Update main.py

main.py

@@ -1,13 +1,13 @@
 # main.py
+
 import os
 import streamlit as st
 import anthropic
+from requests import JSONDecodeError
 
-from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
 from langchain_community.vectorstores import SupabaseVectorStore
 from langchain_community.llms import HuggingFaceEndpoint
-from langchain_community.vectorstores import SupabaseVectorStore
 
 from langchain.chains import ConversationalRetrievalChain
 from langchain.memory import ConversationBufferMemory
@@ -16,122 +16,129 @@ from supabase import Client, create_client
 from streamlit.logger import get_logger
 from stats import get_usage, add_usage
 
-supabase_url = st.secrets.SUPABASE_URL
-supabase_key = st.secrets.SUPABASE_KEY
-openai_api_key = st.secrets.openai_api_key
+# ─────── supabase + secrets ────────────────────────────────────────────────────
+supabase_url    = st.secrets.SUPABASE_URL
+supabase_key    = st.secrets.SUPABASE_KEY
+openai_api_key  = st.secrets.openai_api_key
 anthropic_api_key = st.secrets.anthropic_api_key
-hf_api_key = st.secrets.hf_api_key
-username = st.secrets.username
+hf_api_key      = st.secrets.hf_api_key
+username        = st.secrets.username
 
 supabase: Client = create_client(supabase_url, supabase_key)
 logger = get_logger(__name__)
 
-embeddings = HuggingFaceInferenceAPIEmbeddings(
-    api_key=hf_api_key,
+# ─────── embeddings ─────────────────────────────────────────────────────────────
+# Switch to local BGE embeddings (no JSONDecode errors, no HTTP‑batch issues) :contentReference[oaicite:0]{index=0}
+embeddings = HuggingFaceBgeEmbeddings(
     model_name="BAAI/bge-large-en-v1.5",
-    api_url="https://router.huggingface.co/hf-inference/pipeline/feature-extraction/",
+    model_kwargs={"device": "cpu"},
+    encode_kwargs={"normalize_embeddings": True},
 )
 
-if 'chat_history' not in st.session_state:
-    st.session_state['chat_history'] = []
-
-vector_store = SupabaseVectorStore(supabase, embeddings, query_name='match_documents', table_name="documents")
-memory = ConversationBufferMemory(memory_key="chat_history", input_key='question', output_key='answer', return_messages=True)
+# ─────── vector store + memory ─────────────────────────────────────────────────
+vector_store = SupabaseVectorStore(
+    client=supabase,
+    embedding=embeddings,
+    query_name="match_documents",
+    table_name="documents",
+)
+memory = ConversationBufferMemory(
+    memory_key="chat_history",
+    input_key="question",
+    output_key="answer",
+    return_messages=True,
+)
 
-model = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+# ─────── LLM setup ──────────────────────────────────────────────────────────────
+model        = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+temperature  = 0.1
+max_tokens   = 500
 
-temperature = 0.1
-max_tokens = 500
-stats = str(get_usage(supabase)) 
+def response_generator(query: str) -> str:
+    """Ask the RAG chain to answer `query`, with JSON‑error fallback."""
+    # log usage
+    add_usage(supabase, "chat", "prompt:" + query, {"model": model, "temperature": temperature})
+    logger.info("Using HF model %s", model)
 
