Fix CI/CD (#1492 )

Change dependency to mem0ai (#1476 )
User/dyadav/add platform docs (#1491 )
2024-07-17 23:40:12 -07:00 · 2024-07-17 22:12:25 -07:00 · 2024-07-17 17:39:57 -07:00 · 2024-07-17 14:38:10 -07:00 · 2024-07-17 10:53:21 -07:00 · 2024-07-17 09:47:16 -07:00
706 changed files with 14385 additions and 7457 deletions
@@ -1 +0,0 @@
-OPENAI_API_KEY="your-openai-api-key"
@@ -2,14 +2,13 @@ name: Publish Python 🐍 distributions 📦 to PyPI and TestPyPI

 on:
  release:
-    types: [published] # This will trigger the workflow when you create a new release
+    types: [published]

 jobs:
  build-n-publish:
    name: Build and publish Python 🐍 distributions 📦 to PyPI and TestPyPI
    runs-on: ubuntu-latest
    permissions:
-      # IMPORTANT: this permission is mandatory for trusted publishing
      id-token: write
    steps:
      - uses: actions/checkout@v2
@@ -25,16 +24,23 @@ jobs:
          echo "$HOME/.local/bin" >> $GITHUB_PATH

      - name: Install dependencies
-        run: poetry install
+        run: |
+          cd embedchain
+          poetry install

      - name: Build a binary wheel and a source tarball
-        run: poetry build
+        run: |
+          cd embedchain
+          poetry build

      - name: Publish distribution 📦 to Test PyPI
        uses: pypa/gh-action-pypi-publish@release/v1
        with:
          repository_url: https://test.pypi.org/legacy/
+          packages_dir: embedchain/dist/

      - name: Publish distribution 📦 to PyPI
        if: startsWith(github.ref, 'refs/tags')
-        uses: pypa/gh-action-pypi-publish@release/v1
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages_dir: embedchain/dist/
@@ -5,13 +5,13 @@ on:
    branches: [main]
    paths:
      - 'embedchain/**'
-      - 'tests/**'
-      - 'examples/**'
+      - 'embedchain/tests/**'
+      - 'embedchain/examples/**'
  pull_request:
    paths:
-      - 'embedchain/**'
-      - 'tests/**'
-      - 'examples/**'
+      - 'embedchain/embedchain/**'
+      - 'embedchain/tests/**'
+      - 'embedchain/examples/**'

 jobs:
  build:
@@ -39,12 +39,12 @@ jobs:
          path: .venv
          key: venv-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
      - name: Install dependencies
-        run: make install_all
+        run: cd embedchain && make install_all
        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
      - name: Lint with ruff
-        run: make lint
+        run: cd embedchain && make lint
      - name: Run tests and generate coverage report
-        run: make coverage
+        run: cd embedchain && make coverage
      - name: Upload coverage reports to Codecov
        uses: codecov/codecov-action@v3
        with:
@@ -165,7 +165,7 @@ cython_debug/
 # Database
 db
 test-db
-!embedchain/core/db/
+!embedchain/embedchain/core/db/

 .vscode
 .idea/
@@ -179,3 +179,8 @@ notebooks/*.yaml

 # cache db
 *.db
+
+# local directories for testing
+eval/
+qdrant_storage/
+.crossnote
@@ -1,20 +0,0 @@
-repos:
-  - repo: https://github.com/psf/black
-    rev: 23.3.0
-    hooks:
-      - id: black
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: 'v0.0.252'
-    hooks:
-      - id: ruff
-        name: ruff
-        # Respect `exclude` and `extend-exclude` settings.
-        args: ["--force-exclude"]
-  - repo: local
-    hooks:
-      - id: pytest-check
-        name: pytest-check
-        entry: poetry run pytest
-        language: system
-        pass_filenames: false
-        always_run: true
@@ -1,42 +1,26 @@
+.PHONY: format sort lint
+
 # Variables
-PYTHON := python3
-PIP := $(PYTHON) -m pip
-PROJECT_NAME := embedchain
+RUFF_OPTIONS = --line-length 120
+ISORT_OPTIONS = --profile black

-# Targets
-.PHONY: install format lint clean test ci_lint ci_test coverage
-
-install:
-	poetry install
-
-install_all:
-	poetry install --all-extras
-	poetry run pip install pinecone-text pinecone-client langchain-anthropic "unstructured[local-inference, all-docs]" ollama deepgram-sdk==3.2.7 langchain-huggingface
-
-install_es:
-	poetry install --extras elasticsearch
-
-install_opensearch:
-	poetry install --extras opensearch
-
-install_milvus:
-	poetry install --extras milvus
-
-shell:
-	poetry shell
-
-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
@@ -44,9 +28,5 @@ 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
@@ -1,125 +1,107 @@
 <p align="center">
-  <img src="docs/logo/dark.svg" width="400px" alt="Embedchain Logo">
+  <img src="docs/images/mem0-bg.png" width="500px" alt="Mem0 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 href="https://twitter.com/mem0ai">
+    <img src="https://img.shields.io/twitter/follow/mem0ai" alt="Twitter">
  </a>
 </p>

-<hr />
+# Mem0: The Memory Layer for Personalized AI

-## What is Embedchain?
+Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.

