Update app.py

app.py

@@ -36,30 +36,34 @@ def format_doc(doc: Document) -> str:
     return f"Document_Title: {doc.metadata.get('title', 'Unknown')}\nPage: {doc.metadata.get('page', 'Unknown')}\nContent: {doc.page_content}"
 
 # Extract relevant context function
-def extract_relevant_context(question, documents):
+def extract_relevant_context(question,documents):
     result = []
-    with st.spinner("🔍 Extracting relevant content from document..."):
-        for doc in documents:
-            formatted_documents = format_doc(doc)
-            system_prompt = f"{REAG_SYSTEM_PROMPT}\n\n# Available source\n\n{formatted_documents}"
-            prompt = f"""Determine if the 'Available source' content is sufficient to answer the QUESTION.
-            QUESTION: {question}
-            RESPONSE FORMAT (Strict JSON):
+    for doc in documents:
+        formatted_documents = format_doc(doc)
+        system = f"{REAG_SYSTEM_PROMPT}\n\n# Available source\n\n{formatted_documents}"
+        prompt = f"""Determine if the 'Avaiable source' content supplied is sufficient and relevant to ANSWER the QUESTION asked.
+        QUESTION: {question}
+        #INSTRUCTIONS TO FOLLOW
+        1. Analyze the context provided thoroughly to check its relevancy to help formulizing a response for the QUESTION asked.
+        2, STRICTLY PROVIDE THE RESPONSE IN A JSON STRUCTURE AS DESCRIBED BELOW:
             ```json
-            {{
-                "content": "Extracted relevant content",
-                "reasoning": "Why this was chosen",
-                "is_irrelevant": false
-            }}
+               {{"content":<<The page content of the document that is relevant or sufficient to answer the question asked>>,
+                 "reasoning":<<The reasoning for selecting The page content with respect to the question asked>>,
+                 "is_irrelevant":<<Specify 'True' if the content in the document is not sufficient or relevant.Specify 'False' if the page content is sufficient to answer the QUESTION>>
+                 }}
             ```
-            """
-            messages = [{"role": "system", "content": system_prompt},
-                        {"role": "user", "content": prompt}]
-            response = llm_relevancy.invoke(messages)
-            formatted_response = relevancy_parser.parse(response.content)
-            result.append(formatted_response)
-    
-    final_context = [item['content'] for item in result if not item['is_irrelevant']]
+         """
+        messages =[ {"role": "system", "content": system},
+                       {"role": "user", "content": prompt},
+                    ]
+        response = llm_relevancy.invoke(messages)
+        print(response.content)
+        formatted_response = relevancy_parser.parse(response.content)
+        result.append(formatted_response)
+    final_context = []
+    for items in result:
+        if (items['is_irrelevant'] == False) or ( items['is_irrelevant'] == 'false') or (items['is_irrelevant'] == 'False'):
+            final_context.append(items['content'])
     return final_context
 
 # Generate response using RAG Prompt