Doc changes and Storage fix (#2181)
This commit is contained in:
Dev Khant
194
README.md
194
README.md
@@ -45,50 +45,25 @@
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[Mem0](https://mem0.ai) (pronounced as "mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. Mem0 remembers user preferences, adapts to individual needs, and continuously improves over time, making it ideal for customer support chatbots, AI assistants, and autonomous systems.
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<!-- Start of Selection -->
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<p style="display: flex;">
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<span style="font-size: 1.2em;">New Feature: Introducing Graph Memory. Check out our <a href="https://docs.mem0.ai/open-source/graph-memory" target="_blank">documentation</a>.</span>
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</p>
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<!-- End of Selection -->
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### Features & Use Cases
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Core Capabilities:
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- **Multi-Level Memory**: User, Session, and AI Agent memory retention with adaptive personalization
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- **Developer-Friendly**: Simple API integration, cross-platform consistency, and hassle-free managed service
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### Core Features
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- **Multi-Level Memory**: User, Session, and AI Agent memory retention
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- **Adaptive Personalization**: Continuous improvement based on interactions
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- **Developer-Friendly API**: Simple integration into various applications
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- **Cross-Platform Consistency**: Uniform behavior across devices
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- **Managed Service**: Hassle-free hosted solution
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### How Mem0 works?
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Mem0 leverages a hybrid database approach to manage and retrieve long-term memories for AI agents and assistants. Each memory is associated with a unique identifier, such as a user ID or agent ID, allowing Mem0 to organize and access memories specific to an individual or context.
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When a message is added to the Mem0 using add() method, the system extracts relevant facts and preferences and stores it across data stores: a vector database, a key-value database, and a graph database. This hybrid approach ensures that different types of information are stored in the most efficient manner, making subsequent searches quick and effective.
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When an AI agent or LLM needs to recall memories, it uses the search() method. Mem0 then performs search across these data stores, retrieving relevant information from each source. This information is then passed through a scoring layer, which evaluates their importance based on relevance, importance, and recency. This ensures that only the most personalized and useful context is surfaced.
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The retrieved memories can then be appended to the LLM's prompt as needed, enhancing the personalization and relevance of its responses.
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### Use Cases
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Mem0 empowers organizations and individuals to enhance:
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- **AI Assistants and agents**: Seamless conversations with a touch of déjà vu
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- **Personalized Learning**: Tailored content recommendations and progress tracking
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- **Customer Support**: Context-aware assistance with user preference memory
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- **Healthcare**: Patient history and treatment plan management
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- **Virtual Companions**: Deeper user relationships through conversation memory
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- **Productivity**: Streamlined workflows based on user habits and task history
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- **Gaming**: Adaptive environments reflecting player choices and progress
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Applications:
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- **AI Assistants**: Seamless conversations with context and personalization
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- **Learning & Support**: Tailored content recommendations and context-aware customer assistance
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- **Healthcare & Companions**: Patient history tracking and deeper relationship building
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- **Productivity & Gaming**: Streamlined workflows and adaptive environments based on user behavior
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## Get Started
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The easiest way to set up Mem0 is through the managed [Mem0 Platform](https://app.mem0.ai). This hosted solution offers automatic updates, advanced analytics, and dedicated support. [Sign up](https://app.mem0.ai) to get started.
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Get started quickly with [Mem0 Platform](https://app.mem0.ai) - our fully managed solution that provides automatic updates, advanced analytics, enterprise security, and dedicated support. [Create a free account](https://app.mem0.ai) to begin.
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If you prefer to self-host, use the open-source Mem0 package. Follow the [installation instructions](#install) to get started.
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For complete control, you can self-host Mem0 using our open-source package. See the [Quickstart guide](#quickstart) below to set up your own instance.
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## Installation Instructions <a name="install"></a>
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## Quickstart Guide <a name="quickstart"></a>
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Install the Mem0 package via pip:
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@@ -96,8 +71,6 @@ Install the Mem0 package via pip:
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pip install mem0ai
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```
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Alternatively, you can use Mem0 with one click on the hosted platform [here](https://app.mem0.ai/).
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### Basic Usage
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Mem0 requires an LLM to function, with `gpt-4o` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/llms).
