Delete data/workflow_nodes.py

data/workflow_nodes.py

@@ -1,291 +0,0 @@
-from typing import Dict
-from langchain.prompts import ChatPromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from models import llm, retriever
-from agent_state import AgentState
-from config import config
-
-def expand_query(state: AgentState) -> AgentState:
-    """Expands the user query to improve retrieval of nutrition disorder-related information."""
-    print("---------Expanding Query---------")
-    
-    system_message = '''You are an AI specializing in improving search queries to retrieve the most relevant nutrition disorder-related information.
-Your task is to **refine** and **expand** the given query so that better search results are obtained, while **keeping the original intent** unchanged.
-
-Guidelines:
-- Add **specific details** where needed. Example: If a user asks about "anorexia," specify aspects like symptoms, causes, or treatment options.
-- Include **related terms** to improve retrieval (e.g., "bulimia" → "bulimia nervosa vs binge eating disorder").
-- If the user provides an unclear query, suggest necessary clarifications.
-- **DO NOT** answer the question. Your job is only to enhance the query.
-
-Examples:
-1. User Query: "Tell me about eating disorders."
-   Expanded Query: "Provide details on eating disorders, including types (e.g., anorexia nervosa, bulimia nervosa), symptoms, causes, and treatment options."
-
-2. User Query: "What is anorexia?"
-   Expanded Query: "Explain anorexia nervosa, including its symptoms, causes, risk factors, and treatment options."
-
-3. User Query: "How to treat bulimia?"
-   Expanded Query: "Describe treatment options for bulimia nervosa, including psychotherapy, medications, and lifestyle changes."
-
-4. User Query: "What are the effects of malnutrition?"
-   Expanded Query: "Explain the effects of malnutrition on physical and mental health, including specific nutrient deficiencies and their consequences."
-
-Now, expand the following query:'''
-
-    expand_prompt = ChatPromptTemplate.from_messages([
-        ("system", system_message),
-        ("user", "Expand this query: {query} using the feedback: {query_feedback}")
-    ])
-
-    chain = expand_prompt | llm | StrOutputParser()
-    expanded_query = chain.invoke({"query": state['query'], "query_feedback": state["query_feedback"]})
-    print("expanded_query", expanded_query)
-    state["expanded_query"] = expanded_query
-    return state
-
-def retrieve_context(state: AgentState) -> AgentState:
-    """Retrieves context from the vector store using the expanded or original query."""
-    print("---------retrieve_context---------")
-    query = state['expanded_query']
-    
-    docs = retriever.invoke(query)
-    print("Retrieved documents:", docs)
-    
-    context = [
-        {
-            "content": doc.page_content,
-            "metadata": doc.metadata
-        }
-        for doc in docs
-    ]
-    state['context'] = context
-    print("Extracted context with metadata:", context)
-    return state
-
-def craft_response(state: AgentState) -> AgentState:
-    """Generates a response using the retrieved context, focusing on nutrition disorders."""
-    system_message = '''You are a professional AI nutrition disorder specialist generating responses based on retrieved documents.
-Your task is to use the given **context** to generate a highly accurate, informative, and user-friendly response.
-
-Guidelines:
-- **Be direct and concise** while ensuring completeness.
-- **DO NOT include information that is not present in the context.**
-- If multiple sources exist, synthesize them into a coherent response.
-- If the context does not fully answer the query, state what additional information is needed.
-- Use bullet points when explaining complex concepts.
-
-Example:
-User Query: "What are the symptoms of anorexia nervosa?"
-Context:
-1. Anorexia nervosa is characterized by extreme weight loss and fear of gaining weight.
-2. Common symptoms include restricted eating, distorted body image, and excessive exercise.
-Response:
-"Anorexia nervosa is an eating disorder characterized by extreme weight loss and an intense fear of gaining weight. Common symptoms include:
-- Restricted eating
-- Distorted body image
-- Excessive exercise
-If you or someone you know is experiencing these symptoms, it is important to seek professional help."'''
-
-    response_prompt = ChatPromptTemplate.from_messages([
-        ("system", system_message),
-        ("user", "Query: {query}\nContext: {context}\n\nResponse:")
-    ])
-
-    chain = response_prompt | llm | StrOutputParser()
-    state['response'] = chain.invoke({
-        "query": state['query'],
-        "context": "\n".join([doc["content"] for doc in state['context']])
-    })
-    return state
-
-def score_groundedness(state: AgentState) -> AgentState:
-    """Checks whether the response is grounded in the retrieved context."""
