expanding

chains/diagnoser_chain.py

@@ -2,11 +2,13 @@
 from pydantic import BaseModel
 from typing import Any
 from langchain_core.prompts.chat import ChatPromptTemplate
+from config.exercise_standardizer import standardize_exercise
+
 
 class DiagnoserChain(BaseModel):
     template_standardize: ChatPromptTemplate
-    template_diagnose: ChatPromptTemplate
     llm_standardize: Any  # Fixed LLM for step 1
+    template_diagnose: ChatPromptTemplate
     llm_diagnose: Any  # User-selectable LLM for step 2
 
     async def run(self, user_query: str, exercise_format: str) -> str:
@@ -15,72 +17,18 @@ class DiagnoserChain(BaseModel):
           1. Standardizes the exercise formatting (if exercise_format isn't Raw).
           2. Generates a diagnosis from the standardized format.
         """
-        # --- Step 1: Standardize the exercise formatting ---
-        if exercise_format == "Raw (original)":
-            standardized_exercise = user_query
-        else:
-            mapping = {
-                "Markdown": (
-                    "Please format the exercise in Markdown, similarly to this example:\n\n"
-                    "**Theorie**  \n"
-                    "Eenzaamheid wordt door ieder persoon anders ervaren en is daarom subjectief.\n\n"
-                    "---\n\n"
-                    "**Vraag**  \n"
-                    "Wat is de meest passende definitie van eenzaamheid?\n\n"
-                    "1. Het gevoel geen connectie te hebben met anderen  \n"
-                    "2. Regelmatig in je eentje zijn  \n"
-                    "3. Beide bovenstaande  \n"
-                    "4. Geen van bovenstaande  \n\n"
-                    "**Correct antwoord:**  \n"
-                    "1. Het gevoel geen connectie te hebben met anderen."
-                ),
-                "XML": (
-                    "Please reformat in XML, following this example:\n"
-                    "<exercise>\n"
-                    "    <content>\n"
-                    "        <question>Theorie:\n"
-                    "Eenzaamheid wordt door ieder persoon anders ervaren en is daarom subjectief.\n\n"
-                    "Vraag:\n"
-                    "Wat is de meest passende definitie van eenzaamheid?</question>\n"
-                    "        <choices>\n"
-                    "            <choice id=\"1\">Het gevoel geen connectie te hebben met anderen</choice>\n"
-                    "            <choice id=\"2\">Regelmatig in je eentje zijn</choice>\n"
-                    "            <choice id=\"3\">Beide bovenstaande</choice>\n"
-                    "            <choice id=\"4\">Geen van bovenstaande</choice>\n"
-                    "        </choices>\n"
-                    "    </content>\n"
-                    "    <answer>\n"
-                    "        <correct-choice>1</correct-choice>\n"
-                    "        <explanation></explanation>\n"
-                    "    </answer>\n"
-                    "</exercise>"
-                ),
-                "Plaintext": (
-                    "Please reformat in plain text, following this example:\n\n"
-                    "Theorie\n"
-                    "Eenzaamheid wordt door ieder persoon anders ervaren en is daarom subjectief.\n\n"
-                    "Vraag\n"
-                    "Wat is de meest passende definitie van eenzaamheid?\n\n"
-                    "1. Het gevoel geen connectie te hebben met anderen\n"
-                    "2. Regelmatig in je eentje zijn\n"
-                    "3. Beide bovenstaande\n"
-                    "4. Geen van bovenstaande\n\n"
-                    "Correct antwoord:\n"
-                    "1. Het gevoel geen connectie te hebben met anderen."
-                )
-            }
-            formatting_instructions = mapping.get(exercise_format, "Please reformat the given exercise to ease further processing.")
-            prompt_std = await self.template_standardize.aformat_prompt(
-                user_input=user_query,
-                formatting_instructions=formatting_instructions
-            )
-            std_messages = prompt_std.to_messages()
-            standardized_exercise = await self.llm_standardize.ainvoke(std_messages)
+        # --- Step 1: Standardize the exercise formatting (if exercise_format isn't 'Raw (original)') ---
+        standardized_exercise = await standardize_exercise(
+            user_query, exercise_format, self.template_standardize, self.llm_standardize
+        )
 
