llm_prompts.py

"""
Contains the prompt templates for interacting with the Gemini LLM.

Separating prompts from the application logic makes them easier to manage,
modify, and version. This module provides functions that return the formatted
prompt strings required by the data processing module.
"""


import datetime
import json
from solr_metadata import format_metadata_for_prompt

def get_analysis_plan_prompt(natural_language_query, chat_history, search_fields=None, core_name="news"):
    """
    Generates the prompt for creating a Solr analysis plan from a user query.
    Args:
        natural_language_query (str): The user's query.
        chat_history (list): A list of previous user and bot messages.
        search_fields (list, optional): A list of dictionaries with 'field_name' and 'field_value'.
        core_name (str): The name of the Solr core to use for field metadata.
    """
    # Dynamically get field info for the specified core
    formatted_field_info = format_metadata_for_prompt(core_name)
    formatted_history = ""
    for user_msg, bot_msg in chat_history:
        if user_msg:
            formatted_history += f"- User: \"{user_msg}\"\n"

    dynamic_fields_prompt_section = ""
    if search_fields:
        # The search_fields are now pre-mapped, so we can use them directly
        formatted_fields = "\n".join([f"  - {field['field_name']}: {field['field_value']}" for field in search_fields])
        dynamic_fields_prompt_section = f"""
--- 
### DYNAMIC FIELD SUGGESTIONS (Use Critically)

An external API has suggested the following field-value pairs based on your query.
**These are only HINTS.** Do NOT use them blindly.
Critically evaluate if they make sense. For example, a `molecule_name` associated with a `company_name` might be irrelevant or illogical.
Use only what is logical for the query. Do not construct filters from fields/values that do not make sense.

**Suggested Fields:**
{formatted_fields}
"""

    return f"""
You are an expert data analyst and Solr query engineer. Your task is to convert a natural language question into a structured JSON "Analysis Plan". This plan will be used to run two separate, efficient queries: one for aggregate data (facets) and one for finding illustrative examples (grouping).

Your most important job is to think like an analyst and choose a `analysis_dimension` that provides a meaningful, non-obvious breakdown of the data.

---
### CONTEXT & RULES

1.  **Today's Date for Calculations**: {datetime.datetime.now().date().strftime("%Y-%m-%d")}
2.  **Field Usage**: You MUST use the fields described in the 'Field Definitions'. Pay close attention to the definitions to select the correct field, especially the `_s` fields for searching. Do not use fields ending with `_s` in `group.field` or facet `field` unless necessary for the analysis.
3.  **Crucial Sorting Rules**:
    *   For `group.sort`: If `analysis_measure` involves a function on a field (e.g., `sum(total_deal_value_in_million)`), you MUST use the full function: `group.sort: 'sum(total_deal_value_in_million) desc'`.
    *   If `analysis_measure` is 'count', you MUST OMIT the `group.sort` parameter entirely.
    *   For sorting, NEVER use 'date_year' directly for `sort` in `terms` facets; use 'index asc' or 'index desc' instead. For other sorts, use 'date'.
3.  On **Qualitative Data** Group Operation:
    * We need to show user **standout examples** for each category chosen. 
    For example: if user asks for "USA approved drugs last 5 years" We need to show user standout examples for each year. In this context: standout means the news with the biggest deals in million for each year for example. 
4.  **Output Format**: Your final output must be a single, raw JSON object. Do not add comments or markdown formatting. The JSON MUST include a `reasoning` object explaining your choices.

---
### HOW TO CHOOSE THE ANALYSIS DIMENSION AND MEASURE (ANALYTICAL STRATEGY)

This is the most critical part of your task. A bad choice leads to a useless, boring analysis.

**1. Choosing the `analysis_dimension` (The "Group By" field):**

*   **THE ANTI-REDUNDANCY RULE (MOST IMPORTANT):** If you use a field in the `query_filter` with a specific value (e.g., `news_type:"product approvals"`), you **MUST NOT** use that same field (`news_type`) as the `analysis_dimension`. The user already knows the news type; they want to know something *else* about it. Choosing a redundant dimension is a critical failure.

*   **USER INTENT FIRST:** If the user explicitly asks to group by a field (e.g., "by company", "by country"), use that field.

*   **INFERENCE HEURISTICS (If the user doesn't specify a dimension):** Think "What is the next logical question?" to find the most insightful breakdown.
    *   If the query is about "drug approvals," a good dimension is `therapeutic_category` (what diseases are the approvals for?) or `company_name` (who is getting the approvals?).
    *   If the query compares concepts like "cancer vs. infection," the dimension is `therapeutic_category`.
    *   If the query compares "oral vs. injection," the dimension is `route_branch`.
    *   For general "recent news" or "top deals," `news_type` or `company_name` are often good starting points.
    *   Your goal is to find a dimension that reveals a meaningful pattern in the filtered data.

