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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-c24cc1578b","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"quickstartHeroTitle":{"lines":["Getting Started with AI Observability"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Josh Reini","quickstartHeroForkRepoLink":{"id":"button-f6d532d1a3","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfguide-getting-started-with-ai-observability"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Getting Started with AI Observability","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/getting-started-with-ai-observability","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}],"fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/getting-started-with-ai-observability",":type":"snowflake-site/components/quickstart/quickstart-hero","isDeveloperGuidesPage":false},"flexible_column_cont":{"id":"flexible-column-container-04bc8ba56b","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-69f922decb",":items":{"contentfragment":{"id":"contentfragment-9dbd033ae0","paragraphs":["\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThe widespread integration of large language models (LLMs) and generative AI in mission-critical business processes has created a need for robust AI observability to address the inherent “black box” and nondeterministic nature of these systems and applications. The complexity of AI systems makes it challenging to understand their behavior, performance and resource consumption.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-observability\"\u003EAI observability\u003C/a\u003E enables developers to monitor, analyze and visualize the internal states, inputs and outputs of generative AI applications, increasing accuracy, trust, efficiency and regulatory compliance in real-world environments. AI observability spans all stages of application development, including development, testing and production, and anchors on three key pillars:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETracing\u003C/strong\u003E: As developers build and customize their applications, tracing enables them to visualize the inputs, outputs and intermediate states of the application. This provides granular information of each component within the application to enable better debugging and explainability of the application behavior.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEvaluations\u003C/strong\u003E: After the initial version of the application is ready, developers conduct systematic evaluations to assess their application's performance to proactively improve response accuracy. This allows them to test and compare different models and prompts and finalize the configuration for product deployments.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMonitoring\u003C/strong\u003E: Once the application is deployed in production, developers need to constantly monitor the performance of their application to ensure operational reliability and avoid performance drift. Continuous monitoring also enables them to fine-tune the application by eliminating failure points and accommodating data drift.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIn this quickstart, you'll build a \u003Ca href=\"https://www.snowflake.com/en/fundamentals/rag/\"\u003ERetrieval-Augmented Generation\u003C/a\u003E (RAG) system using Cortex Search and Cortex LLMs. This will be the basis of our example. Last, you'll create a test set and run LLM-as-judge evaluations in batch against your application.\u003C/p\u003E\n","\u003Ch3\u003EWhat is TruLens?\u003C/h3\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.trulens.org/\"\u003ETruLens\u003C/a\u003E is a library for tracking and evaluating Generative AI applications in open source, along with powering Snowflake AI Observability.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to load, parse, and chunk data for RAG\u003C/li\u003E\u003Cli\u003EHow to create a RAG with Cortex Search and Complete, adding TruLens instrumentation\u003C/li\u003E\u003Cli\u003EHow to set application metadata for experiment tracking\u003C/li\u003E\u003Cli\u003EHow to create a run and configure your test set for evaluation\u003C/li\u003E\u003Cli\u003EHow to compute evaluation metrics in batch on the run\u003C/li\u003E\u003Cli\u003EHow to navigate AI Observability to view and compare traces and evaluation results\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA retrieval-augmented generation (RAG) app\u003C/li\u003E\u003Cli\u003EAn evaluation run with computed metrics\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with Cortex LLM Functions and Cortex Search enabled.  If you do not have a Snowflake account, you can register for a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides&_fsi=yYZEVo4S&_fsi=yYZEVo4S\"\u003Efree trial account\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EA Snowflake account login with ACCOUNTADMIN role. If you have this role in your environment, you may choose to use it. If not, you will need to 1) Register for a free trial, 2) Use a different role that has the ability to create database, schema, tables, stages, tasks, user-defined functions, and stored procedures OR 3) Use an existing database and schema in which you are able to create the mentioned objects.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESetup Environment\u003C/h2\u003E\n","\u003Cp\u003EThis phase prepares your environment and creates the dedicated database and warehouse. If you are not working in a Snowflake trial account and do not have ACCOUNTADMIN privileges, ensure that the user role has the following roles granted ():\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESNOWFLAKE.CORTEX_USER database role\u003C/li\u003E\u003Cli\u003ECREATE EXTERNAL AGENT privilege on the schema\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor more information, see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-observability/reference#label-ai-observability-required-privileges\"\u003ERequired Privileges for AI Observability\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EWe will use a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks\"\u003ESnowflake Notebook\u003C/a\u003E to execute all the setup. Open the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-ai-observability/blob/main/getting-started-with-ai-observability.ipynb\"\u003Egetting-started-with-ai-observability.ipynb\u003C/a\u003E file from the GitHub repository and download it to open the Notebook. \u003Cem\u003E\u003Cstrong\u003E(NOTE: Do NOT right-click to download.)