fix(python): huggingface TEI auth, procedural-memory content handling, and proxy pip auto-install (#6947)
This commit is contained in:
Kartik
@@ -1,4 +1,5 @@
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import logging
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import os
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from typing import Literal, Optional
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from openai import OpenAI
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@@ -17,7 +18,8 @@ class HuggingFaceEmbedding(EmbeddingBase):
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super().__init__(config)
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if self.config.huggingface_base_url:
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self.client = OpenAI(base_url=self.config.huggingface_base_url, api_key=self.config.api_key)
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api_key = self.config.api_key or os.getenv("HUGGINGFACE_API_KEY") or "hf"
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self.client = OpenAI(base_url=self.config.huggingface_base_url, api_key=api_key)
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self.config.model = self.config.model or "tei"
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else:
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self.config.model = self.config.model or "multi-qa-MiniLM-L6-cos-v1"
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+13
-1
@@ -2017,6 +2017,12 @@ class Memory(MemoryBase):
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logger.error(f"Error generating procedural memory summary: {e}")
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raise
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if not procedural_memory:
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raise ValueError(
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"The LLM returned no content for the procedural memory summary. "
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"The model may have declined the request or returned an empty response."
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)
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if metadata is None:
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raise ValueError("Metadata cannot be done for procedural memory.")
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@@ -3701,11 +3707,17 @@ class AsyncMemory(MemoryBase):
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else:
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procedural_memory = await asyncio.to_thread(self.llm.generate_response, messages=parsed_messages)
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procedural_memory = remove_code_blocks(procedural_memory)
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except Exception as e:
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logger.error(f"Error generating procedural memory summary: {e}")
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raise
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if not procedural_memory:
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raise ValueError(
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"The LLM returned no content for the procedural memory summary. "
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"The model may have declined the request or returned an empty response."
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)
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if metadata is None:
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raise ValueError("Metadata cannot be done for procedural memory.")
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@@ -121,7 +121,15 @@ def remove_code_blocks(content: str) -> str:
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- If a code block is detected, it returns only the inner content, stripping out the markers.
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- If no code block markers are found, the original content is returned as-is.
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"""
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if content is None:
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if isinstance(content, list):
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parts = []
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for block in content:
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if isinstance(block, str):
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parts.append(block)
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elif isinstance(block, dict):
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parts.append(block.get("text", ""))
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content = "".join(parts)
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if not isinstance(content, str):
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return ""
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pattern = r"^```[a-zA-Z0-9]*\n([\s\S]*?)\n```$"
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match = re.match(pattern, content.strip())
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+1
-8
@@ -1,6 +1,4 @@
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import logging
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import subprocess
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import sys
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import threading
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from typing import List, Optional, Union
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@@ -11,12 +9,7 @@ import mem0
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try:
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import litellm
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except ImportError:
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try:
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subprocess.check_call([sys.executable, "-m", "pip", "install", "litellm"])
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import litellm
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except subprocess.CalledProcessError:
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print("Failed to install 'litellm'. Please install it manually using 'pip install litellm'.")
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sys.exit(1)
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raise ImportError("The 'litellm' library is required. Please install it using 'pip install litellm'.")
