diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 64ddfe42..60c27fc2 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -58,11 +58,10 @@ jobs: run: | pip install --upgrade pip pip install -e ".[test,graph,vector_stores,llms,extras]" + pip install ruff if: steps.cached-hatch-dependencies.outputs.cache-hit != 'true' - - name: Run Formatting - run: | - mkdir -p .ruff_cache && chmod -R 777 .ruff_cache - hatch run format + - name: Run Linting + run: make lint - name: Run tests and generate coverage report run: make test diff --git a/Makefile b/Makefile index a01c84a5..1fe0e376 100644 --- a/Makefile +++ b/Makefile @@ -13,7 +13,7 @@ install: install_all: pip install ruff==0.6.9 groq together boto3 litellm ollama chromadb weaviate weaviate-client sentence_transformers vertexai \ google-generativeai elasticsearch opensearch-py vecs "pinecone<7.0.0" pinecone-text faiss-cpu langchain-community \ - upstash-vector azure-search-documents langchain-memgraph langchain-neo4j rank-bm25 pymochow + upstash-vector azure-search-documents langchain-memgraph langchain-neo4j rank-bm25 pymochow pymongo # Format code with ruff format: diff --git a/evaluation/metrics/llm_judge.py b/evaluation/metrics/llm_judge.py index 8dbae80b..20acc4af 100644 --- a/evaluation/metrics/llm_judge.py +++ b/evaluation/metrics/llm_judge.py @@ -110,7 +110,7 @@ def main(): print("All categories accuracy:") for cat, results in LLM_JUDGE.items(): if results: # Only print if there are results for this category - print(f" Category {cat}: {np.mean(results):.4f} " f"({sum(results)}/{len(results)})") + print(f" Category {cat}: {np.mean(results):.4f} ({sum(results)}/{len(results)})") print("------------------------------------------") index += 1 diff --git a/evaluation/src/rag.py b/evaluation/src/rag.py index b3ce5a39..97a2e1d8 100644 --- a/evaluation/src/rag.py +++ b/evaluation/src/rag.py @@ -68,7 +68,7 @@ class RAGManager: def clean_chat_history(self, chat_history): cleaned_chat_history = "" for c in chat_history: - cleaned_chat_history += f"{c['timestamp']} | {c['speaker']}: " f"{c['text']}\n" + cleaned_chat_history += f"{c['timestamp']} | {c['speaker']}: {c['text']}\n" return cleaned_chat_history diff --git a/examples/graph-db-demo/neo4j-example.ipynb b/examples/graph-db-demo/neo4j-example.ipynb index 1fe2852a..77d225db 100644 --- a/examples/graph-db-demo/neo4j-example.ipynb +++ b/examples/graph-db-demo/neo4j-example.ipynb @@ -1,271 +1,267 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "ApdaLD4Qi30H" - }, - "source": [ - "# Neo4j as Graph Memory" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "l7bi3i21i30I" - }, - "source": [ - "## Prerequisites\n", - "\n", - "### 1. Install Mem0 with Graph Memory support\n", - "\n", - "To use Mem0 with Graph Memory support, install it using pip:\n", - "\n", - "```bash\n", - "pip install \"mem0ai[graph]\"\n", - "```\n", - "\n", - "This command installs Mem0 along with the necessary dependencies for graph functionality.\n", - "\n", - "### 2. Install Neo4j\n", - "\n", - "To utilize Neo4j as Graph Memory, run it with Docker:\n", - "\n", - "```bash\n", - "docker run \\\n", - " -p 7474:7474 -p 7687:7687 \\\n", - " -e NEO4J_AUTH=neo4j/password \\\n", - " neo4j:5\n", - "```\n", - "\n", - "This command starts Neo4j with default credentials (`neo4j` / `password`) and exposes both the HTTP (7474) and Bolt (7687) ports.\n", - "\n", - "You can access the Neo4j browser at [http://localhost:7474](http://localhost:7474).\n", - "\n", - "Additional information can be found in the [Neo4j documentation](https://neo4j.com/docs/).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DkeBdFEpi30I" - }, - "source": [ - "## Configuration\n", - "\n", - "Do all the imports and configure OpenAI (enter your OpenAI API key):" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "d99EfBpii30I" - }, - "outputs": [], - "source": [ - "from mem0 import Memory\n", - "\n", - "import os\n", - "\n", - "os.environ[\"OPENAI_API_KEY\"] = (\n", - " \"\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QTucZJjIi30J" - }, - "source": [ - "Set up configuration to use the embedder model and Neo4j as a graph store:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "QSE0RFoSi30J" - }, - "outputs": [], - "source": [ - "config = {\n", - " \"embedder\": {\n", - " \"provider\": \"openai\",\n", - " \"config\": {\"model\": \"text-embedding-3-large\", \"embedding_dims\": 1536},\n", - " },\n", - " \"graph_store\": {\n", - " \"provider\": \"neo4j\",\n", - " \"config\": {\n", - " \"url\": \"bolt://54.87.227.131:7687\",\n", - " \"username\": \"neo4j\",\n", - " \"password\": \"causes-bins-vines\",\n", - " },\n", - " },\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OioTnv6xi30J" - }, - "source": [ - "## Graph Memory initializiation\n", - "\n", - "Initialize Neo4j as a Graph Memory store:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "fX-H9vgNi30J" - }, - "outputs": [], - "source": [ - "m = Memory.from_config(config_dict=config)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kr1fVMwEi30J" - }, - "source": [ - "## Store memories\n", - "\n", - "Create memories:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "sEfogqp_i30J" - }, - "outputs": [], - "source": [ - "messages = [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"I'm planning to watch a movie tonight. Any recommendations?\",\n", - " },\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"How about a thriller movies? They can be quite engaging.\",\n", - " },\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"I'm not a big fan of thriller movies but I love sci-fi movies.\",\n", - " },\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.\",\n", - " },\n", - "]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gtBHCyIgi30J" - }, - "source": [ - "Store memories in Neo4j:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "BMVGgZMFi30K" - }, - "outputs": [], - "source": [ - "# Store inferred memories (default behavior)\n", - "result = m.add(\n", - " messages, user_id=\"alice\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lQRptOywi30K" - }, - "source": [ - "![](https://github.com/tomasonjo/mem0/blob/neo4jexample/examples/graph-db-demo/alice-memories.png?raw=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LBXW7Gv-i30K" - }, - "source": [ - "## Search memories" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "UHFDeQBEi30K", - "outputId": "2c69de7d-a79a-48f6-e3c4-bd743067857c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loves sci-fi movies 0.3153664287340898\n", - "Planning to watch a movie tonight 0.09683349296551162\n", - "Not a big fan of thriller movies 0.09468540071789466\n" - ] - } - ], - "source": [ - "for result in m.search(\"what does alice love?\", user_id=\"alice\")[\"results\"]:\n", - " print(result[\"memory\"], result[\"score\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "2jXEIma9kK_Q" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.2" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "ApdaLD4Qi30H" + }, + "source": [ + "# Neo4j as Graph Memory" + ] }, - "nbformat": 4, - "nbformat_minor": 0 + { + "cell_type": "markdown", + "metadata": { + "id": "l7bi3i21i30I" + }, + "source": [ + "## Prerequisites\n", + "\n", + "### 1. Install Mem0 with Graph Memory support\n", + "\n", + "To use Mem0 with Graph Memory support, install it using pip:\n", + "\n", + "```bash\n", + "pip install \"mem0ai[graph]\"\n", + "```\n", + "\n", + "This command installs Mem0 along with the necessary dependencies for graph functionality.\n", + "\n", + "### 2. Install Neo4j\n", + "\n", + "To utilize Neo4j as Graph Memory, run it with Docker:\n", + "\n", + "```bash\n", + "docker run \\\n", + " -p 7474:7474 -p 7687:7687 \\\n", + " -e NEO4J_AUTH=neo4j/password \\\n", + " neo4j:5\n", + "```\n", + "\n", + "This command starts Neo4j with default credentials (`neo4j` / `password`) and exposes both the HTTP (7474) and Bolt (7687) ports.