Deploy from GitHub Actions 2025-12-11_03:14:30

app.py

@@ -12,6 +12,7 @@ SUPABASE_ANON_KEY = os.environ.get("SUPABASE_ANON_KEY")
 EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
 LLM_MODEL = os.environ.get("LLM_MODEL", "HuggingFaceH4/zephyr-7b-beta")
 RESULTS_K = int(os.environ.get("RESULTS_K", 5))
+SIMILARITY_THRESHOLD = float(os.environ.get("SIMILARITY_THRESHOLD", 0.35))  # Minimum similarity score
 
 # -------- VALIDATE ----------
 if not HF_API_TOKEN or not SUPABASE_URL or not SUPABASE_ANON_KEY:
@@ -27,10 +28,12 @@ SYSTEM_PROMPT = """You are an SAP documentation assistant. Your job is to answer
 
 STRICT RULES:
 1. ONLY use information from the provided context to answer
-2. If the context doesn't contain enough information to answer, say "I don't have enough information in my knowledge base to answer this question."
+2. If the context doesn't contain enough information to answer, say "I don't have enough information in my knowledge base to answer this question. Please try asking about a different SAP topic or rephrase your question."
 3. DO NOT use any prior knowledge - only the provided documents
 4. Always be helpful and format your answers clearly
 5. If relevant, mention which source document the information came from
+6. For SAP transaction codes, explain what they do and when to use them
+7. Keep answers concise but comprehensive
 
 Remember: You are grounded to the provided context only. Do not make up information."""
 
@@ -67,6 +70,15 @@ def search_supabase(query_vector: List[float], k: int = RESULTS_K):
     return resp.data or []
 
 
+def filter_by_similarity(chunks: List[dict], threshold: float = SIMILARITY_THRESHOLD) -> List[dict]:
+    """
+    Filter chunks by minimum similarity threshold.
+    Only return chunks with similarity >= threshold.
+    """
+    filtered = [c for c in chunks if c.get("similarity", 0) >= threshold]
+    return filtered
+
+
 def format_context(chunks: List[dict]) -> str:
     """
     Format retrieved chunks into a context string for the LLM.
@@ -75,7 +87,9 @@ def format_context(chunks: List[dict]) -> str:
     for i, chunk in enumerate(chunks, 1):
         title = chunk.get("title", "Unknown")
         content = chunk.get("content", "")
-        context_parts.append(f"[Document {i}: {title}]\n{content}\n")
+        similarity = chunk.get("similarity", 0)
+        source = chunk.get("source", "unknown")
+        context_parts.append(f"[Document {i}: {title}]\nSource: {source}\nRelevance: {similarity:.2%}\n\n{content}\n")
     return "\n---\n".join(context_parts)
 
 
@@ -90,7 +104,7 @@ def generate_answer(question: str, context: str) -> str:
 
 Question: {question}
 
-Please answer the question based ONLY on the context documents provided above."""
+Please answer the question based ONLY on the context documents provided above. If the documents don't contain relevant information, say so clearly."""
 
     messages = [
         {"role": "system", "content": SYSTEM_PROMPT},
@@ -126,7 +140,7 @@ for message in st.session_state.messages:
         if message.get("sources"):
             with st.expander("📚 View Sources"):
                 for source in message["sources"]:
-                    st.markdown(f"**{source['title']}** (similarity: {source['similarity']:.4f})")
+                    st.markdown(f"**{source['title']}** (similarity: {source['similarity']:.2%})")
                     st.caption(source['content'][:500] + "..." if len(source['content']) > 500 else source['content'])
                     st.divider()
 
@@ -147,16 +161,33 @@ if question := st.chat_input("Ask a question about SAP..."):
                 query_vector = compute_embedding(question)
                 