-    print("---------check_groundedness---------")
-    
-    system_message = '''You are an AI tasked with evaluating whether a response is grounded in the provided context and includes proper citations.
-
-Guidelines:
-1. **Groundedness Check**:
-   - Verify that the response accurately reflects the information in the context.
-   - Flag any unsupported claims or deviations from the context.
-
-2. **Citation Check**:
-   - Ensure that the response includes citations to the source material (e.g., "According to [Source], ...").
-   - If citations are missing, suggest adding them.
-
-3. **Scoring**:
-   - Assign a groundedness score between 0 and 1, where 1 means fully grounded and properly cited.
-
-Examples:
-1. Response: "Anorexia nervosa is caused by genetic factors (Source 1)."
-   Context: "Anorexia nervosa is influenced by genetic, environmental, and psychological factors (Source 1)."
-   Evaluation: "The response is grounded and properly cited. Groundedness score: 1.0."
-
-2. Response: "Bulimia nervosa can be cured with diet alone."
-   Context: "Treatment for bulimia nervosa involves psychotherapy and medications (Source 2)."
-   Evaluation: "The response is ungrounded and lacks citations. Groundedness score: 0.2."
-
-3. Response: "Anorexia nervosa has a high mortality rate."
-   Context: "Anorexia nervosa has one of the highest mortality rates among psychiatric disorders (Source 3)."
-   Evaluation: "The response is grounded but lacks a citation. Groundedness score: 0.7."
-
-****Return only a float score (e.g., 0.9). Do not provide explanations.****
-
-Now, evaluate the following response:'''
-
-    groundedness_prompt = ChatPromptTemplate.from_messages([
-        ("system", system_message),
-        ("user", "Context: {context}\nResponse: {response}\n\nGroundedness score:")
-    ])
-
-    chain = groundedness_prompt | llm | StrOutputParser()
-    groundedness_score = float(chain.invoke({
-        "context": "\n".join([doc["content"] for doc in state['context']]),
-        "response": state['response']
-    }))
-    print("groundedness_score: ", groundedness_score)
-    state['groundedness_loop_count'] += 1
-    print("#########Groundedness Incremented###########")
-    state['groundedness_score'] = groundedness_score
-    return state
-
-def check_precision(state: AgentState) -> AgentState:
-    """Checks whether the response precisely addresses the user's query."""
-    print("---------check_precision---------")
-    
-    system_message = '''You are an AI evaluator assessing the **precision** of the response.
-Your task is to **score** how well the response addresses the user's original nutrition disorder-related query.
-
-Scoring Criteria:
-- 1.0 → The response is fully precise, directly answering the question.
-- 0.7 → The response is mostly correct but contains some generalization.
-- 0.5 → The response is somewhat relevant but lacks key details.
-- 0.3 → The response is vague or only partially correct.
-- 0.0 → The response is incorrect or misleading.
-
-Examples:
-1. Query: "What are the symptoms of anorexia nervosa?"
-   Response: "The symptoms of anorexia nervosa include extreme weight loss, fear of gaining weight, and a distorted body image."
-   Precision Score: 1.0
-
-2. Query: "How is bulimia nervosa treated?"
-   Response: "Bulimia nervosa is treated with therapy and medications."
-   Precision Score: 0.7
-
-3. Query: "What causes binge eating disorder?"
-   Response: "Binge eating disorder is caused by a combination of genetic, psychological, and environmental factors."
-   Precision Score: 0.5
-
-4. Query: "What are the effects of malnutrition?"
-   Response: "Malnutrition can lead to health problems."
-   Precision Score: 0.3
-
-5. Query: "What is the mortality rate of anorexia nervosa?"
-   Response: "Anorexia nervosa is a type of eating disorder."
-   Precision Score: 0.0
-
-*****Return only a float score (e.g., 0.9). Do not provide explanations.*****
-Now, evaluate the following query and response:'''
-
-    precision_prompt = ChatPromptTemplate.from_messages([
-        ("system", system_message),
-        ("user", "Query: {query}\nResponse: {response}\n\nPrecision score:")
-    ])
-
-    chain = precision_prompt | llm | StrOutputParser()
-    precision_score = float(chain.invoke({
-        "query": state['query'],
-        "response": state['response']
-    }))
-    state['precision_score'] = precision_score
-    print("precision_score:", precision_score)
-    state['precision_loop_count'] += 1
-    print("#########Precision Incremented###########")
-    return state
-
-def refine_response(state: AgentState) -> AgentState:
-    """Suggests improvements for the generated response."""
