added custom agent

main.py

@@ -1,4 +1,5 @@
 from utils import create_index, get_agent_chain, get_prompt_and_tools, get_search_index
+from utils import get_custom_agent, get_prompt_and_tools_for_custom_agent
 question_starters = ['who', 'why', 'what', 'how', 'where', 'when', 'which', 'whom', 'whose']
 
 
@@ -9,9 +10,13 @@ def index():
 def run(question):
     index = get_search_index()
 
-    prompt, tools = get_prompt_and_tools()
+    # prompt, tools = get_prompt_and_tools()
 
-    agent_chain = get_agent_chain(prompt, tools)
+    # agent_chain = get_agent_chain(prompt, tools)
+
+    prompt, tools = get_prompt_and_tools_for_custom_agent()
+
+    agent_chain = get_custom_agent(prompt, tools)
 
     result = None
 

utils.py

@@ -102,7 +102,7 @@ def generate_answer(question) -> str:
     global chat_history, gpt_3_5_index
     gpt_3_5_chain = get_qa_chain(gpt_3_5_index)
     result = gpt_3_5_chain(
-        {"question": question, "chat_history": chat_history, "vectordbkwargs": {"search_distance": 0.4}})
+        {"question": question, "chat_history": chat_history, "vectordbkwargs": {"search_distance": 0.6}})
     chat_history = [(question, result["answer"])]
     sources = []
 
@@ -147,7 +147,69 @@ def get_tools():
         Tool(
             name="Vectorstore",
             func=generate_answer,
-            description="useful for when you need to answer questions about the coursera course about 3D Printing.",
+            description="useful for when you need to answer questions about the coursera course on 3D Printing.",
             return_direct=True
         )]
     return tools
+
+def get_custom_agent(prompt, tools):
+
+    llm_chain = LLMChain(llm=gpt_3_5, prompt=prompt)
+
+    output_parser = CustomOutputParser()
+    tool_names = [tool.name for tool in tools]
+    agent = LLMSingleActionAgent(
+        llm_chain=llm_chain,
+        output_parser=output_parser,
+        stop=["\nObservation:"],
+        allowed_tools=tool_names
+    )
+    agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory,
+                                                        intermediate_steps=True)
+    return agent_executor
+
+def get_prompt_and_tools_for_custom_agent():
+    template = """
+    Have a conversation with a human, answering the following questions as best you can. 
+    Always try to use Vectorstore first. 
+    Your name is Coursera QA Bot because you are a personal assistant of a Coursera Course: The 3D Printing Evolution. You have access to the following tools:
+    
+    {tools}
+
+    To answer for the new input, use the following format:
+    
+    New Input: the input question you must answer
+    Thought: Do I need to use a tool? Yes
+    Action: the action to take, should be one of [{tool_names}]
+    Action Input: the input to the action
+    Observation: the result of the action
+    ... (this Thought/Action/Action Input/Observation can repeat N times)
+    Thought: I now know the final answer
+    Final Answer: the final answer to the original input question. SOURCES: the sources referred to find the final answer
+
+    
+    When you have a response to say to the Human and DO NOT need to use a tool:
+    1. DO NOT return "SOURCES" if you did not use any tool.
+    2. You MUST use this format:
+    ```
+    Thought: Do I need to use a tool? No
+    AI: [your response here]
+    ```
+
+    Begin! Remember to speak as a personal assistant when giving your final answer.
+    ALWAYS return a "SOURCES" part in your answer, if you used any tool. 
+    
+    Previous conversation history:
+    {chat_history}
+    New input: {input}
+    {agent_scratchpad}
+    SOURCES:"""
+    tools = get_tools()
+    prompt = CustomPromptTemplate(
+        template=template,
+        tools=tools,
+        # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
+        # This includes the `intermediate_steps` variable because that is needed
+        input_variables=["input", "intermediate_steps", "chat_history"]
+    )
+    return prompt, tools