\u003C/strong\u003E\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003EThen, create a new Snowflake Notebook by importing the notebook file in Snowsight.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EProjects » Notebooks\u003C/li\u003E\u003Cli\u003EClick the + Notebook drop-down and select Import .ipynb file\u003C/li\u003E\u003Cli\u003ESelect the getting-started-with-ai-observability.ipynb file\u003C/li\u003E\u003Cli\u003EIn Create notebook, configure the fields to your liking and click Create when done\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOnce in the Snowflake Notebook, we will \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-import-packages\"\u003Eimport packages\u003C/a\u003E from Anaconda:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect Packages menu at the top of your Notebook\u003C/li\u003E\u003Cli\u003ESearch for these packages listed in the Snowflake Anaconda channel\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Esnowflake-ml-python\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Esnowflake.core\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Etrulens-core==1.5.2\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Etrulens-providers-cortex==1.5.2\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Etrulens-connectors-snowflake==1.5.2\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003ESelect a package to install it for use in your Notebook, and optionally change the default package version in the list of Installed Packages\u003C/li\u003E\u003Cli\u003EPackages installed by you appear under Installed Packages\u003C/li\u003E\u003Cli\u003EAfter the package is added, it may take some time to be installed. After it is installed, you will see a confirmation message and you can then import and use the libraries in a Python cell\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOnce we have the Snowflake Notebook with the right packages installed, we are ready to go.\u003C/p\u003E\n","\u003Cp\u003EIn the notebook, we will begin by setting up your Snowflake environment. Execute these cells to create a new database, warehouse, and establish a session.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.snowpark.context import get_active_session\nsession = get_active_session()\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS cortex_search_tutorial_db;\n\nCREATE OR REPLACE WAREHOUSE cortex_search_tutorial_wh WITH\n     WAREHOUSE_SIZE='X-SMALL'\n     AUTO_SUSPEND = 120\n     AUTO_RESUME = TRUE\n     INITIALLY_SUSPENDED=TRUE;\n\nUSE WAREHOUSE cortex_search_tutorial_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EPrepare Data\u003C/h2\u003E\n","\u003Cp\u003EThis phase focuses on acquiring the documents needed for your RAG application and securely storing them in Snowflake using an internal stage. These documents will be indexed by Cortex Search in a later step.\u003C/p\u003E\n","\u003Cp\u003EYou will use a sample dataset of the Federal Open Market Committee (FOMC) meeting minutes for this example. This is a sample of twelve 10-page documents with meeting notes from FOMC meetings from 2023 and 2024. Download the files directly from your browser through this link: \u003Ca href=\"https://drive.google.com/file/d/1C6TdVjy6d-GnasGO6ZrIEVJQRcedDQxG/view\"\u003EFOMC minutes sample\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EThe complete set of FOMC minutes can be found at the \u003Ca href=\"https://www.federalreserve.gov/monetarypolicy/fomccalendars.htm\"\u003EUS Federal Reserve’s website\u003C/a\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: Make sure to first unzip all the contents of the zipped folder to upload the PDF files.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EExecute the following SQL commands in your Snowflake Notebook to create a dedicated stage within your database. This stage is where you will upload the downloaded PDF files.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STAGE cortex_search_tutorial_db.public.fomc\n    DIRECTORY = (ENABLE = TRUE)\n    ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow upload the dataset. You can upload the dataset in Snowsight or using SQL. To upload in Snowsight:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESelect Catalog » Database Explorer\u003C/li\u003E\u003Cli\u003ESelect your database \u003Ccode\u003Ecortex_search_tutorial_db\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ESelect your schema \u003Ccode\u003Epublic\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EStages\u003C/strong\u003E and choose \u003Ccode\u003Efomc\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EOn the top right, select the \u003Cstrong\u003E+ Files\u003C/strong\u003E button.\u003C/li\u003E\u003Cli\u003EDrag and drop files into the UI or select \u003Cstrong\u003EBrowse\u003C/strong\u003E to choose a file from the dialog window.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EUpload\u003C/strong\u003E to upload your file.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ERun the following command in your Snowflake Notebook to confirm that your PDF files have been successfully uploaded to the stage.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Els @cortex_search_tutorial_db.public.fomc\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see a list of the PDF files, confirming they are ready to be parsed and indexed for RAG.\u003C/p\u003E\n","\u003Ch3\u003EParse the data\u003C/h3\u003E\n","\u003Cp\u003ETo make the PDF data useful for retrieval, we must first extract the text content and then break that content into small, searchable segments.\u003C/p\u003E\n","\u003Cp\u003EWe use the built in SNOWFLAKE.CORTEX.PARSE_DOCUMENT function to extract text from the PDF files stored in your stage, placing the result into a new table.\u003C/p\u003E\n","\u003Cp\u003EExecute the following SQL commands in your Snowflake Notebook to parse the uploaded PDF files and extract their content.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.PARSED_FOMC_CONTENT AS SELECT \n      relative_path,\n      TO_VARCHAR(\n        SNOWFLAKE.CORTEX.PARSE_DOCUMENT(\n          @cortex_search_tutorial_db.public.fomc, \n          relative_path, \n          {'mode': 'LAYOUT'}\n        ) :content\n      ) AS parsed_text\n    FROM directory(@cortex_search_tutorial_db.public.fomc)\n    WHERE relative_path LIKE '%.pdf'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EChunk the data\u003C/h3\u003E\n","\u003Cp\u003ELarge text blocks are inefficient for search. We use the SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER function to break the parsed content into smaller, overlapping chunks. This overlap ensures context is not lost at the chunk boundaries.