diff --git a/dist/embeddings/llamacpp-provider.js b/dist/embeddings/llamacpp-provider.js
index 16b05b2a7c6b806a3032c9af2eb811c5227d29d9..90431db50bb462a1e7b10902554273e88e1aeaf9 100644
--- a/dist/embeddings/llamacpp-provider.js
+++ b/dist/embeddings/llamacpp-provider.js
@@ -7,8 +7,8 @@
  * Default model: nomic-embed-text-v1.5 (768 dimensions)
  */
 import { existsSync } from 'node:fs';
-import { join } from 'node:path';
 import { homedir } from 'node:os';
+import { join } from 'node:path';
 import { BaseEmbeddingProvider } from './provider.js';
 /**
  * Default embedding config: nomic-embed-text-v1.5 (768 dimensions)
@@ -18,7 +18,8 @@ export const DEFAULT_EMBEDDING_CONFIG = {
     path: join(homedir(), '.cache/llama.cpp/nomic-embed-text-v1.5.Q8_0.gguf'),
     dimensions: 768,
     contextSize: 8192,
-    gpuLayers: 999,
+    // Default to CPU (gpuLayers: 0) for reliability - GPU often has memory issues
+    gpuLayers: parseInt(process.env.LLAMA_GPU_LAYERS ?? '0', 10),
 };
 /**
  * MiniLM embedding config: all-MiniLM-L6-v2 (384 dimensions)
@@ -30,7 +31,6 @@ export const MINILM_EMBEDDING_CONFIG = {
     contextSize: 512,
     gpuLayers: 999,
 };
-// Lazy-loaded llama module
 let llamaModule = null;
 async function getLlamaModule() {
     if (!llamaModule) {
@@ -43,6 +43,15 @@ async function getLlamaModule() {
     }
     return llamaModule;
 }
+/**
+ * Task prefixes for nomic-embed-text model.
+ * These significantly improve semantic similarity scores.
+ * See: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
+ */
+const NOMIC_EMBED_PREFIXES = {
+    document: 'search_document: ',
+    query: 'search_query: ',
+};
 /**
  * LlamaCpp embedding provider with fail-fast initialization.
  *
@@ -53,6 +62,10 @@ export class LlamaCppEmbeddingProvider extends BaseEmbeddingProvider {
     name = 'llamacpp';
     dimensions;
     model;
+    /** Task prefix for document embeddings */
+    documentPrefix;
+    /** Task prefix for query embeddings */
+    queryPrefix;
     modelPath;
     gpuLayers;
     contextSize;
@@ -67,6 +80,15 @@ export class LlamaCppEmbeddingProvider extends BaseEmbeddingProvider {
         this.dimensions = config.dimensions ?? DEFAULT_EMBEDDING_CONFIG.dimensions;
         this.gpuLayers = config.gpuLayers ?? DEFAULT_EMBEDDING_CONFIG.gpuLayers;
         this.contextSize = config.contextSize ?? DEFAULT_EMBEDDING_CONFIG.contextSize;
+        // Set task prefixes based on model
+        if (this.model.toLowerCase().includes('nomic-embed')) {
+            this.documentPrefix = NOMIC_EMBED_PREFIXES.document;
+            this.queryPrefix = NOMIC_EMBED_PREFIXES.query;
+        }
+        else {
+            this.documentPrefix = '';
+            this.queryPrefix = '';
+        }
     }
     /**
      * Initialize the embedding model.
diff --git a/dist/redis/vector-store.js b/dist/redis/vector-store.js
index cb5b781a93d0f1558aca6415a2651c81a70593b4..b2977f239773bf178cd2afa52772ef93dc108203 100644
--- a/dist/redis/vector-store.js
+++ b/dist/redis/vector-store.js
@@ -1,8 +1,8 @@
 /**
  * Redis Vector Store with HNSW indexing via RediSearch.
  */
-import { DEFAULT_INDEX_CONFIG, KeyPatterns, Fields, hashPath, buildIndexCreateArgs, escapeTag, } from './schema.js';
 import { floatArrayToBuffer, bufferToFloatArray } from '../embeddings/provider.js';
+import { DEFAULT_INDEX_CONFIG, KeyPatterns, Fields, hashPath, buildIndexCreateArgs, escapeTag, } from './schema.js';
 /**
  * Redis Vector Store for directory semantic search.
