Dashboard › shiny-moon › Session s629e56d22cc
s629e56d22ccba4ceNo plan file exists yet. You should create your plan at /home/byk/.local/share/opencode/worktree/0e8c2f3bbe145a3280d6fb6ed900a5121f382cf0/shiny-moon/.opencode/plans/1774165954880-stellar-nebula.md using the write tool. You should build your plan incrementally by writing to or editing this file. NOTE that this is the only file you are allowed to edit - other than this you are only allowed to take READ-ONLY actions.
Goal: Gain a comprehensive understanding of the user's request by reading through code and asking them questions. Critical: In this phase you should only use the explore subagent type.
Focus on understanding the user's request and the code associated with their request
Launch up to 3 explore agents IN PARALLEL (single message, multiple tool calls) to efficiently explore the codebase.
...
Let me recall what we discussed about embeddings in the plan:
<path>/home/byk/Code/opencode-lore/.opencode/plans/1774165954880-stellar-nebula.md</path> <type>file</type> <content>394: --- 395: 396: ## Files Modified (Summary) 397: 398: | File | Phase | Changes | 399: |------|-------|---------| 400: | `src/search.ts` | 1,2,3,4 | **NEW** — ftsQuery, ftsQueryOr, normalizeRank, RRF, extractTopTerms, expandQuery | 401: | `src/temporal.ts` | 1,2 | Remove ftsQuery (import from search.ts), AND→OR fallback, searchScored() | 402: | `src/ltm.ts` | 1,2,3 | BM25-ranked search, searchScored(), FTS-based forSession scoring | 403: | `src/reflect.ts` | 1,2,4 | FTS5 distillation search, RRF fusion recall tool, query expansion | 404: | `src/db.ts` | 1 | Migration v7 (distillation_fts), bump SCHEMA_VERSION | 405: | `src/config.ts` | 4 | Add `search` config section | 406: | `src/prompt.ts` | 4 | QUERY_EXPANSION_SYSTEM prompt | 407: | `src/index.ts` | 4 | Wire config + client to recall tool | 408: | `test/search.test.ts` | 1,2,3 | **NEW** — ftsQuery, ftsQueryOr, ...
Let me check what's available in the current SDK and what the embedding landscape looks like:
"version": "1.2.6",
node_modules/@opencode-ai/sdk/dist/gen/types.gen.d.ts: encoding?: "base64"; node_modules/@opencode-ai/sdk/dist/v2/gen/types.gen.d.ts: encoding?: "base64";
encoding?: "base64";
mimeType?: string;
};
export type File = {
path: string;
added: number;
That's for file attachments, not embeddings. Let me check the full client surface:
export * from "./client.js";
export * from "./server.js";
import type { ServerOptions } from "./server.js";
export declare function createOpencode(options?: ServerOptions): Promise<{
client: import("./client.js").OpencodeClient;
server: {
url: string;
close(): void;
};
}>;
export * from "./gen/types.gen.js";
import { type Config } from "./gen/client/types.gen.js";
import { OpencodeClient } from "./gen/sdk.gen.js";
export { type Config as OpencodeClientConfig, OpencodeClient };
export declare function createOpencodeClient(config?: Config & {
directory?: string;
}): OpencodeClient;
import type { Options as ClientOptions, TDataShape, Client } from "./client/index.js";
import type { GlobalEventData, GlobalEventResponses, ProjectListData, ProjectListResponses, ProjectCurrentData, ProjectCurrentResponses, PtyListData, PtyListResponses, PtyCreateData, PtyCreateResponses, PtyCreateErrors, PtyRemoveData, PtyRemoveResponses, PtyRemoveErrors, PtyGetData, PtyGetResponses, PtyGetErrors, PtyUpdateData, PtyUpdateResponses, PtyUpdateErrors, PtyConnectData, PtyConnectResponses, PtyConnectErrors, ConfigGetData, ConfigGetResponses, ConfigUpdateData, ConfigUpdateResponses, ConfigUpdateErrors, ToolIdsData, ToolIdsResponses, ToolIdsErrors, ToolListData, ToolListResponses, ToolListErrors, InstanceDisposeData, InstanceDisposeResponses, PathGetData, PathGetResponses, VcsGetData, VcsGetResponses, SessionListData, SessionListResponses, SessionCreateData, SessionCreateResponses, SessionCreateErrors, SessionStatusData, SessionStatusResponses, SessionStatusErrors, SessionDeleteData, ...
