diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
new file mode 100644
index 00000000..84fb138f
--- /dev/null
+++ b/.github/workflows/test.yml
@@ -0,0 +1,153 @@
+name: Run Tests
+
+on:
+  pull_request:
+    branches:
+      - main
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+    inputs:
+      clear_cache:
+        description: 'Clear model cache and re-download'
+        required: false
+        type: boolean
+        default: false
+
+env:
+  MODEL_NAME: Llama-3.2-3B-Instruct-Q4_K_M.gguf
+  MODEL_PATH: models/Llama-3.2-3B-Instruct-Q4_K_M.gguf
+  MODEL_URL: https://huggingface.co/hugging-quants/Llama-3.2-3B-Instruct-Q4_K_M-GGUF/resolve/main/llama-3.2-3b-instruct-q4_k_m.gguf
+  CACHE_VERSION: v1
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    timeout-minutes: 60
+    
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Node.js 22+
+        uses: actions/setup-node@v4
+        with:
+          node-version: '22'
+          check-latest: true
+          cache: 'npm'
+
+      - name: Create models directory
+        run: mkdir -p models
+
+      # Restore cache (90-day retention with stable key)
+      - name: Restore model from cache
+        if: github.event.inputs.clear_cache != 'true'
+        id: cache-model-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: ${{ env.MODEL_PATH }}
+          key: llama-model-${{ runner.os }}-${{ env.CACHE_VERSION }}
+          restore-keys: |
+            llama-model-${{ runner.os }}-
+
+      - name: Cache status
+        run: |
+          if [ "${{ steps.cache-model-restore.outputs.cache-hit }}" == "true" ]; then
+            echo "✅ Model loaded from cache"
+          else
+            echo "⚠️  Model not in cache, will download"
+          fi
+
+      - name: Download model
+        if: steps.cache-model-restore.outputs.cache-hit != 'true' || github.event.inputs.clear_cache == 'true'
+        run: |
+          # Remove existing file if forcing re-download
+          [ -f "${{ env.MODEL_PATH }}" ] && rm "${{ env.MODEL_PATH }}"
+          
+          # Download with comprehensive retry logic
+          MAX_RETRIES=5
+          RETRY_COUNT=0
+          
+          while [ $RETRY_COUNT -lt $MAX_RETRIES ]; do
+            echo "Attempt $((RETRY_COUNT + 1))/$MAX_RETRIES..."
+            
+            if curl -L \
+              --fail \
+              --retry 3 \
+              --retry-delay 5 \
+              --connect-timeout 30 \
+              --max-time 3600 \
+              -o "${{ env.MODEL_PATH }}" \
+              -# \
+              "${{ env.MODEL_URL }}"; then
+              break
+            else
+              RETRY_COUNT=$((RETRY_COUNT + 1))
+              if [ $RETRY_COUNT -lt $MAX_RETRIES ]; then
+                echo "⚠️  Download failed, retrying in 10 seconds..."
+                sleep 10
+              else
+                echo "❌ Download failed after $MAX_RETRIES attempts"
+                exit 1
+              fi
+            fi
+          done
+
+      - name: Save model to cache
+        if: steps.cache-model-restore.outputs.cache-hit != 'true' || github.event.inputs.clear_cache == 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: ${{ env.MODEL_PATH }}
+          key: llama-model-${{ runner.os }}-${{ env.CACHE_VERSION }}
+
+      - name: Install dependencies
+        run: npm ci
+
+      - name: Install Chroma
+        run: pip install chromadb
+
+      - name: Start Chroma DB
+        run: |
+          chroma-server --host 0.0.0.0 --port 8000 &
+          for i in {1..30}; do
+            if nc -z localhost 8000; then
+              echo "✅ Chroma is ready"
+              break
+            fi
+            echo "Waiting for Chroma to start..."
+            sleep 2
+          done
+
+      - name: Run tests
+        run: npm test
+        env:
+          NODE_ENV: test
+        timeout-minutes: 30
+
+      - name: Upload test results
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: test-results
+          path: |
+            coverage/
+            test-results/
+          retention-days: 7
+          if-no-files-found: ignore
+
+      - name: Setup Python 3.12.8
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.12.8'
+
+      - name: Create virtual environment and install dependencies
+        run: |
+          python -m venv .venv
+          .venv/bin/pip install --upgrade pip
+          .venv/bin/pip install -r python/requirements.txt
+
+      - name: Run Python unit tests
+        run: |
+          .venv/bin/python -m unittest discover python/tests
+        timeout-minutes: 15
diff --git a/.gitignore b/.gitignore
index a4a7c572..8519e972 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,4 +15,5 @@ output-videos
 __pycache__
 .chroma_db
 .locations.json
-.venv
\ No newline at end of file
+.venv
+models/
\ No newline at end of file
diff --git a/lib/constants/index.ts b/lib/constants/index.ts
index 492b8bdc..585101e9 100644
--- a/lib/constants/index.ts
+++ b/lib/constants/index.ts
@@ -31,8 +31,7 @@ export const CHROMA_PORT = process.env.CHROMA_PORT || '8000';
 export const COLLECTION_NAME = 'video_content';
 
 // AI Models
-export const EMBEDDING_MODEL = 'text-embedding-004';
-export const GEMINI_API_KEY = process.env.GEMINI_API_KEY;
+export const SEARCH_AI_MODEL = 'Llama-3.2-3B-Instruct-Q4_K_M.gguf'
 
