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alt=\"e2e-architecture\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAnswer analytical questions  over thousands of unstructured SEC filings using Cortex Agents with Analytical Search.\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ETraditional RAG retrieves 10–50 passages and asks an LLM to summarize. That works for single-document lookups, but fails on questions that require processing hundreds of filings: "How many companies disclosed a cybersecurity incident?" or "List every M&A deal filed this week." Analytical Search solves this by combining semantic search, AI functions, and SQL into one orchestrated loop.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart you will ingest SEC EDGAR filings directly from the SEC's public EDGAR archive using Snowflake's External Access Integration. A stored procedure fetches daily filing archives over HTTPS, parses metadata and document content, enriches filings with stock tickers and industry classification, chunks them into searchable passages, and extracts structured AI signals.\u003C/p\u003E\n","\u003Cp\u003EYou will then build a multi-index Cortex Search service, create a Semantic View for structured analytics, and deploy an Analytical Search agent in Snowflake CoWork — then ask it questions that no standard RAG system can answer. The result is a near-production-ready, end-to-end solution that you can easily reuse: expand the date range for historical depth, schedule ongoing ingestion with Snowflake Tasks, and the agent grows with the data automatically.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWhy traditional RAG breaks on analytical questions (top-k ceiling, no compute, no filters)\u003C/li\u003E\u003Cli\u003EHow Analytical Search combines Cortex Search with AI_FILTER, AI_EXTRACT, and AI_AGG\u003C/li\u003E\u003Cli\u003EHow auto-routing keeps simple questions cheap while powering analytical ones\u003C/li\u003E\u003Cli\u003EHow adaptive depth retrieves exactly as much data as the question requires\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Cstrong\u003EData Pipeline\u003C/strong\u003E that ingests, enriches, chunks, and extracts signals from SEC EDGAR filings\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ECortex Search service\u003C/strong\u003E with text + vector indexes over 3,400+ filing chunks\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ESemantic View\u003C/strong\u003E for structured analytics (counts, sentiment breakdowns, sector comparisons)\u003C/li\u003E\u003Cli\u003EAn \u003Cstrong\u003EAnalytical Search agent\u003C/strong\u003E wired to all Cortex Search and Semantic View tools, registered in Snowflake CoWork\u003C/li\u003E\u003Cli\u003EValidated analytical queries demonstrating counting, listing, hybrid, and auto-routing capabilities\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role\u003C/li\u003E\u003Cli\u003ECortex enabled in you Snowflake account\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESource Code\u003C/h3\u003E\n","\u003Cp\u003EAll SQL files are available in the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/main/site/sfguides/src/analytical-search-over-sec-filings-with-snowflake-cowork/sql\"\u003Esource code repository\u003C/a\u003E:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFile\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esql/00_env_setup.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnvironment setup\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esql/01_pipeline.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull ingestion, enrichment, chunking, and signal extraction pipeline\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esql/02_create_search_service.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMulti-index Cortex Search service\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esql/03_create_semantic_view.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESemantic View for Cortex Analyst\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esql/04_deploy_agent.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAgent deployment + CoWork registration\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esql/99_teardown.sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull cleanup\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWhy RAG Falls Short\u003C/h2\u003E\n","\u003Cp\u003EStandard RAG has three fundamental limitations that make it unsuitable for analytical work over large document collections:\u003C/p\u003E\n","\u003Ch3\u003ETop-K Ceiling\u003C/h3\u003E\n","\u003Cp\u003ERAG retrieves just 10–50 results to fit in the LLM context window. Extending beyond this typically reduces accuracy and increases cost. You cannot count 36 leadership changes by looking at 5 examples.\u003C/p\u003E\n","\u003Ch3\u003ESemantic Only\u003C/h3\u003E\n","\u003Cp\u003ENo structural filters over attributes such as date, status, or region. "Contextual chunking" can help, but doesn't allow for targeted search queries with precise date ranges or category filters.\u003C/p\u003E\n","\u003Ch3\u003ENo Compute\u003C/h3\u003E\n","\u003Cp\u003ECounts and sums are mostly hallucinated, not calculated. The LLM's worldview is limited to the handful of passages in its context — it estimates rather than computes.\u003C/p\u003E\n","\u003Ch3\u003EWhy Agentic Search Isn't Enough Either\u003C/h3\u003E\n","\u003Cp\u003EA natural evolution from standard RAG is \u003Cstrong\u003Eagentic search\u003C/strong\u003E: an AI agent searches, reads, reasons, reformulates its query, and searches again in a loop. By decomposing a problem and chasing missing evidence across multiple turns, agentic search drastically improves recall for complex document QA.\u003C/p\u003E\n","\u003Cp\u003EBut at its core, agentic search is still \u003Cstrong\u003Esearch-and-summarize in a reasoning loop\u003C/strong\u003E. The agent takes more turns to find better evidence, yet the final answer is still synthesized from a relatively small, sampled evidence set. It cannot perform true macro-analysis — there is no grouping, counting, or SQL-level aggregation happening. Ask it "how many filings mention cybersecurity risks?" and it will find more examples than basic RAG, but it still cannot give you a precise count because it never processes the full result set computationally.\u003C/p\u003E\n","\u003Cp\u003EThe evolution looks like this:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStage\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EApproach\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ELimitation\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EStandard RAG\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERetrieve top-k, summarize\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMisses the long tail; no compute\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAgentic Search\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELoop: search → read → reformulate → search again\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBetter recall, but still sampling — no true aggregation\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAnalytical Search\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERetrieve adaptively + compute with AI functions + SQL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETreats documents as a table: filter, aggregate, count, compare\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EWhy Bigger Context Windows Don't Solve This\u003C/h3\u003E\n","\u003Cp\u003EA popular argument says RAG is dead because LLMs can now ingest a million tokens — so just load the documents and let the model reason end-to-end. This solves the context window constraint, but it doesn't give the model a database.\u003C/p\u003E\n","\u003Cp\u003ELoading thousands of documents into a single prompt fails for three reasons:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ECost\u003C/strong\u003E — It is orders of magnitude more expensive than targeted retrieval followed by focused computation.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENo auditable trace\u003C/strong\u003E — The model produces a final answer with no inspectable execution path. You cannot verify what was searched, filtered, counted, or excluded.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENo SQL-level precision\u003C/strong\u003E — LLMs estimate; they don't compute. Grouping, counting, ranking, and calculating deltas require an actual execution engine, not a language model approximating arithmetic over raw text.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EAnalytical Search takes the opposite approach: use retrieval to prune, then hand off to AI functions and SQL for precise computation — getting both scalability and rigor.\u003C/p\u003E\n","\u003Ch3\u003EWhy Coding Agents Fall Short\u003C/h3\u003E\n","\u003Cp\u003ECoding agents offer another alternative: grep through documents as local files, load matches into the agent's context, and write code to reason over the results. This is a strong agentic baseline — the agent can improvise an analytical loop (search, parse, aggregate) on every question and partially succeed.\u003C/p\u003E\n","\u003Cp\u003EHowever, coding agents don't scale once questions demand corpus-wide aggregation. On analytical benchmarks, they trail Analytical Search by 10–45% because:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThey lack adaptive depth — they either over-fetch (expensive) or under-fetch (incomplete)\u003C/li\u003E\u003Cli\u003EThey have no built-in semantic operators (AI_FILTER, AI_EXTRACT) optimized for document-level classification\u003C/li\u003E\u003Cli\u003EEach question requires re-inventing the analytical workflow from scratch rather than executing a systematic, optimized pipeline\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EA system designed around the analytical loop — with retrieval, semantic classification, and SQL as first-class stages — does the work more reliably and at lower cost than ad-hoc coding for each question.\u003C/p\u003E\n","\u003Ch3\u003EQuery Types That Break Traditional RAG\u003C/h3\u003E\n","\u003Cp\u003EHigh-value enterprise queries requiring exhaustiveness, aggregates, or temporal analysis:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuery Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhy RAG Fails\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EList queries with multi-aspect filters\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"List all companies that disclosed a cybersecurity incident this month"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETop-k misses the long tail\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAggregates\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What percentage of filings mention supply chain risk in Finance vs Technology?"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECannot count from a sample\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ETemporal queries\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What new risk themes emerged this quarter compared to last?"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo date filtering, no comparison logic\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ETraditional RAG vs Analytical Search\u003C/h3\u003E\n","\u003Cp\u003EConsider the question: \u003Cem\u003E"How many filings filed on Feb 3, 2025 mention cybersecurity risks?"\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional RAG approach:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOne search query with limit=10: \u003Ccode\u003Esearch("cybersecurity risks")\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EFeeds 10 results back to the LLM to summarize\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EAnswer: "Based on the provided documents, several companies mention cybersecurity risks, including Boeing and GE. However, there may be more that were not analyzed."\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E→ Imprecise answer based on a sample. No actual count.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAnalytical Search approach:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EStructured search\u003C/li\u003E\u003Cli\u003EAI_FILTER to classify which chunks discuss \u003Cem\u003Eactual\u003C/em\u003E cybersecurity risks (not generic boilerplate)\u003C/li\u003E\u003Cli\u003ECOUNT(DISTINCT ACCESSION_NO) over the filtered table\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EAnswer: "5 filings filed on Feb 3, 2025 mention substantive cybersecurity risks. The companies are: [complete list with citations]."