning (in both native and english) or breakdown of the verse, if available in the context"
+    )
+    verse_number: Optional[int] = Field(
+        default=None,
+        description="The absolute verse number from the context if available."
+    )
+    page_number: Optional[int] = Field(
+        default=None,
+        description="The page number from the context if available."
+    )
+    verse_reference: Optional[str] = Field(
+        default=None,
+        description="The reference identifier of the verse or the title (e.g., Bhagavad Gita 2.47)"
+    )
+    reference_link: Optional[str] = Field(
+        default=None,
+        description="The html_url if available"
+    )
+    complexity: Literal["beginner", "intermediate", "advanced"] = Field(
+        description="Difficulty level of the question"
+    )
+    choices: List[str] = Field(
+        description="List of multiple-choice options. Leave empty for open-text questions"
+    )
+    expected_answer: str = Field(
+        description="The correct or expected answer to the question"
+    )
+    answer_explanation: str = Field(
+        description="Explanation why the expected answer is correct"
+    )
+    expected_choice_index: Optional[int] = Field(
+        default=None,
+        description="Index of the correct choice in 'choices'. Null if open-text question"
+    )
+
+
+
+def generate_question(
+    collection: allowedCollections,
+    complexity: Literal["beginner", "intermediate", "advanced"] = "beginner",
+    mode: Literal["mcq", "open"] = "mcq",
+    preferred_lamguage : str = "English"
+) -> Question:
+    """
+    Fetch a random scripture record and have the LLM generate a structured Question.
+    """
+
+    # 1. Fetch random scripture record
+    context = query(
+        collection_name=collection,
+        query=None,
+        metadata_where_clause=None,
+        n_results=1,
+        search_type="random",
+    )
+    if not context:
+        raise ValueError(f"No records found in collection {collection}")
+
+    # 2. Prompt (grounded in record only)
+    prompt = f"""
+    You are a quiz generator. Use ONLY the following scripture record to create a question.
+
+    Context from {collection}:
+    {context}
+
+    Rules:
+    - Do not invent facts beyond the context.
+    - Difficulty level: {complexity}
+    - Mode: {mode}
+    - If mode is 'mcq', generate 3–4 plausible choices (with one correct).
+    - If mode is 'open', leave 'choices' empty and provide a reference answer.
+    - User's preferred language is {preferred_lamguage}. Translate everything except the native verses to this language.
+    """
+
+    # 3. Structured response with Pydantic class reference
+    response = client.chat.completions.parse(
+        model="gpt-5-nano", 
+        messages=[{"role": "user", "content": prompt}],
+        response_format=Question, 
+    )
+
+    # print(response)
+
+    return response.choices[0].message.parsed
+
+
+# Example usage
+if __name__ == "__main__":
+    for i in range(3):
+        scripture = random.choice([s for s in SanatanConfig.scriptures if s["collection_name"] != "yt_metadata"])
+        mode = random.choice(["mcq", "open"])
+        complexity = random.choice(["beginner", "intermediate", "advanced"])
+        q = generate_question(
+            scripture["collection_name"], complexity=complexity, mode=mode
+        )
+        print(q.model_dump_json(indent=1))
+        print("_______________________")

sanatan_assistant.py

@@ -13,7 +13,9 @@ sanatanConfig = SanatanConfig()
 allowedCollections = Literal[
     *[scripture["collection_name"] for scripture in sanatanConfig.scriptures]
 ]
-
+allowedScriptureTitles = Literal[
+    *[scripture["title"] for scripture in sanatanConfig.scriptures]
+]
 
 def format_scripture_answer(
     collection_name: allowedCollections, question: str, query_tool_output: str