Delete neo_sages.py

neo_sages.py

@@ -1,529 +0,0 @@
-import streamlit as st
-from io import BytesIO
-import ibm_watsonx_ai
-import secretsload
-import genparam
-import requests
-import time
-import re
-import json
-
-from ibm_watsonx_ai.foundation_models import ModelInference
-from ibm_watsonx_ai import Credentials, APIClient
-from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
-from ibm_watsonx_ai.metanames import GenTextReturnOptMetaNames as RetParams
-
-from ibm_watsonx_ai.foundation_models import Embeddings
-from ibm_watsonx_ai.foundation_models.utils.enums import EmbeddingTypes
-from pymilvus import MilvusClient
-
-from secretsload import load_stsecrets
-
-credentials = load_stsecrets()
-
-st.set_page_config(
-    page_title="The Solutioning Sages",
-    page_icon="🪄",
-    initial_sidebar_state="collapsed",
-    layout="wide"
-)
-
-# Password protection
-def check_password():
-    def password_entered():
-        if st.session_state["password"] == st.secrets["app_password"]:
-            st.session_state["password_correct"] = True
-            del st.session_state["password"]
-        else:
-            st.session_state["password_correct"] = False
-
-    if "password_correct" not in st.session_state:
-        st.markdown("\n\n")
-        st.text_input("Enter the password", type="password", on_change=password_entered, key="password")
-        st.divider()
-        st.info("Designed and developed by Milan Mrdenovic © IBM Norway 2024")
-        return False
-    elif not st.session_state["password_correct"]:
-        st.markdown("\n\n")
-        st.text_input("Enter the password", type="password", on_change=password_entered, key="password")
-        st.divider()
-        st.error("😕 Incorrect password")
-        st.info("Designed and developed by Milan Mrdenovic © IBM Norway 2024")
-        return False
-    else:
-        return True
-
-def initialize_session_state():
-    if 'chat_history_1' not in st.session_state:
-        st.session_state.chat_history_1 = []
-    if 'chat_history_2' not in st.session_state:
-        st.session_state.chat_history_2 = []
-    if 'chat_history_3' not in st.session_state:
-        st.session_state.chat_history_3 = []
-    if 'first_question' not in st.session_state:
-        st.session_state.first_question = False 
-    if "counter" not in st.session_state:
-        st.session_state["counter"] = 0
-    if 'token_statistics' not in st.session_state:
-        st.session_state.token_statistics = []
-
-# three_column_style = """
-#     <style>
-#     .stColumn {
-#         padding: 0.5rem;
-#         border-right: 1px solid #dedede;
-#     }
-#     .stColumn:last-child {
-#         border-right: none;
-#     }
-#     .chat-container {
-#         height: calc(100vh - 200px);
-#         overflow-y: auto;
-#     }
-#     </style>
-# """
-
-three_column_style = """
-    <style>
-    .stColumn {
-        padding: 0.5rem;
-        border-right: 1px solid #dedede;
-    }
-    .stColumn:last-child {
-        border-right: none;
-    }
-    .chat-container {
-        height: calc(100vh - 200px);
-        overflow-y: auto;
-        display: flex;
-        flex-direction: column;
-    }
-    .chat-messages {
-        display: flex;
-        flex-direction: column;
-        gap: 1rem;
-    }
-    </style>
-""" # Alt Style
-
-#-----
-def get_active_model():
-    return genparam.SELECTED_MODEL_1 if genparam.ACTIVE_MODEL == 0 else genparam.SELECTED_MODEL_2
-
-def get_active_prompt_template():
-    return genparam.PROMPT_TEMPLATE_1 if genparam.ACTIVE_MODEL == 0 else genparam.PROMPT_TEMPLATE_2
-
-def get_active_vector_index():
-    return st.secrets["vector_index_id_1"] if genparam.ACTIVE_INDEX == 0 else st.secrets["vector_index_id_2"]
-#-----
-
-def setup_client(project_id):
-    credentials = Credentials(
-        url=st.secrets["url"],
-        api_key=st.secrets["api_key"]
-    )
-    apo = st.secrets["api_key"]
-    client = APIClient(credentials, project_id=project_id)
-    return credentials, client
-
-wml_credentials, client = setup_client(st.secrets["project_id"])
-
-def setup_vector_index(client, wml_credentials, vector_index_id):