-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.
+> Note: The Mem0 repository now also includes the Embedchain project. We continue to maintain and support Embedchain ❤️. You can find the Embedchain codebase in the [embedchain](https://github.com/mem0ai/mem0/tree/main/embedchain) directory.
+## 🚀 Quick Start

-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
+### Installation

 ```bash
-pip install embedchain
+pip install mem0ai
 ```

-## ✨ Live demo
-
-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).
-
-## 🔍 Usage
-
-<!-- Demo GIF or Image -->
-<p align="center">
-  <img src="docs/images/cover.gif" width="900px" alt="Embedchain Demo">
-</p>
-
-For example, you can create an Elon Musk bot using the following code:
+### Basic Usage

 ```python
-import os
-from embedchain import App
+from mem0 import Memory

-# Create a bot instance
-os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>"
-app = App()
+# Initialize Mem0
+m = Memory()

-# Embed online resources
-app.add("https://en.wikipedia.org/wiki/Elon_Musk")
-app.add("https://www.forbes.com/profile/elon-musk")
+# Store a memory from any unstructured text
+result = m.add("I am working on improving my tennis skills. Suggest some online courses.", user_id="alice", metadata={"category": "hobbies"})
+print(result)
+# Created memory: Improving her tennis skills. Looking for online suggestions.

-# 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.
+# Retrieve memories
+all_memories = m.get_all()
+print(all_memories)
+
+# Search memories
+related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
+print(related_memories)
+
+# Update a memory
+result = m.update(memory_id="m1", data="Likes to play tennis on weekends")
+print(result)
+
+# Get memory history
+history = m.history(memory_id="m1")
+print(history)
 ```

-You can also try it in your browser with Google Colab:
+## 🔑 Core Features

-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing)
+- **Multi-Level Memory**: User, Session, and AI Agent memory retention
+- **Adaptive Personalization**: Continuous improvement based on interactions
+- **Developer-Friendly API**: Simple integration into various applications
+- **Cross-Platform Consistency**: Uniform behavior across devices
+- **Managed Service**: Hassle-free hosted solution

 ## 📖 Documentation
-Comprehensive guides and API documentation are available to help you get the most out of Embedchain:

- [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)
+For detailed usage instructions and API reference, visit our documentation at [docs.mem0.ai](https://docs.mem0.ai).

-## 🔗 Join the Community
+## 🔧 Advanced Usage

-* Connect with fellow developers by joining our [Slack Community](https://embedchain.ai/slack) or [Discord Community](https://embedchain.ai/discord).
+For production environments, you can use Qdrant as a vector store:

-* Dive into [GitHub Discussions](https://github.com/embedchain/embedchain/discussions), ask questions, or share your experiences.
+```python
+from mem0 import Memory

-## 🤝 Schedule a 1-on-1 Session
-
-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.
-
-## 🌐 Contributing
-
-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).
-
-For more reference, please go through [Development Guide](https://docs.embedchain.ai/contribution/dev) and [Documentation Guide](https://docs.embedchain.ai/contribution/docs).
-
-<a href="https://github.com/embedchain/embedchain/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=embedchain/embedchain" />
-</a>
-
-## Anonymous Telemetry
-
-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.
-
-## Citation
-
-If you utilize this repository, please consider citing it with:
-
-```
-@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}},
+config = {
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "host": "localhost",
+            "port": 6333,
+        }
+    },
 }
+
+m = Memory.from_config(config)
 ```
+
+## 🗺️ Roadmap
+
+- Integration with various LLM providers
+- Support for LLM frameworks
+- Integration with AI Agents frameworks
+- Customizable memory creation/update rules
+- Hosted platform support
+
+## 🙋‍♂️ Support
+Join our Slack or Discord community for support and discussions.
+If you have any questions, feel free to reach out to us using one of the following methods:
+
+- [Join our Discord](https://embedchain.ai/discord)
+- [Join our Slack](https://embedchain.ai/slack)
+- [Follow us on Twitter](https://twitter.com/mem0ai)
+- [Email us](mailto:founders@mem0.ai)
@@ -1,7 +1,14 @@
-# Contributing to embedchain docs
+# Mintlify Starter Kit

+Click on `Use this template` to copy the Mintlify starter kit. The starter kit contains examples including

-### 👩‍💻 Development
+- Guide pages
+- Navigation
+- Customizations
+- API Reference pages
+- Use of popular components
+
+### Development

 Install the [Mintlify CLI](https://www.npmjs.com/package/mintlify) to preview the documentation changes locally. To install, use the following command

@@ -15,9 +22,9 @@ Run the following command at the root of your documentation (where mint.json is)
 mintlify dev
 ```

-### 😎 Publishing Changes
+### Publishing Changes

-Changes will be deployed to production automatically after your PR is merged to the main branch.
+Install our Github App to auto propagate changes from your repo to your deployment. Changes will be deployed to production automatically after pushing to the default branch. Find the link to install on your dashboard. 