\u003C/p\u003E\n","\u003Cp\u003EExecute the following commands in your Snowflake Notebook to split the parsed text into manageable chunks for efficient searching.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.CHUNKED_FOMC_CONTENT (\n    file_name VARCHAR,\n    CHUNK VARCHAR\n);\n\nINSERT INTO CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.CHUNKED_FOMC_CONTENT (file_name, CHUNK)\nSELECT\n    relative_path,\n    c.value AS CHUNK\nFROM\n    CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.PARSED_FOMC_CONTENT,\n    LATERAL FLATTEN( input => SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER (\n        parsed_text,\n        'markdown',\n        1800,\n        250\n    )) c;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ECreate a RAG\u003C/h2\u003E\n","\u003Cp\u003EIn this phase, you will create the Cortex Search Service, establish the tracing mechanism using TruLens, and construct the complete RAG application.\u003C/p\u003E\n","\u003Cp\u003EWe create the search index over the chunked FOMC data, defining the warehouse and the embedding model. Execute the following commands in your Snowflake Notebook to set up the Cortex Search service and enable efficient querying of the chunked content.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE CORTEX SEARCH SERVICE CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.FOMC_SEARCH_SERVICE\n    ON chunk\n    WAREHOUSE = cortex_search_tutorial_wh\n    TARGET_LAG = '1 hour'\n    EMBEDDING_MODEL = 'snowflake-arctic-embed-l-v2.0'\n    AS (\n    SELECT\n        file_name,\n        chunk\n    FROM CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.CHUNKED_FOMC_CONTENT\n    );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, we define a class \u003Ccode\u003ECortexSearchRetreiver\u003C/code\u003E to connect to our Cortex Search Service and add the \u003Ccode\u003Eretrieve\u003C/code\u003E method that we can leverage to call it from within the Snowflake Notebook session. Execute the following code in the Snowflake Notebook:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.snowpark.context import get_active_session\nsession = get_active_session()\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport os\nfrom snowflake.core import Root\nfrom typing import List\nfrom snowflake.snowpark.session import Session\n\nclass CortexSearchRetriever:\n\n    def __init__(self, snowpark_session: Session, limit_to_retrieve: int = 4):\n        self._snowpark_session = snowpark_session\n        self._limit_to_retrieve = limit_to_retrieve\n\n    def retrieve(self, query: str) -> List[str]:\n        root = Root(session)\n\n        search_service = (root\n          .databases["CORTEX_SEARCH_TUTORIAL_DB"]\n          .schemas["PUBLIC"]\n          .cortex_search_services["FOMC_SEARCH_SERVICE"]\n        )\n        resp = search_service.search(\n          query=query,\n          columns=["chunk"],\n          limit=self._limit_to_retrieve\n        )\n\n        if resp.results:\n            return [curr["chunk"] for curr in resp.results]\n        else:\n            return []\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eretriever = CortexSearchRetriever(snowpark_session=session, limit_to_retrieve=3)\n\nretrieved_context = retriever.retrieve(query="how was inflation expected to evolve in 2024?")\n\nretrieved_context\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate a database and schema to store our traces and evaluations.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace database observability_db;\nuse database observability_db;\ncreate or replace schema observability_schema;\nuse schema observability_schema;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen, we construct the RAG system with integrated instrumentation using the retriever we created previously. By including the span type and attributes in instrumentation, it will power evaluations of the spans captured.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.cortex import complete\nfrom trulens.core.otel.instrument import instrument\nfrom trulens.otel.semconv.trace import SpanAttributes\n\nclass RAG:\n\n    def __init__(self):\n        self.retriever = CortexSearchRetriever(snowpark_session=session, limit_to_retrieve=4)\n\n    @instrument(\n        span_type=SpanAttributes.SpanType.RETRIEVAL,\n        attributes={\n            SpanAttributes.RETRIEVAL.QUERY_TEXT: "query",\n            SpanAttributes.RETRIEVAL.RETRIEVED_CONTEXTS: "return",\n            }\n    )\n    def retrieve_context(self, query: str) -> list:\n        """\n        Retrieve relevant text from vector store.\n        """\n        return self.retriever.retrieve(query)\n\n\n    @instrument(\n        span_type=SpanAttributes.SpanType.GENERATION)\n    def generate_completion(self, query: str, context_str: list) -> str:\n        """\n        Generate answer from context.\n        """\n        prompt = f"""\n          You are an expert assistant extracting information from context provided.\n          Answer the question in long-form, fully and completely, based on the context. Do not hallucinate.\n          If you don´t have the information just say so. If you do have the information you need, just tell me the answer.\n          Context: {context_str}\n          Question:\n          {query}\n          Answer:\n        """\n        response = ""\n        stream = complete("mistral-large2", prompt, stream = True)\n        for update in stream:    \n          response += update\n          print(update, end = '')\n        return response\n\n    @instrument(\n        span_type=SpanAttributes.SpanType.RECORD_ROOT, \n        attributes={\n            SpanAttributes.RECORD_ROOT.INPUT: "query",\n            SpanAttributes.RECORD_ROOT.OUTPUT: "return",\n        })\n    def query(self, query: str) -> str:\n        context_str = self.retrieve_context(query)\n        return self.generate_completion(query, context_str)\n\n\nrag = RAG()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETest the RAG system by querying it with a sample question. The successful execution of the RAG query confirms that the tracing is active and the system is ready for formal evaluation.