-def response_generator(query):
-    qa = None
-    add_usage(supabase, "chat", "prompt" + query, {"model": model, "temperature": temperature})
-    logger.info('Using HF model %s', model)
-    # print(st.session_state['max_tokens'])
-    endpoint_url = ("https://api-inference.huggingface.co/models/"+ model)
-    model_kwargs = {"temperature" : temperature,
-                    "max_new_tokens" : max_tokens,
-                    # "repetition_penalty" : 1.1,
-                    "return_full_text" : False}
+    # prepare HF text-generation LLM
     hf = HuggingFaceEndpoint(
-        endpoint_url=endpoint_url,
+        endpoint_url=f"https://api-inference.huggingface.co/models/{model}",
         task="text-generation",
         huggingfacehub_api_token=hf_api_key,
-        model_kwargs=model_kwargs
+        model_kwargs={
+            "temperature": temperature,
+            "max_new_tokens": max_tokens,
+            "return_full_text": False,
+        },
+    )
+
+    # conversational RAG chain
+    qa = ConversationalRetrievalChain.from_llm(
+        llm=hf,
+        retriever=vector_store.as_retriever(
+            search_kwargs={"score_threshold": 0.6, "k": 4, "filter": {"user": username}}
+        ),
+        memory=memory,
+        verbose=True,
+        return_source_documents=True,
     )
-    qa = ConversationalRetrievalChain.from_llm(hf, retriever=vector_store.as_retriever(search_kwargs={"score_threshold": 0.6, "k": 4,"filter": {"user": username}}), memory=memory, verbose=True, return_source_documents=True)
-    
-    # Generate model's response 
-    model_response = qa({"question": query})
-    logger.info('Result: %s', model_response["answer"])
-    sources = model_response["source_documents"]
-    logger.info('Sources: %s', model_response["source_documents"])
-
-    if len(sources) > 0:
-        response = model_response["answer"]
-    else:
-        response = "I am sorry, I do not have enough information to provide an answer. If there is a public source of data that you would like to add, please email [email protected]."
-    
-    return response
-    
-# Set the theme
+
+    try:
+        result = qa({"question": query})
+    except JSONDecodeError as e:
+        # fallback logging  
+        logger.error("Embedding JSONDecodeError: %s", e)
+        return "Sorry, I had trouble understanding the embedded data. Please try again."
+
+    answer = result.get("answer", "")
+    sources = result.get("source_documents", [])
+
+    if not sources:
+        return (
+            "I’m sorry, I don’t have enough information to answer that. "
+            "If you have a public data source to add, please email [email protected]."
+        )
+    return answer
+
+# ─────── Streamlit UI ──────────────────────────────────────────────────────────
 st.set_page_config(
     page_title="Securade.ai - Safety Copilot",
     page_icon="https://securade.ai/favicon.ico",
     layout="centered",
     initial_sidebar_state="collapsed",
     menu_items={
-        "About": "# Securade.ai Safety Copilot v0.1\n [https://securade.ai](https://securade.ai)",
-        "Get Help" : "https://securade.ai",
-        "Report a Bug": "mailto:[email protected]"
-    }
+        "About": "# Securade.ai Safety Copilot v0.1\n[https://securade.ai](https://securade.ai)",
+        "Get Help": "https://securade.ai",
+        "Report a Bug": "mailto:[email protected]",
+    },
 )
 
 st.title("👷‍♂️ Safety Copilot 🦺")
+stats = get_usage(supabase)
+st.markdown(f"_{stats} queries answered!_")
+st.markdown(
+    "Chat with your personal safety assistant about any health & safety related queries. "
+    "[[blog](https://securade.ai/blog/how-securade-ai-safety-copilot-transforms-worker-safety.html)"
+    "|[paper](https://securade.ai/assets/pdfs/Securade.ai-Safety-Copilot-Whitepaper.pdf)]"
+)
 
-st.markdown("Chat with your personal safety assistant about any health & safety related queries. [[blog](https://securade.ai/blog/how-securade-ai-safety-copilot-transforms-worker-safety.html)|[paper](https://securade.ai/assets/pdfs/Securade.ai-Safety-Copilot-Whitepaper.pdf)]")
-# st.markdown("Up-to-date with latest OSH regulations for Singapore, Indonesia, Malaysia & other parts of Asia.")
-st.markdown("_"+ stats + " queries answered!_")
-
-if 'chat_history' not in st.session_state:
-    st.session_state['chat_history'] = []
-    
-# Display chat messages from history on app rerun
-for message in st.session_state.chat_history:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-        
-# Accept user input
-if prompt := st.chat_input("Ask a question"): 
-    # print(prompt)
-    # Add user message to chat history
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+# show history
+for msg in st.session_state.chat_history:
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
+
+# new user input
+if prompt := st.chat_input("Ask a question"):
     st.session_state.chat_history.append({"role": "user", "content": prompt})
-    # Display user message in chat message container
     with st.chat_message("user"):
         st.markdown(prompt)
-    
-    with st.spinner('Safety briefing in progress...'):
-        response = response_generator(prompt)
-    
-    # Display assistant response in chat message container
+
+    with st.spinner("Safety briefing in progress..."):
+        answer = response_generator(prompt)
+
     with st.chat_message("assistant"):
-        st.markdown(response)
-    # Add assistant response to chat history
-    # print(response)
-    st.session_state.chat_history.append({"role": "assistant", "content": response})
-
-# query = st.text_area("## Ask a question (" + stats + " queries answered so far)", max_chars=500)
-# columns = st.columns(2)
-# with columns[0]:
-#     button = st.button("Ask")
-# with columns[1]:
-#     clear_history = st.button("Clear History", type='secondary')
-    
-# st.markdown("---\n\n")
-
-# if clear_history:
-#     # Clear memory in Langchain
-#     memory.clear()
-#     st.session_state['chat_history'] = []
-#     st.experimental_rerun()
+        st.markdown(answer)
+    st.session_state.chat_history.append({"role": "assistant", "content": answer})