❄️ Snowflake Quickstart with Cortex LLM Functions¶

In this quickstart you will learn build and evaluate a RAG application with Snowflake Cortex LLM Functions.

Building and evaluating RAG applications with Snowflake Cortex offers developers a unique opportunity to leverage a top-tier, enterprise-focused LLM that is both cost-effective and open-source. Cortex excels in enterprise tasks like SQL generation and coding, providing a robust foundation for developing intelligent applications with significant cost savings.

In this example, we will use Arctic Embed (snowflake-arctic-embed-m) as our embedding model via HuggingFace, and LLM of your choice for both generation and as the LLM judge to power TruLens feedback functions. The LLM models are fully-mananaged by Cortex LLM functions

Note, you'll need to have an active Snowflake account to run Cortex LLM functions from Snowflake's data warehouse.

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# !pip install trulens trulens-providers-cortex chromadb sentence-transformers snowflake-snowpark-python snowflake-ml-python>=1.7.1

# !pip install trulens trulens-providers-cortex chromadb sentence-transformers snowflake-snowpark-python snowflake-ml-python>=1.7.1

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import os

from snowflake.snowpark import Session
from trulens.core.utils.keys import check_keys

check_keys("SNOWFLAKE_ACCOUNT", "SNOWFLAKE_USER", "SNOWFLAKE_USER_PASSWORD")


connection_params = {
    "account": os.environ["SNOWFLAKE_ACCOUNT"],
    "user": os.environ["SNOWFLAKE_USER"],
    "password": os.environ["SNOWFLAKE_USER_PASSWORD"],
    "role": os.environ.get("SNOWFLAKE_ROLE", "ENGINEER"),
    "database": os.environ.get("SNOWFLAKE_DATABASE"),
    "schema": os.environ.get("SNOWFLAKE_SCHEMA"),
    "warehouse": os.environ.get("SNOWFLAKE_WAREHOUSE"),
}


# Create a Snowflake session
snowpark_session = Session.builder.configs(connection_params).create()

import os from snowflake.snowpark import Session from trulens.core.utils.keys import check_keys check_keys("SNOWFLAKE_ACCOUNT", "SNOWFLAKE_USER", "SNOWFLAKE_USER_PASSWORD") connection_params = { "account": os.environ["SNOWFLAKE_ACCOUNT"], "user": os.environ["SNOWFLAKE_USER"], "password": os.environ["SNOWFLAKE_USER_PASSWORD"], "role": os.environ.get("SNOWFLAKE_ROLE", "ENGINEER"), "database": os.environ.get("SNOWFLAKE_DATABASE"), "schema": os.environ.get("SNOWFLAKE_SCHEMA"), "warehouse": os.environ.get("SNOWFLAKE_WAREHOUSE"), } # Create a Snowflake session snowpark_session = Session.builder.configs(connection_params).create()

Get Data¶

In this case, we'll just initialize some simple text in the notebook.

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university_info = """
The University of Washington, founded in 1861 in Seattle, is a public research university
with over 45,000 students across three campuses in Seattle, Tacoma, and Bothell.
As the flagship institution of the six public universities in Washington state,
UW encompasses over 500 buildings and 20 million square feet of space,
including one of the largest library systems in the world.
"""

university_info = """ The University of Washington, founded in 1861 in Seattle, is a public research university with over 45,000 students across three campuses in Seattle, Tacoma, and Bothell. As the flagship institution of the six public universities in Washington state, UW encompasses over 500 buildings and 20 million square feet of space, including one of the largest library systems in the world. """

Create Vector Store¶

Create a chromadb vector store in memory.

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from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m")

from sentence_transformers import SentenceTransformer model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m")

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document_embeddings = model.encode([university_info])

document_embeddings = model.encode([university_info])

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import chromadb

chroma_client = chromadb.Client()
vector_store = chroma_client.get_or_create_collection(name="Universities")

import chromadb chroma_client = chromadb.Client() vector_store = chroma_client.get_or_create_collection(name="Universities")

Add the university_info to the embedding database.

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vector_store.add(
    "uni_info", documents=university_info, embeddings=document_embeddings
)

vector_store.add( "uni_info", documents=university_info, embeddings=document_embeddings )

Build RAG from scratch¶

Build a custom RAG from scratch, and add TruLens custom instrumentation.