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@@ -105,101 +78,70 @@ Mem0 requires an LLM to function, with `gpt-4o` from OpenAI as the default. Howe
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First step is to instantiate the memory:
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```python
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from openai import OpenAI
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from mem0 import Memory
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m = Memory()
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openai_client = OpenAI()
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mem0 = Memory()
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def chat_with_memories(message: str, user_id: str = "default_user") -> str:
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# Retrieve relevant memories
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relevant_memories = mem0.search(query=message, user_id=user_id, limit=3)
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memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories)
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# Generate Assistant response
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system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}"
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messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}]
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response = openai_client.chat.completions.create(model="gpt-4o-mini", messages=messages)
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assistant_response = response.choices[0].message.content
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# Create new memories from the conversation
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messages.append({"role": "assistant", "content": assistant_response})
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mem0.add(messages, user_id=user_id)
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return assistant_response
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def main():
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print("Chat with AI (type 'exit' to quit)")
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while True:
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user_input = input("You: ").strip()
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if user_input.lower() == 'exit':
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print("Goodbye!")
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break
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print(f"AI: {chat_with_memories(user_input)}")
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if __name__ == "__main__":
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main()
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```
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<details>
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<summary>How to set OPENAI_API_KEY</summary>
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```python
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import os
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os.environ["OPENAI_API_KEY"] = "sk-xxx"
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```
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</details>
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You can perform the following task on the memory:
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1. Add: Store a memory from any unstructured text
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2. Update: Update memory of a given memory_id
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3. Search: Fetch memories based on a query
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4. Get: Return memories for a certain user/agent/session
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5. History: Describe how a memory has changed over time for a specific memory ID
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```python
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# 1. Add: Store a memory from any unstructured text
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result = m.add("I am working on improving my tennis skills. Suggest some online courses.", user_id="alice", metadata={"category": "hobbies"})
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# Created memory --> 'Improving her tennis skills.' and 'Looking for online suggestions.'
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```
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```python
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# 2. Update: update the memory
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result = m.update(memory_id=<memory_id_1>, data="Likes to play tennis on weekends")
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# Updated memory --> 'Likes to play tennis on weekends.' and 'Looking for online suggestions.'
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```
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```python
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# 3. Search: search related memories
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related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
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# Retrieved memory --> 'Likes to play tennis on weekends'
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```
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```python
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# 4. Get all memories
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all_memories = m.get_all()
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memory_id = all_memories["memories"][0] ["id"] # get a memory_id
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# All memory items --> 'Likes to play tennis on weekends.' and 'Looking for online suggestions.'
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```
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```python
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# 5. Get memory history for a particular memory_id
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history = m.history(memory_id=<memory_id_1>)
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# Logs corresponding to memory_id_1 --> {'prev_value': 'Working on improving tennis skills and interested in online courses for tennis.', 'new_value': 'Likes to play tennis on weekends' }
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```
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For more advanced usage and API documentation, visit our [documentation](https://docs.mem0.ai).
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> [!TIP]
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> If you prefer a hosted version without the need to set up infrastructure yourself, check out the [Mem0 Platform](https://app.mem0.ai/) to get started in minutes.
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> For a hassle-free experience, try our [hosted platform](https://app.mem0.ai) with automatic updates and enterprise features.
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## Demos
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### Graph Memory
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To initialize Graph Memory you'll need to set up your configuration with graph store providers.
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Currently, we support Neo4j as a graph store provider. You can setup [Neo4j](https://neo4j.com/) locally or use the hosted [Neo4j AuraDB](https://neo4j.com/product/auradb/).
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Moreover, you also need to set the version to `v1.1` (*prior versions are not supported*).
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Here's how you can do it:
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- AI Companion: Experience personalized conversations with an AI that remembers your preferences and past interactions
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```python
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from mem0 import Memory
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config = {
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"graph_store": {
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"provider": "neo4j",
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"config": {
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"url": "neo4j+s://xxx",
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"username": "neo4j",
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"password": "xxx"
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}
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},
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"version": "v1.1"
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}
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<br/><br/>
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m = Memory.from_config(config_dict=config)
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- Enhance your AI interactions by storing memories across ChatGPT, Perplexity, and Claude using our browser extension.
