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With extensions like pgvector and Apache AGE, you can build sophisticated AI systems entirely within your database — no external vector stores, graph databases, or complex data pipelines required.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Can Build\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECapability\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EKey Extension\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EVector storage & search\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStore embeddings and find similar items using approximate nearest-neighbor search\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Epgvector\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESemantic search\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFind content by meaning, not just keywords — combine vector similarity with full-text search\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Epgvector + tsvector\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ERAG\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGround LLM responses in your own data by retrieving relevant context at query time\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Epgvector\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EGraphRAG\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETraverse knowledge graphs to gather richer, multi-hop context for LLM generation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EApache AGE + pgvector\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EWhy This Matters\u003C/h3\u003E\n","\u003Cp\u003EEach of these capabilities typically requires a separate specialized system — a vector database for embeddings, a graph database for relationships, a search engine for full-text. Postgres handles all of them in one place, with one security model, one backup strategy, and full transactional consistency across all your data.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EKeep Everything Together\u003C/h2\u003E\n","\u003Cp\u003EWith Postgres, everything lives in one database:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EJoin vectors with relational data\u003C/strong\u003E — Combine similarity search with filters on metadata, user attributes, or business logic in a single query.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExisting tooling\u003C/strong\u003E — Use familiar ORMs, migration frameworks, backup tools, and monitoring. No new infrastructure to learn.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EACID guarantees\u003C/strong\u003E — Embeddings are transactionally consistent with the data they represent. No stale vectors from failed syncs.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESingle access control\u003C/strong\u003E — One \u003Ccode\u003EGRANT\u003C/code\u003E system governs who can see what — vectors, text, metadata, and graph data alike.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOne connection pool\u003C/strong\u003E — Your application talks to one database, not three or four.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nThe AI landscape moves fast, but your database is the stable foundation. Building on Postgres means you can swap embedding models, try new retrieval strategies, or add graph traversal — all without re-architecting your infrastructure.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStoring and Querying Vectors\u003C/h2\u003E\n","\u003Ch3\u003EEnable pgvector\u003C/h3\u003E\n","\u003Cp\u003Epgvector adds a \u003Ccode\u003Evector\u003C/code\u003E data type and distance operators to Postgres. Enable it in any database:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE EXTENSION vector;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate a Table with Embeddings\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE TABLE documents (\n  id         bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  title      text NOT NULL,\n  content    text NOT NULL,\n  metadata   jsonb DEFAULT '{}',\n  embedding  vector(1536)\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Evector(1536)\u003C/code\u003E type stores a fixed-length array of 32-bit floats. Choose the dimension to match your embedding model:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EModel\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDimensions\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOpenAI \u003Ccode\u003Etext-embedding-3-small\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1536\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOpenAI \u003Ccode\u003Etext-embedding-3-large\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3072\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECohere \u003Ccode\u003Eembed-english-v3\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1024\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOpen-source (e5, BGE, etc.)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E768–1024\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EDistance Operators\u003C/h3\u003E\n","\u003Cp\u003Epgvector provides three distance operators for different use cases:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Cosine distance (most common for normalized embeddings)\nSELECT title, 1 - (embedding <=> $1::vector) AS similarity\nFROM documents\nORDER BY embedding <=> $1::vector\nLIMIT 5;\n\n-- L2 (Euclidean) distance\nSELECT title, embedding <-> $1::vector AS distance\nFROM documents\nORDER BY embedding <-> $1::vector\nLIMIT 5;\n\n-- Inner product (use with non-normalized embeddings)\nSELECT title, (embedding <#> $1::vector) * -1 AS score\nFROM documents\nORDER BY embedding <#> $1::vector\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EOperator\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDistance Metric\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBest For\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<=>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECosine\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENormalized embeddings (most embedding APIs)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<->\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EL2 / Euclidean\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhen magnitude matters\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<#>\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENegative inner product\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMax inner product search\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EIndexing for Performance\u003C/h3\u003E\n","\u003Cp\u003EWithout an index, pgvector performs exact (brute-force) search. For datasets beyond a few thousand rows, create an approximate nearest-neighbor (ANN) index.