Add Qdrant and ChromaDB support to the project

- Added Qdrant service to both docker-compose files for production and development.
- Updated environment variables in .env.example and settings.py to include Qdrant configuration.
- Included necessary dependencies for Qdrant and ChromaDB in requirements.txt.
- Updated .gitignore to exclude ChromaDB data files.

knowledge_base/indexer.py

@@ -0,0 +1,90 @@
+"""
+منطق اصلی indexing: embed کردن chunks و ذخیره در ChromaDB.
+"""
+from pathlib import Path
+
+from .chunks import build_all_chunks
+from .rag_settings import RAGConfig
+from .embeddings import get_embedder
+
+
+COLLECTION_NAME = "croplogic_kb"
+
+
+def build_index(config: RAGConfig) -> int:
+    """
+    ساخت/بازسازی کامل index پایگاه دانش.
+    chunks را از soil_data، sensor_data و فایل لحن تولید، embed و در ChromaDB ذخیره می‌کند.
+
+    Returns:
+        تعداد documentهای اضافه شده.
+    """
+    tone_path = Path(config.tone_file)
+
+    chunks = build_all_chunks(
+        tone_path=tone_path,
+        max_chunk_tokens=config.chunking.max_chunk_tokens,
+        overlap_tokens=config.chunking.overlap_tokens,
+    )
+
+    if not chunks:
+        return 0
+
+    texts = [t for t, _ in chunks]
+    metadatas = [m for _, m in chunks]
+
+    # تبدیل metadata به فرمت ChromaDB (فقط str, int, float)
+    def _serialize_meta(m: dict) -> dict:
+        out = {}
+        for k, v in m.items():
+            if v is None:
+                continue
+            if isinstance(v, (str, int, float, bool)):
+                out[k] = v
+            else:
+                out[k] = str(v)
+        return out
+
+    metadatas = [_serialize_meta(m) for m in metadatas]
+
+    embedder = get_embedder(config)
+    batch_size = config.embedding.batch_size
+
+    all_embeddings = []
+    for i in range(0, len(texts), batch_size):
+        batch = texts[i : i + batch_size]
+        embs = embedder.encode(batch, batch_size=batch_size)
+        all_embeddings.extend(embs)
+
+    # ChromaDB
+    persist_dir = Path(config.chromadb.persist_directory)
+    persist_dir.mkdir(parents=True, exist_ok=True)
+
+    import chromadb
+    from chromadb.config import Settings as ChromaSettings
+
+    client = chromadb.PersistentClient(
+        path=str(persist_dir),
+        settings=ChromaSettings(anonymized_telemetry=False),
+    )
+
+    collection_name = config.chromadb.collection_name or COLLECTION_NAME
+    try:
+        client.delete_collection(collection_name)
+    except Exception:
+        pass
+
+    collection = client.create_collection(
+        name=collection_name,
+        metadata={"hnsw:space": "cosine"},
+    )
+
+    ids = [f"doc_{i}" for i in range(len(texts))]
+    collection.add(
+        ids=ids,
+        embeddings=all_embeddings,
+        documents=texts,
+        metadatas=metadatas,
+    )
+
+    return len(texts)