 #### Troubleshooting

@@ -1,6 +1,6 @@
 <CardGroup cols={3}>
-  <Card title="Talk to founders" icon="calendar" href="https://cal.com/taranjeetio/ec">
-  Schedule a call
+  <Card title="Talk to founders" icon="calendar" href="https://cal.com/taranjeetio/meet">
+  Talk to founders
  </Card>
  <Card title="Slack" icon="slack" href="https://embedchain.ai/slack" color="#4A154B">
    Join our slack community
@@ -0,0 +1,109 @@
+---
+title: Customer Support AI Agent
+---
+
+You can create a personalized Customer Support AI Agent using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
+
+## Overview
+
+The Customer Support AI Agent leverages Mem0 to retain information across interactions, enabling a personalized and efficient support experience.
+
+## Setup
+
+Install the necessary packages using pip:
+
+```bash
+pip install openai mem0ai
+```
+
+## Full Code Example
+
+Below is the simplified code to create and interact with a Customer Support AI Agent using Mem0:
+
+```python
+from openai import OpenAI
+from mem0 import Memory
+
+# Set the OpenAI API key
+os.environ['OPENAI_API_KEY'] = 'sk-xxx'
+
+class CustomerSupportAIAgent:
+    def __init__(self):
+        """
+        Initialize the CustomerSupportAIAgent with memory configuration and OpenAI client.
+        """
+        config = {
+            "vector_store": {
+                "provider": "qdrant",
+                "config": {
+                    "host": "localhost",
+                    "port": 6333,
+                }
+            },
+        }
+        self.memory = Memory.from_config(config)
+        self.client = OpenAI()
+        self.app_id = "customer-support"
+
+    def handle_query(self, query, user_id=None):
+        """
+        Handle a customer query and store the relevant information in memory.
+
+        :param query: The customer query to handle.
+        :param user_id: Optional user ID to associate with the memory.
+        """
+        # Start a streaming chat completion request to the AI
+        stream = self.client.chat.completions.create(
+            model="gpt-4",
+            stream=True,
+            messages=[
+                {"role": "system", "content": "You are a customer support AI agent."},
+                {"role": "user", "content": query}
+            ]
+        )
+        # Store the query in memory
+        self.memory.add(query, user_id=user_id, metadata={"app_id": self.app_id})
+
+        # Print the response from the AI in real-time
+        for chunk in stream:
+            if chunk.choices[0].delta.content is not None:
+                print(chunk.choices[0].delta.content, end="")
+
+    def get_memories(self, user_id=None):
+        """
+        Retrieve all memories associated with the given customer ID.
+
+        :param user_id: Optional user ID to filter memories.
+        :return: List of memories.
+        """
+        return self.memory.get_all(user_id=user_id)
+
+# Instantiate the CustomerSupportAIAgent
+support_agent = CustomerSupportAIAgent()
+
+# Define a customer ID
+customer_id = "jane_doe"
+
+# Handle a customer query
+support_agent.handle_query("I need help with my recent order. It hasn't arrived yet.", user_id=customer_id)
+```
+
+### Fetching Memories
+
+You can fetch all the memories at any point in time using the following code:
+
+```python
+memories = support_agent.get_memories(user_id=customer_id)
+for m in memories:
+    print(m['text'])
+```
+
+### Key Points
+
+- **Initialization**: The CustomerSupportAIAgent class is initialized with the necessary memory configuration and OpenAI client setup.
+- **Handling Queries**: The handle_query method sends a query to the AI and stores the relevant information in memory.
+- **Retrieving Memories**: The get_memories method fetches all stored memories associated with a customer.
+
+### Conclusion
+
+As the conversation progresses, Mem0's memory automatically updates based on the interactions, providing a continuously improving personalized support experience.
@@ -0,0 +1,32 @@
+---
+title: Overview
+description: How to use mem0 in your existing applications?
+---
+
+
+With Mem0, you can create stateful LLM-based applications such as chatbots, virtual assistants, or AI agents. Mem0 enhances your applications by providing a memory layer that makes responses:
+
+- More personalized
+- More reliable
+- Cost-effective by reducing the number of LLM interactions
+- More engaging
+- Enables long-term memory
+
+Here are some examples of how Mem0 can be integrated into various applications:
+
+## Example Use Cases
+
+<CardGroup cols={1}>
+  <Card title="Personal AI Tutor" icon="square-1" href="/examples/personal-ai-tutor">
+    <img width="100%" src="/images/ai-tutor.png" />
+    Create a Personalized AI Tutor that adapts to student progress and learning preferences.
+  </Card>
+  <Card title="Personal Travel Assistant" icon="square-2" href="/examples/personal-travel-assistant">
+    <img src="/images/personal-travel-agent.png" />
+    Build a Personalized AI Travel Assistant that understands your travel preferences and past itineraries.
+  </Card>
+  <Card title="Customer Support Agent" icon="square-3" href="/examples/customer-support-agent">