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHNSW\u003C/strong\u003E (recommended for most use cases):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE INDEX ON documents\n  USING hnsw (embedding vector_cosine_ops)\n  WITH (m = 16, ef_construction = 128);\n\n-- Tune query-time recall vs. speed\nSET hnsw.ef_search = 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIVFFlat\u003C/strong\u003E (faster to build, useful for bulk-load scenarios):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Build after loading data (needs representative sample)\nCREATE INDEX ON documents\n  USING ivfflat (embedding vector_cosine_ops)\n  WITH (lists = 100);\n\n-- Tune: higher probes = better recall, more latency\nSET ivfflat.probes = 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EIndex Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBuild Speed\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuery Speed\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERecall\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EIncremental Inserts\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EHNSW\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESlower\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFaster\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHigher\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYes — no rebuild needed\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EIVFFlat\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFaster\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESlower\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELower (tunable)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERequires periodic rebuild\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EHalf-Precision and Binary Vectors\u003C/h3\u003E\n","\u003Cp\u003Epgvector 0.7+ supports \u003Ccode\u003Ehalfvec\u003C/code\u003E (16-bit floats) and binary quantization for reduced memory usage:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Half-precision vectors use ~50% less memory\nCREATE TABLE documents_half (\n  id        bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  content   text NOT NULL,\n  embedding halfvec(1536)\n);\n\nCREATE INDEX ON documents_half\n  USING hnsw (embedding halfvec_cosine_ops);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nUse \u003Ccode\u003Ehalfvec\u003C/code\u003E when you need to scale to millions of vectors. The recall loss is minimal for most applications, and you cut index memory in half.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESemantic Search\u003C/h2\u003E\n","\u003Cp\u003ESemantic search finds content by meaning rather than exact keyword matches. By combining pgvector's similarity search with Postgres's built-in full-text search, you get hybrid search — the best of both approaches.\u003C/p\u003E\n","\u003Ch3\u003EWhy Hybrid Search?\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EApproach\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStrengths\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWeaknesses\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EKeyword (tsvector)\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExact term matching, fast, no ML model needed\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMisses synonyms and paraphrases\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EVector (pgvector)\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnderstands meaning, handles synonyms\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECan miss exact terms, needs embeddings\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EHybrid (both)\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBest recall — catches both exact matches and semantic similarity\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESlightly more complex\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ESetting Up Hybrid Search\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE TABLE articles (\n  id         bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  title      text NOT NULL,\n  body       text NOT NULL,\n  -- Full-text search column (auto-updated)\n  tsv        tsvector GENERATED ALWAYS AS (\n               to_tsvector('english', title || ' ' || body)\n             ) STORED,\n  -- Vector embedding for semantic similarity\n  embedding  vector(1536)\n);\n\n-- Index both search methods\nCREATE INDEX ON articles USING gin (tsv);\nCREATE INDEX ON articles USING hnsw (embedding vector_cosine_ops);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EHybrid Search Query\u003C/h3\u003E\n","\u003Cp\u003ECombine both signals with Reciprocal Rank Fusion (RRF) — a simple, effective way to merge ranked lists:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EWITH keyword_results AS (\n  SELECT id, ts_rank_cd(tsv, query) AS rank\n  FROM articles, plainto_tsquery('english', $1) query\n  WHERE tsv @@ query\n  ORDER BY rank DESC\n  LIMIT 20\n),\nvector_results AS (\n  SELECT id, 1 - (embedding <=> $2::vector) AS rank\n  FROM articles\n  ORDER BY embedding <=> $2::vector\n  LIMIT 20\n),\ncombined AS (\n  SELECT\n    COALESCE(k.id, v.id) AS id,\n    -- RRF formula: sum of 1/(k + rank_position) across both lists\n    COALESCE(1.0 / (60 + ROW_NUMBER() OVER (ORDER BY k.rank DESC NULLS LAST)), 0) +\n    COALESCE(1.0 / (60 + ROW_NUMBER() OVER (ORDER BY v.rank DESC NULLS LAST)), 0) AS rrf_score\n  FROM keyword_results k\n  FULL OUTER JOIN vector_results v ON k.id = v.id\n)\nSELECT a.id, a.title, a.body, c.rrf_score\nFROM combined c\nJOIN articles a ON a.id = c.id\nORDER BY c.rrf_score DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nThe \u003Ccode\u003E60\u003C/code\u003E constant in RRF is standard (from the original paper). It prevents high-ranked items in one list from dominating. You can tune it, but 60 works well as a default.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ESimpler Hybrid Search with Weighted Scoring\u003C/h3\u003E\n","\u003Cp\u003EIf RRF feels complex, a weighted linear combination works too:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT id, title, body,\n  (0.7 * (1 - (embedding <=> $2::vector))) +\n  (0.3 * ts_rank_cd(tsv, plainto_tsquery('english', $1))) AS score\nFROM articles\nWHERE tsv @@ plainto_tsquery('english', $1)\n   OR embedding <=> $2::vector < 0.5\nORDER BY score DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAdjust the 0.7/0.3 weights based on your domain. Technical content often benefits from higher keyword weight; conversational queries benefit from higher vector weight.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERAG (Retrieval-Augmented Generation)\u003C/h2\u003E\n","\u003Cp\u003ERetrieval-Augmented Generation enhances LLM responses with relevant context from your own data. Postgres is a natural fit for the retrieval layer because your embeddings, source text, and metadata all live together.\u003C/p\u003E\n","\u003Ch3\u003EThe RAG Pattern\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EChunk documents\u003C/strong\u003E — Split long content into overlapping segments.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGenerate embeddings\u003C/strong\u003E — Run each chunk through an embedding model.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStore in Postgres\u003C/strong\u003E — Insert embeddings alongside the source content and metadata.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERetrieve at query time\u003C/strong\u003E — Embed the user's question and find similar chunks via vector search.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGenerate response\u003C/strong\u003E — Pass the retrieved context to an LLM to produce a grounded answer.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ESchema for RAG\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Source documents\nCREATE TABLE documents (\n  id           bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  title        text NOT NULL,\n  source_url   text,\n  content      text NOT NULL,\n  metadata     jsonb DEFAULT '{}',\n  created_at   timestamptz DEFAULT now()\n);\n\n-- Chunked and embedded content\nCREATE TABLE chunks (\n  id           bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  document_id  bigint REFERENCES documents(id) ON DELETE CASCADE,\n  chunk_index  int NOT NULL,\n  content      text NOT NULL,\n  token_count  int,\n  embedding    vector(1536),\n  UNIQUE (document_id, chunk_index)\n);\n\n-- Index for fast retrieval\nCREATE INDEX ON chunks USING hnsw (embedding vector_cosine_ops);\nCREATE INDEX ON chunks (document_id);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERetrieval with Metadata Filtering\u003C/h3\u003E\n","\u003Cp\u003EThe real power of Postgres-based RAG is combining similarity search with structured filters:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Find relevant chunks, filtered by source and recency\nSELECT c.content, c.chunk_index,\n       d.title, d.source_url,\n       1 - (c.embedding <=> $1::vector) AS similarity\nFROM chunks c\nJOIN documents d ON d.id = c.document_id\nWHERE d.metadata->>'category' = 'engineering'\n  AND d.created_at > now() - interval '90 days'\nORDER BY c.embedding <=> $1::vector\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis uses both the HNSW index for vector similarity and standard B-tree indexes on metadata — something that requires complex multi-system orchestration with standalone vector databases.