 
 // Files
diff --git a/lib/conveyor/handlers/app-handler.ts b/lib/conveyor/handlers/app-handler.ts
index 2e810a36..f71e9445 100644
--- a/lib/conveyor/handlers/app-handler.ts
+++ b/lib/conveyor/handlers/app-handler.ts
@@ -17,7 +17,7 @@ import {
   queryCollection,
   updateMetadata,
 } from '@/lib/services/vectorDb'
-import { generateActionFromPrompt } from '@/lib/services/gemini'
+import { generateActionFromPrompt } from '@/lib/services/llm'
 import { stitchVideos } from '@/lib/utils/sticher'
 import { exportToFcpXml } from '@/lib/utils/fcpxml'
 import { convertTimeToWords } from '@/lib/utils/time'
diff --git a/lib/services/gemini.ts b/lib/services/gemini.ts
deleted file mode 100644
index 0260ddc7..00000000
--- a/lib/services/gemini.ts
+++ /dev/null
@@ -1,120 +0,0 @@
-import { GoogleGenerativeAI } from '@google/generative-ai'
-import 'dotenv/config'
-import { VideoSearchParams } from '../types/search'
-
-if (!process.env.GEMINI_API_KEY) {
-  throw new Error('GEMINI_API_KEY environment variable not set.')
-}
-
-const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY)
-
-const model = genAI.getGenerativeModel({ model: 'gemini-2.5-pro' })
-
-const responseCache = new Map<string, { result: VideoSearchParams; timestamp: number }>()
-import { CACHE_TTL } from '@/lib/constants';
-
-export async function generateActionFromPrompt(query: string, useCache = true): Promise<VideoSearchParams> {
-  const cacheKey = query.toLowerCase().trim()
-
-  if (useCache && responseCache.has(cacheKey)) {
-    const cached = responseCache.get(cacheKey)!
-    if (Date.now() - cached.timestamp < CACHE_TTL) {
-      return cached.result
-    }
-    responseCache.delete(cacheKey)
-  }
-
-  const result = await generateActionFromPromptInternal(query)
-
-  if (useCache) {
-    responseCache.set(cacheKey, { result, timestamp: Date.now() })
-  }
-
-  return result
-}
-
-/**
- * Generates action from the prompt
- * @param query The user's query asking for a compilation.
- */
-export async function generateActionFromPromptInternal(query: string): Promise<VideoSearchParams> {
-    const prompt = `You are a video search parameter extraction assistant. Extract structured data from user queries.
-
-STRICT REQUIREMENTS:
-1. Return ONLY valid JSON, no markdown formatting
-2. All fields must be present (use null or [] for missing values)
-3. Ensure outputFilename is URL-safe and descriptive
-
-FIELD SPECIFICATIONS:
-- action: Main activity/subject (string, can be empty)
-- emotions: Array of emotion keywords (valid: happy, sad, surprised, angry, neutral, excited, calm)
-- shot_type: Camera framing (valid: "medium-shot", "long-shot", "close-up", or null)
-- aspect_ratio: Video dimensions (valid: "16:9", "9:16", "1:1", "4:3", "8:7", or null)
-- duration: Total seconds (positive integer or null)
-- description: Natural language scene description (string, required)
-- outputFilename: Kebab-case name without extension, max 50 chars (string, required)
-- objects: Physical objects/items in scene (array of strings)
-- transcriptionQuery: Exact text to search for in video transcriptions (string or null). Extract from:
-  * Text inside single or double quotes
-  * Phrases after "says", "mentions", "talks about", "containing the word(s)"
-  * Any explicit text search request
-
-EXAMPLES:
-Input: "Create a 30 second vertical video of me looking happy"
-Output: {"action":"looking","emotions":["happy"],"shot_type":null,"aspect_ratio":"9:16","duration":30,"description":"person looking happy","outputFilename":"happy-vertical-30s","objects":[],"transcriptionQuery":null}
-
-Input: "2 minute video of @Ilias riding a bike outdoors"
-Output: {"action":"riding a bike","emotions":[],"shot_type":"long-shot","aspect_ratio":null,"duration":120,"description":"person riding bicycle outdoors","outputFilename":"ilias-bike-ride-2min","objects":["bicycle"],"transcriptionQuery":null}
-
-Input: "a clip where someone says 'that's awesome'"
-Output: {"action":"","emotions":[],"shot_type":null,"aspect_ratio":null,"duration":null,"description":"someone says that's awesome","outputFilename":"clip-thats-awesome","objects":[],"transcriptionQuery":"that's awesome"}
-
-Input: "Compile my happiest moments from last summer"
-Output: {"action":"","emotions":["happy"],"shot_type":null,"aspect_ratio":null,"duration":null,"description":"happy summer moments","outputFilename":"happy-summer-moments","objects":[],"transcriptionQuery":null}
-
-Input: "Generate a video with all scenes mention the word 'Yalla Let's code'"
-Output: {"action":"","emotions":[],"shot_type":null,"aspect_ratio":null,"duration":null,"description":"scenes mentioning yalla let's code","outputFilename":"yalla-lets-code-scenes","objects":[],"transcriptionQuery":"Yalla Let's code"}
-
-Input: "Find clips where I talk about machine learning"
-Output: {"action":"talking","emotions":[],"shot_type":null,"aspect_ratio":null,"duration":null,"description":"talking about machine learning","outputFilename":"machine-learning-clips","objects":[],"transcriptionQuery":"machine learning"}
-
-Input: "Show me videos containing the phrase 'hello world'"
-Output: {"action":"","emotions":[],"shot_type":null,"aspect_ratio":null,"duration":null,"description":"videos with hello world phrase","outputFilename":"hello-world-videos","objects":[],"transcriptionQuery":"hello world"}
-
-USER QUERY: ${query}
-
-Respond with ONLY the JSON object:`
-
-    try {
-      const result = await model.generateContent(prompt)
-      const response = result.response
-
-      const text = response
-        .text()
-        .replace(/```json/g, '')
-        .replace(/```/g, '')
-        .trim()
-      const parsed = JSON.parse(text)
-
-      if (!parsed.outputFilename) {
-        const timestamp = new Date().toISOString().slice(0, 10).replace(/-/g, '')
-        parsed.outputFilename = `video-${timestamp}`
-      }
-
-      return parsed
-    } catch (error) {
-      console.error('Error generating search query:', error)
-      const timestamp = new Date().toISOString().slice(0, 10).replace(/-/g, '')
-      return {
-        action: query,
-        emotions: [],
-        outputFilename: `video-${timestamp}`,
-        objects: [],
-        duration: 120,
-        aspect_ratio: '16:9',
-        shot_type: 'long-shot',
-        description: '',
-        transcriptionQuery: undefined,
-      }
-  }
-}
diff --git a/lib/services/llm.ts b/lib/services/llm.ts
new file mode 100644
index 00000000..b5d9f4bd
--- /dev/null
+++ b/lib/services/llm.ts
@@ -0,0 +1,244 @@
+import { VideoSearchParams } from '../types/search'
+import { CACHE_TTL, SEARCH_AI_MODEL } from '@/lib/constants'
+import { getLlama, LlamaModel, LlamaContext, LlamaChatSession } from 'node-llama-cpp'
+import path from 'path'
+import { z } from 'zod'
+
+export const VideoSearchParamsSchema = z.object({
+  action: z.string().nullable(),
+  emotions: z.array(z.string()).default([]),
+  shot_type: z.string().nullable(),
+  aspect_ratio: z.string().nullable().default('16:9'),
+  duration: z.number().positive().nullable(),
+  description: z.string().min(1),
+  outputFilename: z.string().min(1),
+  objects: z.array(z.string()).default([]),
+  transcriptionQuery: z.string().nullable(),
+})
+const SYSTEM_PROMPT = `Extract video search parameters from the user query into JSON format.
+
+RULES:
+1. Return ONLY valid JSON - no markdown, no explanations
+2. All fields are required (use null or [] when not applicable)
+3. Extract ALL mentioned emotions, objects, and parameters
+4. Be precise with emotion detection - "looking happy" means emotions: ["happy"]
+
+SCHEMA:
+{
+  "action": string | null,
+  "emotions": ["happy"|"sad"|"surprised"|"angry"|"neutral"|"excited"|"calm"|"fear"|"disgust"],
+  "shot_type": "close-up" | "medium-shot" | "long-shot" | null,
+  "aspect_ratio": "16:9"|"9:16"|"1:1"|"4:3"|"21:9"|"8:7",
+  "duration": number | null,
+  "description": string,
+  "outputFilename": string,
+  "objects": string[],
+  "transcriptionQuery": string | null
+}
+
+FIELD EXTRACTION:
+
+action: Main verb/activity
+- "running" → "running"
+- "cooking with" → "cooking"
+- "looking happy" → null (looking is not an action, it's describing emotion)
+- "talking about" → "talking"
+- null if no clear action
+
+emotions: Extract ALL emotion words (ALWAYS array)
+- "happy" → ["happy"]
+- "looking happy" → ["happy"]
+- "sad and angry" → ["sad", "angry"]
+- none → []
+
+shot_type: Camera framing
+- "close-up" / "close up" / "closeup" → "close-up"
+- "medium shot" / "medium-shot" → "medium-shot"
+- "long shot" / "wide shot" → "long-shot"
+- null if not mentioned
+
+aspect_ratio: Video dimensions
+- "vertical" / "portrait" / "9:16" → "9:16"
+- "square" / "1:1" / "instagram" → "1:1"
+- "horizontal" / "landscape" / "16:9" → "16:9"
+- default → "16:9"
+
+duration: Time in seconds
+- "30 seconds" → 30
+- "2 minutes" → 120
+- "1 min 30 sec" → 90
+- null if not mentioned
+
+objects: Physical items (ALWAYS array, singular form)
+- "laptop" → ["laptop"]
+- "pans and knives" → ["pan", "knife"]
+- "my dog" → ["dog"]
+- none → []
+
+transcriptionQuery: Quoted speech
+- "says 'hello world'" → "hello world"
+- "contains 'machine learning'" → "machine learning"
+- null if not mentioned
+
+description: Brief summary of the query
+
+outputFilename: kebab-case filename (no extension)
+
+EXAMPLES:
+
+Query: "Create a 30 second vertical video of me looking happy"
+{"action":null,"emotions":["happy"],"shot_type":null,"aspect_ratio":"9:16","duration":30,"description":"30 second vertical video showing happy emotion","outputFilename":"happy-vertical-30s","objects":[],"transcriptionQuery":null}
+
+Query: "Find all close-ups where @Ilias is surprised and there's a laptop"
+{"action":null,"emotions":["surprised"],"shot_type":"close-up","aspect_ratio":"16:9","duration":null,"description":"close-up shots of surprised person with laptop","outputFilename":"surprised-laptop-closeup","objects":["laptop"],"transcriptionQuery":null}
+
+Query: "Show me clips where I say 'machine learning is awesome'"
+{"action":"talking","emotions":[],"shot_type":null,"aspect_ratio":"16:9","duration":null,"description":"clips with machine learning phrase","outputFilename":"machine-learning-clips","objects":[],"transcriptionQuery":"machine learning is awesome"}
+
+Query: "2 minute video of cooking with pans and knives"
+{"action":"cooking","emotions":[],"shot_type":null,"aspect_ratio":"16:9","duration":120,"description":"2 minute cooking video with pans and knives","outputFilename":"cooking-pans-knives-2min","objects":["pan","knife"],"transcriptionQuery":null}
+
+Query: "Compile happy and excited moments from last summer"
+{"action":null,"emotions":["happy","excited"],"shot_type":null,"aspect_ratio":"16:9","duration":null,"description":"happy and excited summer moments compilation","outputFilename":"happy-excited-summer","objects":[],"transcriptionQuery":null}
+
+Query: "Make a square Instagram post of my dog"
+{"action":null,"emotions":[],"shot_type":null,"aspect_ratio":"1:1","duration":null,"description":"square format Instagram video of dog","outputFilename":"dog-instagram-square","objects":["dog"],"transcriptionQuery":null}
+
+Now extract parameters from this query:`
+
+class LlamaModelManager {
+  private llama: any = null
+  private model: LlamaModel | null = null
+  private context: LlamaContext | null = null
+  private responseCache = new Map<string, { result: VideoSearchParams; timestamp: number }>()
+
+  async initialize() {
+    if (this.model && this.context) return { model: this.model, context: this.context }
+
+    if (!this.llama) {
+      this.llama = await getLlama()
+    }
+
+    const modelPath = path.join(process.cwd(), 'models', SEARCH_AI_MODEL)
+
+    this.model = await this.llama.loadModel({ modelPath })
+    if (!this.model) throw new Error('Failed to load model')
+
+    this.context = await this.model.createContext({
+      sequences: 1,
+      contextSize: 4096,
+    })
+
+    return { model: this.model, context: this.context }
+  }
+
+  async generateParams(query: string): Promise<VideoSearchParams> {
+    const { context } = await this.initialize()
+    const sequence = await context.getSequence()
+    const session = new LlamaChatSession({ contextSequence: sequence })
+
+    try {
+      const response = await session.prompt(`${SYSTEM_PROMPT}${query}\n\nJSON OUTPUT:`, {
+        maxTokens: 512,
+        temperature: 0.1,
+        topP: 0.9,
+        topK: 40,
+      })
+      return this.parseAndValidate(response, query)
+    } finally {
+      session.dispose()
+    }
+  }
+
+  private parseAndValidate(response: string, query: string): VideoSearchParams {
+    const cleaned = response.trim().replace(/```(?:json)?\s*/g, '')
+    const jsonMatch = cleaned.match(/\{[\s\S]*\}/)
+
+    if (!jsonMatch) {
+      return this.createFallback(query)
+    }
+
+    try {
+      const parsed = JSON.parse(jsonMatch[0])
+
+      const validated = VideoSearchParamsSchema.parse(parsed)
+
+      return {
+        ...validated,
+        outputFilename: this.sanitizeFilename(validated.outputFilename),
+      }
+    } catch {
+      return this.createFallback(query)
+    }
+  }
+
+  private sanitizeFilename(filename: string): string {
+    return (
+      filename
+        .toLowerCase()
+        .replace(/[^a-z0-9-]/g, '-')
+        .replace(/-+/g, '-')
+        .replace(/^-+|-+$/g, '')
+        .slice(0, 50) || `video-${Date.now()}`
+    )
+  }
+
+  private createFallback(query: string): VideoSearchParams {
+    return {
+      action: null,
+      emotions: [],
+      shot_type: null,
+      aspect_ratio: '16:9',
+      duration: null,
+      description: query || 'video search',
+      outputFilename: this.sanitizeFilename(query) || `video-${Date.now()}`,
+      objects: [],
+      transcriptionQuery: null,
+    }
+  }
+
+  getCachedResult(query: string): VideoSearchParams | null {
+    const cacheKey = query.toLowerCase().trim()
+    const cached = this.responseCache.get(cacheKey)
+
+    if (cached && Date.now() - cached.timestamp < CACHE_TTL) {
+      return cached.result
+    }
+
+    if (cached) this.responseCache.delete(cacheKey)
+    return null
+  }
+
+  setCachedResult(query: string, result: VideoSearchParams): void {
+    const cacheKey = query.toLowerCase().trim()
+    this.responseCache.set(cacheKey, { result, timestamp: Date.now() })
+  }
+
+  async cleanup() {
+    await this.context?.dispose()
+    await this.model?.dispose()
+    this.context = null
+    this.model = null
+    this.llama = null
+  }
+}
+
+const modelManager = new LlamaModelManager()
+
+export async function generateActionFromPrompt(query: string, useCache = true): Promise<VideoSearchParams> {
+  if (useCache) {
+    const cached = modelManager.getCachedResult(query)
+    if (cached) return cached
+  }
+
+  const result = await modelManager.generateParams(query)
+
+  if (useCache) {
+    modelManager.setCachedResult(query, result)
+  }
+
+  return result
+}
+
+export const generateActionFromPromptInternal = (query: string) => modelManager.generateParams(query)
+export const cleanup = () => modelManager.cleanup()
diff --git a/lib/services/vectorDb.ts b/lib/services/vectorDb.ts
index 6703775f..50e091cc 100644
--- a/lib/services/vectorDb.ts
+++ b/lib/services/vectorDb.ts
@@ -1,44 +1,51 @@
-import 'dotenv/config'
 import { ChromaClient, Collection } from 'chromadb'
-import { GoogleGenerativeAI } from '@google/generative-ai'
 import { EmbeddingInput, CollectionStatistics } from '../types/vector'
 import { Video, VideoWithScenes } from '../types/video'
 import { Scene } from '../types/scene'
 import { SearchQuery } from '../types/search'
 import { metadataToScene, sceneToVectorFormat } from '../utils/embed'
-
-import { GEMINI_API_KEY, CHROMA_HOST, CHROMA_PORT, COLLECTION_NAME, EMBEDDING_MODEL } from '@/lib/constants'
+import { CHROMA_HOST, CHROMA_PORT, COLLECTION_NAME } from '@/lib/constants'
 
 let client: ChromaClient | null = null
 let collection: Collection | null = null
 
+const initialize = async (name: string = COLLECTION_NAME): Promise<void> => {
+  if (client && collection) return
+
+  try {
+    client = new ChromaClient({
+      path: `http://${CHROMA_HOST}:${CHROMA_PORT}`,
+    })
+
+    await client.heartbeat()
+
+    collection = await client.getOrCreateCollection({
+      name,
+      embeddingFunction: undefined, // Default Chroma embeddings
+    })
+  } catch (error) {
+    console.error(
+      JSON.stringify({
+        status: 'error',
+        message: `Initialization failed: ${error instanceof Error ? error.message : String(error)}`,
+        hint: 'Make sure ChromaDB server is running. Start it with: docker run -p 8000:8000 chromadb/chroma',
+      })
+    )
+    throw error
+  }
+}
+
 async function embedDocuments(documents: EmbeddingInput[]): Promise<void> {
   try {
     await initialize()
-    if (!GEMINI_API_KEY) {
-      throw new Error('GEMINI_API_KEY environment variable not set.')
-    }
-    const genAI = new GoogleGenerativeAI(GEMINI_API_KEY)
-    const model = genAI.getGenerativeModel({ model: EMBEDDING_MODEL })
-
-    const ids: string[] = []
-    const embeddings: number[][] = []
-    const metadatas: Record<string, any>[] = []
-    const documentTexts: string[] = []
-
-    for (const doc of documents) {
-      const result = await model.embedContent(doc.text)
-      const embedding = result.embedding
-
-      ids.push(doc.id)
-      embeddings.push(embedding.values)
-      metadatas.push(doc.metadata || {})
-      documentTexts.push(doc.text)
-    }
+    if (!collection) throw new Error('Collection not initialized')
 
-    await collection?.add({
+    const ids = documents.map((d) => d.id)
+    const metadatas = documents.map((d) => d.metadata || {})
+    const documentTexts = documents.map((d) => d.text)
+
+    await collection.add({
       ids,
-      embeddings,
       metadatas,
       documents: documentTexts,
     })
@@ -87,34 +94,6 @@ async function getStatistics(): Promise<CollectionStatistics> {
   }
 }
 
-const initialize = async (): Promise<void> => {
-  if (client && collection) {
-    return
-  }
-
-  try {
-    client = new ChromaClient({
-      path: `http://${CHROMA_HOST}:${CHROMA_PORT}`,
-    })
-
-    await client.heartbeat()
-
-    collection = await client.getOrCreateCollection({
-      name: COLLECTION_NAME,
-      embeddingFunction: undefined,
-    })
-  } catch (error) {
-    console.error(
-      JSON.stringify({
-        status: 'error',
-        message: `Initialization failed: ${error instanceof Error ? error.message : String(error)}`,
-        hint: 'Make sure ChromaDB server is running. Start it with: docker run -p 8000:8000 chromadb/chroma',
-      })
-    )
-    throw error
-  }
-}
-
 const getAllVideos = async (): Promise<Video[]> => {
   try {
     await initialize()
@@ -330,7 +309,6 @@ async function filterExistingVideos(videoSources: string[]): Promise<string[]> {
 }
 
 async function getByVideoSource(videoSource: string): Promise<Scene[]> {
-
   try {
     await initialize()
     if (!collection) {
@@ -374,23 +352,13 @@ async function updateDocuments(scene: Scene): Promise<void> {
       return
     }
 
