wrangler.toml

# MindSpring — Cloudflare Workers Configuration
#
# Setup steps:
#   1. Create KV namespace:
#        wrangler kv namespace create MINDSPRING_KV
#        wrangler kv namespace create MINDSPRING_KV --preview
#
#   2. Create R2 bucket:
#        wrangler r2 bucket create mindspring-uploads
#
#   3. Create Vectorize index:
#        wrangler vectorize create mindspring-conversations --dimensions=1024 --metric=cosine
#
#   4. Create Queues:
#        wrangler queues create mindspring-ingestion
#        wrangler queues create mindspring-ingestion-dlq
#
#   5. Deploy:
#        wrangler deploy

name = "mindspring"
main = "src/index.ts"
compatibility_date = "2025-03-01"
compatibility_flags = ["nodejs_compat"]

# --- Static frontend assets ---
[assets]
directory = "./frontend"
binding = "ASSETS"

# --- R2: raw conversation JSON uploads ---
[[r2_buckets]]
binding = "UPLOADS_BUCKET"
bucket_name = "mindspring-uploads"

# --- Vectorize: vector search index ---
[[vectorize]]
binding = "VECTORIZE"
index_name = "mindspring-conversations"

# --- Queue: async ingestion pipeline ---
[[queues.producers]]
binding = "INGESTION_QUEUE"
queue = "mindspring-ingestion"

[[queues.consumers]]
queue = "mindspring-ingestion"
max_batch_size = 1
max_retries = 3
dead_letter_queue = "mindspring-ingestion-dlq"

# --- KV: auth keys, upload progress, conversation text, processed IDs, telemetry ---
[[kv_namespaces]]
binding = "KV"
id = "YOUR_KV_NAMESPACE_ID"        # Create with: npx wrangler kv namespace create KV
preview_id = "YOUR_KV_PREVIEW_ID"  # Create with: npx wrangler kv namespace create KV --preview

# --- Workers AI: embedding generation ---
[ai]
binding = "AI"

# --- Environment variables ---
[vars]
EMBEDDING_MODEL = "@cf/baai/bge-large-en-v1.5"
EMBEDDING_DIMENSION = "1024"
BATCH_SIZE = "100"