ENG-1822 Modify upsert_concepts so it accepts embedded content

apps/roam/src/utils/syncDgNodesToSupabase.ts

@@ -19,12 +19,15 @@ import {
 import { fetchEmbeddingsForNodes } from "./upsertNodesAsContentWithEmbeddings";
 import { convertRoamNodeToLocalContent } from "./upsertNodesAsContentWithEmbeddings";
 import type { DGSupabaseClient } from "@repo/database/lib/client";
-import type { Json, CompositeTypes, Enums } from "@repo/database/dbTypes";
+import type { Json, Enums } from "@repo/database/dbTypes";
 import { render as renderToast } from "roamjs-components/components/Toast";
 import internalError from "~/utils/internalError";
-type LocalContentDataInput = Partial<CompositeTypes<"content_local_input">>;
-type AccountLocalInput = CompositeTypes<"account_local_input">;
 import { FatalError } from "@repo/database/lib/contextFunctions";
+import type {
+  LocalConceptDataInput,
+  LocalContentDataInput,
+  LocalAccountDataInput,
+} from "@repo/database/inputTypes";
 
 const SYNC_FUNCTION = "embedding";
 // Minimal interval between syncs of all clients for this task.
@@ -42,8 +45,6 @@ type SyncTaskInfo = {
   shouldProceed: boolean;
 };
 
-type LocalConceptDataInput = Partial<CompositeTypes<"concept_local_input">>;
-
 const chunk = <T>(array: T[], size: number): T[][] => {
   const chunks: T[][] = [];
   for (let i = 0; i < array.length; i += size) {
@@ -388,7 +389,7 @@ export const upsertNodesToSupabaseAsContentWithEmbeddings = async (
   await uploadBatches(chunk(nodesWithEmbeddings, BATCH_SIZE));
 };
 
-const getAllUsers = async (): Promise<AccountLocalInput[]> => {
+const getAllUsers = async (): Promise<LocalAccountDataInput[]> => {
   const query = `[:find ?author_local_id ?author_name
   :keys author_local_id name
   :where
@@ -412,7 +413,7 @@ const getAllUsers = async (): Promise<AccountLocalInput[]> => {
 };
 
 const upsertUsers = async (
-  users: AccountLocalInput[],
+  users: LocalAccountDataInput[],
   supabaseClient: DGSupabaseClient,
   context: SupabaseContext,
 ) => {

apps/roam/src/utils/upsertNodesAsContentWithEmbeddings.ts

@@ -2,9 +2,8 @@ import { type RoamDiscourseNodeData } from "./getAllDiscourseNodesSince";
 import { type SupabaseContext } from "./supabaseContext";
 import { nextApiRoot } from "@repo/utils/execContext";
 import type { DGSupabaseClient } from "@repo/database/lib/client";
-import type { Json, CompositeTypes } from "@repo/database/dbTypes";
-
-type LocalContentDataInput = Partial<CompositeTypes<"content_local_input">>;
+import type { Json } from "@repo/database/dbTypes";
+import type { LocalContentDataInput } from "@repo/database/inputTypes";
 
 const EMBEDDING_BATCH_SIZE = 200;
 const EMBEDDING_MODEL = "openai_text_embedding_3_small_1536";

