Rename embedchain to mem0 and open sourcing code for long term memory

.gitignore

@@ -179,3 +179,6 @@ notebooks/*.yaml
 
 # cache db
 *.db
+
+# local directories for testing
+eval/

Makefile

@@ -1,56 +1,32 @@
-# Variables
-PYTHON := python3
-PIP := $(PYTHON) -m pip
-PROJECT_NAME := embedchain
-
-# Targets
-.PHONY: install format lint clean test ci_lint ci_test coverage
-
-install:
-	poetry install
-
-# TODO: use a more efficient way to install these packages
-install_all:
-	poetry install --all-extras
-	poetry run pip install pinecone-text pinecone-client langchain-anthropic "unstructured[local-inference, all-docs]" ollama langchain_together==0.1.3 \
-		langchain_cohere==0.1.5 deepgram-sdk==3.2.7 langchain-huggingface psutil clarifai==10.0.1 flask==2.3.3 twilio==8.5.0 fastapi-poe==0.0.16 discord==2.3.2 \
-	 	slack-sdk==3.21.3 huggingface_hub==0.23.0 gitpython==3.1.38 yt_dlp==2023.11.14 PyGithub==1.59.1 feedparser==6.0.10 newspaper3k==0.2.8 listparser==0.19 \
-	 	modal==0.56.4329 dropbox==11.36.2 boto3==1.34.20 youtube-transcript-api==0.6.1 pytube==15.0.0 beautifulsoup4==4.12.3
-
-install_es:
-	poetry install --extras elasticsearch
-
-install_opensearch:
-	poetry install --extras opensearch
+.PHONY: format sort lint
 
-install_milvus:
-	poetry install --extras milvus
-
-shell:
-	poetry shell
+# Variables
+RUFF_OPTIONS = --line-length 120
+ISORT_OPTIONS = --profile black
 
-py_shell:
-	poetry run python
+# Default target
+all: format sort lint
 
+# Format code with ruff
 format:
-	$(PYTHON) -m black .
-	$(PYTHON) -m isort .
+	poetry run ruff check . --fix $(RUFF_OPTIONS)
 
-clean:
-	rm -rf dist build *.egg-info
+# Sort imports with isort
+sort:
+	poetry run isort . $(ISORT_OPTIONS)
 
+# Lint code with ruff
 lint:
-	poetry run ruff .
+	poetry run ruff check . $(RUFF_OPTIONS)
+
+docs:
+	cd docs && mintlify dev
 
 build:
 	poetry build
 
 publish:
 	poetry publish
 
-# for example: make test file=tests/test_factory.py
-test:
-	poetry run pytest $(file)
-
-coverage:
-	poetry run pytest --cov=$(PROJECT_NAME) --cov-report=xml
+clean:
+	poetry run rm -rf dist