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from mem0 import Memory, MemoryClient
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from mem0.configs.prompts import MEMORY_ANSWER_PROMPT
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@@ -72,7 +72,8 @@ def test_embed_with_custom_embedding_dims(mock_sentence_transformer):
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assert result == [1.0, 1.1, 1.2]
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def test_embed_with_huggingface_base_url():
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def test_embed_with_huggingface_base_url(monkeypatch):
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monkeypatch.delenv("HUGGINGFACE_API_KEY", raising=False)
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config = BaseEmbedderConfig(
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huggingface_base_url="http://localhost:8080",
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model="my-custom-model",
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@@ -81,20 +82,20 @@ def test_embed_with_huggingface_base_url():
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with patch("mem0.embeddings.huggingface.OpenAI") as mock_openai:
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mock_client = Mock()
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mock_openai.return_value = mock_client
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# Create a mock for the response object and its attributes
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mock_embedding_response = Mock()
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mock_embedding_response.embedding = [0.1, 0.2, 0.3]
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mock_create_response = Mock()
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mock_create_response.data = [mock_embedding_response]
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mock_client.embeddings.create.return_value = mock_create_response
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embedder = HuggingFaceEmbedding(config)
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result = embedder.embed("Hello from custom endpoint")
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mock_openai.assert_called_once_with(base_url="http://localhost:8080", api_key=None)
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mock_openai.assert_called_once_with(base_url="http://localhost:8080", api_key="hf")
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mock_client.embeddings.create.assert_called_once_with(
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input="Hello from custom endpoint",
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model="my-custom-model",
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@@ -115,6 +116,26 @@ def test_embed_with_huggingface_base_url_forwards_api_key():
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mock_openai.assert_called_once_with(base_url="http://localhost:8080", api_key="tei-token")
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def test_embed_with_huggingface_base_url_falls_back_to_env_var(monkeypatch):
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monkeypatch.setenv("HUGGINGFACE_API_KEY", "env-token")
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config = BaseEmbedderConfig(huggingface_base_url="http://localhost:8080", model="my-custom-model")
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with patch("mem0.embeddings.huggingface.OpenAI") as mock_openai:
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HuggingFaceEmbedding(config)
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mock_openai.assert_called_once_with(base_url="http://localhost:8080", api_key="env-token")
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def test_embed_with_huggingface_base_url_config_key_beats_env_var(monkeypatch):
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monkeypatch.setenv("HUGGINGFACE_API_KEY", "env-token")
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config = BaseEmbedderConfig(
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huggingface_base_url="http://localhost:8080", model="my-custom-model", api_key="config-token"
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)
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with patch("mem0.embeddings.huggingface.OpenAI") as mock_openai:
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HuggingFaceEmbedding(config)
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mock_openai.assert_called_once_with(base_url="http://localhost:8080", api_key="config-token")
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def test_embed_batch_sentence_transformer(mock_sentence_transformer):
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config = BaseEmbedderConfig()
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embedder = HuggingFaceEmbedding(config)
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@@ -0,0 +1,61 @@
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import pytest
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pytest.importorskip("langchain", reason="langchain is an optional extra")
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from langchain.embeddings.base import Embeddings # noqa: E402
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from mem0.configs.embeddings.base import BaseEmbedderConfig # noqa: E402
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from mem0.embeddings.langchain import LangchainEmbedding # noqa: E402
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class DummyEmbeddings(Embeddings):
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def __init__(self):
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self.queries = []
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def embed_documents(self, texts):
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return [[0.1, 0.2, 0.3] for _ in texts]
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def embed_query(self, text):
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self.queries.append(text)
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return [0.1, 0.2, 0.3]
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def test_missing_model_raises():
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with pytest.raises(ValueError, match="`model` parameter is required"):
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LangchainEmbedding(BaseEmbedderConfig())
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def test_model_that_is_not_an_embeddings_instance_raises():
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with pytest.raises(ValueError, match="`model` must be an instance of Embeddings"):
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LangchainEmbedding(BaseEmbedderConfig(model="text-embedding-3-small"))
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def test_configured_model_instance_is_kept():
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model = DummyEmbeddings()
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assert LangchainEmbedding(BaseEmbedderConfig(model=model)).langchain_model is model
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def test_embed_delegates_to_embed_query():
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model = DummyEmbeddings()
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embedder = LangchainEmbedding(BaseEmbedderConfig(model=model))
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assert embedder.embed("hello", "add") == [0.1, 0.2, 0.3]
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assert model.queries == ["hello"]
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def test_memory_action_never_reaches_the_model():
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model = DummyEmbeddings()
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embedder = LangchainEmbedding(BaseEmbedderConfig(model=model))
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embedder.embed("hello", memory_action="add")
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assert model.queries == ["hello"]
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def test_embed_batch_falls_back_to_sequential_embed():
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model = DummyEmbeddings()
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embedder = LangchainEmbedding(BaseEmbedderConfig(model=model))
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assert embedder.embed_batch(["a", "b"]) == [[0.1, 0.2, 0.3], [0.1, 0.2, 0.3]]
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assert model.queries == ["a", "b"]
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@@ -1,3 +1,6 @@
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import builtins
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import importlib
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import sys
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from unittest.mock import Mock, patch
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import pytest
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@@ -94,3 +97,25 @@ def test_embed_batch_count_mismatch_raises(mock_ollama_client):
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with pytest.raises(ValueError, match="returned 1 embeddings for 2 texts"):
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embedder.embed_batch(["first text", "second text"])
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def test_missing_ollama_raises_actionable_import_error(monkeypatch):
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"""Missing ollama raises a catchable ImportError naming the install command, never input()/sys.exit()."""
