{"id":786,"date":"2026-05-13T08:47:14","date_gmt":"2026-05-13T08:47:14","guid":{"rendered":"https:\/\/datascientists.info\/?p=786"},"modified":"2026-05-13T08:47:15","modified_gmt":"2026-05-13T08:47:15","slug":"postgres-unified-graph-rag","status":"publish","type":"post","link":"https:\/\/datascientists.info\/index.php\/2026\/05\/13\/postgres-unified-graph-rag\/","title":{"rendered":"Unified Graph-RAG in a Single Postgres Engine"},"content":{"rendered":"\n<p>Our production benchmarks confirm that consolidating Hybrid Graph-RAG into a single PostgreSQL instance via <code><a href=\"https:\/\/github.com\/pgvector\/pgvector\">pgvector<\/a><\/code> and <a href=\"https:\/\/age.apache.org\/\">Apache AGE<\/a> reduced cross-service network latency and eliminated the consistency lag inherent in multi-database synchronization.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"572\" src=\"https:\/\/datascientists.info\/wp-content\/uploads\/2026\/04\/image-5.png\" alt=\"A technical infographic illustrating the unified &quot;Hybrid Graph-RAG&quot; architecture utilizing PostgreSQL. On the left, &quot;Vector Data (pgvector)&quot; is shown as lists of raw data points merging with &quot;Graph Data (Apache Age)&quot; visualization (nodes and edges). Both streams flow into a central &quot;UNIFIED&quot; PostgreSQL server icon. Below, a &quot;Hybrid Retrieval pipeline&quot; panel details single-query execution, combining vector search and multi-hop traversal via code. Data tables inside the server show &quot;embedding vectors&quot; and &quot;JSONB graph properties&quot; linking to a &quot;Transactional Sync &amp; Schema Enforcement&quot; process. The final output on the right is the &quot;Unified Retrieval Context,&quot; ready for an LLM prompt. The entire setup is labeled as a &quot;PostgreSQL 16 Server.&quot;\" class=\"wp-image-788\" srcset=\"https:\/\/datascientists.info\/wp-content\/uploads\/2026\/04\/image-5.png 1024w, https:\/\/datascientists.info\/wp-content\/uploads\/2026\/04\/image-5-300x168.png 300w, https:\/\/datascientists.info\/wp-content\/uploads\/2026\/04\/image-5-768x429.png 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">The Unified Postgres Architecture<\/h2>\n\n\n\n<p>We enforce a unified data layer by storing vector embeddings and graph property data within the same relational clusters. This allows us to execute retrieval in a single database transaction, ensuring that the graph state and vector state remain synchronized without external coordination logic.<\/p>\n\n\n\n<p>We configured the system to use <code>pgvector<\/code> for HNSW indexing and Apache AGE for Cypher query execution. We observed that while AGE handles the graph traversal, the underlying storage remains standard Postgres tables, allowing us to perform relational joins against the retrieved nodes without external ETL pipelines.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Schema Enforcement and Extension Configuration<\/h3>\n\n\n\n<p>We do not allow unstructured data entry. We enforce a schema where vectors are stored in a dedicated column with a fixed dimension of $1536$.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n-- We enable the necessary extensions within the system schema\nCREATE EXTENSION IF NOT EXISTS age;\nCREATE EXTENSION IF NOT EXISTS vector;\n\n-- We set the search path to include the age namespace for Cypher execution\nSET search_path = ag_catalog, &quot;$user&quot;, public;\n\n-- We initialize the graph container\nSELECT create_graph(&#039;kg_network&#039;);\n\n-- We enforce a structured table for the vector component of the nodes\nCREATE TABLE IF NOT EXISTS entity_embeddings (\n    id UUID PRIMARY KEY,\n    entity_name TEXT,\n    embedding vector(1536),\n    metadata JSONB\n);\n\n-- We create an HNSW index for sub-10ms similarity search\nCREATE INDEX ON entity_embeddings USING hnsw (embedding vector_cosine_ops)\nWITH (m = 16, ef_construction = 64);\n\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\">Integrated Ingestion Pipeline<\/h3>\n\n\n\n<p>We deployed a unified ingestion function that writes to both the AGE graph and the <code>pgvector<\/code> table. We observed that performing these writes in a single transaction reduced our ingestion failure rate, as there is no risk of a &#8220;partial commit&#8221; where a node exists in the graph but its embedding fails to write to a secondary store.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\ndef ingest_node(conn, entity_data, embedding):\n    with conn.cursor() as cur:\n        # Step 1: Insert into Apache AGE graph\n        cur.execute(&quot;&quot;&quot;\n            SELECT * FROM cypher(&#039;kg_network&#039;, $$\n            CREATE (v:Entity {name: %s, type: %s})\n            RETURN id(v)\n            $$, &#x5B;%s, %s])\n        &quot;&quot;&quot;, (entity_data&#x5B;&#039;name&#039;], entity_data&#x5B;&#039;type&#039;]))\n        \n        # Step 2: Insert into pgvector table for similarity search\n        cur.execute(&quot;&quot;&quot;\n            INSERT INTO entity_embeddings (id, entity_name, embedding)\n            VALUES (gen_random_uuid(), %s, %s)\n        &quot;&quot;&quot;, (entity_data&#x5B;&#039;name&#039;], embedding))\n        \n        conn.commit()\n\n<\/pre><\/div>\n\n\n<h2 class=\"wp-block-heading\">The Single-Query Retrieval Logic<\/h2>\n\n\n\n<p>Our retrieval engine executes a two-stage process within the same database session. We observed that by keeping the data in Postgres, we can use Common Table Expressions (CTEs) to feed the results of a vector search directly into a graph traversal.