Vectors & Embeddings

hybrid_search fuses graph + BM25 + vector channels in one governed call. embed produces vectors via the configured embedder; embed_image/embed_face/embed_audio/embed_video cover the media modalities (ADR-0276). A real media vector requires an embedding sidecar (RELATA_ACCEL_ENDPOINT).

12 methods across this domain. Signatures, parameters (incl. keyword-only tunable defaults), and the three SDK spellings are ported from the live SDK source; flagship methods carry a hand-written When to use + example.

RelataClient

face_search(gallery_id, embedding, k=10, threshold=0.7, purpose=None)

face_search(gallery_id, embedding, k=10, threshold=0.7, purpose=None)`

  • Python — client.relataclient.face_search(gallery_id, embedding, k=10, threshold=0.7, purpose=None)
  • Go — FaceSearch(galleryID, embedding)

Parameters: gallery_id, embedding, k=10, threshold=0.7, purpose=None

match_pdq(corpus_id, query_hash, threshold=0.9, purpose=None)

match_pdq(corpus_id, query_hash, threshold=0.9, purpose=None)`

  • Python — client.relataclient.match_pdq(corpus_id, query_hash, threshold=0.9, purpose=None)
  • Go — MatchPdq(corpusID, queryHash)

Parameters: corpus_id, query_hash, threshold=0.9, purpose=None

similar_image(media_ref, threshold=None, index=None, purpose=None)

similar_image(media_ref, threshold=None, index=None, purpose=None)`

  • Python — client.relataclient.similar_image(media_ref, threshold=None, index=None, purpose=None)
  • Go — SimilarImage(mediaRef)

Parameters: media_ref, threshold=None, index=None, purpose=None

VectorClient

embed(text, model=None)

When to use. Produce a vector for caller-side caching or custom ANN; uses the configured embedder.

  • Python — client.vectorclient.embed(text, model=None)
  • TypeScript — embed(text, opts)
  • Go — Embed(text, model)

Parameters: text, model=None

Example

vec = VectorClient.from_client(relata).embed("shell company filings")

hybrid_search(object_type, query_text=None, k=10, purpose=None, rerank=False, metric=None, weights=None)

When to use. Explicit hybrid fusion over a type with a query text (server embeds the text).

  • Python — client.vectorclient.hybrid_search(object_type, query_text=None, k=10, purpose=None, rerank=False, metric=None, weights=None)
  • TypeScript — hybridSearch(objectType, opts, number)
  • Go — HybridSearch(objectType, queryText)

Parameters: object_type, query_text=None, k=10, purpose=None, rerank=False, metric=None, weights=None

Example

from relata import VectorClient
vc = VectorClient.from_client(relata)
hits = vc.hybrid_search("IntelChunk", "sanctions evasion", limit=20)

embed_audio(bytes_b64, model=None)

embed_audio(bytes_b64, model=None)`

  • Python — client.vectorclient.embed_audio(bytes_b64, model=None)
  • TypeScript — embedAudio(bytesB64, opts)
  • Go — EmbedAudio(bytesB64, model)

Parameters: bytes_b64, model=None

embed_batch(texts, model=None)

embed_batch(texts, model=None)`

  • Python — client.vectorclient.embed_batch(texts, model=None)
  • TypeScript — embedBatch(texts, opts)
  • Go — EmbedBatch(texts, model)

Parameters: texts, model=None

embed_face(bytes_b64, model=None)

embed_face(bytes_b64, model=None)`

  • Python — client.vectorclient.embed_face(bytes_b64, model=None)
  • TypeScript — embedFace(bytesB64, opts)
  • Go — EmbedFace(bytesB64, model)

Parameters: bytes_b64, model=None

embed_image(bytes_b64, model=None)

embed_image(bytes_b64, model=None)`

  • Python — client.vectorclient.embed_image(bytes_b64, model=None)
  • TypeScript — embedImage(bytesB64, opts)
  • Go — EmbedImage(bytesB64, model)

Parameters: bytes_b64, model=None

embed_video(bytes_b64, model=None)

embed_video(bytes_b64, model=None)`

  • Python — client.vectorclient.embed_video(bytes_b64, model=None)
  • TypeScript — embedVideo(bytesB64, opts)
  • Go — EmbedVideo(bytesB64, model)

Parameters: bytes_b64, model=None

knn_search(object_type, embedding_slot, query_embedding, k=10, ef_search=None, purpose=None)

knn_search(object_type, embedding_slot, query_embedding, k=10, ef_search=None, purpose=None)`

  • Python — client.vectorclient.knn_search(object_type, embedding_slot, query_embedding, k=10, ef_search=None, purpose=None)
  • TypeScript — knnSearch(objectType, embeddingSlot, queryEmbedding, opts)
  • Go — KNNSearch(objectType, embeddingSlot, queryEmbedding, k)

Parameters: object_type, embedding_slot, query_embedding, k=10, ef_search=None, purpose=None

similar_to(object_type, reference_id, k=10, purpose=None)

similar_to(object_type, reference_id, k=10, purpose=None)`

  • Python — client.vectorclient.similar_to(object_type, reference_id, k=10, purpose=None)
  • TypeScript — similarTo(objectType, referenceId, opts)
  • Go — SimilarTo(objectType, referenceID)

Parameters: object_type, reference_id, k=10, purpose=None