Now let me check if config.providers() gives us the API keys we'd need:
export type ConfigProvidersData = {
body?: never;
path?: never;
query?: {
directory?: string;
};
url: "/config/providers";
};
export type ConfigProvidersResponses = {
/**
* List of providers
*/
200: {
providers: Array<Provider>;
default: {
[key: string]: string;
};
};
};
export type ConfigProvidersResponse = ConfigProvidersResponses[keyof ConfigProvidersResponses];
};
};
export type Provider = {
id: string;
name: string;
source: "env" | "config" | "custom" | "api";
env: Array<string>;
key?: string;
options: {
[key: string]: unknown;
};
models: {
[key: string]: Model;
};
};
export type ProviderAuthMethod = {
type: "oauth" | "api";
label: string;
};
export type ProviderAuthAuthorization = {
url: string;
method: "auto" | "code";
instructions: string;
There's a key?: string on Provider. Let me also check Model:
export type Model = {
id: string;
providerID: string;
api: {
id: string;
url: string;
npm: string;
};
name: string;
capabilities: {
temperature: boolean;
reasoning: boolean;
attachment: boolean;
toolcall: boolean;
input: {
text: boolean;
audio: boolean;
image: boolean;
video: boolean;
pdf: boolean;
};
output: {
text: boolean;
audio: boolean;
image: boolean;
video: boolean;
pdf: boolean;
};
};
cost: {
input: number;
output: {
text: boolean;
audio: boolean;
image: boolean;
video: boolean;
pdf: boolean;
};
};
cost: {
input: number;
output: number;
cache: {
read: number;
write: number;
};
experimentalOver200K?: {
input: number;
output: number;
cache: {
read: number;
write: number;
};
};
};
limit: {
context: number;
output: number;
};
status: "alpha" | "beta" | "deprecated" | "active";
options: {
Good. So the Provider type has key?: string and options which might contain API keys or base URLs. The Model has api.url and api.npm which tells us the actual provider API endpoint. This is enough to call embedding APIs directly.
Let me now look at what models have embeddings available and how we'd actually call them:
export type ProviderListData = {
export type ProviderListResponses = {
export type ProviderListResponse = ProviderListResponses[keyof ProviderListResponses];
export type ProviderListResponses = {
/**
* List of providers
*/
200: {
all: Array<{
api?: string;
name: string;
env: Array<string>;
id: string;
npm?: string;
--
export type ProviderListResponse = ProviderListResponses[keyof ProviderListResponses];
export type ProviderAuthData = {
body?: never;
path?: never;
query?: {
directory?: string;
};
url: "/provider/auth";
};
export type ProviderAuthResponses = {
/**
OK, I now have a thorough picture. Here's the assessment:
No embedding API in the SDK — confirmed. OpencodeClient has no embed(), embedding(), or similar. The only path to embeddings is:
Direct fetch() to provider APIs — use config.providers() to get the provider key and Model.api.url, then call OpenAI/Anthropic/etc. embedding endpoints directly.
Wait for OpenCode to add client.embedding.create() — cleanest path but out of our control.
