Class: Vectorsearch::Weaviate

Inherits:
Base
  • Object
show all
Defined in:
lib/vectorsearch/weaviate.rb

Constant Summary

Constants inherited from Base

Base::DEFAULT_COHERE_DIMENSION, Base::DEFAULT_METRIC, Base::DEFAULT_OPENAI_DIMENSION, Base::LLMS

Instance Attribute Summary

Attributes inherited from Base

#client, #index_name, #llm, #llm_api_key

Instance Method Summary collapse

Methods inherited from Base

#generate_completion, #generate_embedding, #generate_prompt

Constructor Details

#initialize(url:, api_key:, index_name:, llm:, llm_api_key:) ⇒ Weaviate 



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# File 'lib/vectorsearch/weaviate.rb', line 7

def initialize(
  url:,
  api_key:,
  index_name:,
  llm:,
  llm_api_key:
)
  @client = ::Weaviate::Client.new(
    url: url,
    api_key: api_key,
    model_service: llm,
    model_service_api_key: llm_api_key
  )
  @index_name = index_name

  super(llm: llm, llm_api_key: llm_api_key)
end

Instance Method Details

#add_texts(texts:) ⇒ Object 



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# File 'lib/vectorsearch/weaviate.rb', line 25

def add_texts(
  texts:
)
  objects = []  
  texts.each do |text|
    objects.push({
      class_name: index_name,
      properties: {
        content: text
      }
    })
  end

  client.objects.batch_create(
    objects: objects
  )
end

#ask(question:) ⇒ Hash

Ask a question and return the answer



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# File 'lib/vectorsearch/weaviate.rb', line 95

def ask(
  question:
)
  # Weaviate currently supports the `ask:` parameter only for the OpenAI LLM (with `qna-openai` module enabled).
  if llm == :openai
    ask_object = "{ question: \"#{question}\" }"

    client.query.get(
      class_name: index_name,
      ask: ask_object,
      limit: "1",
      fields: "_additional { answer { result } }"
    )
  elsif llm == :cohere
    search_results = similarity_search(query: question)

    context = search_results.map do |result|
      result.dig("content").to_s
    end
    context = context.join("\n---\n")

    prompt = generate_prompt(question: question, context: context)

    generate_completion(prompt: prompt)
  end
end

#create_default_schema ⇒ Object 



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# File 'lib/vectorsearch/weaviate.rb', line 43

def create_default_schema
  client.schema.create(
    class_name: index_name,
    vectorizer: "text2vec-#{llm.to_s}",
    properties: [
      {
        dataType: ["text"],
        name: "content"
      }
    ]
  )
end

#similarity_search(query:, k: 4) ⇒ Hash

Return documents similar to the query



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# File 'lib/vectorsearch/weaviate.rb', line 60

def similarity_search(
  query:,
  k: 4
)
  near_text = "{ concepts: [\"#{query}\"] }"

  client.query.get(
    class_name: index_name,
    near_text: near_text,
    limit: k.to_s,
    fields: "content _additional { id }"
  )
end

#similarity_search_by_vector(embedding:, k: 4) ⇒ Hash

Return documents similar to the vector



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# File 'lib/vectorsearch/weaviate.rb', line 78

def similarity_search_by_vector(
  embedding:,
  k: 4
)
  near_vector = "{ vector: #{embedding} }"

  client.query.get(
    class_name: index_name,
    near_vector: near_vector,
    limit: k.to_s,
    fields: "content recipe_id"
  )
end