**2. Choosing the `analysis_measure` (The metric):**

*   **EXPLICIT METRIC:** If the user asks for a value (e.g., "by total deal value", "highest revenue"), use the corresponding field and function (e.g., `sum(total_deal_value_in_million)`).
*   **IMPLICIT COUNT:** If the user asks a "what," "who," "how many," or "most common" question without specifying a value metric, the measure is `count`.

---
### FIELD DEFINITIONS (Your Source of Truth for Core: {core_name})

{formatted_field_info}
{dynamic_fields_prompt_section}

---
### CHAT HISTORY
{formatted_history}
---
### EXAMPLES

**User Query 1:** "What are the top 5 companies by total deal value in 2023?"
**Correct JSON Output 1:**
```json
{{
  "reasoning": {{
    "dimension_choice": "User explicitly asked for 'top 5 companies', so 'company_name' is the correct dimension.",
    "measure_choice": "User explicitly asked for 'total deal value', so 'sum(total_deal_value_in_million)' is the correct measure."
  }},
  "analysis_dimension": "company_name",
  "analysis_measure": "sum(total_deal_value_in_million)",
  "sort_field_for_examples": "total_deal_value_in_million",
  "query_filter": "date_year:2023 AND total_deal_value_in_million:[0 TO *]",
  "quantitative_request": {{
    "json.facet": {{
      "companies_by_deal_value": {{
        "type": "terms",
        "field": "company_name",
        "limit": 5,
        "sort": "total_value desc",
        "facet": {{
          "total_value": "sum(total_deal_value_in_million)"
        }}
      }}
    }}
  }},
  "qualitative_request": {{
    "group": true,
    "group.field": "company_name",
    "group.limit": 1,
    "group.sort": "sum(total_deal_value_in_million) desc",
    "sort": "total_deal_value_in_million desc"
  }}
}}
```

**User Query 2:** "What are the most common news types for infections this year?"
**Correct JSON Output 2:**
```json
{{
  "reasoning": {{
    "dimension_choice": "User asked for 'most common news types', so 'news_type' is the correct dimension.",
    "measure_choice": "User asked for 'most common', which implies counting occurrences. Therefore, the measure is 'count'."
  }},
  "analysis_dimension": "news_type",
  "analysis_measure": "count",
  "sort_field_for_examples": "date",
  "query_filter": "therapeutic_category_s:infections AND date_year:{datetime.datetime.now().year}",
  "quantitative_request": {{
    "json.facet": {{
      "news_by_type": {{
        "type": "terms",
        "field": "news_type",
        "limit": 10,
        "sort": "count desc"
      }}
    }}
  }},
  "qualitative_request": {{
    "group": true,
    "group.field": "news_type",
    "group.limit": 1,
    "group.sort": "sum(total_deal_value_in_million) desc",
    "sort": "total_deal_value_in_million desc"
  }}
}}
```

**User Query 3 (Insightful Breakdown):** "give me recent news on USA drug approvals"
**Correct JSON Output 3:**
```json
{{
  "reasoning": {{
    "dimension_choice": "The user filtered for 'drug approvals' (news_type) and 'USA' (territory_hq_s). Using 'news_type' as a dimension would be redundant. The next logical question is 'what diseases are these approvals for?'. Therefore, 'therapeutic_category' is the most insightful dimension.",
    "measure_choice": "The user asked for 'news', implying a count of events. 'count' is the appropriate measure."
  }},
  "analysis_dimension": "therapeutic_category",
  "analysis_measure": "count",
  "sort_field_for_examples": "date",
  "query_filter": "territory_hq_s:\"united states of america\" AND news_type:\"product approvals\" AND date_year:{datetime.datetime.now().year}",
  "quantitative_request": {{
    "json.facet": {{
      "approvals_by_therapeutic_category": {{
        "type": "terms",
        "field": "therapeutic_category",
        "limit": 10,
        "sort": "count desc"
      }}
    }}
  }},
  "qualitative_request": {{
    "group": true,
    "group.field": "therapeutic_category",
    "group.limit": 1,
    "sort": "date desc"
  }}
}}
```


