Reverts

app.py

@@ -5,20 +5,20 @@ from aimakerspace.openai_utils.prompts import (
     UserRolePrompt,
     SystemRolePrompt,
 )
-# from aimakerspace.openai_utils.embedding import EmbeddingModel
+from aimakerspace.openai_utils.embedding import EmbeddingModel
 from aimakerspace.vectordatabase import VectorDatabase
-# from aimakerspace.openai_utils.chatmodel import ChatOpenAI
+from aimakerspace.openai_utils.chatmodel import ChatOpenAI
 import chainlit as cl
 # import asyncio
-from operator import itemgetter
+# from operator import itemgetter
 import nest_asyncio
 nest_asyncio.apply()
 from langchain_community.document_loaders import PyMuPDFLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-from langchain_community.vectorstores import Qdrant
-from langchain.prompts import ChatPromptTemplate
-from langchain_core.runnables import RunnablePassthrough
+# from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+# from langchain_community.vectorstores import Qdrant
+# from langchain.prompts import ChatPromptTemplate
+# from langchain_core.runnables import RunnablePassthrough
 
 
 filepath_NIST = "data/NIST.AI.600-1.pdf"
@@ -36,15 +36,15 @@ text_splitter = RecursiveCharacterTextSplitter(
 
 rag_documents = text_splitter.split_documents(documents)
 
-embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+# embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
 
-vectorstore = Qdrant.from_documents(
-    documents=rag_documents,
-    embedding=embeddings,
-    location=":memory:",
-    collection_name="Implications of AI"
-)
-retriever = qdrant_vectorstore.as_retriever()
+# vectorstore = Qdrant.from_documents(
+#     documents=rag_documents,
+#     embedding=embeddings,
+#     location=":memory:",
+#     collection_name="Implications of AI"
+# )
+# retriever = qdrant_vectorstore.as_retriever()
 
 RAG_PROMPT = """\
 Given a provided context and question, you must answer the question based only on context.
@@ -55,46 +55,46 @@ Context: {context}
 Question: {question}
 """
 
-prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
+# prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
 
-# RAG_PROMPT_TEMPLATE = """ \
-# Use the provided context to answer the user's query.
-# You may not answer the user's query unless there is specific context in the following text.
-# If you do not know the answer, or cannot answer, please respond with "I don't know".
-# """
+RAG_PROMPT_TEMPLATE = """ \
+Use the provided context to answer the user's query.
+You may not answer the user's query unless there is specific context in the following text.
+If you do not know the answer, or cannot answer, please respond with "I don't know".
+"""
 
-# rag_prompt = SystemRolePrompt(RAG_PROMPT_TEMPLATE)
+rag_prompt = SystemRolePrompt(RAG_PROMPT_TEMPLATE)
 
-# USER_PROMPT_TEMPLATE = """ \
-# Context:
-# {context}
-# User Query:
-# {user_query}
-# """
+USER_PROMPT_TEMPLATE = """ \
+Context:
+{context}
+User Query:
+{user_query}
+"""
 
-# user_prompt = UserRolePrompt(USER_PROMPT_TEMPLATE)
+user_prompt = UserRolePrompt(USER_PROMPT_TEMPLATE)
 
-# class RetrievalAugmentedQAPipeline:
-#     def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
-#         self.llm = llm
-#         self.vector_db_retriever = vector_db_retriever
+class RetrievalAugmentedQAPipeline:
+    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
+        self.llm = llm
+        self.vector_db_retriever = vector_db_retriever
 
-#     async def arun_pipeline(self, user_query: str):
-#         context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
+    async def arun_pipeline(self, user_query: str):
+        context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
 
-#         context_prompt = ""
-#         for context in context_list:
-#             context_prompt += context[0] + "\n"
+        context_prompt = ""
+        for context in context_list:
+            context_prompt += context[0] + "\n"
 
-#         formatted_system_prompt = rag_prompt.create_message()
+        formatted_system_prompt = rag_prompt.create_message()
 
-#         formatted_user_prompt = user_prompt.create_message(user_query=user_query, context=context_prompt)
+        formatted_user_prompt = user_prompt.create_message(user_query=user_query, context=context_prompt)
 
-#         async def generate_response():
-#             async for chunk in self.llm.astream([formatted_system_prompt, formatted_user_prompt]):
-#                 yield chunk
+        async def generate_response():
+            async for chunk in self.llm.astream([formatted_system_prompt, formatted_user_prompt]):
+                yield chunk
 
-#         return {"response": generate_response(), "context": context_list}
+        return {"response": generate_response(), "context": context_list}
 
 
 # ------------------------------------------------------------
@@ -112,36 +112,22 @@ async def start_chat():
     # }
 
     # # Create a dict vector store
-    # vector_db = VectorDatabase()
-    # vector_db = await vector_db.abuild_from_list(rag_documents)
+    vector_db = VectorDatabase()
+    vector_db = await vector_db.abuild_from_list(rag_documents)
     # vector_db = await vector_db.abuild_from_list(split_documents_NIST)
     # vector_db = await vector_db.abuild_from_list(split_documents_Blueprint)
     
     # # chat_openai = ChatOpenAI()
-    # llm = ChatOpenAI(model="gpt-4o-mini", tags=["base_llm"]) 
+    llm = ChatOpenAI(model="gpt-4o-mini", tags=["base_llm"]) 
 
 
     # # Create a chain
-    # retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
-    #     vector_db_retriever=vector_db,
-    #     llm=llm
-    # )
-    primary_llm = ChatOpenAI(model_name="gpt-4o-mini", temperature=0)
-
-    rag_chain = (
-        # INVOKE CHAIN WITH: {"question" : "<<SOME USER QUESTION>>"}
-        # "question" : populated by getting the value of the "question" key
-        # "context"  : populated by getting the value of the "question" key and chaining it into the base_retriever
-        {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
-        # "context"  : is assigned to a RunnablePassthrough object (will not be called or considered in the next step)
-        #              by getting the value of the "context" key from the previous step
-        | RunnablePassthrough.assign(context=itemgetter("context"))
-        # "response" : the "context" and "question" values are used to format our prompt object and then piped
-        #              into the LLM and stored in a key called "response"
-        # "context"  : populated by getting the value of the "context" key from the previous step
-        | {"response": prompt | primary_llm, "context": itemgetter("context")}
+    rag_chain = RetrievalAugmentedQAPipeline(
+        vector_db_retriever=vector_db,
+        llm=llm
     )
 
+
     # cl.user_session.set("settings", settings)
     cl.user_session.set("chain", rag_chain)
 

requirements.txt

@@ -7,5 +7,6 @@ langchain
 langchain-core
 langchain-community
 langchain-text-splitters
-langchain-openai
-qdrant-client
+# langchain-openai
+# qdrant-client
+# langchain-qdrant