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eresponse = rag.query("how was inflation expected to evolve in 2024?")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ERegister the App\u003C/h2\u003E\n","\u003Cp\u003EWe will register your newly built RAG system with the TruLens observability framework. This registration tells the system where to store the evaluation results and tracing data within Snowflake and defines the specific application you are tracking. We will set metadata, including application name, version, and Snowpark session, to store the experiments.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom trulens.apps.app import TruApp\nfrom trulens.connectors.snowflake import SnowflakeConnector\n\ntru_snowflake_connector = SnowflakeConnector(snowpark_session=session)\n\napp_name = "fed_reserve_rag"\napp_version = "cortex_search"\n\ntru_rag = TruApp(\n        rag,\n        app_name=app_name,\n        app_version=app_version,\n        connector=tru_snowflake_connector\n    )\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYour application is now ready to run formal evaluations.\u003C/p\u003E\n","\u003Ch2\u003ECreate a Run\u003C/h2\u003E\n","\u003Cp\u003EThis phase prepares the formal test set and registers the evaluation experiment with TruLens. We will prepare a set of test queries to evaluate the RAG system.\u003C/p\u003E\n","\u003Cp\u003EThe evaluation requires a dataset of predefined questions and their corresponding correct answers, which are the ground truth responses. The test set can be either a dataframe in Python or a table in Snowflake. In this example, we'll use a table in Snowflake.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EDownload the \u003Ccode\u003Efomc_dataset.csv\u003C/code\u003E \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-ai-observability/blob/main/fomc_dataset.csv\"\u003Edataset\u003C/a\u003E provided on GitHub.\u003C/li\u003E\u003Cli\u003EUpload \u003Ccode\u003Efomc_dataset.csv\u003C/code\u003E to Snowflake via Snowsight:\n\u003Cul\u003E\u003Cli\u003ESelect Ingestion » Add data\u003C/li\u003E\u003Cli\u003ESelect the tile Load data into a Table\u003C/li\u003E\u003Cli\u003EUpload the \u003Ccode\u003Efomc_dataset.csv\u003C/code\u003E file from the \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-ai-observability/tree/main\"\u003EGitHub repository\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EChoose \u003Ccode\u003EOBSERVABILITY_DB.OBSERVABILITY_SCHEMA\u003C/code\u003E for the schema\u003C/li\u003E\u003Cli\u003ESelect + Create new table\u003C/li\u003E\u003Cli\u003EName the new table \u003Ccode\u003EFOMC_DATA\u003C/code\u003E, then click next\u003C/li\u003E\u003Cli\u003EUpdate the column names to \u003Ccode\u003EQUERY\u003C/code\u003E and \u003Ccode\u003EGROUND_TRUTH_RESPONSE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect Load\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EExecute the following code in your Snowflake Notebook. This will set up the configuration for running experiments and add the run to Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom trulens.core.run import Run\nfrom trulens.core.run import RunConfig\n\nrun_name = "experiment_1_run"\n\nrun_config = RunConfig(\n    run_name=run_name,\n    dataset_name="FOMC_DATA",\n    description="Questions about the Federal Open Market Committee meetings",\n    label="fomc_rag_eval",\n    source_type="TABLE",\n    dataset_spec={\n        "input": "QUERY",\n        "ground_truth_output":"GROUND_TRUTH_RESPONSE",\n    },\n)\n\nrun: Run = tru_rag.add_run(run_config=run_config)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EStart the experiment run with the prepared test set. This will invoke the application in batch using the inputs in the dataset you provided in the run.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Erun.start()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis may take around 10 to 15 minutes to load. Once this process completes, all the data will be collected and you can proceed to calculate the evaluation metrics.\u003C/p\u003E\n","\u003Ch2\u003ECompute Evaluation Metrics\u003C/h2\u003E\n","\u003Cp\u003EWe will analyze the performance of the RAG system by computing relevant metrics.\u003C/p\u003E\n","\u003Cp\u003EExecute the following command in your Snowflake Notebook to trigger the batch computation of the three RAG triad.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Erun.compute_metrics([\n    "answer_relevance",\n    "context_relevance",\n    "groundedness",\n])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEvaluation metrics provide a quantifiable way to measure the accuracy and performance of your application. These metrics are computed using specific inputs to the application, LLM-generated outputs and any intermediate information (e.g., retrieved results for a RAG application). Additionally, some metrics can also be computed using ground truth if available.\u003C/p\u003E\n","\u003Cp\u003EMetric computations use the "LLM-as-a-judge" approach, where an LLM is used to generate a score (between 0 - 1) and an associated explanation is given based on the provided information.\u003C/p\u003E\n","\u003Cp\u003EThe starting point for evaluating RAGs is the RAG triad of context relevance, groundedness and answer relevance. These are localized evaluations of a RAG system, so you can pinpoint the root cause of poor performance. They are also reference-free, meaning they can be run without using ground truth data.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-ai-observability/RAG_Triad.png\" alt=\"RAG Triad\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EContext Relevance\u003C/h3\u003E\n","\u003Cp\u003EContext Relevance determines if the retrieved context from the retriever or the search service is relevant to the user query. Given the user query and retrieved context, an LLM judge is used to determine relevance of the retrieved context based on the query.\u003C/p\u003E\n","\u003Ch3\u003EGroundedness\u003C/h3\u003E\n","\u003Cp\u003EGroundedness determines if the generated response is supported by and grounded in the retrieved context from the retriever or the search service. Given the generated response and retrieved context, an LLM judge is used to determine groundedness. The underlying implementation uses Chain-of-thought reasoning when generating the groundedness scores.