\n", + "\n", + "You can access the Neo4j browser at [http://localhost:7474](http://localhost:7474).\n", + "\n", + "Additional information can be found in the [Neo4j documentation](https://neo4j.com/docs/).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DkeBdFEpi30I" + }, + "source": [ + "## Configuration\n", + "\n", + "Do all the imports and configure OpenAI (enter your OpenAI API key):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "d99EfBpii30I" + }, + "outputs": [], + "source": [ + "from mem0 import Memory\n", + "\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QTucZJjIi30J" + }, + "source": [ + "Set up configuration to use the embedder model and Neo4j as a graph store:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "QSE0RFoSi30J" + }, + "outputs": [], + "source": [ + "config = {\n", + " \"embedder\": {\n", + " \"provider\": \"openai\",\n", + " \"config\": {\"model\": \"text-embedding-3-large\", \"embedding_dims\": 1536},\n", + " },\n", + " \"graph_store\": {\n", + " \"provider\": \"neo4j\",\n", + " \"config\": {\n", + " \"url\": \"bolt://54.87.227.131:7687\",\n", + " \"username\": \"neo4j\",\n", + " \"password\": \"causes-bins-vines\",\n", + " },\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OioTnv6xi30J" + }, + "source": [ + "## Graph Memory initializiation\n", + "\n", + "Initialize Neo4j as a Graph Memory store:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "fX-H9vgNi30J" + }, + "outputs": [], + "source": [ + "m = Memory.from_config(config_dict=config)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kr1fVMwEi30J" + }, + "source": [ + "## Store memories\n", + "\n", + "Create memories:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "sEfogqp_i30J" + }, + "outputs": [], + "source": [ + "messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"I'm planning to watch a movie tonight. Any recommendations?\",\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"How about a thriller movies? They can be quite engaging.\",\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"I'm not a big fan of thriller movies but I love sci-fi movies.\",\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.\",\n", + " },\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gtBHCyIgi30J" + }, + "source": [ + "Store memories in Neo4j:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "BMVGgZMFi30K" + }, + "outputs": [], + "source": [ + "# Store inferred memories (default behavior)\n", + "result = m.add(messages, user_id=\"alice\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lQRptOywi30K" + }, + "source": [ + "![](https://github.com/tomasonjo/mem0/blob/neo4jexample/examples/graph-db-demo/alice-memories.png?raw=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LBXW7Gv-i30K" + }, + "source": [ + "## Search memories" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UHFDeQBEi30K", + "outputId": "2c69de7d-a79a-48f6-e3c4-bd743067857c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loves sci-fi movies 0.3153664287340898\n", + "Planning to watch a movie tonight 0.09683349296551162\n", + "Not a big fan of thriller movies 0.09468540071789466\n" + ] + } + ], + "source": [ + "for result in m.search(\"what does alice love?\", user_id=\"alice\")[\"results\"]:\n", + " print(result[\"memory\"], result[\"score\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "2jXEIma9kK_Q" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/examples/misc/diet_assistant_voice_cartesia.py b/examples/misc/diet_assistant_voice_cartesia.py index 9438b729..62b2bf35 100644 --- a/examples/misc/diet_assistant_voice_cartesia.py +++ b/examples/misc/diet_assistant_voice_cartesia.py @@ -45,11 +45,7 @@ def get_food_recommendation(user_query: str, user_id): """Get food recommendation with memory context""" # Search memory for relevant food preferences - memories_result = memory_client.search( - query=user_query, - user_id=user_id, - limit=5 - ) + memories_result = memory_client.search(query=user_query, user_id=user_id, limit=5) # Add memory context to the message memories = [f"- {result['memory']}" for result in memories_result] @@ -71,6 +67,7 @@ def get_food_recommendation(user_query: str, user_id): # Save audio file if response.audio: import time + timestamp = int(time.time()) filename = f"food_recommendation_{timestamp}.mp3" write_audio_to_file( @@ -118,7 +115,11 @@ def initialize_food_memory(user_id): # Initialize the memory for the user once in order for the agent to learn the user preference initialize_food_memory(user_id=USER_ID) -print(get_food_recommendation("Which type of restaurants should I go tonight for dinner and cuisines preferred?", user_id=USER_ID)) +print( + get_food_recommendation( + "Which type of restaurants should I go tonight for dinner and cuisines preferred?", user_id=USER_ID + ) +) # OUTPUT: šŸŽµ Audio saved as food_recommendation_1750162610.mp3 # For dinner tonight, considering your love for healthy spic optionsy, you could try a nice Thai, Indian, or Mexican restaurant. # You might find dishes with quinoa, chickpeas, tofu, and fresh herbs delightful. Enjoy your dinner! diff --git a/examples/misc/test.py b/examples/misc/test.py index c886dba1..06c73a9d 100644 --- a/examples/misc/test.py +++ b/examples/misc/test.py @@ -1,4 +1,4 @@ -from agents import Agent, Runner, function_tool, handoffs, enable_verbose_stdout_logging +from agents import Agent, Runner, function_tool, enable_verbose_stdout_logging from dotenv import load_dotenv from mem0 import MemoryClient @@ -35,7 +35,7 @@ travel_agent = Agent( understand the user's travel preferences and history before making recommendations. After providing your response, use store_conversation to save important details.""", tools=[search_memory, save_memory], - model="gpt-4o" + model="gpt-4o", ) health_agent = Agent( @@ -44,7 +44,7 @@ health_agent = Agent( understand the user's health goals and dietary preferences. After providing advice, use store_conversation to save relevant information.""", tools=[search_memory, save_memory], - model="gpt-4o" + model="gpt-4o", ) # Triage agent with handoffs @@ -55,7 +55,7 @@ triage_agent = Agent( For health-related questions (fitness, diet, wellness, exercise), hand off to Health Advisor. For general questions, you can handle them directly using available tools.""", handoffs=[travel_agent, health_agent], - model="gpt-4o" + model="gpt-4o", ) @@ -74,10 +74,7 @@ def chat_with_handoffs(user_input: str, user_id: str) -> str: result = Runner.run_sync(triage_agent, user_input) # Store the original conversation in memory - conversation = [ - {"role": "user", "content": user_input}, - {"role": "assistant", "content": result.final_output} - ] + conversation = [{"role": "user", "content": user_input}, {"role": "assistant", "content": result.final_output}] mem0.add(conversation, user_id=user_id) return result.final_output diff --git a/examples/misc/vllm_example.py b/examples/misc/vllm_example.py index 3f5d8e48..635cb550 100644 --- a/examples/misc/vllm_example.py +++ b/examples/misc/vllm_example.py @@ -34,96 +34,91 @@ config = { "api_key": "vllm-api-key", "temperature": 0.7, "max_tokens": 100, - } - }, - "embedder": { - "provider": "openai", - "config": { - "model": "text-embedding-3-small" - } + }, }, + "embedder": {"provider": "openai", "config": {"model": "text-embedding-3-small"}}, "vector_store": { "provider": "qdrant", - "config": { - "collection_name": "vllm_memories", - "host": "localhost", - "port": 6333 - } - } + "config": {"collection_name": "vllm_memories", "host": "localhost", "port": 6333}, + }, } + def main(): """ Demonstrate vLLM integration with mem0 """ print("--> Initializing mem0 with vLLM...") - + # Initialize memory with vLLM memory = Memory.from_config(config) - + print("--> Memory initialized successfully!") - + # Example conversations to store conversations = [ { "messages": [ {"role": "user", "content": "I love playing chess on weekends"}, - {"role": "assistant", "content": "That's great! Chess is an excellent strategic game that helps improve critical thinking."