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monkeypatch.delitem(sys.modules, "mem0.embeddings.ollama", raising=False)
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monkeypatch.delitem(sys.modules, "ollama", raising=False)
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real_import = builtins.__import__
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def fake_import(name, *args, **kwargs):
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if name == "ollama":
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raise ImportError("No module named 'ollama'")
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return real_import(name, *args, **kwargs)
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monkeypatch.setattr(builtins, "__import__", fake_import)
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with patch("builtins.input") as mock_input, patch("sys.exit") as mock_exit:
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with pytest.raises(ImportError, match="pip install ollama"):
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importlib.import_module("mem0.embeddings.ollama")
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mock_input.assert_not_called()
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mock_exit.assert_not_called()
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@@ -192,3 +192,16 @@ class TestProcessTelemetryFilters:
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class TestRemoveCodeBlocks:
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def test_none_content_returns_empty_string(self):
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assert remove_code_blocks(None) == ""
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def test_list_content_from_langchain_aimessage_is_joined(self):
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assert remove_code_blocks([{"type": "text", "text": "hello"}]) == "hello"
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def test_list_content_joins_multiple_blocks_and_bare_strings(self):
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content = [{"type": "text", "text": "hello "}, "world", {"type": "thinking", "thinking": "ignored"}]
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assert remove_code_blocks(content) == "hello world"
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def test_list_content_still_strips_code_fences(self):
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assert remove_code_blocks([{"type": "text", "text": "```json\n{}\n```"}]) == "{}"
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def test_unsupported_content_type_returns_empty_string(self):
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assert remove_code_blocks(42) == ""
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+93
-2
@@ -1235,9 +1235,10 @@ class TestHybridSearchWarning:
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def test_warning_for_store_without_keyword_search(
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self, mock_vs_factory, mock_emb, mock_llm, mock_sqlite, _cap, caplog
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):
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from mem0.vector_stores.base import VectorStoreBase
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import logging
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from mem0.vector_stores.base import VectorStoreBase
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class StoreWithoutKeywordSearch(VectorStoreBase):
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def create_col(self, *a, **kw): pass
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def insert(self, *a, **kw): pass
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@@ -1271,9 +1272,10 @@ class TestHybridSearchWarning:
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def test_no_warning_for_store_with_keyword_search(
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self, mock_vs_factory, mock_emb, mock_llm, mock_sqlite, _cap, caplog
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):
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from mem0.vector_stores.base import VectorStoreBase
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import logging
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from mem0.vector_stores.base import VectorStoreBase
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class StoreWithKeywordSearch(VectorStoreBase):
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def keyword_search(self, query, top_k=5, filters=None):
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return []
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@@ -1574,3 +1576,92 @@ async def test_async_procedural_memory_default_path_without_langchain(mock_llm_f
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assert result["results"][0]["event"] == "ADD"
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memory.llm.generate_response.assert_called_once()
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@pytest.mark.asyncio
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@patch("mem0.memory.main.VectorStoreFactory")
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@patch("mem0.memory.main.EmbedderFactory")
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@patch("mem0.memory.main.LlmFactory")
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async def test_async_procedural_memory_langchain_list_content(mock_llm_factory, mock_emb, mock_vs):
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"""Regression #6150: a LangChain block list must be extracted, not dropped."""
