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generate-embeddings.ts
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generate-embeddings.ts
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/**
* Note: this file mostly uses NPM specifiers for its imports.
* Currently, NPM specifiers are incompatible with Fresh.
* This is fine as this file is not used within Fresh in this implementation.
*/
import "std/dotenv/load.ts";
import "xhr";
import { createClient } from "@supabase/supabase-js";
import { createHash } from "node:crypto";
import { walk } from "std/fs/walk.ts";
import GithubSlugger from "github-slugger";
import { fromMarkdown } from "mdast-util-from-markdown";
import { mdxFromMarkdown, MdxjsEsm } from "mdast-util-mdx";
import { toMarkdown } from "mdast-util-to-markdown";
import { toString } from "mdast-util-to-string";
import { mdxjs } from "micromark-extension-mdxjs";
import { Configuration, OpenAIApi } from "openai";
import { u } from "unist-builder";
import { filter } from "unist-util-filter";
import type { Content, Root } from "types/mdast";
import type { ObjectExpression } from "types/estree";
import { ensureGetEnv } from "../utils/env.ts";
/**
* Extracts ES literals from an `estree` `ObjectExpression`
* into a plain JavaScript object.
*/
function getObjectFromExpression(node: ObjectExpression) {
return node.properties.reduce<
Record<string, string | number | bigint | true | RegExp | undefined>
>((object, property) => {
if (property.type !== "Property") {
return object;
}
const key = (property.key.type === "Identifier" && property.key.name) ||
undefined;
const value = (property.value.type === "Literal" && property.value.value) ||
undefined;
if (!key) {
return object;
}
return {
...object,
[key]: value,
};
}, {});
}
/**
* Extracts the `meta` ESM export from the MDX file.
*
* This info is akin to frontmatter.
*/
function extractMetaExport(mdxTree: Root) {
const metaExportNode = mdxTree.children.find((node): node is MdxjsEsm => {
return (
node.type === "mdxjsEsm" &&
node.data?.estree?.body[0]?.type === "ExportNamedDeclaration" &&
node.data.estree.body[0].declaration?.type === "VariableDeclaration" &&
node.data.estree.body[0].declaration.declarations[0]?.id.type ===
"Identifier" &&
node.data.estree.body[0].declaration.declarations[0].id.name === "meta"
);
});
if (!metaExportNode) {
return undefined;
}
const objectExpression =
(metaExportNode.data?.estree?.body[0]?.type === "ExportNamedDeclaration" &&
metaExportNode.data.estree.body[0].declaration?.type ===
"VariableDeclaration" &&
metaExportNode.data.estree.body[0].declaration.declarations[0]?.id
.type === "Identifier" &&
metaExportNode.data.estree.body[0].declaration.declarations[0].id.name ===
"meta" &&
metaExportNode.data.estree.body[0].declaration.declarations[0].init
?.type ===
"ObjectExpression" &&
metaExportNode.data.estree.body[0].declaration.declarations[0].init) ||
undefined;
if (!objectExpression) {
return undefined;
}
return getObjectFromExpression(objectExpression);
}
/**
* Splits a `mdast` tree into multiple trees based on
* a predicate function. Will include the splitting node
* at the beginning of each tree.
*
* Useful to split a markdown file into smaller sections.
*/
function splitTreeBy(tree: Root, predicate: (node: Content) => boolean) {
return tree.children.reduce<Root[]>((trees, node) => {
const [lastTree] = trees.slice(-1);
if (!lastTree || predicate(node)) {
const tree: Root = u("root", [node]);
return trees.concat(tree);
}
lastTree.children.push(node);
return trees;
}, []);
}
type Meta = ReturnType<typeof extractMetaExport>;
type Section = {
content: string;
heading?: string;
slug?: string;
};
type ProcessedMdx = {
checksum: string;
meta: Meta;
sections: Section[];
};
/**
* Processes MDX content for search indexing.
* It extracts metadata, strips it of all JSX,
* and splits it into sub-sections based on criteria.