} + { + "role": "assistant", + "content": "That's great! Chess is an excellent strategic game that helps improve critical thinking.", + }, ], - "user_id": "user_123" + "user_id": "user_123", }, { "messages": [ {"role": "user", "content": "I'm learning Python programming"}, - {"role": "assistant", "content": "Python is a fantastic language for beginners! What specific areas are you focusing on?"} + { + "role": "assistant", + "content": "Python is a fantastic language for beginners! What specific areas are you focusing on?", + }, ], - "user_id": "user_123" + "user_id": "user_123", }, { "messages": [ {"role": "user", "content": "I prefer working late at night, I'm more productive then"}, - {"role": "assistant", "content": "Many people find they're more creative and focused during nighttime hours. It's important to maintain a consistent schedule that works for you."} + { + "role": "assistant", + "content": "Many people find they're more creative and focused during nighttime hours. It's important to maintain a consistent schedule that works for you.", + }, ], - "user_id": "user_123" - } + "user_id": "user_123", + }, ] - + print("\n--> Adding memories using vLLM...") - + # Add memories - now powered by vLLM's high-performance inference for i, conversation in enumerate(conversations, 1): - result = memory.add( - messages=conversation["messages"], - user_id=conversation["user_id"] - ) + result = memory.add(messages=conversation["messages"], user_id=conversation["user_id"]) print(f"Memory {i} added: {result}") - + print("\nšŸ” Searching memories...") - + # Search memories - vLLM will process the search and memory operations search_queries = [ "What does the user like to do on weekends?", "What is the user learning?", - "When is the user most productive?" + "When is the user most productive?", ] - + for query in search_queries: print(f"\nQuery: {query}") - memories = memory.search( - query=query, - user_id="user_123" - ) - + memories = memory.search(query=query, user_id="user_123") + for memory_item in memories: print(f" - {memory_item['memory']}") - + print("\n--> Getting all memories for user...") all_memories = memory.get_all(user_id="user_123") print(f"Total memories stored: {len(all_memories)}") - + for memory_item in all_memories: print(f" - {memory_item['memory']}") - + print("\n--> vLLM integration demo completed successfully!") print("\nBenefits of using vLLM:") print(" -> 2.7x higher throughput compared to standard implementations") diff --git a/mem0/client/main.py b/mem0/client/main.py index 5aafa0de..bccc0437 100644 --- a/mem0/client/main.py +++ b/mem0/client/main.py @@ -89,9 +89,7 @@ class MemoryClient: self.user_id = get_user_id() if not self.api_key: - raise ValueError( - "Mem0 API Key not provided. Please provide an API Key." - ) + raise ValueError("Mem0 API Key not provided. Please provide an API Key.") # Create MD5 hash of API key for user_id self.user_id = hashlib.md5(self.api_key.encode()).hexdigest() @@ -174,9 +172,7 @@ class MemoryClient: response.raise_for_status() if "metadata" in kwargs: del kwargs["metadata"] - capture_client_event( - "client.add", self, {"keys": list(kwargs.keys()), "sync_type": "sync"} - ) + capture_client_event("client.add", self, {"keys": list(kwargs.keys()), "sync_type": "sync"}) return response.json() @api_error_handler @@ -195,9 +191,7 @@ class MemoryClient: params = self._prepare_params() response = self.client.get(f"/v1/memories/{memory_id}/", params=params) response.raise_for_status() - capture_client_event( - "client.get", self, {"memory_id": memory_id, "sync_type": "sync"} - ) + capture_client_event("client.get", self, {"memory_id": memory_id, "sync_type": "sync"}) return response.json() @api_error_handler @@ -224,13 +218,9 @@ class MemoryClient: "page": params.pop("page"), "page_size": params.pop("page_size"), } - response = self.client.post( - f"/{version}/memories/", json=params, params=query_params - ) + response = self.client.post(f"/{version}/memories/", json=params, params=query_params) else: - response = self.client.post( - f"/{version}/memories/", json=params - ) + response = self.client.post(f"/{version}/memories/", json=params) response.raise_for_status() if "metadata" in kwargs: del kwargs["metadata"] @@ -246,9 +236,7 @@ class MemoryClient: return response.json() @api_error_handler - def search( - self, query: str, version: str = "v1", **kwargs - ) -> List[Dict[str, Any]]: + def search(self, query: str, version: str = "v1", **kwargs) -> List[Dict[str, Any]]: """Search memories based on a query. Args: @@ -266,9 +254,7 @@ class MemoryClient: payload = {"query": query} params = self._prepare_params(kwargs) payload.update(params) - response = self.client.post( - f"/{version}/memories/search/", json=payload - ) + response = self.client.post(f"/{version}/memories/search/", json=payload) response.raise_for_status() if "metadata" in kwargs: del kwargs["metadata"] @@ -308,13 +294,9 @@ class MemoryClient: if metadata is not None: payload["metadata"] = metadata - capture_client_event( - "client.update", self, {"memory_id": memory_id, "sync_type": "sync"} - ) + capture_client_event("client.update", self, {"memory_id": memory_id, "sync_type": "sync"}) params = self._prepare_params() - response = self.client.put( - f"/v1/memories/{memory_id}/", json=payload, params=params - ) + response = self.client.put(f"/v1/memories/{memory_id}/", json=payload, params=params) response.raise_for_status() return response.json() @@ -332,13 +314,9 @@ class MemoryClient: APIError: If the API request fails. """ params = self._prepare_params() - response = self.client.delete( - f"/v1/memories/{memory_id}/", params=params - ) + response = self.client.delete(f"/v1/memories/{memory_id}/", params=params) response.raise_for_status() - capture_client_event( - "client.delete", self, {"memory_id": memory_id, "sync_type": "sync"} - ) + capture_client_event("client.delete", self, {"memory_id": memory_id, "sync_type": "sync"}) return response.json() @api_error_handler @@ -379,13 +357,9 @@ class MemoryClient: APIError: If the API request fails. """ params = self._prepare_params() - response = self.client.get( - f"/v1/memories/{memory_id}/history/", params=params - ) + response = self.client.get(f"/v1/memories/{memory_id}/history/", params=params) response.raise_for_status() - capture_client_event( - "client.history", self, {"memory_id": memory_id, "sync_type": "sync"} - ) + capture_client_event("client.history", self, {"memory_id": memory_id, "sync_type": "sync"}) return response.json() @api_error_handler @@ -432,10 +406,7 @@ class MemoryClient: else: entities = self.users() # Filter entities based on provided IDs using list comprehension - to_delete = [ - {"type": entity["type"], "name": entity["name"]} - for entity in entities["results"] - ] + to_delete = [{"type": entity["type"], "name": entity["name"]} for entity in entities["results"]] params = self._prepare_params() @@ -444,9 +415,7 @@ class MemoryClient: # Delete entities and check response immediately for entity in to_delete: - response = self.client.delete( - f"/v2/entities/{entity['type']}/{entity['name']}/", params=params - ) + response = self.client.delete(f"/v2/entities/{entity['type']}/{entity['name']}/", params=params) response.raise_for_status() capture_client_event( @@ -484,9 +453,7 @@ class MemoryClient: self.delete_users() capture_client_event("client.reset", self, {"sync_type": "sync"}) - return { - "message": "Client reset successful. All users and memories deleted." - } + return {"message": "Client reset successful. All users and memories deleted."