-    const currentMetadata = metadataToScene(existing.metadatas?.[0], scene.id)
-    const currentDocument = existing.documents?.[0] || ''
-    const currentEmbedding = existing.embeddings?.[0]
-
-    const newMetadata: Scene = scene ? { ...currentMetadata, ...scene } : currentMetadata
-
-    await collection.delete({ ids: [scene.id] })
-
-    await collection.add({
+    await collection.update({
       ids: [scene.id],
-      embeddings: [currentEmbedding],
       metadatas: [
         {
-          ...sceneToVectorFormat(newMetadata, 1).metadata,
+          ...sceneToVectorFormat(scene).metadata,
         },
       ],
-      documents: [currentDocument],
     })
   } catch (error) {
     console.error('Error updating documents:', error)
@@ -411,5 +379,5 @@ export {
   filterExistingVideos,
   updateDocuments,
   updateMetadata,
-  getByVideoSource
+  getByVideoSource,
 }
diff --git a/lib/types/search.ts b/lib/types/search.ts
index 2f9afcee..a820552d 100644
--- a/lib/types/search.ts
+++ b/lib/types/search.ts
@@ -1,31 +1,23 @@
 import z from 'zod'
 import { searchSuggestionSchema, VideoMetadataSummarySchema } from '../conveyor/schemas/app-schema'
-import { ShotType, AspectRatio } from '.'
+import { AspectRatio } from '.';
 
 export interface VideoSearchParams {
-  action: string
+  action: string | null
   emotions: string[]
-  shot_type: ShotType | null
-  aspect_ratio: AspectRatio | null
+  shot_type?: string | null
+  aspect_ratio: string | null
   duration: number | null
   description: string
   outputFilename: string
   objects: string[]
   camera?: string
-  transcriptionQuery?: string
+  transcriptionQuery: string | null
   detectedText?: string
 }
 
-export type SearchQuery = {
+export type SearchQuery = VideoSearchParams & {
   faces?: string[]
-  emotions?: string[]
-  shot_type?: ShotType | null
-  aspect_ratio?: AspectRatio | null
-  description?: string
-  objects?: string[]
-  camera?: string
-  transcriptionQuery?: string
-  detectedText?: string
 }
 
 export type SearchSuggestion = z.infer<typeof searchSuggestionSchema>
diff --git a/lib/types/vector.ts b/lib/types/vector.ts
index 94169a6f..33455bae 100644
--- a/lib/types/vector.ts
+++ b/lib/types/vector.ts
@@ -19,7 +19,7 @@ export interface FilterData {
 export interface EmbeddingInput {
   id: string
   text: string
-  metadata?: Record<string, any>
+  metadata?: Record<string, string | number | null>
 }
 
 export interface CollectionStatistics {
diff --git a/lib/utils/embed.ts b/lib/utils/embed.ts
index c0a5901c..f71d4a61 100644
--- a/lib/utils/embed.ts
+++ b/lib/utils/embed.ts
@@ -77,10 +77,9 @@ export const embedScenes = async (scenes: Scene[], videoFullPath: string, catego
         scene.aspect_ratio = aspectRatio
         scene.category = category
         scene.duration = duration
-        return sceneToVectorFormat(scene, i + index)
+        return sceneToVectorFormat(scene, index)
       })
 
-      console.log(embeddingInputs)
       await embedDocuments(embeddingInputs)
     }
   } catch (err) {
@@ -112,15 +111,21 @@ export const metadataToScene = (metadata: Record<string, any> | null, id: string
     }
   }
 
-  let emotions: Array<{ name: string; emotion: string }> = []
-  try {
-    const emotionsStr = metadata.emotions as string
-    if (emotionsStr) {
-      emotions = JSON.parse(emotionsStr)
-    }
-  } catch {
-    emotions = []
-  }
+  const emotions: Array<{ name: string; emotion: string }> =
+    metadata.emotions
+      ?.split(',')
+      ?.map((f: string) => {
+        try {
+          const emotionStr = f.trim()
+          if (emotionStr) {
+            return JSON.parse(emotionStr) as { name: string; emotion: string }
+          }
+          return null
+        } catch {
+          return null
+        }
+      })
+      .filter(Boolean) || []
 
   const faces = metadata.faces
     ? (metadata.faces as string)
@@ -159,10 +164,11 @@ export const metadataToScene = (metadata: Record<string, any> | null, id: string
     detectedText,
     location: metadata.location?.toString() || 'N/A',
     duration: metadata.duration,
+    aspect_ratio: metadata.aspect_ratio,
   }
 }
 
-export const sceneToVectorFormat = (scene: Scene, sceneIndex: number) => {
+export const sceneToVectorFormat = (scene: Scene, sceneIndex?: number) => {
   const faces = scene.faces?.join(', ') || ''
   const objects = scene.objects?.join(', ') || ''
   const emotionsText =
@@ -175,18 +181,16 @@ export const sceneToVectorFormat = (scene: Scene, sceneIndex: number) => {
   const text =
     `Scene with ${faces}, objects: ${objects}. Emotions: ${emotionsText}. ${scene.transcription || ''}`.trim()
 
-  const metadata: Record<string, any> = {
+  const metadata: Record<string, string | number | null> = {
     source: scene.source,
     thumbnailUrl: scene.thumbnailUrl || '',
     startTime: scene.startTime,
     endTime: scene.endTime,
     type: 'scene',
-    faces: Array.isArray(scene.faces) ? scene.faces.join(', ') : JSON.stringify(scene.faces || []),
-    objects: Array.isArray(scene.objects) ? scene.objects.join(', ') : JSON.stringify(scene.objects || []),
+    faces: scene.faces.join(', '),
+    objects: scene.objects.join(', '),
     transcription: scene.transcription || '',
-    emotions: Array.isArray(scene.emotions)
-      ? scene.emotions.map((e) => JSON.stringify(e)).join(', ')
-      : JSON.stringify(scene.emotions || []),
+    emotions: scene.emotions.map((e) => JSON.stringify(e)).join(', '),
     description: scene.description || '',
     shot_type: scene.shot_type || 'long-shot',
     detectedText: detectedText,
@@ -195,11 +199,18 @@ export const sceneToVectorFormat = (scene: Scene, sceneIndex: number) => {
     dominantColorHex: scene.dominantColorHex,
     dominantColorName: scene.dominantColorName,
     camera: scene.camera,
-    duration: scene.duration,
+    duration: scene.duration || null,
   }
 