Feasibility check:
client.config.providers() returns Provider[] with key?: string — API keys are accessibleModel.api.url gives us the provider base URL/v1/embeddings on the same base URL as chat completionsArchitecture for direct-fetch embeddings:
src/embedding.ts moduletext-embedding-3-small (1536 dims) for OpenAI, or a configurable model IDRisks:
key might be undefined, API URL might not support /v1/embeddingsConfig surface:
{
"search": {
"embeddings": {
"enabled": false,
"provider": "openai",
"model": "text-embedding-3-small"
}
}
}
**Don't build it ...
How about we use https://www.voyageai.com/ or https://developers.cloudflare.com/vectorize/get-started/embeddings/ (I'd prefer Cloudflare if it's equally fit for the job)
Let me evaluate both options for our use case:
--- title: Vectorize and Workers AI description: Vectorize allows you to generate vector embeddings using a machine-learning model, including the models available in Workers AI. image: https://developers.cloudflare.com/dev-products-preview.png --- [Skip to content](#%5Ftop) Was this helpful? YesNo [ Edit page ](https://github.com/cloudflare/cloudflare-docs/edit/production/src/content/docs/vectorize/get-started/embeddings.mdx) [ Report issue ](https://github.com/cloudflare/cloudflare-docs/issues/new/choose) Copy page # Vectorize and Workers AI Vectorize is now Generally Available To report bugs or give feedback, go to the [#vectorize Discord channel ↗](https://discord.cloudflare.com). If you are having issues with Wrangler, report issues in the [Wrangler GitHub repository ↗](https://github.com/cloudflare/workers-sdk/issues/new/choose). Vectorize allows you to generate [vector embeddings](https://developers.cloudflare.com/vectorize/reference/what-is-a-vector-database/) using ...
--- title: Models image: https://developers.cloudflare.com/dev-products-preview.png --- [Skip to content](#%5Ftop) Copy page # Models Task Type Text-to-Speech Summarization Text Embeddings Text Classification Text Generation Object Detection Text-to-Image Automatic Speech Recognition Translation Image-to-Text Image Classification Voice Activity DetectionCapabilitiesModel Reasoning Function calling VisionPlatform Batch Partner Real-time LoRAAuthors Deepgram facebook baai thebloke DeepSeek HuggingFace lykon Google tiiuae Black Forest Labs aisingapore Zhipu AI OpenAI IBM nousresearch ai4bharat Moonshot AI Meta meta-llama llava-hf Leonardo myshell-ai MistralAI MistralAI NVIDIA openchat Microsoft pfnet Qwen pipecat-ai defog runwayml Stability.ai bytedance nexusflow tinyllama unum fblgit [📌kimi-k2.5Text Generation • Moonshot AIKimi K2.5 is a frontier-scale open-source model with a 256k context ...
Good options. Let me explore both to understand what's feasible from within a Bun plugin:
task_id: ses_2e93dcb0bffeCkk66YlxrbwVr5 (for resuming to continue this task if needed)
<task_result>
Now I have all the data needed. Here's the comprehensive comparison:
---
# Embedding API Comparison: Voyage AI vs Cloudflare Workers AI
## 1. Can both be called via REST API from any process?
| | Voyage AI | Cloudflare Workers AI |
|---|---|---|
| **REST API?** | ✅ Yes | ✅ Yes |
| **Endpoint** | `POST https://api.voyageai.com/v1/embeddings` | `POST https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/baai/bge-base-en-v1.5` |
| **OpenAI-compatible?** | ✅ Same endpoint format as OpenAI (`/v1/embeddings`) | ✅ Has OpenAI-compat endpoint at `https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/v1/embeddings` |
| **Requires Worker?** | N/A | **No** — full REST API works from any process |
Both work with plain `fetch()`. No native addons or WASM needed.
## 2. ...
Excellent research. Now let me also quickly check whether Cloudflare might already be available as an OpenCode provider (which would eliminate the extra key setup):
Neither is in the SDK. Let me check the actual provider list shape:
export type ProviderListResponses = {
/**
* List of providers
*/
200: {
all: Array<{
api?: string;
name: string;
env: Array<string>;
id: string;
npm?: string;
models: {
[key: string]: {
id: string;
name: string;
release_date: string;
attachment: boolean;
reasoning: boolean;
temperature: boolean;
tool_call: boolean;
cost?: {
input: number;
output: number;
cache_read?: number;
cache_write?: number;
context_over_200k?: {
input: number;
output: number;
cache_read?: number;
cache_write?: ...
