Principle:Confident ai Deepeval Context Generation
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| Last Updated | 2026-02-14 09:00 GMT |
Overview
Context generation is the process of producing diverse evaluation contexts from source documents using embeddings and vector stores. It enables the creation of varied test scenarios from a single corpus of source material by combining document chunks through similarity-based retrieval.
Description
Context generation bridges the gap between raw document chunks and the rich, multi-faceted contexts needed for high-quality evaluation data. Rather than using individual chunks in isolation, context generation:
- Combines related chunks -- uses embedding similarity to group chunks that address related topics, producing richer contexts for question generation.
- Ensures diversity -- applies filtering thresholds to avoid generating redundant contexts from highly similar chunks.
- Supports configurable similarity -- allows tuning of similarity and filter thresholds to control the balance between context relevance and diversity.
- Leverages vector stores -- stores chunk embeddings in a vector database for efficient nearest-neighbor retrieval during context construction.
In the DeepEval pipeline, context generation occurs after document chunking and before golden generation. The generated contexts serve as the grounding material from which LLMs synthesize evaluation questions and expected answers.
Usage
Context generation is used whenever evaluation data must be generated from documents, providing the intermediate representation between raw text and structured evaluation goldens. It is especially valuable when:
- Source documents are large and cover many topics
- Evaluation scenarios require multi-document or cross-section contexts
- Diversity of generated test cases is a priority
Theoretical Basis
Context generation for synthetic evaluation data draws from several retrieval and information retrieval techniques:
- Embedding-based retrieval -- documents and chunks are mapped to dense vector representations using embedding models, enabling semantic similarity search beyond lexical matching.
- Context diversity -- by applying filter thresholds, the system ensures that generated contexts are sufficiently distinct from each other, preventing evaluation datasets from being dominated by repetitive scenarios.
- Vector similarity search -- cosine similarity (or other distance metrics) in the embedding space is used to find and combine related chunks into coherent contexts.
The abstract context generation process follows this pattern:
CONTEXT_GENERATION(chunks, embedder, similarity_threshold, filter_threshold):
1. EMBED all chunks using the embedding model
2. STORE embeddings in vector database
3. FOR each chunk as seed:
a. RETRIEVE nearest neighbors above similarity_threshold
b. FILTER contexts below filter_threshold for diversity
c. COMBINE seed + neighbors into a context group
4. RETURN list of context groups (List[List[str]])
Key properties:
- Semantic grounding -- contexts are formed from semantically related content, not random combinations.
- Configurable diversity -- the filter_threshold parameter controls the minimum distinctiveness between generated contexts.
- Scalability -- vector store indexing enables efficient retrieval even with large document collections.