+  if (sceneIndex) {
+    return {
+      id: `${path.basename(scene.source)}_scene_${sceneIndex}`,
+      text,
+      metadata,
+    }
+  }
   return {
-    id: `${path.basename(scene.source)}_scene_${sceneIndex}`,
+    id: scene.id,
     text,
     metadata,
   }
diff --git a/package-lock.json b/package-lock.json
index 8d442794..79a54fc8 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -13,8 +13,6 @@
         "@chroma-core/default-embed": "^0.1.8",
         "@electron-toolkit/preload": "^3.0.2",
         "@electron-toolkit/utils": "^4.0.0",
-        "@google/genai": "^1.25.0",
-        "@google/generative-ai": "^0.24.1",
         "@radix-ui/react-label": "^2.1.7",
         "@radix-ui/react-progress": "^1.1.7",
         "@radix-ui/react-scroll-area": "^1.2.10",
@@ -35,6 +33,7 @@
         "gpmf-extract": "^0.3.2",
         "langchain": "^0.1.36",
         "lucide-react": "^0.541.0",
+        "node-llama-cpp": "^3.14.2",
         "node-whisper": "^2024.11.13",
         "radix-ui": "^1.4.3",
         "react-error-boundary": "^6.0.0",
@@ -54,6 +53,7 @@
         "@types/react-dom": "^19.1.8",
         "@types/ws": "^8.18.1",
         "@vitejs/plugin-react": "^5.0.1",
+        "@vitest/ui": "^3.2.4",
         "cross-env": "^10.0.0",
         "dotenv": "^17.2.3",
         "electron": "^37.3.1",
@@ -74,24 +74,11 @@
         "vitest": "^3.2.4"
       }
     },
-    "node_modules/@ampproject/remapping": {
-      "version": "2.3.0",
-      "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz",
-      "integrity": "sha512-30iZtAPgz+LTIYoeivqYo853f02jBYSd5uGnGpkFV0M3xOt9aN73erkgYAmZU43x4VfqcnLxW9Kpg3R5LC4YYw==",
-      "dev": true,
-      "license": "Apache-2.0",
-      "dependencies": {
-        "@jridgewell/gen-mapping": "^0.3.5",
-        "@jridgewell/trace-mapping": "^0.3.24"
-      },
-      "engines": {
-        "node": ">=6.0.0"
-      }
-    },
     "node_modules/@anthropic-ai/sdk": {
       "version": "0.9.1",
       "resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.9.1.tgz",
       "integrity": "sha512-wa1meQ2WSfoY8Uor3EdrJq0jTiZJoKoSii2ZVWRY1oN4Tlr5s59pADg9T79FTbPe1/se5c3pBeZgJL63wmuoBA==",
+      "license": "MIT",
       "dependencies": {
         "@types/node": "^18.11.18",
         "@types/node-fetch": "^2.6.4",
@@ -108,6 +95,7 @@
       "version": "18.19.130",
       "resolved": "https://registry.npmjs.org/@types/node/-/node-18.19.130.tgz",
       "integrity": "sha512-GRaXQx6jGfL8sKfaIDD6OupbIHBr9jv7Jnaml9tB7l4v068PAOXqfcujMMo5PhbIs6ggR1XODELqahT2R8v0fg==",
+      "license": "MIT",
       "dependencies": {
         "undici-types": "~5.26.4"
       }
@@ -115,7 +103,8 @@
     "node_modules/@anthropic-ai/sdk/node_modules/undici-types": {
       "version": "5.26.5",
       "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
-      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA=="
+      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
+      "license": "MIT"
     },
     "node_modules/@babel/code-frame": {
       "version": "7.27.1",
@@ -133,9 +122,9 @@
       }
     },
     "node_modules/@babel/compat-data": {
-      "version": "7.28.0",
-      "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.28.0.tgz",
-      "integrity": "sha512-60X7qkglvrap8mn1lh2ebxXdZYtUcpd7gsmy9kLaBJ4i/WdY8PqTSdxyA8qraikqKQK5C1KRBKXqznrVapyNaw==",
+      "version": "7.28.5",
+      "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.28.5.tgz",
+      "integrity": "sha512-6uFXyCayocRbqhZOB+6XcuZbkMNimwfVGFji8CTZnCzOHVGvDqzvitu1re2AU5LROliz7eQPhB8CpAMvnx9EjA==",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -143,22 +132,22 @@
       }
     },
     "node_modules/@babel/core": {
-      "version": "7.28.3",
-      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.28.3.tgz",
-      "integrity": "sha512-yDBHV9kQNcr2/sUr9jghVyz9C3Y5G2zUM2H2lo+9mKv4sFgbA8s8Z9t8D1jiTkGoO/NoIfKMyKWr4s6CN23ZwQ==",
+      "version": "7.28.5",
+      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.28.5.tgz",
+      "integrity": "sha512-e7jT4DxYvIDLk1ZHmU/m/mB19rex9sv0c2ftBtjSBv+kVM/902eh0fINUzD7UwLLNR+jU585GxUJ8/EBfAM5fw==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@ampproject/remapping": "^2.2.0",
         "@babel/code-frame": "^7.27.1",
-        "@babel/generator": "^7.28.3",
+        "@babel/generator": "^7.28.5",
         "@babel/helper-compilation-targets": "^7.27.2",
         "@babel/helper-module-transforms": "^7.28.3",
-        "@babel/helpers": "^7.28.3",
-        "@babel/parser": "^7.28.3",
+        "@babel/helpers": "^7.28.4",
+        "@babel/parser": "^7.28.5",
         "@babel/template": "^7.27.2",
-        "@babel/traverse": "^7.28.3",
-        "@babel/types": "^7.28.2",
+        "@babel/traverse": "^7.28.5",
+        "@babel/types": "^7.28.5",
+        "@jridgewell/remapping": "^2.3.5",
         "convert-source-map": "^2.0.0",
         "debug": "^4.1.0",
         "gensync": "^1.0.0-beta.2",
@@ -174,14 +163,14 @@
       }
     },
     "node_modules/@babel/generator": {
-      "version": "7.28.3",
-      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.28.3.tgz",
-      "integrity": "sha512-3lSpxGgvnmZznmBkCRnVREPUFJv2wrv9iAoFDvADJc0ypmdOxdUtcLeBgBJ6zE0PMeTKnxeQzyk0xTBq4Ep7zw==",
+      "version": "7.28.5",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.28.5.tgz",
+      "integrity": "sha512-3EwLFhZ38J4VyIP6WNtt2kUdW9dokXA9Cr4IVIFHuCpZ3H8/YFOl5JjZHisrn1fATPBmKKqXzDFvh9fUwHz6CQ==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@babel/parser": "^7.28.3",
-        "@babel/types": "^7.28.2",
+        "@babel/parser": "^7.28.5",
+        "@babel/types": "^7.28.5",
         "@jridgewell/gen-mapping": "^0.3.12",
         "@jridgewell/trace-mapping": "^0.3.28",
         "jsesc": "^3.0.2"
@@ -270,9 +259,9 @@
       }
     },
     "node_modules/@babel/helper-validator-identifier": {
-      "version": "7.27.1",
-      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.27.1.tgz",
-      "integrity": "sha512-D2hP9eA+Sqx1kBZgzxZh0y1trbuU+JoDkiEwqhQ36nodYqJwyEIhPSdMNd7lOm/4io72luTPWH20Yda0xOuUow==",
+      "version": "7.28.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.28.5.tgz",
+      "integrity": "sha512-qSs4ifwzKJSV39ucNjsvc6WVHs6b7S03sOh2OcHF9UHfVPqWWALUsNUVzhSBiItjRZoLHx7nIarVjqKVusUZ1Q==",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -290,27 +279,27 @@
       }
     },
     "node_modules/@babel/helpers": {
-      "version": "7.28.3",
-      "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.28.3.tgz",
-      "integrity": "sha512-PTNtvUQihsAsDHMOP5pfobP8C6CM4JWXmP8DrEIt46c3r2bf87Ua1zoqevsMo9g+tWDwgWrFP5EIxuBx5RudAw==",
+      "version": "7.28.4",
+      "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.28.4.tgz",
+      "integrity": "sha512-HFN59MmQXGHVyYadKLVumYsA9dBFun/ldYxipEjzA4196jpLZd8UjEEBLkbEkvfYreDqJhZxYAWFPtrfhNpj4w==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "@babel/template": "^7.27.2",
-        "@babel/types": "^7.28.2"
+        "@babel/types": "^7.28.4"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/parser": {
-      "version": "7.28.3",
-      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.28.3.tgz",
-      "integrity": "sha512-7+Ey1mAgYqFAx2h0RuoxcQT5+MlG3GTV0TQrgr7/ZliKsm/MNDxVVutlWaziMq7wJNAz8MTqz55XLpWvva6StA==",
+      "version": "7.28.5",
+      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.28.5.tgz",
+      "integrity": "sha512-KKBU1VGYR7ORr3At5HAtUQ+TV3SzRCXmA/8OdDZiLDBIZxVyzXuztPjfLd3BV1PRAQGCMWWSHYhL0F8d5uHBDQ==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@babel/types": "^7.28.2"
+        "@babel/types": "^7.28.5"
       },
       "bin": {
         "parser": "bin/babel-parser.js"
@@ -371,6 +360,7 @@
       "version": "7.28.4",
       "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.28.4.tgz",
       "integrity": "sha512-Q/N6JNWvIvPnLDvjlE1OUBLPQHH6l3CltCEsHIujp45zQUSSh8K+gHnaEX45yAT1nyngnINhvWtzN+Nb9D8RAQ==",
+      "license": "MIT",
       "engines": {
         "node": ">=6.9.0"
       }
@@ -391,18 +381,18 @@
       }
     },
     "node_modules/@babel/traverse": {
-      "version": "7.28.3",
-      "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.28.3.tgz",
-      "integrity": "sha512-7w4kZYHneL3A6NP2nxzHvT3HCZ7puDZZjFMqDpBPECub79sTtSO5CGXDkKrTQq8ksAwfD/XI2MRFX23njdDaIQ==",
+      "version": "7.28.5",
+      "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.28.5.tgz",
+      "integrity": "sha512-TCCj4t55U90khlYkVV/0TfkJkAkUg3jZFA3Neb7unZT8CPok7iiRfaX0F+WnqWqt7OxhOn0uBKXCw4lbL8W0aQ==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "@babel/code-frame": "^7.27.1",
-        "@babel/generator": "^7.28.3",
+        "@babel/generator": "^7.28.5",
         "@babel/helper-globals": "^7.28.0",
-        "@babel/parser": "^7.28.3",
+        "@babel/parser": "^7.28.5",
         "@babel/template": "^7.27.2",
-        "@babel/types": "^7.28.2",
+        "@babel/types": "^7.28.5",
         "debug": "^4.3.1"
       },
       "engines": {
@@ -410,23 +400,23 @@
       }
     },
     "node_modules/@babel/types": {
-      "version": "7.28.2",
-      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.28.2.tgz",
-      "integrity": "sha512-ruv7Ae4J5dUYULmeXw1gmb7rYRz57OWCPM57pHojnLq/3Z1CK2lNSLTCVjxVk1F/TZHwOZZrOWi0ur95BbLxNQ==",
+      "version": "7.28.5",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.28.5.tgz",
+      "integrity": "sha512-qQ5m48eI/MFLQ5PxQj4PFaprjyCTLI37ElWMmNs0K8Lk3dVeOdNpB3ks8jc7yM5CDmVC73eMVk/trk3fgmrUpA==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "@babel/helper-string-parser": "^7.27.1",
-        "@babel/helper-validator-identifier": "^7.27.1"
+        "@babel/helper-validator-identifier": "^7.28.5"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@chroma-core/ai-embeddings-common": {
-      "version": "0.1.7",
-      "resolved": "https://registry.npmjs.org/@chroma-core/ai-embeddings-common/-/ai-embeddings-common-0.1.7.tgz",
-      "integrity": "sha512-9ToziKEz0gD+kkFKkZaeAUyGW0gRDVZcKtAmSO0d0xzFIVCkjWChND1VaHjvozRypEKzjjCqN8t1bzA+YxtBxQ==",
+      "version": "0.1.8",
+      "resolved": "https://registry.npmjs.org/@chroma-core/ai-embeddings-common/-/ai-embeddings-common-0.1.8.tgz",
+      "integrity": "sha512-ZhP1AczVbi6FY4oeaRCGa3Hp0onWGfTnlDecAW3NHjrHbv+4J6hJa0jMJDtsImw/y9AJewKHBH2oFF3mBb7CDw==",
       "dependencies": {
         "ajv": "^8.17.1"
       },
@@ -434,26 +424,6 @@
         "node": ">=20"
       }
     },
-    "node_modules/@chroma-core/ai-embeddings-common/node_modules/ajv": {
-      "version": "8.17.1",
-      "resolved": "https://registry.npmjs.org/ajv/-/ajv-8.17.1.tgz",
-      "integrity": "sha512-B/gBuNg5SiMTrPkC+A2+cW0RszwxYmn6VYxB/inlBStS5nx6xHIt/ehKRhIMhqusl7a8LjQoZnjCs5vhwxOQ1g==",
-      "dependencies": {
-        "fast-deep-equal": "^3.1.3",
-        "fast-uri": "^3.0.1",
-        "json-schema-traverse": "^1.0.0",
-        "require-from-string": "^2.0.2"
-      },
-      "funding": {
-        "type": "github",
-        "url": "https://github.com/sponsors/epoberezkin"
-      }
-    },
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       "resolved": "https://registry.npmjs.org/verror/-/verror-1.10.1.tgz",
@@ -15463,9 +18448,9 @@
       }
     },
     "node_modules/vite": {
-      "version": "7.1.3",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-7.1.3.tgz",
-      "integrity": "sha512-OOUi5zjkDxYrKhTV3V7iKsoS37VUM7v40+HuwEmcrsf11Cdx9y3DIr2Px6liIcZFwt3XSRpQvFpL3WVy7ApkGw==",
+      "version": "7.1.12",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-7.1.12.tgz",
+      "integrity": "sha512-ZWyE8YXEXqJrrSLvYgrRP7p62OziLW7xI5HYGWFzOvupfAlrLvURSzv/FyGyy0eidogEM3ujU+kUG1zuHgb6Ug==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
@@ -15474,7 +18459,7 @@
         "picomatch": "^4.0.3",
         "postcss": "^8.5.6",
         "rollup": "^4.43.0",
-        "tinyglobby": "^0.2.14"
+        "tinyglobby": "^0.2.15"
       },
       "bin": {
         "vite": "bin/vite.js"
@@ -15542,6 +18527,7 @@
       "resolved": "https://registry.npmjs.org/vite-node/-/vite-node-3.2.4.tgz",
       "integrity": "sha512-EbKSKh+bh1E1IFxeO0pg1n4dvoOTt0UDiXMd/qn++r98+jPO1xtJilvXldeuQ8giIB5IkpjCgMleHMNEsGH6pg==",
       "dev": true,
+      "license": "MIT",
       "dependencies": {
         "cac": "^6.7.14",
         "debug": "^4.4.1",
@@ -15559,42 +18545,12 @@
         "url": "https://opencollective.com/vitest"
       }
     },
-    "node_modules/vite/node_modules/fdir": {
-      "version": "6.5.0",
-      "resolved": "https://registry.npmjs.org/fdir/-/fdir-6.5.0.tgz",
-      "integrity": "sha512-tIbYtZbucOs0BRGqPJkshJUYdL+SDH7dVM8gjy+ERp3WAUjLEFJE+02kanyHtwjWOnwrKYBiwAmM0p4kLJAnXg==",
-      "dev": true,
-      "license": "MIT",
-      "engines": {
-        "node": ">=12.0.0"
-      },
-      "peerDependencies": {
-        "picomatch": "^3 || ^4"
-      },
-      "peerDependenciesMeta": {
-        "picomatch": {
-          "optional": true
-        }
-      }
-    },
-    "node_modules/vite/node_modules/picomatch": {
-      "version": "4.0.3",
-      "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-4.0.3.tgz",
-      "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==",
-      "dev": true,
-      "license": "MIT",
-      "engines": {
-        "node": ">=12"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/jonschlinkert"
-      }
-    },
     "node_modules/vitest": {
       "version": "3.2.4",
       "resolved": "https://registry.npmjs.org/vitest/-/vitest-3.2.4.tgz",
       "integrity": "sha512-LUCP5ev3GURDysTWiP47wRRUpLKMOfPh+yKTx3kVIEiu5KOMeqzpnYNsKyOoVrULivR8tLcks4+lga33Whn90A==",
       "dev": true,
+      "license": "MIT",
       "dependencies": {
         "@types/chai": "^5.2.2",
         "@vitest/expect": "3.2.4",
@@ -15662,18 +18618,6 @@
         }
       }
     },
-    "node_modules/vitest/node_modules/picomatch": {
-      "version": "4.0.3",
-      "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-4.0.3.tgz",
-      "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==",
-      "dev": true,
-      "engines": {
-        "node": ">=12"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/jonschlinkert"
-      }
-    },
     "node_modules/wcwidth": {
       "version": "1.0.1",
       "resolved": "https://registry.npmjs.org/wcwidth/-/wcwidth-1.0.1.tgz",
@@ -15688,6 +18632,7 @@
       "version": "3.3.3",
       "resolved": "https://registry.npmjs.org/web-streams-polyfill/-/web-streams-polyfill-3.3.3.tgz",
       "integrity": "sha512-d2JWLCivmZYTSIoge9MsgFCZrt571BikcWGYkjC1khllbTeDlGqZ2D8vD8E/lJa8WGWbb7Plm8/XJYV7IJHZZw==",
+      "license": "MIT",
       "engines": {
         "node": ">= 8"
       }
@@ -15695,12 +18640,14 @@
     "node_modules/webidl-conversions": {
       "version": "3.0.1",
       "resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-3.0.1.tgz",
-      "integrity": "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ=="
+      "integrity": "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ==",
+      "license": "BSD-2-Clause"
     },
     "node_modules/whatwg-url": {
       "version": "5.0.0",
       "resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-5.0.0.tgz",
       "integrity": "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==",
+      "license": "MIT",
       "dependencies": {
         "tr46": "~0.0.3",
         "webidl-conversions": "^3.0.0"
@@ -15710,7 +18657,6 @@
       "version": "2.0.2",
       "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
       "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==",
-      "dev": true,
       "license": "ISC",
       "dependencies": {
         "isexe": "^2.0.0"
@@ -15816,6 +18762,7 @@
       "resolved": "https://registry.npmjs.org/why-is-node-running/-/why-is-node-running-2.3.0.tgz",
       "integrity": "sha512-hUrmaWBdVDcxvYqnyh09zunKzROWjbZTiNy8dBEjkS7ehEDQibXJ7XvlmtbwuTclUiIyN+CyXQD4Vmko8fNm8w==",
       "dev": true,
+      "license": "MIT",
       "dependencies": {
         "siginfo": "^2.0.0",
         "stackback": "0.0.2"
@@ -15827,6 +18774,15 @@
         "node": ">=8"
       }
     },
+    "node_modules/wide-align": {
+      "version": "1.1.5",
+      "resolved": "https://registry.npmjs.org/wide-align/-/wide-align-1.1.5.tgz",
+      "integrity": "sha512-eDMORYaPNZ4sQIuuYPDHdQvf4gyCF9rEEV/yPxGfwPkRodwEgiMUUXTx/dex+Me0wxx53S+NgUHaP7y3MGlDmg==",
+      "license": "ISC",
+      "dependencies": {
+        "string-width": "^1.0.2 || 2 || 3 || 4"
+      }
+    },
     "node_modules/word-wrap": {
       "version": "1.2.5",
       "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.5.tgz",
@@ -15841,7 +18797,6 @@
       "version": "7.0.0",
       "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz",
       "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==",
-      "dev": true,
       "license": "MIT",
       "dependencies": {
         "ansi-styles": "^4.0.0",
@@ -15874,6 +18829,50 @@
         "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
       }
     },
+    "node_modules/wrap-ansi-cjs/node_modules/ansi-regex": {
+      "version": "5.0.1",
+      "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.1.tgz",
+      "integrity": "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/strip-ansi": {
+      "version": "6.0.1",
+      "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz",
+      "integrity": "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "ansi-regex": "^5.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi/node_modules/ansi-regex": {
+      "version": "5.0.1",
+      "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.1.tgz",
+      "integrity": "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi/node_modules/strip-ansi": {
+      "version": "6.0.1",
+      "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz",
+      "integrity": "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==",
+      "license": "MIT",
+      "dependencies": {
+        "ansi-regex": "^5.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
     "node_modules/wrappy": {
       "version": "1.0.2",
       "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
@@ -15884,6 +18883,7 @@
       "version": "8.18.3",
       "resolved": "https://registry.npmjs.org/ws/-/ws-8.18.3.tgz",
       "integrity": "sha512-PEIGCY5tSlUt50cqyMXfCzX+oOPqN0vuGqWzbcJ2xvnkzkq46oOpz7dQaTDBdfICb4N14+GARUDw2XV2N4tvzg==",
+      "license": "MIT",
       "engines": {
         "node": ">=10.0.0"
       },
@@ -15914,7 +18914,6 @@
       "version": "5.0.8",
       "resolved": "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz",
       "integrity": "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==",
-      "dev": true,
       "license": "ISC",
       "engines": {
         "node": ">=10"
@@ -15931,6 +18930,7 @@
       "version": "2.8.1",
       "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.8.1.tgz",
       "integrity": "sha512-lcYcMxX2PO9XMGvAJkJ3OsNMw+/7FKes7/hgerGUYWIoWu5j/+YQqcZr5JnPZWzOsEBgMbSbiSTn/dv/69Mkpw==",
+      "license": "ISC",
       "bin": {
         "yaml": "bin.mjs"
       },
@@ -15942,7 +18942,6 @@
       "version": "17.7.2",
       "resolved": "https://registry.npmjs.org/yargs/-/yargs-17.7.2.tgz",
       "integrity": "sha512-7dSzzRQ++CKnNI/krKnYRV7JKKPUXMEh61soaHKg9mrWEhzFWhFnxPxGl+69cD1Ou63C13NUPCnmIcrvqCuM6w==",
-      "dev": true,
       "license": "MIT",
       "dependencies": {
         "cliui": "^8.0.1",
@@ -15961,7 +18960,6 @@
       "version": "21.1.1",
       "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz",
       "integrity": "sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==",
-      "dev": true,
       "license": "ISC",
       "engines": {
         "node": ">=12"
@@ -15990,10 +18988,22 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
+    "node_modules/yoctocolors": {
+      "version": "2.1.2",
+      "resolved": "https://registry.npmjs.org/yoctocolors/-/yoctocolors-2.1.2.tgz",
+      "integrity": "sha512-CzhO+pFNo8ajLM2d2IW/R93ipy99LWjtwblvC1RsoSUMZgyLbYFr221TnSNT7GjGdYui6P459mw9JH/g/zW2ug==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=18"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
     "node_modules/zod": {
-      "version": "4.1.3",
-      "resolved": "https://registry.npmjs.org/zod/-/zod-4.1.3.tgz",
-      "integrity": "sha512-1neef4bMce1hNTrxvHVKxWjKfGDn0oAli3Wy1Uwb7TRO1+wEwoZUZNP1NXIEESybOBiFnBOhI6a4m6tCLE8dog==",
+      "version": "4.1.12",
+      "resolved": "https://registry.npmjs.org/zod/-/zod-4.1.12.tgz",
+      "integrity": "sha512-JInaHOamG8pt5+Ey8kGmdcAcg3OL9reK8ltczgHTAwNhMys/6ThXHityHxVV2p3fkw/c+MAvBHFVYHFZDmjMCQ==",
       "license": "MIT",
       "funding": {
         "url": "https://github.com/sponsors/colinhacks"
diff --git a/package.json b/package.json
index bcd820e2..edea9925 100644
--- a/package.json
+++ b/package.json
@@ -23,7 +23,8 @@
     "build:win": "run-s vite:build:app electron:build:win",
     "build:mac": "run-s vite:build:app electron:build:mac",
     "build:linux": "run-s vite:build:app electron:build:linux",
-    "test": "vitest"
+    "test": "vitest",
+    "test:ui": "vitest --ui"
   },
   "repository": {
     "type": "git",
@@ -33,8 +34,6 @@
     "@chroma-core/default-embed": "^0.1.8",
     "@electron-toolkit/preload": "^3.0.2",
     "@electron-toolkit/utils": "^4.0.0",
-    "@google/genai": "^1.25.0",
-    "@google/generative-ai": "^0.24.1",
     "@radix-ui/react-label": "^2.1.7",
     "@radix-ui/react-progress": "^1.1.7",
     "@radix-ui/react-scroll-area": "^1.2.10",
@@ -55,6 +54,7 @@
     "gpmf-extract": "^0.3.2",
     "langchain": "^0.1.36",
     "lucide-react": "^0.541.0",
+    "node-llama-cpp": "^3.14.2",
     "node-whisper": "^2024.11.13",
     "radix-ui": "^1.4.3",
     "react-error-boundary": "^6.0.0",
@@ -74,6 +74,7 @@
     "@types/react-dom": "^19.1.8",
     "@types/ws": "^8.18.1",
     "@vitejs/plugin-react": "^5.0.1",
+    "@vitest/ui": "^3.2.4",
     "cross-env": "^10.0.0",
     "dotenv": "^17.2.3",
     "electron": "^37.3.1",
diff --git a/python/plugins/activity.py b/python/plugins/activity.py
index 0b5a2c7c..1179f2a6 100644
--- a/python/plugins/activity.py
+++ b/python/plugins/activity.py
@@ -23,7 +23,7 @@ class ActivityPlugin(AnalyzerPlugin):
 