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```
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## Documentation
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For detailed usage instructions and API reference, visit our documentation at [docs.mem0.ai](https://docs.mem0.ai). Here, you can find more information on both the open-source version and the hosted [Mem0 Platform](https://app.mem0.ai).
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## Star History
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[](https://star-history.com/#mem0ai/mem0&Date)
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For detailed usage instructions and API reference, visit our [documentation](https://docs.mem0.ai). You'll find:
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- Complete API reference
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- Integration guides
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- Advanced configuration options
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- Best practices and examples
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- More details about:
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- Open-source version
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- [Hosted Mem0 Platform](https://app.mem0.ai)
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## Support
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@@ -209,20 +151,6 @@ Join our community for support and discussions. If you have any questions, feel
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- [Follow us on Twitter](https://x.com/mem0ai)
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- [Email founders](mailto:founders@mem0.ai)
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## Contributors
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Join our [Discord community](https://mem0.dev/DiG) to learn about memory management for AI agents and LLMs, and connect with Mem0 users and contributors. Share your ideas, questions, or feedback in our [GitHub Issues](https://github.com/mem0ai/mem0/issues).
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We value and appreciate the contributions of our community. Special thanks to our contributors for helping us improve Mem0.
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<a href="https://github.com/mem0ai/mem0/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=mem0ai/mem0" />
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</a>
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## Anonymous Telemetry
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We collect anonymous usage metrics to enhance our package's quality and user experience. This includes data like feature usage frequency and system info, but never personal details. The data helps us prioritize improvements and ensure compatibility. If you wish to opt-out, set the environment variable MEM0_TELEMETRY=false. We prioritize data security and don't share this data externally.
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## License
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This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.
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@@ -53,7 +53,6 @@
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"pages": [
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"overview",
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"quickstart",
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"playground",
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"features"
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]
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},
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@@ -65,8 +64,7 @@
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{
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"group": "Features",
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"pages": ["features/selective-memory", "features/custom-categories", "features/custom-instructions", "features/direct-import", "features/async-client", "features/memory-export"]
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},
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"features/langchain-tools"
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}
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]
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},
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{
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@@ -221,7 +219,8 @@
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"integrations/autogen",
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"integrations/langchain",
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"integrations/langgraph",
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"integrations/llama-index"
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"integrations/llama-index",
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"integrations/langchain-tools"
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]
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},
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{
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@@ -18,7 +18,7 @@ Mem0 offers two powerful ways to leverage our technology: our [managed platform]
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<Card title="Playground" icon="play" href="playground">
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Mem0 in action
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</Card>
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<Card title="Examples" icon="lightbulb" href="/open-source/quickstart">
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<Card title="Examples" icon="lightbulb" href="/examples">
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See what you can build with Mem0
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</Card>
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</CardGroup>
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@@ -27,6 +27,6 @@ Check out our [Platform Guide](/platform/guide) to start using Mem0 platform qui
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## Next Steps
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- Sign up to the [Mem0 Platform](https://mem0.dev/pd)
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- Join our [Discord](https://mem0.dev/Did) or [Slack](https://mem0.ai/slack) with other developers and get support.
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- Join our [Discord](https://mem0.dev/Did) or [Slack](https://mem0.dev/slack) with other developers and get support.
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We're excited to see what you'll build with Mem0 Platform. Let's create smarter, more personalized AI experiences together!
