The AI Evolution of Graph Search at Netflix (opens in new tab)
Netflix is evolving Graph Search from structured DSL queries toward natural-language search using large language models (LLMs). The goal is to let users describe what they want in everyday language while preserving the accuracy and trustworthiness required by Netflix’s complex, federated GraphQL data. Rather than replacing existing applications, Netflix plans to augment them with AI-generated filters and future retrieval-augmented generation capabilities. ## Why Natural-Language Search Is Needed - Graph Search currently relies on a Filter DSL, with applications translating UI interactions into structured queries. - Netflix has hundreds of applications with inconsistent query-building experiences, forcing users to learn different interfaces. - Some indexes contain hundreds of filterable fields, making large forms slow and cumbersome even for subject-matter experts. - Users naturally express goals in language such as “show all movies from the 90s about robots from the US,” not through query builders or DSL syntax. - Natural-language input could reduce friction while allowing each application to retain its own domain-specific presentation. ## Converting Text into Graph Search Filters - The core task is translating an ambiguous natural-language request into a valid Graph Search Filter DSL statement. - Graph Search indexes are defined through GraphQL queries containing typed fields, including booleans, strings, enums, and controlled vocabularies. - Generated filters can combine: - Comparisons such as `>` and `==` - Inclusion or exclusion operators such as `IN` - Logical operators such as `AND` - Netflix evaluates generated queries at three levels: - **Syntactic correctness:** The statement follows the DSL grammar and can be parsed. - **Semantic correctness:** The query uses existing fields, respects field types, and selects valid controlled-vocabulary values. - **Pragmatic correctness:** The filter accurately reflects the user’s intended meaning. ## Context Engineering for the LLM - The LLM needs index metadata to generate semantically valid filters. - Netflix derives much of this context from GraphQL schemas, including: - Field paths - Field descriptions from schema comments - Field types - Valid enum or controlled-vocabulary values - Controlled vocabularies define finite, governed sets of values, such as countries, and prevent generated queries from using invalid alternatives. - Supplying all metadata works for simple examples but does not scale: - Some indexes contain hundreds of fields. - Some vocabularies contain thousands of values. - Larger prompts increase latency and can reduce generation accuracy. - Netflix therefore needs ways to provide the LLM with relevant metadata without overwhelming its context, while still grounding generated queries in the actual schema and allowed values. Netflix’s approach combines schema-aware context, LLM-based query generation, and validation to make natural-language Graph Search practical. The key recommendation is to use AI as an augmentation layer over existing Graph Search applications, with strong grounding and correctness checks rather than treating generated queries as inherently reliable.
