LeMUR

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require 'assemblyai'
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client = AssemblyAI::Client.new(api_key: '<YOUR_API_KEY>')
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# You can upload and transcribe a local file:
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# uploaded_file = client.files.upload(file: '/path/to/your/file')
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# transcript = client.transcripts.transcribe(audio_url: uploaded_file.upload_url, speaker_labels: true)
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# Or use a publicly-accessible URL:
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audio_url = 'https://assembly.ai/sports_injuries.mp3'
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transcript = client.transcripts.transcribe(audio_url: audio_url)
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result = client.lemur.task(
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  transcript_ids: [transcript.id],
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  prompt: 'Provide a brief summary of the transcript.',
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  final_model: AssemblyAI::Lemur::LemurModel::ANTHROPIC_CLAUDE3_5_SONNET
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)
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puts result.response

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require 'assemblyai'
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client = AssemblyAI::Client.new(api_key: '<YOUR_API_KEY>')
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# Step 1: Transcribe an audio file. For local files see our Getting Started guides.
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audio_url = 'https://assembly.ai/sports_injuries.mp3'
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transcript = client.transcripts.transcribe(audio_url: audio_url)
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# Step 2: Define a prompt with your question(s).
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prompt = "What is a runner's knee?"
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# Step 3: Apply LeMUR.
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response = client.lemur.task(
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  prompt: prompt,
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  transcript_ids: [transcript.id],
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  final_model: AssemblyAI::Lemur::LemurModel::ANTHROPIC_CLAUDE3_5_SONNET
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)
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puts response.response

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require 'assemblyai'
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client = AssemblyAI::Client.new(api_key: '<YOUR_API_KEY>')
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audio_url = 'https://assembly.ai/meeting.mp4'
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transcript = client.transcripts.transcribe(audio_url: audio_url)
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response = client.lemur.question_answer(
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  transcript_ids: [transcript.id],
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  final_model: AssemblyAI::Lemur::LemurModel::ANTHROPIC_CLAUDE3_5_SONNET,
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  context: 'A GitLab meeting to discuss logistic',
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  questions: [
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    {
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      question: 'What are the top level KPIs for engineering?',
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      context: 'KPI stands for key performance indicator',
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      answer_format: 'short sentence'
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    },
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    {
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      question: 'How many days has it been since the data team has gotten updated metrics?',
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      context: 'KPI stands for key performance indicator',
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      answer_options: ['1', '2', '3', '4', '5', '6', '7', 'more than 7']
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    }
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  ]
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)
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response.response.each do |qa|
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  printf("Question: %<question>s\n", question: qa.question)
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  printf("Answer: %<answer>s\n", answer: qa.answer)
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end

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response = client.lemur.task(
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  prompt: prompt,
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  transcript_ids: [transcript_id],
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  final_model: AssemblyAI::Lemur::LemurModel::ANTHROPIC_CLAUDE3_5_SONNET
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)

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response = client.lemur.task(
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  prompt: prompt,
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  transcript_ids: [transcript_id],
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  max_output_size: 1000
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)

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response = client.lemur.task(
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  prompt: prompt,
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  transcript_ids: [transcript_id],
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  temperature: 0.7
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)

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transcript = client.transcripts.transcribe(
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  audio_url: audio_url,
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  speaker_labels: true
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)
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text_with_speaker_labels = (transcript.utterances.map do |utterance|
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  sprintf(
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    "Speaker %<speaker>s:\n%<text>s\n",
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    speaker: utterance.speaker,
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    text: utterance.text
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  )
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end).join("\n")
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response = client.lemur.task(
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  prompt: prompt,
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  input_text: text_with_speaker_labels
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)
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puts response.response

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response = client.lemur.task(
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  prompt: prompt,
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  transcript_ids: [id1, id2, id3]
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)

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response = client.lemur.task(
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  prompt: prompt,
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  transcript_ids: [transcript_id],
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)
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deletion_response = client.lemur.purge_request_data(request_id: response.request_id)