🔍 Motivation
Current VectorStore implementations (e.g., ChromaVectorStore, PgVectorStore) automatically compute embeddings from Document.content via the configured EmbeddingModel. This rigid behavior is limiting in real-world applications where:

1. Embeddings are precomputed externally using fine-tuned or specialized models (offline pipelines).
2. Embeddings may represent a prompt, summary, or condensed form, not the entire content.
3. Structured data (e.g., JSON) may be stored as content, but embedding the full structure reduces semantic quality.

✅ What This Proposal Adds
This feature introduces support for user-provided embeddings at ingestion time, improving flexibility and performance. Highlights include:

• Overloaded add(List, List<float[]>) method in the VectorStore interface.
• AbstractObservationVectorStore refactored to call a centralized doAdd with validation.
• Embedding generation logic removed from VectorStore doAdd() implementations — instead, embeddings must be passed explicitly.
• No need to modify the Document model.
• No extra user config required for backward-compatible usage (existing add(List) continues to auto-embed).

⚙️ Implementation Benefits

• Clean separation of embedding generation from storage logic.
• Maintains full backward compatibility.
• Enables efficient batch ingestion using external embedding workflows.

📎 Related Work
#1600 – Discusses the need for prompt-based or user-controlled embedding logic.
#1239 – Adds prompt-based embedding, but doesn't support full injection of embeddings per document.

✅ Acceptance Criteria

• Overloaded add(documents, embeddings) method available in all VectorStore implementations.
• Embedding validation (dimension, NaN/Inf check) is done before ingestion.
• If add(documents) is called, embeddings are generated as before.
• Supports batching where applicable (no batching enforced by user; store decides).
• Works out-of-the-box for existing stores (e.g., Pinecone, PGVector, Milvus).