Update app.py

app.py

@@ -1,6 +1,4 @@
 import pandas as pd
-
-
 df = pd.read_csv('./anime.csv')
 
 context_data = []
@@ -24,19 +22,21 @@ from langchain_groq import ChatGroq
 
 llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_key)
 
-## Embedding model!
+!Embedding model
 from langchain_huggingface import HuggingFaceEmbeddings
 embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
 
-# create vector store!
+# create vector store
 from langchain_chroma import Chroma
 
 vectorstore = Chroma(
-    collection_name="medical_dataset_store",
+    collection_name="Anime_dataset_store",
     embedding_function=embed_model,
     persist_directory="./",
 )
 
+vectorstore.get().keys()
+
 # add data to vector nstore
 vectorstore.add_texts(context_data)
 
@@ -44,7 +44,8 @@ retriever = vectorstore.as_retriever()
 
 from langchain_core.prompts import PromptTemplate
 
-template = ("""You are a medical expert.
+# Modified template for anime dataset
+template = ("""You are an anime expert.
     Use the provided context to answer the question.
     If you don't know the answer, say so. Explain your answer in detail.
     Do not discuss the context in your response; just provide the answer directly.
@@ -55,6 +56,7 @@ template = ("""You are a medical expert.
 
     Answer:""")
 
+# Create the prompt
 rag_prompt = PromptTemplate.from_template(template)
 
 from langchain_core.output_parsers import StrOutputParser