ChromaDB facilitates searching for documents based on their embeddings, which represent the semantic content of the text. When a query is made to ChromaDB, it performs a similarity search operation to find documents that are most similar to the query document or chunk.
Here’s how the search process typically works:
- Query Embedding: The user provides a query document or chunk, which is then embedded using the same embedding function used to generate embeddings for the documents stored in ChromaDB. This results in a query embedding that represents the semantic content of the query.
- Similarity Calculation: ChromaDB compares the query embedding with the embeddings of documents stored in its database. This is typically done using a similarity metric such as cosine similarity or Euclidean distance. The goal is to find documents whose embeddings are most similar to the query embedding.
- Retrieval: Based on the similarity scores calculated for each document, ChromaDB retrieves the top-k most similar documents. These documents are considered the closest matches to the query document in terms of their semantic content.
- Ranking and Filtering: ChromaDB may further rank or filter the retrieved documents based on additional criteria, such as relevance or recency, before returning them to the user.
Overall, ChromaDB performs a search operation within its database of document embeddings to find documents that closely match a given query document or chunk in terms of their semantic content. This functionality is essential for various NLP applications, including retrieval-based question answering, information retrieval, and recommendation systems.
