Retrieval-Augmented Generation (RAG) is an architecture which enhances the capabilities of Large Language Models (LLMs) by integrating them with external knowledge sources. This integration allows LLMs to access up-to-date, domain-specific information which helps in improving the accuracy and relevance of generated responses. RAG is effective in addressing challenges such as hallucinations and outdated knowledge.
RAG Architecture
The Retrieval-Augmented Generation (RAG) architecture is a two-part process involving a retriever component and a generator component.
1. Retrieval Component: The retrieval component identifies relevant data to assist in generating accurate responses. Dense Passage Retrieval (DPR) is a common model that is used to perform retrieval. Lets see how DPR works:
- Query Encoding: When we submits a query such as a question or prompt, it is converted into a dense vector using an encoder. This vector represents the query's semantic meaning in a high-dimensional space.
- Passage Encoding: Each document in the knowledge base is also encoded into vectors. This encoding process is done offline and stored in a way that allows for fast retrieval when the query is entered.
- Retrieval: Upon receiving the query the system compares the query vector with the vectors of all the documents in the knowledge base. It then retrieves the most relevant passages.
2. Generative Component: When the retrieval model identifies the relevant match it is then passed to the generative component. The generative component is based on Transformer architecture like BART or GPT. The generated response will be a combination of the retrieved information along with a newly generated output from the model.
The generative component uses two main strategies i.e Fusion-in-decoder (FiD) and Fusion-in-Encoder (FiE). The final output is based on the user's input. In fusion management both FiD and FiE combine the retrieved information with the user's input to generate a response.
- FiD (Fusion-in-Decoder): The retrieval and generation processes are kept separate. The generative model only merges the retrieved information during the decoding phase. This allows the model to focus on the most relevant parts of each document when generating the final response, offering greater flexibility in the integration of retrieved data.
- FiE (Fusion-in-Encoder): FiE combines the query and the retrieved passages at the beginning of the process. Both are processed simultaneously by the encoder. While this method can be more efficient, it offers less flexibility in integrating the retrieved information compared to FiD.
Lets see the key difference between FiD and FiE:
Aspect | Fusion-in-Decoder(FiD) | Fusion-in-Encoder(FiE) |
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Fusion Point | Fusion occurs in the decoding phase. | Fusion happens at the encoding phase before decoding. |
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Process Separation | Retrieval and generation are kept separate. | Retrieval and generation are processed together. |
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Efficiency | Slower due to separate retrieval and generation steps. | Faster due to simultaneous process in encoder phase |
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Complexity | More Complex | Simpler |
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Performance | Higher-quality response | Quicker response generation |
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Workflow of a Retrieval-Augmented Generation (RAG) system
The RAG architecture’s workflow can be broken down into the following steps:
Retrieval-Augmented Generation- Query Processing: The input query which could be a natural language question or prompt is first pre-processed. It is then passed to an embedding model that transforms the query into a high-dimensional vector representation.
- Embedding Model: The query is passed through an embedding model which transforms it into a vector that captures the deeper meaning of the query.
- Vector Database Retrieval: The query is in vector form which is used to search through a vector database. The system uses these vectors to find the most relevant documents that match the query.
- Retrieved Contexts: The system retrieves the documents that are closest to the query. These documents are then forwarded to the generative model to help it craft a response.
- LLM Response Generation: The LLM combines the original query with the additional retrieved context using its internal mechanisms to generate a response. It uses its trained knowledge alongside the fresh data to create a contextually accurate and coherent answer.
- Response: A response that blends the model's inherent knowledge with the up-to-date information retrieved during the process is then presented. This makes the response more accurate and detailed.
Techniques for Optimisation of RAG
The efficiency of Retrieval-Augmented Generation Systems can be enhanced by using various optimisation techniques. These techniques improve performance, reduce latency and ensures the relevance of responses by the system.
- Query Expansion: It enhances retrieval accuracy by adding related terms or synonyms to the original query. Using semantic embeddings, queries can be enriched with contextually similar terms which helps in improving the chances of finding the best match in large datasets.
- Early Stopping in Generative Models: Early stopping in generative models prevents unnecessary token generation by stopping the response once it is sufficiently complete. This helps to avoid redundancy while keeping responses relevant and concise.
- Pipeline Parallelism: It optimises the RAG process by running retrieval and generation tasks simultaneously on different hardware, reducing wait times. While one part of the system retrieves documents, the other can generate the response. Parallel execution cuts down processing time and maximises hardware potentials.
- Multi-Stage Retrieval: It involves an initial broad search followed by a refined search for the most relevant documents. This step-by-step approach ensures the retrieval process is efficient and precise. This helps in reducing overhead by narrowing the focus progressively.
Lets see how RAG architecture performs on various metrics,
- Accuracy: RAG models tend to have higher accuracy for task that require dynamic knowledge since they use current and domain-specific data.
- Latency: They may experience higher latency due to the retrieval step, but the use of efficient indexing systems like FAISS can minimise this issue.
- Cost Efficiency: RAG models are more cost-effective in cases where frequent updates to the knowledge base are required as it does not require the model to be retrained.
- Memory Usage: They require more memory for storing external knowledge but can offload retrieval tasks unlike transformer models that store everything within the model itself.
Applications of RAG
Lets see the applications of RAG architecture in various fields,
- Customer Support: By integrating RAG with chatbots, businesses can provide more accurate and context-aware answers by pulling in real-time information from company knowledge bases, FAQs and support documents.
- Healthcare: Medical professionals get benefit from RAG by retrieving the latest research, treatment guidelines and clinical data to assist in decision-making and it ensures that the information is current and evidence-based.
- Legal: Legal professionals use RAG to retrieve up-to-date case laws, regulations and legal precedents to generate responses that are grounded in the most relevant and recent legal information.
- Finance: Financial analysts use RAG systems to access the latest market data and financial reports which helps them make informed decisions quickly and accurately.
Advantages of RAG Architecture
- Up-to-Date Responses: RAG enables LLMs to generate answers based on the most current external data rather than being limited to pre-trained knowledge that may be outdated.
- Reduced Hallucinations: By grounding the LLM's response in reliable external knowledge RAG reduces the risk of hallucinations or generation of incorrect data, ensuring that model provides more factual accuracy.
- Domain-Specific Responses: RAG allows LLMs to provide answers that are more relevant to specific organizational needs or industries without retraining.
- Efficiency: RAG is cost-effective compared to traditional fine-tuning as it allows models to be updated with new data without needing retraining.
Challenges
- Data Quality: The accuracy of RAG’s output depends heavily on the quality of the retrieved documents. If the retrieval mechanism pulls in irrelevant or incorrect data, the response may be affected.
- Latency: The retrieval process can introduce delays, especially when dealing with large datasets. Optimizing retrieval efficiency is crucial to maintaining a responsive system.
- Complexity: RAG systems involve multiple moving parts which are embedding models, retrieval mechanisms and generative models. Ensuring all these components work seamlessly together can be technically complex.
- Scalability: As the volume of data grows, scaling the retrieval and generative component to handle larger datasets becomes increasingly challenging.
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