Add feature : add chromadb support as a vector database

flask4modelcache.py

@@ -41,10 +41,16 @@ def response_hitquery(cache_resp):
 # redis_config = configparser.ConfigParser()
 # redis_config.read('modelcache/config/redis_config.ini')
 
+# chromadb_config = configparser.ConfigParser()
+# chromadb_config.read('modelcache/config/chromadb_config.ini')
 
 data_manager = get_data_manager(CacheBase("mysql", config=mysql_config),
                                 VectorBase("milvus", dimension=data2vec.dimension, milvus_config=milvus_config))
 
+
+# data_manager = get_data_manager(CacheBase("mysql", config=mysql_config),
+#                                 VectorBase("chromadb", dimension=data2vec.dimension, chromadb_config=chromadb_config))
+
 # data_manager = get_data_manager(CacheBase("mysql", config=mysql_config),
 #                                 VectorBase("redis", dimension=data2vec.dimension, redis_config=redis_config))
 

modelcache/config/chromadb_config.ini

@@ -0,0 +1,2 @@
+[chromadb]
+persist_directory=''

modelcache/manager/vector_data/chroma.py

@@ -0,0 +1,92 @@
+from typing import List
+
+import numpy as np
+import logging
+from modelcache.manager.vector_data.base import VectorBase, VectorData
+from modelcache.utils import import_chromadb, import_torch
+
+import_torch()
+import_chromadb()
+
+import chromadb
+
+
+class Chromadb(VectorBase):
+
+    def __init__(
+            self,
+            persist_directory="./chromadb",
+            top_k: int = 1,
+    ):
+        self.collection_name = "modelcache"
+        self.top_k = top_k
+
+        self._client = chromadb.PersistentClient(path=persist_directory)
+        self._collection = None
+
+    def mul_add(self, datas: List[VectorData], model=None):
+        collection_name_model = self.collection_name + '_' + model
+        self._collection = self._client.get_or_create_collection(name=collection_name_model)
+
+        data_array, id_array = map(list, zip(*((data.data.tolist(), str(data.id)) for data in datas)))
+        self._collection.add(embeddings=data_array, ids=id_array)
+
+    def search(self, data: np.ndarray, top_k: int = -1, model=None):
+        collection_name_model = self.collection_name + '_' + model
+        self._collection = self._client.get_or_create_collection(name=collection_name_model)
+
+        if self._collection.count() == 0:
+            return []
+        if top_k == -1:
+            top_k = self.top_k
+        results = self._collection.query(
+            query_embeddings=[data.tolist()],
+            n_results=top_k,
+            include=["distances"],
+        )
+        return list(zip(results["distances"][0], [int(x) for x in results["ids"][0]]))
+
+    def rebuild(self, ids=None):
+        pass
+
+    def delete(self, ids, model=None):
+        try:
+            collection_name_model = self.collection_name + '_' + model
+            self._collection = self._client.get_or_create_collection(name=collection_name_model)
+            # 查询集合中实际存在的 ID
+            ids_str = [str(x) for x in ids]
+            existing_ids = set(self._collection.get(ids=ids_str).ids)
+
+            # 删除存在的 ID
+            if existing_ids:
+                self._collection.delete(list(existing_ids))
+
+            # 返回实际删除的条目数量
+            return len(existing_ids)
+
+        except Exception as e:
+            logging.error('Error during deletion: {}'.format(e))
+            raise ValueError(str(e))
+
+    def rebuild_col(self, model):
+        collection_name_model = self.collection_name + '_' + model
+
+        # 检查集合是否存在，如果存在则删除
+        collections = self._client.list_collections()
+        if any(col.name == collection_name_model for col in collections):
+            self._client.delete_collection(collection_name_model)
+        else:
+            return 'model collection not found, please check!'
+
+        try:
+            self._client.create_collection(collection_name_model)
+        except Exception as e:
+            logging.info(f'rebuild_collection: {e}')
+            raise ValueError(str(e))
+
+    def flush(self):
+        # chroma无flush方法
+        pass
+
+    def close(self):
+        pass

modelcache/manager/vector_data/manager.py

@@ -102,13 +102,11 @@ def get(name, **kwargs):
         elif name == "chromadb":
             from modelcache.manager.vector_data.chroma import Chromadb
 