+    <img width="100%" src="/images/customer-support-agent.png" />
+    Develop a Personal AI Assistant that remembers user preferences, past interactions, and context to provide personalized and efficient assistance.
+  </Card>
+</CardGroup>
@@ -0,0 +1,111 @@
+---
+title: Personalized AI Tutor
+---
+
+You can create a personalized AI Tutor using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
+
+## Overview
+
+The Personalized AI Tutor leverages Mem0 to retain information across interactions, enabling a tailored learning experience. By integrating with OpenAI's GPT-4 model, the tutor can provide detailed and context-aware responses to user queries.
+
+## Setup
+Before you begin, ensure you have the required dependencies installed. You can install the necessary packages using pip:
+
+```bash
+pip install openai mem0ai
+```
+
+## Full Code Example
+
+Below is the complete code to create and interact with a Personalized AI Tutor using Mem0:
+
+```python
+from openai import OpenAI
+from mem0 import Memory
+
+# Set the OpenAI API key
+os.environ['OPENAI_API_KEY'] = 'sk-xxx'
+
+# Initialize the OpenAI client
+client = OpenAI()
+
+class PersonalAITutor:
+    def __init__(self):
+        """
+        Initialize the PersonalAITutor with memory configuration and OpenAI client.
+        """
+        config = {
+            "vector_store": {
+                "provider": "qdrant",
+                "config": {
+                    "host": "localhost",
+                    "port": 6333,
+                }
+            },
+        }
+        self.memory = Memory.from_config(config)
+        self.client = client
+        self.app_id = "app-1"
+
+    def ask(self, question, user_id=None):
+        """
+        Ask a question to the AI and store the relevant facts in memory
+
+        :param question: The question to ask the AI.
+        :param user_id: Optional user ID to associate with the memory.
+        """
+        # Start a streaming chat completion request to the AI
+        stream = self.client.chat.completions.create(
+            model="gpt-4",
+            stream=True,
+            messages=[
+                {"role": "system", "content": "You are a personal AI Tutor."},
+                {"role": "user", "content": question}
+            ]
+        )
+        # Store the question in memory
+        self.memory.add(question, user_id=user_id, metadata={"app_id": self.app_id})
+
+        # Print the response from the AI in real-time
+        for chunk in stream:
+            if chunk.choices[0].delta.content is not None:
+                print(chunk.choices[0].delta.content, end="")
+
+    def get_memories(self, user_id=None):
+        """
+        Retrieve all memories associated with the given user ID.
+
+        :param user_id: Optional user ID to filter memories.
+        :return: List of memories.
+        """
+        return self.memory.get_all(user_id=user_id)
+
+# Instantiate the PersonalAITutor
+ai_tutor = PersonalAITutor()
+
+# Define a user ID
+user_id = "john_doe"
+
+# Ask a question
+ai_tutor.ask("I am learning introduction to CS. What is queue? Briefly explain.", user_id=user_id)
+```
+
+### Fetching Memories
+
+You can fetch all the memories at any point in time using the following code:
+
+```python
+memories = ai_tutor.get_memories(user_id=user_id)
+for m in memories:
+    print(m['text'])
+```
+
+### Key Points
+
+- **Initialization**: The PersonalAITutor class is initialized with the necessary memory configuration and OpenAI client setup.
+- **Asking Questions**: The ask method sends a question to the AI and stores the relevant information in memory.
+- **Retrieving Memories**: The get_memories method fetches all stored memories associated with a user.
+
+### Conclusion
+
+As the conversation progresses, Mem0's memory automatically updates based on the interactions, providing a continuously improving personalized learning experience. This setup ensures that the AI Tutor can offer contextually relevant and accurate responses, enhancing the overall educational process.
@@ -0,0 +1,101 @@
+---
+title: Personal AI Travel Assistant
+---
+Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.
+
+## Overview
+
+The Personalized AI Travel Assistant uses Mem0 to store and retrieve information across interactions, enabling a tailored travel planning experience. It integrates with OpenAI's GPT-4 model to provide detailed and context-aware responses to user queries.
+
+## Setup
+
+Install the required dependencies using pip:
+
+```bash
+pip install openai mem0ai
+```
+
+## Full Code Example
+
+Here's the complete code to create and interact with a Personalized AI Travel Assistant using Mem0:
+
+```python
+import os
+from openai import OpenAI
+from mem0 import Memory
+
+# Set the OpenAI API key
+os.environ['OPENAI_API_KEY'] = 'sk-xxx'
+
+class PersonalTravelAssistant:
+    def __init__(self):
+        self.client = OpenAI()
+        self.memory = Memory()
+        self.messages = [{"role": "system", "content": "You are a personal AI Assistant."}]
+
+    def ask_question(self, question, user_id):
+        # Fetch previous related memories
+        previous_memories = self.search_memories(question, user_id=user_id)