\u003C/p\u003E\n","\u003Ch3\u003EAnswer Relevance\u003C/h3\u003E\n","\u003Cp\u003EAnswer relevance determines if the generated response is relevant to the user query. Given the user query and generated response, an LLM judge is used to determine how relevant the response is when answering the user’s query. Note that this doesn’t rely on ground truth answer reference, and therefore this is not equivalent to assessing answer correctness.\u003C/p\u003E\n","\u003Ch2\u003EExamine Results\u003C/h2\u003E\n","\u003Cp\u003ETo view evaluation results, navigate to Snowsight. Select AI & ML » Evaluations in the side navigation menu. The following user journey navigates you through the steps to view the evaluation results for your application runs\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EView all applications: Navigate to Snowsight. Select AI & ML » Evaluations\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-ai-observability/apps.png\" alt=\"apps\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EView runs corresponding to an application: Select a specific application to view the list of runs.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-ai-observability/runs.png\" alt=\"runs\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EView evaluation results for a run: Select a run to view the aggregated results and the results corresponding to each record.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-ai-observability/results.png\" alt=\"results\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EView traces for each record: Select a specific record to view detailed traces, metadata and evaluation results for the record.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-ai-observability/trace.png\" alt=\"record\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built a RAG by combining Cortex Search and Cortex Complete. You also created your first run and computed evaluation metrics on the run. Last, you learned how to navigate the AI Observability interface to understand the detailed traces and individual evaluation results.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to build a RAG with Cortex Search and Cortex LLM Functions.\u003C/li\u003E\u003Cli\u003EHow to create a run for batch evaluation\u003C/li\u003E\u003Cli\u003EHow to compute evaluation metrics on a run\u003C/li\u003E\u003Cli\u003EHow to view results\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-observability/\"\u003EAI Observability Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.trulens.org/getting_started/\"\u003EOpen Source TruLens Documentation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Monitor AI model performance with Snowflake AI Observability for drift detection, quality metrics, and production monitoring.","title":"Getting Started with AI Observability",":items":{},":itemsOrder":[],":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nThe widespread integration of large language models (LLMs) and generative AI in mission-critical business processes has created a need for robust AI observability to address the inherent “black box” and nondeterministic nature of these systems and applications. The complexity of AI systems makes it challenging to understand their behavior, performance and resource consumption.\n\n[AI observability](https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-observability) enables developers to monitor, analyze and visualize the internal states, inputs and outputs of generative AI applications, increasing accuracy, trust, efficiency and regulatory compliance in real-world environments. AI observability spans all stages of application development, including development, testing and production, and anchors on three key pillars:\n* **Tracing**: As developers build and customize their applications, tracing enables them to visualize the inputs, outputs and intermediate states of the application. This provides granular information of each component within the application to enable better debugging and explainability of the application behavior.\n* **Evaluations**: After the initial version of the application is ready, developers conduct systematic evaluations to assess their application's performance to proactively improve response accuracy. This allows them to test and compare different models and prompts and finalize the configuration for product deployments.\n* **Monitoring**: Once the application is deployed in production, developers need to constantly monitor the performance of their application to ensure operational reliability and avoid performance drift. Continuous monitoring also enables them to fine-tune the application by eliminating failure points and accommodating data drift.\n\nIn this quickstart, you'll build a [Retrieval-Augmented Generation](https://www.snowflake.com/en/fundamentals/rag/) (RAG) system using Cortex Search and Cortex LLMs. This will be the basis of our example. Last, you'll create a test set and run LLM-as-judge evaluations in batch against your application.\n\n### What is TruLens?\n\n[TruLens](https://www.trulens.org/) is a library for tracking and evaluating Generative AI applications in open source, along with powering Snowflake AI Observability.\n\n### What You Will Learn\n- How to load, parse, and chunk data for RAG\n- How to create a RAG with Cortex Search and Complete, adding TruLens instrumentation\n- How to set application metadata for experiment tracking\n- How to create a run and configure your test set for evaluation\n- How to compute evaluation metrics in batch on the run\n- How to navigate AI Observability to view and compare traces and evaluation results\n\n### What You Will Build\n- A retrieval-augmented generation (RAG) app\n- An evaluation run with computed metrics\n\n### Prerequisites\n- A Snowflake account with Cortex LLM Functions and Cortex Search enabled.  If you do not have a Snowflake account, you can register for a [free trial account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides&_fsi=yYZEVo4S&_fsi=yYZEVo4S).\n- A Snowflake account login with ACCOUNTADMIN role. If you have this role in your environment, you may choose to use it. If not, you will need to 1) Register for a free trial, 2) Use a different role that has the ability to create database, schema, tables, stages, tasks, user-defined functions, and stored procedures OR 3) Use an existing database and schema in which you are able to create the mentioned objects.