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@@ -27,8 +27,12 @@ npm install mem0ai
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<CodeGroup>
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```python Python
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import os
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from mem0 import MemoryClient
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client = MemoryClient(api_key="your-api-key")
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os.environ["MEM0_API_KEY"] = "your-api-key"
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client = MemoryClient()
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```
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```javascript JavaScript
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@@ -43,9 +47,12 @@ const client = new MemoryClient({ apiKey: 'your-api-key' });
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For asynchronous operations in Python, you can use the AsyncMemoryClient:
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```python Python
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import os
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from mem0 import AsyncMemoryClient
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client = AsyncMemoryClient(api_key="your-api-key")
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os.environ["MEM0_API_KEY"] = "your-api-key"
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client = AsyncMemoryClient()
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async def main():
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@@ -1641,7 +1648,18 @@ curl -X POST "https://api.mem0.ai/v1/memories/" \
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```
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```json Output
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{'message': 'ok'}
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[{'id': '3f4eccba-3b09-497a-81ab-cca1ababb36b',
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'memory': 'Is allergic to nuts',
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'event': 'DELETE'},
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{'id': 'f5dcfbf4-5f0b-422a-8ad4-cadb9e941e25',
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'memory': 'Is a vegetarian',
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'event': 'DELETE'},
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{'id': 'dd32f70c-fa69-4fc7-997b-fb4a66d1a0fa',
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'memory': 'Name is Alex',
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'event': 'DELETE'}]{'message': 'ok'}
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{'id': '3f4eccba-3b09-497a-81ab-cca1ababb36b',
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'memory': 'Likes Chicken',
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'event': 'DELETE'}
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```
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</CodeGroup>
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@@ -1,25 +0,0 @@
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---
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title: Interactive Playground
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---
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Watch Mem0 in action with our Playground tool.
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<Steps>
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<Step title="Create Mem0 Account">
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You'll need to create a free Mem0 account to use the playground – this helps ensure responses are more tailored to you by associating interactions with an individual profile.
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<Card title="Sign up to Mem0" icon="right-to-bracket" href="https://mem0.dev/pd" horizontal="true">
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</Card>
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</Step>
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<Step title="Go to Playground">
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<Card title="Mem0 Playground" icon="play" href="https://mem0.dev/pd-pg" horizontal="true">
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</Card>
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</Step>
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<Step title="Start adding memories">
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Chat with the assistant to start adding memories.
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</Step>
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<Step title="Experience the power of Mem0">
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Memories are stored in context for all future conversations, creating truly personal AI.
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</Step>
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</Steps>
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@@ -3,6 +3,8 @@ title: Quickstart
|
||||
---
|
||||
Mem0 offers two powerful ways to leverage our technology: [our managed platform](#mem0-platform-managed-solution) and [our open source solution](#mem0-open-source).
|
||||
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||||
Check out our [Playground](https://mem0.dev/pd-pg) to see Mem0 in action.
|
||||
|
||||
<CardGroup cols={2}>
|
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<Card title="Mem0 Platform" icon="chart-simple" href="#mem0-platform-managed-solution">
|
||||
Better, faster, fully managed, and hassle free solution.
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||||
@@ -13,7 +15,7 @@ Mem0 offers two powerful ways to leverage our technology: [our managed platform]
|
||||
</CardGroup>
|
||||
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||||
|
||||
## Mem0 Platform (Managed Solution)
|
||||
## Mem0 Platform
|
||||
|
||||
Our fully managed platform provides a hassle-free way to integrate Mem0's capabilities into your AI agents and assistants. Sign up for Mem0 platform [here](https://mem0.dev/pd).
|
||||
|
||||
@@ -53,8 +55,12 @@ npm install mem0ai
|
||||
<Accordion title="Instantiate client">
|
||||
<CodeGroup>
|
||||
```python Python
|
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import os
|
||||
from mem0 import MemoryClient
|
||||
client = MemoryClient(api_key="your-api-key")
|
||||
|
||||
os.environ["MEM0_API_KEY"] = "your-api-key"
|
||||
|
||||
client = MemoryClient()
|
||||
```
|
||||
|
||||
```javascript JavaScript
|
||||
@@ -98,7 +104,15 @@ curl -X POST "https://api.mem0.ai/v1/memories/" \
|
||||
```
|
||||
|
||||
```json Output
|
||||
{'message': 'ok'}
|
||||
[{'id': '24e466b5-e1c6-4bde-8a92-f09a327ffa60',
|
||||
'memory': 'Name is Alex',
|
||||
'event': 'ADD'},
|
||||
{'id': 'f2d874ac-09c7-49db-b34a-22cf666bd4ad',
|
||||
'memory': 'Is a vegetarian',
|
||||
'event': 'ADD'},
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||||
{'id': 'bce04006-01e8-4dbc-8a22-67fa46f1822c',
|
||||
'memory': 'Is allergic to nuts',
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||||
'event': 'ADD'}]
|
||||
```
|
||||
</CodeGroup>
|
||||
</Accordion>
|
||||
@@ -112,23 +126,43 @@ curl -X POST "https://api.mem0.ai/v1/memories/" \
|
||||
|
||||
```python Python
|
||||
query = "What can I cook for dinner tonight?"