+        prompt = question
+        if previous_memories:
+            prompt = f"User input: {question}\n Previous memories: {previous_memories}"
+        self.messages.append({"role": "user", "content": prompt})
+
+        # Generate response using GPT-4o
+        response = self.client.chat.completions.create(
+            model="gpt-4o",
+            messages=self.messages
+        )
+        answer = response.choices[0].message.content
+        self.messages.append({"role": "assistant", "content": answer})
+
+        # Store the question in memory
+        self.memory.add(question, user_id=user_id)
+        return answer
+
+    def get_memories(self, user_id):
+        memories = self.memory.get_all(user_id=user_id)
+        return [m['text'] for m in memories]
+
+    def search_memories(self, query, user_id):
+        memories = self.memory.search(query, user_id=user_id)
+        return [m['text'] for m in memories]
+
+# Usage example
+user_id = "traveler_123"
+ai_assistant = PersonalTravelAssistant()
+
+def main():
+    while True:
+        question = input("Question: ")
+        if question.lower() in ['q', 'exit']:
+            print("Exiting...")
+            break
+
+        answer = ai_assistant.ask_question(question, user_id=user_id)
+        print(f"Answer: {answer}")
+        memories = ai_assistant.get_memories(user_id=user_id)
+        print("Memories:")
+        for memory in memories:
+            print(f"- {memory}")
+        print("-----")
+
+if __name__ == "__main__":
+    main()
+```
+
+## Key Components
+
+- **Initialization**: The `PersonalTravelAssistant` class is initialized with the OpenAI client and Mem0 memory setup.
+- **Asking Questions**: The `ask_question` method sends a question to the AI, incorporates previous memories, and stores new information.
+- **Memory Management**: The `get_memories` and search_memories methods handle retrieval and searching of stored memories.
+
+## Usage
+
+1. Set your OpenAI API key in the environment variable.
+2. Instantiate the `PersonalTravelAssistant`.
+3. Use the `main()` function to interact with the assistant in a loop.
+
+## Conclusion
+
+This Personalized AI Travel Assistant leverages Mem0's memory capabilities to provide context-aware responses. As you interact with it, the assistant learns and improves, offering increasingly personalized travel advice and information.
@@ -0,0 +1,49 @@
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+<linearGradient id="paint4_linear_101_2703" x1="7.60205" y1="5.8709" x2="15.5561" y2="16.3719" gradientUnits="userSpaceOnUse">
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+</linearGradient>
+<radialGradient id="paint5_radial_101_2703" cx="0" cy="0" r="1" gradientUnits="userSpaceOnUse" gradientTransform="translate(7.84537 21.5181) rotate(-20.3525) scale(18.5603 17.32)">
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+<stop/>
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+</linearGradient>
+</defs>
+</svg>
@@ -0,0 +1,85 @@
+---
+title: MultiOn
+---
+
+Build personal browser agent remembers user preferences and automates web tasks. It integrates Mem0 for memory management with MultiOn for executing browser actions, enabling personalized and efficient web interactions.
+
+## Overview
+
+In this example, we will create a Browser based AI Agent that searches [arxiv.org](https://arxiv.org) for research papers relevant to user's research interests.
+
+## Setup and Configuration
+
+Install necessary libraries:
+
+```bash
+pip install mem0ai multion
+```
+
+First, we'll import the necessary libraries and set up our configurations.
+
+```python
+import os
+from mem0 import Memory
+from multion.client import MultiOn
+
+# Configuration
+OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
+MULTION_API_KEY = 'your-multion-key'  # Replace with your actual MultiOn API key
+USER_ID = "deshraj"
+
+# Set up OpenAI API key
+os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
+
+# Initialize Mem0 and MultiOn
+memory = Memory()
+multion = MultiOn(api_key=MULTION_API_KEY)
+```
+
+## Add memories to Mem0
+
+Next, we'll define our user data and add it to Mem0.
+
+```python
+# Define user data
+USER_DATA = """
+About me
+- I'm Deshraj Yadav, Co-founder and CTO at Mem0, interested in AI and ML Infrastructure.
+- Previously, I was a Senior Autopilot Engineer at Tesla, leading the AI Platform for Autopilot.
+- I built EvalAI at Georgia Tech, an open-source platform for evaluating ML algorithms.
+- Outside of work, I enjoy playing cricket in two leagues in the San Francisco.
+"""
+
+# Add user data to memory
+memory.add(USER_DATA, user_id=USER_ID)
+print("User data added to memory.")
+```
+
+## Retrieving Relevant Memories
+
+Now, we'll define our search command and retrieve relevant memories from Mem0.
+
+```python
+# Define search command and retrieve relevant memories
+command = "Find papers on arxiv that I should read based on my interests."
+
+relevant_memories = memory.search(command, user_id=USER_ID, limit=3)
+relevant_memories_text = '\n'.join(mem['text'] for mem in relevant_memories)
+print(f"Relevant memories:")
+print(relevant_memories_text)
+```
+
+## Browsing arXiv
+
