Building a Movie Recommendation RAG with Agentic LlamaIndex on GKE

Tags:

• Serving
• Llamaindex
• Agentic
• RAG

LlamaIndex bridges the gap between LLMs and domain-specific datasets, enabling efficient indexing and querying of unstructured data for intelligent, data-driven responses. When deployed on a GKE cluster, it ensures scalability and security by leveraging containerized workloads, GPU-based nodes, and seamless cloud-native integrations, making it ideal for ML-powered applications like RAG systems.

Overview

This tutorial will guide you through creating a Intelligent Agent & robust Retrieval-Augmented Generation (RAG) system using LlamaIndex and deploying it on Google Kubernetes Engine (GKE). The system indexes a dataset of 1000 top-rated movies and uses an LLM to generate recommendations based on user queries (e.g., “best drama movies” or “movies like Inception”).

What you will learn?

1. Data Preparation and Ingestion: Use LlamaIndex to structure and index your data for efficient querying.
2. Model Integration: Connect LlamaIndex with an LLM to build a RAG pipeline that can generate precise responses based on your indexed data.
3. Containerization: Package the application as a container for deployment.
4. GKE Deployment: Set up a GKE cluster using Terraform and deploy your RAG system, leveraging Kubernetes features.

Before you begin

Ensure you have a GCP project with a billing account.

Ensure you have the following tools installed on your workstation:

• gcloud CLI
• kubectl
• terraform (for an automated deployment)
• git

If you previously installed the gcloud CLI, get the latest version by running:

gcloud components update

Ensure that you are signed in using the gcloud CLI tool. Run the following commands:

gcloud auth application-default login

Download the guide files

Clone the repository with our guides and cd to the llamaindex/rag directory by running these commands:

git clone https://github.com/ai-on-gke/tutorials-and-examples.git
cd tutorials-and-examples/agentic-llamaindex/rag

Filesystem structure

• app - folder with demo Python application that uses llamaindex to ingest data to RAG and infer it through web API.
• templates - folder with Kubernetes manifests that require additional processing to specify additional values that are not known from the start.
• terraform - folder with Terraform config that executes automated provisioning of required infrastructure resources.

Demo application

The demo application consists of following components:

Data ingestion

The data ingestion process indexes a movie dataset (imdb_top_1000.csv) into a Redis vector store for use by the RAG system. It is defined in app/cmd/ingest_data.py.

File: app/cmd/ingest_data.py

pipeline = IngestionPipeline(
    transformations=[embed_model],
    vector_store=vector_store,
)

# Process documents individually
nodes = []
for doc in docs:
    logger.info(f"Ingesting document: {doc.id_}")
    try:
        embedding = embed_model.get_text_embedding(doc.text)
        logger.info(f"Embedding generated for {doc.id_}: {len(embedding)} dimensions")
        node_list = pipeline.run(documents=[doc], show_progress=True)
        nodes.extend(node_list)
        logger.info(f"Nodes created for {doc.id_}: {len(node_list)}")
    except Exception as e:
        logger.error(f"Error ingesting document {doc.id_}: {str(e)}")

File app/rag_demo/__init__.py:

custom_schema = IndexSchema.from_dict(
    {
        "index": {"name": "movies", "prefix": "movie"},
        "fields": [
            {"type": "tag", "name": "id"},
            {"type": "tag", "name": "doc_id"},
            {"type": "text", "name": "text"},
            {
                "type": "vector",
                "name": "vector",
                "attrs": {
                    "dims": 384,
                    "algorithm": "hnsw",
                    "distance_metric": "cosine",
                },
            },
        ],
    }
)

RAG server

The RAG server is a FastAPI application that exposes a /recommend endpoint to run a multi-step, self-refining movie recommender. It is defined in app/rag_demo/main.py.

