Document multimodal embedder flag and Voyage/Cohere providers

CHANGELOG.md

@@ -10,7 +10,7 @@
 
 ### Changed
 
-- `provider: vllm` is text-only unless `embeddings.model.multimodal: true` is set. Existing multimodal vLLM configs must add the flag.
+- `provider: vllm` is text-only unless `embeddings.model.multimodal: true` is set. Existing multimodal vLLM configs must add the flag; `multimodal` is not part of the stored embedding identity, so changing it raises no drift error — re-ingest or `rebuild` to add or drop picture chunks.
 
 ## [0.60.0] - 2026-06-22
 

README.md

@@ -10,14 +10,14 @@ Agentic RAG built on [LanceDB](https://lancedb.com/), [Pydantic AI](https://ai.p
 ## Features
 
 - **Hybrid search** — Vector + full-text with Reciprocal Rank Fusion
-- **Multimodal & cross-modal search** — Multimodal embedders (vLLM) put picture vectors in the same space as text; supports text-as-query → figure hits and image-as-query
+- **Multimodal & cross-modal search** — Multimodal embedders (vLLM, VoyageAI, Cohere) put picture vectors in the same space as text; supports text-as-query → figure hits and image-as-query
 - **Question answering** — RAG skill with citations (page numbers, section headings)
 - **Vision QA** — Vision-capable models receive figure bytes alongside chunk text
 - **Reranking** — MxBAI, Cohere, Zero Entropy, or vLLM
 - **Analysis skill** — Complex analytical tasks via sandboxed Python code execution (aggregation, computation, multi-document analysis)
 - **Conversational RAG** — Chat TUI and web application for multi-turn conversations with session memory
 - **Document structure** — Stores full [DoclingDocument](https://docling-project.github.io/docling/concepts/docling_document/), enabling structure-aware context expansion
-- **Multiple providers** — Embeddings: Ollama, OpenAI, VoyageAI, LM Studio, vLLM (multimodal). QA: any model supported by Pydantic AI
+- **Multiple providers** — Embeddings: Ollama, OpenAI, VoyageAI, Cohere, LM Studio, vLLM (multimodal via `multimodal: true` on vLLM/VoyageAI/Cohere). QA: any model supported by Pydantic AI
 - **Local-first** — Embedded LanceDB, no servers required. Also supports S3, GCS, Azure, and LanceDB Cloud
 - **CLI & Python API** — Full functionality from command line or code
 - **MCP server** — Expose as tools for AI assistants (Claude Desktop, etc.)

docs/configuration/processing.md

@@ -283,9 +283,11 @@ Three independent settings drive ingest, retrieval, and QA:
 | Setting | Question it answers | Values |
 |---|---|---|
 | `processing.pictures` | Generate and/or describe pictures at ingest? | `none` / `description` / `image` (default) |
-| `embeddings.model.provider` | Can the embedder index image content? | text-only (`ollama`, `openai`, `cohere`, `sentence-transformers`) vs `vllm` (multimodal) |
+| `embeddings.model.multimodal` | Can the embedder index image content? | `false` (default, text-only) / `true` (supported on `vllm`, `voyageai`, `cohere`) |
 | `qa.model.vision` | Can the QA model interpret images? | `false` (default) / `true` |
 
+The Embedder column below is driven by `embeddings.model.multimodal`, not the provider name — a vision-capable model under a text-only configuration still indexes no images, and an image-only document then produces zero chunks. See [Multimodal embedders](providers.md#multimodal-embedders).
+
 **What gets stored** by `pictures` × embedder:
 
 | `pictures` | Embedder | Text chunks | Synthetic picture chunks |

docs/configuration/providers.md

@@ -228,11 +228,15 @@ embeddings:
     base_url: http://localhost:1234/v1
 ```
 
-**Note:** The `base_url` must include the `/v1` path for OpenAI-compatible endpoints.
+**Note:** The `base_url` must include the `/v1` path for OpenAI-compatible endpoints. This path is text-only. For a vision-language model served by vLLM, use `provider: vllm` with `multimodal: true` (below), not `provider: openai`.
 
-### vLLM (multimodal)
+### Multimodal embedders
 
-For cross-modal retrieval (text and pictures share a single vector space), use the dedicated `vllm` provider against a vLLM server hosting a multimodal embedding model:
+For cross-modal retrieval (text and pictures share a single vector space), set `embeddings.model.multimodal: true`. Capability is decided by this flag, not the provider name: each provider passes images in its own wire format, so multimodal is supported only on `vllm`, `voyageai`, and `cohere`. Setting it on any other provider raises at startup.
+
+A model produces picture chunks at ingest only when its embedder is multimodal. Without the flag, an image-only document produces zero chunks and is not retrievable. Switching `multimodal` on or off does not change the stored embedding identity, so it raises no drift error; re-ingest or `rebuild` to add or drop picture chunks.
+
+**vLLM** — a vLLM server hosting a multimodal embedding model. Text inputs use the standard OpenAI `input` field; image inputs use vLLM's `messages`-with-`image_url` superset. Tested with `Qwen/Qwen3-VL-Embedding-8B` (4096-dim) and `jinaai/jina-embeddings-v4` (2048-dim). Run vLLM separately; haiku.rag adds no Python ML dependencies for this path.
 
 ```yaml
 embeddings:
@@ -241,11 +245,34 @@ embeddings:
     name: Qwen/Qwen3-VL-Embedding-8B
     vector_dim: 4096
     base_url: http://localhost:8000/v1
+    multimodal: true
 ```
 
-Tested with `Qwen/Qwen3-VL-Embedding-8B` (4096-dim) and `jinaai/jina-embeddings-v4` (2048-dim). Run vLLM separately. haiku.rag adds no Python ML dependencies for this path. Text inputs use the standard OpenAI `input` field. Image inputs use vLLM's `messages`-with-`image_url` superset, transparently to the caller.
+**VoyageAI** — `voyage-multimodal-3` (1024-dim) via the `voyageai` extra. Reads `VOYAGE_API_KEY` from the environment.
 
-Picture chunks for retrieval are emitted at ingest under any embedder reporting `supports_images=True`. See [Picture Handling](processing.md#picture-handling).
+```yaml
+embeddings:
+  model:
+    provider: voyageai
+    name: voyage-multimodal-3
+    vector_dim: 1024
+    multimodal: true
+```
+
+**Cohere** — `embed-v4.0` (configurable `vector_dim`, e.g. 1536) via the `cohere` extra. Reads `CO_API_KEY` from the environment.
+
+```yaml
+embeddings:
+  model:
+    provider: cohere
+    name: embed-v4.0
+    vector_dim: 1536
+    multimodal: true
+```
+
+A text-only model served by vLLM uses `provider: vllm` without the flag (or `provider: openai` with a `base_url`).
+
+Picture chunks for retrieval are emitted at ingest under any multimodal embedder. See [Picture Handling](processing.md#picture-handling).
 
 ## Question Answering Providers
 

docs/python.md

@@ -260,7 +260,7 @@ results = await client.search(
 
 ### Image queries
 
-`client.search()` accepts an image instead of a text query when the configured embedder is multimodal (e.g. `provider: vllm` against a vision-language embedding model). The image is embedded once and the chunks table is searched vector-only. Full-text search and reranking don't apply without a text query.
+`client.search()` accepts an image instead of a text query when the configured embedder is multimodal (`embeddings.model.multimodal: true` on a vLLM, VoyageAI, or Cohere model). The image is embedded once and the chunks table is searched vector-only. Full-text search and reranking don't apply without a text query.
 
 ```python
 from PIL import Image