RAGComponentFactory
Factory for creating components of the Retrieval Augmented Generation (RAG) pipeline Provides methods to instantiate and configure all necessary elements for vector search, document ingestion, query processing, and AI-assisted retrieval
Attributes
- Graph
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- Supertypes
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class Objecttrait Matchableclass Any
- Known subtypes
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class RAGComponentFactoryImpl
Members list
Value members
Abstract methods
Creates a StreamingAssistant. This service is the main entry point for the RAG pipeline.
Creates a StreamingAssistant. This service is the main entry point for the RAG pipeline.
Value parameters
- augmentor
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The retrieval augmentor
- model
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The chat model
Attributes
- Returns
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A StreamingAssistant
Creates an embedding model for code.
Creates an embedding model for code.
Attributes
- Returns
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An OllamaEmbeddingModel
Creates a retriever for code.
Creates a retriever for code.
Value parameters
- embeddingModel
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The embedding model
- embeddingStore
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The embedding store
- indexName
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The specific collection to be used
- modelRouter
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The LLM router used for dynamic filtering
Attributes
- Returns
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A retriever for the specified index
Creates a collection in Qdrant.
Creates a collection in Qdrant.
Value parameters
- client
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The Qdrant client to use
- distance
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The distance metric to use for vector similarity
- name
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The name of the collection
Attributes
- Returns
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A Try indicating success or failure of the operation
Creates a content aggregator for ranking and filtering retrieved content. This allows to rerank results, potentially yielding more relevant content.
Creates a content aggregator for ranking and filtering retrieved content. This allows to rerank results, potentially yielding more relevant content.
Value parameters
- scoringModel
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The scoring model used to rank retrieved content
Attributes
- Returns
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A configured ReRankingContentAggregator
Creates an embedding store.
Creates an embedding store.
Value parameters
- client
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The Qdrant client to use
- name
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The name of the collection to store embeddings
Attributes
- Returns
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A QdrantEmbeddingStore
Creates an ingestor for adding document embeddings to the vector database
Creates an ingestor for adding document embeddings to the vector database
Value parameters
- embeddingModel
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The embedding model to use for vectorizing documents
- embeddingStore
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The vector store where embeddings will be saved
- language
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The programming language of the documents to ingest
- strategy
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The strategy defining how documents are processed and split
Attributes
- Returns
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A configured EmbeddingStoreIngestor for the specified parameters
Creates a specified retriever type. The retriever is used to embed documents using the embedding model and store them in the embedding store.
Creates a specified retriever type. The retriever is used to embed documents using the embedding model and store them in the embedding store.
Value parameters
- embeddingModel
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The embedding model
- embeddingStore
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The embedding store
- indexName
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The specific collection to be used
Attributes
- Returns
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A retriever for the specified index
Creates a model router for routing queries
Creates a model router for routing queries
Attributes
- Returns
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An OllamaChatModel configured for query routing
Creates a Qdrant client.
Creates a Qdrant client.
Attributes
- Returns
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A QdrantClient
Creates a QueryRouter based on the provided retrievers
Creates a QueryRouter based on the provided retrievers
Value parameters
- modelRouter
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The LLM model used for query routing decisions
- retrievers
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List of content retrievers to use
Attributes
- Returns
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A configured QueryRouter
Creates a RetrievalAugmentor that combines router and aggregator
Creates a RetrievalAugmentor that combines router and aggregator
Value parameters
- aggregator
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The ContentAggregator to use
- router
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The QueryRouter to use
Attributes
- Returns
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A configured DefaultRetrievalAugmentor
Creates a scoring model. It is used to rerank documents (potentially yield better results).
Creates a scoring model. It is used to rerank documents (potentially yield better results).
Attributes
- Returns
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A ScoringModel
Creates a streaming chat model.
Creates a streaming chat model.
Attributes
- Returns
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A streaming chat model implementation
Creates an embedding model for text.
Creates an embedding model for text.
Attributes
- Returns
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An OllamaEmbeddingModel