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.

rag/vector_store.py

@@ -0,0 +1,117 @@
+"""
+Qdrant Vector Store — ذخیره و جستجوی وکتورها
+"""
+from qdrant_client import QdrantClient
+from qdrant_client.http import models as qmodels
+
+from .client import get_qdrant_client
+from .config import load_rag_config, RAGConfig
+
+
+class QdrantVectorStore:
+    """
+    ذخیره و جستجوی documents در Qdrant.
+    """
+
+    def __init__(self, config: RAGConfig | None = None):
+        self.config = config or load_rag_config()
+        self.qdrant = self.config.qdrant
+        self._client: QdrantClient | None = None
+
+    @property
+    def client(self) -> QdrantClient:
+        if self._client is None:
+            self._client = get_qdrant_client(self.qdrant)
+        return self._client
+
+    def ensure_collection(self, recreate: bool = False) -> None:
+        """
+        اطمینان از وجود collection با نام و اندازه مناسب.
+        """
+        name = self.qdrant.collection_name
+        size = self.qdrant.vector_size
+
+        try:
+            self.client.get_collection(name)
+            if recreate:
+                self.client.delete_collection(name)
+                self.client.create_collection(
+                    collection_name=name,
+                    vectors_config=qmodels.VectorParams(
+                        size=size,
+                        distance=qmodels.Distance.COSINE,
+                    ),
+                )
+        except Exception:
+            self.client.create_collection(
+                collection_name=name,
+                vectors_config=qmodels.VectorParams(
+                    size=size,
+                    distance=qmodels.Distance.COSINE,
+                ),
+            )
+
+    def add_documents(
+        self,
+        ids: list[str],
+        embeddings: list[list[float]],
+        documents: list[str],
+        metadatas: list[dict] | None = None,
+    ) -> int:
+        """
+        افزودن documents به collection.
+        metadata فقط str, int, float, bool پشتیبانی می‌شود.
+        """
+        self.ensure_collection()
+        metas = metadatas or [{}] * len(ids)
+
+        def _serialize(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
+
+        payloads = [
+            {"text": doc, "doc_id": sid, **_serialize(m)}
+            for doc, m, sid in zip(documents, metas, ids)
+        ]
+
+        self.client.upsert(
+            collection_name=self.qdrant.collection_name,
+            points=[
+                qmodels.PointStruct(id=pid, vector=emb, payload=pl)
+                for pid, emb, pl in zip(ids, embeddings, payloads)
+            ],
+        )
+        return len(ids)
+
+    def search(
+        self,
+        query_vector: list[float],
+        limit: int = 5,
+        score_threshold: float | None = None,
+    ) -> list[dict]:
+        """
+        جستجوی شباهت بر اساس query vector.
+        """
+        results = self.client.search(
+            collection_name=self.qdrant.collection_name,
+            query_vector=query_vector,
+            limit=limit,
+            score_threshold=score_threshold,
+        )
+
+        return [
+            {
+                "id": str(r.id),
+                "score": r.score,
+                "text": r.payload.get("text", ""),
+                "metadata": {k: v for k, v in r.payload.items() if k != "text"},
+            }
+            for r in results
+        ]