spring-projects/spring-ai · warning

Could not parse text search score: ${doc.getString("$score")

Error message

Could not parse text search score: ${doc.getString("$score")}

What it means

RedisVectorStore.similarityScore parses the '$score' field from the Redis search result document into a float; on NumberFormatException it warns and falls back to a default similarity of 0.9f. This means the score string was malformed or absent, and the returned Similarity value is fabricated, not measured.

Source

Thrown at vector-stores/spring-ai-redis-store/src/main/java/org/springframework/ai/vectorstore/redis/RedisVectorStore.java:562

			try {
				// Text search scores can be very high (like 10.0), normalize to 0.0-1.0
				// range
				float textScore = Float.parseFloat(doc.getString("$score"));
				// A simple normalization strategy - text scores are usually positive,
				// scale to 0.0-1.0
				// Assuming 10.0 is a "perfect" score, but capping at 1.0
				float normalizedTextScore = Math.min(textScore / 10.0f, 1.0f);

				if (logger.isDebugEnabled()) {
					logger.debug("Text search raw score: " + textScore + ", normalized: " + normalizedTextScore);
				}

				return normalizedTextScore;
			}
			catch (NumberFormatException e) {
				// If we can't parse the score, fall back to default
				if (logger.isWarnEnabled()) {
					logger.warn("Could not parse text search score: " + doc.getString("$score"));
				}
				return 0.9f; // Default high similarity
			}
		}

		// Handle the case where the distance field might not be present (like in text
		// search)
		if (!doc.hasProperty(DISTANCE_FIELD_NAME)) {
			// For text search, we don't have a vector distance, so use a default high
			// similarity
			logger.debug("No vector distance score found. Using default similarity.");
			return 0.9f; // Default high similarity
		}

		float rawScore = Float.parseFloat(doc.getString(DISTANCE_FIELD_NAME));

		// Different distance metrics need different score transformations
		if (logger.isDebugEnabled()) {

View on GitHub (pinned to 98a7beda4f)

Solutions

1. Verify the index was created by RedisVectorStore so the '$score' KNN attribute is present
2. Check the actual '$score' value in Redis (FT.SEARCH output) for malformed content
3. Upgrade spring-ai-redis-store / RediSearch to a version returning parseable numeric scores
4. Treat results with default 0.9 score with suspicion; re-run the query after fixing the index

Example fix

// before
// score field missing -> silently 0.9
List<Document> docs = vectorStore.similaritySearch(query);

// after
// recreate index via RedisVectorStore#afterPropertiesSet or check FT.INFO index
// ensure KNN query includes return field "$score"

String score = doc.getString("$score");
if (score == null || !score.matches("-?\\d+(\\.\\d+)?")) {
    logger.warn("'$score' missing or non-numeric: " + score);
}

boolean hasNumericScore(Document doc) {
    String s = doc.getMetadata().get("$score", "");
    try { Float.parseFloat(s); return true; } catch (Exception e) { return false; }
}

try {
    List<Document> docs = redisVectorStore.similaritySearch(req);
    docs.stream().filter(d -> !hasNumericScore(d)).forEach(d -> logger.warn("fabricated 0.9 score for " + d.getId()));
} catch (RuntimeException e) {
    logger.error("Redis similarity search failed", e);
}

When it happens

Trigger: Executing similaritySearch where the KNN '$score' attribute in the Redis reply cannot be parsed as a float — e.g. score field missing/empty, a non-numeric string, or locale/format issues.

Common situations: Custom Redis indexes missing the score attribute; mixing index definitions; Redis module (RediSearch) versions returning scores in unexpected format; queries executed against an index not created by the store.

Understand the failure class

Background: "Invalid ... format", "must be in format X", "does not look like a ..." — invalid argument format errors across CLI tools and libraries — this error's family across 17 libraries.

Related errors

• Error reserving atomic timestamps for conversation + convers
• Unknown message type: + type + , returning generic UserMessa
• Failed to create index:
• Could not initialize Redis schema
• Failed to initialize Lucene index for session:

AI-assisted analysis of spring-projects/spring-ai@98a7beda4f (2026-09-11). Data as JSON: /api/errors/532a378939eb2f0e. Report an issue: GitHub.