-    vector_index_details = client.data_assets.get_details(vector_index_id)
-    vector_index_properties = vector_index_details["entity"]["vector_index"]
-
-    emb = Embeddings(
-        model_id=vector_index_properties["settings"]["embedding_model_id"],
-        #model_id="sentence-transformers/all-minilm-l12-v2",
-        credentials=wml_credentials,
-        project_id=st.secrets["project_id"],
-        params={
-            "truncate_input_tokens": 512
-        }
-    )
-    
-    vector_store_schema = vector_index_properties["settings"]["schema_fields"]
-    connection_details = client.connections.get_details(vector_index_details["entity"]["vector_index"]["store"]["connection_id"])
-    connection_properties = connection_details["entity"]["properties"]
-    
-    milvus_client = MilvusClient(
-        uri=f'https://{connection_properties.get("host")}:{connection_properties.get("port")}',
-        user=connection_properties.get("username"),
-        password=connection_properties.get("password"),
-        db_name=vector_index_properties["store"]["database"]
-    )
-
-    return milvus_client, emb, vector_index_properties, vector_store_schema
-
-def proximity_search(question, milvus_client, emb, vector_index_properties, vector_store_schema):
-    query_vectors = emb.embed_query(question)
-    milvus_response = milvus_client.search(
-        collection_name=vector_index_properties["store"]["index"],
-        data=[query_vectors],
-        limit=vector_index_properties["settings"]["top_k"],
-        metric_type="L2",
-        output_fields=[
-            vector_store_schema.get("text"),
-            vector_store_schema.get("document_name"),
-            vector_store_schema.get("page_number")
-        ]
-    )
-    
-    documents = []
-    
-    for hit in milvus_response[0]:
-        text = hit["entity"].get(vector_store_schema.get("text"), "")
-        doc_name = hit["entity"].get(vector_store_schema.get("document_name"), "Unknown Document")
-        page_num = hit["entity"].get(vector_store_schema.get("page_number"), "N/A")
-        
-        formatted_result = f"Document: {doc_name}\nContent: {text}\nPage: {page_num}\n"
-        documents.append(formatted_result)
-
-    joined = "\n".join(documents)
-    retrieved = f"""Number of Retrieved Documents: {len(documents)}\n\n{joined}"""
-
-    return retrieved
-
-def prepare_prompt(prompt, chat_history):
-    if genparam.TYPE == "chat" and chat_history:
-        chats = "\n".join([f"{message['role']}: \"{message['content']}\"" for message in chat_history])
-        prompt = f"""Retrieved Contextual Information:\n__grounding__\n\nConversation History:\n{chats}\n\nNew User Input: {prompt}"""
-        return prompt
-    else:
-        prompt = f"""Retrieved Contextual Information:\n__grounding__\n\nUser Input: {prompt}"""
-        return prompt
-
-def apply_prompt_syntax(prompt, system_prompt, prompt_template, bake_in_prompt_syntax):
-    model_family_syntax = {
-        "llama3-instruct (llama-3, 3.1 & 3.2) - system": """<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n""",
-        "llama3-instruct (llama-3, 3.1 & 3.2) - user": """<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n""",
-        "granite-13b-chat & instruct - system": """<|system|>\n{system_prompt}\n<|user|>\n{prompt}\n<|assistant|>\n\n""",
-        "granite-13b-chat & instruct - user": """<|user|>\n{prompt}\n<|assistant|>\n\n""",
-        "mistral & mixtral v2 tokenizer - system": """<s>[INST] System Prompt: {system_prompt} [/INST][INST] {prompt} [/INST]\n\n""",
-        "mistral & mixtral v2 tokenizer - user": """<s>[INST] {prompt} [/INST]\n\n""",
-        "no syntax - system": """{system_prompt}\n\n{prompt}""",
-        "no syntax - user": """{prompt}"""
-    }
-    
-    if bake_in_prompt_syntax:
-        template = model_family_syntax[prompt_template]
-        if system_prompt:
-            return template.format(system_prompt=system_prompt, prompt=prompt)
-    return prompt
-
-def generate_response(watsonx_llm, prompt_data, params):
-    generated_response = watsonx_llm.generate_text_stream(prompt=prompt_data, params=params)
-    for chunk in generated_response:
-        yield chunk
-