File: app/rag_demo/main.py

# Agentic pipeline: expansion, validation, correction, coordination
expander  = QueryExpander(llm=llm)       # Context‑aware sub‑query generation
validator = ValidationAgent(llm=llm)     # YES/NO relevance check
corrector = CorrectionAgent(llm=llm)     # JSON‑based correction step
coordinator = MultiStepCoordinator(
    expander=expander,
    query_engine=query_engine,
    validator=validator,
    corrector=corrector,
    max_steps=3
)

# FastAPI endpoints
app = FastAPI()
class QueryRequest(BaseModel):
    query: str

@app.post("/recommend")
async def recommend_movies(request: QueryRequest):
    """
    1) Expand query via LLM  
    2) Retrieve top‑K from Redis  
    3) Validate relevance (YES/NO)  
    4) If invalid, correct and retry 
    5) Return final JSON with `message` + `recommendations`
    """
    result = await coordinator.run(request.query)
    return JSONResponse(content=jsonable_encoder(result))

@app.get("/recommend")
async def recommend_movies_get(query: str):
    if not query:
        raise HTTPException(400, "Query parameter is required")
    result = await coordinator.run(query)
    return JSONResponse(content=jsonable_encoder(result))

Key components:

• Query Expander:

  File: app/rag_demo/agents/query_expansion.py

  The system uses an LLM to generate 3-5 expanded queries or keywords for the original query. For example, for “movies like inception,” the LLM doesn’t just look for synonyms of “movie” or “Inception.” It understands the context (e.g., Inception’s themes of mind-bending sci-fi, dream sequences, and Nolan’s style) and generates expansions like ‘mind bending movies’, ‘science fiction thrillers’, ‘films with complex narratives’, ‘psychological action movies’, ’time manipulation movies’. This makes the expansion context-aware and improves retrieval relevance.

  Expand to see action log

    13:58:04 rag_demo.agents.coordinator INFO   [Step 1] Expanding query: movies like inception
    13:58:09 httpx INFO   HTTP Request: POST http://ollama-service:11434/api/chat "HTTP/1.1 200 OK"
    13:58:09 rag_demo.agents.query_expansion INFO   Expanded query 'movies like inception' to: ['mind bending sci-fi', 'films with dream sequences', 'neo noir thrillers', 'Christopher Nolan movies', 'thought provoking action']
    13:58:09 rag_demo.agents.coordinator INFO   [Step 1] Expanded queries: ['mind bending sci-fi', 'films with dream sequences', 'neo noir thrillers', 'Christopher Nolan movies', 'thought provoking action']
    13:58:09 rag_demo.agents.coordinator INFO   [Step 1] Querying with expanded: mind bending sci-fi
  

• Multi-Step Coordinator:

  File: app/rag_demo/agents/coordinator.py

  The multi-step coordinator orchestrates the entire workflow. If validation fails, the system doesn’t just give up — it reasons that the current approach was flawed and enters a correction phase, using the CorrectionAgent (app/rag/demo/agents/correction.py) to fix the results. This demonstrates the “expand -> retrieve -> validate -> correct” loop in action.