} @api_error_handler def batch_update(self, memories: List[Dict[str, Any]]) -> Dict[str, Any]: @@ -507,9 +474,7 @@ class MemoryClient: response = self.client.put("/v1/batch/", json={"memories": memories}) response.raise_for_status() - capture_client_event( - "client.batch_update", self, {"sync_type": "sync"} - ) + capture_client_event("client.batch_update", self, {"sync_type": "sync"}) return response.json() @api_error_handler @@ -527,14 +492,10 @@ class MemoryClient: Raises: APIError: If the API request fails. """ - response = self.client.request( - "DELETE", "/v1/batch/", json={"memories": memories} - ) + response = self.client.request("DELETE", "/v1/batch/", json={"memories": memories}) response.raise_for_status() - capture_client_event( - "client.batch_delete", self, {"sync_type": "sync"} - ) + capture_client_event("client.batch_delete", self, {"sync_type": "sync"}) return response.json() @api_error_handler @@ -574,9 +535,7 @@ class MemoryClient: Returns: Dict containing the exported data """ - response = self.client.post( - "/v1/exports/get/", json=self._prepare_params(kwargs) - ) + response = self.client.post("/v1/exports/get/", json=self._prepare_params(kwargs)) response.raise_for_status() capture_client_event( "client.get_memory_export", @@ -586,9 +545,7 @@ class MemoryClient: return response.json() @api_error_handler - def get_summary( - self, filters: Optional[Dict[str, Any]] = None - ) -> Dict[str, Any]: + def get_summary(self, filters: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: """Get the summary of a memory export. Args: @@ -598,17 +555,13 @@ class MemoryClient: Dict containing the export status and summary data """ - response = self.client.post( - "/v1/summary/", json=self._prepare_params({"filters": filters}) - ) + response = self.client.post("/v1/summary/", json=self._prepare_params({"filters": filters})) response.raise_for_status() capture_client_event("client.get_summary", self, {"sync_type": "sync"}) return response.json() @api_error_handler - def get_project( - self, fields: Optional[List[str]] = None - ) -> Dict[str, Any]: + def get_project(self, fields: Optional[List[str]] = None) -> Dict[str, Any]: """Get instructions or categories for the current project. Args: @@ -622,10 +575,7 @@ class MemoryClient: ValueError: If org_id or project_id are not set. """ if not (self.org_id and self.project_id): - raise ValueError( - "org_id and project_id must be set to access instructions or " - "categories" - ) + raise ValueError("org_id and project_id must be set to access instructions or categories") params = self._prepare_params({"fields": fields}) response = self.client.get( @@ -666,10 +616,7 @@ class MemoryClient: ValueError: If org_id or project_id are not set. """ if not (self.org_id and self.project_id): - raise ValueError( - "org_id and project_id must be set to update instructions or " - "categories" - ) + raise ValueError("org_id and project_id must be set to update instructions or categories") if ( custom_instructions is None @@ -826,10 +773,7 @@ class MemoryClient: feedback = feedback.upper() if feedback else None if feedback is not None and feedback not in VALID_FEEDBACK_VALUES: - raise ValueError( - f'feedback must be one of {", ".join(VALID_FEEDBACK_VALUES)} ' - "or None" - ) + raise ValueError(f"feedback must be one of {', '.join(VALID_FEEDBACK_VALUES)} or None") data = { "memory_id": memory_id, @@ -839,14 +783,10 @@ class MemoryClient: response = self.client.post("/v1/feedback/", json=data) response.raise_for_status() - capture_client_event( - "client.feedback", self, data, {"sync_type": "sync"} - ) + capture_client_event("client.feedback", self, data, {"sync_type": "sync"}) return response.json() - def _prepare_payload( - self, messages: List[Dict[str, str]], kwargs: Dict[str, Any] - ) -> Dict[str, Any]: + def _prepare_payload(self, messages: List[Dict[str, str]], kwargs: Dict[str, Any]) -> Dict[str, Any]: """Prepare the payload for API requests. Args: @@ -862,9 +802,7 @@ class MemoryClient: payload.update({k: v for k, v in kwargs.items() if v is not None}) return payload - def _prepare_params( - self, kwargs: Optional[Dict[str, Any]] = None - ) -> Dict[str, Any]: + def _prepare_params(self, kwargs: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: """Prepare query parameters for API requests. Args: @@ -929,9 +867,7 @@ class AsyncMemoryClient: self.user_id = get_user_id() if not self.api_key: - raise ValueError( - "Mem0 API Key not provided. Please provide an API Key." - ) + raise ValueError("Mem0 API Key not provided. Please provide an API Key.") # Create MD5 hash of API key for user_id self.user_id = hashlib.md5(self.api_key.encode()).hexdigest() @@ -989,9 +925,7 @@ class AsyncMemoryClient: error_message = str(e) raise ValueError(f"Error: {error_message}") - def _prepare_payload( - self, messages: List[Dict[str, str]], kwargs: Dict[str, Any] - ) -> Dict[str, Any]: + def _prepare_payload(self, messages: List[Dict[str, str]], kwargs: Dict[str, Any]) -> Dict[str, Any]: """Prepare the payload for API requests. Args: @@ -1007,9 +941,7 @@ class AsyncMemoryClient: payload.update({k: v for k, v in kwargs.items() if v is not None}) return payload - def _prepare_params( - self, kwargs: Optional[Dict[str, Any]] = None - ) -> Dict[str, Any]: + def _prepare_params(self, kwargs: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: """Prepare query parameters for API requests. Args: @@ -1041,9 +973,7 @@ class AsyncMemoryClient: await self.async_client.aclose() @api_error_handler - async def add( - self, messages: List[Dict[str, str]], **kwargs - ) -> Dict[str, Any]: + async def add(self, messages: List[Dict[str, str]], **kwargs) -> Dict[str, Any]: kwargs = self._prepare_params(kwargs) if kwargs.get("output_format") != "v1.1": kwargs["output_format"] = "v1.1" @@ -1062,45 +992,31 @@ class AsyncMemoryClient: response.raise_for_status() if "metadata" in kwargs: del kwargs["metadata"] - capture_client_event( - "client.add", self, {"keys": list(kwargs.keys()), "sync_type": "async"} - ) + capture_client_event("client.add", self, {"keys": list(kwargs.keys()), "sync_type": "async"}) return response.json() @api_error_handler async def get(self, memory_id: str) -> Dict[str, Any]: params = self._prepare_params() - response = await self.async_client.get( - f"/v1/memories/{memory_id}/", params=params - ) + response = await self.async_client.get(f"/v1/memories/{memory_id}/", params=params) response.raise_for_status() - capture_client_event( - "client.get", self, {"memory_id": memory_id, "sync_type": "async"} - ) + capture_client_event("client.get", self, {"memory_id": memory_id, "sync_type": "async"}) return response.json() @api_error_handler - async def get_all( - self, version: str = "v1", **kwargs - ) -> List[Dict[str, Any]]: + async def get_all(self, version: str = "v1", **kwargs) -> List[Dict[str, Any]]: params = self._prepare_params(kwargs) if version == "v1": - response = await self.async_client.get( - f"/{version}/memories/", params=params - ) + response = await self.async_client.get(f"/{version}/memories/", params=params) elif version == "v2": if "page" in params and "page_size" in params: query_params = { "page": params.pop("page"), "page_size": params.pop("page_size"), } - response = await self.async_client.post( - f"/{version}/memories/", json=params, params=query_params - ) + response = await self.async_client.post(f"/{version}/memories/", json=params, params=query_params) else: - response = await self.async_client.post( - f"/{version}/memories/", json=params - ) + response = await self.async_client.post(f"/{version}/memories/", json=params) response.raise_for_status() if "metadata" in kwargs: del kwargs["metadata"] @@ -1116,14 +1032,10 @@ class AsyncMemoryClient: return response.json() @api_error_handler - async def search( - self, query: str, version: str = "v1", **kwargs - ) -> List[Dict[str, Any]]: + async def search(self, query: str, version: str = "v1", **kwargs) -> List[Dict[str, Any]]: payload = {"query": query} payload.update(self._prepare_params(kwargs)) - response = await self.async_client.post( - f"/{version}/memories/search/", json=payload - ) + response = await self.async_client.post(f"/{version}/memories/search/", json=payload) response.raise_for_status() if "metadata" in kwargs: del kwargs["metadata"] @@ -1139,7 +1051,9 @@ class AsyncMemoryClient: return response.json() @api_error_handler - async def update(self, memory_id: str, text: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + async def update( + self, memory_id: str, text: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None + ) -> Dict[str, Any]: """ Update a memory by ID. Args: @@ -1265,10 +1179,7 @@ class AsyncMemoryClient: else: entities = await self.users() # Filter entities based on provided IDs using list comprehension - to_delete = [ - {"type": entity["type"], "name": entity["name"]} - for entity in entities["results"] - ] + to_delete = [{"type": entity["type"], "name": entity["name"]} for entity in entities["results"]] params = self._prepare_params() @@ -1277,9 +1188,7 @@ class AsyncMemoryClient: # Delete entities and check response immediately for entity in to_delete: - response = await self.async_client.delete( - f"/v2/entities/{entity['type']}/{entity['name']}/", params=params - ) + response = await self.async_client.delete(f"/v2/entities/{entity['type']}/{entity['name']}/", params=params) response.raise_for_status() capture_client_event( @@ -1335,9 +1244,7 @@ class AsyncMemoryClient: response = await self.async_client.put("/v1/batch/", json={"memories": memories}) response.raise_for_status() - capture_client_event( - "client.batch_update", self, {"sync_type": "async"} - ) + capture_client_event("client.batch_update", self, {"sync_type": "async"}) return response.json() @api_error_handler @@ -1355,14 +1262,10 @@ class AsyncMemoryClient: Raises: APIError: If the API request fails. """ - response = await self.async_client.request( - "DELETE", "/v1/batch/", json={"memories": memories} - ) + response = await self.async_client.request("DELETE", "/v1/batch/", json={"memories": memories}) response.raise_for_status() - capture_client_event( - "client.batch_delete", self, {"sync_type": "async"} - ) + capture_client_event("client.batch_delete", self, {"sync_type": "async"}) return response.json() @api_error_handler @@ -1614,7 +1517,7 @@ class AsyncMemoryClient: feedback = feedback.upper() if feedback else None if feedback is not None and feedback not in VALID_FEEDBACK_VALUES: - raise ValueError(f'feedback must be one of {", ".join(VALID_FEEDBACK_VALUES)} or None') + raise ValueError(f"feedback must be one of {', '.join(VALID_FEEDBACK_VALUES)} or None") data = {"memory_id": memory_id, "feedback": feedback, "feedback_reason": feedback_reason} diff --git a/mem0/configs/vector_stores/baidu.py b/mem0/configs/vector_stores/baidu.py index 7a1ca6a4..ad27f4cb 100644 --- a/mem0/configs/vector_stores/baidu.py +++ b/mem0/configs/vector_stores/baidu.py @@ -1,4 +1,3 @@ -from enum import Enum from typing import Any, Dict from pydantic import BaseModel, Field, model_validator diff --git a/mem0/configs/vector_stores/mongodb.py b/mem0/configs/vector_stores/mongodb.py index 0b0c0929..36f0c0fd 100644 --- a/mem0/configs/vector_stores/mongodb.py +++ b/mem0/configs/vector_stores/mongodb.py @@ -11,7 +11,7 @@ class MongoDBConfig(BaseModel): embedding_model_dims: Optional[int] = Field(1536, description="Dimensions of the embedding vectors") mongo_uri: str = Field("mongodb://localhost:27017", description="MongoDB URI. Default is mongodb://localhost:27017") - @model_validator(mode='before') + @model_validator(mode="before") @classmethod def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]: allowed_fields = set(cls.model_fields.keys()) diff --git a/mem0/configs/vector_stores/opensearch.py b/mem0/configs/vector_stores/opensearch.py index 1afe6cf3..05681b9b 100644 --- a/mem0/configs/vector_stores/opensearch.py +++ b/mem0/configs/vector_stores/opensearch.py @@ -36,6 +36,6 @@ class OpenSearchConfig(BaseModel): extra_fields = input_fields - allowed_fields if extra_fields: raise ValueError( - f"Extra fields not allowed: {', '.join(extra_fields)}. " f"Allowed fields: {', '.join(allowed_fields)}" + f"Extra fields not allowed: {', '.join(extra_fields)}. Allowed fields: {', '.join(allowed_fields)}" ) return values diff --git a/mem0/embeddings/gemini.py b/mem0/embeddings/gemini.py index 59444b42..203b311c 100644 --- a/mem0/embeddings/gemini.py +++ b/mem0/embeddings/gemini.py @@ -36,4 +36,4 @@ class GoogleGenAIEmbedding(EmbeddingBase): # Call the embed_content method with the correct parameters response = self.client.models.embed_content(model=self.config.model, contents=text, config=config) - return response.embeddings[0].values \ No newline at end of file + return response.embeddings[0].values diff --git a/mem0/llms/aws_bedrock.py b/mem0/llms/aws_bedrock.py index 8b2d2d19..b170f298 100644 --- a/mem0/llms/aws_bedrock.py +++ b/mem0/llms/aws_bedrock.py @@ -92,10 +92,12 @@ class AWSBedrockLLM(LLMBase): if response["output"]["message"]["content"]: for item in response["output"]["message"]["content"]: if "toolUse" in item: - processed_response["tool_calls"].append({ - "name": item["toolUse"]["name"], - "arguments": item["toolUse"]["input"], - }) + processed_response["tool_calls"].append( + { + "name": item["toolUse"]["name"], + "arguments": item["toolUse"]["input"], + } + ) return processed_response diff --git a/mem0/llms/gemini.py b/mem0/llms/gemini.py index 7085f158..1e1c7872 100644 --- a/mem0/llms/gemini.py +++ b/mem0/llms/gemini.py @@ -165,7 +165,6 @@ class GeminiLLM(LLMBase): if system_instruction: config_params["system_instruction"] = system_instruction - if response_format is not None and response_format["type"] == "json_object": config_params["response_mime_type"] = "application/json" if "schema" in response_format: @@ -175,7 +174,6 @@ class GeminiLLM(LLMBase): formatted_tools = self._reformat_tools(tools) config_params["tools"] = formatted_tools - if tool_choice: if tool_choice == "auto": mode = types.FunctionCallingConfigMode.AUTO diff --git a/mem0/llms/sarvam.py b/mem0/llms/sarvam.py index 30a28f3d..b3389c94 100644 --- a/mem0/llms/sarvam.py +++ b/mem0/llms/sarvam.py @@ -18,7 +18,7 @@ class SarvamLLM(LLMBase): if not self.api_key: raise ValueError( - "Sarvam API key is required. Set SARVAM_API_KEY environment variable " "or provide api_key in config." + "Sarvam API key is required. Set SARVAM_API_KEY environment variable or provide api_key in config." ) # Set base URL - use config value or environment or default diff --git a/mem0/llms/vllm.py b/mem0/llms/vllm.py index bf66d4aa..52abf427 100644 --- a/mem0/llms/vllm.py +++ b/mem0/llms/vllm.py @@ -7,7 +7,6 @@ from openai import OpenAI from mem0.configs.llms.base import BaseLlmConfig from mem0.llms.base import LLMBase from mem0.memory.utils import extract_json -from openai import OpenAI class VllmLLM(LLMBase): @@ -41,10 +40,12 @@ class VllmLLM(LLMBase): if response.choices[0].message.tool_calls: for tool_call in response.choices[0].message.tool_calls: - processed_response["tool_calls"].append({ - "name": tool_call.function.name, - "arguments": json.loads(extract_json(tool_call.function.arguments)), - }) + processed_response["tool_calls"].append( + { + "name": tool_call.function.name, + "arguments": json.loads(extract_json(tool_call.function.arguments)), + } + ) return processed_response else: diff --git a/mem0/memory/graph_memory.py b/mem0/memory/graph_memory.py index 95e5077b..0a1aaa9b 100644 --- a/mem0/memory/graph_memory.py +++ b/mem0/memory/graph_memory.py @@ -136,7 +136,6 @@ class MemoryGraph: params = {"user_id": filters["user_id"]} self.graph.query(cypher, params=params) - def get_all(self, filters, limit=100): """ Retrieves all nodes and relationships from the graph database based on optional filtering criteria. @@ -176,7 +175,6 @@ class MemoryGraph: return