No Relevant Chunks Retrieved

The Problem

Vector search fails to retrieve relevant documents, returning either no results or highly irrelevant ones despite the knowledge base containing the answer.

Symptoms

  • ❌ "I don't have that information" despite doc existing

  • ❌ Retrieved chunks completely off-topic

  • ❌ Similarity scores all below threshold

  • ❌ Right document exists but not retrieved

  • ❌ Semantic search misses keyword matches

Real-World Example

Knowledge base contains:
"How to reset your password: Click 'Forgot Password' on login page..."

User query: "I can't log in, forgot my password"

Vector search retrieves:
→ Chunks about account creation
→ Chunks about security policies
→ Nothing about password reset

AI: "I don't have information about password reset."

Problem: Query embedding didn't match document embedding

Deep Technical Analysis

Query-Document Mismatch

Vocabulary Gap:

Query Too Short:

Embedding Quality Issues

Out-of-Domain Text:

Example:

Threshold Tuning

Similarity Score Cutoff:

Hybrid Search Benefits

Semantic-Only Limitations:

Keyword + Semantic:


How to Solve

Implement hybrid search (semantic + keyword) + fine-tune embeddings on domain-specific data + expand queries with synonyms/related terms + use lower similarity thresholds or always return top-K + apply query rewriting (expand short/ambiguous queries) + add fallback to full-text search if no semantic matches. See Retrieval Failures.

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