Update app/llm.py

app/llm.py

@@ -15,7 +15,14 @@ from langchain_community.document_loaders import WebBaseLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_chroma import Chroma
 from langchain_community.embeddings import GPT4AllEmbeddings
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import PromptTemplate
 
+from langchain import hub
+from langchain_core.runnables import RunnablePassthrough, RunnablePick
+
+rag_prompt = hub.pull("rlm/rag-prompt")
+rag_prompt.messages
 
 class RagChat:
     def agent(self): 
@@ -28,10 +35,19 @@ class RagChat:
     def download_embedding(self):
         vectorstore = Chroma.from_documents(documents=self.agent, embedding=GPT4AllEmbeddings())
         return vectorstore
+
+    def prompt_template(self, question):
+        chain = (
+            RunnablePassthrough.assign(context=RunnablePick("context") | format_docs)
+            | rag_prompt
+            | llm
+            | StrOutputParser()
+        )
+        chain.invoke({"context": self.search, "question": question})
         
     def search(self, question):
         docs = vectorstore.similarity_search(question)
-        return len(docs)
+        return docs