basic FAQ functionality is working

agent/_create.py

@@ -1,11 +1,12 @@
 
 def agent(payload):
 
-    from agent.toolset import tool_executor, converted_tools
-   
+    
     from langchain_openai import ChatOpenAI
-    model = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
-    model = model.bind_functions(converted_tools)
+    llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
+
+    from agent.toolset import tool_executor, converted_tools
+    model = llm.bind_functions(converted_tools)
 
     from langgraph.prebuilt import ToolInvocation
     import json
@@ -44,18 +45,21 @@ def agent(payload):
 
     app = workflow.compile(checkpointer=memory.checkpoints)
 
-
     from agent.prompt import prompt
     prompt=prompt[memory.isNew]
 
-    input = payload.get("input") or "hi! I'm bob"
+    input = payload.get("input") or "What is Rise for?"
 
     prompt = prompt.format(input=input, thread_id=memory.thread_id)
 
+
+
     response = app.invoke(prompt, {"configurable": {"thread_id": memory.thread_id}})
     print(response[-1].content)
 
-    print(payload)
+    #for s in app.stream(prompt, {"configurable": {"thread_id": memory.thread_id}}):
+    #    print(list(s.values())[0])
+    #    print("----")
 
     return response[-1].content
     

agent/datastructures.py

@@ -22,7 +22,7 @@ class FrontEndActions(BaseModel):
     type: ActionTypes = Field(description="This should be a string that identifies the type of action. It can be one of: SuggestGoal, SuggestActivity")
 
 class ResponseSchema(BaseModel):
-    """Final response to the question being asked. Some of the fields are optional"""
+    """Always use this to format the final response to the user. This will be passed back to the frontend."""
     message: str = Field(description="final answer to respond to the user")
     thread_id: int = Field(description="The ID of the checkpointer memory thread that this response is associated with. This is used to keep track of the conversation.")
     tools: Optional[List[str]] = Field(description="A list of the tools used to generate the response.") 

agent/prompt.py

@@ -10,13 +10,30 @@ prompt = {
     1: # IF THE THREAD IS NEW, THE CHATBOT NEEDS TO BE PUMP-PROMPTED
     ChatPromptTemplate.from_messages([
         SystemMessagePromptTemplate.from_template("""                                               
+                                                  
+            You are a backend service to a JavaScript frontend chatbot hosted on another website.
 
-            You are an assistant that only responds in JSON. Do not write normal text.
+            You are interacting with students from Manchester Metropolitan University as part of its mission to help students to make the most of their time here.
+            
+            Your tone should be personable and enthusiastic
+                                                  
+            ### ANSWERING QUESTIONS ABOUT RISE AND FUTURE ME ###
 
-            [no prose][Output only valid JSON]
+            You have been provided with the FrequentlyAskedQuestions tool to answer questions that students might have about the Rise programme and Future me initiative. Please rely on this tool and do not make up answers if you are unsure. 
 
-            {response_format}
+            If a question seems relevant to Rise and Future me, but you are unsure of the answer, you are able to refer it to the Rise team using the EmailTeam tool. Before you do this, please confirm with the user.
+
+            ###########
             
+            ### PROVIDING A FINAL ANSWER ###
+
+            {response_format}
+
+            Never output anything outside of the JSON Blob beginning with '{{' and ending with '}}'
+
+            ###########
+                                                
+
             """
                                                   
         ).format(response_format=parser.get_format_instructions()),

agent/toolset.py

@@ -49,13 +49,6 @@ def recommend_activity(profile: str) -> str:
 
 tools = [frequently_asked_questions]
 
-
-## NEW FROM HERE
-
-from langchain_community.tools.tavily_search import TavilySearchResults
-
-tools = [TavilySearchResults(max_results=1)]
-
 from langgraph.prebuilt import ToolExecutor
 
 tool_executor = ToolExecutor(tools)

app.py

@@ -26,5 +26,8 @@ def train_products():
     from train.products import train
     return train();
 
-from agent._create import agent
-agent({})
+#from train.faq import train
+#train();
+
+#from agent._create import agent
+#agent({})

requirements.txt

@@ -3,6 +3,7 @@ Flask
 Flask-cors
 gunicorn
 python-dotenv
+playwright
 
 #LLM
 bs4
@@ -13,19 +14,8 @@ transformers
 sentence-transformers
 datasets
 faiss-cpu
-
-#LLAMA TEST
-torch
-transformers
-langchain
-chromadb
-xformers
-sentence_transformers
-tokenizers
-optimum
-auto-gptq
-unstructured
+langchain-fireworks 
 
 #LANGRAPH
 Langgraph
-Operator
+#Operator

train/faq.py

@@ -6,6 +6,7 @@ def train():
     from langchain_community.document_loaders import WebBaseLoader
     
     documents = WebBaseLoader("https://rise.mmu.ac.uk/what-is-rise/").load()
+    documents[0].page_content = documents[0].page_content.split("Everything You Need To Know About Rise – Students")[1].strip();
 
     # Split document in chunks
     text_splitter = RecursiveCharacterTextSplitter(
@@ -21,4 +22,4 @@ def train():
     # Persist the vectors locally on disk
     vectorstore.save_local("_rise_faq_db");
 
-    return {"trained":"success"}r
+    return {"trained":"success"}