\n\n## Setup Environment\n\nThis phase prepares your environment and creates the dedicated database and warehouse. If you are not working in a Snowflake trial account and do not have ACCOUNTADMIN privileges, ensure that the user role has the following roles granted ():\n\n- SNOWFLAKE.CORTEX_USER database role\n- CREATE EXTERNAL AGENT privilege on the schema\n\nFor more information, see [Required Privileges for AI Observability](https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-observability/reference#label-ai-observability-required-privileges).\n\nWe will use a [Snowflake Notebook](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks) to execute all the setup. Open the [getting-started-with-ai-observability.ipynb](https://github.com/Snowflake-Labs/sfguide-getting-started-with-ai-observability/blob/main/getting-started-with-ai-observability.ipynb) file from the GitHub repository and download it to open the Notebook. ***(NOTE: Do NOT right-click to download.)***\n\nThen, create a new Snowflake Notebook by importing the notebook file in Snowsight.\n* Projects » Notebooks\n* Click the + Notebook drop-down and select Import .ipynb file\n* Select the getting-started-with-ai-observability.ipynb file\n* In Create notebook, configure the fields to your liking and click Create when done\n\nOnce in the Snowflake Notebook, we will [import packages](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks-import-packages) from Anaconda: \n* Select Packages menu at the top of your Notebook\n* Search for these packages listed in the Snowflake Anaconda channel\n     * `snowflake-ml-python`\n     * `snowflake.core`\n     * `trulens-core==1.5.2`\n     * `trulens-providers-cortex==1.5.2`\n     * `trulens-connectors-snowflake==1.5.2`\n * Select a package to install it for use in your Notebook, and optionally change the default package version in the list of Installed Packages\n * Packages installed by you appear under Installed Packages\n * After the package is added, it may take some time to be installed. After it is installed, you will see a confirmation message and you can then import and use the libraries in a Python cell\n\nOnce we have the Snowflake Notebook with the right packages installed, we are ready to go.\n\nIn the notebook, we will begin by setting up your Snowflake environment. Execute these cells to create a new database, warehouse, and establish a session.\n\n```python\nfrom snowflake.snowpark.context import get_active_session\nsession = get_active_session()\n```\n\n```sql\nCREATE DATABASE IF NOT EXISTS cortex_search_tutorial_db;\n\nCREATE OR REPLACE WAREHOUSE cortex_search_tutorial_wh WITH\n     WAREHOUSE_SIZE='X-SMALL'\n     AUTO_SUSPEND = 120\n     AUTO_RESUME = TRUE\n     INITIALLY_SUSPENDED=TRUE;\n\nUSE WAREHOUSE cortex_search_tutorial_wh;\n```\n\n## Prepare Data\n\nThis phase focuses on acquiring the documents needed for your RAG application and securely storing them in Snowflake using an internal stage. These documents will be indexed by Cortex Search in a later step. \n\nYou will use a sample dataset of the Federal Open Market Committee (FOMC) meeting minutes for this example. This is a sample of twelve 10-page documents with meeting notes from FOMC meetings from 2023 and 2024. Download the files directly from your browser through this link: [FOMC minutes sample](https://drive.google.com/file/d/1C6TdVjy6d-GnasGO6ZrIEVJQRcedDQxG/view).\n\nThe complete set of FOMC minutes can be found at the [US Federal Reserve’s website](https://www.federalreserve.gov/monetarypolicy/fomccalendars.htm).\n\n\u003E NOTE: Make sure to first unzip all the contents of the zipped folder to upload the PDF files.\n\nExecute the following SQL commands in your Snowflake Notebook to create a dedicated stage within your database. This stage is where you will upload the downloaded PDF files.\n\n```sql\nCREATE OR REPLACE STAGE cortex_search_tutorial_db.public.fomc\n    DIRECTORY = (ENABLE = TRUE)\n    ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');\n```\n\nNow upload the dataset. You can upload the dataset in Snowsight or using SQL. To upload in Snowsight:\n\n1. Select Catalog » Database Explorer\n2. Select your database `cortex_search_tutorial_db`.\n3. Select your schema `public`.\n4. Select **Stages** and choose `fomc`.\n5. On the top right, select the **+ Files** button.\n6. Drag and drop files into the UI or select **Browse** to choose a file from the dialog window.\n7. Select **Upload** to upload your file.\n\nRun the following command in your Snowflake Notebook to confirm that your PDF files have been successfully uploaded to the stage.\n\n```sql\nls @cortex_search_tutorial_db.public.fomc\n```\n\nYou should see a list of the PDF files, confirming they are ready to be parsed and indexed for RAG.\n\n### Parse the data\n\nTo make the PDF data useful for retrieval, we must first extract the text content and then break that content into small, searchable segments.\n\nWe use the built in SNOWFLAKE.CORTEX.PARSE_DOCUMENT function to extract text from the PDF files stored in your stage, placing the result into a new table. \n\nExecute the following SQL commands in your Snowflake Notebook to parse the uploaded PDF files and extract their content.\n\n```sql\nCREATE OR REPLACE TABLE CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.PARSED_FOMC_CONTENT AS SELECT \n      relative_path,\n      TO_VARCHAR(\n        SNOWFLAKE.CORTEX.PARSE_DOCUMENT(\n          @cortex_search_tutorial_db.public.fomc, \n          relative_path, \n          {'mode': 'LAYOUT'}\n        ) :content\n      ) AS parsed_text\n    FROM directory(@cortex_search_tutorial_db.public.fomc)\n    WHERE relative_path LIKE '%.pdf'\n```\n\n### Chunk the data\n\nLarge text blocks are inefficient for search. We use the SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER function to break the parsed content into smaller, overlapping chunks. This overlap ensures context is not lost at the chunk boundaries. \n\nExecute the following commands in your Snowflake Notebook to split the parsed text into manageable chunks for efficient searching.\n\n```sql\nCREATE OR REPLACE TABLE CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.CHUNKED_FOMC_CONTENT (\n    file_name VARCHAR,\n    CHUNK VARCHAR\n);\n\nINSERT INTO CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.CHUNKED_FOMC_CONTENT (file_name, CHUNK)\nSELECT\n    relative_path,\n    c.value AS CHUNK\nFROM\n    CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.PARSED_FOMC_CONTENT,\n    LATERAL FLATTEN( input =\u003E SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER (\n        parsed_text,\n        'markdown',\n        1800,\n        250\n    )) c;\n```\n\n## Create a RAG\n\nIn this phase, you will create the Cortex Search Service, establish the tracing mechanism using TruLens, and construct the complete RAG application.