README.md

@@ -1,125 +1,197 @@
-<p align="center">
-  <img src="docs/logo/dark.svg" width="400px" alt="Embedchain Logo">
-</p>
-
-<p align="center">
-  <a href="https://pypi.org/project/embedchain/">
-    <img src="https://img.shields.io/pypi/v/embedchain" alt="PyPI">
-  </a>
-  <a href="https://pepy.tech/project/embedchain">
-    <img src="https://static.pepy.tech/badge/embedchain" alt="Downloads">
-  </a>
-  <a href="https://embedchain.ai/slack">
-    <img src="https://img.shields.io/badge/slack-embedchain-brightgreen.svg?logo=slack" alt="Slack">
-  </a>
-  <a href="https://embedchain.ai/discord">
-    <img src="https://dcbadge.vercel.app/api/server/6PzXDgEjG5?style=flat" alt="Discord">
-  </a>
-  <a href="https://twitter.com/embedchain">
-    <img src="https://img.shields.io/twitter/follow/embedchain" alt="Twitter">
-  </a>
-  <a href="https://colab.research.google.com/drive/138lMWhENGeEu7Q1-6lNbNTHGLZXBBz_B?usp=sharing">
-    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab">
-  </a>
-  <a href="https://codecov.io/gh/embedchain/embedchain">
-    <img src="https://codecov.io/gh/embedchain/embedchain/graph/badge.svg?token=EMRRHZXW1Q" alt="codecov">
-  </a>
-</p>
-
-<hr />
-
-## What is Embedchain?
-
-Embedchain is an Open Source Framework for personalizing LLM responses. It makes it easy to create and deploy personalized AI apps. At its core, Embedchain follows the design principle of being *"Conventional but Configurable"* to serve both software engineers and machine learning engineers.
-
-Embedchain streamlines the creation of personalized LLM applications, offering a seamless process for managing various types of unstructured data. It efficiently segments data into manageable chunks, generates relevant embeddings, and stores them in a vector database for optimized retrieval. With a suite of diverse APIs, it enables users to extract contextual information, find precise answers, or engage in interactive chat conversations, all tailored to their own data.
-
-## 🔧 Quick install
-
-### Python API
+# Mem0: Long-Term Memory for LLMs
+
+Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.
+
+## Features
+
+- Persistent memory for users, sessions, and agents
+- Self-improving personalization
+- Simple API for easy integration
+- Cross-platform consistency
+
+## Quick Start
+
+### Installation
+
 
 ```bash
-pip install embedchain
+pip install mem0ai
 ```
 
-## ✨ Live demo
+## Usage
 
-Checkout the [Chat with PDF](https://embedchain.ai/demo/chat-pdf) live demo we created using Embedchain. You can find the source code [here](https://github.com/embedchain/embedchain/tree/main/examples/chat-pdf).
+### Instantiate
 
-## 🔍 Usage
+```python
+from mem0 import Memory
+
+m = Memory()
+```
 
-<!-- Demo GIF or Image -->
-<p align="center">
-  <img src="docs/images/cover.gif" width="900px" alt="Embedchain Demo">
-</p>
+If you want to use Qdrant in server mode, use the following method to instantiate.
 
-For example, you can create an Elon Musk bot using the following code:
+Run qdrant first:
+
+```bash
+docker pull qdrant/qdrant
+
+docker run -p 6333:6333 -p 6334:6334 \
+    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
+    qdrant/qdrant
+```
+
+Then, instantiate memory with qdrant server:
 
 ```python
-import os
-from embedchain import App
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "host": "localhost",
+            "port": 6333,
+        }
+    },
+}
 
-# Create a bot instance
-os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>"
-app = App()
+m = Memory.from_config(config)
+```
 
-# Embed online resources
-app.add("https://en.wikipedia.org/wiki/Elon_Musk")
-app.add("https://www.forbes.com/profile/elon-musk")
+### Store a Memory
 
-# Query the app
-app.query("How many companies does Elon Musk run and name those?")
-# Answer: Elon Musk currently runs several companies. As of my knowledge, he is the CEO and lead designer of SpaceX, the CEO and product architect of Tesla, Inc., the CEO and founder of Neuralink, and the CEO and founder of The Boring Company. However, please note that this information may change over time, so it's always good to verify the latest updates.
+```python
+m.add("Likes to play cricket over weekend", user_id="alex", metadata={"foo": "bar"})
+# Output:
+# [
+#   {
+#     'id': 'm1',
+#     'event': 'add',
+#     'data': 'Likes to play cricket over weekend'
+#   }
+# ]
+
+# Similarly, you can store a memory for an agent
+m.add("Agent X is best travel agent in Paris", agent_id="agent-x", metadata={"type": "long-term"})
 ```
 
-You can also try it in your browser with Google Colab:
+### Retrieve all memories
 
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing)
+#### 1. Get all memories
+```python
+m.get_all()
+# Output:
+# [
+#   {
+#     'id': 'm1',
+#     'text': 'Likes to play cricket over weekend',
+#     'metadata': {
+#       'data': 'Likes to play cricket over weekend'
+#     }
+#   },
+#   {
+#     'id': 'm2',
+#     'text': 'Agent X is best travel agent in Paris',
+#     'metadata': {
+#       'data': 'Agent X is best travel agent in Paris'
+#     }
+#   }
+# ]
 