**User Query 4 :** "Compare deal values for injection vs oral related to infection news."
**Correct JSON Output 3:**
```json
{{
  "reasoning": {{
    "dimension_choice": "The user wants to compare deal values for 'injection' vs 'oral' drug delivery methods within 'infection' news.  The user has specified the comparison ('injection vs oral'), making 'route_branch' the appropriate analysis dimension to directly reflect this comparison. Using 'drug_delivery_branch' would be too granular for this high-level comparison.",
    "measure_choice": "The user explicitly asks to compare 'deal values', so 'sum(total_deal_value_in_million)' is the correct measure."
  }},
  "analysis_dimension": "route_branch",
  "analysis_measure": "sum(total_deal_value_in_million)",
  "sort_field_for_examples": "total_deal_value_in_million",
  "query_filter": "drug_delivery_branch_s:(injection OR oral) AND therapeutic_category_s:infections AND date_year:2025 AND total_deal_value_in_million:[0 TO *]",
  "quantitative_request": {{
    "json.facet": {{
      "deal_values_by_route": {{
        "type": "terms",
        "field": "route_branch",
        "limit": 2,
        "sort": "total_deal_value desc",
        "facet": {{
          "total_deal_value": "sum(total_deal_value_in_million)"
        }}
      }}
    }}
  }},
  "qualitative_request": {{
    "group": true,
    "group.field": "route_branch",
    "group.limit": 1,
    "group.sort": "sum(total_deal_value_in_million) desc",
    "sort": "total_deal_value_in_million desc"
  }}
}}
```
---
### YOUR TASK

Convert the following user query into a single, raw JSON "Analysis Plan" object. Strictly follow all rules, especially the analytical strategy for choosing the dimension and measure. Your JSON output MUST include the `reasoning` field.

**Current User Query:** `{natural_language_query}`
"""

def get_synthesis_report_prompt(query, quantitative_data, qualitative_data, plan):
    """
    Generates the prompt for synthesizing a final report from the query results.
    """
    qualitative_prompt_str = ""
    dimension = plan.get('analysis_dimension', 'N/A')
    if qualitative_data and dimension in qualitative_data:
        for group in qualitative_data.get(dimension, {}).get('groups', []):
            group_value = group.get('groupValue', 'N/A')
            if group.get('doclist', {}).get('docs'):
                doc = group.get('doclist', {}).get('docs', [{}])[0]
                title = doc.get('abstract', ['No Title'])
                content_list = doc.get('content', [])
                content_snip = (' '.join(content_list[0].split()[:40]) + '...') if content_list else 'No content available.'
                metric_val_raw = doc.get(plan.get('sort_field_for_examples'), 'N/A')
                metric_val = metric_val_raw[0] if isinstance(metric_val_raw, list) else metric_val_raw

                qualitative_prompt_str += f"- **For category `{group_value}`:**\n"
                qualitative_prompt_str += f"  - **Top Example Title:** {title}\n"
                qualitative_prompt_str += f"  - **Metric Value:** {metric_val}\n"
                qualitative_prompt_str += f"  - **Content Snippet:** {content_snip}\n\n"

    return f"""
You are a top-tier business intelligence analyst. Your task is to write an insightful, data-driven report for an executive. You must synthesize quantitative data (the 'what') with qualitative examples (the 'why') to tell a complete story.

---
### AVAILABLE INFORMATION

**1. The User's Core Question:**
\"{query}\"

**2. Quantitative Data (The 'What'):**
This data shows the high-level aggregates.
```json
{json.dumps(quantitative_data, indent=2)}
```

**3. Qualitative Data (The 'Why'):**
These are the single most significant documents driving the numbers for each category.
{qualitative_prompt_str}

---
### REPORTING INSTRUCTIONS

Your report must be in clean, professional Markdown and follow this structure precisely.

**Report Structure:**

`## Executive Summary`
- A 1-2 sentence, top-line answer to the user's question based on the quantitative data.

`### Key Findings`
- Use bullet points to highlight the main figures from the quantitative data. Interpret the numbers.

`### Key Drivers & Illustrative Examples`
- **This is the most important section.** Explain the "so what?" behind the numbers.
- Use the qualitative examples to explain *why* a category is high or low. Reference the top example document for each main category.

`### Deeper Dive: Suggested Follow-up Analyses`
- Propose 2-3 logical next questions based on your analysis to uncover deeper trends.

---
**Generate the full report now, paying close attention to all formatting and spacing rules.**
"""

def get_visualization_code_prompt(query_context, facet_data):
    """
    Generates a flexible prompt for creating Python visualization code.
    """
    return f"""
You are a world-class Python data visualization expert specializing in Matplotlib and Seaborn.
Your primary task is to generate a single, insightful, and robust Python script to visualize the provided data. The visualization should directly answer the user's analytical goal.

**1. User's Analytical Goal:**
"{query_context}"

**2. Aggregated Data (from Solr Facets):**
```json
{json.dumps(facet_data, indent=2)}
```

---
### **CRITICAL INSTRUCTIONS: CODE GENERATION RULES**
You MUST follow these rules meticulously to ensure the code runs without errors in a server environment.