\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/cybersecurity-result.png\" alt=\"cybersecurity-result\"\u003E\u003C/p\u003E\n","\u003Cp\u003E→ Quantitative, exhaustive answer with precise filtering and SQL-level computation.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EHow Analytical Search Works\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/analytical-search-intro.png\" alt=\"analytical-search-intro\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAnalytical Search is an orchestration capability in Cortex Agents that enables analytical queries over large document collections. It operates in two layers:\u003C/p\u003E\n","\u003Ch3\u003ELayer 1: Search to Prune\u003C/h3\u003E\n","\u003Cp\u003ECortex Search narrows the full corpus to a relevant candidate set — finding documents about a specific topic, isolating records with a particular attribute, or filtering to a date range. This happens without scanning every document with a large model.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAdaptive depth\u003C/strong\u003E controls how far to search. Rather than using a fixed top-k limit, the agent dynamically adjusts search depth based on the relevance of results. A fixed top-k gets two things wrong: too shallow (the missing item is often the data point that changes the answer), or too deep (the user asks for k=1,000 but only a few dozen documents are relevant — the rest is wasted compute).\u003C/p\u003E\n","\u003Cp\u003EAdaptive depth makes the cutoff a function of the data, not a guessed parameter, and works in two phases:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPhase 1 — Bound the relevant region.\u003C/strong\u003E The system fetches an initial batch of results and uses a fast LLM to judge small samples at the head and tail of the ranked list. If the tail is still relevant, the system extends the fetch limit and rejudges the new tail — repeating until the tail goes off-topic or a hard ceiling is reached. If even the top results are irrelevant, the search returns nothing rather than feed AI functions material that would only generate noise.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPhase 2 — Find the exact cutoff.\u003C/strong\u003E Once the relevant region is bounded, the system binary-searches inside it, LLM-judging a sample around the midpoint and tightening the window to land on the precise boundary in a handful of rounds.\u003C/p\u003E\n","\u003Cp\u003EThe result: a narrow fact question stays shallow; a trend across a broad corpus goes deep; a question against a sparse corpus stops early — all without any parameter tuning.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECost note:\u003C/strong\u003E Adaptive depth optimizes spend in both directions. Better coverage reduces wasted downstream AI function calls that would produce wrong answers. Better restraint avoids paying to extract and classify documents that would not have answered the question in the first place. AI functions are powerful but not free — every unnecessary call adds latency and dollars. Adaptive depth is the layer that decides how many of those calls are worth making.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ELayer 2: AI Functions and SQL to Analyze\u003C/h3\u003E\n","\u003Cp\u003EOnce the corpus is pruned, the agent applies semantic operators directly on the result set:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFunction\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAI_FILTER\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESemantic yes/no classification per row\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Is this about an actual cybersecurity incident (not generic risk boilerplate)?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAI_EXTRACT\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPull structured fields from unstructured text\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExtract person name, role, and whether it's a departure or appointment\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAI_AGG\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDeduplicate, cluster, and summarize across rows\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECollapse 200 risk phrases into top-10 categories with counts\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESQL\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGroup, count, join, rank, trend, calculate\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECOUNT BY sector, percentage breakdowns, temporal comparisons\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EAuto-Routing\u003C/h3\u003E\n","\u003Cp\u003EThe agent classifies query intent at runtime:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESimple, single-passage questions\u003C/strong\u003E → standard RAG path (no persist, no AI functions, low cost)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECorpus-wide analytical questions\u003C/strong\u003E → full Analytical Search loop\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou don't need to specify which mode to use. The agent decides automatically.\u003C/p\u003E\n","\u003Ch3\u003EPlanning Mode\u003C/h3\u003E\n","\u003Cp\u003EBefore executing analytical queries, the agent generates a clear execution plan and presents it for review. This lets you verify the logical steps before any data is processed. Example of an execution planned proposed for a review below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/cybersecurity-plan.png\" alt=\"cybersecurity-plan\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEnvironment Setup\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFile:\u003C/strong\u003E \u003Ccode\u003Esql/00_env_setup.sql\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EOpen a SQL file in Snowflake Workspace and run the following to create the infrastructure:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\n-- Configuration — edit these values\nSET config_database   = 'SEC_FILINGS';\nSET config_schema     = 'FILING_DATA';\nSET config_warehouse  = 'FILING_WH';\nSET config_user_agent = 'YourOrg SEC-Filing-Demo your_name@company.com';\nSET config_start_date = '2025-02-03';  -- single day for quickstart\nSET config_end_date   = '2025-02-03';\n\n-- Create database and schema\nCREATE DATABASE IF NOT EXISTS IDENTIFIER($config_database);\nUSE DATABASE IDENTIFIER($config_database);\nCREATE SCHEMA IF NOT EXISTS IDENTIFIER($config_schema);\nUSE SCHEMA IDENTIFIER($config_schema);\n\n-- Create a dedicated warehouse\nCREATE WAREHOUSE IF NOT EXISTS IDENTIFIER($config_warehouse)\n    WAREHOUSE_SIZE = 'SMALL'\n    AUTO_SUSPEND = 60\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE\n    COMMENT = 'SEC pipeline: dynamically resized by RUN_PIPELINE()';\nUSE WAREHOUSE IDENTIFIER($config_warehouse);\n\n-- Network access for SEC EDGAR\nCREATE OR REPLACE NETWORK RULE SEC_EDGAR_NETWORK_RULE\n    MODE = EGRESS\n    TYPE = HOST_PORT\n    VALUE_LIST = ('www.sec.gov:443', 'data.sec.gov:443', 'efts.sec.gov:443');\n\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION SEC_EDGAR_EAI\n    ALLOWED_NETWORK_RULES = (SEC_EDGAR_NETWORK_RULE)\n    ENABLED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E SEC EDGAR requires a valid \u003Ccode\u003EUser-Agent\u003C/code\u003E header with your organization name and contact email. Edit \u003Ccode\u003Econfig_user_agent\u003C/code\u003E above — this value is passed as a parameter to \u003Ccode\u003ERUN_PIPELINE()\u003C/code\u003E, which forwards it to all HTTP calls. You only need to set it in this one place. See \u003Ca href=\"https://www.sec.gov/os/accessing-edgar-data\"\u003ESEC EDGAR access policies\u003C/a\u003E for details.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the Data Pipeline\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFile:\u003C/strong\u003E \u003Ccode\u003Esql/01_pipeline.sql\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe data pipeline ingests SEC EDGAR filings, enriches them with tickers and industry classification, chunks them for search, and extracts AI signals.\u003C/p\u003E\n","\u003Ch3\u003ECore Tables\u003C/h3\u003E\n","\u003Cp\u003EThe pipeline creates four main tables:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETable\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EKey Columns\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EFILING_INDEX\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFiling metadata registry\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EACCESSION_NO, CIK, COMPANY_NAME, FORM_TYPE, FILED_AT, TICKER, INDUSTRY_SECTOR\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EFILING_CONTENT\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERaw filing text\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EACCESSION_NO, CONTENT_TEXT, PARSE_STATUS, SIGNAL_STATUS\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EFILING_CHUNKS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESearchable passages\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECHUNK_ID, CHUNK_TEXT, SECTION_NAME, ~800 chars avg\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EFILING_SIGNALS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAI-extracted structured signals\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESIGNAL_ID, EVENT_TYPE, SENTIMENT, REVENUE, KEY_METRICS\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ERunning the Pipeline\u003C/h3\u003E\n","\u003Cp\u003EExecute the entire \u003Ccode\u003Esql/01_pipeline.sql\u003C/code\u003E file in Snowflake workspace.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/pipeline-script.png\" alt=\"pipeline-script\"\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- The last command in the script calls the pipeline:\nCALL RUN_PIPELINE('2025-02-03', '2025-02-03', 'YourOrg SEC-Filing-Demo your_name@company.com');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe pipeline executes four phases automatically:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPhase\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWarehouse Size\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1. Ingest\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDownloads daily EDGAR archives, parses filing metadata and content\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELARGE (SNOWPARK-OPTIMIZED)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2. Enrich\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EResolves stock tickers via SEC company search, maps SIC codes to industry sectors\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESMALL\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3. Chunk\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESplits filings into searchable passages by section (Risk Factors, MD&A, Financial Statements, etc.)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESMALL\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4. Extract\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAI extracts structured signals: sentiment, event type, key metrics, forward guidance\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESMALL\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E The pipeline dynamically resizes the warehouse between phases. \u003Cstrong\u003ETotal runtime for a single day: ~3-5 minutes\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScaling Beyond One Day\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThis quickstart ingests a single day for speed (~3-5 minutes). To load more historical data, simply widen \u003Ccode\u003Econfig_start_date\u003C/code\u003E and \u003Ccode\u003Econfig_end_date\u003C/code\u003E in \u003Ccode\u003E00_env_setup.sql\u003C/code\u003E — the pipeline handles any date range up to a full year (runtime scales at ~3-5 minutes per business day). For large backfills (e.g., a full year), you can parallelize by creating multiple Snowflake Tasks that each call \u003Ccode\u003ERUN_PIPELINE()\u003C/code\u003E with non-overlapping monthly date ranges (e.g., Jan, Feb, Mar…), allowing months to process concurrently.\u003C/p\u003E\n","\u003Cp\u003EFor continuous automated ingestion, schedule a single Snowflake Task with a daily CRON to call \u003Ccode\u003ERUN_PIPELINE()\u003C/code\u003E for the current day — new filings flow into the Cortex Search service and Semantic View automatically with no manual intervention.