add quoting rules to prompt while crafting solr filter query

llm_prompts.py

@@ -1,3 +1,4 @@
+
 """
 Contains the prompt templates for interacting with the Gemini LLM.
 
@@ -32,8 +33,7 @@ def get_analysis_plan_prompt(natural_language_query, chat_history, 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"""
----
-### MANDATORY DYNAMIC FILTERS
+---### MANDATORY DYNAMIC FILTERS
 
 An external API has identified the following field-value pairs from the user query.
 **You MUST use ALL of these fields and values to construct the `query_filter`.**
@@ -59,6 +59,7 @@ Your most important job is to think like an analyst and choose a `analysis_dimen
     *   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'.
+    *   **Quoting**: When a field value in the `query_filter` contains spaces (e.g., 'phase 3'), you MUST enclose it in double quotes (e.g., `highest_phase:("phase 3" OR "phase 2")`).
 5.  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.
@@ -117,7 +118,7 @@ This is the most critical part of your task. A bad choice leads to a useless, bo
   "analysis_dimension": "company_name",
   "analysis_measure": "sum(total_deal_value_in_million)",
   "sort_field_for_examples": "total_deal_value_in_million",
-  "query_filter": "date:["2023-01-01T00:00:00Z" TO \"2023-12-31T23:59:59Z\"]",
+  "query_filter": "date:[\"2023-01-01T00:00:00Z\" TO \"2023-12-31T23:59:59Z\"]",
   "quantitative_request": {{
     "json.facet": {{
       "companies_by_deal_value": {{
@@ -159,7 +160,7 @@ This is the most critical part of your task. A bad choice leads to a useless, bo
   "analysis_dimension": "news_type",
   "analysis_measure": "count",
   "sort_field_for_examples": "date",
-  "query_filter": "therapeutic_category_s:infections AND date:["2025-01-01T00:00:00Z" TO *]",
+  "query_filter": "therapeutic_category_s:infections AND date:[\"2025-01-01T00:00:00Z\" TO *]",
   "quantitative_request": {{
     "json.facet": {{
       "news_by_type": {{
@@ -306,7 +307,7 @@ You are a world-class Python data visualization expert specializing in Matplotli
 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}"
+\"{query_context}\"
 
 **2. Aggregated Data (from Solr Facets):**
 ```json
@@ -331,7 +332,7 @@ You MUST follow these rules meticulously to ensure the code runs without errors
 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'`).
+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.
@@ -401,4 +402,4 @@ plt.tight_layout()
 
 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 ... ```.
-"""
+"""