         # --- Step 2: Generate a diagnosis using the standardized exercise ---
-        prompt_diag = await self.template_diagnose.aformat_prompt(standardized_exercise=standardized_exercise)
-        diag_messages = prompt_diag.to_messages()
-        diagnosis = await self.llm_diagnose.ainvoke(diag_messages)
+        prompt_diagnose = await self.template_diagnose.aformat_prompt(standardized_exercise=standardized_exercise)
+        diagnose_messages = prompt_diagnose.to_messages()
+        diagnosis = ""
+        async for token in self.llm_diagnose.astream(diagnose_messages):
+            diagnosis += token
+            # Here you could, for example, update a UI element if you were streaming tokens to the frontend.
         return diagnosis
 
     class Config:

chains/distractors_chain.py

@@ -2,12 +2,39 @@
 from pydantic import BaseModel
 from typing import Any
 from langchain_core.prompts.chat import ChatPromptTemplate
+from config.exercise_standardizer import standardize_exercise
+
 
 class DistractorsChain(BaseModel):
+    llm_standardize: Any    # Fixed LLM for step 1
+    template_standardize: ChatPromptTemplate
     template: ChatPromptTemplate
-    llm: Any
+    llm: Any                # User-selectable LLM for step 2
+
+    async def run(self, user_query: str, exercise_format: str) -> str:
+        """
+        Runs the composite chain:
+          1. Standardizes the exercise formatting (if exercise_format isn't Raw).
+          2. Generates new distractors from the standardized format.
+        """
+        # --- Step 1: Standardize the exercise formatting (if exercise_format isn't 'Raw (original)') ---
+        standardized_exercise = await standardize_exercise(
+            user_query, exercise_format, self.template_standardize, self.llm_standardize
+        )
+
+        # --- Step 2: Generate new distractors using the standardized exercise ---
+        prompt_distractors = await self.template_distractors.aformat_prompt(standardized_exercise=standardized_exercise)
+        distractors_messages = prompt_distractors.to_messages()
+        distractors = ""
+        async for token in self.llm_distr.astream(distractors_messages):
+            distractors += token
+            # Here you could, for example, update a UI element if you were streaming tokens to the frontend.
+        return distractors
+
+
+
+
 
-    async def run(self, user_query: str) -> str:
         prompt = await self.template.aformat_prompt(user_input=user_query)
         messages = prompt.to_messages()
         result = await self.llm.ainvoke(messages)

config/chain_configs.py

@@ -9,13 +9,16 @@ chain_configs = {
     "diagnoser": {
         "class": DiagnoserChain,
         "template_standardize": standardize_template,
+        "llm_standardize": llms["GPT-4o-mini"],     # Always fixed
         "template_diagnose": diagnose_template,
-        "llm_standardize": llms["GPT-4o-mini"],  # Always fixed
-        "llm_diagnose": llms["GPT-4o"],          # Default; can be replaced in UI
+        "llm_diagnose": llms["GPT-4o"],             # Default; can be replaced in UI
+
     },
     "distractors": {
         "class": DistractorsChain,
-        "template": distractors_template,
-        "llm": llms["GPT-4o"],
+        "template_standardize": standardize_template,
+        "llm_standardize": llms["GPT-4o-mini"],     # Always fixed
+        "template_distractors": distractors_template,
+        "llm_distractors": llms["GPT-4o"],                # Default; can be replaced in UI
     },
 }

config/exercise_standardizer.py

@@ -0,0 +1,32 @@
+# exercise_standardizer.py
+from format_mappings import FORMAT_MAPPINGS
+from langchain_core.prompts import ChatPromptTemplate
+from typing import Any
+from config.format_mappings import FORMAT_MAPPINGS
+
+async def standardize_exercise(user_query: str, exercise_format: str, template: ChatPromptTemplate, llm: Any) -> str:
+    """
+    Standardizes an exercise's format using the specified template and LLM.
+    Uses token streaming for efficiency.
+    """
+    if exercise_format == "Raw (original)":
+        return user_query  # No transformation needed
+
+    formatting_instructions = FORMAT_MAPPINGS.get(
+        exercise_format,
+        "Please reformat the given exercise to ease further processing."
+    )
+
+    prompt_std = await template.aformat_prompt(
+        user_input=user_query,
+        formatting_instructions=formatting_instructions
+    )
+
+    std_messages = prompt_std.to_messages()
+
+    # Stream tokens to construct the standardized response
+    standardized_exercise = ""
+    async for token in llm.astream(std_messages):
+        standardized_exercise += token
+
+    return standardized_exercise