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EVerify the Pipeline Output\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check corpus statistics\nSELECT\n    COUNT(*)                       AS total_chunks,\n    COUNT(DISTINCT ACCESSION_NO)   AS distinct_filings,\n    COUNT(DISTINCT TICKER)         AS distinct_tickers,\n    COUNT(DISTINCT FORM_TYPE)      AS distinct_form_types,\n    MIN(FILED_AT)                  AS earliest_filing,\n    MAX(FILED_AT)                  AS latest_filing,\n    AVG(LENGTH(CHUNK_TEXT))::INT   AS avg_chunk_chars\nFROM FILING_CHUNKS\nWHERE CHUNK_TEXT IS NOT NULL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExpected output for Feb 3, 2025:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EMetric\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EValue\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETotal chunks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~3,453\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDistinct filings\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~259\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDistinct tickers\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~197\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EForm types\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10-K, 10-Q, 8-K, 8-K/A\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAvg chunk chars\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~1,250\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate the Cortex Search Service\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFile:\u003C/strong\u003E \u003Ccode\u003Esql/02_create_search_service.sql\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E If you already have a Cortex Search service, you can reuse it; you don't need to create a new service specifically for analytical search. If your agent already has a Cortex Search tool configured, you don't need to add another one.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate the Service\u003C/h3\u003E\n","\u003Cp\u003EIn this example we will be creating Cortex Search service from scratch.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nUSE WAREHOUSE FILING_WH;\n\nCREATE OR REPLACE CORTEX SEARCH SERVICE SEC_FILINGS.FILING_DATA.SEC_FILING_SEARCH\n    TEXT INDEXES CHUNK_TEXT, CHUNK_ID, ACCESSION_NO, SECTION_NAME, COMPANY_NAME\n    VECTOR INDEXES CHUNK_TEXT (model='snowflake-arctic-embed-l-v2.0')\n    ATTRIBUTES COMPANY_NAME, TICKER, FORM_TYPE, SECTION_NAME, FILED_AT,\n               PERIOD_OF_REPORT, INDUSTRY_SECTOR, INDUSTRY_TITLE, CHUNK_ID, ACCESSION_NO\n    WAREHOUSE = FILING_WH\n    TARGET_LAG = '1 day'\n    COMMENT = 'SEC filing search - multi-index with text and vector'\nAS (\n    SELECT\n        CHUNK_ID, CHUNK_TEXT, ACCESSION_NO, COMPANY_NAME, TICKER, FORM_TYPE,\n        SECTION_NAME,\n        TO_VARCHAR(FILED_AT, 'YYYY-MM-DD') AS FILED_AT,\n        TO_VARCHAR(PERIOD_OF_REPORT, 'YYYY-MM-DD') AS PERIOD_OF_REPORT,\n        COALESCE(INDUSTRY_SECTOR, 'Other') AS INDUSTRY_SECTOR,\n        INDUSTRY_TITLE\n    FROM SEC_FILINGS.FILING_DATA.FILING_CHUNKS\n    WHERE CHUNK_TEXT IS NOT NULL AND LENGTH(CHUNK_TEXT) > 100\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhy Each Design Choice Matters for Analytical Search\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EConfig Element\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhy It Matters\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETEXT INDEXES\u003C/code\u003E on 5 columns\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnables keyword search on content, company names, section names — the agent searches on meaning AND exact names\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EVECTOR INDEXES\u003C/code\u003E with Arctic embed\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESemantic similarity — "cybersecurity risks" matches "unauthorized access to our systems" even with no keyword overlap. \u003Ccode\u003Esnowflake-arctic-embed-l-v2.0\u003C/code\u003E is the recommended model for analytical search\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EATTRIBUTES\u003C/code\u003E (10 columns)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBecome \u003Cstrong\u003Efilterable\u003C/strong\u003E and \u003Cstrong\u003Ereturnable\u003C/strong\u003E in search results. The agent can filter by \u003Ccode\u003EFORM_TYPE='10-K'\u003C/code\u003E or \u003Ccode\u003EINDUSTRY_SECTOR='Finance'\u003C/code\u003E server-side\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETARGET_LAG = '1 day'\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERefresh frequency. For a demo corpus that doesn't change, this is sufficient\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E The search service indexes ~3,283 of the 3,453 total chunks. The \u003Ccode\u003EWHERE LENGTH(CHUNK_TEXT) > 100\u003C/code\u003E filter excludes very short chunks (section headers, boilerplate) that would add noise to search results.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate the Semantic View\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFile:\u003C/strong\u003E \u003Ccode\u003Esql/03_create_semantic_view.sql\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe Semantic View is required for Cortex Analyst tool execution. It allows the agent to answer counting and aggregation questions with SQL precision.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Semantic View\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nUSE DATABASE SEC_FILINGS;\nUSE SCHEMA FILING_DATA;\nUSE WAREHOUSE FILING_WH;\n\nCREATE OR REPLACE SEMANTIC VIEW SEC_FILING_ANALYTICS\n  TABLES (\n    signals AS FILING_SIGNALS\n      PRIMARY KEY (SIGNAL_ID)\n      WITH SYNONYMS = ('investment signals', 'filing signals', 'EDGAR signals', 'SEC filings')\n      COMMENT = 'AI-extracted investment signals from SEC EDGAR filings.',\n    meta AS FILING_INDEX\n      PRIMARY KEY (ACCESSION_NO)\n      WITH SYNONYMS = ('filing metadata', 'EDGAR index', 'filing registry')\n      COMMENT = 'SEC EDGAR filing metadata: accession numbers, CIKs, filing URLs, dates'\n  )\n  RELATIONSHIPS (\n    signals_to_meta AS signals(ACCESSION_NO) REFERENCES meta(ACCESSION_NO)\n  )\n  FACTS (\n    signals.accession_no AS signals.ACCESSION_NO\n      WITH SYNONYMS = ('accession number', 'filing id')\n      COMMENT = 'EDGAR accession number uniquely identifying the filing',\n    signals.revenue AS signals.REVENUE\n      WITH SYNONYMS = ('total revenue', 'sales', 'top line')\n      COMMENT = 'Revenue in millions USD. NULL if not extractable.',\n    signals.net_income AS signals.NET_INCOME\n      WITH SYNONYMS = ('net income', 'profit', 'bottom line')\n      COMMENT = 'Net income figure extracted from filing.',\n    signals.eps AS signals.EPS\n      WITH SYNONYMS = ('earnings per share', 'diluted EPS')\n      COMMENT = 'Normalized EPS value.',\n    signals.yoy_change AS signals.YOY_CHANGE\n      WITH SYNONYMS = ('year over year', 'YoY growth', 'growth rate')\n      COMMENT = 'Year-over-year change percentage.',\n    signals.forward_guidance AS signals.FORWARD_GUIDANCE\n      WITH SYNONYMS = ('guidance', 'outlook', 'forecast')\n      COMMENT = 'Forward-looking financial guidance from MD&A.'\n  )\n  DIMENSIONS (\n    signals.company_name AS signals.COMPANY_NAME\n      WITH SYNONYMS = ('company', 'filer', 'issuer')\n      COMMENT = 'Company that filed the SEC document',\n    signals.ticker AS signals.TICKER\n      WITH SYNONYMS = ('stock ticker', 'symbol')\n      COMMENT = 'Stock ticker symbol. May be NULL for non-public filers.',\n    signals.form_type AS signals.FORM_TYPE\n      WITH SYNONYMS = ('filing type', 'SEC form')\n      COMMENT = '10-K (annual), 10-Q (quarterly), 8-K (current report)',\n    signals.event_type AS COALESCE(signals.EVENT_TYPE_NORMALIZED, signals.EVENT_TYPE)\n      WITH SYNONYMS = ('event', 'signal type', 'event classification')\n      COMMENT = 'AI-classified event type: Earnings, M&A, Leadership Change, Risk Disclosure, Guidance Update, Regulatory, Capital Markets, Bankruptcy, Annual Report, Quarterly Report, Current Report, Other.',\n    signals.sentiment AS signals.SENTIMENT\n      WITH SYNONYMS = ('tone', 'filing sentiment')\n      COMMENT = 'AI-assessed sentiment: POSITIVE, NEGATIVE, NEUTRAL, MIXED.',\n    signals.industry_sector AS COALESCE(signals.INDUSTRY_SECTOR, 'Other')\n      WITH SYNONYMS = ('sector', 'industry')\n      COMMENT = 'SEC Office-based industry sector: Technology, Life Sciences, Finance, Real Estate & Construction, Energy & Transportation, Manufacturing, Trade & Services, Other.',\n    signals.industry_title AS signals.INDUSTRY_TITLE\n      WITH SYNONYMS = ('specific industry', 'sub-sector')\n      COMMENT = 'Specific SEC industry title.',\n    signals.is_amendment AS signals.IS_AMENDMENT\n      WITH SYNONYMS = ('amendment', 'restated')\n      COMMENT = 'TRUE if this is an amended filing.',\n    meta.cik AS meta.CIK\n      WITH SYNONYMS = ('SEC CIK', 'central index key')\n      COMMENT = 'SEC Central Index Key',\n    signals.signal_date AS signals.SIGNAL_DATE\n      WITH SYNONYMS = ('filing date', 'date filed', 'when filed')\n      COMMENT = 'The date the SEC received the filing.',\n    signals.period_of_report AS signals.PERIOD_OF_REPORT\n      WITH SYNONYMS = ('fiscal period', 'report period', 'period end')\n      COMMENT = 'Fiscal period end date the filing covers.'\n  )\n  METRICS (\n    signals.filing_count AS COUNT(signals.SIGNAL_ID)\n      WITH SYNONYMS = ('number of filings', 'total filings', 'how many filings')\n      COMMENT = 'Total number of filings matching filters',\n    signals.positive_signals AS COUNT(CASE WHEN signals.SENTIMENT = 'POSITIVE' THEN 1 END)\n      WITH SYNONYMS = ('positive filings', 'bullish signals')\n      COMMENT = 'Count of filings with positive sentiment',\n    signals.negative_signals AS COUNT(CASE WHEN signals.SENTIMENT = 'NEGATIVE' THEN 1 END)\n      WITH SYNONYMS = ('negative filings', 'bearish signals')\n      COMMENT = 'Count of filings with negative sentiment',\n    signals.ma_count AS COUNT(CASE WHEN signals.EVENT_TYPE = 'M&A' THEN 1 END)\n      WITH SYNONYMS = ('merger filings', 'M&A events', 'deals')\n      COMMENT = 'Count of merger and acquisition events',\n    signals.leadership_change_count AS COUNT(CASE WHEN signals.EVENT_TYPE = 'Leadership Change' THEN 1 END)\n      WITH SYNONYMS = ('leadership events', 'management changes')\n      COMMENT = 'Count of leadership change events',\n    signals.negative_sentiment_pct AS\n      ROUND(100.0 * COUNT(CASE WHEN signals.SENTIMENT = 'NEGATIVE' THEN 1 END)\n            / NULLIF(COUNT(signals.SIGNAL_ID), 0), 2)\n      WITH SYNONYMS = ('negative rate', 'percent negative')\n      COMMENT = 'Percentage of filings with negative sentiment (0-100 scale)'\n  )\n  COMMENT = 'Investment signal analytics over SEC EDGAR filing corpus.'\n  AI_SQL_GENERATION 'This semantic view covers SEC EDGAR filings. SIGNAL_DATE is the authoritative filing timestamp. EVENT_TYPE and SENTIMENT are AI-extracted.';\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploy the Analytical Search Agent\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFile:\u003C/strong\u003E \u003Ccode\u003Esql/04_deploy_agent.sql\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate the Agent\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nUSE DATABASE SEC_FILINGS;\nUSE SCHEMA FILING_DATA;\n\n-- Ensure Snowflake Intelligence (CoWork) object exists\nCREATE SNOWFLAKE INTELLIGENCE IF NOT EXISTS SNOWFLAKE_INTELLIGENCE_OBJECT_DEFAULT;\n\n-- Deploy the Analytical Search agent (co-located with search service + semantic view)\nCREATE OR REPLACE AGENT SEC_FILINGS.FILING_DATA.SEC_ANALYTICAL_SEARCH_AGENT\nCOMMENT = 'SEC filing research agent v7 - FILED_AT/PERIOD_OF_REPORT are now native DATE in the search service, range filters supported.'\nFROM SPECIFICATION $$\n{\n  "models": { "orchestration": "claude-opus-4-7" },\n  "orchestration": { "budget": { "seconds": 600, "tokens": 200000 } },\n  "instructions": {\n    "orchestration": "You are SEC Filing Analyst, an investment-research agent over a corpus of SEC EDGAR 10-K, 10-Q, 8-K filings.\\n\\nFor questions about counts, comparisons, exhaustive lists, or cross-filing patterns:\\n1. Search broadly using multiple relevant queries — use synonyms and related phrasings.