**A. Analyze the Data & Choose the Right Chart:**
- **Inspect the Data:** Before writing any code, carefully examine the structure of the `facet_data` JSON. Is it a simple list of categories and counts? Is it a nested structure comparing metrics across categories? Is it a time-series?
- **Select the Best Chart Type:** Based on the data and the user's goal, choose the most effective chart.
    - **Bar Chart:** Ideal for comparing quantities across different categories (e.g., top companies by deal value).
    - **Grouped Bar Chart:** Use when comparing a metric across categories for a few groups (e.g., deal values for 2023 vs. 2024 by company).
    - **Line Chart:** Best for showing a trend over time (e.g., number of approvals per year).
    - **Pie Chart:** Use ONLY for showing parts of a whole, and only with a few (2-5) categories. Generally, bar charts are better.
- **Tell a Story:** Your visualization should be more than just a plot; it should reveal the key insight from the data.
- **Direct Answer** If user ask for like this: compare x with y there should be a comparison visualization between x and y nothing more.

**B. Non-Negotiable Code Requirements:**
1.  **Imports:** You must import `matplotlib.pyplot as plt`, `seaborn as sns`, and `pandas as pd`.
2.  **Use Pandas:** ALWAYS parse the `facet_data` into a pandas DataFrame. This is more robust and flexible than iterating through dictionaries directly.
3.  **Figure and Axes:** Use `fig, ax = plt.subplots()` to create the figure and axes objects. This gives you better control.
4.  **Styling:** Apply a clean and professional style, for example: `plt.style.use('seaborn-v0_8-whitegrid')` and use a suitable Seaborn palette (e.g., `palette='viridis'`).
5.  **NO `plt.show()`:** Your code will be run on a server. **DO NOT** include `plt.show()`.
6.  **Save the Figure:** The execution environment expects a Matplotlib figure object named `fig`. Your code does not need to handle the saving path directly, but it **MUST** produce the final `fig` object correctly. The calling function will handle saving it.
7.  **Titles and Labels:** You MUST set a clear and descriptive title and labels for the x and y axes. The title should reflect the user's query.
8.  **Axis Label Readability:** If x-axis labels are long, you MUST rotate them to prevent overlap. Use this robust method: `plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")`.
9.  **Layout:** Use `plt.tight_layout()` at the end to ensure all elements fit nicely.
10. **Error Handling:** Your code should be robust. If the `facet_data` contains no "buckets" or data to plot, the code should not crash. It should instead produce a plot with a message like "No data available to plot."

---
### **High-Quality Example (Grouped Bar Chart)**

This example shows how to parse a nested facet structure into a DataFrame and create an insightful grouped bar chart. Adapt its principles to your specific task.

```python
# --- Imports and Style ---
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

plt.style.use('seaborn-v0_8-whitegrid')
fig, ax = plt.subplots(figsize=(14, 8))

# --- Data Parsing ---
# Dynamically find the main facet key (the one with 'buckets')
facet_key = None
for key, value in facet_data.items():
    if isinstance(value, dict) and 'buckets' in value:
        facet_key = key
        break

plot_data = []
# Check if a valid key and buckets were found
if facet_key and facet_data[facet_key].get('buckets'):
    # This robustly parses nested metrics (e.g., a sum for each year)
    for bucket in facet_data[facet_key]['buckets']:
        category = bucket.get('val', 'N/A')
        # Find all nested metrics inside the bucket
        for sub_key, sub_value in bucket.items():
            if isinstance(sub_value, dict) and 'sum' in sub_value:
                # Extracts '2025' from a key like 'total_value_2025'
                group = sub_key.split('_')[-1]
                value = sub_value['sum']
                plot_data.append({{'Category': category, 'Group': group, 'Value': value}})

# --- Plotting ---
if plot_data:
    df = pd.DataFrame(plot_data)
    sns.barplot(data=df, x='Category', y='Value', hue='Group', ax=ax, palette='viridis')
    
    # --- Labels and Titles ---
    ax.set_title('Comparison of Total Value by Category and Group')
    ax.set_xlabel('Category')
    ax.set_ylabel('Total Value')
    
    # --- Formatting ---
    plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
else:
    # --- Handle No Data ---
    ax.text(0.5, 0.5, 'No data available to plot.', horizontalalignment='center', verticalalignment='center', transform=ax.transAxes)
    ax.set_title('Data Visualization')

# --- Final Layout ---
plt.tight_layout()
```

---
### **Your Task:**

Now, generate the raw Python code to create the best possible visualization for the user's goal based on the provided data.
Do not wrap the code in ```python ... ```.
"""