config/format_mappings.py

@@ -0,0 +1,52 @@
+# format_mappings.py
+
+FORMAT_MAPPINGS = {
+    "Markdown": (
+        "Please format the exercise in Markdown, similarly to this example:\n\n"
+        "**Theorie**  \n"
+        "Eenzaamheid wordt door ieder persoon anders ervaren en is daarom subjectief.\n\n"
+        "---\n\n"
+        "**Vraag**  \n"
+        "Wat is de meest passende definitie van eenzaamheid?\n\n"
+        "1. Het gevoel geen connectie te hebben met anderen  \n"
+        "2. Regelmatig in je eentje zijn  \n"
+        "3. Beide bovenstaande  \n"
+        "4. Geen van bovenstaande  \n\n"
+        "**Correct antwoord:**  \n"
+        "1. Het gevoel geen connectie te hebben met anderen."
+    ),
+    "XML": (
+        "Please reformat in XML, following this example:\n"
+        "<exercise>\n"
+        "    <content>\n"
+        "        <question>Theorie:\n"
+        "Eenzaamheid wordt door ieder persoon anders ervaren en is daarom subjectief.\n\n"
+        "Vraag:\n"
+        "Wat is de meest passende definitie van eenzaamheid?</question>\n"
+        "        <choices>\n"
+        "            <choice id=\"1\">Het gevoel geen connectie te hebben met anderen</choice>\n"
+        "            <choice id=\"2\">Regelmatig in je eentje zijn</choice>\n"
+        "            <choice id=\"3\">Beide bovenstaande</choice>\n"
+        "            <choice id=\"4\">Geen van bovenstaande</choice>\n"
+        "        </choices>\n"
+        "    </content>\n"
+        "    <answer>\n"
+        "        <correct-choice>1</correct-choice>\n"
+        "        <explanation></explanation>\n"
+        "    </answer>\n"
+        "</exercise>"
+    ),
+    "Plaintext": (
+        "Please reformat in plain text, following this example:\n\n"
+        "Theorie\n"
+        "Eenzaamheid wordt door ieder persoon anders ervaren en is daarom subjectief.\n\n"
+        "Vraag\n"
+        "Wat is de meest passende definitie van eenzaamheid?\n\n"
+        "1. Het gevoel geen connectie te hebben met anderen\n"
+        "2. Regelmatig in je eentje zijn\n"
+        "3. Beide bovenstaande\n"
+        "4. Geen van bovenstaande\n\n"
+        "Correct antwoord:\n"
+        "1. Het gevoel geen connectie te hebben met anderen."
+    )
+}

config/templates.py

@@ -13,7 +13,7 @@ standardize_template = ChatPromptTemplate(
 # Template to generate a diagnosis from the standardized exercise.
 diagnose_template = ChatPromptTemplate(
     messages=[
-        ("system", "You are a diagnostic assistant. Based on the given exercise(s), provide a detailed diagnosis of potential issues. What makes this exercise sub-par, worse than it could be, not yet perfect?"),
+        ("system", "You are a diagnostic assistant. Based on the given exercise(s), provide a detailed diagnosis of potential issues. What makes this exercise sub-par, worse than it could be, not yet perfect? Only give the diagnosis, no solutions."),
         ("human", "{standardized_exercise}")
     ],
     input_variables=["standardized_exercise"]
@@ -22,8 +22,8 @@ diagnose_template = ChatPromptTemplate(
 # Template for the distractors brainstorm (a single-step chain).
 distractors_template = ChatPromptTemplate(
     messages=[
-        ("system", "You are a brainstorming assistant. Provide creative distractors and brainstorm ideas based on the user input."),
+        ("system", "You are a brainstorming assistant. Based on the given multiple choice exercise, come up with 10 additional distractors: alternative answer options that are not correct, yet plausible enough that a poorly informed student might pick them. Vary the degree of 'almost correctness' and 'clearly incorrectness' between them to provide a wide range of options."),
         ("human", "{user_input}")
     ],
-    input_variables=["user_input"]
+    input_variables=["standardized_exercise"]
 )