Class: Langchain::Vectorsearch::Milvus
- Defined in:
- lib/langchain/vectorsearch/milvus.rb
Constant Summary
Constants inherited from Base
Instance Attribute Summary
Attributes inherited from Base
Instance Method Summary collapse
- #add_texts(texts:) ⇒ Object
-
#ask(question:, k: 4) {|String| ... } ⇒ String
Ask a question and return the answer.
-
#create_default_index ⇒ Boolean
Create the default index.
-
#create_default_schema ⇒ Hash
Create default schema.
-
#destroy_default_schema ⇒ Hash
Delete default schema.
-
#get_default_schema ⇒ Hash
Get the default schema.
-
#initialize(url:, index_name:, llm:, api_key: nil) ⇒ Milvus
constructor
Wrapper around Milvus REST APIs.
-
#load_default_schema ⇒ Boolean
Load default schema into memory.
-
#remove_texts(ids:) ⇒ Boolean
Deletes a list of texts in the index.
- #similarity_search(query:, k: 4) ⇒ Object
- #similarity_search_by_vector(embedding:, k: 4) ⇒ Object
Methods inherited from Base
#add_data, #generate_hyde_prompt, #generate_rag_prompt, #similarity_search_with_hyde, #update_texts
Methods included from DependencyHelper
Constructor Details
#initialize(url:, index_name:, llm:, api_key: nil) ⇒ Milvus
Wrapper around Milvus REST APIs.
Gem requirements:
gem "milvus", "~> 0.10.3"
Usage:
milvus = Langchain::Vectorsearch::Milvus.new(url:, index_name:, llm:, api_key:)
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# File 'lib/langchain/vectorsearch/milvus.rb', line 14 def initialize(url:, index_name:, llm:, api_key: nil) depends_on "milvus" @client = ::Milvus::Client.new( url: url, logger: Langchain.logger ) @index_name = index_name super(llm: llm) end |
Instance Method Details
#add_texts(texts:) ⇒ Object
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# File 'lib/langchain/vectorsearch/milvus.rb', line 26 def add_texts(texts:) client.entities.insert( collection_name: index_name, data: texts.map do |text| {content: text, vector: llm.(text: text).} end ) end |
#ask(question:, k: 4) {|String| ... } ⇒ String
Ask a question and return the answer
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# File 'lib/langchain/vectorsearch/milvus.rb', line 144 def ask(question:, k: 4, &block) search_results = similarity_search(query: question, k: k) content_data = search_results.dig("data").map { |result| result.dig("content") } context = content_data.join("\n---\n") prompt = generate_rag_prompt(question: question, context: context) = [{role: "user", content: prompt}] response = llm.chat(messages: , &block) response.context = context response end |
#create_default_index ⇒ Boolean
Create the default index
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# File 'lib/langchain/vectorsearch/milvus.rb', line 84 def create_default_index client.indexes.create( collection_name: index_name, index_params: [ { metricType: "L2", fieldName: "vector", indexName: "vector_idx", indexConfig: { index_type: "AUTOINDEX" } } ] ) end |
#create_default_schema ⇒ Hash
Create default schema
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# File 'lib/langchain/vectorsearch/milvus.rb', line 54 def create_default_schema client.collections.create( auto_id: true, collection_name: index_name, fields: [ { fieldName: "id", isPrimary: true, dataType: "Int64" }, { fieldName: "content", isPrimary: false, dataType: "VarChar", elementTypeParams: { max_length: "32768" # Largest allowed value } }, { fieldName: "vector", isPrimary: false, dataType: "FloatVector", elementTypeParams: { dim: llm.default_dimensions.to_s } } ] ) end |
#destroy_default_schema ⇒ Hash
Delete default schema
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# File 'lib/langchain/vectorsearch/milvus.rb', line 108 def destroy_default_schema client.collections.drop(collection_name: index_name) end |
#get_default_schema ⇒ Hash
Get the default schema
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# File 'lib/langchain/vectorsearch/milvus.rb', line 102 def get_default_schema client.collections.describe(collection_name: index_name) end |
#load_default_schema ⇒ Boolean
Load default schema into memory
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# File 'lib/langchain/vectorsearch/milvus.rb', line 114 def load_default_schema client.collections.load(collection_name: index_name) end |
#remove_texts(ids:) ⇒ Boolean
Deletes a list of texts in the index
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# File 'lib/langchain/vectorsearch/milvus.rb', line 41 def remove_texts(ids:) raise ArgumentError, "ids must be an array" unless ids.is_a?(Array) client.entities.delete( collection_name: index_name, filter: "id in #{ids}" ) end |
#similarity_search(query:, k: 4) ⇒ Object
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# File 'lib/langchain/vectorsearch/milvus.rb', line 118 def similarity_search(query:, k: 4) = llm.(text: query). similarity_search_by_vector( embedding: , k: k ) end |
#similarity_search_by_vector(embedding:, k: 4) ⇒ Object
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# File 'lib/langchain/vectorsearch/milvus.rb', line 127 def similarity_search_by_vector(embedding:, k: 4) load_default_schema client.entities.search( collection_name: index_name, anns_field: "vector", data: [], limit: k, output_fields: ["content", "id", "vector"] ) end |