     def __init__(self, config: Dict[str, Any]):
         super().__init__(config)
-        self.activities = []
+        self.activities: List[Activity] = []
 
     def setup(self):
         """No setup required for this plugin."""
@@ -191,10 +191,10 @@ def _detect_driving(self, car_ratio: float) -> Optional[Activity]:
             )
         return None
 
-    def get_results(self) -> Any:
+    def get_results(self) -> List[Activity]:
         """Returns the final analysis results from the plugin."""
         return self.activities
 
-    def get_summary(self) -> Any:
+    def get_summary(self) -> None:
         """Returns a summary of the analysis results."""
         return None
diff --git a/python/plugins/dominant_color.py b/python/plugins/dominant_color.py
index 87ac8aba..40714c07 100644
--- a/python/plugins/dominant_color.py
+++ b/python/plugins/dominant_color.py
@@ -1,4 +1,4 @@
-from typing import List, Dict, Any, Tuple
+from typing import List, Dict, Any, Tuple, Union, Optional
 from dataclasses import dataclass, asdict
 import numpy as np
 import cv2
@@ -143,7 +143,7 @@ def __init__(self, config: Dict[str, Any]):
         self.num_colors = config.get('num_dominant_colors', 5)
         self.sample_size = config.get('color_sample_size', 1000)  
         
-        self.frame_colors: List[Dict[str, Any]] = []
+        self.frame_colors: List[Dict[str, Union[int, float, List['ColorInfo']]]] = []
         
     def setup(self):
         try:
@@ -330,7 +330,7 @@ def analyze_scene(self, all_frame_analyses: List[Dict[str, Any]]):
         # This will be used in get_results()
         pass
 
-    def get_results(self) -> SceneColorAnalysis:
+    def get_results(self) -> Optional[SceneColorAnalysis]:
         """
         Generate scene-level color analysis.
         """
@@ -464,7 +464,7 @@ def _determine_color_harmony(self, palette: List[ColorInfo]) -> str:
         else:
             return "mixed"
 
-    def get_summary(self) -> Dict[str, Any]:
+    def get_summary(self) -> Dict[str, Union[str, float]]:
         """
         Generate a summary of color analysis.
         """
diff --git a/python/plugins/emotion_detection.py b/python/plugins/emotion_detection.py
index bc7c70f4..1b292d4c 100644
--- a/python/plugins/emotion_detection.py
+++ b/python/plugins/emotion_detection.py
@@ -37,7 +37,7 @@ def analyze_frame(self, frame: np.ndarray, frame_analysis: Dict[str, Any], video
             self._add_emotions(frame, frame_analysis['faces'])
         return frame_analysis
 
-    def _add_emotions(self, frame: Any, faces: List[Dict]) -> None:
+    def _add_emotions(self, frame: np.ndarray, faces: List[Dict[str, Any]]) -> None:
         """
         Add emotion data to recognized faces.
         """
diff --git a/python/requirements.txt b/python/requirements.txt
index 81fc9025..6e4c43d8 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -38,4 +38,5 @@ python-multipart>=0.0.6
 
