Create create_crop_db.py

crop_db/create_crop_db.py

@@ -0,0 +1,42 @@
+import chromadb
+from datasets import load_dataset
+from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction
+
+# Initialize ChromaDB
+client = chromadb.PersistentClient(path="crop_db")
+embedder = SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
+
+# Create collection with metadata
+collection = client.get_or_create_collection(
+    name="crop_data",
+    embedding_function=embedder,
+    metadata={"hnsw:space": "cosine"}
+)
+
+# Load crop optimization dataset
+dataset = load_dataset("DARJYO/sawotiQ29_crop_optimization")
+
+# Prepare documents and metadata
+documents = []
+metadatas = []
+ids = []
+
+for idx, item in enumerate(dataset['train']):
+    doc = f"{item['crop_name']} - {item['region']}. Optimal conditions: {item['optimal_temperature']}°C, {item['annual_rainfall']}mm rainfall. Soil: {item['preferred_soil']}. Yield: {item['average_yield']}"
+    
+    documents.append(doc)
+    metadatas.append({
+        "type": "crop",
+        "region": item['region'],
+        "season": item['best_season']
+    })
+    ids.append(str(idx))
+
+# Add to collection
+collection.add(
+    documents=documents,
+    metadatas=metadatas,
+    ids=ids
+)
+
+print(f"Created crop database with {len(dataset['train'])} entries")