  Expand to see action log

    13:58:04 rag_demo.agents.coordinator INFO   [Step 1] Expanding query: movies like inception
    13:58:09 httpx INFO   HTTP Request: POST http://ollama-service:11434/api/chat "HTTP/1.1 200 OK"
    13:58:09 rag_demo.agents.query_expansion INFO   Expanded query 'movies like inception' to: ['mind bending sci-fi', 'films with dream sequences', 'neo noir thrillers', 'Christopher Nolan movies', 'thought     provoking action']
    13:58:09 rag_demo.agents.coordinator INFO   [Step 1] Expanded queries: ['mind bending sci-fi', 'films with dream sequences', 'neo noir thrillers', 'Christopher Nolan movies', 'thought provoking action']
    13:58:09 rag_demo.agents.coordinator INFO   [Step 1] Querying with expanded: mind bending sci-fi
    Batches:   0%|          | 0/1 [00:00<?, ?it/s]/usr/local/lib/python3.12/site-packages/torch/nn/modules/module.py:1747: FutureWarning: `encoder_attention_mask` is deprecated and will be removed in version 4.55.0    for `BertSdpaSelfAttention.forward`.
      return forward_call(*args, **kwargs)
    Batches: 100%|██████████| 1/1 [00:00<00:00,  1.51it/s]
    13:58:10 llama_index.vector_stores.redis.base INFO   Querying index movies with query *=>[KNN 3 @vector $vector AS vector_distance] RETURN 5 id doc_id text _node_content vector_distance SORTBY vector_distance    ASC DIALECT 2 LIMIT 0 3
    13:58:10 llama_index.vector_stores.redis.base INFO   Found 3 results for query with id ['movie:movie_Avatar', 'movie:movie_Inception', 'movie:movie_Alien']
    13:58:12 httpx INFO   HTTP Request: POST http://ollama-service:11434/api/chat "HTTP/1.1 200 OK"
    13:58:12 rag_demo.agents.coordinator INFO   [Step 1] Querying with expanded: films with dream sequences
    Batches: 100%|██████████| 1/1 [00:00<00:00, 15.60it/s]
    13:58:12 llama_index.vector_stores.redis.base INFO   Querying index movies with query *=>[KNN 3 @vector $vector AS vector_distance] RETURN 5 id doc_id text _node_content vector_distance SORTBY vector_distance    ASC DIALECT 2 LIMIT 0 3
    13:58:12 llama_index.vector_stores.redis.base INFO   Found 3 results for query with id ['movie:movie_Le_charme_discret_de_la_bourgeoisie', 'movie:movie_8½', 'movie:movie_Requiem_for_a_Dream']
    13:58:23 httpx INFO   HTTP Request: POST http://ollama-service:11434/api/chat "HTTP/1.1 200 OK"
    13:58:23 rag_demo.agents.coordinator INFO   [Step 1] Querying with expanded: neo noir thrillers
    Batches: 100%|██████████| 1/1 [00:00<00:00, 25.27it/s]
    13:58:23 llama_index.vector_stores.redis.base INFO   Querying index movies with query *=>[KNN 3 @vector $vector AS vector_distance] RETURN 5 id doc_id text _node_content vector_distance SORTBY vector_distance    ASC DIALECT 2 LIMIT 0 3
    13:58:23 llama_index.vector_stores.redis.base INFO   Found 3 results for query with id ['movie:movie_Shadow_of_a_Doubt', 'movie:movie_Prisoners', 'movie:movie_Sicario']
    13:58:35 httpx INFO   HTTP Request: POST http://ollama-service:11434/api/chat "HTTP/1.1 200 OK"
    13:58:35 rag_demo.agents.coordinator INFO   [Step 1] Querying with expanded: Christopher Nolan movies
    Batches: 100%|██████████| 1/1 [00:00<00:00, 24.42it/s]
    13:58:35 llama_index.vector_stores.redis.base INFO   Querying index movies with query *=>[KNN 3 @vector $vector AS vector_distance] RETURN 5 id doc_id text _node_content vector_distance SORTBY vector_distance    ASC DIALECT 2 LIMIT 0 3
    13:58:35 llama_index.vector_stores.redis.base INFO   Found 3 results for query with id ['movie:movie_Dunkirk', 'movie:movie_The_Dark_Knight', 'movie:movie_The_Prestige']
    13:58:48 httpx INFO   HTTP Request: POST http://ollama-service:11434/api/chat "HTTP/1.1 200 OK"
    13:58:48 rag_demo.agents.coordinator INFO   [Step 1] Querying with expanded: thought provoking action
    Batches: 100%|██████████| 1/1 [00:00<00:00, 24.75it/s]
    13:58:48 llama_index.vector_stores.redis.base INFO   Querying index movies with query *=>[KNN 3 @vector $vector AS vector_distance] RETURN 5 id doc_id text _node_content vector_distance SORTBY vector_distance    ASC DIALECT 2 LIMIT 0 3
    13:58:48 llama_index.vector_stores.redis.base INFO   Found 3 results for query with id ['movie:movie_The_Conversation', 'movie:movie_Terminator_2:_Judgment_Day', 'movie:movie_Saw']
    13:58:50 httpx INFO   HTTP Request: POST http://ollama-service:11434/api/chat "HTTP/1.1 200 OK"
    13:58:50 rag_demo.agents.coordinator INFO   [Step 1] Got 15 unique recs; validating…
    13:58:54 httpx INFO   HTTP Request: POST http://ollama-service:11434/api/chat "HTTP/1.1 200 OK"
    13:58:54 rag_demo.agents.coordinator INFO   [Step 1] Validation failed; correcting…
    13:59:06 httpx INFO   HTTP Request: POST http://ollama-service:11434/api/chat "HTTP/1.1 200 OK"
    13:59:06 rag_demo.agents.correction INFO   Successfully parsed corrected recommendations: [{'title': 'The Matrix', 'imdb_rating': 8.7, 'overview': 'A computer hacker learns from mysterious rebels about the true    nature of his reality and his role in the war against machines.', 'genre': 'Action, Sci-Fi', 'released_year': 1999, 'director': 'Lana Wachowski, Lilly Wachowski', 'stars': ['Keanu Reeves', 'Laurence Fishburne',    'Carrie-Anne Moss']}, {'title': 'Interstellar', 'imdb_rating': 8.6, 'overview': 'A team of explorers travel through a wormhole in space in an attempt to find a new home for humanity.', 'genre': 'Adventure,    Sci-Fi', 'released_year': 2014, 'director': 'Christopher Nolan', 'stars': ['Matthew McConaughey', 'Anne Hathaway', 'Jessica Chastain']}, {'title': 'Paprika', 'imdb_rating': 8.0, 'overview': 'A device allows   therapists to enter the dreams of their patients, but when the technology falls into the wrong hands, reality and dreams begin to blur.', 'genre': 'Animation, Sci-Fi', 'released_year': 2006, 'director':     'Satoshi Kon', 'stars': ['Shinobu Otake', 'Yuko  Miyamura', 'Megumi Hayashibara']}]
    13:59:06 rag_demo.agents.coordinator INFO   [Step 1] Correction succeeded; stopping loop
  