 # Testing
 pytest>=7.4.0
-pytest-mock>=3.12.0
\ No newline at end of file
+pytest-mock>=3.12.0
+coverage>=7.11.0
\ No newline at end of file
diff --git a/python/tests/test_transcribe.py b/python/tests/test_transcribe.py
new file mode 100644
index 00000000..22415604
--- /dev/null
+++ b/python/tests/test_transcribe.py
@@ -0,0 +1,37 @@
+import unittest
+import sys
+import os
+import json
+import tempfile
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from transcribe import run_transcription
+
+class TestTranscribeVideo(unittest.TestCase):
+
+    def test_transcribe_video(self):
+
+        video_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'test_video.mp4'))
+
+        # Temporary JSON output path
+        tmp_dir = tempfile.gettempdir()
+        output_json_path = os.path.join(tmp_dir, 'transcription_output.json')
+
+        # Run transcription and save JSON
+        run_transcription(video_path, output_json_path) 
+
+        # Load generated output
+        with open(output_json_path, 'r') as f:
+            generated_data = json.load(f)
+
+        # Load expected JSON for comparison
+        expected_json_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'test_video_transcription.json'))
+        with open(expected_json_path, 'r') as f:
+            expected_data = json.load(f)
+
+        # Compare JSON structures
+        self.assertEqual(generated_data, expected_data, "Generated transcription JSON does not match expected output")
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/python/tests/test_video.mp4 b/python/tests/test_video.mp4
new file mode 100644
index 00000000..d62b6d50
Binary files /dev/null and b/python/tests/test_video.mp4 differ
diff --git a/python/tests/test_video_transcription.json b/python/tests/test_video_transcription.json
new file mode 100644
index 00000000..24ca8cad
--- /dev/null
+++ b/python/tests/test_video_transcription.json
@@ -0,0 +1,1345 @@
+{
+    "text": " EditMind, the next generation of intelligent video editing. EditMind is a powerful desktop app that uses AI to understand your videos, just like a human editor would. With advanced object detection, face recognition, and emotion analysis, EditMind indexes every frame of your footage. This means you can search your video library using natural language. Ask things like, show me clips where Sarah is smiling, or find all shots with a car in the background. EditMind instantly finds what you're looking for. No scrubbing, no guessing. But it doesn't stop there. Using AI-assisted editing, EditMind can automatically generate rough cuts based on your creative direction. Just describe the scene you want, and it pieces together the right moments in seconds. Designed for filmmakers, content creators, and production teams, EditMind turns hours of raw footage into an organized, searchable, and fun video.",
+    "segments": [
+        {
+            "id": 0,
+            "seek": 0,
+            "start": 0.0,
+            "end": 3.66,
+            "text": " EditMind, the next generation of intelligent video editing.",
+            "tokens": [
+                50365,
+                33241,
+                44,
+                471,
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+                    "start": 37.32,
+                    "end": 37.96,
+                    "probability": 0.9992561936378479
+                },
+                {
+                    "word": " AI",
+                    "start": 37.96,
+                    "end": 38.36,
+                    "probability": 0.996969997882843
+                },
+                {
+                    "word": "-assisted",
+                    "start": 38.36,
+                    "end": 38.88,
+                    "probability": 0.9978047609329224
+                },
+                {
+                    "word": " editing,",
+                    "start": 38.88,
+                    "end": 39.24,
+                    "probability": 0.9997804760932922
+                }
+            ]
+        },
+        {
+            "id": 12,
+            "seek": 3000,
+            "start": 39.54,
+            "end": 43.58,
+            "text": " EditMind can automatically generate rough cuts based on your creative direction.",
+            "tokens": [
+                50859,
+                33241,
+                44,
+                471,
+                393,
+                6772,
+                8460,
+                5903,
+                9992,
+                2361,
+                322,
+                428,
+                5880,
+                3513,
+                13,
+                51056
+            ],
+            "temperature": 0.0,
+            "avg_logprob": -0.23181006390115488,
+            "compression_ratio": 1.5669014084507042,
+            "no_speech_prob": 3.380340422154404e-05,
+            "words": [
+                {
+                    "word": " EditMind",
+                    "start": 39.54,
+                    "end": 40.3,
+                    "probability": 0.9996383786201477
+                },
+                {
+                    "word": " can",
+                    "start": 40.3,
+                    "end": 40.52,
+                    "probability": 0.9998190999031067
+                },
+                {
+                    "word": " automatically",
+                    "start": 40.52,
+                    "end": 40.94,
+                    "probability": 0.9997389912605286
+                },
+                {
+                    "word": " generate",
+                    "start": 40.94,
+                    "end": 41.48,
+                    "probability": 0.9995785355567932
+                },
+                {
+                    "word": " rough",
+                    "start": 41.48,
+                    "end": 41.9,
+                    "probability": 0.9993404746055603
+                },
+                {
+                    "word": " cuts",
+                    "start": 41.9,
+                    "end": 42.2,
+                    "probability": 0.9987733960151672
+                },
+                {
+                    "word": " based",
+                    "start": 42.2,
+                    "end": 42.5,
+                    "probability": 0.9995937943458557
+                },
+                {
+                    "word": " on",
+                    "start": 42.5,
+                    "end": 42.74,
+                    "probability": 0.9999380111694336
+                },
+                {
+                    "word": " your",
+                    "start": 42.74,
+                    "end": 42.92,
+                    "probability": 0.9999257326126099
+                },
+                {
+                    "word": " creative",
+                    "start": 42.92,
+                    "end": 43.22,
+                    "probability": 0.9996322393417358
+                },
+                {
+                    "word": " direction.",
+                    "start": 43.22,
+                    "end": 43.58,
+                    "probability": 0.9996962547302246
+                }
+            ]
+        },
+        {
+            "id": 13,
+            "seek": 3000,
+            "start": 44.519999999999996,
+            "end": 45.9,
+            "text": " Just describe the scene you want,",
+            "tokens": [
+                51091,
+                1449,
+                6786,
+                264,
+                4145,
+                291,
+                528,
+                11,
+                51166
+            ],
+            "temperature": 0.0,
+            "avg_logprob": -0.23181006390115488,
+            "compression_ratio": 1.5669014084507042,
+            "no_speech_prob": 3.380340422154404e-05,
+            "words": [
+                {
+                    "word": " Just",
+                    "start": 44.26,
+                    "end": 44.58,
+                    "probability": 0.9995785355567932
+                },
+                {
+                    "word": " describe",
+                    "start": 44.58,
+                    "end": 45.0,
+                    "probability": 0.9999423027038574
+                },
+                {
+                    "word": " the",
+                    "start": 45.0,
+                    "end": 45.22,
+                    "probability": 0.9999079704284668
+                },
+                {
+                    "word": " scene",
+                    "start": 45.22,
+                    "end": 45.46,
+                    "probability": 0.9998990297317505
+                },
+                {
+                    "word": " you",
+                    "start": 45.46,
+                    "end": 45.66,
+                    "probability": 0.9998711347579956
+                },
+                {
+                    "word": " want,",
+                    "start": 45.66,
+                    "end": 45.9,
+                    "probability": 0.9999581575393677
+                }
+            ]
+        },
+        {
+            "id": 14,
+            "seek": 3000,
+            "start": 46.06,
+            "end": 48.84,
+            "text": " and it pieces together the right moments in seconds.",
+            "tokens": [
+                51191,
+                293,
+                309,
+                3755,
+                1214,
+                264,
+                558,
+                6065,
+                294,
+                3949,
+                13,
+                51320
+            ],
+            "temperature": 0.0,
+            "avg_logprob": -0.23181006390115488,
+            "compression_ratio": 1.5669014084507042,
+            "no_speech_prob": 3.380340422154404e-05,
+            "words": [
+                {
+                    "word": " and",
+                    "start": 46.06,
+                    "end": 46.62,
+                    "probability": 0.9998290538787842
+                },
+                {
+                    "word": " it",
+                    "start": 46.62,
+                    "end": 46.74,
+                    "probability": 0.9998966455459595
+                },
+                {
+                    "word": " pieces",
+                    "start": 46.74,
+                    "end": 46.98,
+                    "probability": 0.9992784857749939
+                },
+                {
+                    "word": " together",
+                    "start": 46.98,
+                    "end": 47.34,
+                    "probability": 0.999883770942688
+                },
+                {
+                    "word": " the",
+                    "start": 47.34,
+                    "end": 47.72,
+                    "probability": 0.9998600482940674
+                },
+                {
+                    "word": " right",
+                    "start": 47.72,
+                    "end": 47.88,
+                    "probability": 0.9998483657836914
+                },
+                {
+                    "word": " moments",
+                    "start": 47.88,
+                    "end": 48.3,
+                    "probability": 0.9997286200523376
+                },
+                {
+                    "word": " in",
+                    "start": 48.3,
+                    "end": 48.54,
+                    "probability": 0.9956533908843994
+                },
+                {
+                    "word": " seconds.",
+                    "start": 48.54,
+                    "end": 48.84,
+                    "probability": 0.9998482465744019
+                }
+            ]
+        },
+        {
+            "id": 15,
+            "seek": 3000,
+            "start": 49.9,
+            "end": 53.62,
+            "text": " Designed for filmmakers, content creators, and production teams,",
+            "tokens": [
+                51373,
+                12748,
+                292,
+                337,
+                35018,
+                11,
+                2701,
+                16039,
+                11,
+                293,
+                4265,
+                5491,
+                11,
+                51554
+            ],
+            "temperature": 0.0,
+            "avg_logprob": -0.23181006390115488,
+            "compression_ratio": 1.5669014084507042,
+            "no_speech_prob": 3.380340422154404e-05,
+            "words": [
+                {
+                    "word": " Designed",
+                    "start": 49.9,
+                    "end": 50.54,
+                    "probability": 0.9991483390331268
+                },
+                {
+                    "word": " for",
+                    "start": 50.54,
+                    "end": 50.6,
+                    "probability": 0.9999561309814453
+                },
+                {
+                    "word": " filmmakers,",
+                    "start": 50.6,
+                    "end": 51.0,
+                    "probability": 0.9989211559295654
+                },
+                {
+                    "word": " content",
+                    "start": 51.46,
+                    "end": 51.98,
+                    "probability": 0.9998670816421509
+                },
+                {
+                    "word": " creators,",
+                    "start": 51.98,
+                    "end": 52.42,
+                    "probability": 0.9998534917831421
+                },
+                {
+                    "word": " and",
+                    "start": 52.6,
+                    "end": 52.88,
+                    "probability": 0.999955415725708
+                },
+                {
+                    "word": " production",
+                    "start": 52.88,
+                    "end": 53.18,
+                    "probability": 0.9998334646224976
+                },
+                {
+                    "word": " teams,",
+                    "start": 53.18,
+                    "end": 53.62,
+                    "probability": 0.9998987913131714
+                }
+            ]
+        },
+        {
+            "id": 16,
+            "seek": 3000,
+            "start": 53.9,
+            "end": 58.18,
+            "text": " EditMind turns hours of raw footage into an organized, searchable,",
+            "tokens": [
+                51571,
+                33241,
+                44,
+                471,
+                4523,
+                2496,
+                295,
+                8936,
+                9556,
+                666,
+                364,
+                9983,
+                11,
+                3164,
+                712,
+                11,
+                51781
+            ],
+            "temperature": 0.0,
+            "avg_logprob": -0.23181006390115488,
+            "compression_ratio": 1.5669014084507042,
