[CHORES]

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "2.6.2"
+version = "2.6.4"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -95,7 +95,7 @@ preview = true
 
 
 [tool.poetry.scripts]
-swarms = "swarms.cli._cli:run_file"
+swarms = "swarms.cli.run_file:run_file"
 
 
 

swarms/cli/_cli.py

@@ -59,7 +59,7 @@ def run_file():
     parser.add_argument(
         "-z", "--logs", help="Get a deployment's logs"
     )
-    
+
     # Parse the arguments
     args = parser.parse_args()
 
@@ -73,4 +73,3 @@ def run_file():
     except Exception as e:
         print(f"Error executing file '{args.file_name}': {e}")
         sys.exit(1)
-

swarms/cli/run_file.py

@@ -1,11 +1,12 @@
 import sys
 import subprocess
 
+
 def run_file():
     """Run a given file.
-    
+
     Usage: swarms run file_name.py
-    
+
     """
     if len(sys.argv) != 3 or sys.argv[1] != "run":
         print("Usage: swarms run file_name.py")

swarms/memory/chroma.py

@@ -1,106 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any, Dict, List
-from chromadb.utils import embedding_functions
-from httpx import RequestError
-import chromadb
-
-from swarms.memory.base_vector_db import VectorDatabase
-
-
-class ChromaClient(VectorDatabase):
-    def __init__(
-        self,
-        collection_name: str = "chromadb-collection",
-        model_name: str = "BAAI/bge-small-en-v1.5",
-    ):
-        try:
-            self.client = chromadb.Client()
-            self.collection_name = collection_name
-            self.model = None
-            self.collection = None
-            self._load_embedding_model(model_name)
-            self._setup_collection()
-        except RequestError as e:
-            print(f"Error setting up QdrantClient: {e}")
-
-    def _load_embedding_model(self, model_name: str):
-        """
-        Loads the sentence embedding model specified by the model name.
-
-        Args:
-            model_name (str): The name of the model to load for generating embeddings.
-        """
-        try:
-            self.model =embedding_functions.SentenceTransformerEmbeddingFunction(model_name=model_name)
-        except Exception as e:
-            print(f"Error loading embedding model: {e}")
-
-    def _setup_collection(self):
-        try:
-            self.collection = self.client.get_collection(name=self.collection_name, embedding_function=self.model)
-        except Exception as e:
-            print(f"{e}. Creating new collection: {self.collection}")
-
-        self.collection = self.client.create_collection(name=self.collection_name, embedding_function=self.model)
-
-
-    def add_vectors(self, docs: List[str]):
-        """
-        Adds vector representations of documents to the Qdrant collection.
-
-        Args:
-            docs (List[dict]): A list of documents where each document is a dictionary with at least a 'page_content' key.
-
-        Returns:
-            OperationResponse or None: Returns the operation information if successful, otherwise None.
-        """
-        points = []
-        ids = []
-        for i, doc in enumerate(docs):
-            try:
-                points.append(doc)
-                ids.append("id"+str(i))
-            except Exception as e:
-                print(f"Error processing document at index {i}: {e}")
-
-        try:
-            self.collection.add(
-                documents=points,
-                ids=ids
-            )
-        except Exception as e:
-            print(f"Error adding vectors: {e}")
-            return None
-
-    def search_vectors(self, query: str, limit: int = 2):
-        """
-        Searches the collection for vectors similar to the query vector.
-
-        Args:
-            query (str): The query string to be converted into a vector and used for searching.
-            limit (int): The number of search results to return. Defaults to 3.
-
-        Returns:
-            SearchResult or None: Returns the search results if successful, otherwise None.
-        """
-        try:
-            search_result = self.collection.query(
-                                    query_texts=query,
-                                    n_results=limit,
-                                )
-            return search_result
-        except Exception as e:
-            print(f"Error searching vectors: {e}")
-            return None
-
-    def add(self, vector: Dict[str, Any], metadata: Dict[str, Any]) -> None:
-        pass
-
-    def query(self, vector: Dict[str, Any], num_results: int) -> Dict[str, Any]:
-        pass
-
-    def delete(self, vector_id: str) -> None:
-        pass
-
-    def update(self, vector_id: str, vector: Dict[str, Any], metadata: Dict[str, Any]) -> None:
-        pass