+Finally, we'll use MultiOn to browse arXiv based on our command and relevant memories.
+
+```python
+# Create prompt and browse arXiv
+prompt = f"{command}\n My past memories: {relevant_memories_text}"
+browse_result = multion.browse(cmd=prompt, url="https://arxiv.org/")
+print(browse_result)
+```
+
+## Conclusion
+
+By integrating Mem0 with MultiOn, you've created a personalized browser agent that remembers user preferences and automates web tasks. For more details and advanced usage, refer to the full [cookbook here](https://github.com/mem0ai/mem0/blob/main/cookbooks/mem0-multion.ipynb).
@@ -0,0 +1,125 @@
+---
+title: 🤖 Overview
+---
+
+## Overview
+
+Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.
+
+<CardGroup cols={4}>
+  <Card title="OpenAI" href="#openai"></Card>
+  <Card title="Groq" href="#groq"></Card>
+  <Card title="Together" href="#together"></Card>
+  <Card title="AWS Bedrock" href="#aws_bedrock"></Card>
+</CardGroup>
+
+## OpenAI
+
+To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
+
+Once you have obtained the key, you can use it like this:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ['OPENAI_API_KEY'] = 'xxx'
+
+config = {
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Groq
+
+[Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.
+
+In order to use LLMs from Groq, go to their [platform](https://console.groq.com/keys) and get the API key. Set the API key as `GROQ_API_KEY` environment variable to use the model as given below in the example.
+
+```python
+import os
+from mem0 import Memory
+
+os.environ['GROQ_API_KEY'] = 'xxx'
+
+config = {
+    "llm": {
+        "provider": "groq",
+        "config": {
+            "model": "mixtral-8x7b-32768",
+            "temperature": 0.1,
+            "max_tokens": 1000,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## TogetherAI
+
+To use TogetherAI LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the TogetherAI API key from their [Account settings page](https://api.together.xyz/settings/api-keys).
+
+Once you have obtained the key, you can use it like this:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ['TOGETHER_API_KEY'] = 'xxx'
+
+config = {
+    "llm": {
+        "provider": "togetherai",
+        "config": {
+            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## AWS Bedrock
+
+### Setup
+- Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
+- You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
+- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
+
+```python
+import os
+from mem0 import Memory
+
+os.environ['AWS_REGION'] = 'us-east-1'
+os.environ["AWS_ACCESS_KEY"] = "xx"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "xx"
+
+config = {
+    "llm": {
+        "provider": "aws_bedrock",
+        "config": {
+            "model": "arn:aws:bedrock:us-east-1:123456789012:model/your-model-name",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
@@ -1,12 +1,7 @@
 {
  "$schema": "https://mintlify.com/schema.json",
-  "name": "Embedchain",
-  "logo": {
-    "dark": "/logo/dark-rt.svg",
-    "light": "/logo/light-rt.svg",
-    "href": "https://github.com/embedchain/embedchain"
-  },
-  "favicon": "/favicon.png",
+  "name": "Mem0.ai",
+  "favicon": "/logo/favicon.png",
  "colors": {
    "primary": "#3B2FC9",
    "light": "#6673FF",
@@ -16,269 +11,84 @@
      "light": "#fff"
    }
  },
-  "modeToggle": {
-    "default": "dark"
-  },
-  "openapi": [
-    "/rest-api.json"
-  ],
-  "metadata": {
-    "og:image": "/images/og.png",
-    "twitter:site": "@embedchain"
+  "logo": {
+    "dark": "/logo/dark.svg",
+    "light": "/logo/light.svg",
+    "href": "https://github.com/embedchain/embedchain"
  },
  "tabs": [
    {
-      "name": "Examples",
+      "name": "💡 Examples",
      "url": "examples"
    },
    {
-      "name": "API Reference",
-      "url": "api-reference"
-    }
-  ],
-  "anchors": [
-    {
-      "name": "Talk to founders",
-      "icon": "calendar",
-      "url": "https://cal.com/taranjeetio/ec"
+      "name": "🖥️  Platform",
+      "url": "platform"
    }
  ],
  "topbarLinks": [
    {
-      "name": "GitHub",
-      "url": "https://github.com/embedchain/embedchain"
+      "name": "Support",
+      "url": "mailto:founders@mem0.ai"
+    }
+  ],
+  "anchors": [
+    {
+      "name": "Slack",
+      "icon": "slack",
+      "url": "https://mem0.ai/slack/"
+    },
+    {
+      "name": "Discord",
+      "icon": "discord",
+      "url": "https://mem0.ai/discord/"
+    },
+    {
+      "name": "Talk to founders",
+      "icon": "calendar",
+      "url": "https://cal.com/taranjeetio/meet"
    }
  ],
-  "topbarCtaButton": {
-    "name": "Join our slack",
-    "url": "https://embedchain.ai/slack"
-  },
-  "primaryTab": {
-    "name": "Documentation"
-  },
  "navigation": [
    {
      "group": "Get Started",
      "pages": [
-        "get-started/quickstart",
-        "get-started/introduction",
-        "get-started/faq",
-        "get-started/full-stack",
-        {
-          "group": "🔗 Integrations",
-          "pages": [
-            "integration/langsmith",
-            "integration/chainlit",
-            "integration/streamlit-mistral",
-            "integration/openlit"
