Fix docker pg hardcoded dimensions

init.sql

@@ -1,94 +0,0 @@
--- Enable pgvector extension
-CREATE EXTENSION IF NOT EXISTS vector;
-
--- Create documents table
-CREATE TABLE IF NOT EXISTS documents (
-    external_id VARCHAR PRIMARY KEY,
-    owner JSONB,
-    content_type VARCHAR,
-    filename VARCHAR,
-    doc_metadata JSONB DEFAULT '{}',
-    storage_info JSONB DEFAULT '{}',
-    system_metadata JSONB DEFAULT '{}',
-    additional_metadata JSONB DEFAULT '{}',
-    access_control JSONB DEFAULT '{}',
-    chunk_ids JSONB DEFAULT '[]'
-);
-
--- Create indexes for documents table
-CREATE INDEX IF NOT EXISTS idx_owner_id ON documents USING gin(owner);
-CREATE INDEX IF NOT EXISTS idx_access_control ON documents USING gin(access_control);
-CREATE INDEX IF NOT EXISTS idx_system_metadata ON documents USING gin(system_metadata);
-
--- Create vector_embeddings table
-CREATE TABLE IF NOT EXISTS vector_embeddings (
-    id SERIAL PRIMARY KEY,
-    document_id VARCHAR(255),
-    chunk_number INTEGER,
-    content TEXT,
-    chunk_metadata TEXT,
-    embedding vector(768),
-    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
-);
-
--- Create caches table
-CREATE TABLE IF NOT EXISTS caches (
-    name TEXT PRIMARY KEY,
-    metadata JSON NOT NULL,
-    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
-    updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
-);
-
--- Create vector index
-CREATE INDEX IF NOT EXISTS vector_idx
-ON vector_embeddings USING ivfflat (embedding vector_l2_ops)
-WITH (lists = 100);
-
--- Initialize multi-vector embeddings table for new multi-vector functionality.
-CREATE TABLE IF NOT EXISTS multi_vector_embeddings (
-    id BIGSERIAL PRIMARY KEY,
-    embeddings BIT(128)[]
-);
-
--- Create function for multi-vector similarity computation
-CREATE OR REPLACE FUNCTION max_sim(document bit[], query bit[]) RETURNS double precision AS $$
-    WITH queries AS (
-        SELECT row_number() OVER () AS query_number, *
-        FROM (SELECT unnest(query) AS query) AS foo
-    ),
-    documents AS (
-        SELECT unnest(document) AS document
-    ),
-    similarities AS (
-        SELECT
-            query_number,
-            1.0 - (bit_count(document # query)::float / greatest(bit_length(query), 1)::float) AS similarity
-        FROM queries CROSS JOIN documents
-    ),
-    max_similarities AS (
-        SELECT MAX(similarity) AS max_similarity FROM similarities GROUP BY query_number
-    )
-    SELECT SUM(max_similarity) FROM max_similarities;
-$$ LANGUAGE SQL;
-
--- Create graphs table for knowledge graph functionality
-CREATE TABLE IF NOT EXISTS graphs (
-    id VARCHAR PRIMARY KEY,
-    name VARCHAR NOT NULL,
-    entities JSONB DEFAULT '[]',
-    relationships JSONB DEFAULT '[]',
-    graph_metadata JSONB DEFAULT '{}',
-    document_ids JSONB DEFAULT '[]',
-    filters JSONB DEFAULT NULL,
-    created_at VARCHAR,
-    updated_at VARCHAR,
-    owner JSONB DEFAULT '{}',
-    access_control JSONB DEFAULT '{"readers": [], "writers": [], "admins": []}'
-);
-
--- Create index for graph name and owner for faster lookups
-CREATE INDEX IF NOT EXISTS idx_graph_name ON graphs(name);
-CREATE INDEX IF NOT EXISTS idx_graph_owner ON graphs USING gin(owner);
-
--- Create unique constraint on name scoped by owner
-CREATE UNIQUE INDEX IF NOT EXISTS idx_graph_owner_name ON graphs((owner->>'id'), name);

postgres.dockerfile

@@ -18,8 +18,7 @@ RUN git clone --branch v0.5.1 https://github.com/pgvector/pgvector.git \
 RUN apk del git build-base clang llvm postgresql-dev \
     && rm -rf /pgvector
 
-# Copy initialization scripts
-COPY init.sql /docker-entrypoint-initdb.d/
-
 # Copy data dump
 COPY dump.sql /tmp/dump.sql
+
+# (No database init script necessary — schema is created by the application at runtime)