\\n2. Return a clear answer with company name, form type, filing date, and the specific evidence from each filing chunk."\n  },\n  "tools": [\n    {\n      "tool_spec": {\n        "type": "cortex_search",\n        "name": "filing_semantic_search",\n        "description": "Multi-index Cortex Search over SEC filing chunks."\n      }\n    },\n    {\n      "tool_spec": {\n        "type": "cortex_analyst_text_to_sql",\n        "name": "filing_analyst",\n        "description": "Structured analytics over pre-extracted filing signals (sector counts, sentiment percentages, signal trends)."\n      }\n    }\n  ],\n  "tool_resources": {\n    "filing_semantic_search": {\n      "name": "SEC_FILINGS.FILING_DATA.SEC_FILING_SEARCH",\n      "search_service": "SEC_FILINGS.FILING_DATA.SEC_FILING_SEARCH",\n      "database_schema": "SEC_FILINGS.FILING_DATA",\n      "is_multi_index": true,\n      "max_results": 1000,\n      "id_column": "CHUNK_ID",\n      "title_column": "COMPANY_NAME",\n      "base_table": "SEC_FILINGS.FILING_DATA.FILING_CHUNKS",\n      "base_table_columns": [\n        "CHUNK_ID","CHUNK_TEXT","ACCESSION_NO","COMPANY_NAME","TICKER",\n        "FORM_TYPE","SECTION_NAME","FILED_AT","PERIOD_OF_REPORT",\n        "INDUSTRY_SECTOR","INDUSTRY_TITLE"\n      ],\n      "execution_environment": {"type": "warehouse", "warehouse": "FILING_WH"},\n      "columns_and_descriptions": {\n        "CHUNK_ID":         {"description": "Chunk primary key", "type": "string", "searchable": true,  "filterable": true},\n        "CHUNK_TEXT":       {"description": "Full filing passage text. Search here for filing content.", "type": "string", "searchable": true, "filterable": false},\n        "COMPANY_NAME":     {"description": "Filer company name. Search here for specific companies.", "type": "string", "searchable": true, "filterable": true},\n        "TICKER":           {"description": "Stock ticker (e.g. NVDA, MSFT, GOOGL). Use for searching or filtering filings by specific public companies. Approximately 85% of filings have a populated ticker.", "type": "string", "searchable": true, "filterable": true},\n        "FORM_TYPE":        {"description": "10-K, 10-K/A, 10-Q, 10-Q/A, 8-K, 8-K/A.", "type": "string", "searchable": false, "filterable": true},\n        "SECTION_NAME":     {"description": "Filing section (Risk Factors, MD&A, Item 1.01, etc.).", "type": "string", "searchable": true, "filterable": true},\n        "FILED_AT":         {"description": "SEC filing date (DATE). Supports @gte / @lte range filters.", "type": "date", "searchable": false, "filterable": true},\n        "PERIOD_OF_REPORT": {"description": "Fiscal period end date (DATE). Supports @gte / @lte range filters.", "type": "date", "searchable": false, "filterable": true},\n        "INDUSTRY_SECTOR":  {"description": "SIC-based sector grouping.", "type": "string", "searchable": false, "filterable": true},\n        "INDUSTRY_TITLE":   {"description": "Detailed SIC industry title.", "type": "string", "searchable": false, "filterable": true},\n        "ACCESSION_NO":     {"description": "SEC accession number, unique per filing.", "type": "string", "searchable": false, "filterable": true}\n      }\n    },\n    "filing_analyst": {\n      "semantic_view": "SEC_FILINGS.FILING_DATA.SEC_FILING_ANALYTICS",\n      "execution_environment": {"type": "warehouse", "warehouse": "FILING_WH"}\n    }\n  }\n}\n$$;\n\n-- Register in Snowflake CoWork\nALTER SNOWFLAKE INTELLIGENCE SNOWFLAKE_INTELLIGENCE_OBJECT_DEFAULT\n    ADD AGENT SEC_FILINGS.FILING_DATA.SEC_ANALYTICAL_SEARCH_AGENT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETools and Their Roles\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETool\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERole in Analytical Search\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Efiling_semantic_search\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecortex_search\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnstructured data search, prerequisite for Analytical Search\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Efiling_analyst\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecortex_analyst_text_to_sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStructured analytics via the Semantic View (counts, breakdowns, percentages)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E Column descriptions are the single most impactful thing you can do to improve analytical search quality. The agent uses them to decide which columns to filter on, how to interpret values, and how to frame AI_FILTER and AI_EXTRACT calls. For each column, describe:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWhat it contains and its expected format or value range\u003C/li\u003E\u003Cli\u003ESample values or enumerations (e.g., \u003Ccode\u003E"Values: 10-K, 10-Q, 8-K, 8-K/A"\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EWhether it's suitable for filtering, searching, or extraction\u003C/li\u003E\u003Cli\u003EAny relationships to other columns\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EColumns without descriptions are harder for the agent to use effectively, especially filterable attributes that determine how the search is scoped.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E Snowflake recommends setting \u003Ccode\u003Emax_results\u003C/code\u003E to 1,000 for analytical search. This gives the agent enough breadth to surface the full relevant set of documents. Adaptive depth limits actual compute to what the question requires — you won't pay for 1,000 AI function calls on a narrow question.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAnalytical Search in Action\u003C/h2\u003E\n","\u003Cp\u003EIn Snowflake UI click Cortex AI and open \u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E and select the \u003Ccode\u003ESEC_ANALYTICAL_SEARCH_AGENT\u003C/code\u003E. Try each exercise below to see different Analytical Search capabilities in action.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/cowork.png\" alt=\"cowork\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPerformance expectations:\u003C/strong\u003E Simple RAG questions typically complete in ~30 seconds. Analytical search workflows take \u003Cstrong\u003E2–6 minutes\u003C/strong\u003E for most questions. Complex analyses over large corpora (thousands of documents with multi-step extraction and aggregation) may take up to \u003Cstrong\u003E15 minutes\u003C/strong\u003E. This is expected — the agent is doing real computation, not just summarizing a handful of passages.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECost considerations:\u003C/strong\u003E Analytical search incurs costs from agent orchestration and the AI functions it uses (AI_FILTER, AI_EXTRACT, AI_AGG). Each AI function call processes one row. Adaptive depth limits unnecessary calls by stopping retrieval when results are no longer relevant — so you pay only for the documents that matter to the answer. For detailed AI function pricing, see the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql-cost#label-cortex-llm-cost-considerations\"\u003ECortex AI cost documentation\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EExercise 1: Auto-Routing (RAG Path)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAsk the agent:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EWhat does Boeing's 10-K say about supply chain risk?\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/boeing-10k.png\" alt=\"boeing-10k\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to observe:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThis is a per-document question — the agent auto-routes to standard top-k RAG\u003C/li\u003E\u003Cli\u003EYou get a focused answer with citations from Boeing's Risk Factors section\u003C/li\u003E\u003Cli\u003EResponse time: fast (~30 seconds)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy this matters:\u003C/strong\u003E Auto-routing keeps simple questions cheap. Analytical Search is for analytical questions, not every question.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EExercise 2: Counting (Analytical Search Path)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAsk the agent:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EHow many filings filed on Feb 3, 2025 mention cybersecurity risks?\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/cybersecurity-plan.png\" alt=\"cybersecurity-plan\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to observe:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe agent shows a \u003Cstrong\u003Eplan\u003C/strong\u003E before executing (probe → search → AI_FILTER → COUNT)\u003C/li\u003E\u003Cli\u003EAI_FILTER classifies each chunk: is this about an \u003Cem\u003Eactual\u003C/em\u003E cybersecurity risk (not generic boilerplate)?\u003C/li\u003E\u003Cli\u003EThe answer is a precise integer with a list of companies\u003C/li\u003E\u003Cli\u003EResponse time: 1–3 minutes\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected result:\u003C/strong\u003E ~5 filings mention substantive cybersecurity risks. The agent will list each company with its form type and filing date.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/cybersecurity-result.png\" alt=\"cybersecurity-result\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey insight:\u003C/strong\u003E A RAG agent would say "here are some examples..." — it cannot count because it only sees 5–10 documents. The Analytical Search agent counts from the full result set using SQL.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EExercise 3: Exhaustive Listing with Extraction\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAsk the agent:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EList every company that announced a leadership change in an 8-K filing on Feb 3, 2025.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/leadership-change-plan.png\" alt=\"leadership-change-plan\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to observe:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAdaptive depth\u003C/strong\u003E retrieves exactly as many results as needed\u003C/li\u003E\u003Cli\u003EThe agent shows a \u003Cstrong\u003Eplan\u003C/strong\u003E before executing (probe → search → AI_FILTER → COUNT (deduplicate)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EAI_FILTER\u003C/code\u003E identifies genuine leadership-change language\u003C/li\u003E\u003Cli\u003EThe result is a \u003Cstrong\u003Etable artifact\u003C/strong\u003E with one row per event\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected result:\u003C/strong\u003E ~50 leadership changes in a single day — a table with company, person, role, and type. Notable examples: RTX Corp (Gregory Hayes stepping down), Baxter International (CEO transition).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/leadership-change-execute.png\" alt=\"leadership-change-execute\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey insight:\u003C/strong\u003E A RAG agent would return 5–10 examples and hedge with "there may be more." The Analytical Search agent finds all ~50 and structures them into an audit-ready table.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EExercise 4: Honest Refusal\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAsk the agent:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ECompare Apple and Microsoft 10-K risk factor language about AI — what specific concerns does each company raise?\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/honest-refusal.png\" alt=\"honest-refusal\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to observe:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe agent \u003Cstrong\u003Edoes not hallucinate\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EIt explains: Apple's fiscal year ends Sept 30 (10-K filed Oct/Nov), Microsoft's ends June 30 (10-K filed Jul/Aug) — neither is in this Feb 3 corpus\u003C/li\u003E\u003Cli\u003EIt searched, found nothing, and says so explicitly\u003C/li\u003E\u003Cli\u003EIt offers alternatives: "I can compare Boeing and RTX, whose 10-Ks are in the corpus"\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey insight:\u003C/strong\u003E Trust over completeness. The agent refuses to fabricate when the corpus lacks data, and offers constructive alternatives. This is as important as getting answers right.