\u003C/p\u003E\n","\u003Ch3\u003EChunking Strategies\u003C/h3\u003E\n","\u003Cp\u003EHow you split documents affects retrieval quality:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Example: inserting chunks with overlap tracking\nINSERT INTO chunks (document_id, chunk_index, content, token_count, embedding)\nVALUES\n  ($1, 0, $2, $3, $4),  -- tokens 0-500\n  ($1, 1, $5, $6, $7),  -- tokens 450-950 (50-token overlap)\n  ($1, 2, $8, $9, $10); -- tokens 900-1400\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStrategy\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EChunk Size\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EOverlap\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBest For\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFixed-size\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E500 tokens\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E50–100 tokens\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGeneral purpose\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EParagraph-based\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVaries\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENone\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWell-structured documents\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESemantic\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVaries\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENone\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDocuments with clear topic shifts\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide negative\nChunk size matters. Too large and the embedding loses specificity; too small and you lose context. Start with 500–1000 tokens with 10–20% overlap and iterate based on retrieval quality.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EContext Window Assembly\u003C/h3\u003E\n","\u003Cp\u003EAfter retrieval, assemble the context for your LLM call:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Get chunks with surrounding context (previous and next chunks)\nSELECT c.content,\n       lag(c.content) OVER (PARTITION BY c.document_id ORDER BY c.chunk_index) AS prev_chunk,\n       lead(c.content) OVER (PARTITION BY c.document_id ORDER BY c.chunk_index) AS next_chunk\nFROM chunks c\nWHERE c.id IN (SELECT id FROM retrieved_chunk_ids)\nORDER BY c.embedding <=> $1::vector;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis gives your LLM not just the most relevant chunk, but its surrounding context for better answer generation.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGraphRAG\u003C/h2\u003E\n","\u003Cp\u003EGraphRAG combines knowledge graph traversal with vector retrieval to provide richer, multi-hop context to LLMs. Standard RAG retrieves isolated chunks; GraphRAG follows relationships between entities to gather connected information.\u003C/p\u003E\n","\u003Ch3\u003EWhen to Use GraphRAG\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EQuestions that span multiple documents ("How does project X relate to team Y's goals?")\u003C/li\u003E\u003Cli\u003EQueries requiring multi-hop reasoning ("Who manages the person who wrote this code?")\u003C/li\u003E\u003Cli\u003EDomains with rich entity relationships (org charts, supply chains, codebases)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESetting Up a Knowledge Graph with Apache AGE\u003C/h3\u003E\n","\u003Cp\u003EApache AGE adds graph database capabilities to Postgres using the openCypher query language:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Enable the AGE extension\nCREATE EXTENSION age;\nLOAD 'age';\nSET search_path = ag_catalog, "$user", public;\n\n-- Create a knowledge graph\nSELECT create_graph('knowledge_base');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EBuilding the Graph\u003C/h3\u003E\n","\u003Cp\u003EStore entities and relationships extracted from your documents:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Add entities as graph nodes\nSELECT * FROM cypher('knowledge_base', $$\n  CREATE (:Document {\n    id: 1,\n    title: 'Postgres Performance Guide',\n    category: 'engineering'\n  })\n$$) AS (v agtype);\n\nSELECT * FROM cypher('knowledge_base', $$\n  CREATE (:Concept {\n    name: 'Index Tuning',\n    description: 'Optimizing database indexes for query performance'\n  })\n$$) AS (v agtype);\n\n-- Create relationships\nSELECT * FROM cypher('knowledge_base', $$\n  MATCH (d:Document {id: 1}), (c:Concept {name: 'Index Tuning'})\n  CREATE (d)-[:COVERS {section: 'chapter_3', depth: 'detailed'}]->(c)\n$$) AS (e agtype);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGraphRAG Retrieval Pattern\u003C/h3\u003E\n","\u003Cp\u003ECombine vector similarity with graph traversal for multi-hop context:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Step 1: Find relevant chunks via vector search\nWITH seed_chunks AS (\n  SELECT c.id, c.document_id, c.content,\n         1 - (c.embedding <=> $1::vector) AS similarity\n  FROM chunks c\n  ORDER BY c.embedding <=> $1::vector\n  LIMIT 5\n),\n-- Step 2: Find related entities via graph traversal\nrelated_entities AS (\n  SELECT entity_id, relationship, depth\n  FROM cypher('knowledge_base', $$\n    MATCH (d:Document {id: $doc_id})-[r*1..2]-(related)\n    RETURN id(related) AS entity_id, type(r) AS relationship, length(r) AS depth\n  $$) AS (entity_id agtype, relationship agtype, depth agtype)\n  WHERE doc_id IN (SELECT document_id FROM seed_chunks)\n),\n-- Step 3: Get chunks from related documents\ngraph_chunks AS (\n  SELECT c.content, c.embedding, 'graph' AS source\n  FROM chunks c\n  JOIN documents d ON d.id = c.document_id\n  WHERE d.id IN (SELECT entity_id::int FROM related_entities)\n  ORDER BY c.embedding <=> $1::vector\n  LIMIT 5\n)\n-- Combine vector-similar and graph-connected chunks\nSELECT content, similarity, source FROM (\n  SELECT content, similarity, 'vector' AS source FROM seed_chunks\n  UNION ALL\n  SELECT content, 1 - (embedding <=> $1::vector), source FROM graph_chunks\n) combined\nORDER BY similarity DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEntity Extraction and Graph Maintenance\u003C/h3\u003E\n","\u003Cp\u003EKeep your knowledge graph in sync with your documents using triggers:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Table to track extracted entities\nCREATE TABLE entities (\n  id            bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  document_id   bigint REFERENCES documents(id) ON DELETE CASCADE,\n  entity_name   text NOT NULL,\n  entity_type   text NOT NULL,  -- 'person', 'concept', 'tool', etc.