-    print("---------refine_response---------")
-
-    system_message = '''You are an AI response refinement assistant. Your task is to suggest **improvements** for the given response.
-
-### Guidelines:
-- Identify **gaps in the explanation** (missing key details).
-- Highlight **unclear or vague parts** that need elaboration.
-- Suggest **additional details** that should be included for better accuracy.
-- Ensure the refined response is **precise** and **grounded** in the retrieved context.
-
-### Examples:
-1. Query: "What are the symptoms of anorexia nervosa?"
-   Response: "The symptoms include weight loss and fear of gaining weight."
-   Suggestions: "The response is missing key details about behavioral and emotional symptoms. Add details like 'distorted body image' and 'restrictive eating patterns.'"
-
-2. Query: "How is bulimia nervosa treated?"
-   Response: "Bulimia nervosa is treated with therapy."
-   Suggestions: "The response is too vague. Specify the types of therapy (e.g., cognitive-behavioral therapy) and mention other treatments like nutritional counseling and medications."
-
-3. Query: "What causes binge eating disorder?"
-   Response: "Binge eating disorder is caused by psychological factors."
-   Suggestions: "The response is incomplete. Add details about genetic and environmental factors, and explain how they contribute to the disorder."
-
-Now, suggest improvements for the following response:'''
-
-    refine_response_prompt = ChatPromptTemplate.from_messages([
-        ("system", system_message),
-        ("user", "Query: {query}\nResponse: {response}\n\n"
-                 "What improvements can be made to enhance accuracy and completeness?")
-    ])
-
-    chain = refine_response_prompt | llm | StrOutputParser()
-    feedback = f"Previous Response: {state['response']}\nSuggestions: {chain.invoke({'query': state['query'], 'response': state['response']})}"
-    print("feedback: ", feedback)
-    print(f"State: {state}")
-    state['feedback'] = feedback
-    return state
-
-def refine_query(state: AgentState) -> AgentState:
-    """Suggests improvements for the expanded query."""
-    print("---------refine_query---------")
-    
-    system_message = '''You are an AI query refinement assistant. Your task is to suggest **improvements** for the expanded query.
-
-### Guidelines:
-- Add **specific keywords** to improve document retrieval.
-- Identify **missing details** that should be included.
-- Suggest **ways to narrow the scope** for better precision.
-
-### Examples:
-1. Original Query: "Tell me about eating disorders."
-   Expanded Query: "Provide details on eating disorders, including types, symptoms, causes, and treatment options."
-   Suggestions: "Add specific types of eating disorders like 'anorexia nervosa' and 'bulimia nervosa' to improve retrieval."
-
-2. Original Query: "What is anorexia?"
-   Expanded Query: "Explain anorexia nervosa, including its symptoms and causes."
-   Suggestions: "Include details about treatment options and risk factors to make the query more comprehensive."
-
-3. Original Query: "How to treat bulimia?"
-   Expanded Query: "Describe treatment options for bulimia nervosa."
-   Suggestions: "Specify types of treatments like 'cognitive-behavioral therapy' and 'medications' for better precision."
-
-Now, suggest improvements for the following expanded query:'''
-
-    refine_query_prompt = ChatPromptTemplate.from_messages([
-        ("system", system_message),
-        ("user", "Original Query: {query}\nExpanded Query: {expanded_query}\n\n"
-                 "What improvements can be made for a better search?")
-    ])
-
-    chain = refine_query_prompt | llm | StrOutputParser()
-    query_feedback = f"Previous Expanded Query: {state['expanded_query']}\nSuggestions: {chain.invoke({'query': state['query'], 'expanded_query': state['expanded_query']})}"
-    print("query_feedback: ", query_feedback)
-    print(f"Groundedness loop count: {state['groundedness_loop_count']}")
-    state['query_feedback'] = query_feedback
-    return state
-
-def max_iterations_reached(state: AgentState) -> AgentState:
-    """Handles the case when the maximum number of iterations is reached."""
-    print("---------max_iterations_reached---------")
-    response = "I'm unable to refine the response further. Please provide more context or clarify your question."
-    state['response'] = response
-    return state