final_results - def _retrieve_nodes_from_data(self, data, filters): """Extracts all the entities mentioned in the query.""" _tools = [EXTRACT_ENTITIES_TOOL] @@ -213,7 +211,7 @@ class MemoryGraph: def _establish_nodes_relations_from_data(self, data, filters, entity_type_map): """Establish relations among the extracted nodes.""" - # Compose user identification string for prompt + # Compose user identification string for prompt user_identity = f"user_id: {filters['user_id']}" if filters.get("agent_id"): user_identity += f", agent_id: {filters['agent_id']}" @@ -221,9 +219,7 @@ class MemoryGraph: if self.config.graph_store.custom_prompt: system_content = EXTRACT_RELATIONS_PROMPT.replace("USER_ID", user_identity) # Add the custom prompt line if configured - system_content = system_content.replace( - "CUSTOM_PROMPT", f"4. {self.config.graph_store.custom_prompt}" - ) + system_content = system_content.replace("CUSTOM_PROMPT", f"4. {self.config.graph_store.custom_prompt}") messages = [ {"role": "system", "content": system_content}, {"role": "user", "content": data}, @@ -336,7 +332,7 @@ class MemoryGraph: user_id = filters["user_id"] agent_id = filters.get("agent_id", None) results = [] - + for item in to_be_deleted: source = item["source"] destination = item["destination"] @@ -349,7 +345,7 @@ class MemoryGraph: "dest_name": destination, "user_id": user_id, } - + if agent_id: agent_filter = "AND n.agent_id = $agent_id AND m.agent_id = $agent_id" params["agent_id"] = agent_id @@ -366,10 +362,10 @@ class MemoryGraph: m.name AS target, type(r) AS relationship """ - + result = self.graph.query(cypher, params=params) results.append(result) - + return results def _add_entities(self, to_be_added, filters, entity_type_map): @@ -430,7 +426,7 @@ class MemoryGraph: r.mentions = coalesce(r.mentions, 0) + 1 RETURN source.name AS source, type(r) AS relationship, destination.name AS target """ - + params = { "source_id": source_node_search_result[0]["elementId(source_candidate)"], "destination_name": destination, @@ -592,7 +588,6 @@ class MemoryGraph: result = self.graph.query(cypher, params=params) return result - def _search_destination_node(self, destination_embedding, filters, threshold=0.9): agent_filter = "" if filters.get("agent_id"): diff --git a/mem0/memory/main.py b/mem0/memory/main.py index 312e6dfc..54e43c82 100644 --- a/mem0/memory/main.py +++ b/mem0/memory/main.py @@ -338,7 +338,7 @@ class Memory(MemoryBase): except Exception as e: logger.error(f"Error in new_retrieved_facts: {e}") new_retrieved_facts = [] - + if not new_retrieved_facts: logger.debug("No new facts retrieved from input. Skipping memory update LLM call.") @@ -1166,7 +1166,7 @@ class AsyncMemory(MemoryBase): except Exception as e: logger.error(f"Error in new_retrieved_facts: {e}") new_retrieved_facts = [] - + if not new_retrieved_facts: logger.debug("No new facts retrieved from input. Skipping memory update LLM call.") diff --git a/mem0/memory/memgraph_memory.py b/mem0/memory/memgraph_memory.py index 9b289f78..beb8af2b 100644 --- a/mem0/memory/memgraph_memory.py +++ b/mem0/memory/memgraph_memory.py @@ -162,7 +162,7 @@ class MemoryGraph: LIMIT $limit """ params = {"user_id": filters["user_id"], "limit": limit} - + results = self.graph.query(query, params=params) final_results = [] @@ -318,7 +318,7 @@ class MemoryGraph: "user_id": filters["user_id"], "limit": limit, } - + ans = self.graph.query(cypher_query, params=params) result_relations.extend(ans) @@ -356,7 +356,7 @@ class MemoryGraph: user_id = filters["user_id"] agent_id = filters.get("agent_id", None) results = [] - + for item in to_be_deleted: source = item["source"] destination = item["destination"] @@ -369,7 +369,7 @@ class MemoryGraph: "dest_name": destination, "user_id": user_id, } - + if agent_id: agent_filter = "AND n.agent_id = $agent_id AND m.agent_id = $agent_id" params["agent_id"] = agent_id @@ -386,10 +386,10 @@ class MemoryGraph: m.name AS target, type(r) AS relationship """ - + result = self.graph.query(cypher, params=params) results.append(result) - + return results # added Entity label to all nodes for vector search to work @@ -398,7 +398,7 @@ class MemoryGraph: user_id = filters["user_id"] agent_id = filters.get("agent_id", None) results = [] - + for item in to_be_added: # entities source = item["source"] @@ -421,7 +421,7 @@ class MemoryGraph: agent_id_clause = "" if agent_id: agent_id_clause = ", agent_id: $agent_id" - + # TODO: Create a cypher query and common params for all the cases if not destination_node_search_result and source_node_search_result: cypher = f""" @@ -446,7 +446,7 @@ class MemoryGraph: } if agent_id: params["agent_id"] = agent_id - + elif destination_node_search_result and not source_node_search_result: cypher = f""" MATCH (destination:Entity) @@ -470,7 +470,7 @@ class MemoryGraph: } if agent_id: params["agent_id"] = agent_id - + elif source_node_search_result and destination_node_search_result: cypher = f""" MATCH (source:Entity) @@ -490,7 +490,7 @@ class MemoryGraph: } if agent_id: params["agent_id"] = agent_id - + else: cypher = f""" MERGE (n:{source_type}:Entity {{name: $source_name, user_id: $user_id{agent_id_clause}}}) @@ -512,7 +512,7 @@ class MemoryGraph: } if agent_id: params["agent_id"] = agent_id - + result = self.graph.query(cypher, params=params) results.append(result) return results @@ -528,7 +528,7 @@ class MemoryGraph: """Search for source nodes with similar embeddings.""" user_id = filters["user_id"] agent_id = filters.get("agent_id", None) - + if agent_id: cypher = """ CALL vector_search.search("memzero", 1, $source_embedding) @@ -567,7 +567,7 @@ class MemoryGraph: """Search for destination nodes with similar embeddings.""" user_id = filters["user_id"] agent_id = filters.get("agent_id", None) - + if agent_id: cypher = """ CALL vector_search.search("memzero", 1, $destination_embedding) diff --git a/mem0/vector_stores/mongodb.py b/mem0/vector_stores/mongodb.py index 49a539f6..5b212eed 100644 --- a/mem0/vector_stores/mongodb.py +++ b/mem0/vector_stores/mongodb.py @@ -1,5 +1,5 @@ import logging -from typing import List, Optional, Dict, Any, Callable +from typing import List, Optional, Dict, Any from pydantic import BaseModel @@ -26,13 +26,7 @@ class MongoDB(VectorStoreBase): VECTOR_TYPE = "knnVector" SIMILARITY_METRIC = "cosine" - def __init__( - self, - db_name: str, - collection_name: str, - embedding_model_dims: int, - mongo_uri: str - ): + def __init__(self, db_name: str, collection_name: str, embedding_model_dims: int, mongo_uri: str): """ Initialize the MongoDB vector store with vector search capabilities. @@ -46,9 +40,7 @@ class MongoDB(VectorStoreBase): self.embedding_model_dims = embedding_model_dims self.db_name = db_name - self.client = MongoClient( - mongo_uri - ) + self.client = MongoClient(mongo_uri) self.db = self.client[db_name] self.collection = self.create_col() @@ -119,7 +111,9 @@ class MongoDB(VectorStoreBase): except PyMongoError as e: logger.error(f"Error inserting data: {e}") - def search(self, query: str, query_vector: List[float], limit=5, filters: Optional[Dict] = None) -> List[OutputData]: + def search( + self, query: str, query_vector: List[float], limit=5, filters: Optional[Dict] = None + ) -> List[OutputData]: """ Search for similar vectors using the vector search index. @@ -285,7 +279,7 @@ class MongoDB(VectorStoreBase): except PyMongoError as e: logger.error(f"Error listing documents: {e}") return [] - + def reset(self): """Reset the index by deleting and recreating it.""" logger.warning(f"Resetting index {self.collection_name}...") diff --git a/mem0/vector_stores/opensearch.py b/mem0/vector_stores/opensearch.py index 211970fc..7d417570 100644 --- a/mem0/vector_stores/opensearch.py +++ b/mem0/vector_stores/opensearch.py @@ -88,7 +88,7 @@ class OpenSearchDB(VectorStoreBase): self.client.indices.create(index=name, 