-def fetch_response(user_input, milvus_client, emb, vector_index_properties, vector_store_schema, system_prompt, chat_history):
-    # Get grounding documents
-    grounding = proximity_search(
-        question=user_input,
-        milvus_client=milvus_client,
-        emb=emb,
-        vector_index_properties=vector_index_properties,
-        vector_store_schema=vector_store_schema
-    )
-
-    # Special handling for PATH-er B. (first column)
-    if chat_history == st.session_state.chat_history_1:
-        # Display user question first
-        with st.chat_message("user", avatar=genparam.USER_AVATAR):
-            st.markdown(user_input)
-        
-        # Parse and display each document from the grounding
-        documents = grounding.split("\n\n")[2:]  # Skip the count line and first newline
-        for doc in documents:
-            if doc.strip():  # Only process non-empty strings
-                parts = doc.split("\n")
-                doc_name = parts[0].replace("Document: ", "")
-                content = parts[1].replace("Content: ", "")
-                
-                # Display document with delay
-                time.sleep(0.5)
-                st.markdown(f"**{doc_name}**")
-                st.code(content)
-
-        # Store in chat history
-        return grounding
-
-    # For MOD-ther S. (second column)
-    elif chat_history == st.session_state.chat_history_2:
-        prompt = prepare_prompt(user_input, chat_history)
-        prompt_data = apply_prompt_syntax(
-            prompt,
-            system_prompt,
-            get_active_prompt_template(),
-            genparam.BAKE_IN_PROMPT_SYNTAX
-        )
-        prompt_data = prompt_data.replace("__grounding__", grounding)
-
-        # Add debug information to column 1 if enabled
-        if genparam.INPUT_DEBUG_VIEW == 1:
-            with st.columns(3)[0]:  # Access first column
-                st.markdown(f"**{genparam.BOT_2_AVATAR} {genparam.BOT_2_NAME} Prompt Data:**")
-                st.code(prompt_data, language="text")
-
-    # For SYS-ter V. (third column)
-    else:
-        # Get chat history from MOD-ther S.
-        mod_ther_history = st.session_state.chat_history_2
-        prompt = prepare_prompt(user_input, mod_ther_history)
-        prompt_data = apply_prompt_syntax(
-            prompt,
-            system_prompt,
-            get_active_prompt_template(),
-            genparam.BAKE_IN_PROMPT_SYNTAX
-        )
-        prompt_data = prompt_data.replace("__grounding__", grounding)
-
-            # Add debug information to column 1 if enabled
-        if genparam.INPUT_DEBUG_VIEW == 1:
-            with st.columns(3)[0]:  # Access first column
-                st.markdown(f"**{genparam.BOT_3_AVATAR} {genparam.BOT_3_NAME} Prompt Data:**")
-                st.code(prompt_data, language="text")
-
-    # Continue with normal processing for columns 2 and 3
-    watsonx_llm = ModelInference(
-        api_client=client, 
-        model_id=get_active_model(),
-        verify=genparam.VERIFY
-    )
-
-    params = {
-        GenParams.DECODING_METHOD: genparam.DECODING_METHOD,
-        GenParams.MAX_NEW_TOKENS: genparam.MAX_NEW_TOKENS,
-        GenParams.MIN_NEW_TOKENS: genparam.MIN_NEW_TOKENS,
-        GenParams.REPETITION_PENALTY: genparam.REPETITION_PENALTY,
-        GenParams.STOP_SEQUENCES: genparam.STOP_SEQUENCES
-    }
-
-    bot_name = None
-    bot_avatar = None
-    if chat_history == st.session_state.chat_history_1:
-        bot_name = genparam.BOT_1_NAME
-        bot_avatar = genparam.BOT_1_AVATAR
-    elif chat_history == st.session_state.chat_history_2:
-        bot_name = genparam.BOT_2_NAME
-        bot_avatar = genparam.BOT_2_AVATAR
-    else:
-        bot_name = genparam.BOT_3_NAME
-        bot_avatar = genparam.BOT_3_AVATAR
-
-    with st.chat_message(bot_name, avatar=bot_avatar):
-        if chat_history != st.session_state.chat_history_1:  # Only generate responses for columns 2 and 3
-            stream = generate_response(watsonx_llm, prompt_data, params)
-            response = st.write_stream(stream)
-            
-            # Only capture tokens for MOD-ther S. and SYS-ter V.
