Snowflake AI Stack: Notebook¶

In [ ]:

from dotenv import load_dotenv

load_dotenv()

from dotenv import load_dotenv load_dotenv()

In [ ]:

from src.observability import start_observability

session = start_observability()

session.reset_database()

from src.observability import start_observability session = start_observability() session.reset_database()

Create and Load Vector Store¶

In [ ]:

from src.retrieval import VectorStore

vector_store = VectorStore()

docs = vector_store.load_text_files(file_path="./data.txt")

chunks = vector_store.split_documents(documents = docs)

vector_store.add_chunks(chunks)

from src.retrieval import VectorStore vector_store = VectorStore() docs = vector_store.load_text_files(file_path="./data.txt") chunks = vector_store.split_documents(documents = docs) vector_store.add_chunks(chunks)

Set LLM¶

In [ ]:

from src.generation import ChatModel

chat_model = ChatModel()

from src.generation import ChatModel chat_model = ChatModel()

Create Evals, Register App¶

In [ ]:

from src.observability import create_evals
from trulens.providers.openai import OpenAI

provider = OpenAI()

evals = create_evals(provider = provider)

from src.observability import create_evals from trulens.providers.openai import OpenAI provider = OpenAI() evals = create_evals(provider = provider)

In [ ]:

from src.rag import Rag
from trulens.apps.app import TruApp

rag = Rag(chat_model=chat_model, vector_store=vector_store, use_context_filter=True)

tru_rag = TruApp(
    rag,
    app_name="RAG",
    app_version="snowflake-oss",
    feedbacks = evals
)

from src.rag import Rag from trulens.apps.app import TruApp rag = Rag(chat_model=chat_model, vector_store=vector_store, use_context_filter=True) tru_rag = TruApp( rag, app_name="RAG", app_version="snowflake-oss", feedbacks = evals )

Generate an Answer¶

Next, we define a query and use the retrieval function to find relevant documents.

In [ ]:

with tru_rag:
    llm_response = rag.retrieve_and_generate("What datasets are used to evaluate the RAG triad?")

with tru_rag: llm_response = rag.retrieve_and_generate("What datasets are used to evaluate the RAG triad?")

Display¶

In [ ]:

from IPython.display import HTML, display

html_content = f"<div style='white-space: pre-wrap; border: 1px solid #ccc; padding: 10px; background-color: white; color: black; font-size: 16px;'>{llm_response}</div>"
display(HTML(html_content))

from IPython.display import HTML, display html_content = f"

{llm_response}

" display(HTML(html_content))

Run TruLens Dashboard¶

In [ ]:

from trulens.dashboard import run_dashboard

run_dashboard()

from trulens.dashboard import run_dashboard run_dashboard()