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFollow-up with Boeing and RTX instead of Apple and Microsoft\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ECompare Boeing and RTX 10-K risk factor language — what does each emphasize?\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/boeing-rtx.png\" alt=\"boeing-rtx\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis works: both have substantial risk-factor sections in the corpus.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EBonus Exercises\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExercise\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPrompt\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAS Feature Demonstrated\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EM&A details\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"List every M&A transaction filed on Feb 3, 2025. Extract acquirer, target, and deal summary."\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAI_EXTRACT (structured extraction)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERisk themes\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What are the most common risk themes across the 10-K annual reports filed today?"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAI_AGG (deduplication/clustering)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull breakdown\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Break down all filings filed today by industry sector and sentiment."\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Analyst (pure structured)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFile:\u003C/strong\u003E \u003Ccode\u003Esql/99_teardown.sql\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EWhen you're done exploring, run the teardown script to remove all objects:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\n-- Drop database (removes all tables, views, UDFs, procedures)\nDROP DATABASE IF EXISTS SEC_FILINGS;\n\n-- Drop warehouse\nDROP WAREHOUSE IF EXISTS FILING_WH;\n\n-- Drop external access integration\nDROP INTEGRATION IF EXISTS SEC_EDGAR_EAI;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYou have built an Analytical Search agent that answers precise analytical questions over hundreds of SEC filing passages — going far beyond what traditional RAG can do.\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You Built\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Cstrong\u003EData Pipeline\u003C/strong\u003E ingesting SEC EDGAR filings with ticker enrichment, section-aware chunking, and AI signal extraction\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003EMulti-Index Cortex Search service\u003C/strong\u003E with text and vector indexes over 3,400+ filing chunks enabling Analytical Search\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ESemantic View\u003C/strong\u003E enabling natural-language SQL analytics over structured filing signals\u003C/li\u003E\u003Cli\u003EValidated exercises demonstrating counting, exhaustive listing, hybrid queries, auto-routing, and honest refusal\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EFrom Quickstart to Production\u003C/h3\u003E\n","\u003Cp\u003EThis quickstart is not just a demo — it produces a near-production-ready, end-to-end solution you can put to real use:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EHistorical depth\u003C/strong\u003E — Widen the date range to ingest weeks, months, or a full year of filings for richer analytical insights\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EParallel backfills\u003C/strong\u003E — For large date ranges, create multiple Snowflake Tasks calling \u003Ccode\u003ERUN_PIPELINE()\u003C/code\u003E with non-overlapping monthly ranges to process months concurrently\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EContinuous ingestion\u003C/strong\u003E — Schedule a daily Snowflake Task (e.g., \u003Ccode\u003ESCHEDULE = 'USING CRON 0 7 * * * America/New_York'\u003C/code\u003E) to call \u003Ccode\u003ERUN_PIPELINE()\u003C/code\u003E for the current day, keeping your corpus fresh with no manual intervention\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAutomatic growth\u003C/strong\u003E — As new data flows in, the Cortex Search service and Semantic View automatically incorporate new filings — no additional configuration needed\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey Takeaways\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAnalytical Search = Retrieve + Compute\u003C/strong\u003E — it goes beyond RAG's "retrieve and summarize" to deliver SQL-level analytical rigor over unstructured data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAI_FILTER understands meaning, not keywords\u003C/strong\u003E — it distinguishes "actual cybersecurity incident" from generic "we maintain security programs"\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAuto-routing keeps simple questions cheap\u003C/strong\u003E — per-document lookups skip the analytical path entirely\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EHybrid answers combine structured + unstructured\u003C/strong\u003E — Cortex Analyst for counts, Cortex Search for evidence, in one turn\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EThe agent refuses honestly when data is missing\u003C/strong\u003E — trust over completeness, with constructive alternatives\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-analytical-search\"\u003EAnalytical Search Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-manage\"\u003ECortex Agents — Configure and Interact\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-cortex-search\"\u003ECortex Search Service DDL\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-agent\"\u003ECREATE AGENT Reference\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_filter\"\u003EAI_FILTER Function\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_extract\"\u003EAI_EXTRACT Function\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_agg\"\u003EAI_AGG Function\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/overview\"\u003ESemantic Views\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/main/site/sfguides/src/analytical-search-over-sec-filings-with-snowflake-cowork\"\u003ESource Code Repository\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Base Quickstart CF",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\n![e2e-architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/e2e-architecture.png)\n\n## Overview\n\n**Answer analytical questions  over thousands of unstructured SEC filings using Cortex Agents with Analytical Search.**\n\nTraditional RAG retrieves 10–50 passages and asks an LLM to summarize. That works for single-document lookups, but fails on questions that require processing hundreds of filings: \"How many companies disclosed a cybersecurity incident?\" or \"List every M&A deal filed this week.\" Analytical Search solves this by combining semantic search, AI functions, and SQL into one orchestrated loop.\n\nIn this quickstart you will ingest SEC EDGAR filings directly from the SEC's public EDGAR archive using Snowflake's External Access Integration. A stored procedure fetches daily filing archives over HTTPS, parses metadata and document content, enriches filings with stock tickers and industry classification, chunks them into searchable passages, and extracts structured AI signals. \n\nYou will then build a multi-index Cortex Search service, create a Semantic View for structured analytics, and deploy an Analytical Search agent in Snowflake CoWork — then ask it questions that no standard RAG system can answer. The result is a near-production-ready, end-to-end solution that you can easily reuse: expand the date range for historical depth, schedule ongoing ingestion with Snowflake Tasks, and the agent grows with the data automatically.\n\n### What You'll Learn\n\n-   Why traditional RAG breaks on analytical questions (top-k ceiling, no compute, no filters)\n-   How Analytical Search combines Cortex Search with AI_FILTER, AI_EXTRACT, and AI_AGG\n-   How auto-routing keeps simple questions cheap while powering analytical ones\n-   How adaptive depth retrieves exactly as much data as the question requires\n\n### What You'll Build\n\n-   A **Data Pipeline** that ingests, enriches, chunks, and extracts signals from SEC EDGAR filings\n-   A **Cortex Search service** with text + vector indexes over 3,400+ filing chunks\n-   A **Semantic View** for structured analytics (counts, sentiment breakdowns, sector comparisons)\n-   An **Analytical Search agent** wired to all Cortex Search and Semantic View tools, registered in Snowflake CoWork\n-   Validated analytical queries demonstrating counting, listing, hybrid, and auto-routing capabilities\n\n### What You'll Need\n\n-   Snowflake account with `ACCOUNTADMIN` role\n-   Cortex enabled in you Snowflake account \n\n### Source Code\n\nAll SQL files are available in the [source code repository](https://github.com/Snowflake-Labs/sfquickstarts/tree/main/site/sfguides/src/analytical-search-over-sec-filings-with-snowflake-cowork/sql):\n\n| File | Purpose |\n|------|---------|\n| `sql/00_env_setup.sql` | Environment setup |\n| `sql/01_pipeline.sql` | Full ingestion, enrichment, chunking, and signal extraction pipeline |\n| `sql/02_create_search_service.sql` | Multi-index Cortex Search service |\n| `sql/03_create_semantic_view.sql` | Semantic View for Cortex Analyst |\n| `sql/04_deploy_agent.sql` | Agent deployment + CoWork registration |\n| `sql/99_teardown.sql` | Full cleanup |\n\n\u003C!-- ------------------------ --\u003E\n## Why RAG Falls Short\n\nStandard RAG has three fundamental limitations that make it unsuitable for analytical work over large document collections:\n\n### Top-K Ceiling\n\nRAG retrieves just 10–50 results to fit in the LLM context window. Extending beyond this typically reduces accuracy and increases cost. You cannot count 36 leadership changes by looking at 5 examples.\n\n### Semantic Only\n\nNo structural filters over attributes such as date, status, or region. \"Contextual chunking\" can help, but doesn't allow for targeted search queries with precise date ranges or category filters.\n\n### No Compute\nCounts and sums are mostly hallucinated, not calculated. The LLM's worldview is limited to the handful of passages in its context — it estimates rather than computes.\n\n### Why Agentic Search Isn't Enough Either\n\nA natural evolution from standard RAG is **agentic search**: an AI agent searches, reads, reasons, reformulates its query, and searches again in a loop. By decomposing a problem and chasing missing evidence across multiple turns, agentic search drastically improves recall for complex document QA.\n\nBut at its core, agentic search is still **search-and-summarize in a reasoning loop**. The agent takes more turns to find better evidence, yet the final answer is still synthesized from a relatively small, sampled evidence set. It cannot perform true macro-analysis — there is no grouping, counting, or SQL-level aggregation happening. Ask it \"how many filings mention cybersecurity risks?\" and it will find more examples than basic RAG, but it still cannot give you a precise count because it never processes the full result set computationally.\n\nThe evolution looks like this:\n\n| Stage | Approach | Limitation |\n|-------|----------|-----------|\n| **Standard RAG** | Retrieve top-k, summarize | Misses the long tail; no compute |\n| **Agentic Search** | Loop: search → read → reformulate → search again | Better recall, but still sampling — no true aggregation |\n| **Analytical Search** | Retrieve adaptively + compute with AI functions + SQL | Treats documents as a table: filter, aggregate, count, compare |\n\n### Why Bigger Context Windows Don't Solve This\n\nA popular argument says RAG is dead because LLMs can now ingest a million tokens — so just load the documents and let the model reason end-to-end. This solves the context window constraint, but it doesn't give the model a database.\n\nLoading thousands of documents into a single prompt fails for three reasons:\n\n1. **Cost** — It is orders of magnitude more expensive than targeted retrieval followed by focused computation.\n2. **No auditable trace** — The model produces a final answer with no inspectable execution path. You cannot verify what was searched, filtered, counted, or excluded.\n3. **No SQL-level precision** — LLMs estimate; they don't compute. Grouping, counting, ranking, and calculating deltas require an actual execution engine, not a language model approximating arithmetic over raw text.