*/
function processMdxForSearch(content: string): ProcessedMdx {
const checksum = createHash("sha256")
.update(content)
.digest("base64");
const mdxTree = fromMarkdown(content, {
extensions: [mdxjs()],
mdastExtensions: [mdxFromMarkdown()],
});
const meta = extractMetaExport(mdxTree);
// Remove all MDX elements from markdown
const mdTree = filter(
mdxTree,
(node) =>
![
"mdxjsEsm",
"mdxJsxFlowElement",
"mdxJsxTextElement",
"mdxFlowExpression",
"mdxTextExpression",
].includes(node.type),
);
if (!mdTree) {
return {
checksum,
meta,
sections: [],
};
}
const sectionTrees = splitTreeBy(mdTree, (node) => node.type === "heading");
const slugger = new GithubSlugger();
const sections = sectionTrees.map((tree) => {
const [firstNode] = tree.children;
const heading = firstNode.type === "heading"
? toString(firstNode)
: undefined;
const slug = heading ? slugger.slug(heading) : undefined;
return {
content: toMarkdown(tree),
heading,
slug,
};
});
return {
checksum,
meta,
sections,
};
}
abstract class BaseEmbeddingSource {
checksum?: string;
meta?: Meta;
sections?: Section[];
constructor(
public source: string,
public path: string,
public parentPath?: string,
) {}
abstract load(): Promise<{
checksum: string;
meta?: Meta;
sections: Section[];
}>;
}
class MarkdownEmbeddingSource extends BaseEmbeddingSource {
type: "markdown" = "markdown";
constructor(
source: string,
public filePath: string,
public parentFilePath?: string,
) {
const path = filePath.replace(/^docs/, "").replace(/\.mdx?$/, "");
const parentPath = parentFilePath?.replace(/^docs/, "").replace(
/\.mdx?$/,
"",
);
super(source, path, parentPath);
}
async load() {
const contents = await Deno.readTextFile(this.filePath);
const { checksum, meta, sections } = processMdxForSearch(contents);
this.checksum = checksum;
this.meta = meta;
this.sections = sections;
return {
checksum,
meta,
sections,
};
}
}
type EmbeddingSource = MarkdownEmbeddingSource;
async function generateEmbeddings() {
const supabaseClient = createClient(
ensureGetEnv("SUPABASE_URL"),
ensureGetEnv("SUPABASE_SERVICE_ROLE_KEY"),
{
auth: {
persistSession: false,
autoRefreshToken: false,
},
},
);
const embeddingSources: EmbeddingSource[] = [];
for await (const { path } of walk("./docs", { includeDirs: false })) {
embeddingSources.push(new MarkdownEmbeddingSource("guide", path));
}
console.log(`Discovered ${embeddingSources.length} docs`);
for (const embeddingSource of embeddingSources) {
const { type, source, path, parentPath } = embeddingSource;
try {
const { checksum, meta, sections } = await embeddingSource.load();
// Check for existing page in DB and compare checksums
const { data: existingPage } = await supabaseClient
.from("dfods_page")
.select("id, path, checksum, parentPage:parent_page_id(id, path)")
.filter("path", "eq", path)
.maybeSingle()
.throwOnError();
// deno-lint-ignore no-explicit-any
type Singular<T> = T extends any[] ? undefined : T;
// We use checksum to determine if this page & its sections need to be regenerated
if (existingPage?.checksum === checksum) {
const existingParentPage = existingPage?.parentPage as Singular<
typeof existingPage.parentPage
>;
// If parent page changed, update it
if (existingParentPage?.path !== parentPath) {
console.log(
`[${path}] Parent page has changed. Updating to '${parentPath}'...`,
);
const { data: parentPage } = await supabaseClient
.from("dfods_page")
.select()
.filter("path", "eq", parentPath)
.maybeSingle()
.throwOnError();
await supabaseClient
.from("dfods_page")
.update({ parent_page_id: parentPage?.id })
.filter("id", "eq", existingPage.id)
.throwOnError();
}
continue;
}
if (existingPage) {
console.log(
`[${path}] Docs have changed, removing old page sections and their embeddings`,
);
await supabaseClient
.from("dfods_page_section")
.delete()
.filter("page_id", "eq", existingPage.id)
.throwOnError();
}
const { data: parentPage } = await supabaseClient
.from("dfods_page")
.select()
.filter("path", "eq", parentPath)
.maybeSingle()
.throwOnError();
// Create/update page record. Intentionally clear checksum until we
// have successfully generated all page sections.
const { data: page } = await supabaseClient
.from("dfods_page")
.upsert(
{
checksum: null,
path,
type,
source,
meta,
parent_page_id: parentPage?.id,
},
{ onConflict: "path" },
)
.select()
.single()
.throwOnError();
console.log(
`[${path}] Adding ${sections.length} page sections (with embeddings)`,
);
for (const { slug, heading, content } of sections) {
// OpenAI recommends replacing newlines with spaces for best results (specific to embeddings)
const input = content.replace(/\n/g, " ");
try {
const configuration = new Configuration({
apiKey: ensureGetEnv("OPENAI_KEY"),
});
const openai = new OpenAIApi(configuration);
const embeddingResponse = await openai.createEmbedding({
model: "text-embedding-ada-002",
input,
});
if (embeddingResponse.status !== 200) {
throw new Error(
Deno.inspect(embeddingResponse.data, {
showHidden: false,
depth: 2,
}),
);
}
const [responseData] = embeddingResponse.data.data;
await supabaseClient
.from("dfods_page_section")
.insert({
page_id: page!.id,
slug,
heading,
content,
token_count: embeddingResponse.data.usage.total_tokens,
embedding: responseData.embedding,
})
.select()
.single()
.throwOnError();
} catch (err) {
// TODO: decide how to better handle failed embeddings
console.error(
`Failed to generate embeddings for '${path}' page section starting with '${
input.slice(
0,
40,
)
}...'`,
);
throw err;
}
}
// Set page checksum so that we know this page was stored successfully
await supabaseClient
.from("dfods_page")
.update({ checksum })
.filter("id", "eq", page!.id)
.throwOnError();
} catch (err) {
console.error(
`Page '${path}' or one/multiple of its page sections failed to store properly. Page has been marked with null checksum to indicate that it needs to be re-generated.`,
);
console.error(err);
}
}
console.log("Embedding generation complete");
}
async function main() {
await generateEmbeddings();
}
main().catch((err) => console.error(err));