-          ]
-        }
+        "overview",
+        "quickstart"
      ]
    },
    {
-      "group": "Use cases",
+      "group": "LLMs",
      "pages": [
-        "use-cases/introduction",
-        "use-cases/chatbots",
-        "use-cases/question-answering",
-        "use-cases/semantic-search"
+        "llms"
      ]
    },
    {
-      "group": "Components",
+      "group": "Integrations",
      "pages": [
-        "components/introduction",
-        {
-          "group": "🗂️ Data sources",
-          "pages": [
-            "components/data-sources/overview",
-            {
-              "group": "Data types",
-              "pages": [
-                "components/data-sources/pdf-file",
-                "components/data-sources/csv",
-                "components/data-sources/json",
-                "components/data-sources/text",
-                "components/data-sources/directory",
-                "components/data-sources/web-page",
-                "components/data-sources/youtube-channel",
-                "components/data-sources/youtube-video",
-                "components/data-sources/docs-site",
-                "components/data-sources/mdx",
-                "components/data-sources/docx",
-                "components/data-sources/notion",
-                "components/data-sources/sitemap",
-                "components/data-sources/xml",
-                "components/data-sources/qna",
-                "components/data-sources/openapi",
-                "components/data-sources/gmail",
-                "components/data-sources/github",
-                "components/data-sources/postgres",
-                "components/data-sources/mysql",
-                "components/data-sources/slack",
-                "components/data-sources/discord",
-                "components/data-sources/discourse",
-                "components/data-sources/substack",
-                "components/data-sources/beehiiv",
-                "components/data-sources/directory",
-                "components/data-sources/dropbox",
-                "components/data-sources/image",
-                "components/data-sources/custom"
-              ]
-            },
-            "components/data-sources/data-type-handling"
-          ]
-        },
-        {
-          "group": "🗄️ Vector databases",
-          "pages": [
-                "components/vector-databases/chromadb",
-                "components/vector-databases/elasticsearch",
-                "components/vector-databases/pinecone",
-                "components/vector-databases/opensearch",
-                "components/vector-databases/qdrant",
-                "components/vector-databases/weaviate",
-                "components/vector-databases/zilliz"
-          ]
-        },
-        "components/llms",
-        "components/embedding-models",
-        "components/evaluation"
+        "integrations/multion"
      ]
    },
    {
-      "group": "Deployment",
+      "group": "💡 Examples",
      "pages": [
-        "get-started/deployment",
-        "deployment/fly_io",
-        "deployment/modal_com",
-        "deployment/render_com",
-        "deployment/railway",
-        "deployment/streamlit_io",
-        "deployment/gradio_app",
-        "deployment/huggingface_spaces"
+        "examples/overview",
+        "examples/personal-ai-tutor",
+        "examples/customer-support-agent",
+        "examples/personal-travel-assistant"
      ]
    },
    {
-      "group": "Community",
+      "group": "🖥️  Platform",
      "pages": [
-        "community/connect-with-us"
-      ]
-    },
-    {
-      "group": "Examples",
-      "pages": [
-        "examples/chat-with-PDF",
-        "examples/notebooks-and-replits",
-        {
-          "group": "REST API Service",
-          "pages": [
-            "examples/rest-api/getting-started",
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-      "group": "Showcase",
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-          "group": "App methods",
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-            "api-reference/app/add",
-            "api-reference/app/query",
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-            "api-reference/app/deploy",
-            "api-reference/app/reset",
-            "api-reference/app/delete"
-          ]
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-        "api-reference/store/openai-assistant",
-        "api-reference/store/ai-assistants",
-        "api-reference/advanced/configuration"
-      ]
-    },
-    {
-      "group": "Contributing",
-      "pages": [
-        "contribution/guidelines",
-        "contribution/dev",
-        "contribution/docs",
-        "contribution/python"
-      ]
-    },
-    {
-      "group": "Product",
-      "pages": [
-        "product/release-notes"
+        "platform/overview",
+        "platform/quickstart"
      ]
    }
  ],
  "footerSocials": {
-    "website": "https://embedchain.ai",
-    "github": "https://github.com/embedchain/embedchain",
-    "slack": "https://embedchain.ai/slack",
-    "discord": "https://discord.gg/6PzXDgEjG5",
-    "twitter": "https://twitter.com/embedchain",
