commented apppy

LLM_test.py

@@ -1,6 +1,6 @@
 import groq, os
 from groq import Groq
-from RAG_test import fetch
+from retrieval_helper import fetch
 
 client = Groq(
     api_key="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",  # This is the default and can be omitted
@@ -16,11 +16,9 @@ query = "Show campaigns where spend is greater than 11 and labels include holida
 top_documents = fetch(query)
 
 
-
-
 USER_INPUT = "Show campaigns where spend is greater than 11 and labels include holiday and with impressions less than 500"
 
-def generate_chat_completion(client, SYSTEM_PROMPT, USER_INPUT, related_vectors):
+def generate_chat_completion(client, USER_INPUT, related_vectors):
 
 
     SYSTEM_PROMPT = f'''

app.py

@@ -1,46 +1,46 @@
 import gradio as gr
 import json
 from LLM_test import generate_chat_completion
-from RAG_test import fetch
+from retrieval_helper import fetch
 from groq import Groq
 
 client = Groq(
     api_key="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",  # This is the default and can be omitted
 )
 
-related_vectors = '''
-attribute: spend, operators_supported: [">", "<", ">=", "<=", "=", "!="], value_type: "Number"
-attribute: clicks, operators_supported: [">", "<", ">=", "<=", "=", "!="], value_type: "Integer"
-attribute: impressions, operators_supported: [">", "<", ">=", "<=", "=", "!="], value_type: "Integer"
-'''
+# related_vectors = '''
+# attribute: spend, operators_supported: [">", "<", ">=", "<=", "=", "!="], value_type: "Number"
+# attribute: clicks, operators_supported: [">", "<", ">=", "<=", "=", "!="], value_type: "Integer"
+# attribute: impressions, operators_supported: [">", "<", ">=", "<=", "=", "!="], value_type: "Integer"
+# '''
 
 
-SYSTEM_PROMPT = f'''
-You are a system that converts natural language queries into a structured filter schema.
-    The filter schema consists of a list of conditions, each represented as:
-    {{
-        "attribute": "<attribute_name>",
-        "op": "<operator>",
-        "value": "<value>"
-    }}
-    There can be any number of conditions. You have to list them all.
+# SYSTEM_PROMPT = f'''
+# You are a system that converts natural language queries into a structured filter schema.
+#     The filter schema consists of a list of conditions, each represented as:
+#     {{
+#         "attribute": "<attribute_name>",
+#         "op": "<operator>",
+#         "value": "<value>"
+#     }}
+#     There can be any number of conditions. You have to list them all.
 
-    Supported attributes and their operators are:
-    {related_vectors}
+#     Supported attributes and their operators are:
+#     {related_vectors}
 
-    Example:
-    Input: "Show campaigns where spend is greater than 11"
-    Output: [{{"attribute": "spend", "op": ">", "value": 11}}]
+#     Example:
+#     Input: "Show campaigns where spend is greater than 11"
+#     Output: [{{"attribute": "spend", "op": ">", "value": 11}}]
 
-    Input: "Find ads with clicks less than 100 and impressions greater than 500"
-    Output: [
-        {{"attribute": "clicks", "op": "<", "value": 100}},
-        {{"attribute": "impressions", "op": ">", "value": 500}}
-    ]
+#     Input: "Find ads with clicks less than 100 and impressions greater than 500"
+#     Output: [
+#         {{"attribute": "clicks", "op": "<", "value": 100}},
+#         {{"attribute": "impressions", "op": ">", "value": 500}}
+#     ]
 
-    STRICLY PROVIDE IN THE ABOVE JSON FORMAT WITHOUT ANY METADATA
+#     STRICLY PROVIDE IN THE ABOVE JSON FORMAT WITHOUT ANY METADATA
 
-'''
+# '''
 
 # Define the Gradio interface
 def generate_chat_completion_interface(USER_INPUT):
@@ -48,17 +48,17 @@ def generate_chat_completion_interface(USER_INPUT):
     top_documents = fetch(USER_INPUT)
     related_vectors = "\n".join(top_documents)
 
-    result = generate_chat_completion(client, SYSTEM_PROMPT, USER_INPUT, related_vectors)
+    result = generate_chat_completion(client, USER_INPUT, related_vectors)
 
     return result
 
 # Set up the Gradio app interface
 iface = gr.Interface(
     fn=generate_chat_completion_interface,  # Function to run on input
-    inputs=gr.Textbox(label="Enter your sentence"),  # Input field
-    outputs=gr.Textbox(label="Generated Completion"),  # Output field
-    title="Chat Completion Generator",  # Title of the app
-    description="This app generates chat completions based on a user-provided input sentence."
+    inputs=gr.Textbox(label="Enter your query"),  # Input field
+    outputs=gr.Textbox(label="Generated JSON"),  # Output field
+    title="RAG based search",  # Title of the app
+    description="Provide your natural language searhc query"
 )
 
 # Launch the interface