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"opensearch-py>=2.0.0", "langchain-memgraph>=0.1.0", diff --git a/tests/embeddings/test_gemini_emeddings.py b/tests/embeddings/test_gemini_emeddings.py index 32c55982..e3254702 100644 --- a/tests/embeddings/test_gemini_emeddings.py +++ b/tests/embeddings/test_gemini_emeddings.py @@ -1,4 +1,4 @@ -from unittest.mock import patch +from unittest.mock import patch, ANY import pytest @@ -8,8 +8,10 @@ from mem0.embeddings.gemini import GoogleGenAIEmbedding @pytest.fixture def mock_genai(): - with patch("mem0.embeddings.gemini.genai.embed_content") as mock_genai: - yield mock_genai + with patch("mem0.embeddings.gemini.genai.Client") as mock_client_class: + mock_client = mock_client_class.return_value + mock_client.models.embed_content.return_value = None + yield mock_client.models.embed_content @pytest.fixture @@ -18,7 +20,9 @@ def config(): def test_embed_query(mock_genai, config): - mock_embedding_response = {"embedding": [0.1, 0.2, 0.3, 0.4]} + mock_embedding_response = type('Response', (), { + 'embeddings': [type('Embedding', (), {'values': [0.1, 0.2, 0.3, 0.4]})] + })() mock_genai.return_value = mock_embedding_response embedder = GoogleGenAIEmbedding(config) @@ -27,10 +31,11 @@ def test_embed_query(mock_genai, config): embedding = embedder.embed(text) assert embedding == [0.1, 0.2, 0.3, 0.4] - mock_genai.assert_called_once_with(model="test_model", content="Hello, world!", output_dimensionality=786) + mock_genai.assert_called_once_with(model="test_model", contents="Hello, world!", config=ANY) + def test_embed_returns_empty_list_if_none(mock_genai, config): - mock_genai.return_value = None + mock_genai.return_value = type('Response', (), {'embeddings': [type('Embedding', (), {'values': []})]})() embedder = GoogleGenAIEmbedding(config) result = embedder.embed("test") @@ -47,10 +52,10 @@ def test_embed_raises_on_error(mock_genai, config): with pytest.raises(RuntimeError, match="Embedding failed"): embedder.embed("some input") + def test_config_initialization(config): embedder = GoogleGenAIEmbedding(config) assert embedder.config.api_key == "dummy_api_key" assert embedder.config.model == "test_model" assert embedder.config.embedding_dims == 786 - diff --git a/tests/llms/test_gemini.py b/tests/llms/test_gemini.py index b086d150..e28f7062 100644 --- a/tests/llms/test_gemini.py +++ b/tests/llms/test_gemini.py @@ -9,7 +9,7 @@ from mem0.llms.gemini import GeminiLLM @pytest.fixture def mock_gemini_client(): - with patch("mem0.llms.gemini.genai") as mock_client_class: + with patch("mem0.llms.gemini.genai.Client") as mock_client_class: mock_client = Mock() mock_client_class.return_value = mock_client yield mock_client @@ -24,43 +24,30 @@ def test_generate_response_without_tools(mock_gemini_client: Mock): ] mock_part = Mock(text="I'm doing well, thank you for asking!") - mock_embedding = Mock() - mock_embedding.values = [0.1, 0.2, 0.3] - - mock_response = Mock() - mock_response.candidates = [Mock()] - mock_response.candidates[0].content.parts = [Mock()] - mock_response.candidates[0].content.parts[0].text = "I'm doing well, thank you for asking!" + mock_content = Mock(parts=[mock_part]) + mock_candidate = Mock(content=mock_content) + mock_response = Mock(candidates=[mock_candidate]) mock_gemini_client.models.generate_content.return_value = mock_response - mock_content = Mock(parts=[mock_part]) - mock_message = Mock(content=mock_content) - mock_response = Mock(candidates=[mock_message]) - mock_gemini_client.generate_content.return_value = mock_response response = llm.generate_response(messages) - mock_gemini_client.generate_content.assert_called_once_with( - contents=[ - {"parts": "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: You are a helpful assistant.", "role": "user"}, - {"parts": "Hello, how are you?", "role": "user"}, - ], - config=types.GenerateContentConfig( - temperature=0.7, - max_output_tokens=100, - top_p=1.0, - tools=None, - tool_config=types.ToolConfig( - function_calling_config=types.FunctionCallingConfig( - allowed_function_names=None, - mode="auto" - - ) - ) - ) ) - - assert response == "I'm doing well, thank you for asking!" + # Check the actual call - system instruction is now in config + mock_gemini_client.models.generate_content.assert_called_once() + call_args = mock_gemini_client.models.generate_content.call_args + # Verify model and contents + assert call_args.kwargs['model'] == "gemini-2.0-flash-latest" + assert len(call_args.kwargs['contents']) == 1 # Only user message + + # Verify config has system instruction + config_arg = call_args.kwargs['config'] + assert config_arg.system_instruction == "You are a helpful assistant." + assert config_arg.temperature == 0.7 + assert config_arg.max_output_tokens == 100 + assert config_arg.top_p == 1.0 + + assert response == "I'm doing well, thank you for asking!" def test_generate_response_with_tools(mock_gemini_client: Mock): @@ -89,64 +76,42 @@ def test_generate_response_with_tools(mock_gemini_client: Mock): mock_tool_call.name = "add_memory" mock_tool_call.args = {"data": "Today is a sunny day."} - mock_part = Mock() - mock_part.function_call = mock_tool_call - mock_part.text = "I've added the memory for you." + # Create mock parts with both text and function_call + mock_text_part = Mock() + mock_text_part.text = "I've added the memory for you." + mock_text_part.function_call = None + + mock_func_part = Mock() + mock_func_part.text = None + mock_func_part.function_call = mock_tool_call mock_content = Mock() - mock_content.parts = [mock_part] + mock_content.parts = [mock_text_part, mock_func_part] - mock_message = Mock() - mock_message.content = mock_content + mock_candidate = Mock() + mock_candidate.content = mock_content - mock_response = Mock(candidates=[mock_message]) - mock_gemini_client.generate_content.return_value = mock_response + mock_response = Mock(candidates=[mock_candidate]) + mock_gemini_client.models.generate_content.return_value = mock_response response = llm.generate_response(messages, tools=tools) - mock_gemini_client.generate_content.assert_called_once_with( - contents=[ - { - "parts": "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: You are a helpful assistant.", - "role": "user" - }, - { - "parts": "Add a new memory: Today is a sunny day.", - "role": "user" - }, - ], - config=types.GenerateContentConfig( - temperature=0.7, - max_output_tokens=100, - top_p=1.0, - tools=[ - types.Tool( - function_declarations=[ - types.FunctionDeclaration( - name="add_memory", - description="Add a memory", - parameters={ - "type": "object", - "properties": { - "data": { - "type": "string", - "description": "Data to add to memory" - } - }, - "required": ["data"] - } - ) - ] - ) - ], - tool_config=types.ToolConfig( - function_calling_config=types.FunctionCallingConfig( - allowed_function_names=None, - mode="auto" - ) - ) - ) - ) + # Check the actual call + mock_gemini_client.models.generate_content.assert_called_once() + call_args = mock_gemini_client.models.generate_content.call_args + + # Verify model and contents + assert call_args.kwargs['model'] == "gemini-1.5-flash-latest" + assert len(call_args.kwargs['contents']) == 1 # Only user message + + # Verify config has system instruction and tools + config_arg = call_args.kwargs['config'] + assert config_arg.system_instruction == "You are a helpful assistant." + assert config_arg.temperature == 0.7 + assert config_arg.max_output_tokens == 100 + assert config_arg.top_p == 1.0 + assert len(config_arg.tools) == 1 + assert config_arg.tool_config.function_calling_config.mode == types.FunctionCallingConfigMode.AUTO assert response["content"] == "I've added the memory for you." assert len(response["tool_calls"]) == 1 diff --git a/tests/llms/test_lm_studio.py b/tests/llms/test_lm_studio.py index 37033e02..c2eace61 100644 --- a/tests/llms/test_lm_studio.py +++ b/tests/llms/test_lm_studio.py @@ -43,6 +43,7 @@ def test_generate_response_without_tools(mock_lm_studio_client): assert response == "I'm doing well, thank you for asking!" + def test_generate_response_specifying_response_format(mock_lm_studio_client): config = BaseLlmConfig( model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", @@ -68,4 +69,4 @@ def test_generate_response_specifying_response_format(mock_lm_studio_client): response_format={"type": "json_schema"}, ) - assert response == "I'm doing well, thank you for asking!" \ No newline at end of file + assert response == "I'm doing well, thank you for asking!" diff --git a/tests/llms/test_vllm.py b/tests/llms/test_vllm.py index fd5c272b..7b2bfcac 100644 --- a/tests/llms/test_vllm.py +++ b/tests/llms/test_vllm.py @@ -71,7 +71,13 @@ def test_generate_response_with_tools(mock_vllm_client): response = llm.generate_response(messages, tools=tools) mock_vllm_client.chat.completions.create.assert_called_once_with( - model="Qwen/Qwen2.5-32B-Instruct", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0, tools=tools, tool_choice="auto" + model="Qwen/Qwen2.5-32B-Instruct", + messages=messages, + temperature=0.7, + max_tokens=100, + top_p=1.0, + tools=tools, + tool_choice="auto", ) assert response["content"] == "I've added the memory for you." diff --git a/tests/test_main.py b/tests/test_main.py index c5b45b34..c89e7bc3 100644 --- a/tests/test_main.py +++ b/tests/test_main.py @@ -253,10 +253,10 @@ def test_get_all(memory_instance, version, enable_graph, expected_result): def test_custom_prompts(memory_custom_instance): messages = [{"role": "user", "content": "Test message"}] from mem0.embeddings.mock import MockEmbeddings + memory_custom_instance.llm.generate_response = Mock() memory_custom_instance.llm.generate_response.return_value = '{"facts": ["fact1", "fact2"]}' memory_custom_instance.embedding_model = MockEmbeddings() - with patch("mem0.memory.main.parse_messages", return_value="Test message") as mock_parse_messages: with patch( diff --git a/tests/vector_stores/test_baidu.py b/tests/vector_stores/test_baidu.py index c5ef5734..987c298b 100644 --- a/tests/vector_stores/test_baidu.py +++ b/tests/vector_stores/test_baidu.py @@ -2,10 +2,9 @@ from unittest.mock import Mock, patch, PropertyMock import pytest -from mem0.vector_stores.baidu import BaiduDB, OutputData -from pymochow.model.enum import MetricType, TableState, ServerErrCode -from pymochow.model.schema import Field, Schema, VectorIndex, FilteringIndex, HNSWParams, AutoBuildRowCountIncrement -from pymochow.model.table import Partition, Row, VectorSearchConfig, VectorTopkSearchRequest, FloatVector, Table +from mem0.vector_stores.baidu import BaiduDB +from pymochow.model.enum import TableState, ServerErrCode +from pymochow.model.table import VectorSearchConfig, VectorTopkSearchRequest, FloatVector, Table from pymochow.exception import ServerError diff --git a/tests/vector_stores/test_mongodb.py b/tests/vector_stores/test_mongodb.py index 066c7b17..812abfc2 100644 --- a/tests/vector_stores/test_mongodb.py +++ b/tests/vector_stores/test_mongodb.py @@ -1,7 +1,7 @@ import pytest from unittest.mock import MagicMock, patch from mem0.vector_stores.mongodb import MongoDB -from pymongo.operations import SearchIndexModel + @pytest.fixture @patch("mem0.vector_stores.mongodb.MongoClient") @@ -19,10 +19,11 @@ def mongo_vector_fixture(mock_mongo_client): db_name="test_db", collection_name="test_collection", embedding_model_dims=1536, - mongo_uri="mongodb://username:password@localhost:27017" + mongo_uri="mongodb://username:password@localhost:27017", ) return mongo_vector, mock_collection, mock_db + def test_initalize_create_col(mongo_vector_fixture): mongo_vector, mock_collection, mock_db = mongo_vector_fixture assert mongo_vector.collection_name == "test_collection" @@ -49,12 +50,13 @@ def test_initalize_create_col(mongo_vector_fixture): "dimensions": 1536, "similarity": "cosine", } - } + }, } - } + }, } assert mongo_vector.collection == mock_collection + def test_insert(mongo_vector_fixture): mongo_vector, mock_collection, _ = mongo_vector_fixture vectors = [[0.1] * 1536, [0.2] * 1536] @@ -62,12 +64,13 @@ def test_insert(mongo_vector_fixture): ids = ["id1", "id2"] mongo_vector.insert(vectors, payloads, ids) - expected_records=[ + expected_records = [ ({"_id": ids[0], "embedding": vectors[0], "payload": payloads[0]}), - ({"_id": ids[1], "embedding": vectors[1], "payload": payloads[1]}) + ({"_id": ids[1], "embedding": vectors[1], "payload": payloads[1]}), ] mock_collection.insert_many.assert_called_once_with(expected_records) + def test_search(mongo_vector_fixture): mongo_vector, mock_collection, _ = mongo_vector_fixture query_vector = [0.1] * 1536 @@ -79,25 +82,28 @@ def test_search(mongo_vector_fixture): results = mongo_vector.search("query_str", query_vector, limit=2) mock_collection.list_search_indexes.assert_called_with(name="test_collection_vector_index") - mock_collection.aggregate.assert_called_once_with([ - { - "$vectorSearch": { - "index": "test_collection_vector_index", - "limit": 2, - "numCandidates": 2, - "queryVector": query_vector, - "path": "embedding", + mock_collection.aggregate.assert_called_once_with( + [ + { + "$vectorSearch": { + "index": "test_collection_vector_index", + "limit": 2, + "numCandidates": 2, + "queryVector": query_vector, + "path": "embedding", + }, }, - }, - {"$set": {"score": {"$meta": "vectorSearchScore"}}}, - {"$project": {"embedding": 0}}, - ]) + {"$set": {"score": {"$meta": "vectorSearchScore"}}}, + {"$project": {"embedding": 0}}, + ] + ) assert len(results) == 2 assert results[0].id == "id1" assert results[0].score == 0.9 assert results[1].id == "id2" assert results[1].score == 0.8 + def test_delete(mongo_vector_fixture): mongo_vector, mock_collection, _ = mongo_vector_fixture mock_delete_result = MagicMock() @@ -107,6 +113,7 @@ def test_delete(mongo_vector_fixture): mongo_vector.delete("id1") mock_collection.delete_one.assert_called_with({"_id": "id1"}) + def test_update(mongo_vector_fixture): mongo_vector, mock_collection, _ = mongo_vector_fixture mock_update_result = MagicMock() @@ -122,6 +129,7 @@ def test_update(mongo_vector_fixture): {"$set": {"embedding": vectorValue, "payload": payloadValue}}, ) + def test_get(mongo_vector_fixture): mongo_vector, mock_collection, _ = mongo_vector_fixture mock_collection.find_one.return_value = {"_id": "id1", "payload": {"key": "value1"}} @@ -131,6 +139,7 @@ def test_get(mongo_vector_fixture): assert result.id == "id1" assert result.payload == {"key": "value1"} + def test_list_cols(mongo_vector_fixture): mongo_vector, _, mock_db = mongo_vector_fixture mock_db.list_collection_names.return_value = ["col1", "col2"] @@ -138,12 +147,14 @@ def test_list_cols(mongo_vector_fixture): collections = mongo_vector.list_cols() assert collections == ["col1", "col2"] + def test_delete_col(mongo_vector_fixture): mongo_vector, mock_collection, _ = mongo_vector_fixture mongo_vector.delete_col() mock_collection.drop.assert_called_once() + def test_col_info(mongo_vector_fixture): mongo_vector, _, mock_db = mongo_vector_fixture mock_db.command.return_value = {"count": 10, "size": 1024} @@ -154,6 +165,7 @@ def test_col_info(mongo_vector_fixture): assert info["count"] == 10 assert info["size"] == 1024 + def test_list(mongo_vector_fixture): mongo_vector, mock_collection, _ = mongo_vector_fixture mock_cursor = MagicMock() diff --git a/tests/vector_stores/test_pinecone.py b/tests/vector_stores/test_pinecone.py index 2cfaf996..0a7fad05 100644 --- a/tests/vector_stores/test_pinecone.py +++ b/tests/vector_stores/test_pinecone.py @@ -88,7 +88,7 @@ def test_get_vector_found(pinecone_db): # or a list of dictionaries, not a dictionary with an 'id' field # Create a mock Vector object - from pinecone.data.dataclasses.vector import Vector + from pinecone import Vector mock_vector = Vector(id="id1", values=[0.1] * 128, metadata={"name": "vector1"})