Skip to content
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion lib/ruby_llm/provider.rb
Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@ def list_models(connection:)
def embed(text, model:, connection:, dimensions:)
payload = render_embedding_payload(text, model:, dimensions:)
response = connection.post(embedding_url(model:), payload)
parse_embedding_response(response, model:)
parse_embedding_response(response, model:, text:)
end

def paint(prompt, model:, size:, connection:)
Expand Down
6 changes: 4 additions & 2 deletions lib/ruby_llm/providers/gemini/embeddings.rb
Original file line number Diff line number Diff line change
Expand Up @@ -15,9 +15,11 @@ def render_embedding_payload(text, model:, dimensions:)
{ requests: [text].flatten.map { |t| single_embedding_payload(t, model:, dimensions:) } }
end

def parse_embedding_response(response, model:)
def parse_embedding_response(response, model:, text:)
vectors = response.body['embeddings']&.map { |e| e['values'] }
vectors in [vectors]
# If we only got one embedding AND the input was a single string (not an array),
# return it as a single vector
vectors = vectors.first if vectors&.length == 1 && !text.is_a?(Array)

Embedding.new(vectors:, model:, input_tokens: 0)
end
Expand Down
7 changes: 4 additions & 3 deletions lib/ruby_llm/providers/openai/embeddings.rb
Original file line number Diff line number Diff line change
Expand Up @@ -19,13 +19,14 @@ def render_embedding_payload(text, model:, dimensions:)
}.compact
end

def parse_embedding_response(response, model:)
def parse_embedding_response(response, model:, text:)
data = response.body
input_tokens = data.dig('usage', 'prompt_tokens') || 0
vectors = data['data'].map { |d| d['embedding'] }

# If we only got one embedding, return it as a single vector
vectors in [vectors]
# If we only got one embedding AND the input was a single string (not an array),
# return it as a single vector
vectors = vectors.first if vectors.length == 1 && !text.is_a?(Array)

Embedding.new(vectors:, model:, input_tokens:)
end
Expand Down
Loading