implement history stream

app/callbacks.py

@@ -15,7 +15,9 @@ class LogResponseCallback(BaseCallbackHandler):
         """Run when llm ends running."""
         # TODO: The function on_llm_end is going to be called when the LLM stops sending 
         # the response. Use the crud.add_message function to capture that response.
-        raise NotImplemented
+        print(outputs)
+        message = schemas.MessageBase(message=outputs.get('text'), type='AI')
+        crud.add_message(self.db, message=message, username=self.user_request.username)
 
     def on_llm_start(
         self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any

app/chains.py

@@ -9,6 +9,7 @@ import schemas
 from prompts import (
     raw_prompt,
     raw_prompt_formatted,
+    history_prompt_formatted,
     format_context,
     tokenizer
 )
@@ -34,7 +35,7 @@ simple_chain = (raw_prompt | llm).with_types(input_type=schemas.UserQuestion)
 formatted_chain = (raw_prompt_formatted | llm).with_types(input_type=schemas.UserQuestion)
 
 # # TODO: use history_prompt_formatted and HistoryInput to create the history_chain
-# history_chain = None
+history_chain = (history_prompt_formatted | llm).with_types(input_type=schemas.HistoryInput)
 
 # # TODO: Let's construct the standalone_chain by piping standalone_prompt_formatted with the LLM
 # standalone_chain = None

app/main.py

@@ -10,18 +10,32 @@ from typing import List
 from sqlalchemy.orm import Session
 
 import schemas
-from chains import simple_chain, formatted_chain
-import crud, models, schemas
+from chains import simple_chain, formatted_chain, history_chain
+import crud, models, schemas, prompts
 from database import SessionLocal, engine
 from callbacks import LogResponseCallback
 
-
+# models.Base comes from SQLAlchemy’s declarative_base() in database.py. 
+# It acts as the base class for all ORM models (defined in models.py). 
+# .metadata.create_all(): Tells SQLAlchemy to create all the tables defined 
+# in the models module if they don’t already exist in the database.
+# -> metadata is a catalog of all the tables and other schema constructs in your database.
+# -> create_all() method creates all the tables that don't exist yet in the database.
+# -> bind=engine specifies which database engine to use for this operation.
 models.Base.metadata.create_all(bind=engine)
 
 app = FastAPI()
 
 def get_db():
+    """This is a dependency function used to create and provide a 
+    database session to various endpoints in the FastAPI app.
+    """    
+    # A new SQLAlchemy session is created using the SessionLocal session factory. 
+    # This session will be used for database transactions.
     db = SessionLocal()
+
+    # This pattern ensures that each request gets its own database session and that 
+    # the session is properly closed when the request is finished, preventing resource leaks.
     try:
         yield db
     finally:
@@ -71,16 +85,31 @@ async def formatted_stream(request: Request):
     return EventSourceResponse(generate_stream(user_question, formatted_chain))
 
 
-# @app.post("/history/stream")
-# async def history_stream(request: Request, db: Session = Depends(get_db)):  
-#     # TODO: Let's implement the "/history/stream" endpoint. The endpoint should follow those steps:
-#     # - The endpoint receives the request
-#     # - The request is parsed into a user request
-#     # - The user request is used to pull the chat history of the user
-#     # - We add as part of the user history the current question by using add_message.
-#     # - We create an instance of HistoryInput by using format_chat_history.
-#     # - We use the history input within the history chain.
-#     raise NotImplemented
+@app.post("/history/stream")
+async def history_stream(request: Request, db: Session = Depends(get_db)):  
+    # TODO: Let's implement the "/history/stream" endpoint. The endpoint should follow those steps:
+    # - The endpoint receives the request
+    data = await request.json()
+
+    # - The request is parsed into a user request
+    user_request = schemas.UserRequest(**data['input'])
+
+    # - The user request is used to pull the chat history of the user
+    chat_history = crud.get_user_chat_history(db=db, username=user_request.username)
+
+    # - We add as part of the user history the current question by using add_message.
+    message = schemas.MessageBase(message=user_request.question, type='User')
+    crud.add_message(db, message=message, username=user_request.username)
+
+    # - We create an instance of HistoryInput by using format_chat_history.
+    history_input = schemas.HistoryInput(question=user_request.username, chat_history=chat_history)
+
+    # - We use the history input within the history chain.
+    return EventSourceResponse(generate_stream(
+        history_input, history_chain, [LogResponseCallback(user_request, db)]
+    ))
+    
+
 
 
 # @app.post("/rag/stream")

app/prompts.py

@@ -36,7 +36,14 @@ def format_prompt(prompt) -> PromptTemplate:
 def format_chat_history(messages: List[models.Message]):
     # TODO:  implement format_chat_history to format 
     # the list of Message into a text of chat history.
-    raise NotImplemented
+    
+    return '\n'.join([
+        '[{}] {}: {}'.format(
+            message.timestamp.strftime("%Y-%m-%d %H:%M:%S"),
+            message.type,
+            message.message
+        ) for message in messages
+    ])
 
 
 def format_context(docs: List[str]):
@@ -46,11 +53,20 @@ def format_context(docs: List[str]):
     # like of strings and returns the context as one string.
     raise NotImplemented
 
-raw_prompt = "{question}"
+prompt = "{question}"
 
 # TODO: Create the history_prompt prompt that will capture the question and the conversation history. 
 # The history_prompt needs a {chat_history} placeholder and a {question} placeholder.
-history_prompt: str = None
+history_prompt: str = """
+Given the follwoing conversation provide a helpful answer to the follow up question.
+
+Chat History:
+{chat_history}
+
+Follow Up Question: {question}
+
+helpful answer:
+"""
 
 # TODO: Create the standalone_prompt prompt that will capture the question and the chat history
 # to generate a standalone question. It needs a {chat_history} placeholder and a {question} placeholder,
@@ -61,11 +77,19 @@ standalone_prompt: str = None
 rag_prompt: str = None
 
 # TODO: create raw_prompt_formatted by using format_prompt
-raw_prompt_formatted = format_prompt(raw_prompt)
-raw_prompt = PromptTemplate.from_template(raw_prompt)
+#raw_prompt_formatted = format_prompt(raw_prompt)
+#raw_prompt = PromptTemplate.from_template(raw_prompt)
+
+# i) raw prompt
+raw_prompt = PromptTemplate.from_template(prompt)
+
+# ii) formatted prompt
+raw_prompt_formatted = format_prompt(prompt)
+
 
 # TODO: use format_prompt to create history_prompt_formatted
-history_prompt_formatted: PromptTemplate = None
+history_prompt_formatted =  format_prompt(history_prompt)
+
 # TODO: use format_prompt to create standalone_prompt_formatted
 standalone_prompt_formatted: PromptTemplate = None
 # TODO: use format_prompt to create rag_prompt_formatted