\n\nWe create the search index over the chunked FOMC data, defining the warehouse and the embedding model. Execute the following commands in your Snowflake Notebook to set up the Cortex Search service and enable efficient querying of the chunked content.\n\n```sql\nCREATE OR REPLACE CORTEX SEARCH SERVICE CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.FOMC_SEARCH_SERVICE\n    ON chunk\n    WAREHOUSE = cortex_search_tutorial_wh\n    TARGET_LAG = '1 hour'\n    EMBEDDING_MODEL = 'snowflake-arctic-embed-l-v2.0'\n    AS (\n    SELECT\n        file_name,\n        chunk\n    FROM CORTEX_SEARCH_TUTORIAL_DB.PUBLIC.CHUNKED_FOMC_CONTENT\n    );\n```\n\nNext, we define a class `CortexSearchRetreiver` to connect to our Cortex Search Service and add the `retrieve` method that we can leverage to call it from within the Snowflake Notebook session. Execute the following code in the Snowflake Notebook:\n\n```python\nfrom snowflake.snowpark.context import get_active_session\nsession = get_active_session()\n```\n\n```python\nimport os\nfrom snowflake.core import Root\nfrom typing import List\nfrom snowflake.snowpark.session import Session\n\nclass CortexSearchRetriever:\n\n    def __init__(self, snowpark_session: Session, limit_to_retrieve: int = 4):\n        self._snowpark_session = snowpark_session\n        self._limit_to_retrieve = limit_to_retrieve\n\n    def retrieve(self, query: str) -\u003E List[str]:\n        root = Root(session)\n\n        search_service = (root\n          .databases[\"CORTEX_SEARCH_TUTORIAL_DB\"]\n          .schemas[\"PUBLIC\"]\n          .cortex_search_services[\"FOMC_SEARCH_SERVICE\"]\n        )\n        resp = search_service.search(\n          query=query,\n          columns=[\"chunk\"],\n          limit=self._limit_to_retrieve\n        )\n\n        if resp.results:\n            return [curr[\"chunk\"] for curr in resp.results]\n        else:\n            return []\n```\n\n```python\nretriever = CortexSearchRetriever(snowpark_session=session, limit_to_retrieve=3)\n\nretrieved_context = retriever.retrieve(query=\"how was inflation expected to evolve in 2024?\")\n\nretrieved_context\n```\n\nCreate a database and schema to store our traces and evaluations.\n\n```sql\ncreate or replace database observability_db;\nuse database observability_db;\ncreate or replace schema observability_schema;\nuse schema observability_schema;\n```\n\nThen, we construct the RAG system with integrated instrumentation using the retriever we created previously. By including the span type and attributes in instrumentation, it will power evaluations of the spans captured.\n\n```python\nfrom snowflake.cortex import complete\nfrom trulens.core.otel.instrument import instrument\nfrom trulens.otel.semconv.trace import SpanAttributes\n\nclass RAG:\n\n    def __init__(self):\n        self.retriever = CortexSearchRetriever(snowpark_session=session, limit_to_retrieve=4)\n\n    @instrument(\n        span_type=SpanAttributes.SpanType.RETRIEVAL,\n        attributes={\n            SpanAttributes.RETRIEVAL.QUERY_TEXT: \"query\",\n            SpanAttributes.RETRIEVAL.RETRIEVED_CONTEXTS: \"return\",\n            }\n    )\n    def retrieve_context(self, query: str) -\u003E list:\n        \"\"\"\n        Retrieve relevant text from vector store.\n        \"\"\"\n        return self.retriever.retrieve(query)\n\n\n    @instrument(\n        span_type=SpanAttributes.SpanType.GENERATION)\n    def generate_completion(self, query: str, context_str: list) -\u003E str:\n        \"\"\"\n        Generate answer from context.\n        \"\"\"\n        prompt = f\"\"\"\n          You are an expert assistant extracting information from context provided.\n          Answer the question in long-form, fully and completely, based on the context. Do not hallucinate.\n          If you don´t have the information just say so. If you do have the information you need, just tell me the answer.\n          Context: {context_str}\n          Question:\n          {query}\n          Answer:\n        \"\"\"\n        response = \"\"\n        stream = complete(\"mistral-large2\", prompt, stream = True)\n        for update in stream:    \n          response += update\n          print(update, end = '')\n        return response\n\n    @instrument(\n        span_type=SpanAttributes.SpanType.RECORD_ROOT, \n        attributes={\n            SpanAttributes.RECORD_ROOT.INPUT: \"query\",\n            SpanAttributes.RECORD_ROOT.OUTPUT: \"return\",\n        })\n    def query(self, query: str) -\u003E str:\n        context_str = self.retrieve_context(query)\n        return self.generate_completion(query, context_str)\n\n\nrag = RAG()\n```\n\nTest the RAG system by querying it with a sample question. The successful execution of the RAG query confirms that the tracing is active and the system is ready for formal evaluation.\n\n```python\nresponse = rag.query(\"how was inflation expected to evolve in 2024?\")\n```\n\n## Register the App\n\nWe will register your newly built RAG system with the TruLens observability framework. This registration tells the system where to store the evaluation results and tracing data within Snowflake and defines the specific application you are tracking. We will set metadata, including application name, version, and Snowpark session, to store the experiments.\n\n```python\nfrom trulens.apps.app import TruApp\nfrom trulens.connectors.snowflake import SnowflakeConnector\n\ntru_snowflake_connector = SnowflakeConnector(snowpark_session=session)\n\napp_name = \"fed_reserve_rag\"\napp_version = \"cortex_search\"\n\ntru_rag = TruApp(\n        rag,\n        app_name=app_name,\n        app_version=app_version,\n        connector=tru_snowflake_connector\n    )\n```\nYour application is now ready to run formal evaluations.\n\n## Create a Run\n\nThis phase prepares the formal test set and registers the evaluation experiment with TruLens. We will prepare a set of test queries to evaluate the RAG system.\n\nThe evaluation requires a dataset of predefined questions and their corresponding correct answers, which are the ground truth responses. The test set can be either a dataframe in Python or a table in Snowflake. In this example, we'll use a table in Snowflake.