+            "no_speech_prob": 3.380340422154404e-05,
+            "words": [
+                {
+                    "word": " EditMind",
+                    "start": 53.9,
+                    "end": 54.72,
+                    "probability": 0.9997626741727194
+                },
+                {
+                    "word": " turns",
+                    "start": 54.72,
+                    "end": 54.98,
+                    "probability": 0.9996140599250793
+                },
+                {
+                    "word": " hours",
+                    "start": 54.98,
+                    "end": 55.32,
+                    "probability": 0.9998528957366943
+                },
+                {
+                    "word": " of",
+                    "start": 55.32,
+                    "end": 55.64,
+                    "probability": 0.9999170303344727
+                },
+                {
+                    "word": " raw",
+                    "start": 55.64,
+                    "end": 55.84,
+                    "probability": 0.9867357611656189
+                },
+                {
+                    "word": " footage",
+                    "start": 55.84,
+                    "end": 56.2,
+                    "probability": 0.9997938275337219
+                },
+                {
+                    "word": " into",
+                    "start": 56.2,
+                    "end": 56.54,
+                    "probability": 0.9999097585678101
+                },
+                {
+                    "word": " an",
+                    "start": 56.54,
+                    "end": 56.76,
+                    "probability": 0.9999264478683472
+                },
+                {
+                    "word": " organized,",
+                    "start": 56.76,
+                    "end": 57.22,
+                    "probability": 0.9995367527008057
+                },
+                {
+                    "word": " searchable,",
+                    "start": 57.5,
+                    "end": 58.18,
+                    "probability": 0.9999381899833679
+                }
+            ]
+        },
+        {
+            "id": 17,
+            "seek": 3000,
+            "start": 58.3,
+            "end": 58.7,
+            "text": " and fun video.",
+            "tokens": [
+                51801,
+                293,
+                1019,
+                960,
+                13,
+                51854
+            ],
+            "temperature": 0.0,
+            "avg_logprob": -0.23181006390115488,
+            "compression_ratio": 1.5669014084507042,
+            "no_speech_prob": 3.380340422154404e-05,
+            "words": [
+                {
+                    "word": " and",
+                    "start": 58.3,
+                    "end": 58.7,
+                    "probability": 0.8338812589645386
+                },
+                {
+                    "word": " fun",
+                    "start": 58.7,
+                    "end": 58.7,
+                    "probability": 0.06599029153585434
+                },
+                {
+                    "word": " video.",
+                    "start": 58.7,
+                    "end": 58.7,
+                    "probability": 0.07364847511053085
+                }
+            ]
+        }
+    ],
+    "language": "en"
+}
\ No newline at end of file
diff --git a/tests/helpers/llm.ts b/tests/helpers/llm.ts
new file mode 100644
index 00000000..251ae3e1
--- /dev/null
+++ b/tests/helpers/llm.ts
@@ -0,0 +1,69 @@
+import { VideoSearchParamsSchema } from '@/lib/services/llm'
+import { VideoSearchParams } from '@/lib/types/search'
+import { expect } from 'vitest'
+
+export const TEST_QUERIES = {
+  simple: {
+    query: 'Create a 30 second vertical video of me looking happy',
+    expected: {
+      emotions: expect.arrayContaining(['happy']),
+      aspect_ratio: '9:16',
+      duration: 30,
+    },
+  },
+  complex: {
+    query: "Find all close-ups where @Ilias is surprised and there's a laptop",
+    expected: {
+      emotions: expect.arrayContaining(['surprised']),
+      shot_type: 'close-up',
+      objects: expect.arrayContaining(['laptop']),
+    },
+  },
+  transcription: {
+    query: "Show me clips where I say 'machine learning is awesome'",
+    expected: {
+      transcriptionQuery: expect.stringMatching(/machine learning/i),
+    },
+  },
+  withObjects: {
+    query: '2 minute video of cooking with pans and knives',
+    expected: {
+      action: expect.stringMatching(/cook/i),
+      duration: 120,
+      objects: expect.arrayContaining([expect.stringMatching(/pan|knife/i)]),
+    },
+  },
+  multiEmotion: {
+    query: 'Compile happy and excited moments from last summer',
+    expected: {
+      emotions: expect.arrayContaining(['happy', 'excited']),
+    },
+  },
+  aspectRatio: {
+    query: 'Make a square Instagram post of my dog',
+    expected: {
+      aspect_ratio: '1:1',
+      objects: expect.arrayContaining([expect.stringMatching(/dog/i)]),
+    },
+  },
+  shotType: {
+    query: 'Give me all medium shots videos',
+    expected: {
+      shot_type: 'medium-shot',
+    },
+  },
+}
+
+export function compareResults(result: VideoSearchParams, expected: Partial<VideoSearchParams>): void {
+  const validated = VideoSearchParamsSchema.safeParse(result)
+  expect(validated.success).toEqual(true)
+  Object.entries(expected).forEach(([key, expectedValue]) => {
+    const actualValue = result[key as keyof VideoSearchParams]
+
+    if (expectedValue && typeof expectedValue === 'object' && 'asymmetricMatch' in expectedValue) {
+      expect(actualValue).toEqual(expectedValue)
+    } else {
+      expect(actualValue).toEqual(expectedValue)
+    }
+  })
+}
diff --git a/tests/services/llm.test.ts b/tests/services/llm.test.ts
new file mode 100644
index 00000000..9e3735f2
--- /dev/null
+++ b/tests/services/llm.test.ts
@@ -0,0 +1,105 @@
+import { cleanup, generateActionFromPromptInternal } from '@/lib/services/llm'
+import { describe, it, expect, afterEach } from 'vitest'
+import { TEST_QUERIES, compareResults } from '../helpers/llm'
+
+const TEST_TIMEOUT = 30000
+
+afterEach(cleanup)
+
+describe('Video Search Parameter Generation', () => {
+  const runTest = async (query: string, expected?: Partial<any>) => {
+    const result = await generateActionFromPromptInternal(query)
+    if (expected) compareResults(result, expected)
+  }
+
+  describe('Core Functionality', () => {
+    it('parses simple queries', () => runTest(TEST_QUERIES.simple.query, TEST_QUERIES.simple.expected), TEST_TIMEOUT)
+
+    it(
+      'handles complex multi-parameter queries',
+      () => runTest(TEST_QUERIES.complex.query, TEST_QUERIES.complex.expected),
+      TEST_TIMEOUT
+    )
+
+    it(
+      'extracts transcription requirements',
+      () => runTest(TEST_QUERIES.transcription.query, TEST_QUERIES.transcription.expected),
+      TEST_TIMEOUT
+    )
+  })
+
+  describe('Advanced Features', () => {
+    it(
+      'identifies objects in scene',
+      () => runTest(TEST_QUERIES.withObjects.query, TEST_QUERIES.withObjects.expected),
+      TEST_TIMEOUT
+    )
+
+    it(
+      'parses multiple emotions',
+      () => runTest(TEST_QUERIES.multiEmotion.query, TEST_QUERIES.multiEmotion.expected),
+      TEST_TIMEOUT
+    )
+
+    it(
+      'detects aspect ratio requirements',
+      () => runTest(TEST_QUERIES.aspectRatio.query, TEST_QUERIES.aspectRatio.expected),
+      TEST_TIMEOUT
+    )
+    it(
+      'detects shot type requirements',
+      () => runTest(TEST_QUERIES.shotType.query, TEST_QUERIES.shotType.expected),
+      TEST_TIMEOUT
+    )
+  })
+
+  describe('Action Field Integrity', () => {
+    it(
+      'returns action as string primitive',
+      async () => {
+        const queries = ['Show me running clips', 'Find videos where I am cooking', 'Videos of me laughing']
+
+        await Promise.all(queries.map((query) => runTest(query)))
+      },
+      TEST_TIMEOUT * 2
+    )
+  })
+
+  describe('Edge Cases', () => {
+    it(
+      'handles minimal input gracefully',
+      async () => {
+        const edgeCases = ['video', 'a', 'create clip', 'show me stuff']
+        await Promise.all(edgeCases.map((query) => runTest(query)))
+      },
+      TEST_TIMEOUT * 2
+    )
+
+    it(
+      'sanitizes filenames correctly',
+      async () => {
+        const queries = ["John's @birthday party!!!", "video with 'special' characters#$%", 'UPPERCASE VIDEO NAME']
+        await Promise.all(queries.map((query) => runTest(query)))
+      },
+      TEST_TIMEOUT * 2
+    )
+  })
+
+  describe('Consistency', () => {
+    it(
+      'produces consistent results with low temperature',
+      async () => {
+        const query = '30 second happy video with music'
+        const [result1, result2] = await Promise.all([
+          generateActionFromPromptInternal(query),
+          generateActionFromPromptInternal(query),
+        ])
+
+        expect(result1.emotions).toEqual(result2.emotions)
+        expect(result1.duration).toEqual(result2.duration)
+        expect(result1.aspect_ratio).toEqual(result2.aspect_ratio)
+      },
+      TEST_TIMEOUT * 2
+    )
+  })
+})
diff --git a/tests/services/vectorDb.test.ts b/tests/services/vectorDb.test.ts
new file mode 100644
index 00000000..c499ce5f
--- /dev/null
+++ b/tests/services/vectorDb.test.ts
@@ -0,0 +1,701 @@
+import { describe, it, expect, beforeAll, beforeEach } from 'vitest'
+import {
+  embedDocuments,
+  getStatistics,
+  initialize,
+  queryCollection,
+  getAllVideos,
+  getAllVideosWithScenes,
+  filterExistingVideos,
+  updateMetadata,
+  getByVideoSource,
+} from '@/lib/services/vectorDb'
+import { EmbeddingInput } from '@/lib/types/vector'
+import { Scene } from '@/lib/types/scene'
+import { SearchQuery } from '@/lib/types/search'
+
+const TEST_TIMEOUT = 30000
+
+const createMockScene = (overrides?: Partial<Scene>): Scene => ({
+  id: `scene-${Date.now()}-${Math.random()}`,
+  source: 'test-video.mp4',
+  startTime: 0,
+  endTime: 5,
+  duration: 5,
+  description: 'A test scene with a person smiling',
+  faces: ['person1'],
+  objects: ['laptop', 'desk'],
+  emotions: [{ emotion: 'happy', name: 'person1' }],
+  transcription: 'Hello world, this is a test',
+  detectedText: ['lorem ipsum'],
+  shot_type: 'medium-shot',
+  aspect_ratio: '16:9',
+  camera: 'static',
+  category: 'test',
+  createdAt: new Date().toISOString(),
+  dominantColorName: 'black',
+  dominantColorHex: '#000',
+  location: '',
+  ...overrides,
+})
+
+const defaultQuery: SearchQuery = {
+  faces: [],
+  action: null,
+  emotions: [],
+  shot_type: null,
+  aspect_ratio: '16:9',
+  duration: null,
+  description: '',
+  outputFilename: '',
+  objects: [],
+  camera: undefined,
+  transcriptionQuery: null,
+  detectedText: undefined,
+}
+
+const createMockEmbeddingInput = (overrides?: Partial<EmbeddingInput>): EmbeddingInput => ({
+  id: `doc-${Date.now()}-${Math.random()}`,
+  text: 'Test document for embedding',
+  metadata: {
+    source: 'test-video.mp4',
+    startTime: 0,
+    endTime: 5,
+    category: 'test',
+  },
+  ...overrides,
+})
+
+describe('Embedding Service', () => {
+  beforeAll(async () => {
+    await initialize('testing')
+  }, TEST_TIMEOUT)
+
+  describe('Initialization', () => {
+    it(
+      'should initialize ChromaDB client and collection',
+      async () => {
+        await expect(initialize('testing')).resolves.not.toThrow()
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should be idempotent (safe to call multiple times)',
+      async () => {
+        await initialize('testing')
+        await initialize('testing')
+        await initialize('testing')
+        // Should not throw
+        expect(true).toBe(true)
+      },
+      TEST_TIMEOUT
+    )
+
+    it('should throw error if ChromaDB is not running', async () => {
+      // This test requires ChromaDB to be stopped
+      // Skip in normal test runs, useful for debugging
+      // You can enable this by stopping ChromaDB manually
+    })
+  })
+
+  describe('Document Embedding', () => {
+    it(
+      'should embed a single document',
+      async () => {
+        const doc = createMockEmbeddingInput({
+          text: 'A person walking in the park on a sunny day',
+        })
+
+        await expect(embedDocuments([doc])).resolves.not.toThrow()
+
+        // Verify the document was added
+        const stats = await getStatistics()
+        expect(stats.totalDocuments).toBeGreaterThan(0)
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should embed multiple documents in batch',
+      async () => {
+        const docs: EmbeddingInput[] = [
+          createMockEmbeddingInput({ text: 'Person running outdoors' }),
+          createMockEmbeddingInput({ text: 'Dog playing with a ball' }),
+          createMockEmbeddingInput({ text: 'Sunset over the ocean' }),
+        ]
+
+        await expect(embedDocuments(docs)).resolves.not.toThrow()
+
+        const stats = await getStatistics()
+        expect(stats.totalDocuments).toBeGreaterThanOrEqual(docs.length)
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should generate embeddings with correct dimensions',
+      async () => {
+        const doc = createMockEmbeddingInput({
+          text: 'Test embedding dimension',
+        })
+
+        await embedDocuments([doc])
+
+        const stats = await getStatistics()
+        // all-MiniLM-L6-v2 produces 384-dimensional embeddings
+        expect(stats.embeddingDimension).toBe(384)
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should handle documents with metadata',
+      async () => {
+        const doc = createMockEmbeddingInput({
+          text: 'Document with rich metadata',
+          metadata: {
+            source: 'metadata-test.mp4',
+            startTime: 10,
+            endTime: 15,
+            category: 'sports',
+            faces: ['athlete1', 'athlete2'].join(','),
+            objects: ['basketball', 'court'].join(','),
+            emotions: ['excited'].join(','),
+          },
+        })
+
+        await embedDocuments([doc])
+
+        const stats = await getStatistics()
+        expect(stats.metadataKeys).toContain('source')
+        expect(stats.metadataKeys).toContain('startTime')
+        expect(stats.metadataKeys).toContain('endTime')
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should handle empty text gracefully',
+      async () => {
+        const doc = createMockEmbeddingInput({ text: '' })
+
+        // Should either succeed or fail gracefully
+        try {
+          await embedDocuments([doc])
+          expect(true).toBe(true)
+        } catch (error) {
+          expect(error).toBeDefined()
+        }
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should normalize embeddings',
+      async () => {
+        const doc = createMockEmbeddingInput({
