fix: OSS Python SDK hygiene batch, 9 small bug fixes (#6770)

mem0/embeddings/fastembed.py

@@ -1,7 +1,7 @@
-from typing import Optional, Literal
+from typing import Literal, Optional
 
-from mem0.embeddings.base import EmbeddingBase
 from mem0.configs.embeddings.base import BaseEmbedderConfig
+from mem0.embeddings.base import EmbeddingBase
 
 try:
     from fastembed import TextEmbedding
@@ -29,4 +29,4 @@ class FastEmbedEmbedding(EmbeddingBase):
         """
         text = text.replace("\n", " ")
         embeddings = list(self.dense_model.embed(text))
-        return embeddings[0]
+        return embeddings[0].tolist()

mem0/embeddings/huggingface.py

@@ -17,7 +17,7 @@ class HuggingFaceEmbedding(EmbeddingBase):
         super().__init__(config)
 
         if self.config.huggingface_base_url:
-            self.client = OpenAI(base_url=self.config.huggingface_base_url)
+            self.client = OpenAI(base_url=self.config.huggingface_base_url, api_key=self.config.api_key)
             self.config.model = self.config.model or "tei"
         else:
             self.config.model = self.config.model or "multi-qa-MiniLM-L6-cos-v1"

mem0/embeddings/ollama.py

@@ -1,5 +1,3 @@
-import subprocess
-import sys
 from typing import Literal, Optional
 
 from mem0.configs.embeddings.base import BaseEmbedderConfig
@@ -8,17 +6,7 @@ from mem0.embeddings.base import EmbeddingBase
 try:
     from ollama import Client
 except ImportError:
-    user_input = input("The 'ollama' library is required. Install it now? [y/N]: ")
-    if user_input.lower() == "y":
-        try:
-            subprocess.check_call([sys.executable, "-m", "pip", "install", "ollama"])
-            from ollama import Client
-        except subprocess.CalledProcessError:
-            print("Failed to install 'ollama'. Please install it manually using 'pip install ollama'.")
-            sys.exit(1)
-    else:
-        print("The required 'ollama' library is not installed.")
-        sys.exit(1)
+    raise ImportError("The 'ollama' library is required. Please install it using 'pip install ollama'.")
 
 
 class OllamaEmbedding(EmbeddingBase):

mem0/memory/utils.py

@@ -121,6 +121,8 @@ def remove_code_blocks(content: str) -> str:
     - If a code block is detected, it returns only the inner content, stripping out the markers.
     - If no code block markers are found, the original content is returned as-is.
     """
+    if content is None:
+        return ""
     pattern = r"^```[a-zA-Z0-9]*\n([\s\S]*?)\n```$"
     match = re.match(pattern, content.strip())
     match_res=match.group(1).strip() if match else content.strip()
@@ -213,8 +215,8 @@ def parse_vision_messages(messages, llm=None, vision_details="auto"):
             try:
                 description = get_image_description(image_url, llm, vision_details)
                 returned_messages.append({"role": role, "content": description})
-            except Exception:
-                raise Exception(f"Error while downloading {image_url}.")
+            except Exception as e:
+                raise Exception(f"Error while downloading {image_url}.") from e
         else:
             # Regular text content
             returned_messages.append(msg)
@@ -227,7 +229,7 @@ def process_telemetry_filters(filters):
     Process the telemetry filters
     """
     if filters is None:
-        return {}
+        return [], {}
 
     encoded_ids = {}
     if "user_id" in filters:

mem0/reranker/cohere_reranker.py

@@ -1,6 +1,6 @@
 import logging
 import os
-from typing import List, Dict, Any
+from typing import Any, Dict, List
 
 from mem0.reranker.base import BaseReranker
 
@@ -84,7 +84,10 @@ class CohereReranker(BaseReranker):
         except Exception as e:
             # Fallback to original order if reranking fails
             logger.warning("Cohere reranking failed, falling back to original order: %s", e)
+            fallback_docs = []
             for doc in documents:
-                doc['rerank_score'] = 0.0
+                fallback_doc = doc.copy()
+                fallback_doc['rerank_score'] = 0.0
+                fallback_docs.append(fallback_doc)
             final_top_k = top_k or self.config.top_k
-            return documents[:final_top_k] if final_top_k else documents
+            return fallback_docs[:final_top_k] if final_top_k else fallback_docs

mem0/reranker/huggingface_reranker.py

@@ -1,14 +1,15 @@
 import logging
-from typing import List, Dict, Any, Union
+from typing import Any, Dict, List, Union
+
 import numpy as np
 