|
||||
client.search(query, user_id="alex")
|
||||
filters = {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alex"
|
||||
}
|
||||
]
|
||||
}
|
||||
client.search(query, version="v2", filters=filters)
|
||||
```
|
||||
|
||||
```javascript JavaScript
|
||||
const query = "What can I cook for dinner tonight?";
|
||||
client.search(query, { user_id: "alex" })
|
||||
const filters = {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alex"
|
||||
}
|
||||
]
|
||||
};
|
||||
client.search(query, { version: "v2", filters })
|
||||
.then(results => console.log(results))
|
||||
.catch(error => console.error(error));
|
||||
```
|
||||
|
||||
```bash cURL
|
||||
curl -X POST "https://api.mem0.ai/v1/memories/search/" \
|
||||
curl -X POST "https://api.mem0.ai/v1/memories/search/?version=v2" \
|
||||
-H "Authorization: Token your-api-key" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"query": "What can I cook for dinner tonight?",
|
||||
"user_id": "alex"
|
||||
"filters": {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alex"
|
||||
}
|
||||
]
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
@@ -163,18 +197,44 @@ curl -X POST "https://api.mem0.ai/v1/memories/search/" \
|
||||
<CodeGroup>
|
||||
|
||||
```python Python
|
||||
user_memories = client.get_all(user_id="alex")
|
||||
filters = {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alice"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
all_memories = client.get_all(version="v2", filters=filters, page=1, page_size=50)
|
||||
```
|
||||
|
||||
```javascript JavaScript
|
||||
client.getAll({ user_id: "alex" })
|
||||
const filters = {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alice"
|
||||
}
|
||||
]
|
||||
};
|
||||
|
||||
client.getAll({ version: "v2", filters, page: 1, page_size: 50 })
|
||||
.then(memories => console.log(memories))
|
||||
.catch(error => console.error(error));
|
||||
```
|
||||
|
||||
```bash cURL
|
||||
curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex" \
|
||||
-H "Authorization: Token your-api-key"
|
||||
curl -X GET "https://api.mem0.ai/v1/memories/?version=v2&page=1&page_size=50" \
|
||||
-H "Authorization: Token your-api-key" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"filters": {
|
||||
"AND": [
|
||||
{
|
||||
"user_id": "alice"
|
||||
}
|
||||
]
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
```json Output
|
||||
@@ -244,7 +304,9 @@ result = m.add("I like to take long walks on weekends.", user_id="alice", metada
|
||||
```
|
||||
|
||||
```json Output
|
||||
{'message': 'ok'}
|
||||
[{'id': 'ea9b08ee-09d7-4e8b-9912-687ad65548b4',
|
||||
'memory': 'Likes to take long walks on weekends',
|
||||
'event': 'ADD'}]
|
||||
```
|
||||
</CodeGroup>
|
||||
</Accordion>
|
||||
|
||||
@@ -432,7 +432,7 @@ class Memory(MemoryBase):
|
||||
return {"results": original_memories}
|
||||
else:
|
||||
warnings.warn(
|
||||
"The current get_all API output format is deprecated. "
|
||||
"The current search API output format is deprecated. "
|
||||
"To use the latest format, set `api_version='v1.1'`. "
|
||||
"The current format will be removed in mem0ai 1.1.0 and later versions.",
|
||||
category=DeprecationWarning,
|
||||
|
||||
@@ -1,89 +1,93 @@
|
||||
import sqlite3
|
||||
import uuid
|
||||
import threading
|
||||
|
||||
|
||||
class SQLiteManager:
|
||||
def __init__(self, db_path=":memory:"):
|
||||
self.connection = sqlite3.connect(db_path, check_same_thread=False)
|
||||
