From 80506ec3cebafb815b3056b0cfeea49ab3dbec7c Mon Sep 17 00:00:00 2001
From: root <root@DESKTOP-DFC1405>
Date: Mon, 14 Aug 2023 21:59:48 +0800
Subject: [PATCH] add param annotations for qdrant

---
 metagpt/document_store/qdrant_store.py | 40 ++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)

diff --git a/metagpt/document_store/qdrant_store.py b/metagpt/document_store/qdrant_store.py
index 07791565e..9968794ef 100644
--- a/metagpt/document_store/qdrant_store.py
+++ b/metagpt/document_store/qdrant_store.py
@@ -10,6 +10,14 @@ from metagpt.document_store.base_store import BaseStore
 
 @dataclass
 class QdrantConnection:
+    """
+   Args:
+       url: qdrant url
+       host: qdrant host
+       port: qdrant port
+       memory: qdrant service use memory mode
+       api_key: qdrant cloud api_key
+   """
     url: str = None
     host: str = None
     port: int = None
@@ -37,6 +45,17 @@ class QdrantStore(BaseStore):
         force_recreate=False,
         **kwargs,
     ):
+        """
+        create a collection
+        Args:
+            collection_name: collection name
+            vectors_config: VectorParams object,detail in https://github.com/qdrant/qdrant-client
+            force_recreate: default is False, if True, will delete exists collection,then create it
+            **kwargs:
+
+        Returns:
+
+        """
         try:
             self.client.get_collection(collection_name)
             if force_recreate:
@@ -63,6 +82,15 @@ class QdrantStore(BaseStore):
             raise Exception(f"Delete collection {collection_name} failed.")
 
     def add(self, collection_name: str, points: List[PointStruct]):
+        """
+        add some vector data to qdrant
+        Args:
+            collection_name: collection name
+            points: list of PointStruct object, about PointStruct detail in https://github.com/qdrant/qdrant-client
+
+        Returns: None
+
+        """
         # self.client.upload_records()
         self.client.upsert(
             collection_name,
@@ -77,6 +105,18 @@ class QdrantStore(BaseStore):
         k=10,
         return_vector=False,
     ):
+        """
+        vector search
+        Args:
+            collection_name: qdrant collection name
+            query: input vector
+            query_filter: Filter object, detail in https://github.com/qdrant/qdrant-client
+            k: return the most similar k pieces of data
+            return_vector: whether return vector
+
+        Returns: list of dict
+
+        """
         hits = self.client.search(
             collection_name=collection_name,
             query_vector=query,