\n\nAnalytical Search takes the opposite approach: use retrieval to prune, then hand off to AI functions and SQL for precise computation — getting both scalability and rigor.\n\n### Why Coding Agents Fall Short\n\nCoding agents offer another alternative: grep through documents as local files, load matches into the agent's context, and write code to reason over the results. This is a strong agentic baseline — the agent can improvise an analytical loop (search, parse, aggregate) on every question and partially succeed.\n\nHowever, coding agents don't scale once questions demand corpus-wide aggregation. On analytical benchmarks, they trail Analytical Search by 10–45% because:\n\n- They lack adaptive depth — they either over-fetch (expensive) or under-fetch (incomplete)\n- They have no built-in semantic operators (AI_FILTER, AI_EXTRACT) optimized for document-level classification\n- Each question requires re-inventing the analytical workflow from scratch rather than executing a systematic, optimized pipeline\n\nA system designed around the analytical loop — with retrieval, semantic classification, and SQL as first-class stages — does the work more reliably and at lower cost than ad-hoc coding for each question.\n\n### Query Types That Break Traditional RAG\n\nHigh-value enterprise queries requiring exhaustiveness, aggregates, or temporal analysis:\n\n| Query Type | Example | Why RAG Fails |\n|------------|---------|---------------|\n| **List queries with multi-aspect filters** | \"List all companies that disclosed a cybersecurity incident this month\" | Top-k misses the long tail |\n| **Aggregates** | \"What percentage of filings mention supply chain risk in Finance vs Technology?\" | Cannot count from a sample |\n| **Temporal queries** | \"What new risk themes emerged this quarter compared to last?\" | No date filtering, no comparison logic |\n\n### Traditional RAG vs Analytical Search\n\nConsider the question: *\"How many filings filed on Feb 3, 2025 mention cybersecurity risks?\"*\n\n**Traditional RAG approach:**\n1. One search query with limit=10: `search(\"cybersecurity risks\")`\n2. Feeds 10 results back to the LLM to summarize\n\n\u003E Answer: \"Based on the provided documents, several companies mention cybersecurity risks, including Boeing and GE. However, there may be more that were not analyzed.\"\n\n→ Imprecise answer based on a sample. No actual count.\n\n**Analytical Search approach:**\n1. Structured search\n2. AI_FILTER to classify which chunks discuss *actual* cybersecurity risks (not generic boilerplate)\n3. COUNT(DISTINCT ACCESSION_NO) over the filtered table\n\n\u003E Answer: \"5 filings filed on Feb 3, 2025 mention substantive cybersecurity risks. The companies are: [complete list with citations].\"\n\n![cybersecurity-result](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/cybersecurity-result.png)\n\n→ Quantitative, exhaustive answer with precise filtering and SQL-level computation.\n\n\u003C!-- ------------------------ --\u003E\n## How Analytical Search Works\n\n![analytical-search-intro](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/analytical-search-intro.png)\n\nAnalytical Search is an orchestration capability in Cortex Agents that enables analytical queries over large document collections. It operates in two layers:\n\n### Layer 1: Search to Prune\n\nCortex Search narrows the full corpus to a relevant candidate set — finding documents about a specific topic, isolating records with a particular attribute, or filtering to a date range. This happens without scanning every document with a large model.\n\n**Adaptive depth** controls how far to search. Rather than using a fixed top-k limit, the agent dynamically adjusts search depth based on the relevance of results. A fixed top-k gets two things wrong: too shallow (the missing item is often the data point that changes the answer), or too deep (the user asks for k=1,000 but only a few dozen documents are relevant — the rest is wasted compute).\n\nAdaptive depth makes the cutoff a function of the data, not a guessed parameter, and works in two phases:\n\n**Phase 1 — Bound the relevant region.** The system fetches an initial batch of results and uses a fast LLM to judge small samples at the head and tail of the ranked list. If the tail is still relevant, the system extends the fetch limit and rejudges the new tail — repeating until the tail goes off-topic or a hard ceiling is reached. If even the top results are irrelevant, the search returns nothing rather than feed AI functions material that would only generate noise.\n\n**Phase 2 — Find the exact cutoff.** Once the relevant region is bounded, the system binary-searches inside it, LLM-judging a sample around the midpoint and tightening the window to land on the precise boundary in a handful of rounds.\n\nThe result: a narrow fact question stays shallow; a trend across a broad corpus goes deep; a question against a sparse corpus stops early — all without any parameter tuning.\n\n\u003E **Cost note:** Adaptive depth optimizes spend in both directions. Better coverage reduces wasted downstream AI function calls that would produce wrong answers. Better restraint avoids paying to extract and classify documents that would not have answered the question in the first place. AI functions are powerful but not free — every unnecessary call adds latency and dollars. Adaptive depth is the layer that decides how many of those calls are worth making.\n\n### Layer 2: AI Functions and SQL to Analyze\n\nOnce the corpus is pruned, the agent applies semantic operators directly on the result set:\n\n| Function | What It Does | Example |\n|----------|-------------|--------|\n| **AI_FILTER** | Semantic yes/no classification per row | \"Is this about an actual cybersecurity incident (not generic risk boilerplate)?\" |\n| **AI_EXTRACT** | Pull structured fields from unstructured text | Extract person name, role, and whether it's a departure or appointment |\n| **AI_AGG** | Deduplicate, cluster, and summarize across rows | Collapse 200 risk phrases into top-10 categories with counts |\n| **SQL** | Group, count, join, rank, trend, calculate | COUNT BY sector, percentage breakdowns, temporal comparisons |\n\n### Auto-Routing\n\nThe agent classifies query intent at runtime:\n- **Simple, single-passage questions** → standard RAG path (no persist, no AI functions, low cost)\n- **Corpus-wide analytical questions** → full Analytical Search loop\n\nYou don't need to specify which mode to use. The agent decides automatically.\n\n### Planning Mode\n\nBefore executing analytical queries, the agent generates a clear execution plan and presents it for review. This lets you verify the logical steps before any data is processed. Example of an execution planned proposed for a review below.\n\n![cybersecurity-plan](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/cybersecurity-plan.png)\n\n\u003C!-- ------------------------ --\u003E\n## Environment Setup\n\n\u003E **File:** `sql/00_env_setup.sql`\n\nOpen a SQL file in Snowflake Workspace and run the following to create the infrastructure:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\n-- Configuration — edit these values\nSET config_database   = 'SEC_FILINGS';\nSET config_schema     = 'FILING_DATA';\nSET config_warehouse  = 'FILING_WH';\nSET config_user_agent = 'YourOrg SEC-Filing-Demo your_name@company.com';\nSET config_start_date = '2025-02-03';  -- single day for quickstart\nSET config_end_date   = '2025-02-03';\n\n-- Create database and schema\nCREATE DATABASE IF NOT EXISTS IDENTIFIER($config_database);\nUSE DATABASE IDENTIFIER($config_database);\nCREATE SCHEMA IF NOT EXISTS IDENTIFIER($config_schema);\nUSE SCHEMA IDENTIFIER($config_schema);\n\n-- Create a dedicated warehouse\nCREATE WAREHOUSE IF NOT EXISTS IDENTIFIER($config_warehouse)\n    WAREHOUSE_SIZE = 'SMALL'\n    AUTO_SUSPEND = 60\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE\n    COMMENT = 'SEC pipeline: dynamically resized by RUN_PIPELINE()';\nUSE WAREHOUSE IDENTIFIER($config_warehouse);\n\n-- Network access for SEC EDGAR\nCREATE OR REPLACE NETWORK RULE SEC_EDGAR_NETWORK_RULE\n    MODE = EGRESS\n    TYPE = HOST_PORT\n    VALUE_LIST = ('www.sec.gov:443', 'data.sec.gov:443', 'efts.sec.gov:443');\n\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION SEC_EDGAR_EAI\n    ALLOWED_NETWORK_RULES = (SEC_EDGAR_NETWORK_RULE)\n    ENABLED = TRUE;\n```\n\n\u003E **NOTE:** SEC EDGAR requires a valid `User-Agent` header with your organization name and contact email. Edit `config_user_agent` above — this value is passed as a parameter to `RUN_PIPELINE()`, which forwards it to all HTTP calls. You only need to set it in this one place. See [SEC EDGAR access policies](https://www.sec.gov/os/accessing-edgar-data) for details.\n\n\u003C!-- ------------------------ --\u003E\n## Build the Data Pipeline\n\n\u003E **File:** `sql/01_pipeline.sql`\n\nThe data pipeline ingests SEC EDGAR filings, enriches them with tickers and industry classification, chunks them for search, and extracts AI signals.\n\n### Core Tables\n\nThe pipeline creates four main tables:\n\n| Table | Purpose | Key Columns |\n|-------|---------|-------------|\n| `FILING_INDEX` | Filing metadata registry | ACCESSION_NO, CIK, COMPANY_NAME, FORM_TYPE, FILED_AT, TICKER, INDUSTRY_SECTOR |\n| `FILING_CONTENT` | Raw filing text | ACCESSION_NO, CONTENT_TEXT, PARSE_STATUS, SIGNAL_STATUS |\n| `FILING_CHUNKS` | Searchable passages | CHUNK_ID, CHUNK_TEXT, SECTION_NAME, ~800 chars avg |\n| `FILING_SIGNALS` | AI-extracted structured signals | SIGNAL_ID, EVENT_TYPE, SENTIMENT, REVENUE, KEY_METRICS |\n\n### Running the Pipeline\n\nExecute the entire `sql/01_pipeline.sql` file in Snowflake workspace. \n\n![pipeline-script](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/pipeline-script.png)\n\n```sql\n-- The last command in the script calls the pipeline:\nCALL RUN_PIPELINE('2025-02-03', '2025-02-03', 'YourOrg SEC-Filing-Demo your_name@company.com');\n```\n\nThe pipeline executes four phases automatically:\n\n| Phase | What It Does | Warehouse Size |\n|-------|-------------|---------------|\n| 1. Ingest | Downloads daily EDGAR archives, parses filing metadata and content | LARGE (SNOWPARK-OPTIMIZED) |\n| 2. Enrich | Resolves stock tickers via SEC company search, maps SIC codes to industry sectors | SMALL |\n| 3. Chunk | Splits filings into searchable passages by section (Risk Factors, MD&A, Financial Statements, etc.) | SMALL |\n| 4. Extract | AI extracts structured signals: sentiment, event type, key metrics, forward guidance | SMALL |\n\n\u003E **NOTE:** The pipeline dynamically resizes the warehouse between phases. **Total runtime for a single day: ~3-5 minutes**.\n\n\u003E **Scaling Beyond One Day**\n\u003E\n\u003E This quickstart ingests a single day for speed (~3-5 minutes). To load more historical data, simply widen `config_start_date` and `config_end_date` in `00_env_setup.sql` — the pipeline handles any date range up to a full year (runtime scales at ~3-5 minutes per business day). For large backfills (e.g., a full year), you can parallelize by creating multiple Snowflake Tasks that each call `RUN_PIPELINE()` with non-overlapping monthly date ranges (e.g., Jan, Feb, Mar…), allowing months to process concurrently.\n\u003E\n\u003E For continuous automated ingestion, schedule a single Snowflake Task with a daily CRON to call `RUN_PIPELINE()` for the current day — new filings flow into the Cortex Search service and Semantic View automatically with no manual intervention.\n\n### Verify the Pipeline Output\n\n```sql\n-- Check corpus statistics\nSELECT\n    COUNT(*)                       AS total_chunks,\n    COUNT(DISTINCT ACCESSION_NO)   AS distinct_filings,\n    COUNT(DISTINCT TICKER)         AS distinct_tickers,\n    COUNT(DISTINCT FORM_TYPE)      AS distinct_form_types,\n    MIN(FILED_AT)                  AS earliest_filing,\n    MAX(FILED_AT)                  AS latest_filing,\n    AVG(LENGTH(CHUNK_TEXT))::INT   AS avg_chunk_chars\nFROM FILING_CHUNKS\nWHERE CHUNK_TEXT IS NOT NULL;\n```\n\nExpected output for Feb 3, 2025:\n\n| Metric | Value |\n|--------|-------|\n| Total chunks | ~3,453 |\n| Distinct filings | ~259 |\n| Distinct tickers | ~197 |\n| Form types | 10-K, 10-Q, 8-K, 8-K/A |\n| Avg chunk chars | ~1,250 |\n\n\u003C!