try passing prompt with chat template to check response

app.py

@@ -14,6 +14,12 @@ from langchain.embeddings import CacheBackedEmbeddings
 from langchain.storage import LocalFileStore
 from langchain_community.vectorstores import FAISS
 from datasets import load_dataset
+from langchain_core.runnables.base import RunnableSequence
+from langchain_core.runnables.passthrough import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+import asyncio
 
 
 
@@ -24,9 +30,20 @@ system_template = """You are a helpful assistant who always speaks in a pleasant
 """
 
 user_template = """{input}
+
+
+
+
 Think through your response step by step.
 """
 
+# used for imdb chat 
+template = """Answer the question based only on the following context:
+{context}
+
+Question: {question}
+"""
+
 def setup():
     dataset = load_dataset("ShubhamChoksi/IMDB_Movies")
     print(dataset['train'][0])
@@ -67,19 +84,61 @@ def setup():
 
     vector_store.save_local("./vector_store")
     
+def input_query(query):
+    openai_api_key =  os.getenv("OPENAI_API_KEY")
+    embedding_model = OpenAIEmbeddings(openai_api_key=openai_api_key)
+
+    store = LocalFileStore("./cache/")
+    embedder = CacheBackedEmbeddings.from_bytes_store(
+        embedding_model, store, namespace=embedding_model.model
+    )
     vector_store = FAISS.load_local("./vector_store", embedder, allow_dangerous_deserialization=True)
 
     retriever = vector_store.as_retriever()
     
-    query = "What are some good westerns movies?"
+    # query = "What are some good westerns movies?"
+    # embedded_query = embedding_model.embed_query(query)
+    # similar_documents = vector_store.similarity_search_by_vector(embedded_query)
+    # for page in similar_documents:
+    #     # TODO: Print the similar documents that the similarity search returns?
+    #     print(page)
+    #     print("00-----0000")
+    #     print(page)
+    #     print("-------------")
     embedded_query = embedding_model.embed_query(query)
     similar_documents = vector_store.similarity_search_by_vector(embedded_query)
-    for page in similar_documents:
-        # TODO: Print the similar documents that the similarity search returns?
-        print(page)
-        print("00-----0000")
-        print(page)
-        print("-------------")
+    similar_documents_for_prompt = list(map(lambda page: ("assistant", page.page_content), similar_documents))
+    # print(similar_documents_for_prompt)
+
+    similar_documents_for_prompt.append(("human", query))
+    # print(similar_documents_for_prompt)
+
+    # Create the components (chefs)
+    # prompt_template = # TODO: How do we create a prompt template to send to our LLM that will incorporate the documents from our retriever with the question we ask the chat model?
+    alternative_prompt = ChatPromptTemplate.from_messages(
+        similar_documents_for_prompt
+    )
+    print("alternative prompt")
+    print(alternative_prompt.messages)
+
+
+    prompt = ChatPromptTemplate.from_template(template)
+
+
+    #retriever = # TODO: How to we create a retriever for our documents?
+    retriever = vector_store.as_retriever()
+
+
+    #chat_model = # TODO: How do we create a chat model / LLM?
+    chat_model = ChatOpenAI(openai_api_key=openai_api_key, temperature=0)
+
+
+
+    #parser = # TODO: How do we create a parser to parse the output of our LLM?
+    parser = StrOutputParser()
+    runnable_chain = alternative_prompt | chat_model | parser
+    return alternative_prompt, chat_model, parser
+
 
 @cl.on_chat_start  # marks a function that will be executed at the start of a user session
 async def start_chat():
@@ -94,7 +153,7 @@ async def start_chat():
     setup()
     cl.user_session.set("settings", settings)
 
-
+# need to pass the query to the input_query function
 @cl.on_message  # marks a function that should be run each time the chatbot receives a message from a user
 async def main(message: cl.Message):
     settings = cl.user_session.get("settings")
@@ -102,24 +161,32 @@ async def main(message: cl.Message):
     client = AsyncOpenAI()
 
     print(message.content)
+    # message.content is the input query from the user
+    prompt, model, parser = input_query(message.content)
+
+    # prompt = Prompt(
+    #     provider=ChatOpenAI.id,
+    #     messages=[
+    #         PromptMessage(
+    #             role="system",
+    #             template=system_template,
+    #             formatted=system_template,
+    #         ),
+    #         PromptMessage(
+    #             role="user",
+    #             template=user_template,
+    #             formatted=user_template.format(input=message.content),
+    #         ),
+    #     ],
+    #     inputs={"input": message.content},
+    #     settings=settings,
+    # )
+    
+    runnable_chain = prompt | model | parser
+    output_chunks = runnable_chain.invoke({})
+    print(''.join(output_chunks))
+    print("output chunks")
 
-    prompt = Prompt(
-        provider=ChatOpenAI.id,
-        messages=[
-            PromptMessage(
-                role="system",
-                template=system_template,
-                formatted=system_template,
-            ),
-            PromptMessage(
-                role="user",
-                template=user_template,
-                formatted=user_template.format(input=message.content),
-            ),
-        ],
-        inputs={"input": message.content},
-        settings=settings,
-    )
 
     print([m.to_openai() for m in prompt.messages])