\n  properties    jsonb DEFAULT '{}'\n);\n\nCREATE TABLE entity_relationships (\n  id            bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  source_id     bigint REFERENCES entities(id) ON DELETE CASCADE,\n  target_id     bigint REFERENCES entities(id) ON DELETE CASCADE,\n  relationship  text NOT NULL,  -- 'mentions', 'depends_on', 'authored_by'\n  properties    jsonb DEFAULT '{}'\n);\n\n-- Indexes for fast graph traversal\nCREATE INDEX ON entity_relationships (source_id);\nCREATE INDEX ON entity_relationships (target_id);\nCREATE INDEX ON entities (entity_type, entity_name);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGraphRAG with Common Table Expression (CTE)\u003C/h3\u003E\n","\u003Cp\u003EIf you don't want to install AGE, you can implement graph traversal with recursive CTEs:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Traverse relationships up to 3 hops from seed entities\nWITH RECURSIVE graph_walk AS (\n  -- Start from entities mentioned in relevant chunks\n  SELECT e.id, e.entity_name, e.entity_type, 0 AS depth\n  FROM entities e\n  JOIN chunks c ON c.document_id = e.document_id\n  WHERE c.id IN (SELECT id FROM seed_chunk_ids)\n\n  UNION ALL\n\n  -- Walk relationships\n  SELECT e2.id, e2.entity_name, e2.entity_type, gw.depth + 1\n  FROM graph_walk gw\n  JOIN entity_relationships er ON er.source_id = gw.id\n  JOIN entities e2 ON e2.id = er.target_id\n  WHERE gw.depth < 3\n)\nSELECT DISTINCT entity_name, entity_type, min(depth) AS min_depth\nFROM graph_walk\nGROUP BY entity_name, entity_type\nORDER BY min_depth, entity_name;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nYou don't need a dedicated graph database for GraphRAG. Postgres recursive CTEs handle most knowledge graph traversal patterns. Apache AGE adds openCypher syntax for complex graph queries, but the relational approach works for simpler graphs.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Cortex Integration\u003C/h2\u003E\n","\u003Cp\u003ESnowflake Cortex provides serverless AI functions that pair naturally with Postgres. Use Cortex to generate embeddings and run LLM inference at scale, then serve the results from Postgres at application speed.\u003C/p\u003E\n","\u003Ch3\u003EGenerating Embeddings with Cortex\u003C/h3\u003E\n","\u003Cp\u003EYou don't need to manage embedding model infrastructure yourself. The \u003Ccode\u003EAI_EMBED\u003C/code\u003E function generates vector embeddings on demand:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Generate an embedding with AI_EMBED (recommended)\nSELECT AI_EMBED('snowflake-arctic-embed-l-v2.0', 'Your text here');\n\n-- Specify output dimensions (model-dependent)\nSELECT AI_EMBED('snowflake-arctic-embed-l-v2.0', 'Your text here');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EModel\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDimensions\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBest For\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake-arctic-embed-l-v2.0\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1024\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHigh accuracy, general purpose\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake-arctic-embed-l-v2.0-8k\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1024\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELonger documents (8K token context)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esnowflake-arctic-embed-m-v1.5\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E768\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGood balance of speed and accuracy\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Evoyage-multilingual-2\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1024\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMultilingual content\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EBatch Embedding Generation\u003C/h3\u003E\n","\u003Cp\u003EGenerate embeddings for an entire table of content in Snowflake:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Embed all rows in a content table\nCREATE OR REPLACE TABLE my_db.my_schema.embedded_chunks AS\nSELECT\n  id,\n  document_id,\n  chunk_index,\n  content,\n  AI_EMBED('snowflake-arctic-embed-l-v2.0', content) AS embedding\nFROM my_db.my_schema.chunks;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EScripting Between Snowflake Postgres and Snowflake\u003C/h3\u003E\n","\u003Cp\u003ESnowflake Postgres and Snowflake can exchange data bidirectionally through \u003Cstrong\u003Epg_lake stages\u003C/strong\u003E. This enables a powerful pattern: export text from Postgres, generate embeddings in Snowflake with Cortex, and write the vectors back to Postgres — all without leaving the Snowflake ecosystem.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 1: Set up the stage connection\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- In Snowflake: create a storage integration pointing to your Postgres instance\nCREATE STORAGE INTEGRATION my_pg_stage_integration\n  TYPE = POSTGRES_INTERNAL_STORAGE\n  POSTGRES_INSTANCE = 'my_postgres_instance';\n\n-- Create a stage for file exchange\nCREATE STAGE my_pg_stage\n  RELATIVE_URL = '/embeddings'\n  STORAGE_INTEGRATION = my_pg_stage_integration;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 2: Export text from Postgres to the stage\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- In Postgres: export content that needs embeddings\nCOPY (\n  SELECT id, content\n  FROM documents\n  WHERE embedding IS NULL\n)\nTO '@STAGE/embeddings/to_embed.parquet'\nWITH (FORMAT 'parquet');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 3: Generate embeddings in Snowflake\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- In Snowflake: read the exported file and generate embeddings\nCREATE OR REPLACE TABLE embedded_results AS\nSELECT\n  $1:id::int AS id,\n  AI_EMBED('snowflake-arctic-embed-l-v2.0', $1:content::text) AS embedding\nFROM @my_pg_stage/to_embed.parquet;\n\n-- Write results back to the stage\nCOPY INTO @my_pg_stage/embeddings_done.parquet\nFROM embedded_results\nFILE_FORMAT = (TYPE = PARQUET);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStep 4: Load embeddings back into Postgres\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- In Postgres: load the generated embeddings\nCREATE TEMP TABLE embedding_import (\n  id       int,\n  embedding vector(1024)\n);\n\nCOPY embedding_import\nFROM '@STAGE/embeddings/embeddings_done.parquet'\nWITH (FORMAT 'parquet');\n\n-- Update the source table\nUPDATE documents d\nSET embedding = e.embedding\nFROM embedding_import e\nWHERE d.id = e.id;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nWith pg_lake stages, you can script the entire embedding pipeline without any external infrastructure. Postgres exports text, Snowflake generates embeddings with Cortex, and the vectors flow right back — no S3 buckets, no custom ETL code, no third-party orchestrators.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ESyncing Embeddings with Shared Iceberg\u003C/h3\u003E\n","\u003Cp\u003EFor continuous sync rather than batch scripting, use the \u003Cstrong\u003EShared Iceberg\u003C/strong\u003E pattern. Postgres writes an Iceberg table that Snowflake reads through a catalog integration:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- In Postgres: create an Iceberg table with your content\nCREATE TABLE documents_iceberg (\n  id      int,\n  content text\n) USING iceberg;\n\n-- In Snowflake: read the table, generate embeddings, and write back via stage\nCREATE OR REPLACE ICEBERG TABLE my_iceberg_docs\n  CATALOG = 'my_postgres_catalog'\n  CATALOG_TABLE_NAME = 'documents_iceberg';\n\n-- Generate embeddings from the synced data\nSELECT id, AI_EMBED('snowflake-arctic-embed-l-v2.0', content) AS embedding\nFROM my_iceberg_docs;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EEmbedding Queries at Runtime\u003C/h3\u003E\n","\u003Cp\u003EFor real-time RAG, you also need to embed user queries. Two approaches:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOption A: Embed in Snowflake, query in Postgres\u003C/strong\u003E — Call Cortex from your application, then pass the vector to Postgres:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Python example using Snowflake connector + psycopg\nimport snowflake.connector\nimport psycopg\n\n# Generate query embedding via Cortex\nsf_conn = snowflake.connector.connect(...)\ncursor = sf_conn.cursor()\ncursor.execute("""\n    SELECT AI_EMBED('snowflake-arctic-embed-l-v2.0', %s)::ARRAY\n""", (user_question,))\nquery_embedding = cursor.fetchone()[0]\n\n# Search Postgres with the embedding\npg_conn = psycopg.connect(...)