<\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li><strong>Semantic Retrieval:<\/strong> We query <code>entity_embeddings<\/code> for the top $k=15$ entities.<\/li>\n\n\n\n<li><strong>Relational Traversal:<\/strong> We pass those entity names into an AGE Cypher query to find neighbors.<\/li>\n\n\n\n<li><strong>Filtered Aggregation:<\/strong> We join the results back to relational tables for final context grounding.<\/li>\n<\/ol>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n-- We use a CTE to combine vector and graph lookups\nWITH vector_matches AS (\n    SELECT entity_name, metadata\n    FROM entity_embeddings\n    ORDER BY embedding &amp;lt;=&gt; %s -- Cosine distance\n    LIMIT 15\n)\nSELECT * FROM cypher(&#039;kg_network&#039;, $$\n    MATCH (e:Entity)-&#x5B;r:DEPENDS_ON]-&gt;(neighbor)\n    WHERE e.name IN $entity_list\n    RETURN e.name, type(r), neighbor.name\n$$) AS (source_name agtype, edge_type agtype, target_name agtype);\n\n<\/pre><\/div>\n\n\n<p>We settled on the <code>&lt;=&gt;<\/code> operator for cosine distance. During initial testing, we attempted to use Euclidean distance (<code>&lt;-&gt;<\/code>), but the $F_1$ score for our specific embedding model decayed because the model was optimized for angular similarity.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Operational Frustrations and Unresolved Hacks<\/h2>\n\n\n\n<p>We have not resolved the <strong>AGE-to-PG Join Complexity<\/strong>. Apache AGE returns data in a custom <code>agtype<\/code> format. We are currently hacking the transformation by casting <code>agtype<\/code> to <code>text<\/code> and then back to <code>jsonb<\/code> to join with standard relational tables. This adds a measurable overhead to our retrieval pipeline which we have not yet optimized out of the driver layer.<\/p>\n\n\n\n<p>We are also hacking around <strong>Parallel Index Scans<\/strong>. Currently, Postgres does not effectively parallelize HNSW index scans when combined with complex AGE graph traversals in a single execution plan. This forces us to rely on high single-core clock speeds. We are currently testing a manual sharding approach, but it has not reached production stability.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Memory and Performance Tuning<\/h2>\n\n\n\n<p>We configured <code>shared_buffers<\/code> to $25\\%$ of total RAM to ensure the HNSW index stays resident. It was then observed that if the <code>pgvector<\/code> index spills to disk, retrieval latency increases significantly.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n# Postgres Configuration for Hybrid Graph-Vector Workloads\nmax_connections = 300\nshared_buffers = 16GB\nwork_mem = 64MB\nmaintenance_work_mem = 2GB\neffective_cache_size = 48GB\n\n<\/pre><\/div>\n\n\n<p>We enforce a <code>maintenance_work_mem<\/code> of <strong>2GB<\/strong> specifically for HNSW index builds. We discovered that lower values caused the index construction to thrash, increasing our build time for a $1M$ vector set from minutes to hours.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Grounding and Context Injection<\/h2>\n\n\n\n<p>We linearized the output of the AGE traversal into the prompt. Because all data is in Postgres, we can now include relational metadata (e.g., <code>last_updated<\/code>, <code>author_reliability_score<\/code>) that was previously trapped in isolated silos.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\ndef build_grounded_context(cursor, query_vector):\n    # Vector Search\n    cursor.execute(&quot;SELECT entity_name FROM entity_embeddings ORDER BY embedding &amp;lt;=&gt; %s LIMIT 10&quot;, (query_vector,))\n    entities = &#x5B;row&#x5B;0] for row in cursor.fetchall()]\n\n    # Graph Traversal via AGE\n    cursor.execute(&quot;&quot;&quot;\n        SELECT * FROM cypher(&#039;kg_network&#039;, $$\n            MATCH (n:Entity)-&#x5B;r]-&gt;(m)\n            WHERE n.name ANY($list)\n            RETURN n.name, type(r), m.name\n        $$, &#x5B;%s])\n    &quot;&quot;&quot;, (entities,))\n    \n    # We observed that formatting as &#039;Source -&gt; Relation -&gt; Target&#039; \n    # provides the clearest signal for the LLM&#039;s reasoning engine.\n\n<\/pre><\/div>\n\n\n<p>We found that removing the network hop between separate databases reduced our total &#8220;Time to First Token&#8221; by eliminating the overhead of multiple TLS handshakes and serialized data transfer between disparate environments.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Our production benchmarks confirm that consolidating Hybrid Graph-RAG into a single PostgreSQL instance via pgvector and Apache AGE reduced cross-service network latency and eliminated the consistency lag inherent in multi-database synchronization. The Unified Postgres Architecture We enforce a unified data layer by storing vector embeddings and graph property data within the same relational clusters. This [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[125,137],"tags":[136,165,166,138],"ppma_author":[144,145],"class_list":["post-786","post","type-post","status-publish","format-standard","hentry","category-data-engineering","category-generative-ai","tag-genai","tag-graphrag","tag-postgres","tag-rag","author-marc","author-saidah"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Unified Graph-RAG in a Single Postgres Engine - DATA DO - \u30c7\u30fc\u30bf \u9053<\/title>\n<meta name=\"description\" content=\"Switching from multi-database RAG to unified Postgres (pgvector + Apache AGE) eliminated our sync lag and network overhead. 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