packages/database/doc/upsert_examples.ts

@@ -0,0 +1,236 @@
+/*
+A few examples of using upsert_authors / upsert_document / upsert_content / upsert_concept etc.
+
+In general, for external references such as `author_id`, you can either:
+
+1. use the database id as is: `author_id`
+2. Use the platform id, with the name transformed such as `author_local_id`
+3. Put the data inline as a subobject, with the name transformed such as `author_inline`
+
+Note that embeddings are always inline.
+
+Here are complete examples:
+*/
+
+import type {
+  LocalAccountDataInput,
+  LocalDocumentDataInput,
+  LocalContentDataInput,
+  LocalConceptDataInput,
+} from "@repo/database/inputTypes";
+import type { Json } from "@repo/database/dbTypes";
+import type { DGSupabaseClient } from "@repo/database/lib/client";
+
+export const demoCode = async (client: DGSupabaseClient) => {
+  const accounts: LocalAccountDataInput[] = [
+    {
+      account_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+      name: "maparent",
+    },
+  ];
+
+  const docs: LocalDocumentDataInput[] = [
+    {
+      source_local_id: "page1_uid",
+      created: "2000/01/01",
+      last_modified: "2001/01/02",
+      author_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+    },
+  ];
+
+  const contents: LocalContentDataInput[] = [
+    {
+      author_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+      author_inline: {
+        account_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+        name: "maparent",
+      },
+      document_inline: {
+        source_local_id: "page1_uid",
+        created: "2000/01/01",
+        last_modified: "2001/01/02",
+        author_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+      },
+      source_local_id: "a_roam_uid",
+      scale: "document",
+      created: "2000/01/01",
+      last_modified: "2001/01/02",
+      text: "[[CLM]] a claim",
+    },
+    {
+      author_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+      document_local_id: "page1_uid",
+      source_local_id: "a_roam_uid2",
+      scale: "section",
+      created: "2000/01/02",
+      last_modified: "2001/01/03",
+      part_of_local_id: "a_roam_uid",
+      text: "Some subsection",
+    },
+    {
+      author_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+      document_inline: docs[0],
+      source_local_id: "a_roam_uid3",
+      scale: "paragraph",
+      created: "2000/01/02",
+      last_modified: "2001/01/03",
+      part_of_local_id: "a_roam_uid2",
+      text: "Some paragraph",
+      embedding_inline: {
+        model: "openai_text_embedding_3_small_1536",
+        vector: [0], // assume that the vector has the requisite length
+      },
+    },
+  ];
+
+  const concepts: LocalConceptDataInput[] = [
+    {
+      author_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+      source_local_id: "a_roam_uid3",
+      created: "2000/01/01",
+      last_modified: "2001/01/02",
+      name: "Some subsubtext",
+      schema_represented_by_local_id: "known_claim_schema_local_id",
+    },
+  ];
+
+  // Base scenario: insert references in order of dependencies
+  // 1. upsert accounts.
+  {
+    const { data, error } = await client.rpc("upsert_accounts_in_space", {
+      space_id_: 12,
+      accounts,
+    });
+    if (error) console.error(error);
+    console.log(data);
+  }
+
+  // 2. upsert documents.
+  {
+    const { data, error } = await client.rpc("upsert_documents", {
+      v_space_id: 12,
+      data: docs as Json,
+    });
+    if (error) console.error(error);
+    console.log(data);
+  }
+
+  // 3. content
+  {
+    const { data, error } = await client.rpc("upsert_content", {
+      v_space_id: 12,
+      data: contents as Json,
+      v_creator_id: 63,
+    });
+    if (error) console.error(error);
+    console.log(data);
+  }
+
+  // 4. upsert concept
+  {
+    const { data, error } = await client.rpc("upsert_concepts", {
+      v_space_id: 12,
+      data: concepts as Json,
+    });
+    if (error) console.error(error);
+    console.log(data);
+  }
+
+  // Variant: if all content is known to also be a document, you can upsert both as if it were a single object
+  const pageContents: LocalContentDataInput[] = [
+    {
+      author_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+      source_local_id: "a_page_uid2",
+      created: "2000/01/02",
+      last_modified: "2001/01/03",
+      text: "Some other page",
+    },
+  ];
+
+  {
+    const { data, error } = await client.rpc("upsert_content", {
+      v_space_id: 12,
+      data: pageContents as Json,
+      v_creator_id: 63,
+      content_as_document: true,
+    });
+    if (error) console.error(error);
+    console.log(data);
+  }
+
+  // Nesting: you can nest information. Nested types can inherit information from the type above.
+  // Eg 1: upserting content with nested documents and authors
+  const contentWithNestedDoc: LocalContentDataInput[] = [
+    {
+      author_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+      source_local_id: "a_page_uid3",
+      created: "2000/01/02",
+      scale: "document",
+      last_modified: "2001/01/03",
+      text: "Yet another page",
+      author_inline: {
+        account_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+        name: "maparent",
+      },
+      document_inline: {
+        source_local_id: "a_page_uid3",
+        // note that created, last_modified and author_inline will be inherited
+      },
+    },
+  ];
+  {
+    const { data, error } = await client.rpc("upsert_content", {
+      v_space_id: 12,
+      data: contentWithNestedDoc as Json,
+      v_creator_id: 63,
+    });
+    if (error) console.error(error);
+    console.log(data);
+  }
+
+  // eg 2: Nesting multiple contents in a concept
+  const conceptsWithNestedContent: LocalConceptDataInput[] = [
+    {
+      author_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+      source_local_id: "a_roam_uid4",
+      created: "2000/01/01",
+      last_modified: "2001/01/02",
+      name: "[[CLM]] another claim",
+      schema_represented_by_local_id: "known_claim_schema_local_id",
+      author_inline: {
+        account_local_id: "sR22zZ470dNPkIf9PpjQXXdTBjG2",
+        name: "maparent",
+      },
+      document_inline: {
+        source_local_id: "a_page_uid3",
+        // note that created, last_modified and author_inline will be inherited
+      },
+      contents_inline: [
+        {
+          text: "The claim page contents",
+          variant: "full",
+          // here, source_local_id, document_inline, author_inline, created, last_modified are inherited
+        },
+        {
+          text: "[[CLM]] another claim",
+          variant: "direct",
+          embedding_inline: {
+            model: "openai_text_embedding_3_small_1536",
+            vector: [0], // assume that the vector has the requisite length
+            // again source_local_id etc. are inherited
+          },
+          // same variables are intherited
+        },
+      ],
+    },
+  ];
+
+  {
+    const { data, error } = await client.rpc("upsert_concepts", {
+      v_space_id: 12,
+      data: conceptsWithNestedContent as Json,
+    });
+    if (error) console.error(error);
+    console.log(data);
+  }
+};