Create tribunal_4.py

tribunal_4.py

@@ -0,0 +1,421 @@
+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_capture' not in st.session_state:
+        st.session_state.token_capture = []
+
+# 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):
+    grounding = proximity_search(
+        question=user_input,
+        milvus_client=milvus_client,
+        emb=emb,
+        vector_index_properties=vector_index_properties,
+        vector_store_schema=vector_store_schema
+    )
+    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)
+    
+    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 genparam.TOKEN_CAPTURE_ENABLED:
+            st.code(prompt_data, line_numbers=True, wrap_lines=True)
+        stream = generate_response(watsonx_llm, prompt_data, params)
+        response = st.write_stream(stream)
+       # response = st.write_stream(stream, f"<span style='color: {color};'>", unsafe_allow_html=True)
+
+    if genparam.TOKEN_CAPTURE_ENABLED:
+        chat_number = len(chat_history) // 2
+        token_calculations = capture_tokens(prompt_data, response, chat_number)
+        if token_calculations:
+            st.sidebar.code(token_calculations)
+
+    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
+
+    st.session_state.token_capture.append(f"chat {chat_number}: {input_tokens} + {output_tokens} = {total_tokens}")
+
+    token_calculations = "\n".join(st.session_state.token_capture)
+    return token_calculations
+
+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.write('')
+    
+    # Display user chat history in sidebar
+    st.sidebar.subheader("Your Questions")
+    for i, message in enumerate(st.session_state.chat_history_1):
+        if message["role"] == "user":
+            st.sidebar.markdown(f"**Question {i//2 + 1}:** {message['content']}")
+    
+    st.sidebar.write('')
+    st.sidebar.write('')
+
+    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)
+        
+        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 only bot responses
+            for message in st.session_state.chat_history_1:
+                if message["role"] != "user":  # Only show bot messages
+                    with st.chat_message(message["role"], avatar=genparam.BOT_1_AVATAR):
+                        st.markdown(message['content'])
+            
+            # Add user message to history but don't display
+            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,
+                get_active_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)
+            
+        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)
+            
+            # Display only bot responses
+            for message in st.session_state.chat_history_2:
+                if message["role"] != "user":  # Only show bot messages
+                    with st.chat_message(message["role"], avatar=genparam.BOT_2_AVATAR):
+                        st.markdown(message['content'])
+            
+            # Add user message to history but don't display
+            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,
+                get_active_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)
+            
+        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)
+            
+            # Display only bot responses
+            for message in st.session_state.chat_history_3:
+                if message["role"] != "user":  # Only show bot messages
+                    with st.chat_message(message["role"], avatar=genparam.BOT_3_AVATAR):
+                        st.markdown(message['content'])
+            
+            # Add user message to history but don't display
+            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"]
+            )
+            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)