-            if genparam.TOKEN_CAPTURE_ENABLED and chat_history != st.session_state.chat_history_1:
-                token_stats = capture_tokens(prompt_data, response, bot_name)
-                if token_stats:
-                    st.session_state.token_statistics.append(token_stats)
-        else:
-            response = grounding  # For column 1, we already displayed the content
-
-    return response
-
-def capture_tokens(prompt_data, response, chat_number):
-    if not genparam.TOKEN_CAPTURE_ENABLED:
-        return
-
-    watsonx_llm = ModelInference(
-        api_client=client, 
-        model_id=genparam.SELECTED_MODEL,
-        verify=genparam.VERIFY
-    )
-
-    input_tokens = watsonx_llm.tokenize(prompt=prompt_data)["result"]["token_count"]
-    output_tokens = watsonx_llm.tokenize(prompt=response)["result"]["token_count"]
-    total_tokens = input_tokens + output_tokens
-
-    return {
-        "bot_name": bot_name,
-        "input_tokens": input_tokens,
-        "output_tokens": output_tokens,
-        "total_tokens": total_tokens,
-        "timestamp": time.strftime("%H:%M:%S")
-    }
-
-def main():
-    initialize_session_state()
-
-    # Apply custom styles
-    st.markdown(three_column_style, unsafe_allow_html=True)
-    
-    # Sidebar configuration
-    st.sidebar.header('The Solutioning Sages')
-    st.sidebar.divider()
-    
-    # Display token statistics in sidebar
-    st.sidebar.subheader("Token Usage Statistics")
-    
-    # Group token statistics by interaction (for MOD-ther S. and SYS-ter V. only)
-    if st.session_state.token_statistics:
-        current_timestamp = None
-        interaction_count = 0
-        stats_by_time = {}
-        
-        # Group stats by timestamp
-        for stat in st.session_state.token_statistics:
-            if stat["timestamp"] not in stats_by_time:
-                stats_by_time[stat["timestamp"]] = []
-            stats_by_time[stat["timestamp"]].append(stat)
-        
-        # Display grouped stats
-        for timestamp, stats in stats_by_time.items():
-            interaction_count += 1
-            st.sidebar.markdown(f"**Interaction {interaction_count}** ({timestamp})")
-            
-            # Calculate total tokens for this interaction
-            total_input = sum(stat['input_tokens'] for stat in stats)
-            total_output = sum(stat['output_tokens'] for stat in stats)
-            total = total_input + total_output
-            
-            # Display individual bot statistics
-            for stat in stats:
-                st.sidebar.markdown(
-                    f"_{stat['bot_name']}_  \n"
-                    f"Input: {stat['input_tokens']} tokens  \n"
-                    f"Output: {stat['output_tokens']} tokens  \n"
-                    f"Total: {stat['total_tokens']} tokens"
-                )
-            
-            # Display interaction totals
-            st.sidebar.markdown("**Interaction Totals:**")
-            st.sidebar.markdown(
-                f"Total Input: {total_input} tokens  \n"
-                f"Total Output: {total_output} tokens  \n"
-                f"Total Usage: {total} tokens"
-            )
-            st.sidebar.markdown("---")
-    
-    st.sidebar.markdown("")
-
-
-    if not check_password():
-        st.stop()
-
-    # Get user input before column creation
-    user_input = st.chat_input("Ask your question here", key="user_input")
-    
-    if user_input:
-        # Create three columns
-        col1, col2, col3 = st.columns(3)
-        
-        # First column - PATH-er B. (Document Display)
-        with col1:
-            st.markdown("<div class='chat-container'>", unsafe_allow_html=True)
-            st.subheader(f"{genparam.BOT_1_AVATAR} {genparam.BOT_1_NAME}")
-            st.markdown("<div class='chat-messages'>", unsafe_allow_html=True)
-            
-            # Display previous messages
-            for message in st.session_state.chat_history_1:
-                if message["role"] == "user":
-                    with st.chat_message(message["role"], avatar=genparam.USER_AVATAR):
-                        st.markdown(message['content'])
-                else:
-                    # Parse and display stored documents
-                    documents = message['content'].split("\n\n")[2:]  # Skip count line
-                    for doc in documents:
-                        if doc.strip():
-                            parts = doc.split("\n")
-                            doc_name = parts[0].replace("Document: ", "")