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mock_vs.return_value = MagicMock()
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mock_emb.return_value = MagicMock()
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mock_emb.return_value.embed.return_value = [0.1] * 1536
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mock_llm_factory.return_value = MagicMock()
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from mem0.memory.main import AsyncMemory
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memory = AsyncMemory(MemoryConfig())
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memory.vector_store = MagicMock()
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memory.vector_store.insert = MagicMock()
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mock_langchain_llm = MagicMock()
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mock_response = MagicMock()
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mock_response.content = [{"type": "text", "text": "- deploy with the release script"}]
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mock_langchain_llm.invoke.return_value = mock_response
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await memory._create_procedural_memory(
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[{"role": "user", "content": "how do we deploy"}],
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metadata={"user_id": "test_user"},
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llm=mock_langchain_llm,
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)
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stored_data = memory.vector_store.insert.call_args[1]["payloads"][0]["data"]
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assert stored_data == "- deploy with the release script"
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@pytest.mark.asyncio
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@patch("mem0.memory.main.VectorStoreFactory")
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@patch("mem0.memory.main.EmbedderFactory")
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@patch("mem0.memory.main.LlmFactory")
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async def test_async_procedural_memory_empty_content_raises(mock_llm_factory, mock_emb, mock_vs):
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"""Regression #6150: a refusal (content=None) must raise, not store an empty memory."""
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mock_vs.return_value = MagicMock()
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mock_emb.return_value = MagicMock()
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mock_llm_factory.return_value = MagicMock()
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from mem0.memory.main import AsyncMemory
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memory = AsyncMemory(MemoryConfig())
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memory.vector_store = MagicMock()
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memory.vector_store.insert = MagicMock()
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mock_langchain_llm = MagicMock()
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mock_response = MagicMock()
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mock_response.content = None
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mock_langchain_llm.invoke.return_value = mock_response
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with pytest.raises(ValueError, match="no content for the procedural memory summary"):
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await memory._create_procedural_memory(
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[{"role": "user", "content": "how do we deploy"}],
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metadata={"user_id": "test_user"},
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llm=mock_langchain_llm,
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)
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memory.vector_store.insert.assert_not_called()
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@patch("mem0.utils.factory.EmbedderFactory.create")
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@patch("mem0.utils.factory.VectorStoreFactory.create")
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@patch("mem0.utils.factory.LlmFactory.create")
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@patch("mem0.memory.storage.SQLiteManager")
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def test_sync_procedural_memory_empty_content_raises(
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mock_sqlite, mock_llm_factory, mock_vector_factory, mock_embedder_factory
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):
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"""Regression #6150: the sync path must raise on empty LLM content too."""
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mock_embedder_factory.return_value = MagicMock()
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mock_vector_store = MagicMock()
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mock_vector_factory.return_value = mock_vector_store
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mock_llm_factory.return_value = MagicMock()
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mock_sqlite.return_value = MagicMock()
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memory = Memory(MemoryConfig())
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memory.llm.generate_response = Mock(return_value=None)
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with pytest.raises(ValueError, match="no content for the procedural memory summary"):
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memory._create_procedural_memory(
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[{"role": "user", "content": "how do we deploy"}],
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metadata={"user_id": "test_user"},
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)
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mock_vector_store.insert.assert_not_called()
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@@ -1,4 +1,7 @@
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import builtins
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import importlib
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import inspect
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import sys
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from unittest.mock import Mock, patch
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import pytest
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@@ -138,3 +141,20 @@ def test_completions_create_messages_default_does_not_leak_between_calls(mock_me
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f"Completions.create(messages=...) must default to None to avoid the "
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f"B006 shared-default-list bug; got {messages_default!r}."
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)
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def test_missing_litellm_raises_actionable_import_error(monkeypatch):
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monkeypatch.delitem(sys.modules, "mem0.proxy.main", raising=False)
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monkeypatch.delitem(sys.modules, "litellm", raising=False)
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real_import = builtins.__import__
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def fake_import(name, *args, **kwargs):
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if name == "litellm":
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raise ImportError("No module named 'litellm'")
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return real_import(name, *args, **kwargs)
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monkeypatch.setattr(builtins, "__import__", fake_import)
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with pytest.raises(ImportError, match="pip install litellm"):
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importlib.import_module("mem0.proxy.main")
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