updates

app.py

@@ -1,117 +1,15 @@
-
-import os
 from PIL import Image
 import sys
 
-from omegaconf import OmegaConf
 import streamlit as st
 from streamlit_pills import pills
 
-from dotenv import load_dotenv
-load_dotenv(override=True)
+from vectara_agent.agent import AgentStatusType
 
-from pydantic import Field, BaseModel
-from vectara_agent.agent import Agent, AgentStatusType
-from vectara_agent.tools import ToolsFactory, VectaraToolFactory
-from vectara_agent.tools_catalog import rephrase_text
+from agent import initialize_agent, get_agent_config, teaching_styles, languages
 
-teaching_styles = ['Inquiry-based', 'Socratic', 'traditional']
-languages = {'English': 'en', 'Spanish': 'es', 'French': 'fr', 'German': 'de', 'Arabic': 'ar', 'Chinese': 'zh-cn', 
-             'Hebrew': 'he', 'Hindi': 'hi', 'Italian': 'it', 'Japanese': 'ja', 'Korean': 'ko', 'Portuguese': 'pt'}
 initial_prompt = "How can I help you today?"
 
-def create_assistant_tools(cfg):        
-
-    def adjust_response_to_student(
-            text: str = Field(description='the text to adjust. may include citations in markdown format.'), 
-            age: int = Field(description='the age of the student. An integer'), 
-            style: str = Field(description='teaching style'), 
-            language: str = Field(description='the language')
-
-        ) -> str:
-        """
-        Rephrase the provided text to match the student's age, desired teaching style and language.
-        """
-        instructions = f'''
-        The following is some text, which may include citations in markdown format.
-        Adjust the response to match the student's age of {age}, the {style} teaching style.
-        Where possible, maintain the citations in context of the response (instead of listing them at the end). 
-        When showing citations, use markdown in the following format: [[N](URL)], where N is the citation sequential number.
-        You can renumber citations if needed.
-        For example, in the inquiry-based teaching style, choose to ask questions that encourage the student to think critically instead of repsonding directly with the answer.
-        Or in the socratic teaching style, choose to ask questions that lead the student to the answer.
-        Always respond in the {language} language.''' \
-            .replace("{style}", cfg.style) \
-            .replace("{language}", cfg.language) \
-            .replace("{student_age}", str(cfg.student_age))
-
-        return rephrase_text(text, instructions)
-
-
-    class JusticeHarvardArgs(BaseModel):
-        query: str = Field(..., description="The user query.")
-
-    vec_factory = VectaraToolFactory(vectara_api_key=cfg.api_key, 
-                                     vectara_customer_id=cfg.customer_id, 
-                                     vectara_corpus_id=cfg.corpus_id)
-    tools_factory = ToolsFactory()
-    query_tool = vec_factory.create_rag_tool(
-        tool_name = "justice_harvard_query",
-        tool_description = """
-        Answer questions about the justice, morality, politics and related topics,
-        based on transcripts of recordings from the Justice Harvard class that includes a lot of content on these topics.
-        When using the tool it's best to ask simple short questions. You can break complex questions into sub-queries.
-        """,
-        tool_args_schema = JusticeHarvardArgs,
-        reranker = "multilingual_reranker_v1", rerank_k = 100, 
-        n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.005,
-        summary_num_results = 10,
-        vectara_summarizer = 'vectara-summary-ext-24-05-med-omni',
-        include_citations = True,
-    )
-
-    return (
-            [tools_factory.create_tool(tool) for tool in
-                [
-                    adjust_response_to_student,
-                ]
-            ] +
-            tools_factory.standard_tools() + 
-            tools_factory.guardrail_tools() +
-            [query_tool]
-    )
-
-def initialize_agent(_cfg):
-    if 'agent' in st.session_state:
-        return st.session_state.agent
-
-    bot_instructions = f"""
-    - You are a helpful teacher assistant, with expertise in education in various teaching styles.
-    - Obtain information using tools to answer the user's query.
-    - If the tool cannot provide information relevant to the user's query, tell the user that you are unable to provide an answer.
-    - If the tool can provide relevant information, use the adjust_response_to_student tool 
-      to rephrase the text (including citations if any) to ensure it fits the student's age of {_cfg.student_age}, 
-      the {_cfg.style} teaching style and the {_cfg.language} language.
-    - When including links to the Justice Harvard videos, the url should include the start time (from the metadata).
-      For example: https://youtube.com/watch?v=0O2Rq4HJBxw&t=1234 represents a link to a youtube video starting at 1234 seconds. 
-    - When showing citations, renumber them if needed and use markdown in the following format: [[N](URL)], where N is the citation sequential number.
-    - Response in a concise and clear manner, and provide the most relevant information to the student.
-    - Never discuss politics, and always respond politely.
-    """
-
-    def update_func(status_type: AgentStatusType, msg: str):
-        if status_type != AgentStatusType.AGENT_UPDATE:
-            output = f"{status_type.value} - {msg}"
-            st.session_state.log_messages.append(output)
-
-    agent = Agent(
-        tools=create_assistant_tools(_cfg),
-        topic="justice, morality, politics, and philosophy",
-        custom_instructions=bot_instructions,
-        update_func=update_func
-    )
-    return agent
-
 def toggle_logs():
     st.session_state.show_logs = not st.session_state.show_logs
 