self._lock = threading.Lock()
|
||||
self._migrate_history_table()
|
||||
self._create_history_table()
|
||||
|
||||
def _migrate_history_table(self):
|
||||
with self.connection:
|
||||
cursor = self.connection.cursor()
|
||||
with self._lock:
|
||||
with self.connection:
|
||||
cursor = self.connection.cursor()
|
||||
|
||||
cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='history'")
|
||||
table_exists = cursor.fetchone() is not None
|
||||
cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='history'")
|
||||
table_exists = cursor.fetchone() is not None
|
||||
|
||||
if table_exists:
|
||||
# Get the current schema of the history table
|
||||
cursor.execute("PRAGMA table_info(history)")
|
||||
current_schema = {row[1]: row[2] for row in cursor.fetchall()}
|
||||
if table_exists:
|
||||
# Get the current schema of the history table
|
||||
cursor.execute("PRAGMA table_info(history)")
|
||||
current_schema = {row[1]: row[2] for row in cursor.fetchall()}
|
||||
|
||||
# Define the expected schema
|
||||
expected_schema = {
|
||||
"id": "TEXT",
|
||||
"memory_id": "TEXT",
|
||||
"old_memory": "TEXT",
|
||||
"new_memory": "TEXT",
|
||||
"new_value": "TEXT",
|
||||
"event": "TEXT",
|
||||
"created_at": "DATETIME",
|
||||
"updated_at": "DATETIME",
|
||||
"is_deleted": "INTEGER",
|
||||
}
|
||||
# Define the expected schema
|
||||
expected_schema = {
|
||||
"id": "TEXT",
|
||||
"memory_id": "TEXT",
|
||||
"old_memory": "TEXT",
|
||||
"new_memory": "TEXT",
|
||||
"new_value": "TEXT",
|
||||
"event": "TEXT",
|
||||
"created_at": "DATETIME",
|
||||
"updated_at": "DATETIME",
|
||||
"is_deleted": "INTEGER",
|
||||
}
|
||||
|
||||
# Check if the schemas are the same
|
||||
if current_schema != expected_schema:
|
||||
# Rename the old table
|
||||
cursor.execute("ALTER TABLE history RENAME TO old_history")
|
||||
# Check if the schemas are the same
|
||||
if current_schema != expected_schema:
|
||||
# Rename the old table
|
||||
cursor.execute("ALTER TABLE history RENAME TO old_history")
|
||||
|
||||
cursor.execute(
|
||||
cursor.execute(
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS history (
|
||||
id TEXT PRIMARY KEY,
|
||||
memory_id TEXT,
|
||||
old_memory TEXT,
|
||||
new_memory TEXT,
|
||||
new_value TEXT,
|
||||
event TEXT,
|
||||
created_at DATETIME,
|
||||
updated_at DATETIME,
|
||||
is_deleted INTEGER
|
||||
)
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS history (
|
||||
id TEXT PRIMARY KEY,
|
||||
memory_id TEXT,
|
||||
old_memory TEXT,
|
||||
new_memory TEXT,
|
||||
new_value TEXT,
|
||||
event TEXT,
|
||||
created_at DATETIME,
|
||||
updated_at DATETIME,
|
||||
is_deleted INTEGER
|
||||
)
|
||||
"""
|
||||
)
|
||||
|
||||
# Copy data from the old table to the new table
|
||||
cursor.execute(
|
||||
"""
|
||||
INSERT INTO history (id, memory_id, old_memory, new_memory, new_value, event, created_at, updated_at, is_deleted)
|
||||
SELECT id, memory_id, prev_value, new_value, new_value, event, timestamp, timestamp, is_deleted
|
||||
FROM old_history
|
||||
""" # noqa: E501
|
||||
)
|
||||
# Copy data from the old table to the new table
|
||||
cursor.execute(
|
||||
"""
|
||||
INSERT INTO history (id, memory_id, old_memory, new_memory, new_value, event, created_at, updated_at, is_deleted)
|
||||
SELECT id, memory_id, prev_value, new_value, new_value, event, timestamp, timestamp, is_deleted
|
||||
FROM old_history
|
||||
""" # noqa: E501
|
||||
)
|
||||
|
||||
