How to improve the recall accuracy of vector database
A practical guide to improving the recall accuracy of vector databases
Core content:
1. The importance and challenges of recall accuracy of vector databases
2. Strategies to improve vector quality: optimizing embedding models, fine-tuning models, dimensionality reduction and regularization
3. Methods to improve index structure: choose appropriate indexing methods, such as FAISS, Annoy, HNSW, etc.
“ Vector databases need to strike a business balance between recall efficiency and recall accuracy. ”
The core technology of RAG lies in the accuracy of recall data. In the similarity search process, the technical carrier is mainly based on the vector database; therefore, how to improve the recall accuracy of the vector database has become an urgent problem to be solved.
In the previous article about vector databases, I mentioned that one thing that makes vector databases different from traditional relational databases is that they use vector metrics to recall data; this means that the recalled data is related at the vector level, but may be completely unrelated in terms of semantics. For example, in the example given in the previous article, searching for Lin Daiyu in the vector database of Journey to the West actually yielded results.
Therefore, how to optimize the recall accuracy of the vector database, how should this problem be solved?
The recall accuracy problem of vector database
There are many factors that affect the recall accuracy of vector databases. It is difficult or even impossible to solve this problem from a single aspect. Therefore, improving the accuracy of vector databases requires multiple aspects.
Improving the accuracy of vector database search can be improved from the following aspects:
1. Improve the quality of vectors
Optimize embedding models : Using higher quality embedding models can generate more accurate vectors. For example, you can use the latest pre-trained models (such as OpenAI's GPT series, CLIP, BERT, SimCSE, etc.) to generate more semantically accurate vectors.
Fine-tune the model : If possible, fine-tune the model on domain-specific data to make the embeddings more relevant to your data.
Dimensionality reduction and regularization : Before storing vector data, you can consider reducing the dimensionality of high-dimensional vectors (such as PCA or t-SNE) and performing regularization processing to ensure the uniformity of data distribution and reduce noise interference.
2. Improve the index structure
Choose the right indexing method : Vector databases such as FAISS, Annoy, and HNSW provide different indexing methods. Choosing the right indexing method based on the amount of data, real-time query requirements, and computing resources is the key to improving accuracy.
FAISS : Suitable for large-scale vector search and supports multiple index structures such as inverted file index (IVF) and quantization (PQ).
HNSW (Hierarchical Navigable Small World) : Suitable for processing sparse vectors or large data sets, and generally provides higher accuracy.
Fine-tuning of the index : Adjusting the index parameters (such as distance measurement method, size of candidate list, etc.) helps improve search accuracy.
3. Optimizing distance metrics
Choose an appropriate distance metric : Common distance metrics include Euclidean distance, cosine similarity, Manhattan distance, etc. Different data sets and application scenarios may require different distance metrics. You can choose the metric that best suits your application scenario through experiments.
Distance metric normalization : Ensure that when calculating the distance, the input vector is properly normalized (such as L2 normalization or Z-score normalization). This helps avoid certain features from biasing the distance calculation.
4. Improve query strategy
Multiple rounds of query optimization : The most relevant results can be gradually and precisely located through step-by-step screening and hierarchical querying. That is, more candidates are returned in the initial search, and then a deeper search is conducted on the candidates.
Query re-ranking : After the initial search, use a more accurate ranking algorithm to further improve the relevance of the search results. For example, combining traditional ranking learning algorithms (such as learning to rank, RankNet) or using deep learning models to optimize the ranking.
5. Data enhancement and processing
Data preprocessing : Ensure that the data input into the vector database is effectively cleaned and normalized. For example, for text data, stop words should be removed, and word segmentation and stemming should be performed.
Data augmentation : Enhance the dataset by introducing additional semantic information or transformations to increase the diversity of training and embeddings and reduce the bias of the model on certain edge cases.
6. Optimize through feedback mechanisms
User feedback learning : Continuously optimize search results based on user click behavior or feedback. Use machine learning models to adjust vector weights or improve index structures, so that the system can more accurately reflect users’ actual needs.
Active learning : Introduce an active learning mechanism to continuously select the samples that can best improve the model from a small amount of labeled data for training, further improving the accuracy of retrieval.
7. Multimodal Fusion
If your dataset includes multiple types (such as text, images, audio, etc.), you can consider fusing vectors of different modalities for retrieval. For example, using a cross-modal embedding model to fuse text and image vectors can achieve cross-modal retrieval during querying and improve accuracy.
By combining the above methods, you can gradually improve the accuracy of vector database searches to meet different retrieval needs.