+          text: 'Test normalization',
+        })
+
+        await embedDocuments([doc])
+
+        // Verify via statistics that embeddings exist
+        const stats = await getStatistics()
+        expect(stats.totalDocuments).toBeGreaterThan(0)
+      },
+      TEST_TIMEOUT
+    )
+  })
+
+  describe('Statistics', () => {
+    beforeEach(async () => {
+      // Add some test data
+      const docs = [
+        createMockEmbeddingInput({ text: 'Stats test 1' }),
+        createMockEmbeddingInput({ text: 'Stats test 2' }),
+      ]
+      await embedDocuments(docs)
+    }, TEST_TIMEOUT)
+
+    it(
+      'should return collection statistics',
+      async () => {
+        const stats = await getStatistics()
+
+        expect(stats).toHaveProperty('name')
+        expect(stats).toHaveProperty('totalDocuments')
+        expect(stats).toHaveProperty('embeddingDimension')
+        expect(stats).toHaveProperty('metadataKeys')
+        expect(stats).toHaveProperty('documentIds')
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should report correct document count',
+      async () => {
+        const statsBefore = await getStatistics()
+        const countBefore = statsBefore.totalDocuments
+
+        const newDoc = createMockEmbeddingInput({ text: 'New document for counting' })
+        await embedDocuments([newDoc])
+
+        const statsAfter = await getStatistics()
+        expect(statsAfter.totalDocuments).toBe(countBefore + 1)
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should list all metadata keys',
+      async () => {
+        const stats = await getStatistics()
+
+        expect(Array.isArray(stats.metadataKeys)).toBe(true)
+        expect(stats.metadataKeys.length).toBeGreaterThan(0)
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should list all document IDs',
+      async () => {
+        const stats = await getStatistics()
+
+        expect(Array.isArray(stats.documentIds)).toBe(true)
+        expect(stats.documentIds.length).toBe(stats.totalDocuments)
+      },
+      TEST_TIMEOUT
+    )
+  })
+
+  describe('Semantic Search', () => {
+    beforeEach(async () => {
+      // Add diverse test data
+      const docs: EmbeddingInput[] = [
+        createMockEmbeddingInput({
+          id: 'happy-person',
+          text: 'A happy person smiling at the camera',
+          metadata: {
+            source: 'video1.mp4',
+            emotions: 'person:happy',
+            shot_type: 'close-up',
+            aspect_ratio: '16:9',
+          },
+        }),
+        createMockEmbeddingInput({
+          id: 'sad-person',
+          text: 'A sad person looking down',
+          metadata: {
+            source: 'video2.mp4',
+            emotions: 'person:sad',
+            shot_type: 'medium-shot',
+            aspect_ratio: '9:16',
+          },
+        }),
+        createMockEmbeddingInput({
+          id: 'dog-playing',
+          text: 'A dog playing with a ball in the park',
+          metadata: {
+            source: 'video3.mp4',
+            objects: 'dog,ball',
+            shot_type: 'long-shot',
+            aspect_ratio: '16:9',
+          },
+        }),
+      ]
+
+      await embedDocuments(docs)
+    }, TEST_TIMEOUT)
+
+    it(
+      'should filter by aspect ratio',
+      async () => {
+        const query: SearchQuery = {
+          ...defaultQuery,
+          aspect_ratio: '16:9',
+        }
+
+        const results = await queryCollection(query)
+
+        expect(results.length).toBeGreaterThan(0)
+        results.forEach((scene) => {
+          expect(scene.aspect_ratio).toBe('16:9')
+        })
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should filter by shot type',
+      async () => {
+        const query: SearchQuery = {
+          ...defaultQuery,
+          shot_type: 'close-up',
+        }
+
+        const results = await queryCollection(query)
+
+        results.forEach((scene) => {
+          expect(scene.shot_type).toBe('close-up')
+        })
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should filter by emotions',
+      async () => {
+        const query: SearchQuery = {
+          ...defaultQuery,
+          emotions: ['happy'],
+        }
+
+        const results = await queryCollection(query)
+
+        results.forEach((scene) => {
+          const emotions = scene.emotions.map((e) => e.emotion.toLowerCase())
+          expect(emotions).toContain('happy')
+        })
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should filter by objects',
+      async () => {
+        const query: SearchQuery = {
+          ...defaultQuery,
+          objects: ['dog'],
+        }
+
+        const results = await queryCollection(query)
+
+        results.forEach((scene) => {
+          const objects = scene.objects.map((o) => o.toLowerCase())
+          expect(objects).toContain('dog')
+        })
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should filter by transcription query',
+      async () => {
+        const testDoc = createMockEmbeddingInput({
+          text: 'Speech transcript test',
+          metadata: {
+            transcription: 'hello world this is a test',
+          },
+        })
+
+        await embedDocuments([testDoc])
+
+        const query: SearchQuery = {
+          ...defaultQuery,
+          transcriptionQuery: 'hello world',
+        }
+
+        const results = await queryCollection(query)
+
+        results.forEach((scene) => {
+          expect(scene.transcription.toLowerCase()).toContain('hello world')
+        })
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should combine multiple filters',
+      async () => {
+        const query: SearchQuery = {
+          ...defaultQuery,
+          aspect_ratio: '16:9',
+          shot_type: 'close-up',
+          emotions: ['happy'],
+        }
+
+        const results = await queryCollection(query)
+
+        results.forEach((scene) => {
+          expect(scene.aspect_ratio).toBe('16:9')
+          expect(scene.shot_type).toBe('close-up')
+          const emotions = scene.emotions.map((e) => e.emotion.toLowerCase())
+          expect(emotions).toContain('happy')
+        })
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should respect nResults limit',
+      async () => {
+        const query: SearchQuery = {
+          ...defaultQuery,
+        }
+        const limit = 5
+
+        const results = await queryCollection(query, limit)
+
+        expect(results.length).toBeLessThanOrEqual(limit)
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should return empty array when no matches found',
+      async () => {
+        const query: SearchQuery = {
+          ...defaultQuery,
+
+          emotions: ['nonexistent-emotion'],
+        }
+
+        const results = await queryCollection(query)
+
+        expect(Array.isArray(results)).toBe(true)
+        // May or may not be empty depending on test data
+      },
+      TEST_TIMEOUT
+    )
+  })
+
+  describe('Video Management', () => {
+    const testVideoSource = `test-video-${Date.now()}.mp4`
+
+    beforeEach(async () => {
+      // Add test video with multiple scenes
+      const scenes: EmbeddingInput[] = [
+        createMockEmbeddingInput({
+          text: 'Scene 1',
+          metadata: {
+            source: testVideoSource,
+            startTime: 0,
+            endTime: 5,
+            duration: 120,
+            aspect_ratio: '16:9',
+            category: 'sports',
+          },
+        }),
+        createMockEmbeddingInput({
+          text: 'Scene 2',
+          metadata: {
+            source: testVideoSource,
+            startTime: 5,
+            endTime: 10,
+            duration: 120,
+            aspect_ratio: '16:9',
+            category: 'sports',
+          },
+        }),
+      ]
+
+      await embedDocuments(scenes)
+    }, TEST_TIMEOUT)
+
+    it(
+      'should get all videos',
+      async () => {
+        const videos = await getAllVideos()
+
+        expect(Array.isArray(videos)).toBe(true)
+        expect(videos.length).toBeGreaterThan(0)
+
+        videos.forEach((video) => {
+          expect(video).toHaveProperty('source')
+          expect(video).toHaveProperty('duration')
+          expect(video).toHaveProperty('aspect_ratio')
+        })
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should get all videos with scenes',
+      async () => {
+        const videos = await getAllVideosWithScenes()
+
+        expect(Array.isArray(videos)).toBe(true)
+        expect(videos.length).toBeGreaterThan(0)
+
+        videos.forEach((video) => {
+          expect(video).toHaveProperty('source')
+          expect(video).toHaveProperty('scenes')
+          expect(video).toHaveProperty('sceneCount')
+          expect(Array.isArray(video.scenes)).toBe(true)
+          expect(video.sceneCount).toBe(video.scenes.length)
+        })
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should sort scenes by start time',
+      async () => {
+        const videos = await getAllVideosWithScenes()
+        const testVideo = videos.find((v) => v.source === testVideoSource)
+
+        if (testVideo && testVideo.scenes.length > 1) {
+          for (let i = 1; i < testVideo.scenes.length; i++) {
+            expect(testVideo.scenes[i].startTime).toBeGreaterThanOrEqual(testVideo.scenes[i - 1].startTime)
+          }
+        }
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should get scenes by video source',
+      async () => {
+        const scenes = await getByVideoSource(testVideoSource)
+
+        expect(Array.isArray(scenes)).toBe(true)
+        expect(scenes.length).toBeGreaterThan(0)
+
+        scenes.forEach((scene) => {
+          expect(scene.source).toBe(testVideoSource)
+        })
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should filter existing videos',
+      async () => {
+        const sources = [testVideoSource, 'nonexistent-video.mp4', 'another-nonexistent.mp4']
+
+        const newSources = await filterExistingVideos(sources)
+
+        expect(newSources).not.toContain(testVideoSource)
+        expect(newSources).toContain('nonexistent-video.mp4')
+        expect(newSources).toContain('another-nonexistent.mp4')
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should return empty array when filtering empty list',
+      async () => {
+        const newSources = await filterExistingVideos([])
+
+        expect(newSources).toEqual([])
+      },
+      TEST_TIMEOUT
+    )
+  })
+
+  describe('Metadata Updates', () => {
+    let testScene: Scene
+
+    beforeEach(async () => {
+      testScene = createMockScene({
+        id: `update-test-${Date.now()}`,
+        source: `update-video-${Date.now()}.mp4`,
+        description: 'Original description',
+        faces: ['person1'],
+      })
+
+      const doc = createMockEmbeddingInput({
+        id: testScene.id,
+        text: testScene.description,
+        metadata: {
+          source: testScene.source,
+          startTime: testScene.startTime,
+          endTime: testScene.endTime,
+          faces: testScene.faces.join(','),
+        },
+      })
+
+      await embedDocuments([doc])
+    }, TEST_TIMEOUT)
+
+    it(
+      'should update scene metadata',
+      async () => {
+        const updatedScene: Scene = {
+          ...testScene,
+          description: 'Updated description',
+          faces: ['person1', 'person2'],
+        }
+
+        await expect(updateMetadata(updatedScene)).resolves.not.toThrow()
+
+        // Verify update
+        const scenes = await getByVideoSource(testScene.source)
+        const updated = scenes.find((s) => s.id === testScene.id)
+
+        expect(updated).toBeDefined()
+        if (updated) {
+          expect(updated.faces).toContain('person2')
+        }
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should preserve embeddings during update',
+      async () => {
+        const statsBefore = await getStatistics()
+        const countBefore = statsBefore.totalDocuments
+
+        await updateMetadata(testScene)
+
+        const statsAfter = await getStatistics()
+        expect(statsAfter.totalDocuments).toBe(countBefore)
+      },
+      TEST_TIMEOUT
+    )
+  })
+
+  describe('Edge Cases', () => {
+    it(
+      'should handle special characters in text',
+      async () => {
+        const doc = createMockEmbeddingInput({
+          text: 'Text with special chars: @#$%^&*()[]{}|\\/<>?`~',
+        })
+
+        await expect(embedDocuments([doc])).resolves.not.toThrow()
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should handle very long text',
+      async () => {
+        const longText = 'A '.repeat(1000) + 'very long description'
+        const doc = createMockEmbeddingInput({ text: longText })
+
+        await expect(embedDocuments([doc])).resolves.not.toThrow()
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should handle unicode characters',
+      async () => {
+        const doc = createMockEmbeddingInput({
+          text: '你好世界 مرحبا بالعالم Привет мир 🌍🎉',
+        })
+
+        await expect(embedDocuments([doc])).resolves.not.toThrow()
+      },
+      TEST_TIMEOUT
+    )
+
+    it(
+      'should handle concurrent embedding requests',
+      async () => {
+        const docs = Array.from({ length: 10 }, (_, i) =>
+          createMockEmbeddingInput({
+            text: `Concurrent test ${i}`,
+          })
+        )
+
+        await expect(Promise.all(docs.map((doc) => embedDocuments([doc])))).resolves.not.toThrow()
+      },
+      TEST_TIMEOUT * 2
+    )
+  })
+})
diff --git a/tsconfig.node.json b/tsconfig.node.json
index dcc5ca96..1c1b6449 100644
--- a/tsconfig.node.json
+++ b/tsconfig.node.json
@@ -1,6 +1,6 @@
 {
   "extends": "@electron-toolkit/tsconfig/tsconfig.node.json",
-  "include": ["lib/main/index.d.ts", "electron.vite.config.*", "lib/**/*", "resources/**/*", "app/**/*"],
+  "include": ["lib/main/index.d.ts", "electron.vite.config.*", "lib/**/*", "resources/**/*", "app/**/*", "tests/**/*", "vitest.config.ts"],
   "compilerOptions": {
     "composite": true,
     "moduleResolution": "bundler",
diff --git a/vitest.config.ts b/vitest.config.ts
new file mode 100644
index 00000000..31f8c883
--- /dev/null
+++ b/vitest.config.ts
@@ -0,0 +1,25 @@
+import { defineConfig } from 'vitest/config'
+import path from 'path'
+
+export default defineConfig({
+  test: {
+    globals: true,
+    logHeapUsage: true,
+    environment: 'node',
+    coverage: {
+      provider: 'v8',
+      reporter: ['text', 'json', 'html'],
+      exclude: ['node_modules/', 'tests/', '**/*.spec.ts', '**/*.test.ts'],
+    },
+    testTimeout: 30000 * 2,
+    hookTimeout: 30000 * 2,
+  },
+  resolve: {
+    alias: {
+      '@': path.resolve(__dirname),
+      '@/app': path.resolve(__dirname, './app'),
+      '@/lib': path.resolve(__dirname, './lib'),
+      '@/resources': path.resolve(__dirname, './resources'),
+    },
+  },
+})