• Validation & Correction Agents:

  The MultiStepCoordinator runs a loop where it expands the query, retrieves recommendations from all expansions, aggregates unique results, validates relevance, and corrects if needed. If the validation fails (e.g., recommendations don’t match the intent), it enters correction phase using the CorrectionAgent to generate new results based on the original query and previous recs. This shows multi-step reasoning and agent coordination.

  Expand to see action log

    13:58:50 rag_demo.agents.coordinator INFO   [Step 1] Got 15 unique recs; validating…
    13:58:54 httpx INFO   HTTP Request: POST http://ollama-service:11434/api/chat "HTTP/1.1 200 OK"
    13:58:54 rag_demo.agents.coordinator INFO   [Step 1] Validation failed; correcting…
    13:59:06 httpx INFO   HTTP Request: POST http://ollama-service:11434/api/chat "HTTP/1.1 200 OK"
    13:59:06 rag_demo.agents.correction INFO   Successfully parsed corrected recommendations: [{'title': 'The Matrix', 'imdb_rating': 8.7, 'overview': 'A computer hacker learns from mysterious rebels about the true    nature of his reality and his role in the war against machines.', 'genre': 'Action, Sci-Fi', 'released_year': 1999, 'director': 'Lana Wachowski, Lilly Wachowski', 'stars': ['Keanu Reeves', 'Laurence Fishburne',    'Carrie-Anne Moss']}, {'title': 'Interstellar', 'imdb_rating': 8.6, 'overview': 'A team of explorers travel through a wormhole in space in an attempt to find a new home for humanity.', 'genre': 'Adventure,    Sci-Fi', 'released_year': 2014, 'director': 'Christopher Nolan', 'stars': ['Matthew McConaughey', 'Anne Hathaway', 'Jessica Chastain']}, {'title': 'Paprika', 'imdb_rating': 8.0, 'overview': 'A device allows   therapists to enter the dreams of their patients, but when the technology falls into the wrong hands, reality and dreams begin to blur.', 'genre': 'Animation, Sci-Fi', 'released_year': 2006, 'director':     'Satoshi Kon', 'stars': ['Shinobu Otake', 'Yuko  Miyamura', 'Megumi Hayashibara']}]
    13:59:06 rag_demo.agents.coordinator INFO   [Step 1] Correction succeeded; stopping loop
  