  */
@@ -29,10 +29,20 @@ export class RedisVectorStore {
             // Index exists, nothing to do
         }
         catch {
-            // Index doesn't exist, create it
-            const args = buildIndexCreateArgs(this.indexName, this.keyPrefix, this.config);
-            const [command, ...rest] = args;
-            await this.redis.call(command, ...rest);
+            // Index doesn't exist (or FT.INFO failed), try to create it
+            try {
+                const args = buildIndexCreateArgs(this.indexName, this.keyPrefix, this.config);
+                const [command, ...rest] = args;
+                await this.redis.call(command, ...rest);
+            }
+            catch (createError) {
+                // Ignore "Index already exists" error - this can happen in race conditions
+                // or if FT.INFO failed for a different reason
+                const errorMessage = createError instanceof Error ? createError.message : String(createError);
+                if (!errorMessage.includes('Index already exists')) {
+                    throw createError;
+                }
+            }
         }
     }
     /**
@@ -77,8 +87,9 @@ export class RedisVectorStore {
      * Store multiple chunks in a batch using pipeline.
      */
     async storeBatch(chunks) {
-        if (chunks.length === 0)
+        if (chunks.length === 0) {
             return;
+        }
         const pipeline = this.redis.pipeline();
         for (const chunk of chunks) {
             const key = KeyPatterns.chunk(this.dirHash, chunk.id);
@@ -105,7 +116,7 @@ export class RedisVectorStore {
             : '*';
         // KNN query
         const queryExpr = `${filterExpr}=>[KNN ${limit} @${Fields.embedding} $query_vec AS score]`;
-        const result = (await this.redis.call('FT.SEARCH', this.indexName, queryExpr, 'PARAMS', '2', 'query_vec', embeddingBuffer, 'SORTBY', 'score', 'RETURN', '5', Fields.chunkId, 'score', Fields.chunkText, Fields.metadata, Fields.position, 'DIALECT', '2', 'LIMIT', '0', limit.toString()));
+        const result = (await this.redis.call('FT.SEARCH', this.indexName, queryExpr, 'PARAMS', '2', 'query_vec', embeddingBuffer, 'SORTBY', 'score', 'RETURN', '6', Fields.chunkId, Fields.docId, 'score', Fields.chunkText, Fields.metadata, Fields.position, 'DIALECT', '2', 'LIMIT', '0', limit.toString()));
         return this.parseSearchResults(result);
     }
     /**
@@ -115,12 +126,14 @@ export class RedisVectorStore {
         const results = [];
         // Result format: [count, key1, [field, value, ...], key2, [field, value, ...], ...]
         const count = result[0];
-        if (count === 0)
+        if (count === 0) {
             return results;
+        }
         for (let i = 1; i < result.length; i += 2) {
             const fields = result[i + 1];
-            if (!Array.isArray(fields))
+            if (!Array.isArray(fields)) {
                 continue;
+            }
             const fieldMap = {};
             for (let j = 0; j < fields.length; j += 2) {
                 fieldMap[fields[j]] = fields[j + 1];
@@ -144,7 +157,7 @@ export class RedisVectorStore {
                 score,
                 text: fieldMap[Fields.chunkText] || '',
                 position: parseInt(fieldMap[Fields.position] || '0', 10),
-                docId: metadata['docId'] || '',
+                docId: fieldMap[Fields.docId] || metadata['docId'] || '',
                 metadata,
             });
         }
@@ -156,8 +169,9 @@ export class RedisVectorStore {
     async getChunk(chunkId) {
         const key = KeyPatterns.chunk(this.dirHash, chunkId);
         const data = (await this.redis.hgetallBuffer(key));
-        if (!data || Object.keys(data).length === 0)
+        if (!data || Object.keys(data).length === 0) {
             return null;
+        }
         // Convert buffers to strings/values
         const getString = (field) => data[field]?.toString('utf-8') || '';
         const getBuffer = (field) => data[field] || null;
@@ -253,8 +267,9 @@ export class RedisVectorStore {
     async getDirectoryMeta() {
         const key = KeyPatterns.directoryMeta(this.dirHash);
         const data = await this.redis.get(key);
-        if (!data)
+        if (!data) {
             return null;
+        }
         try {
             return JSON.parse(data);
         }