cursor.execute("DROP TABLE old_history")
|
||||
cursor.execute("DROP TABLE old_history")
|
||||
|
||||
self.connection.commit()
|
||||
self.connection.commit()
|
||||
|
||||
def _create_history_table(self):
|
||||
with self.connection:
|
||||
self.connection.execute(
|
||||
with self._lock:
|
||||
with self.connection:
|
||||
self.connection.execute(
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS history (
|
||||
id TEXT PRIMARY KEY,
|
||||
memory_id TEXT,
|
||||
old_memory TEXT,
|
||||
new_memory TEXT,
|
||||
new_value TEXT,
|
||||
event TEXT,
|
||||
created_at DATETIME,
|
||||
updated_at DATETIME,
|
||||
is_deleted INTEGER
|
||||
)
|
||||
"""
|
||||
CREATE TABLE IF NOT EXISTS history (
|
||||
id TEXT PRIMARY KEY,
|
||||
memory_id TEXT,
|
||||
old_memory TEXT,
|
||||
new_memory TEXT,
|
||||
new_value TEXT,
|
||||
event TEXT,
|
||||
created_at DATETIME,
|
||||
updated_at DATETIME,
|
||||
is_deleted INTEGER
|
||||
)
|
||||
"""
|
||||
)
|
||||
|
||||
def add_history(
|
||||
self,
|
||||
@@ -95,49 +99,52 @@ class SQLiteManager:
|
||||
updated_at=None,
|
||||
is_deleted=0,
|
||||
):
|
||||
with self.connection:
|
||||
self.connection.execute(
|
||||
"""
|
||||
INSERT INTO history (id, memory_id, old_memory, new_memory, event, created_at, updated_at, is_deleted)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""",
|
||||
(
|
||||
str(uuid.uuid4()),
|
||||
memory_id,
|
||||
old_memory,
|
||||
new_memory,
|
||||
event,
|
||||
created_at,
|
||||
updated_at,
|
||||
is_deleted,
|
||||
),
|
||||
)
|
||||
with self._lock:
|
||||
with self.connection:
|
||||
self.connection.execute(
|
||||
"""
|
||||
INSERT INTO history (id, memory_id, old_memory, new_memory, event, created_at, updated_at, is_deleted)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""",
|
||||
(
|
||||
str(uuid.uuid4()),
|
||||
memory_id,
|
||||
old_memory,
|
||||
new_memory,
|
||||
event,
|
||||
created_at,
|
||||
updated_at,
|
||||
is_deleted,
|
||||
),
|
||||
)
|
||||
|
||||
def get_history(self, memory_id):
|
||||
cursor = self.connection.execute(
|
||||
"""
|
||||
SELECT id, memory_id, old_memory, new_memory, event, created_at, updated_at
|
||||
FROM history
|
||||
WHERE memory_id = ?
|
||||
ORDER BY updated_at ASC
|
||||
""",
|
||||
(memory_id,),
|
||||
)
|
||||
rows = cursor.fetchall()
|
||||
return [
|
||||
{
|
||||
"id": row[0],
|
||||
"memory_id": row[1],
|
||||
"old_memory": row[2],
|
||||
"new_memory": row[3],
|
||||
"event": row[4],
|
||||
"created_at": row[5],
|
||||
"updated_at": row[6],
|
||||
}
|
||||
for row in rows
|
||||
]
|
||||
with self._lock:
|
||||
cursor = self.connection.execute(
|
||||
"""
|
||||
SELECT id, memory_id, old_memory, new_memory, event, created_at, updated_at
|
||||
FROM history
|
||||
WHERE memory_id = ?
|
||||
ORDER BY updated_at ASC
|
||||
""",
|
||||
(memory_id,),
|
||||
)
|
||||
rows = cursor.fetchall()
|
||||
return [
|
||||
{
|
||||
"id": row[0],
|
||||
"memory_id": row[1],
|
||||
"old_memory": row[2],
|
||||
"new_memory": row[3],
|
||||
"event": row[4],
|
||||
"created_at": row[5],
|
||||
"updated_at": row[6],
|
||||
}
|
||||
for row in rows
|
||||
]
|
||||
|
||||
def reset(self):
|
||||
with self.connection:
|
||||
self.connection.execute("DROP TABLE IF EXISTS history")
|
||||
self._create_history_table()
|
||||
with self._lock:
|
||||
with self.connection:
|
||||
self.connection.execute("DROP TABLE IF EXISTS history")
|
||||
self._create_history_table()
|
||||
|
||||
Reference in New Issue
Block a user