feat: add semantic embedding text prefixes

[lastguru-net]

Summary

Adds optional role-specific literal text prefixes for semantic embeddings:

• semantic_embedding_document_prefix
• semantic_embedding_query_prefix

These prefixes are prepended to the actual text sent to the embedding provider:

• document prefix for indexed chunks during sync/reindex
• query prefix for vector and hybrid search queries

Fixes #1008.

Why

Some embedding models require role-specific text prefixes as part of the input string, not as API parameters.

Examples include models where the recommended input format is something like:

• documents: title: none | text: ...
• queries: task: search result | query: ...

Basic Memory already supports LiteLLM input_type settings, but that only helps providers that understand an API-level role parameter. OpenAI-compatible endpoints such as TEI just receive the input strings, so prefix-sensitive models cannot currently be used in their recommended form without a proxy or local patch.
Without this, retrieval quality evaluation is misleading: a model can look worse than it really is because documents and queries are embedded without the format it was trained/evaluated with.

Implementation

• Adds document/query prefix config fields.
• Wraps embedding providers with a generic prefixing provider.
• Works across fastembed, openai, and litellm.
• Empty or unset prefixes preserve current behavior.
• Includes prefixes in the provider cache key.
• Includes prefixes in stored vector identity, so changing either prefix invalidates existing embeddings and requires reindexing.
• Exposes prefix-enabled status in runtime/project status surfaces.
• Documents the difference between LiteLLM input_type and literal text prefixes.

Validation

• 201 passed focused tests
• Ruff format/check on touched Python files
• ty check src tests test-int
• git diff --check

[chatgpt-codex-connector]

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[lastguru-net]

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Codex Review: Didn't find any major issues. Breezy!

Reviewed commit: 0818b8d7da

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