## Workflow

The system follows a well-structured workflow to ensure accurate, secure, and context-aware responses to user queries:

### 1. **Input Query**
- A user provides a query that can be a general question, ambiguous statement, or potentially malicious intent.

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### 2. **Detection Module**
- **Purpose**: Classify the query as "bad" or "good."
- **Steps**:
  1. Use a sentiment analysis model (`distilbert-base-uncased-finetuned-sst-2-english`) to detect malicious or inappropriate intent.
  2. If the query is classified as "bad" (e.g., SQL injection or inappropriate tone), block further processing and provide a warning message.
  3. If "good," proceed to the **Transformation Module**.

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### 3. **Transformation Module**
- **Purpose**: Rephrase or enhance ambiguous or poorly structured queries for better retrieval.
- **Steps**:
  1. Identify missing context or ambiguous phrasing.
  2. Transform the query using:
     - Rule-based transformations for simple fixes.
     - Text-to-text models (e.g., `google/flan-t5-small`) for more sophisticated rephrasing.
  3. Pass the transformed query to the **RAG Pipeline**.

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### 4. **RAG Pipeline**
- **Purpose**: Retrieve relevant data and generate a context-aware response.
- **Steps**:
  1. **Document Retrieval**:
     - Encode the transformed query and documents into embeddings using `all-MiniLM-L6-v2`.
     - Compute semantic similarity between the query and stored documents.
     - Retrieve the top-k documents relevant to the query.
  2. **Response Generation**:
     - Use the retrieved documents as context.
     - Pass the query and context to a generative model (e.g., `distilgpt2`) to synthesize a meaningful response.

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### 5. **Semantic Response Generation**
- **Purpose**: Provide a concise and meaningful answer.
- **Steps**:
  1. Combine the retrieved documents into a coherent context.
  2. Generate a response tailored to the query using the generative model.
  3. Return the response to the user, ensuring clarity and relevance.

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### End-to-End Example

#### Input Query:
```plaintext
"How to improve acting skills?"
````