-- ------------------------ --\u003E\n## Create the Cortex Search Service\n\n\u003E **File:** `sql/02_create_search_service.sql`\n\n\u003E **NOTE:** If you already have a Cortex Search service, you can reuse it; you don't need to create a new service specifically for analytical search. If your agent already has a Cortex Search tool configured, you don't need to add another one.\n\n### Create the Service\nIn this example we will be creating Cortex Search service from scratch.\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nUSE WAREHOUSE FILING_WH;\n\nCREATE OR REPLACE CORTEX SEARCH SERVICE SEC_FILINGS.FILING_DATA.SEC_FILING_SEARCH\n    TEXT INDEXES CHUNK_TEXT, CHUNK_ID, ACCESSION_NO, SECTION_NAME, COMPANY_NAME\n    VECTOR INDEXES CHUNK_TEXT (model='snowflake-arctic-embed-l-v2.0')\n    ATTRIBUTES COMPANY_NAME, TICKER, FORM_TYPE, SECTION_NAME, FILED_AT,\n               PERIOD_OF_REPORT, INDUSTRY_SECTOR, INDUSTRY_TITLE, CHUNK_ID, ACCESSION_NO\n    WAREHOUSE = FILING_WH\n    TARGET_LAG = '1 day'\n    COMMENT = 'SEC filing search - multi-index with text and vector'\nAS (\n    SELECT\n        CHUNK_ID, CHUNK_TEXT, ACCESSION_NO, COMPANY_NAME, TICKER, FORM_TYPE,\n        SECTION_NAME,\n        TO_VARCHAR(FILED_AT, 'YYYY-MM-DD') AS FILED_AT,\n        TO_VARCHAR(PERIOD_OF_REPORT, 'YYYY-MM-DD') AS PERIOD_OF_REPORT,\n        COALESCE(INDUSTRY_SECTOR, 'Other') AS INDUSTRY_SECTOR,\n        INDUSTRY_TITLE\n    FROM SEC_FILINGS.FILING_DATA.FILING_CHUNKS\n    WHERE CHUNK_TEXT IS NOT NULL AND LENGTH(CHUNK_TEXT) \u003E 100\n);\n```\n\n### Why Each Design Choice Matters for Analytical Search\n\n| Config Element | Why It Matters |\n|---------------|----------------|\n| `TEXT INDEXES` on 5 columns | Enables keyword search on content, company names, section names — the agent searches on meaning AND exact names |\n| `VECTOR INDEXES` with Arctic embed | Semantic similarity — \"cybersecurity risks\" matches \"unauthorized access to our systems\" even with no keyword overlap. `snowflake-arctic-embed-l-v2.0` is the recommended model for analytical search |\n| `ATTRIBUTES` (10 columns) | Become **filterable** and **returnable** in search results. The agent can filter by `FORM_TYPE='10-K'` or `INDUSTRY_SECTOR='Finance'` server-side |\n| `TARGET_LAG = '1 day'` | Refresh frequency. For a demo corpus that doesn't change, this is sufficient |\n\n\u003E **NOTE:** The search service indexes ~3,283 of the 3,453 total chunks. The `WHERE LENGTH(CHUNK_TEXT) \u003E 100` filter excludes very short chunks (section headers, boilerplate) that would add noise to search results.\n\n\u003C!-- ------------------------ --\u003E\n## Create the Semantic View\n\n\u003E **File:** `sql/03_create_semantic_view.sql`\n\nThe Semantic View is required for Cortex Analyst tool execution. It allows the agent to answer counting and aggregation questions with SQL precision.\n\n### Create the Semantic View\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nUSE DATABASE SEC_FILINGS;\nUSE SCHEMA FILING_DATA;\nUSE WAREHOUSE FILING_WH;\n\nCREATE OR REPLACE SEMANTIC VIEW SEC_FILING_ANALYTICS\n  TABLES (\n    signals AS FILING_SIGNALS\n      PRIMARY KEY (SIGNAL_ID)\n      WITH SYNONYMS = ('investment signals', 'filing signals', 'EDGAR signals', 'SEC filings')\n      COMMENT = 'AI-extracted investment signals from SEC EDGAR filings.',\n    meta AS FILING_INDEX\n      PRIMARY KEY (ACCESSION_NO)\n      WITH SYNONYMS = ('filing metadata', 'EDGAR index', 'filing registry')\n      COMMENT = 'SEC EDGAR filing metadata: accession numbers, CIKs, filing URLs, dates'\n  )\n  RELATIONSHIPS (\n    signals_to_meta AS signals(ACCESSION_NO) REFERENCES meta(ACCESSION_NO)\n  )\n  FACTS (\n    signals.accession_no AS signals.ACCESSION_NO\n      WITH SYNONYMS = ('accession number', 'filing id')\n      COMMENT = 'EDGAR accession number uniquely identifying the filing',\n    signals.revenue AS signals.REVENUE\n      WITH SYNONYMS = ('total revenue', 'sales', 'top line')\n      COMMENT = 'Revenue in millions USD. NULL if not extractable.',\n    signals.net_income AS signals.NET_INCOME\n      WITH SYNONYMS = ('net income', 'profit', 'bottom line')\n      COMMENT = 'Net income figure extracted from filing.',\n    signals.eps AS signals.EPS\n      WITH SYNONYMS = ('earnings per share', 'diluted EPS')\n      COMMENT = 'Normalized EPS value.',\n    signals.yoy_change AS signals.YOY_CHANGE\n      WITH SYNONYMS = ('year over year', 'YoY growth', 'growth rate')\n      COMMENT = 'Year-over-year change percentage.',\n    signals.forward_guidance AS signals.FORWARD_GUIDANCE\n      WITH SYNONYMS = ('guidance', 'outlook', 'forecast')\n      COMMENT = 'Forward-looking financial guidance from MD&A.'\n  )\n  DIMENSIONS (\n    signals.company_name AS signals.COMPANY_NAME\n      WITH SYNONYMS = ('company', 'filer', 'issuer')\n      COMMENT = 'Company that filed the SEC document',\n    signals.ticker AS signals.TICKER\n      WITH SYNONYMS = ('stock ticker', 'symbol')\n      COMMENT = 'Stock ticker symbol. May be NULL for non-public filers.',\n    signals.form_type AS signals.FORM_TYPE\n      WITH SYNONYMS = ('filing type', 'SEC form')\n      COMMENT = '10-K (annual), 10-Q (quarterly), 8-K (current report)',\n    signals.event_type AS COALESCE(signals.EVENT_TYPE_NORMALIZED, signals.EVENT_TYPE)\n      WITH SYNONYMS = ('event', 'signal type', 'event classification')\n      COMMENT = 'AI-classified event type: Earnings, M&A, Leadership Change, Risk Disclosure, Guidance Update, Regulatory, Capital Markets, Bankruptcy, Annual Report, Quarterly Report, Current Report, Other.',\n    signals.sentiment AS signals.SENTIMENT\n      WITH SYNONYMS = ('tone', 'filing sentiment')\n      COMMENT = 'AI-assessed sentiment: POSITIVE, NEGATIVE, NEUTRAL, MIXED.',\n    signals.industry_sector AS COALESCE(signals.INDUSTRY_SECTOR, 'Other')\n      WITH SYNONYMS = ('sector', 'industry')\n      COMMENT = 'SEC Office-based industry sector: Technology, Life Sciences, Finance, Real Estate & Construction, Energy & Transportation, Manufacturing, Trade & Services, Other.',\n    signals.industry_title AS signals.INDUSTRY_TITLE\n      WITH SYNONYMS = ('specific industry', 'sub-sector')\n      COMMENT = 'Specific SEC industry title.',\n    signals.is_amendment AS signals.IS_AMENDMENT\n      WITH SYNONYMS = ('amendment', 'restated')\n      COMMENT = 'TRUE if this is an amended filing.',\n    meta.cik AS meta.CIK\n      WITH SYNONYMS = ('SEC CIK', 'central index key')\n      COMMENT = 'SEC Central Index Key',\n    signals.signal_date AS signals.SIGNAL_DATE\n      WITH SYNONYMS = ('filing date', 'date filed', 'when filed')\n      COMMENT = 'The date the SEC received the filing.',\n    signals.period_of_report AS signals.PERIOD_OF_REPORT\n      WITH SYNONYMS = ('fiscal period', 'report period', 'period end')\n      COMMENT = 'Fiscal period end date the filing covers.'\n  )\n  METRICS (\n    signals.filing_count AS COUNT(signals.SIGNAL_ID)\n      WITH SYNONYMS = ('number of filings', 'total filings', 'how many filings')\n      COMMENT = 'Total number of filings matching filters',\n    signals.positive_signals AS COUNT(CASE WHEN signals.SENTIMENT = 'POSITIVE' THEN 1 END)\n      WITH SYNONYMS = ('positive filings', 'bullish signals')\n      COMMENT = 'Count of filings with positive sentiment',\n    signals.negative_signals AS COUNT(CASE WHEN signals.SENTIMENT = 'NEGATIVE' THEN 1 END)\n      WITH SYNONYMS = ('negative filings', 'bearish signals')\n      COMMENT = 'Count of filings with negative sentiment',\n    signals.ma_count AS COUNT(CASE WHEN signals.EVENT_TYPE = 'M&A' THEN 1 END)\n      WITH SYNONYMS = ('merger filings', 'M&A events', 'deals')\n      COMMENT = 'Count of merger and acquisition events',\n    signals.leadership_change_count AS COUNT(CASE WHEN signals.EVENT_TYPE = 'Leadership Change' THEN 1 END)\n      WITH SYNONYMS = ('leadership events', 'management changes')\n      COMMENT = 'Count of leadership change events',\n    signals.negative_sentiment_pct AS\n      ROUND(100.0 * COUNT(CASE WHEN signals.SENTIMENT = 'NEGATIVE' THEN 1 END)\n            / NULLIF(COUNT(signals.SIGNAL_ID), 0), 2)\n      WITH SYNONYMS = ('negative rate', 'percent negative')\n      COMMENT = 'Percentage of filings with negative sentiment (0-100 scale)'\n  )\n  COMMENT = 'Investment signal analytics over SEC EDGAR filing corpus.'\n  AI_SQL_GENERATION 'This semantic view covers SEC EDGAR filings. SIGNAL_DATE is the authoritative filing timestamp. EVENT_TYPE and SENTIMENT are AI-extracted.';\n```\n\n\u003C!-- ------------------------ --\u003E\n## Deploy the Analytical Search Agent\n\n\u003E **File:** `sql/04_deploy_agent.sql`\n\n### Create the Agent\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nUSE DATABASE SEC_FILINGS;\nUSE SCHEMA FILING_DATA;\n\n-- Ensure Snowflake Intelligence (CoWork) object exists\nCREATE SNOWFLAKE INTELLIGENCE IF NOT EXISTS SNOWFLAKE_INTELLIGENCE_OBJECT_DEFAULT;\n\n-- Deploy the Analytical Search agent (co-located with search service + semantic view)\nCREATE OR REPLACE AGENT SEC_FILINGS.FILING_DATA.SEC_ANALYTICAL_SEARCH_AGENT\nCOMMENT = 'SEC filing research agent v7 - FILED_AT/PERIOD_OF_REPORT are now native DATE in the search service, range filters supported.'\nFROM SPECIFICATION $$\n{\n  \"models\": { \"orchestration\": \"claude-opus-4-7\" },\n  \"orchestration\": { \"budget\": { \"seconds\": 600, \"tokens\": 200000 } },\n  \"instructions\": {\n    \"orchestration\": \"You are SEC Filing Analyst, an investment-research agent over a corpus of SEC EDGAR 10-K, 10-Q, 8-K filings.\\n\\nFor questions about counts, comparisons, exhaustive lists, or cross-filing patterns:\\n1. Search broadly using multiple relevant queries — use synonyms and related phrasings.\\n2. Return a clear answer with company name, form type, filing date, and the specific evidence from each filing chunk.\"\n  },\n  \"tools\": [\n    {\n      \"tool_spec\": {\n        \"type\": \"cortex_search\",\n        \"name\": \"filing_semantic_search\",\n        \"description\": \"Multi-index Cortex Search over SEC filing chunks.\"\n      }\n    },\n    {\n      \"tool_spec\": {\n        \"type\": \"cortex_analyst_text_to_sql\",\n        \"name\": \"filing_analyst\",\n        \"description\": \"Structured analytics over pre-extracted filing signals (sector counts, sentiment percentages, signal trends).\"\n      }\n    }\n  ],\n  \"tool_resources\": {\n    \"filing_semantic_search\": {\n      \"name\": \"SEC_FILINGS.FILING_DATA.SEC_FILING_SEARCH\",\n      \"search_service\": \"SEC_FILINGS.FILING_DATA.SEC_FILING_SEARCH\",\n      \"database_schema\": \"SEC_FILINGS.FILING_DATA\",\n      \"is_multi_index\": true,\n      \"max_results\": 1000,\n      \"id_column\": \"CHUNK_ID\",\n      \"title_column\": \"COMPANY_NAME\",\n      \"base_table\": \"SEC_FILINGS.FILING_DATA.FILING_CHUNKS\",\n      \"base_table_columns\": [\n        \"CHUNK_ID\",\"CHUNK_TEXT\",\"ACCESSION_NO\",\"COMPANY_NAME\",\"TICKER\",\n        \"FORM_TYPE\",\"SECTION_NAME\",\"FILED_AT\",\"PERIOD_OF_REPORT\",\n        \"INDUSTRY_SECTOR\",\"INDUSTRY_TITLE\"\n      ],\n      \"execution_environment\": {\"type\": \"warehouse\", \"warehouse\": \"FILING_WH\"},\n      \"columns_and_descriptions\": {\n        \"CHUNK_ID\":         {\"description\": \"Chunk primary key\", \"type\": \"string\", \"searchable\": true,  \"filterable\": true},\n        \"CHUNK_TEXT\":       {\"description\": \"Full filing passage text. Search here for filing content.\", \"type\": \"string\", \"searchable\": true, \"filterable\": false},\n        \"COMPANY_NAME\":     {\"description\": \"Filer company name. Search here for specific companies.\", \"type\": \"string\", \"searchable\": true, \"filterable\": true},\n        \"TICKER\":           {\"description\": \"Stock ticker (e.g. NVDA, MSFT, GOOGL). Use for searching or filtering filings by specific public companies. Approximately 85% of filings have a populated ticker.\", \"type\": \"string\", \"searchable\": true, \"filterable\": true},\n        \"FORM_TYPE\":        {\"description\": \"10-K, 10-K/A, 10-Q, 10-Q/A, 8-K, 8-K/A.\", \"type\": \"string\", \"searchable\": false, \"filterable\": true},\n        \"SECTION_NAME\":     {\"description\": \"Filing section (Risk Factors, MD&A, Item 1.01, etc.).\", \"type\": \"string\", \"searchable\": true, \"filterable\": true},\n        \"FILED_AT\":         {\"description\": \"SEC filing date (DATE). Supports @gte / @lte range filters.\", \"type\": \"date\", \"searchable\": false, \"filterable\": true},\n        \"PERIOD_OF_REPORT\": {\"description\": \"Fiscal period end date (DATE). Supports @gte / @lte range filters.