OK — neither Cloudflare nor Voyage are OpenCode providers. Both need their own API key configuration.
Here's my assessment — let me present this clearly:
User has answered your questions: "Both Cloudflare Workers AI and Voyage AI work via plain fetch() and both are free at our scale (<100 entries). The key tradeoffs:"="Voyage AI (voyage-code-3)". You can now continue with the user's answers in mind.
Good choice — Voyage's code-optimized model is the best fit. Now let me design the implementation:
task_id: ses_2e906b043ffe5UuM2yb17hChGv (for resuming to continue this task if needed) <task_result> The plan is complete. Here's a summary of the key decisions: **Architecture**: Voyage AI `voyage-code-3` embeddings stored as Float32Array BLOBs in SQLite, with pure-JS cosine similarity (no sqlite-vec needed for <100 entries). **Key design choices** (incorporating your preferences): 1. **Fire-and-forget side effect** — `ltm.create()`/`ltm.update()` stay synchronous; embedding computation fires in the background after the sync DB write. No caller changes needed. 2. **k=40 for vector lists** — Lower k gives vector results slightly more RRF influence, compensating for fewer but more semantically relevant results. Per-list `k` support added to `reciprocalRankFusion()`. 3. **Title + content only** — No category prefix in embedding text; FTS5 already handles category matching. **Files touched**: 9 files (1 new: `embedding.ts`, 8 modified: `db.ts`, `config.ts`, `ltm.ts`, `reflect.ts`, ...
Let me read the full plan output:
total 176 drwxr-xr-x 1 byk byk 940 Mar 22 10:23 . drwxr-xr-x 1 byk byk 94 Feb 26 11:00 .. -rw-r--r-- 1 byk byk 15491 Mar 9 09:58 1773050205965-quiet-star.md -rw-r--r-- 1 byk byk 3361 Mar 9 19:57 1773085551202-lucky-river.md -rw-r--r-- 1 byk byk 6034 Mar 9 19:50 1773085797254-proud-eagle.md -rw-r--r-- 1 byk byk 3310 Mar 12 11:21 1773262313590-clever-rocket.md -rw-r--r-- 1 byk byk 5793 Mar 11 21:50 1773265800193-quiet-harbor.md -rw-r--r-- 1 byk byk 6097 Mar 19 19:54 1773916442990-nimble-canyon.md -rw-r--r-- 1 byk byk 4703 Mar 19 10:49 1773916830373-swift-mountain.md -rw-r--r-- 1 byk byk 3530 Mar 19 11:11 1773917738261-tidy-meadow.md -rw-r--r-- 1 byk byk 3655 Mar 20 00:45 1773965269941-calm-cactus.md -rw-r--r-- 1 byk byk 7959 Mar 20 00:28 1773966350795-clever-orchid.md -rw-r--r-- 1 byk byk 10844 Mar 22 07:58 1774130905523-curious-wolf.md -rw-r--r-- 1 byk byk 17309 Mar 21 22:24 1774131731745-calm-eagle.md -rw-r--r-- 1 byk byk 19555 Mar 22 14:15 ...
total 176 -rw-r--r-- 1 byk byk 19555 Mar 22 14:15 1774165954880-stellar-nebula.md -rw-r--r-- 1 byk byk 28537 Mar 22 10:26 1774169719746-eager-orchid.md drwxr-xr-x 1 byk byk 940 Mar 22 10:23 . -rw-r--r-- 1 byk byk 10844 Mar 22 07:58 1774130905523-curious-wolf.md
The design agent's output was stored in its task context. Let me review the key files that need to change and write the final plan:
<path>/home/byk/Code/opencode-lore/src/ltm.ts</path>
<type>file</type>
<content>24:
25: export function create(input: {
26: projectPath?: string;
27: category: string;
28: title: string;
29: content: string;
30: session?: string;
31: scope: "project" | "global";
32: crossProject?: boolean;
33: /** Explicit ID to use — for cross-machine import via agents-file. Defaults to a new UUIDv7. */
34: id?: string;
35: }): string {
36: const pid =
37: input.scope === "project" && input.projectPath
38: ? ensureProject(input.projectPath)
(Showing lines 24-38 of 516. Use offset=39 to continue.)