\nresults = pg_conn.execute("""\n    SELECT content, 1 - (embedding <=> %s::vector) AS similarity\n    FROM chunks\n    ORDER BY embedding <=> %s::vector\n    LIMIT 5\n""", (query_embedding, query_embedding)).fetchall()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOption B: Use the same model via an API\u003C/strong\u003E — If you're using an open model like Arctic Embed, you can also run it locally or via a model serving endpoint for query-time embeddings, avoiding the round-trip to Snowflake.\u003C/p\u003E\n","\u003Ch3\u003ECortex AI Functions\u003C/h3\u003E\n","\u003Cp\u003EBeyond embeddings, Cortex provides additional serverless AI functions you can use to enrich data before storing it in Postgres:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EAI_COMPLETE()\u003C/code\u003E\u003C/strong\u003E — Run LLM inference (GPT-4, Llama, Mistral, and more) over your data.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EAI_EXTRACT()\u003C/code\u003E\u003C/strong\u003E — Extract structured information from text or documents.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EAI_CLASSIFY()\u003C/code\u003E\u003C/strong\u003E — Classify text or images into user-defined categories.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EAI_SENTIMENT()\u003C/code\u003E\u003C/strong\u003E — Analyze sentiment of text content.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EAI_TRANSLATE()\u003C/code\u003E\u003C/strong\u003E — Translate text between languages.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EAI_SUMMARIZE_AGG()\u003C/code\u003E\u003C/strong\u003E — Summarize across multiple rows without context window limits.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECortex Search\u003C/h3\u003E\n","\u003Cp\u003EBuild full semantic search applications without managing embedding pipelines. Cortex Search handles chunking, embedding, indexing, and retrieval automatically.\u003C/p\u003E\n","\u003Ch3\u003EIntegration with Postgres\u003C/h3\u003E\n","\u003Cp\u003ECombine Snowflake's managed AI services with Postgres's operational database strengths:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Epg_lake stages\u003C/strong\u003E — Bidirectional file exchange for scripting embedding pipelines between Postgres and Snowflake.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EShared Iceberg\u003C/strong\u003E — Postgres writes Iceberg tables that Snowflake reads for analytics and AI processing.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStreamlit in Snowflake\u003C/strong\u003E — Build interactive AI applications that query both Snowflake and Postgres data.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EHybrid retrieval\u003C/strong\u003E — Use Postgres for low-latency operational RAG and Snowflake for analytical queries over the same data.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nUsing Snowflake Cortex for embeddings means you don't need to provision GPU infrastructure, manage model versions, or handle scaling. Generate embeddings at Snowflake scale, then serve them from Postgres at application speed.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/pgvector/pgvector\"\u003E🐘 pgvector — Open-source vector similarity search for Postgres\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EThe pgvector extension GitHub repository with installation instructions, distance operators, and indexing documentation.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.crunchydata.com/blog/topic/ai\"\u003E🦛 Crunchy Data Blog — AI with Postgres\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003ECollection of articles on building AI applications with Postgres, including RAG patterns, embedding strategies, and pgvector performance tuning.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql\"\u003E❄️ Snowflake Docs — Cortex AI Functions\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EReference for all Snowflake Cortex AI functions including AI_EMBED, AI_COMPLETE, AI_EXTRACT, and more.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-pg_lake\"\u003E❄️ Snowflake Docs — Moving data between Snowflake Postgres and Snowflake\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EGuide to pg_lake stages, Shared Iceberg, and bidirectional data movement for embedding pipelines.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://age.apache.org/\"\u003E🐘 Apache AGE — Graph extension for PostgreSQL\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EOpen-source extension that adds graph database capabilities and openCypher query support to Postgres.\u003C/p\u003E"],"description":"","title":"AI in Postgres","elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\nPostgres is a complete platform for AI-powered applications. With extensions like pgvector and Apache AGE, you can build sophisticated AI systems entirely within your database — no external vector stores, graph databases, or complex data pipelines required.\n\n### What You Can Build\n\n| Capability | What It Does | Key Extension |\n|-----------|--------------|---------------|\n| **Vector storage & search** | Store embeddings and find similar items using approximate nearest-neighbor search | pgvector |\n| **Semantic search** | Find content by meaning, not just keywords — combine vector similarity with full-text search | pgvector + tsvector |\n| **RAG** | Ground LLM responses in your own data by retrieving relevant context at query time | pgvector |\n| **GraphRAG** | Traverse knowledge graphs to gather richer, multi-hop context for LLM generation | Apache AGE + pgvector |\n\n### Why This Matters\n\nEach of these capabilities typically requires a separate specialized system — a vector database for embeddings, a graph database for relationships, a search engine for full-text. Postgres handles all of them in one place, with one security model, one backup strategy, and full transactional consistency across all your data.\n\n\u003C!-- ------------------------ --\u003E\n## Keep Everything Together\n\nWith Postgres, everything lives in one database:\n\n- **Join vectors with relational data** — Combine similarity search with filters on metadata, user attributes, or business logic in a single query.\n- **Existing tooling** — Use familiar ORMs, migration frameworks, backup tools, and monitoring. No new infrastructure to learn.\n- **ACID guarantees** — Embeddings are transactionally consistent with the data they represent. No stale vectors from failed syncs.\n- **Single access control** — One `GRANT` system governs who can see what — vectors, text, metadata, and graph data alike.\n- **One connection pool** — Your application talks to one database, not three or four.\n\n\u003E aside positive\n\u003E The AI landscape moves fast, but your database is the stable foundation. Building on Postgres means you can swap embedding models, try new retrieval strategies, or add graph traversal — all without re-architecting your infrastructure.\n\n\u003C!-- ------------------------ --\u003E\n## Storing and Querying Vectors\n### Enable pgvector\n\npgvector adds a `vector` data type and distance operators to Postgres. Enable it in any database:\n\n```sql\nCREATE EXTENSION vector;\n```\n\n### Create a Table with Embeddings\n\n```sql\nCREATE TABLE documents (\n  id         bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  title      text NOT NULL,\n  content    text NOT NULL,\n  metadata   jsonb DEFAULT '{}',\n  embedding  vector(1536)\n);\n```\n\nThe `vector(1536)` type stores a fixed-length array of 32-bit floats. Choose the dimension to match your embedding model:\n\n| Model | Dimensions |\n|-------|-----------|\n| OpenAI `text-embedding-3-small` | 1536 |\n| OpenAI `text-embedding-3-large` | 3072 |\n| Cohere `embed-english-v3` | 1024 |\n| Open-source (e5, BGE, etc.) | 768–1024 |\n\n### Distance Operators\n\npgvector provides three distance operators for different use cases:\n\n```sql\n-- Cosine distance (most common for normalized embeddings)\nSELECT title, 1 - (embedding \u003C=\u003E $1::vector) AS similarity\nFROM documents\nORDER BY embedding \u003C=\u003E $1::vector\nLIMIT 5;\n\n-- L2 (Euclidean) distance\nSELECT title, embedding \u003C-\u003E $1::vector AS distance\nFROM documents\nORDER BY embedding \u003C-\u003E $1::vector\nLIMIT 5;\n\n-- Inner product (use with non-normalized embeddings)\nSELECT title, (embedding \u003C#\u003E $1::vector) * -1 AS score\nFROM documents\nORDER BY embedding \u003C#\u003E $1::vector\nLIMIT 5;\n```\n\n| Operator | Distance Metric | Best For |\n|----------|----------------|----------|\n| `\u003C=\u003E` | Cosine | Normalized embeddings (most embedding APIs) |\n| `\u003C-\u003E` | L2 / Euclidean | When magnitude matters |\n| `\u003C#\u003E` | Negative inner product | Max inner product search |\n\n### Indexing for Performance\n\nWithout an index, pgvector performs exact (brute-force) search. For datasets beyond a few thousand rows, create an approximate nearest-neighbor (ANN) index.