-## 📖 Documentation
-Comprehensive guides and API documentation are available to help you get the most out of Embedchain:
+```
+#### 2. Get memories for specific user
 
-- [Introduction](https://docs.embedchain.ai/get-started/introduction#what-is-embedchain)
-- [Getting Started](https://docs.embedchain.ai/get-started/quickstart)
-- [Examples](https://docs.embedchain.ai/examples)
-- [Supported data types](https://docs.embedchain.ai/components/data-sources/overview)
+```python
+m.get_all(user_id="alex")
+```
 
-## 🔗 Join the Community
+#### 3. Get memories for specific agent
 
-* Connect with fellow developers by joining our [Slack Community](https://embedchain.ai/slack) or [Discord Community](https://embedchain.ai/discord).
+```python
+m.get_all(agent_id="agent-x")
+```
 
-* Dive into [GitHub Discussions](https://github.com/embedchain/embedchain/discussions), ask questions, or share your experiences.
+#### 4. Get memories for a user during an agent run
 
-## 🤝 Schedule a 1-on-1 Session
+```python
+m.get_all(agent_id="agent-x", user_id="alex")
+```
+
+### Retrieve a Memory
 
-Book a [1-on-1 Session](https://cal.com/taranjeetio/ec) with the founders, to discuss any issues, provide feedback, or explore how we can improve Embedchain for you.
+```python
+memory_id = "m1"
+m.get(memory_id)
+# Output:
+# {
+#   'id': '1',
+#   'text': 'Likes to play cricket over weekend',
+#   'metadata': {
+#     'data': 'Likes to play cricket over weekend'
+#   }
+# }
+```
 
-## 🌐 Contributing
+### Search for related memories
 
-Contributions are welcome! Please check out the issues on the repository, and feel free to open a pull request.
-For more information, please see the [contributing guidelines](CONTRIBUTING.md).
+```python
+m.search(query="What is my name", user_id="deshraj")
+```
+
+### Update a Memory
+
+```python
+m.update(memory_id="m1", data="Likes to play tennis")
+```
 
-For more reference, please go through [Development Guide](https://docs.embedchain.ai/contribution/dev) and [Documentation Guide](https://docs.embedchain.ai/contribution/docs).
+### Get history of a Memory
 
-<a href="https://github.com/embedchain/embedchain/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=embedchain/embedchain" />
-</a>
+```python
+m.history(memory_id="m1")
+# Output:
+# [
+#   {
+#     'id': 'h1',
+#     'memory_id': 'm1',
+#     'prev_value': None,
+#     'new_value': 'Likes to play cricket over weekend',
+#     'event': 'add',
+#     'timestamp': '2024-06-12 21:00:54.466687',
+#     'is_deleted': 0
+#   },
+#   {
+#     'id': 'h2',
+#     'memory_id': 'm1',
+#     'prev_value': 'Likes to play cricket over weekend',
+#     'new_value': 'Likes to play tennis',
+#     'event': 'update',
+#     'timestamp': '2024-06-12 21:01:17.230943',
+#     'is_deleted': 0
+#   }
+# ]
+```
 
-## Anonymous Telemetry
+### Delete a Memory
 
-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 `EC_TELEMETRY=false`. We prioritize data security and don't share this data externally.
+#### Delete specific memory
 
-## Citation
+```python
+m.delete(memory_id="m1")
+```
 
-If you utilize this repository, please consider citing it with:
+#### Delete memories for a user or agent
 
+```python
+m.delete_all(user_id="alex")
+m.delete_all(agent_id="agent-x")
 ```
-@misc{embedchain,
-  author = {Taranjeet Singh, Deshraj Yadav},
-  title = {Embedchain: The Open Source RAG Framework},
-  year = {2023},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/embedchain/embedchain}},
-}
+
+#### Delete all Memories
+
+```python
+m.reset()
 ```
+
+## License
+
+[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)