feat(embedding): pluggable OpenAI-compatible embedding provider#178
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asperty567 wants to merge 1 commit intogarrytan:masterfrom
Open
feat(embedding): pluggable OpenAI-compatible embedding provider#178asperty567 wants to merge 1 commit intogarrytan:masterfrom
asperty567 wants to merge 1 commit intogarrytan:masterfrom
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Adds three env overrides so gbrain can run against any
OpenAI-compatible /v1/embeddings endpoint (LM Studio, Ollama,
vLLM, Together, self-hosted) instead of hardcoded OpenAI.
Environment variables:
EMBEDDING_BASE_URL custom endpoint (optional)
EMBEDDING_API_KEY api key (falls back to OPENAI_API_KEY)
EMBEDDING_MODEL model name (default: text-embedding-3-large)
EMBEDDING_DIMENSIONS vector dimension (default: 1536)
EMBEDDING_SEND_DIMENSIONS auto (default): sends dimensions only
when MODEL starts with text-embedding-3
Schema templates (pglite-schema.ts, schema-embedded.ts) now
interpolate EMBEDDING_MODEL and EMBEDDING_DIMENSIONS so the
pgvector column and config metadata match at gbrain init time.
Forces encoding_format: float in the request. The OpenAI SDK
defaults to base64 which LM Studio mis-decodes (returns 192
floats for a 768-dim nomic vector). float is spec-compliant
and all providers support it.
Verified end-to-end against LM Studio with
text-embedding-nomic-embed-text-v1.5 (768 dim): import,
embed, vector search all pass. Existing embed tests still
green.
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Adds three env overrides so gbrain can run against any OpenAI-compatible /v1/embeddings endpoint (LM Studio, Ollama, vLLM, Together, self-hosted) instead of hardcoded OpenAI.
Environment variables:
EMBEDDING_BASE_URL custom endpoint (optional)
EMBEDDING_API_KEY api key (falls back to OPENAI_API_KEY)
EMBEDDING_MODEL model name (default: text-embedding-3-large)
EMBEDDING_DIMENSIONS vector dimension (default: 1536)
EMBEDDING_SEND_DIMENSIONS auto (default): sends dimensions only
when MODEL starts with text-embedding-3
Schema templates (pglite-schema.ts, schema-embedded.ts) now interpolate EMBEDDING_MODEL and EMBEDDING_DIMENSIONS so the pgvector column and config metadata match at gbrain init time.
Forces encoding_format: float in the request. The OpenAI SDK defaults to base64 which LM Studio mis-decodes (returns 192 floats for a 768-dim nomic vector). float is spec-compliant and all providers support it.
Verified end-to-end against LM Studio with
text-embedding-nomic-embed-text-v1.5 (768 dim): import, embed, vector search all pass. Existing embed tests still green.