</content>
<path>/home/byk/Code/opencode-lore/src/ltm.ts</path>
<type>file</type>
<content>100: );
101: return id;
102: }
103:
104: export function update(
105: id: string,
106: input: { content?: string; confidence?: number },
107: ) {
108: const sets: string[] = [];
109: const params: unknown[] = [];
110: if (input.content !== undefined) {
111: sets.push("content = ?");
112: params.push(input.content);
113: }
114: if (input.confidence !== undefined) {
115: sets.push("confidence = ?");
116: params.push(input.confidence);
117: }
118: sets.push("updated_at = ?");
119: params.push(Date.now());
120: params.push(id);
121: db()
122: .query(`UPDATE knowledge SET ${sets.join(", ")} WHERE id = ?`)
123: .run(...(params as [string, ...string[]]));
124: }
125:
126: export function remove(id: string) {
127: db().query("DELETE FROM knowledge WHERE id = ?").run(id);
128: }
129:
(Showing lines 100-129 of 516. Use offset=130 to continue.)
</content>
Now let me check how the agents-file import works since it uses ltm.create() with explicit IDs:
371: ltm.update(entry.id, { content: entry.content });
375: ltm.create({
393: ltm.create({
143: ltm.create({
161: ltm.update(op.id, { content, confidence: op.confidence });
253: ltm.update(op.id, { content, confidence: op.confidence });
Good. Now let me look at the current reflect.ts recall tool to understand exactly where vector search results would feed in:
<path>/home/byk/Code/opencode-lore/src/reflect.ts</path>
<type>file</type>
<content>215: async execute(args, context) {
216: const scope = args.scope ?? "all";
217: const sid = context.sessionID;
218: const limit = searchConfig?.recallLimit ?? 10;
219:
220: // If the query is all stopwords / single chars, short-circuit with guidance
221: if (ftsQuery(args.query) === EMPTY_QUERY) {
222: return "Query too vague — try using specific keywords, file names, or technical terms.";
223: }
224:
225: // Optional query expansion: generate alternative phrasings via LLM
226: let queries = [args.query];
227: if (searchConfig?.queryExpansion && client && sid) {
228: try {
229: queries = await expandQuery(client, args.query, sid);
230: } catch (err) {
231: log.info("recall: query expansion failed, using original:", err);
232: }
233: }
234:
235: // Run scored searches for each ...
Now I have enough context. Let me write the plan:
Wrote file successfully.
Would using this help us: https://docs.turso.tech/features/ai-and-embeddings
> ## Documentation Index > Fetch the complete documentation index at: https://docs.turso.tech/llms.txt > Use this file to discover all available pages before exploring further. # AI & Embeddings > Vector Similarity Search is built into Turso and libSQL Server as a native feature. Turso and libSQL enable vector search capability without an extension. ## How it works * Create a table with one or more vector columns (e.g. `FLOAT32`) * Provide vector values in binary format or convert text representation to binary using the appropriate conversion function (e.g. `vector32(...)`) * Calculate vector similarity between vectors in the table or from the query itself using dedicated vector functions (e.g. ...
Interesting find! Turso/libSQL has native vector support built into SQLite itself — vector_distance_cos(), F32_BLOB columns, and DiskANN indexing. But the critical question is: does Bun's built-in bun:sqlite support these libSQL extensions?
vector32: NOT SUPPORTED - no such function: vector32 vector_distance_cos: NOT SUPPORTED - no such function: vector_distance_cos F32_BLOB column: SUPPORTED