\n\n**HNSW** (recommended for most use cases):\n\n```sql\nCREATE INDEX ON documents\n  USING hnsw (embedding vector_cosine_ops)\n  WITH (m = 16, ef_construction = 128);\n\n-- Tune query-time recall vs. speed\nSET hnsw.ef_search = 100;\n```\n\n**IVFFlat** (faster to build, useful for bulk-load scenarios):\n\n```sql\n-- Build after loading data (needs representative sample)\nCREATE INDEX ON documents\n  USING ivfflat (embedding vector_cosine_ops)\n  WITH (lists = 100);\n\n-- Tune: higher probes = better recall, more latency\nSET ivfflat.probes = 10;\n```\n\n| Index Type | Build Speed | Query Speed | Recall | Incremental Inserts |\n|-----------|-------------|-------------|--------|---------------------|\n| **HNSW** | Slower | Faster | Higher | Yes — no rebuild needed |\n| **IVFFlat** | Faster | Slower | Lower (tunable) | Requires periodic rebuild |\n\n### Half-Precision and Binary Vectors\n\npgvector 0.7+ supports `halfvec` (16-bit floats) and binary quantization for reduced memory usage:\n\n```sql\n-- Half-precision vectors use ~50% less memory\nCREATE TABLE documents_half (\n  id        bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  content   text NOT NULL,\n  embedding halfvec(1536)\n);\n\nCREATE INDEX ON documents_half\n  USING hnsw (embedding halfvec_cosine_ops);\n```\n\n\u003E aside positive\n\u003E Use `halfvec` when you need to scale to millions of vectors. The recall loss is minimal for most applications, and you cut index memory in half.\n\n\u003C!-- ------------------------ --\u003E\n## Semantic Search\nSemantic search finds content by meaning rather than exact keyword matches. By combining pgvector's similarity search with Postgres's built-in full-text search, you get hybrid search — the best of both approaches.\n\n### Why Hybrid Search?\n\n| Approach | Strengths | Weaknesses |\n|---------|-----------|------------|\n| **Keyword (tsvector)** | Exact term matching, fast, no ML model needed | Misses synonyms and paraphrases |\n| **Vector (pgvector)** | Understands meaning, handles synonyms | Can miss exact terms, needs embeddings |\n| **Hybrid (both)** | Best recall — catches both exact matches and semantic similarity | Slightly more complex |\n\n### Setting Up Hybrid Search\n\n```sql\nCREATE TABLE articles (\n  id         bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  title      text NOT NULL,\n  body       text NOT NULL,\n  -- Full-text search column (auto-updated)\n  tsv        tsvector GENERATED ALWAYS AS (\n               to_tsvector('english', title || ' ' || body)\n             ) STORED,\n  -- Vector embedding for semantic similarity\n  embedding  vector(1536)\n);\n\n-- Index both search methods\nCREATE INDEX ON articles USING gin (tsv);\nCREATE INDEX ON articles USING hnsw (embedding vector_cosine_ops);\n```\n\n### Hybrid Search Query\n\nCombine both signals with Reciprocal Rank Fusion (RRF) — a simple, effective way to merge ranked lists:\n\n```sql\nWITH keyword_results AS (\n  SELECT id, ts_rank_cd(tsv, query) AS rank\n  FROM articles, plainto_tsquery('english', $1) query\n  WHERE tsv @@ query\n  ORDER BY rank DESC\n  LIMIT 20\n),\nvector_results AS (\n  SELECT id, 1 - (embedding \u003C=\u003E $2::vector) AS rank\n  FROM articles\n  ORDER BY embedding \u003C=\u003E $2::vector\n  LIMIT 20\n),\ncombined AS (\n  SELECT\n    COALESCE(k.id, v.id) AS id,\n    -- RRF formula: sum of 1/(k + rank_position) across both lists\n    COALESCE(1.0 / (60 + ROW_NUMBER() OVER (ORDER BY k.rank DESC NULLS LAST)), 0) +\n    COALESCE(1.0 / (60 + ROW_NUMBER() OVER (ORDER BY v.rank DESC NULLS LAST)), 0) AS rrf_score\n  FROM keyword_results k\n  FULL OUTER JOIN vector_results v ON k.id = v.id\n)\nSELECT a.id, a.title, a.body, c.rrf_score\nFROM combined c\nJOIN articles a ON a.id = c.id\nORDER BY c.rrf_score DESC\nLIMIT 10;\n```\n\n\u003E aside negative\n\u003E The `60` constant in RRF is standard (from the original paper). It prevents high-ranked items in one list from dominating. You can tune it, but 60 works well as a default.\n\n### Simpler Hybrid Search with Weighted Scoring\n\nIf RRF feels complex, a weighted linear combination works too:\n\n```sql\nSELECT id, title, body,\n  (0.7 * (1 - (embedding \u003C=\u003E $2::vector))) +\n  (0.3 * ts_rank_cd(tsv, plainto_tsquery('english', $1))) AS score\nFROM articles\nWHERE tsv @@ plainto_tsquery('english', $1)\n   OR embedding \u003C=\u003E $2::vector \u003C 0.5\nORDER BY score DESC\nLIMIT 10;\n```\n\nAdjust the 0.7/0.3 weights based on your domain. Technical content often benefits from higher keyword weight; conversational queries benefit from higher vector weight.\n\n\u003C!-- ------------------------ --\u003E\n## RAG (Retrieval-Augmented Generation)\nRetrieval-Augmented Generation enhances LLM responses with relevant context from your own data. Postgres is a natural fit for the retrieval layer because your embeddings, source text, and metadata all live together.\n\n### The RAG Pattern\n\n1. **Chunk documents** — Split long content into overlapping segments.\n2. **Generate embeddings** — Run each chunk through an embedding model.\n3. **Store in Postgres** — Insert embeddings alongside the source content and metadata.\n4. **Retrieve at query time** — Embed the user's question and find similar chunks via vector search.\n5. **Generate response** — Pass the retrieved context to an LLM to produce a grounded answer.\n\n### Schema for RAG\n\n```sql\n-- Source documents\nCREATE TABLE documents (\n  id           bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  title        text NOT NULL,\n  source_url   text,\n  content      text NOT NULL,\n  metadata     jsonb DEFAULT '{}',\n  created_at   timestamptz DEFAULT now()\n);\n\n-- Chunked and embedded content\nCREATE TABLE chunks (\n  id           bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  document_id  bigint REFERENCES documents(id) ON DELETE CASCADE,\n  chunk_index  int NOT NULL,\n  content      text NOT NULL,\n  token_count  int,\n  embedding    vector(1536),\n  UNIQUE (document_id, chunk_index)\n);\n\n-- Index for fast retrieval\nCREATE INDEX ON chunks USING hnsw (embedding vector_cosine_ops);\nCREATE INDEX ON chunks (document_id);\n```\n\n### Retrieval with Metadata Filtering\n\nThe real power of Postgres-based RAG is combining similarity search with structured filters:\n\n```sql\n-- Find relevant chunks, filtered by source and recency\nSELECT c.content, c.chunk_index,\n       d.title, d.source_url,\n       1 - (c.embedding \u003C=\u003E $1::vector) AS similarity\nFROM chunks c\nJOIN documents d ON d.id = c.document_id\nWHERE d.metadata-\u003E\u003E'category' = 'engineering'\n  AND d.created_at \u003E now() - interval '90 days'\nORDER BY c.embedding \u003C=\u003E $1::vector\nLIMIT 5;\n```\n\nThis uses both the HNSW index for vector similarity and standard B-tree indexes on metadata — something that requires complex multi-system orchestration with standalone vector databases.