\", \"type\": \"date\", \"searchable\": false, \"filterable\": true},\n        \"INDUSTRY_SECTOR\":  {\"description\": \"SIC-based sector grouping.\", \"type\": \"string\", \"searchable\": false, \"filterable\": true},\n        \"INDUSTRY_TITLE\":   {\"description\": \"Detailed SIC industry title.\", \"type\": \"string\", \"searchable\": false, \"filterable\": true},\n        \"ACCESSION_NO\":     {\"description\": \"SEC accession number, unique per filing.\", \"type\": \"string\", \"searchable\": false, \"filterable\": true}\n      }\n    },\n    \"filing_analyst\": {\n      \"semantic_view\": \"SEC_FILINGS.FILING_DATA.SEC_FILING_ANALYTICS\",\n      \"execution_environment\": {\"type\": \"warehouse\", \"warehouse\": \"FILING_WH\"}\n    }\n  }\n}\n$$;\n\n-- Register in Snowflake CoWork\nALTER SNOWFLAKE INTELLIGENCE SNOWFLAKE_INTELLIGENCE_OBJECT_DEFAULT\n    ADD AGENT SEC_FILINGS.FILING_DATA.SEC_ANALYTICAL_SEARCH_AGENT;\n```\n### Tools and Their Roles\n\n| Tool | Type | Role in Analytical Search |\n|------|------|---------------------------|\n| `filing_semantic_search` | `cortex_search` | Unstructured data search, prerequisite for Analytical Search  |\n| `filing_analyst` | `cortex_analyst_text_to_sql` | Structured analytics via the Semantic View (counts, breakdowns, percentages) |\n\n\u003E **NOTE:** Column descriptions are the single most impactful thing you can do to improve analytical search quality. The agent uses them to decide which columns to filter on, how to interpret values, and how to frame AI_FILTER and AI_EXTRACT calls. For each column, describe:\n\u003E - What it contains and its expected format or value range\n\u003E - Sample values or enumerations (e.g., `\"Values: 10-K, 10-Q, 8-K, 8-K/A\"`)\n\u003E - Whether it's suitable for filtering, searching, or extraction\n\u003E - Any relationships to other columns\n\u003E\n\u003E Columns without descriptions are harder for the agent to use effectively, especially filterable attributes that determine how the search is scoped.\n\n\u003E **NOTE:** Snowflake recommends setting `max_results` to 1,000 for analytical search. This gives the agent enough breadth to surface the full relevant set of documents. Adaptive depth limits actual compute to what the question requires — you won't pay for 1,000 AI function calls on a narrow question.\n\n\u003C!-- ------------------------ --\u003E\n## Analytical Search in Action\n\nIn Snowflake UI click Cortex AI and open **Snowflake CoWork** and select the `SEC_ANALYTICAL_SEARCH_AGENT`. Try each exercise below to see different Analytical Search capabilities in action.\n\n![cowork](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/cowork.png)\n\n\u003E **Performance expectations:** Simple RAG questions typically complete in ~30 seconds. Analytical search workflows take **2–6 minutes** for most questions. Complex analyses over large corpora (thousands of documents with multi-step extraction and aggregation) may take up to **15 minutes**. This is expected — the agent is doing real computation, not just summarizing a handful of passages.\n\n\u003E **Cost considerations:** Analytical search incurs costs from agent orchestration and the AI functions it uses (AI_FILTER, AI_EXTRACT, AI_AGG). Each AI function call processes one row. Adaptive depth limits unnecessary calls by stopping retrieval when results are no longer relevant — so you pay only for the documents that matter to the answer. For detailed AI function pricing, see the [Cortex AI cost documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql-cost#label-cortex-llm-cost-considerations).\n\n### Exercise 1: Auto-Routing (RAG Path)\n\n**Ask the agent:**\n\n\u003E What does Boeing's 10-K say about supply chain risk?\n\n![boeing-10k](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/boeing-10k.png)\n\n**What to observe:**\n- This is a per-document question — the agent auto-routes to standard top-k RAG\n- You get a focused answer with citations from Boeing's Risk Factors section\n- Response time: fast (~30 seconds)\n\n**Why this matters:** Auto-routing keeps simple questions cheap. Analytical Search is for analytical questions, not every question.\n\n---\n\n### Exercise 2: Counting (Analytical Search Path)\n\n**Ask the agent:**\n\n\u003E How many filings filed on Feb 3, 2025 mention cybersecurity risks?\n\n![cybersecurity-plan](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/cybersecurity-plan.png)\n\n**What to observe:**\n- The agent shows a **plan** before executing (probe → search → AI_FILTER → COUNT)\n- AI_FILTER classifies each chunk: is this about an *actual* cybersecurity risk (not generic boilerplate)?\n- The answer is a precise integer with a list of companies\n- Response time: 1–3 minutes\n\n**Expected result:** ~5 filings mention substantive cybersecurity risks. The agent will list each company with its form type and filing date.\n\n![cybersecurity-result](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/cybersecurity-result.png)\n\n**Key insight:** A RAG agent would say \"here are some examples...\" — it cannot count because it only sees 5–10 documents. The Analytical Search agent counts from the full result set using SQL.\n\n---\n\n### Exercise 3: Exhaustive Listing with Extraction\n\n**Ask the agent:**\n\n\u003E List every company that announced a leadership change in an 8-K filing on Feb 3, 2025.\n\n![leadership-change-plan](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/leadership-change-plan.png)\n\n**What to observe:**\n- **Adaptive depth** retrieves exactly as many results as needed\n- The agent shows a **plan** before executing (probe → search → AI_FILTER → COUNT (deduplicate)\n- `AI_FILTER` identifies genuine leadership-change language\n- The result is a **table artifact** with one row per event\n\n**Expected result:** ~50 leadership changes in a single day — a table with company, person, role, and type. Notable examples: RTX Corp (Gregory Hayes stepping down), Baxter International (CEO transition).\n\n![leadership-change-execute](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/leadership-change-execute.png)\n\n**Key insight:** A RAG agent would return 5–10 examples and hedge with \"there may be more.\" The Analytical Search agent finds all ~50 and structures them into an audit-ready table.\n\n---\n\n### Exercise 4: Honest Refusal\n\n**Ask the agent:**\n\n\u003E Compare Apple and Microsoft 10-K risk factor language about AI — what specific concerns does each company raise?\n\n![honest-refusal](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/honest-refusal.png)\n\n**What to observe:**\n- The agent **does not hallucinate**\n- It explains: Apple's fiscal year ends Sept 30 (10-K filed Oct/Nov), Microsoft's ends June 30 (10-K filed Jul/Aug) — neither is in this Feb 3 corpus\n- It searched, found nothing, and says so explicitly\n- It offers alternatives: \"I can compare Boeing and RTX, whose 10-Ks are in the corpus\"\n\n**Key insight:** Trust over completeness. The agent refuses to fabricate when the corpus lacks data, and offers constructive alternatives. This is as important as getting answers right.\n\n**Follow-up with Boeing and RTX instead of Apple and Microsoft**\n\n\u003E Compare Boeing and RTX 10-K risk factor language — what does each emphasize?\n\n![boeing-rtx](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analytical-search-over-sec-filings-with-snowflake-cowork/boeing-rtx.png)\n\nThis works: both have substantial risk-factor sections in the corpus.\n\n---\n\n### Bonus Exercises\n\n| Exercise | Prompt | AS Feature Demonstrated |\n|----------|--------|------------------------|\n| M&A details | \"List every M&A transaction filed on Feb 3, 2025. Extract acquirer, target, and deal summary.\" | AI_EXTRACT (structured extraction) |\n| Risk themes | \"What are the most common risk themes across the 10-K annual reports filed today?\" | AI_AGG (deduplication/clustering) |\n| Full breakdown | \"Break down all filings filed today by industry sector and sentiment.\" | Cortex Analyst (pure structured) |\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\n\u003E **File:** `sql/99_teardown.sql`\n\nWhen you're done exploring, run the teardown script to remove all objects:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\n-- Drop database (removes all tables, views, UDFs, procedures)\nDROP DATABASE IF EXISTS SEC_FILINGS;\n\n-- Drop warehouse\nDROP WAREHOUSE IF EXISTS FILING_WH;\n\n-- Drop external access integration\nDROP INTEGRATION IF EXISTS SEC_EDGAR_EAI;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\n**You have built an Analytical Search agent that answers precise analytical questions over hundreds of SEC filing passages — going far beyond what traditional RAG can do.**\n\n### What You Built\n\n-   A **Data Pipeline** ingesting SEC EDGAR filings with ticker enrichment, section-aware chunking, and AI signal extraction\n-   A **Multi-Index Cortex Search service** with text and vector indexes over 3,400+ filing chunks enabling Analytical Search\n-   A **Semantic View** enabling natural-language SQL analytics over structured filing signals\n-   Validated exercises demonstrating counting, exhaustive listing, hybrid queries, auto-routing, and honest refusal\n\n### From Quickstart to Production\n\nThis quickstart is not just a demo — it produces a near-production-ready, end-to-end solution you can put to real use:\n\n-   **Historical depth** — Widen the date range to ingest weeks, months, or a full year of filings for richer analytical insights\n-   **Parallel backfills** — For large date ranges, create multiple Snowflake Tasks calling `RUN_PIPELINE()` with non-overlapping monthly ranges to process months concurrently\n-   **Continuous ingestion** — Schedule a daily Snowflake Task (e.g., `SCHEDULE = 'USING CRON 0 7 * * * America/New_York'`) to call `RUN_PIPELINE()` for the current day, keeping your corpus fresh with no manual intervention\n-   **Automatic growth** — As new data flows in, the Cortex Search service and Semantic View automatically incorporate new filings — no additional configuration needed\n\n### Key Takeaways\n\n-   **Analytical Search = Retrieve + Compute** — it goes beyond RAG's \"retrieve and summarize\" to deliver SQL-level analytical rigor over unstructured data\n-   **AI_FILTER understands meaning, not keywords** — it distinguishes \"actual cybersecurity incident\" from generic \"we maintain security programs\"\n-   **Auto-routing keeps simple questions cheap** — per-document lookups skip the analytical path entirely\n-   **Hybrid answers combine structured + unstructured** — Cortex Analyst for counts, Cortex Search for evidence, in one turn\n-   **The agent refuses honestly when data is missing** — trust over completeness, with constructive alternatives\n\n### Resources\n\n-   [Analytical Search Documentation](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-analytical-search)\n-   [Cortex Agents — Configure and Interact](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-manage)\n-   [Cortex Search Service DDL](https://docs.snowflake.com/en/sql-reference/sql/create-cortex-search)\n-   [CREATE AGENT Reference](https://docs.snowflake.com/en/sql-reference/sql/create-agent)\n-   [AI_FILTER Function](https://docs.snowflake.com/en/sql-reference/functions/ai_filter)\n-   [AI_EXTRACT Function](https://docs.snowflake.com/en/sql-reference/functions/ai_extract)\n-   [AI_AGG Function](https://docs.snowflake.com/en/sql-reference/functions/ai_agg)\n-   [Semantic Views](https://docs.snowflake.com/en/user-guide/views-semantic/overview)\n-   [Source Code Repository](https://github.com/Snowflake-Labs/sfquickstarts/tree/main/site/sfguides/src/analytical-search-over-sec-filings-with-snowflake-cowork)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo 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It may be out of date with current Snowflake 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RAG Falls Short\u003C/h2\u003E","\u003Ch2\u003EHow Analytical Search Works\u003C/h2\u003E","\u003Ch2\u003EEnvironment Setup\u003C/h2\u003E","\u003Ch2\u003EBuild the Data Pipeline\u003C/h2\u003E","\u003Ch2\u003ECreate the Cortex Search Service\u003C/h2\u003E","\u003Ch2\u003ECreate the Semantic View\u003C/h2\u003E","\u003Ch2\u003EDeploy the Analytical Search Agent\u003C/h2\u003E","\u003Ch2\u003EAnalytical Search in Action\u003C/h2\u003E","\u003Ch2\u003ECleanup\u003C/h2\u003E","\u003Ch2\u003EConclusion And 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