@@ -124,58 +22,56 @@ def show_example_questions():
             return True
     return False
 
+def update_func(status_type: AgentStatusType, msg: str):
+    if status_type != AgentStatusType.AGENT_UPDATE:
+        output = f"{status_type.value} - {msg}"
+        st.session_state.log_messages.append(output)
 
 def launch_bot():
     def reset():
-        cfg = st.session_state.cfg
         st.session_state.messages = [{"role": "assistant", "content": initial_prompt, "avatar": "🦖"}]
         st.session_state.thinking_message = "Agent at work..."
-        st.session_state.agent = initialize_agent(cfg)
         st.session_state.log_messages = []
         st.session_state.prompt = None
         st.session_state.ex_prompt = None
         st.session_state.first_turn = True
         st.session_state.show_logs = False
+        st.session_state.agent = initialize_agent(cfg, update_func=update_func) # must reset agent to apply new settings
 
-    st.set_page_config(page_title="Justice Harvard Teaching Assistant", layout="wide")
     if 'cfg' not in st.session_state:
-        cfg = OmegaConf.create({
-            'customer_id': str(os.environ['VECTARA_CUSTOMER_ID']),
-            'corpus_id': str(os.environ['VECTARA_CORPUS_ID']),
-            'api_key': str(os.environ['VECTARA_API_KEY']),
-            'style': teaching_styles[0],
-            'language': 'English',
-            'student_age': 18,
-            'examples': os.environ.get('QUERY_EXAMPLES', None)
-        })
-        st.session_state.cfg = cfg
+        cfg = get_agent_config()
         st.session_state.style = cfg.style
         st.session_state.language = cfg.language
         st.session_state.student_age = cfg.student_age
+        st.session_state.cfg = cfg
         st.session_state.ex_prompt = None
         example_messages = [example.strip() for example in cfg.examples.split(",")] if cfg.examples else []
         st.session_state.example_messages = [em for em in example_messages if len(em)>0]
         reset()
 
     cfg = st.session_state.cfg
+    st.set_page_config(page_title=cfg['title'], layout="wide")
 
     # left side content
     with st.sidebar:
         image = Image.open('Vectara-logo.png')
         st.image(image, width=175)
-        st.markdown("## Welcome to the Justice Harvard e-learning assistant demo.\n\n\n")
+        st.markdown(f"## {cfg['demo_welcome']}")
+        st.markdown(f"{cfg['demo_description']}")
 
         st.markdown("\n")
         cfg.style = st.selectbox('Teacher Style:', teaching_styles)
         if st.session_state.style != cfg.style:
             st.session_state.style = cfg.style
             reset()
+            st.rerun()
 
         st.markdown("\n")
         cfg.language = st.selectbox('Language:', languages.keys())
         if st.session_state.language != cfg.language:
-            st.session_state.langage = cfg.language
+            st.session_state.language = cfg.language
             reset()
+            st.rerun()
 
         st.markdown("\n") 
         cfg.student_age = st.number_input(
@@ -185,6 +81,7 @@ def launch_bot():
         if st.session_state.student_age != cfg.student_age:
             st.session_state.student_age = cfg.student_age
             reset()
+            st.rerun()
 
         st.markdown("\n\n")
         bc1, _ = st.columns([1, 1])
@@ -197,10 +94,8 @@ def launch_bot():
         st.markdown(
             "## How this works?\n"
             "This app was built with [Vectara](https://vectara.com).\n\n"
-            "It demonstrates the use of Agentic Chat functionality with Vectara"
+            "It demonstrates the use of Agentic RAG functionality with Vectara"
         )
-        st.markdown("---")
-
 
     if "messages" not in st.session_state.keys():
         reset()
@@ -243,6 +138,7 @@ def launch_bot():
             st.markdown(res)
         st.session_state.ex_prompt = None
         st.session_state.prompt = None
+        st.session_state.first_turn = False
         st.rerun()
 
     log_placeholder = st.empty()
@@ -256,8 +152,6 @@ def launch_bot():
                 st.button("Show Logs", on_click=toggle_logs)
 
     sys.stdout.flush()
-    
- 
+
 if __name__ == "__main__":
-    launch_bot()
-    
+    launch_bot()