• FastAPI Endpoint: The /recommend endpoint accepts queries (e.g., {"query": "movies like inception"}) and returns JSON responses with up to 3 movie recommendations.

Infrastructure Setup

In this section we will use Terraform to automate the creation of infrastructure resources. For more details how it is done please refer to the terraform config in the terraform folder. By default it creates an Autopilot GKE cluster but it can be changed to standard by setting autopilot_cluster=false

It creates:

• IAM service accounts:
  • for a cluster
  • for Kubernetes permissions for app deployments (using Workload Identity Federation)
  • GCS bucket to store data to be ingested to the RAG.
    • Artifact registry as storage for an app-demo image

1. Go the terraform directory:

   cd terraform
   

2. Specify the following values inside the default_env.tfvars file (or make a separate copy):

   • <PROJECT_ID> – replace with your project id (you can find it in the project settings).

   Other values can be changed, if needed, but can be left with default values.

3. Optional. You can use a GCS bucket as a storage for a Terraform state. Create a bucket manually and then uncomment the content of the file terraform/backend.tf and specify your bucket:

   terraform {
     backend "gcs" {
       bucket = "<BUCKET_NAME>"
       prefix = "terraform/state/agentic-llamaindex"
     }
   }
   

4. Init terraform modules:

   terraform init
   

5. Optionally run the plan command to view an execution plan:

   terraform plan -var-file=default_env.tfvars
   

6. Execute the plan:

   terraform apply -var-file=default_env.tfvars
   

   And you should see your resources created:

   Apply complete! Resources: 16 added, 0 changed, 0 destroyed.
   
   Outputs:
   
   bucket_name = "llamaindex-rag-demo-tf"
   demo_app_image_repo_name = "llamaindex-rag-demo-tf"
   gke_cluster_location = "us-central1"
   gke_cluster_name = "llamaindex-rag-demo-tf"
   project_id = "akvelon-gke-aieco"
   

7. Connect the cluster:

   gcloud container clusters get-credentials $(terraform output -raw gke_cluster_name) --region $(terraform output -raw gke_cluster_location) --project $(terraform output -raw project_id)
   

Deploy the application to the cluster

1. Deploy Redis-stack.

For this guide it was decided to use redis-stack as a vector store, but there are many other options.

IMPORTANT: For the simplicity of this guide, Redis is deployed without persistent volumes, so the database is not persistent as well. Please consider proper persistence configuration for production.

1. Apply Redis-stack manifest:

   kubectl apply -f ../redis-stack.yaml
   

2. Wait for Redis-stack is successfully deployed

   kubectl rollout status deployment/redis-stack
   

2. Deploy Ollama server

Ollama is a tool that will run LLMs. It interacts with Llama-index through its ollama integration and will serve the desired model, the gemma2-9b in our case.