-from mem0.reranker.base import BaseReranker
 from mem0.configs.rerankers.base import BaseRerankerConfig
 from mem0.configs.rerankers.huggingface import HuggingFaceRerankerConfig
+from mem0.reranker.base import BaseReranker
 
 try:
-    from transformers import AutoTokenizer, AutoModelForSequenceClassification
     import torch
+    from transformers import AutoModelForSequenceClassification, AutoTokenizer
     TRANSFORMERS_AVAILABLE = True
 except ImportError:
     TRANSFORMERS_AVAILABLE = False
@@ -163,7 +164,10 @@ class HuggingFaceReranker(BaseReranker):
         except Exception as e:
             # Fallback to original order if reranking fails
             logger.warning("HuggingFace reranking failed, falling back to original order: %s", e)
+            fallback_docs = []
             for doc in documents:
-                doc['rerank_score'] = 0.0
+                fallback_doc = doc.copy()
+                fallback_doc['rerank_score'] = 0.0
+                fallback_docs.append(fallback_doc)
             final_top_k = top_k or self.config.top_k
-            return documents[:final_top_k] if final_top_k else documents
+            return fallback_docs[:final_top_k] if final_top_k else fallback_docs

mem0/reranker/sentence_transformer_reranker.py

@@ -1,10 +1,13 @@
 import logging
-from typing import List, Dict, Any, Union
+from typing import Any, Dict, List, Union
+
 import numpy as np
 
-from mem0.reranker.base import BaseReranker
 from mem0.configs.rerankers.base import BaseRerankerConfig
-from mem0.configs.rerankers.sentence_transformer import SentenceTransformerRerankerConfig
+from mem0.configs.rerankers.sentence_transformer import (
+    SentenceTransformerRerankerConfig,
+)
+from mem0.reranker.base import BaseReranker
 
 try:
     from sentence_transformers import CrossEncoder
@@ -108,7 +111,10 @@ class SentenceTransformerReranker(BaseReranker):
         except Exception as e:
             # Fallback to original order if reranking fails
             logger.warning("SentenceTransformer reranking failed, falling back to original order: %s", e)
+            fallback_docs = []
             for doc in documents:
-                doc['rerank_score'] = 0.0
+                fallback_doc = doc.copy()
+                fallback_doc['rerank_score'] = 0.0
+                fallback_docs.append(fallback_doc)
             final_top_k = top_k or self.config.top_k
-            return documents[:final_top_k] if final_top_k else documents
+            return fallback_docs[:final_top_k] if final_top_k else fallback_docs

mem0/reranker/zero_entropy_reranker.py

@@ -1,6 +1,6 @@
 import logging
 import os
-from typing import List, Dict, Any
+from typing import Any, Dict, List
 
 from mem0.reranker.base import BaseReranker
 
@@ -95,7 +95,10 @@ class ZeroEntropyReranker(BaseReranker):
         except Exception as e:
             # Fallback to original order if reranking fails
             logger.warning("Zero Entropy reranking failed, falling back to original order: %s", e)
+            fallback_docs = []
             for doc in documents:
-                doc['rerank_score'] = 0.0
+                fallback_doc = doc.copy()
+                fallback_doc['rerank_score'] = 0.0
+                fallback_docs.append(fallback_doc)
             final_top_k = top_k or self.config.top_k
-            return documents[:final_top_k] if final_top_k else documents
+            return fallback_docs[:final_top_k] if final_top_k else fallback_docs

mem0/utils/factory.py

@@ -19,7 +19,9 @@ from mem0.configs.rerankers.base import BaseRerankerConfig
 from mem0.configs.rerankers.cohere import CohereRerankerConfig
 from mem0.configs.rerankers.huggingface import HuggingFaceRerankerConfig
 from mem0.configs.rerankers.llm import LLMRerankerConfig
-from mem0.configs.rerankers.sentence_transformer import SentenceTransformerRerankerConfig
+from mem0.configs.rerankers.sentence_transformer import (
+    SentenceTransformerRerankerConfig,
+)
 from mem0.configs.rerankers.zero_entropy import ZeroEntropyRerankerConfig
 from mem0.embeddings.mock import MockEmbeddings
 
@@ -86,7 +88,7 @@ class LlmFactory:
             config = config_class(**kwargs)
         elif isinstance(config, dict):
             # Merge dict config with kwargs
-            config.update(kwargs)
+            config = {**config, **kwargs}
             config = config_class(**config)
         elif isinstance(config, BaseLlmConfig):
             # Convert base config to provider-specific config if needed

mem0/vector_stores/mongodb.py

@@ -15,7 +15,6 @@ except ImportError:
 from mem0.vector_stores.base import VectorStoreBase
 
 logger = logging.getLogger(__name__)
-logging.basicConfig(level=logging.INFO)
 
 _DRIVER_METADATA = DriverInfo(name="Mem0", version=version("mem0ai"))
 

mem0/vector_stores/vertex_ai_vector_search.py

@@ -22,7 +22,6 @@ from mem0.configs.vector_stores.vertex_ai_vector_search import (
 from mem0.vector_stores.base import VectorStoreBase
 
 # Configure logging
-logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger(__name__)