\n\n1. Download the `fomc_dataset.csv` [dataset](https://github.com/Snowflake-Labs/sfguide-getting-started-with-ai-observability/blob/main/fomc_dataset.csv) provided on GitHub.\n2. Upload `fomc_dataset.csv` to Snowflake via Snowsight:\n     * Select Ingestion » Add data\n     * Select the tile Load data into a Table\n     * Upload the `fomc_dataset.csv` file from the [GitHub repository](https://github.com/Snowflake-Labs/sfguide-getting-started-with-ai-observability/tree/main)\n     * Choose `OBSERVABILITY_DB.OBSERVABILITY_SCHEMA` for the schema\n     * Select + Create new table\n     * Name the new table `FOMC_DATA`, then click next\n     * Update the column names to `QUERY` and `GROUND_TRUTH_RESPONSE`\n     * Select Load\n\nExecute the following code in your Snowflake Notebook. This will set up the configuration for running experiments and add the run to Snowflake.\n\n```python\nfrom trulens.core.run import Run\nfrom trulens.core.run import RunConfig\n\nrun_name = \"experiment_1_run\"\n\nrun_config = RunConfig(\n    run_name=run_name,\n    dataset_name=\"FOMC_DATA\",\n    description=\"Questions about the Federal Open Market Committee meetings\",\n    label=\"fomc_rag_eval\",\n    source_type=\"TABLE\",\n    dataset_spec={\n        \"input\": \"QUERY\",\n        \"ground_truth_output\":\"GROUND_TRUTH_RESPONSE\",\n    },\n)\n\nrun: Run = tru_rag.add_run(run_config=run_config)\n```\n\nStart the experiment run with the prepared test set. This will invoke the application in batch using the inputs in the dataset you provided in the run.\n\n```python\nrun.start()\n```\n\nThis may take around 10 to 15 minutes to load. Once this process completes, all the data will be collected and you can proceed to calculate the evaluation metrics.\n\n## Compute Evaluation Metrics\n\nWe will analyze the performance of the RAG system by computing relevant metrics.\n\nExecute the following command in your Snowflake Notebook to trigger the batch computation of the three RAG triad.\n\n```python\nrun.compute_metrics([\n    \"answer_relevance\",\n    \"context_relevance\",\n    \"groundedness\",\n])\n```\n\nEvaluation metrics provide a quantifiable way to measure the accuracy and performance of your application. These metrics are computed using specific inputs to the application, LLM-generated outputs and any intermediate information (e.g., retrieved results for a RAG application). Additionally, some metrics can also be computed using ground truth if available.\n\nMetric computations use the \"LLM-as-a-judge\" approach, where an LLM is used to generate a score (between 0 - 1) and an associated explanation is given based on the provided information.\n\nThe starting point for evaluating RAGs is the RAG triad of context relevance, groundedness and answer relevance. These are localized evaluations of a RAG system, so you can pinpoint the root cause of poor performance. They are also reference-free, meaning they can be run without using ground truth data.\n\n![RAG Triad](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-ai-observability/RAG_Triad.png)\n\n### Context Relevance\nContext Relevance determines if the retrieved context from the retriever or the search service is relevant to the user query. Given the user query and retrieved context, an LLM judge is used to determine relevance of the retrieved context based on the query. \n\n### Groundedness\nGroundedness determines if the generated response is supported by and grounded in the retrieved context from the retriever or the search service. Given the generated response and retrieved context, an LLM judge is used to determine groundedness. The underlying implementation uses Chain-of-thought reasoning when generating the groundedness scores. \n\n### Answer Relevance\nAnswer relevance determines if the generated response is relevant to the user query. Given the user query and generated response, an LLM judge is used to determine how relevant the response is when answering the user’s query. Note that this doesn’t rely on ground truth answer reference, and therefore this is not equivalent to assessing answer correctness. \n\n## Examine Results\n\nTo view evaluation results, navigate to Snowsight. Select AI & ML » Evaluations in the side navigation menu. The following user journey navigates you through the steps to view the evaluation results for your application runs\n\n1. View all applications: Navigate to Snowsight. Select AI & ML » Evaluations\n   \n![apps](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-ai-observability/apps.png)\n\n2. View runs corresponding to an application: Select a specific application to view the list of runs.\n    \n![runs](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-ai-observability/runs.png)\n\n3. View evaluation results for a run: Select a run to view the aggregated results and the results corresponding to each record.\n\n![results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-ai-observability/results.png)\n\n4. View traces for each record: Select a specific record to view detailed traces, metadata and evaluation results for the record.\n   \n![record](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-ai-observability/trace.png)\n\n## Conclusion And Resources\n\nCongratulations! You've successfully built a RAG by combining Cortex Search and Cortex Complete. You also created your first run and computed evaluation metrics on the run. Last, you learned how to navigate the AI Observability interface to understand the detailed traces and individual evaluation results.\n\n### What You Learned\n\n- How to build a RAG with Cortex Search and Cortex LLM Functions.\n- How to create a run for batch evaluation\n- How to compute evaluation metrics on a run\n- How to view results\n\n### Related Resources\n\n- [AI Observability Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/ai-observability/)\n- [Open Source TruLens Documentation](https://www.trulens.org/getting_started/)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-6e74f56cc2","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-021a11e2b8",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-36bb48d014","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-02-12",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-516f7f2dee","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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