honcho/docs
Vansh-Sharma27 e83ab842c1
fix(embedding): configurable tokenizer for non-OpenAI embedding models
The embedding client resolved every model's tokenizer through tiktoken,
silently falling back to cl100k_base for models tiktoken doesn't know
(e.g. baai/bge-m3). cl100k_base undercounts vs the model's real
tokenizer on technical/mixed text (runtime-measured +44% for bge-m3),
so prepare_chunks emits "within-limit" chunks the provider then rejects
with HTTP 400. The reconciler retries the unchanged payload 20 times
over ~3h, marks MessageEmbedding.sync_state='failed', and the message
is permanently excluded from vector search (search.py filters
embedding IS NOT NULL).

Add EMBEDDING_MODEL_CONFIG__TOKENIZER: unset keeps tiktoken
auto-detection (backwards compatible); tiktoken:<encoding>, hf:<repo>,
or file:<path> select an explicit tokenizer. HF/file tokenizers use the
optional honcho[tokenizers] extra. The HuggingFace adapter encodes
without special tokens and reserves the special-token overhead
([CLS]/[SEP]) from the chunk budget so provider-side counts stay
exactly within limit. Unknown models now log a warning pointing at the
new setting. Invalid specs raise ValidationException (repo-standard).
The singleton rebuild signature includes tokenizer so runtime config
changes take effect.

Runtime-verified end-to-end without a live provider: 24,360 chars of
technical text with bge-m3 went from 1 chunk (8,355 real tokens > 8,192
-> provider 400 -> failed) to 2 chunks (8,192 / 1,803, both within
limit).

Out of scope (noted for follow-up): recovery/reindex of existing failed
rows, scripts/generate_message_embeddings.py chunk-identity bug,
ConclusionCreate o200k_base validator alignment, typed
dimension-vs-token-limit exceptions, live-embedding CI matrix.

Fixes #827
2026-08-20 01:49:49 +05:30
..
changelog August Changelog Docs Sync (#1009) 2026-08-12 17:35:51 -04:00
images chore(docs): Add detailed system diagram to docs 2026-07-01 16:55:15 -04:00
logo color theme and broken link fix (#213) 2025-09-24 16:06:54 -04:00
snippets feat(cli): add `honcho session view` transcript command (#1006) 2026-08-11 10:12:16 -04:00
v1 feat: retry on more httpx exceptions (#467) 2026-04-03 12:20:53 -04:00
v2 Connection Exponential Backoff (#758) 2026-06-01 12:57:07 -04:00
v3 fix(embedding): configurable tokenizer for non-OpenAI embedding models 2026-08-20 01:49:49 +05:30
README.md Add Pre-commit Hooks (#165) 2025-07-22 15:17:53 -04:00
bun.lock v3.0.6 Release Candidate (#550) 2026-04-10 13:16:42 -04:00
docs.json August Changelog Docs Sync (#1009) 2026-08-12 17:35:51 -04:00
favicon.svg [0.0.7] — 2024-04-01 (#50) 2024-04-01 10:58:42 -07:00
package.json v3.0.6 Release Candidate (#550) 2026-04-10 13:16:42 -04:00

README.md

Honcho Docs

These docs are built using Next.js via mintlify.

Setting Up Honcho's Docs Locally

  1. Clone the repository:
git clone git@github.com:plastic-labs/honcho.git
  1. Navigate into the docs folder:
cd honcho/docs/

The docs folder contains the markdown files that make up the documentation. The majority of the files are in the pages directory. Some notable files in this folder include:

  1. Verify that you have Node.js and npm installed in your system. You can check by running:
node --version
npm --version
  1. If not installed, download Node.js and npm from the respective official websites.

  2. Once you have Node.js and npm running, proceed to install pnpm - another package manager that helps to manage project dependencies:

npm install -g pnpm
  1. Install the project dependencies using pnpm:
pnpm i
  1. After the successful installation of the project dependencies, start the local server:
pnpm dev

Now, you should be able to view the docs on your local environment by visiting http://localhost:3000. You can explore the different markdown files and make changes as you see fit.