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

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Deep Technical Analysis

Query-Document Mismatch

Vocabulary Gap:

User query: "How do I nuke my account?"
Document: "Account deletion procedure..."

Embeddings:
→ "nuke" → casual/slang vector space
→ "deletion" → formal vector space
→ Low cosine similarity despite same intent

Query Too Short:

Query: "API"
→ Too generic
→ Matches hundreds of chunks equally
→ Top-K full of weak matches

vs

Query: "API authentication using OAuth tokens"
→ Specific
→ Better discrimination
→ Retrieves relevant OAuth docs

Embedding Quality Issues

Out-of-Domain Text:

Embedding model trained on:
→ General web text
→ Wikipedia
→ Books

Your domain:
→ Technical product docs
→ Domain-specific jargon
→ Internal terminology

Embedding model doesn't understand:
→ Your acronyms
→ Your product names
→ Your technical terms
→ Lower retrieval quality

Example:

Model doesn't know "Redwood Strategy" is your RAG approach:
→ Query: "When should I use Redwood?"
→ Model treats "Redwood" as tree type
→ Fails to retrieve your docs about Redwood Strategy

Threshold Tuning

Similarity Score Cutoff:

Config: Only return chunks with score > 0.7

Problem:
→ Best match: 0.65 (good enough, but below threshold)
→ Result: No chunks returned
→ "I don't have that information"

Should be: Dynamic threshold or return top-K regardless

Hybrid Search Benefits

Semantic-Only Limitations:

Query: "Order #12345 status"
→ Semantic embedding focuses on "order status" (concept)
→ Misses exact match on "12345"
→ Retrieves generic order status docs, not specific order

Keyword + Semantic:

Hybrid search:
1. Keyword: Find docs containing "12345" (exact match)
2. Semantic: Find docs about order status
3. Combine results (weighted)

More likely to find specific order #12345 status

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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.