\n\n### Chunking Strategies\n\nHow you split documents affects retrieval quality:\n\n```sql\n-- Example: inserting chunks with overlap tracking\nINSERT INTO chunks (document_id, chunk_index, content, token_count, embedding)\nVALUES\n  ($1, 0, $2, $3, $4),  -- tokens 0-500\n  ($1, 1, $5, $6, $7),  -- tokens 450-950 (50-token overlap)\n  ($1, 2, $8, $9, $10); -- tokens 900-1400\n```\n\n| Strategy | Chunk Size | Overlap | Best For |\n|----------|-----------|---------|----------|\n| Fixed-size | 500 tokens | 50–100 tokens | General purpose |\n| Paragraph-based | Varies | None | Well-structured documents |\n| Semantic | Varies | None | Documents with clear topic shifts |\n\n\u003E aside negative\n\u003E Chunk size matters. Too large and the embedding loses specificity; too small and you lose context. Start with 500–1000 tokens with 10–20% overlap and iterate based on retrieval quality.\n\n### Context Window Assembly\n\nAfter retrieval, assemble the context for your LLM call:\n\n```sql\n-- Get chunks with surrounding context (previous and next chunks)\nSELECT c.content,\n       lag(c.content) OVER (PARTITION BY c.document_id ORDER BY c.chunk_index) AS prev_chunk,\n       lead(c.content) OVER (PARTITION BY c.document_id ORDER BY c.chunk_index) AS next_chunk\nFROM chunks c\nWHERE c.id IN (SELECT id FROM retrieved_chunk_ids)\nORDER BY c.embedding \u003C=\u003E $1::vector;\n```\n\nThis gives your LLM not just the most relevant chunk, but its surrounding context for better answer generation.\n\n\u003C!-- ------------------------ --\u003E\n## GraphRAG\nGraphRAG combines knowledge graph traversal with vector retrieval to provide richer, multi-hop context to LLMs. Standard RAG retrieves isolated chunks; GraphRAG follows relationships between entities to gather connected information.\n\n### When to Use GraphRAG\n\n- Questions that span multiple documents (\"How does project X relate to team Y's goals?\")\n- Queries requiring multi-hop reasoning (\"Who manages the person who wrote this code?\")\n- Domains with rich entity relationships (org charts, supply chains, codebases)\n\n### Setting Up a Knowledge Graph with Apache AGE\n\nApache AGE adds graph database capabilities to Postgres using the openCypher query language:\n\n```sql\n-- Enable the AGE extension\nCREATE EXTENSION age;\nLOAD 'age';\nSET search_path = ag_catalog, \"$user\", public;\n\n-- Create a knowledge graph\nSELECT create_graph('knowledge_base');\n```\n\n### Building the Graph\n\nStore entities and relationships extracted from your documents:\n\n```sql\n-- Add entities as graph nodes\nSELECT * FROM cypher('knowledge_base', $$\n  CREATE (:Document {\n    id: 1,\n    title: 'Postgres Performance Guide',\n    category: 'engineering'\n  })\n$$) AS (v agtype);\n\nSELECT * FROM cypher('knowledge_base', $$\n  CREATE (:Concept {\n    name: 'Index Tuning',\n    description: 'Optimizing database indexes for query performance'\n  })\n$$) AS (v agtype);\n\n-- Create relationships\nSELECT * FROM cypher('knowledge_base', $$\n  MATCH (d:Document {id: 1}), (c:Concept {name: 'Index Tuning'})\n  CREATE (d)-[:COVERS {section: 'chapter_3', depth: 'detailed'}]-\u003E(c)\n$$) AS (e agtype);\n```\n\n### GraphRAG Retrieval Pattern\n\nCombine vector similarity with graph traversal for multi-hop context:\n\n```sql\n-- Step 1: Find relevant chunks via vector search\nWITH seed_chunks AS (\n  SELECT c.id, c.document_id, c.content,\n         1 - (c.embedding \u003C=\u003E $1::vector) AS similarity\n  FROM chunks c\n  ORDER BY c.embedding \u003C=\u003E $1::vector\n  LIMIT 5\n),\n-- Step 2: Find related entities via graph traversal\nrelated_entities AS (\n  SELECT entity_id, relationship, depth\n  FROM cypher('knowledge_base', $$\n    MATCH (d:Document {id: $doc_id})-[r*1..2]-(related)\n    RETURN id(related) AS entity_id, type(r) AS relationship, length(r) AS depth\n  $$) AS (entity_id agtype, relationship agtype, depth agtype)\n  WHERE doc_id IN (SELECT document_id FROM seed_chunks)\n),\n-- Step 3: Get chunks from related documents\ngraph_chunks AS (\n  SELECT c.content, c.embedding, 'graph' AS source\n  FROM chunks c\n  JOIN documents d ON d.id = c.document_id\n  WHERE d.id IN (SELECT entity_id::int FROM related_entities)\n  ORDER BY c.embedding \u003C=\u003E $1::vector\n  LIMIT 5\n)\n-- Combine vector-similar and graph-connected chunks\nSELECT content, similarity, source FROM (\n  SELECT content, similarity, 'vector' AS source FROM seed_chunks\n  UNION ALL\n  SELECT content, 1 - (embedding \u003C=\u003E $1::vector), source FROM graph_chunks\n) combined\nORDER BY similarity DESC\nLIMIT 10;\n```\n\n### Entity Extraction and Graph Maintenance\n\nKeep your knowledge graph in sync with your documents using triggers:\n\n```sql\n-- Table to track extracted entities\nCREATE TABLE entities (\n  id            bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  document_id   bigint REFERENCES documents(id) ON DELETE CASCADE,\n  entity_name   text NOT NULL,\n  entity_type   text NOT NULL,  -- 'person', 'concept', 'tool', etc.\n  properties    jsonb DEFAULT '{}'\n);\n\nCREATE TABLE entity_relationships (\n  id            bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n  source_id     bigint REFERENCES entities(id) ON DELETE CASCADE,\n  target_id     bigint REFERENCES entities(id) ON DELETE CASCADE,\n  relationship  text NOT NULL,  -- 'mentions', 'depends_on', 'authored_by'\n  properties    jsonb DEFAULT '{}'\n);\n\n-- Indexes for fast graph traversal\nCREATE INDEX ON entity_relationships (source_id);\nCREATE INDEX ON entity_relationships (target_id);\nCREATE INDEX ON entities (entity_type, entity_name);\n```\n\n### GraphRAG with Common Table Expression (CTE)\n\nIf you don't want to install AGE, you can implement graph traversal with recursive CTEs:\n\n```sql\n-- Traverse relationships up to 3 hops from seed entities\nWITH RECURSIVE graph_walk AS (\n  -- Start from entities mentioned in relevant chunks\n  SELECT e.id, e.entity_name, e.entity_type, 0 AS depth\n  FROM entities e\n  JOIN chunks c ON c.document_id = e.document_id\n  WHERE c.id IN (SELECT id FROM seed_chunk_ids)\n\n  UNION ALL\n\n  -- Walk relationships\n  SELECT e2.id, e2.entity_name, e2.entity_type, gw.depth + 1\n  FROM graph_walk gw\n  JOIN entity_relationships er ON er.source_id = gw.id\n  JOIN entities e2 ON e2.id = er.target_id\n  WHERE gw.depth \u003C 3\n)\nSELECT DISTINCT entity_name, entity_type, min(depth) AS min_depth\nFROM graph_walk\nGROUP BY entity_name, entity_type\nORDER BY min_depth, entity_name;\n```\n\n\u003E aside positive\n\u003E You don't need a dedicated graph database for GraphRAG. Postgres recursive CTEs handle most knowledge graph traversal patterns. Apache AGE adds openCypher syntax for complex graph queries, but the relational approach works for simpler graphs.\n\n\u003C!-- ------------------------ --\u003E\n## Snowflake Cortex Integration\nSnowflake Cortex provides serverless AI functions that pair naturally with Postgres. Use Cortex to generate embeddings and run LLM inference at scale, then serve the results from Postgres at application speed.\n\n### Generating Embeddings with Cortex\n\nYou don't need to manage embedding model infrastructure yourself. The `AI_EMBED` function generates vector embeddings on demand:\n\n```sql\n-- Generate an embedding with AI_EMBED (recommended)\nSELECT AI_EMBED('snowflake-arctic-embed-l-v2.0', 'Your text here');\n\n-- Specify output dimensions (model-dependent)\nSELECT AI_EMBED('snowflake-arctic-embed-l-v2.0', 'Your text here');\n```\n\n| Model | Dimensions | Best For |\n|-------|-----------|----------|\n| `snowflake-arctic-embed-l-v2.0` | 1024 | High accuracy, general purpose |\n| `snowflake-arctic-embed-l-v2.0-8k` | 1024 | Longer documents (8K token context) |\n| `snowflake-arctic-embed-m-v1.5` | 768 | Good balance of speed and accuracy |\n| `voyage-multilingual-2` | 1024 | Multilingual content |\n\n### Batch Embedding Generation\n\nGenerate embeddings for an entire table of content in Snowflake:\n\n```sql\n-- Embed all rows in a content table\nCREATE OR REPLACE TABLE my_db.my_schema.embedded_chunks AS\nSELECT\n  id,\n  document_id,\n  chunk_index,\n  content,\n  AI_EMBED('snowflake-arctic-embed-l-v2.0', content) AS embedding\nFROM my_db.my_schema.chunks;\n```\n\n### Scripting Between Snowflake Postgres and Snowflake\n\nSnowflake Postgres and Snowflake can exchange data bidirectionally through **pg_lake stages**. This enables a powerful pattern: export text from Postgres, generate embeddings in Snowflake with Cortex, and write the vectors back to Postgres — all without leaving the Snowflake ecosystem.