Update latest app

app.py

@@ -1,10 +1,15 @@
 from datasets import load_dataset
-from langchain_community.document_loaders.csv_loader import CSVLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import CacheBackedEmbeddings
 from langchain.storage import LocalFileStore
-from langchain_openai import OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+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_community.document_loaders.csv_loader import CSVLoader
 from langchain_community.vectorstores import FAISS
+from langchain_openai import OpenAIEmbeddings
+from langchain_openai import ChatOpenAI
 
 dataset = load_dataset('ShubhamChoksi/IMDB_Movies')
 dataset_dict = dataset
@@ -31,12 +36,19 @@ vector_file = "local_vector"
 vector_store = FAISS.from_documents(chunked_documents, cached_embedder)
 vector_store.save_local(vector_file)
 
-query = "What are some good sci-fi movies from the 1980s?"
-
-embedded_query = embedding_model.embed_query(query)
-
-similar_documents = vector_store.similarity_search_by_vector(embedded_query) # TODO: How do we do a similarity search to find documents similar to our query?
-
-for page in similar_documents:
-  print(page.page_content)
+prompt_template = ChatPromptTemplate.from_template(
+    "You are a movie recommendation system, for a given {query} find recommendations from {content}."
+)
+retriever = vector_store.as_retriever()
+chat_model = ChatOpenAI(model="gpt-4o", temperature=0.2, openai_api_key=openai_api_key)
+parser = StrOutputParser()
+
+runnable_chain = (
+    {"query": RunnablePassthrough(), "content": retriever}
+    | prompt_template
+    | chat_model
+    | StrOutputParser()
+)
 
+output_chunks = runnable_chain.invoke(query)
+print(''.join(output_chunks))