Elasticsearch is an open source distributed search and analytics engine, often used to store and search through text data (e.g., logs, documents, articles). It’s commonly integrated with NLP workflows for:
- Storing extracted named entities or keywords
- Enabling full-text search over processed corpora
- Ranking documents based on custom scoring
Use Cases:
- Search systems over preprocessed corpora
- Document similarity lookup
- Named entity indexing
Integration Example:
- Use spaCy to extract keywords or metadata
- Store results in Elasticsearch index
- Use query interface to retrieve matching or related docs
Exploratory Questions:
- How does spaCy output map to ElasticSearch indexing fields?
- Can entity relationships or dependency trees be indexed effectively?
- How can TF-IDF or vector search (e.g., via Elastic’s k-NN or OpenSearch) be layered in?