\n\n**Step 1: Set up the stage connection**\n\n```sql\n-- In Snowflake: create a storage integration pointing to your Postgres instance\nCREATE STORAGE INTEGRATION my_pg_stage_integration\n  TYPE = POSTGRES_INTERNAL_STORAGE\n  POSTGRES_INSTANCE = 'my_postgres_instance';\n\n-- Create a stage for file exchange\nCREATE STAGE my_pg_stage\n  RELATIVE_URL = '/embeddings'\n  STORAGE_INTEGRATION = my_pg_stage_integration;\n```\n\n**Step 2: Export text from Postgres to the stage**\n\n```sql\n-- In Postgres: export content that needs embeddings\nCOPY (\n  SELECT id, content\n  FROM documents\n  WHERE embedding IS NULL\n)\nTO '@STAGE/embeddings/to_embed.parquet'\nWITH (FORMAT 'parquet');\n```\n\n**Step 3: Generate embeddings in Snowflake**\n\n```sql\n-- In Snowflake: read the exported file and generate embeddings\nCREATE OR REPLACE TABLE embedded_results AS\nSELECT\n  $1:id::int AS id,\n  AI_EMBED('snowflake-arctic-embed-l-v2.0', $1:content::text) AS embedding\nFROM @my_pg_stage/to_embed.parquet;\n\n-- Write results back to the stage\nCOPY INTO @my_pg_stage/embeddings_done.parquet\nFROM embedded_results\nFILE_FORMAT = (TYPE = PARQUET);\n```\n\n**Step 4: Load embeddings back into Postgres**\n\n```sql\n-- In Postgres: load the generated embeddings\nCREATE TEMP TABLE embedding_import (\n  id       int,\n  embedding vector(1024)\n);\n\nCOPY embedding_import\nFROM '@STAGE/embeddings/embeddings_done.parquet'\nWITH (FORMAT 'parquet');\n\n-- Update the source table\nUPDATE documents d\nSET embedding = e.embedding\nFROM embedding_import e\nWHERE d.id = e.id;\n```\n\n\u003E aside positive\n\u003E With pg_lake stages, you can script the entire embedding pipeline without any external infrastructure. Postgres exports text, Snowflake generates embeddings with Cortex, and the vectors flow right back — no S3 buckets, no custom ETL code, no third-party orchestrators.\n\n### Syncing Embeddings with Shared Iceberg\n\nFor continuous sync rather than batch scripting, use the **Shared Iceberg** pattern. Postgres writes an Iceberg table that Snowflake reads through a catalog integration:\n\n```sql\n-- In Postgres: create an Iceberg table with your content\nCREATE TABLE documents_iceberg (\n  id      int,\n  content text\n) USING iceberg;\n\n-- In Snowflake: read the table, generate embeddings, and write back via stage\nCREATE OR REPLACE ICEBERG TABLE my_iceberg_docs\n  CATALOG = 'my_postgres_catalog'\n  CATALOG_TABLE_NAME = 'documents_iceberg';\n\n-- Generate embeddings from the synced data\nSELECT id, AI_EMBED('snowflake-arctic-embed-l-v2.0', content) AS embedding\nFROM my_iceberg_docs;\n```\n\n### Embedding Queries at Runtime\n\nFor real-time RAG, you also need to embed user queries. Two approaches:\n\n**Option A: Embed in Snowflake, query in Postgres** — Call Cortex from your application, then pass the vector to Postgres:\n\n```python\n# Python example using Snowflake connector + psycopg\nimport snowflake.connector\nimport psycopg\n\n# Generate query embedding via Cortex\nsf_conn = snowflake.connector.connect(...)\ncursor = sf_conn.cursor()\ncursor.execute(\"\"\"\n    SELECT AI_EMBED('snowflake-arctic-embed-l-v2.0', %s)::ARRAY\n\"\"\", (user_question,))\nquery_embedding = cursor.fetchone()[0]\n\n# Search Postgres with the embedding\npg_conn = psycopg.connect(...)\nresults = pg_conn.execute(\"\"\"\n    SELECT content, 1 - (embedding \u003C=\u003E %s::vector) AS similarity\n    FROM chunks\n    ORDER BY embedding \u003C=\u003E %s::vector\n    LIMIT 5\n\"\"\", (query_embedding, query_embedding)).fetchall()\n```\n\n**Option B: Use the same model via an API** — If you're using an open model like Arctic Embed, you can also run it locally or via a model serving endpoint for query-time embeddings, avoiding the round-trip to Snowflake.\n\n### Cortex AI Functions\n\nBeyond embeddings, Cortex provides additional serverless AI functions you can use to enrich data before storing it in Postgres:\n\n- **`AI_COMPLETE()`** — Run LLM inference (GPT-4, Llama, Mistral, and more) over your data.\n- **`AI_EXTRACT()`** — Extract structured information from text or documents.\n- **`AI_CLASSIFY()`** — Classify text or images into user-defined categories.\n- **`AI_SENTIMENT()`** — Analyze sentiment of text content.\n- **`AI_TRANSLATE()`** — Translate text between languages.\n- **`AI_SUMMARIZE_AGG()`** — Summarize across multiple rows without context window limits.\n\n### Cortex Search\n\nBuild full semantic search applications without managing embedding pipelines. Cortex Search handles chunking, embedding, indexing, and retrieval automatically.\n\n### Integration with Postgres\n\nCombine Snowflake's managed AI services with Postgres's operational database strengths:\n\n- **pg_lake stages** — Bidirectional file exchange for scripting embedding pipelines between Postgres and Snowflake.\n- **Shared Iceberg** — Postgres writes Iceberg tables that Snowflake reads for analytics and AI processing.\n- **Streamlit in Snowflake** — Build interactive AI applications that query both Snowflake and Postgres data.\n- **Hybrid retrieval** — Use Postgres for low-latency operational RAG and Snowflake for analytical queries over the same data.\n\n\u003E aside positive\n\u003E Using Snowflake Cortex for embeddings means you don't need to provision GPU infrastructure, manage model versions, or handle scaling. Generate embeddings at Snowflake scale, then serve them from Postgres at application speed.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion\n### Related Resources\n\n\u003Cbutton\u003E\n\n[🐘 pgvector — Open-source vector similarity search for Postgres](https://github.com/pgvector/pgvector)\n\u003C/button\u003E\n\nThe pgvector extension GitHub repository with installation instructions, distance operators, and indexing documentation.\n\n---\n\n\u003Cbutton\u003E\n\n[🦛 Crunchy Data Blog — AI with Postgres](https://www.crunchydata.com/blog/topic/ai)\n\u003C/button\u003E\n\nCollection of articles on building AI applications with Postgres, including RAG patterns, embedding strategies, and pgvector performance tuning.\n\n---\n\n\u003Cbutton\u003E\n\n[❄️ Snowflake Docs — Cortex AI Functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql)\n\u003C/button\u003E\n\nReference for all Snowflake Cortex AI functions including AI_EMBED, AI_COMPLETE, AI_EXTRACT, and more.\n\n---\n\n\u003Cbutton\u003E\n\n[❄️ Snowflake Docs — Moving data between Snowflake Postgres and Snowflake](https://docs.snowflake.com/en/user-guide/snowflake-postgres/postgres-pg_lake)\n\u003C/button\u003E\n\nGuide to pg_lake stages, Shared Iceberg, and bidirectional data movement for embedding pipelines.\n\n---\n\n\u003Cbutton\u003E\n\n[🐘 Apache AGE — Graph extension for PostgreSQL](https://age.apache.org/)\n\u003C/button\u003E\n\nOpen-source extension that adds graph database capabilities and openCypher query support to Postgres.\n\n","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":true,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-0784212950","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-4b8425bb0d",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-a19f98bf45","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-07-08",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-f1e2c7035b","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake 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