1. Deploy resulting Ollama manifest:

   kubectl apply -f ../gen/ollama-deployment.yaml
   

   Key notes on the Ollama deployment 'ollama-deployment.yaml' file:

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: ollama
   spec:
     ...
     template:
       metadata:
         ...
         annotations:
           gke-gcsfuse/volumes: 'true' # <- use FUSE to mount our bucket
       spec:
         serviceAccount: ... # <- our service account
         nodeSelector:
           cloud.google.com/gke-accelerator: nvidia-l4 # <- specify GPU type for LLM
         containers:
           ...
             volumeMounts: # <- mount bucket volume to be used by Ollama
               - name: ollama-data
                 mountPath: /root/.ollama/ # <- Ollama's path where it stores models
             resources:
               limits:
                 nvidia.com/gpu: 1 # <- Enable GPU
         volumes:
           - name: ollama-data # <- Volume with a bucket mounted with FUSE
             csi:
   

2. Wait for Ollama to be successfully deployed:

   kubectl rollout status deployment/ollama
   

3. Pull the gemma2:9b model within the Ollama server pod:

   kubectl exec $(kubectl get pod -l app=ollama -o name) -c ollama -- ollama pull gemma2:9b
   

3. Build the demo app image

1. Build the llamaindex-rag-demo container image using Cloud Build and push it to the repository that is created by terraform. It uses cloudbuild.yaml file which uses the app/Dockerfile for a build. This may take some time:

   gcloud builds submit ../app \
   --substitutions _IMAGE_REPO_NAME="$(terraform output -raw demo_app_image_repo_name)" \
   --config ../cloudbuild.yaml
   

   More information about the container image and demo application can be found in the app folder.

4. Ingest data to the vector database by running a Kubernetes job.

1. Upload sample data into our bucket, which is created by the Terraform. This data will then be ingested to our RAG’s vector store.

   curl -o imdb_top_1000.zip -L https://www.kaggle.com/api/v1/datasets/download/harshitshankhdhar/imdb-dataset-of-top-1000-movies-and-tv-shows | \
   unzip -p - imdb_top_1000 | \         
   gcloud storage cp - gs://$(terraform output -raw bucket_name)/datalake/imdb_top_1000.csv
   

2. Create ingestion job:

   The manifests are generated from templates in the templates directory and put in the gen directory.

   kubectl apply -f ../gen/ingest-data-job.yaml
   

   Key notes on `ingest-data-job.yaml`

   apiVersion: batch/v1
   kind: Job
   spec:
     template:
       metadata:
         annotations:
           gke-gcsfuse/volumes: 'true' # Enable GCS Fuse
       spec:
         serviceAccount: ... # Service account for GCP access
         containers:
           - name: ingest-data
             image: ${IMAGE_NAME} # Built application image
             command: ["python3", "cmd/ingest_data.py"] # Run ingestion script
             env:
               - name: INPUT_DIR
                 value: /datalake # Path to mounted dataset
             volumeMounts:
               - name: datalake
                 mountPath: /datalake
         volumes:
           - name: datalake
             csi:
               driver: gcsfuse.csi.storage.gke.io
               volumeAttributes:
                 bucketName: ... # GCS bucket
                 mountOptions: implicit-dirs,only-dir=datalake
   

   • Mounts the GCS bucket containing imdb_top_1000.csv via GCS Fuse.
   • Runs ingest_data.py to index the dataset into Redis.

3. Wait for data to be ingested. It may take few minutes:

   kubectl wait --for=condition=complete --timeout=600s job/llamaindex-ingest-data
   

4. Verify that data has been ingested:

   kubectl logs -f -l name=ingest-data
   

   Expected output:

   INFO:__main__:Loaded 1000 documents
   ...
   INFO:__main__:Embedding generated for movie_The_Shawshank_Redemption: 384 dimensions
   INFO:__main__:Nodes created for movie_The_Shawshank_Redemption: 1
   ...
   INFO:__main__:Ingested 1000 nodes
   

   Note: Completed job pods may be deleted by Kubernetes after some time, so check logs promptly.

5. Deploy RAG server

1. Apply created manifest:

   kubectl apply -f ../gen/rag-deployment.yaml
   

   Key notes on `rag-deployment.yaml`

   apiVersion: apps/v1
   kind: Deployment
   spec:
     template:
       spec:
         containers:
           - name: llamaindex-rag
             image: ${IMAGE_NAME} # Built application image
             env:
               - name: MODEL_NAME
                 value: gemma2:9b # LLM model
               - name: OLLAMA_SERVER_URL
                 value: http://ollama-service:11434 # Ollama service URL
   

   • Deploys the FastAPI application with access to the indexed Redis vector store and Ollama LLM.

