[FEAT]: Embedder Config: add chunking strategy and embedder query/passage prefix

[chrisjunlee]

What would you like to see?

For the Embedder, there's two missing configurable parameters:

1. Chunking strategy: fixed (current default), recursive (model figures out dynamically), structural, etc. You don't have to include the options since they're evolving. Just an optional text field to pass to embedder
2. Query prefix: models like Qwen3-Embedding-8B require a query and instruct prefix. This could also be a text field in the Embedder settings

[jhsmith409]

Confirming this is a real, reproducible accuracy regression for any asymmetric embedding model (Qwen3-Embedding family, BGE-M3 in retrieval mode, E5-instruct, multilingual-e5-*, etc.). Caught it on a deployment serving Qwen3-Embedding-0.6B via an OpenAI-compatible gateway through EMBEDDING_ENGINE=generic-openai.

Source-level evidence

server/utils/EmbeddingEngines/genericOpenAi/index.js passes input straight through with no query/passage distinction:

async embedChunks(textChunks = []) {
  // ...
  this.openai.embeddings
    .create({
      model: this.model,
      input: chunk,
    })
  // ...
}

async embedTextInput(textInput) {
  const result = await this.embedChunks(
    Array.isArray(textInput) ? textInput : [textInput]
  );
  return result?.[0] || [];
}

No env var or constructor option exists to inject a prefix.

Wire-level evidence

Spied on the OpenAI client inside the running container to capture the actual request body:

--- query path (LLMConnector.embedTextInput) ---
WIRE_REQUEST_BODY: {"model":"qwen3-embedding-0.6b","input":["what is the snow load on a barn roof"]}

--- doc path (EmbedderEngine.embedChunks) ---
WIRE_REQUEST_BODY: {"model":"qwen3-embedding-0.6b","input":["Section 7.3.2 specifies a ground snow load of 30 psf for the building."]}

Per the Qwen3-Embedding model card, queries should be wrapped:

Instruct: Given a web search query, retrieve relevant passages that answer the query
Query:{query}

…with passages left raw. Skipping the wrap costs ~1–5% on the asymmetric MTEB tasks the model was trained for.

Why a fix is straightforward (the prefix half of this issue)

The architecture is already prefix-friendly: every vector-DB provider in server/utils/vectorDbProviders/* cleanly separates the two paths — embedTextInput is called only for queries, embedChunks only for ingest. So the change can be local to each embedder class with zero call-site churn. Grep confirms (lance, chroma, qdrant, pinecone, milvus, weaviate, astra, pgvector all follow the pattern):

LLMConnector.embedTextInput(input)        // queries
EmbedderEngine.embedChunks(textChunks)    // ingest

Proposed minimal scope

Two new env vars on the GenericOpenAI embedder (with empty defaults, fully backward-compatible):

• EMBEDDING_QUERY_PREFIX — prepended to inputs in embedTextInput
• EMBEDDING_PASSAGE_PREFIX — prepended to inputs in embedChunks (often left empty for Qwen3)

For Qwen3-Embedding the user would set:

EMBEDDING_QUERY_PREFIX="Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:"
EMBEDDING_PASSAGE_PREFIX=

Happy to put up a PR for the prefix half of this issue if maintainers are open to that scoping (chunking-strategy half can stay as a follow-up). Could also extend the same env knobs to the sibling OpenAI-compatible embedders (openai, lmstudio, localai, voyageai, ollama) in the same PR or a follow-up — preference?