Gemini informational agent + improved prompt (#30)

* feat: improve informational agent prompt

* feat: gemini model informational agent

Author: neagualexa

.github/workflows/dev.yml

@@ -15,6 +15,8 @@ jobs:
     env:
       OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
       OPENAI_MODEL: ${{ vars.OPENAI_MODEL }}
+      GOOGLE_AI_API_KEY: ${{ secrets.GOOGLE_AI_API_KEY }}
+      GOOGLE_AI_MODEL: ${{ vars.GOOGLE_AI_MODEL }}
     steps:
       - name: Checkout Code
         uses: actions/checkout@v4

.github/workflows/main.yml

@@ -15,6 +15,8 @@ jobs:
     env:
       OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
       OPENAI_MODEL: ${{ vars.OPENAI_MODEL }}
+      GOOGLE_AI_API_KEY: ${{ secrets.GOOGLE_AI_API_KEY }}
+      GOOGLE_AI_MODEL: ${{ vars.GOOGLE_AI_MODEL }}
     steps:
       - name: Checkout Code
         uses: actions/checkout@v4

src/agents/informational_agent/informational_agent.py

@@ -1,10 +1,10 @@
 try:
-    from ..llm_factory import OpenAILLMs
+    from ..llm_factory import OpenAILLMs, GoogleAILLMs
     from .informational_prompts import \
         informational_role_prompt, conv_pref_prompt, update_conv_pref_prompt, summary_prompt, update_summary_prompt, summary_system_prompt
     from ..utils.types import InvokeAgentResponseType
 except ImportError:
-    from src.agents.llm_factory import OpenAILLMs
+    from src.agents.llm_factory import OpenAILLMs, GoogleAILLMs
     from src.agents.informational_agent.informational_prompts import \
         informational_role_prompt, conv_pref_prompt, update_conv_pref_prompt, summary_prompt, update_summary_prompt, summary_system_prompt
     from src.agents.utils.types import InvokeAgentResponseType
@@ -37,7 +37,7 @@ class State(TypedDict):
 
 class InformationalAgent:
     def __init__(self):
-        llm = OpenAILLMs(temperature=0.25)
+        llm = GoogleAILLMs()
         self.llm = llm.get_llm()
         summarisation_llm = OpenAILLMs()
         self.summarisation_llm = summarisation_llm.get_llm()
@@ -135,7 +135,6 @@ def summarize_conversation(self, state: State, config: RunnableConfig) -> dict:
         delete_messages: list[AllMessageTypes] = [RemoveMessage(id=m.id) for m in state["messages"][:-3]]
 
         return {"summary": summary_response.content, "conversationalStyle": conversationalStyle_response.content, "messages": delete_messages}
-        # return {"summary": summary_response.content, "messages": delete_messages}
 
     def should_summarize(self, state: State) -> str:
         """

src/agents/informational_agent/informational_prompts.py

@@ -31,8 +31,10 @@
 Follow up with guided hints or clarifications based on their response.
 
 ## Flexibility:
-Adjust your approach dynamically, whether the student seeks detailed guidance, prefers a hands-off approach, or demonstrates unique problem-solving strategies. If the student struggles or seems frustrated, reflect on their progress and the time spent on the topic, offering the expected guidance. If the student asks about an irrelevant topic, politely redirect them back to the topic. Do not end your responses with a concluding statement."""
+Restrict your response's length to quickly resolve the student's query. However, adjust your approach dynamically, if the student seeks detailed guidance, prefers a hands-off approach, or demonstrates unique problem-solving strategies. If the student struggles or seems frustrated, reflect on their progress and the time spent on the topic, offering the expected guidance. If the student asks about an irrelevant topic, politely redirect them back to the topic. Do not end your responses with a concluding statement.
 
+## Governance
+You are a chatbot deployed in Lambda Feedback, an online self-study platform. You are discussing with students from Imperial College London."""
 
 pref_guidelines = """**Guidelines:**
 - Use concise, objective language.

src/agents/utils/example_inputs/example_input_4.json

@@ -2,10 +2,7 @@
   "message": "hi",
   "params": {
     "include_test_data": true,
-    "conversation_history": [
-      { "type": "user", "content": "hi" },
-      { "type": "ai", "content": "Hello! How can I help you today?" }
-    ],
+    "conversation_history": [{ "type": "user", "content": "hi" }],
     "summary": "",
     "conversational_style": "",
     "question_response_details": {

src/agents/utils/testbench_agents.py

@@ -30,7 +30,7 @@
       STEP 2: Extract the parameters from the JSON
     """
     # NOTE: #### This is the testing message!! #####
-    message = "Hi" 
+    message = "What do you know about me?" 
     # NOTE: ########################################
 
     # replace "mock" in the message and conversation history with the actual message
@@ -57,7 +57,7 @@
             question_information,
             question_access_information
         )
-        print("Question Response Details Prompt:", question_response_details_prompt, "\n\n")
+        # print("Question Response Details Prompt:", question_response_details_prompt, "\n\n")
 
     if "agent_type" in params:
         agent_type = params["agent_type"]