2. Wait for deployment is completed:

   kubectl rollout status deployment/llamaindex-rag
   

Test the RAG

1. Forward the port to get access from a local machine:

   kubectl port-forward svc/llamaindex-rag-service 8000:8000
   

2. Open http://127.0.0.1:8000/docs in your browser to access the FastAPI Swagger UI, which provides an interactive interface for the /recommend endpoint.

3. Test movie recommendation queries:

   • Query: {"query": "best drama movies"}
     • Expected response:

       {
         "message": "Here are the top movie recommendations:",
         "recommendations": [
           {
             "title": "The Shawshank Redemption",
             "imdb_rating": 9.3,
             "overview": "Framed for the murder of his wife and her lover, Andy Dufresne begins a new life sentence at the notorious Shawshank prison. Over time, he befriends fellow inmate Red and uses his intelligence to navigate the brutal realities of prison life while secretly plotting a daring escape.",
             "genre": "Drama",
             "released_year": "1994",
             "director": "Frank Darabont",
             "stars": "Tim Robbins, Morgan Freeman"
           },
           {
             "title": "Schindler's List",
             "imdb_rating": 8.9,
             "overview": "During the Holocaust in World War II, Oskar Schindler, a German businessman, saves the lives of over a thousand Jewish refugees by employing them in his factory. The film is a powerful and moving testament to the human capacity for both good and evil.",
             "genre": "Drama, History",
             "released_year": "1993",
             "director": "Steven Spielberg",
             "stars": "Liam Neeson, Ralph Fiennes"
           },
           {
             "title": "The Godfather",
             "imdb_rating": 9.2,
             "overview": "Don Vito Corleone, the aging patriarch of a powerful Mafia family, faces threats from rival gangsters and tries to protect his empire. When his youngest son Michael enters the family business, he is thrust into a world of violence and betrayal.",
             "genre": "Crime, Drama",
             "released_year": "1972",
             "director": "Francis Ford Coppola",
             "stars": "Marlon Brando, Al Pacino"
           }
         ]
       }
       

   • Query: {"query": "movies like inception"}
     • Expected response:

       {
         "message": "Here are the top movie recommendations:",
         "recommendations": [
           {
             "title": "Interstellar",
             "imdb_rating": 8.6,
             "overview": "A team of explorers travel through a wormhole in space in an attempt to find a new home for humanity.",
             "genre": "Science Fiction, Adventure",
             "released_year": "2014",
             "director": "Christopher Nolan",
             "stars": "Matthew McConaughey, Anne Hathaway, Jessica Chastain"
           },
           {
             "title": "Predestination",
             "imdb_rating": 7.9,
             "overview": "A temporal agent travels through time to prevent a devastating attack, but his mission leads him down a mind-bending path of paradox and self-discovery.",
             "genre": "Science Fiction, Thriller",
             "released_year": "2014",
             "director": "Michael & Peter Spierig",
             "stars": "Ethan Hawke, Sarah Snook"
           },
           {
             "title": "The Matrix",
             "imdb_rating": 8.7,
             "overview": "A computer hacker learns that reality is a simulation controlled by machines and joins a rebellion to free humanity.",
             "genre": "Science Fiction, Action",
             "released_year": "1999",
             "director": "The Wachowskis",
             "stars": "Keanu Reeves, Laurence Fishburne, Carrie-Anne Moss"
           }
         ]
       }
       

Cleanup

terraform destroy -var-file=default_env.tfvars

Troubleshooting

There may be a temporary error in the pods, where we mount buckets by using FUSE. Normally, they should be resolved without any additional actions.

MountVolume.SetUp failed for volume "datalake" : kubernetes.io/csi: mounter.SetUpAt failed to get CSI client: driver name gcsfuse.csi.storage.gke.io not found in the list of registered CSI drivers