feat: add Forge embedders integration

[JCorners68]

What this adds

A new forge embedders integration (forge-haystack) under integrations/forge/, providing:

• ForgeTextEmbedder(OpenAITextEmbedder)
• ForgeDocumentEmbedder(OpenAIDocumentEmbedder)

Forge serves an OpenAI-compatible embeddings API, so both components subclass Haystack's built-in OpenAI embedders and default to the Forge endpoint:

• api_base_url="https://api.voxell.ai/v1"
• api_key=Secret.from_env_var("FORGE_API_KEY")
• model="forge-pro" (other accepted strings: forge-turbo, forge-ultra-4k, plus OpenAI-compatible aliases text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002)

A dimensions parameter is exposed (passed through to the underlying OpenAI embedder) since Forge models support Matryoshka representation learning. to_dict()/from_dict() are implemented via default_to_dict/default_from_dict, and a SUPPORTED_MODELS ClassVar lists the accepted model strings.

This mirrors the existing OpenAI-compatible embedder integrations — the merged Perplexity integration (#3262) and integrations/mistral/ — using the same thin-subclass pattern. No new SDK dependency is added; haystack-ai already ships the OpenAI client.

How it was tested

From integrations/forge/:

• hatch run fmt-check — passes
• hatch run test:types — mypy clean (3 source files)
• hatch run test:unit-cov-retry — 16 unit tests pass, 100% coverage of both modules (integration tests requiring a real FORGE_API_KEY skip, as expected)

Unit tests mirror the Perplexity/Mistral embedder tests: they assert init defaults/metadata and to_dict/from_dict round-trips, and do not make live API calls.

The scaffold was generated with scripts/create_new_integration.py --name forge --type embedders, which also added the GitHub workflow, labeler entry, coverage-comment workflow entry, and the root README row.

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[julian-risch]

@JCorners68 Thank you for opening this pull request. Great to see that you used the scaffolding script! However, without the CLA agreed to we can't merge this PR. Either way, we recommend to publish and release this integration yourself using our repository template if you believe it benefits a larger group of users. https://github.com/deepset-ai/custom-component For example, as a repo under https://github.com/VoxellInc/

For the moment, Haystack users can use OpenAITextEmbedder class as follows

from haystack.components.embedders import OpenAITextEmbedder
from haystack.utils import Secret

OpenAITextEmbedder(
    api_key=Secret.from_env_var("FORGE_API_KEY"),
    api_base_url="https://api.voxell.ai/v1",
    model="forge-pro",
)

We'll monitor requests from the community and if there is an increase in demand for a dedicated integration, potentially with additional features, we will reconsider.