                 # Step 2: Search Supabase for relevant chunks
-                chunks = search_supabase(query_vector, RESULTS_K)
+                all_chunks = search_supabase(query_vector, RESULTS_K)
+                
+                # Step 3: Filter by similarity threshold
+                chunks = filter_by_similarity(all_chunks, SIMILARITY_THRESHOLD)
                 
                 if not chunks:
-                    answer = "I couldn't find any relevant documents in my knowledge base for your question. Please try rephrasing or ask about a different SAP topic."
+                    # Check if we got results but they were all below threshold
+                    if all_chunks:
+                        best_score = max(c.get("similarity", 0) for c in all_chunks)
+                        answer = f"""I couldn't find sufficiently relevant information in my knowledge base for your question.
+
+**What I found:** The best matching documents had only {best_score:.1%} relevance, which is below my confidence threshold of {SIMILARITY_THRESHOLD:.0%}.
+
+**Suggestions:**
+- Try rephrasing your question with different keywords
+- Ask about a specific SAP topic like "SAP Basis administration", "SAP authorization", or "SAP HANA"
+- Check if you're asking about a very specific transaction code - my knowledge base may not cover all of them yet
+
+Would you like to try a different question?"""
+                    else:
+                        answer = "I couldn't find any relevant documents in my knowledge base for your question. Please try asking about a different SAP topic."
                     sources = []
                 else:
-                    # Step 3: Format context from retrieved chunks
+                    # Step 4: Format context from retrieved chunks
                     context = format_context(chunks)
                     
-                    # Step 4: Generate answer using LLM
+                    # Step 5: Generate answer using LLM
                     with st.spinner("🤔 Generating answer..."):
                         answer = generate_answer(question, context)
                     
@@ -165,7 +196,8 @@ if question := st.chat_input("Ask a question about SAP..."):
                         {
                             "title": chunk.get("title", "Unknown"),
                             "content": chunk.get("content", ""),
-                            "similarity": chunk.get("similarity", 0.0)
+                            "similarity": chunk.get("similarity", 0.0),
+                            "source": chunk.get("source", "unknown")
                         }
                         for chunk in chunks
                     ]
@@ -175,10 +207,18 @@ if question := st.chat_input("Ask a question about SAP..."):
                 
                 # Display sources
                 if sources:
-                    with st.expander("📚 View Sources"):
+                    with st.expander(f"📚 View Sources ({len(sources)} relevant documents)"):
                         for source in sources:
-                            st.markdown(f"**{source['title']}** (similarity: {source['similarity']:.4f})")
-                            st.caption(source['content'][:500] + "..." if len(source['content']) > 500 else source['content'])
+                            sim_pct = source['similarity'] * 100
+                            if sim_pct >= 70:
+                                badge = "🟢"
+                            elif sim_pct >= 50:
+                                badge = "🟡"
+                            else:
+                                badge = "🟠"
+                            st.markdown(f"{badge} **{source['title']}** ({source['similarity']:.1%} match)")
+                            st.caption(f"Source: {source.get('source', 'unknown')}")
+                            st.text(source['content'][:600] + "..." if len(source['content']) > 600 else source['content'])
                             st.divider()
                 
                 # Add to history
@@ -205,17 +245,29 @@ with st.sidebar:
     
     1. 🔍 **Search**: Your question is converted to embeddings and matched against our SAP knowledge base
     2. 📚 **Retrieve**: The most relevant document chunks are retrieved from Supabase
-    3. 🤖 **Generate**: An LLM generates an answer based *only* on the retrieved documents
+    3. 🎯 **Filter**: Only documents above the similarity threshold are used
+    4. 🤖 **Generate**: An LLM generates an answer based *only* on the retrieved documents
     
     This ensures answers are grounded in real documentation, not hallucinated!
     """)
     
     st.divider()
     
-    st.header("⚙️ Settings")
-    st.caption(f"Embedding Model: `{EMBEDDING_MODEL}`")
-    st.caption(f"LLM Model: `{LLM_MODEL}`")
-    st.caption(f"Results per query: `{RESULTS_K}`")
+    st.header("⚙️ Configuration")
+    st.caption(f"**Embedding Model:** `{EMBEDDING_MODEL}`")
+    st.caption(f"**LLM Model:** `{LLM_MODEL}`")
+    st.caption(f"**Results per query:** `{RESULTS_K}`")
+    st.caption(f"**Similarity threshold:** `{SIMILARITY_THRESHOLD:.0%}`")
+    
+    st.divider()
+    
+    st.header("💡 Tips")
+    st.markdown("""
+    - Ask specific questions about SAP topics
+    - Try questions about SAP Basis, HANA, Security, etc.
+    - Mention transaction codes (SM50, SU01, etc.)
+    - Check the sources to verify answers
+    """)
     
     st.divider()