-                            content = parts[1].replace("Content: ", "")
-                            st.markdown(f"**{doc_name}**")
-                            st.code(content)
-            
-            # Add user message and get new response
-            st.session_state.chat_history_1.append({"role": "user", "content": user_input, "avatar": genparam.USER_AVATAR})
-            milvus_client, emb, vector_index_properties, vector_store_schema = setup_vector_index(
-                client, 
-                wml_credentials,
-                st.secrets["vector_index_id_1"]  # Use first vector index
-            )
-            system_prompt = genparam.BOT_1_PROMPT
-            
-            response = fetch_response(
-                user_input, 
-                milvus_client, 
-                emb, 
-                vector_index_properties, 
-                vector_store_schema,
-                system_prompt,
-                st.session_state.chat_history_1
-            )
-            st.session_state.chat_history_1.append({"role": genparam.BOT_1_NAME, "content": response, "avatar": genparam.BOT_1_AVATAR})
-            st.markdown("</div></div>", unsafe_allow_html=True)
-
-        # Second column - MOD-ther S. (Uses documents from first vector index)
-        with col2:
-            st.markdown("<div class='chat-container'>", unsafe_allow_html=True)
-            st.subheader(f"{genparam.BOT_2_AVATAR} {genparam.BOT_2_NAME}")
-            st.markdown("<div class='chat-messages'>", unsafe_allow_html=True)
-            
-            for message in st.session_state.chat_history_2:
-                if message["role"] != "user":
-                    with st.chat_message(message["role"], avatar=genparam.BOT_2_AVATAR):
-                        st.markdown(message['content'])
-            
-            st.session_state.chat_history_2.append({"role": "user", "content": user_input, "avatar": genparam.USER_AVATAR})
-            milvus_client, emb, vector_index_properties, vector_store_schema = setup_vector_index(
-                client, 
-                wml_credentials,
-                st.secrets["vector_index_id_1"]  # Use first vector index
-            )
-            system_prompt = genparam.BOT_2_PROMPT
-            
-            response = fetch_response(
-                user_input, 
-                milvus_client, 
-                emb, 
-                vector_index_properties, 
-                vector_store_schema,
-                system_prompt,
-                st.session_state.chat_history_2
-            )
-            st.session_state.chat_history_2.append({"role": genparam.BOT_2_NAME, "content": response, "avatar": genparam.BOT_2_AVATAR})
-            st.markdown("</div></div>", unsafe_allow_html=True)
-            
-        # Third column - SYS-ter V. (Uses second vector index and chat history from second column)
-        with col3:
-            st.markdown("<div class='chat-container'>", unsafe_allow_html=True)
-            st.subheader(f"{genparam.BOT_3_AVATAR} {genparam.BOT_3_NAME}")
-            st.markdown("<div class='chat-messages'>", unsafe_allow_html=True)
-            
-            for message in st.session_state.chat_history_3:
-                if message["role"] != "user":
-                    with st.chat_message(message["role"], avatar=genparam.BOT_3_AVATAR):
-                        st.markdown(message['content'])
-            
-            st.session_state.chat_history_3.append({"role": "user", "content": user_input, "avatar": genparam.USER_AVATAR})
-            milvus_client, emb, vector_index_properties, vector_store_schema = setup_vector_index(
-                client, 
-                wml_credentials,
-                st.secrets["vector_index_id_2"]  # Use second vector index
-            )
-            system_prompt = genparam.BOT_3_PROMPT
-            
-            response = fetch_response(
-                user_input, 
-                milvus_client, 
-                emb, 
-                vector_index_properties, 
-                vector_store_schema,
-                system_prompt,
-                st.session_state.chat_history_3
-            )
-            st.session_state.chat_history_3.append({"role": genparam.BOT_3_NAME, "content": response, "avatar": genparam.BOT_3_AVATAR})
-            st.markdown("</div></div>", unsafe_allow_html=True)
-
-
-        # Update sidebar with new question
-        st.sidebar.markdown("---")
-        st.sidebar.markdown("**Latest Question:**")
-        st.sidebar.markdown(f"_{user_input}_")
-
-if __name__ == "__main__":
-    main()