app.py

#Complete code for rag.py
from langchain.llms import LlamaCpp
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores.chroma import Chroma
from langchain.prompts import ChatPromptTemplate
from langchain.schema.runnable import RunnablePassthrough
 
 
llm = LlamaCpp(
    model_path="llama-2-7b-chat.Q5_K_M.gguf",
    temperature=0.75,
    max_tokens=2000,
    top_p=1,
    verbose=True,
)
 
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
 
vectorstore = Chroma(
    persist_directory="data",
    embedding_function=embeddings
)
 
retriever = vectorstore.as_retriever(search_kwargs={"k": 1})
 
template = """Answer the question based only on the following context
 
{context}
 
Question:{question}
Answer:
"""
 
prompt = ChatPromptTemplate.from_template(template)
 
chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
)
 
for chunk in chain.stream("What is a chat model?"):
    print(chunk, end="", flush=True)