-            client_settings = kwargs.get("client_settings", None)
-            persist_directory = kwargs.get("persist_directory", None)
-            collection_name = kwargs.get("collection_name", COLLECTION_NAME)
+            chromadb_config = kwargs.get("chromadb_config", None)
+            persist_directory = chromadb_config.get('chromadb','persist_directory')
+
             vector_base = Chromadb(
-                client_settings=client_settings,
                 persist_directory=persist_directory,
-                collection_name=collection_name,
                 top_k=top_k,
             )
         elif name == "hnswlib":

modelcache/utils/__init__.py

@@ -73,3 +73,7 @@ def import_pillow():
 
 def import_redis():
     _check_library("redis")
+
+
+def import_chromadb():
+    _check_library("chromadb", package="chromadb")

modelcache_mm/config/chromadb_config.ini

@@ -0,0 +1,2 @@
+[chromadb]
+persist_directory=./chromadb

modelcache_mm/manager/vector_data/chroma.py

@@ -0,0 +1,99 @@
+from typing import List
+
+import numpy as np
+import logging
+from modelcache_mm.manager.vector_data.base import VectorBase, VectorData
+from modelcache_mm.utils import import_chromadb, import_torch
+from modelcache_mm.utils.index_util import get_mm_index_name
+
+import_torch()
+import_chromadb()
+
+import chromadb
+
+
+class Chromadb(VectorBase):
+
+    def __init__(
+            self,
+            persist_directory="./chromadb",
+            top_k: int = 1,
+    ):
+        self.top_k = top_k
+
+        self._client = chromadb.PersistentClient(path=persist_directory)
+        self._collection = None
+
+    def create(self, model=None, mm_type=None):
+        try:
+            collection_name_model = get_mm_index_name(model, mm_type)
+            # collection_name_model = self.collection_name + '_' + model
+            self._client.get_or_create_collection(name=collection_name_model)
+        except Exception as e:
+            raise ValueError(str(e))
+
+    def add(self, datas: List[VectorData], model=None, mm_type=None):
+        collection_name_model = get_mm_index_name(model, mm_type)
+        self._collection = self._client.get_or_create_collection(name=collection_name_model)
+
+        data_array, id_array = map(list, zip(*((data.data.tolist(), str(data.id)) for data in datas)))
+        self._collection.add(embeddings=data_array, ids=id_array)
+
+    def search(self, data: np.ndarray, top_k: int = -1, model=None, mm_type='mm'):
+        collection_name_model = get_mm_index_name(model, mm_type)
+        self._collection = self._client.get_or_create_collection(name=collection_name_model)
+
+        if self._collection.count() == 0:
+            return []
+        if top_k == -1:
+            top_k = self.top_k
+        results = self._collection.query(
+            query_embeddings=[data.tolist()],
+            n_results=top_k,
+            include=["distances"],
+        )
+        return list(zip(results["distances"][0], [int(x) for x in results["ids"][0]]))
+
+    def delete(self, ids, model=None, mm_type=None):
+        try:
+            collection_name_model = get_mm_index_name(model, mm_type)
+            self._collection = self._client.get_or_create_collection(name=collection_name_model)
+            # 查询集合中实际存在的 ID
+            ids_str = [str(x) for x in ids]
+            existing_ids = set(self._collection.get(ids=ids_str).ids)
+
+            # 删除存在的 ID
+            if existing_ids:
+                self._collection.delete(list(existing_ids))
+
+            # 返回实际删除的条目数量
+            return len(existing_ids)
+
+        except Exception as e:
+            logging.error('Error during deletion: {}'.format(e))
+            raise ValueError(str(e))
+
+    def rebuild_idx(self, model, mm_type=None):
+        collection_name_model = get_mm_index_name(model, mm_type)
+
+        # 检查集合是否存在，如果存在则删除
+        collections = self._client.list_collections()
+        if any(col.name == collection_name_model for col in collections):
+            self._client.delete_collection(collection_name_model)
+        else:
+            return 'model collection not found, please check!'
+
+        try:
+            self._client.create_collection(collection_name_model)
+        except Exception as e:
+            logging.info(f'rebuild_collection: {e}')
+            raise ValueError(str(e))
+
+    def rebuild(self, ids=None):
+        pass
+
+    def flush(self):
+        pass
+
+    def close(self):
+        pass

modelcache_mm/manager/vector_data/manager.py

@@ -108,6 +108,15 @@ def get(name, **kwargs):
                 dimension=dimension,
                 top_k=top_k
             )
+        elif name == "chromadb":
+            from modelcache_mm.manager.vector_data.chroma import Chromadb
+
+            chromadb_config = kwargs.get("chromadb_config", None)
+            persist_directory = chromadb_config.get('chromadb', 'persist_directory')
+            vector_base = Chromadb(
+                persist_directory=persist_directory,
+                top_k=top_k,
+            )
         else:
             raise NotFoundError("vector store", name)
         return vector_base

modelcache_mm/utils/__init__.py

@@ -73,3 +73,7 @@ def import_pillow():
 
 def import_redis():
     _check_library("redis")
+
+
+def import_chromadb():
+    _check_library("chromadb", package="chromadb")

requirements.txt

@@ -13,4 +13,5 @@ faiss-cpu==1.7.4
 redis==5.0.1
 modelscope==1.14.0
 fastapi==0.115.5
-uvicorn==0.32.0
+uvicorn==0.32.0
+chromadb==0.5.23