In [ ]:

from trulens.apps.custom import instrument
from trulens.core import TruSession

session = TruSession()
session.reset_database()

from trulens.apps.custom import instrument from trulens.core import TruSession session = TruSession() session.reset_database()

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from snowflake.cortex import Complete


class RAG_from_scratch:
    @instrument
    def retrieve(self, query: str) -> list:
        """
        Retrieve relevant text from vector store.
        """
        results = vector_store.query(
            query_embeddings=model.encode([query], prompt_name="query"),
            n_results=2,
        )
        return results["documents"]

    @instrument
    def generate_completion(self, query: str, context_str: list) -> str:
        """
        Generate answer from context.
        """
        prompt = f"""
         We have provided context information below. 
            {context_str}
            Given this information, please answer the question: {query}
        """
        resp = Complete(model='mistral-large2', prompt=[{'role': 'user', 'content': prompt}], session=snowpark_session)
        
        return resp


    @instrument
    def query(self, query: str) -> str:
        context_str = self.retrieve(query)
        completion = self.generate_completion(query, context_str)
        return completion


rag = RAG_from_scratch()

from snowflake.cortex import Complete class RAG_from_scratch: @instrument def retrieve(self, query: str) -> list: """ Retrieve relevant text from vector store. """ results = vector_store.query( query_embeddings=model.encode([query], prompt_name="query"), n_results=2, ) return results["documents"] @instrument def generate_completion(self, query: str, context_str: list) -> str: """ Generate answer from context. """ prompt = f""" We have provided context information below. {context_str} Given this information, please answer the question: {query} """ resp = Complete(model='mistral-large2', prompt=[{'role': 'user', 'content': prompt}], session=snowpark_session) return resp @instrument def query(self, query: str) -> str: context_str = self.retrieve(query) completion = self.generate_completion(query, context_str) return completion rag = RAG_from_scratch()

Set up feedback functions.¶

Here we'll use groundedness, answer relevance and context relevance to detect hallucination.

In [ ]:

import numpy as np
from trulens.core import Feedback
from trulens.core import Select
from trulens.providers.cortex import Cortex

provider = Cortex(
    snowpark_session=snowpark_session,
    model_engine="llama3.1-8b",
)


# Define a groundedness feedback function
f_groundedness = (
    Feedback(
        provider.groundedness_measure_with_cot_reasons, name="Groundedness"
    )
    .on(Select.RecordCalls.retrieve.rets.collect())
    .on_output()
)

# Question/answer relevance between overall question and answer.
f_answer_relevance = (
    Feedback(provider.relevance_with_cot_reasons, name="Answer Relevance")
    .on(Select.RecordCalls.retrieve.args.query)
    .on_output()
)

# Question/statement relevance between question and each context chunk.
f_context_relevance = (
    Feedback(
        provider.context_relevance_with_cot_reasons, name="Context Relevance"
    )
    .on(Select.RecordCalls.retrieve.args.query)
    .on(Select.RecordCalls.retrieve.rets.collect())
    .aggregate(np.mean)
)

f_coherence = Feedback(
    provider.coherence_with_cot_reasons, name="coherence"
).on_output()

import numpy as np from trulens.core import Feedback from trulens.core import Select from trulens.providers.cortex import Cortex provider = Cortex( snowpark_session=snowpark_session, model_engine="llama3.1-8b", ) # Define a groundedness feedback function f_groundedness = ( Feedback( provider.groundedness_measure_with_cot_reasons, name="Groundedness" ) .on(Select.RecordCalls.retrieve.rets.collect()) .on_output() ) # Question/answer relevance between overall question and answer. f_answer_relevance = ( Feedback(provider.relevance_with_cot_reasons, name="Answer Relevance") .on(Select.RecordCalls.retrieve.args.query) .on_output() ) # Question/statement relevance between question and each context chunk. f_context_relevance = ( Feedback( provider.context_relevance_with_cot_reasons, name="Context Relevance" ) .on(Select.RecordCalls.retrieve.args.query) .on(Select.RecordCalls.retrieve.rets.collect()) .aggregate(np.mean) ) f_coherence = Feedback( provider.coherence_with_cot_reasons, name="coherence" ).on_output()

Construct the app¶

Wrap the custom RAG with TruCustomApp, add list of feedbacks for eval

In [ ]:

from trulens.apps.custom import TruCustomApp

tru_rag = TruCustomApp(
    rag,
    app_name="RAG",
    app_version="v1",
    feedbacks=[
        f_groundedness,
        f_answer_relevance,
        f_context_relevance,
        f_coherence,
    ],
)

from trulens.apps.custom import TruCustomApp tru_rag = TruCustomApp( rag, app_name="RAG", app_version="v1", feedbacks=[ f_groundedness, f_answer_relevance, f_context_relevance, f_coherence, ], )

Run the app¶

Use tru_rag as a context manager for the custom RAG-from-scratch app.

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with tru_rag as recording:
    resp = rag.query("When is University of Washington founded?")

with tru_rag as recording: resp = rag.query("When is University of Washington founded?")

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resp

resp

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session.get_leaderboard(app_ids=[])

session.get_leaderboard(app_ids=[])

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from trulens.dashboard import run_dashboard

run_dashboard(session)

from trulens.dashboard import run_dashboard run_dashboard(session)