-    "linkedin": "https://www.linkedin.com/company/embedchain"
-  },
-  "isWhiteLabeled": true,
-  "analytics": {
-    "posthog": {
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-    "thumbsRating": true
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-  "search": {
-    "prompt": "✨ Search embedchain docs..."
-  },
-  "api": {
-    "baseUrl": "http://localhost:8080"
-  },
-  "redirects": [
-    {
-      "source": "/changelog/command-line",
-      "destination": "/get-started/introduction"
-    }
-  ]
+    "x": "https://x.com/mem0ai",
+    "github": "https://github.com/embedchain/embedchain/mem0",
+    "linkedin": "https://www.linkedin.com/company/mem0/"
+  }
 }
@@ -0,0 +1,59 @@
+---
+title: 📚 Overview
+description: 'Welcome to the Mem0 docs!'
+---
+
+> Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.
+
+## Core features
+
+- **User, Session, and AI Agent Memory**: Retains information across user sessions, interactions, and AI agents, ensuring continuity and context.
+- **Adaptive Personalization**: Continuously improves personalization based on user interactions and feedback.
+- **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
+- **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
+- **Managed Service**: Provides a hosted solution for easy deployment and maintenance.
+
+If you are looking to quick start, jump to one of the following links:
+
+<CardGroup cols={2}>
+  <Card title="Quickstart" icon="square-1" href="/quickstart/">
+    Jump to quickstart section to get started
+  </Card>
+  <Card title="Examples" icon="square-2" href="/examples/overview/">
+    Checkout curated examples
+  </Card>
+</CardGroup>
+
+## Common Use Cases
+
+- **Personalized Learning Assistants**: Long-term memory allows learning assistants to remember user preferences, past interactions, and progress, providing a more tailored and effective learning experience.
+
+- **Customer Support AI Agents**: By retaining information from previous interactions, customer support bots can offer more accurate and context-aware assistance, improving customer satisfaction and reducing resolution times.
+
+- **Healthcare Assistants**: Long-term memory enables healthcare assistants to keep track of patient history, medication schedules, and treatment plans, ensuring personalized and consistent care.
+
+- **Virtual Companions**: Virtual companions can use long-term memory to build deeper relationships with users by remembering personal details, preferences, and past conversations, making interactions more meaningful.
+
+- **Productivity Tools**: Long-term memory helps productivity tools remember user habits, frequently used documents, and task history, streamlining workflows and enhancing efficiency.
+
+- **Gaming AI**: In gaming, AI with long-term memory can create more immersive experiences by remembering player choices, strategies, and progress, adapting the game environment accordingly.
+
+## How is Mem0 different from RAG?
+
+Mem0's memory implementation for Large Language Models (LLMs) offers several advantages over Retrieval-Augmented Generation (RAG):
+
+- **Entity Relationships**: Mem0 can understand and relate entities across different interactions, unlike RAG which retrieves information from static documents. This leads to a deeper understanding of context and relationships.
+
+- **Recency, Relevancy, and Decay**: Mem0 prioritizes recent interactions and gradually forgets outdated information, ensuring the memory remains relevant and up-to-date for more accurate responses.
+
+- **Contextual Continuity**: Mem0 retains information across sessions, maintaining continuity in conversations and interactions, which is essential for long-term engagement applications like virtual companions or personalized learning assistants.
+
+- **Adaptive Learning**: Mem0 improves its personalization based on user interactions and feedback, making the memory more accurate and tailored to individual users over time.
+
+- **Dynamic Updates**: Mem0 can dynamically update its memory with new information and interactions, unlike RAG which relies on static data. This allows for real-time adjustments and improvements, enhancing the user experience.
+
+These advanced memory capabilities make Mem0 a powerful tool for developers aiming to create personalized and context-aware AI applications.
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />
@@ -0,0 +1,45 @@
+---
+title: Introduction
+description: 'Empower your AI applications with long-term memory and personalization'
+---
+
+## Welcome to Mem0 Platform
+
+Mem0 Platform is a managed service that revolutionizes the way AI applications handle memory. By providing a smart, self-improving memory layer for Large Language Models (LLMs), we enable developers to create personalized AI experiences that evolve with each user interaction.
+
+## Why Choose Mem0 Platform?
+
+1. **Enhanced User Experience**: Deliver tailored interactions that make your AI applications truly stand out.
+2. **Simplified Development**: Our API-first approach streamlines integration, allowing you to focus on building great features.
+3. **Scalable Solution**: Designed to grow with your application, from prototypes to production-ready systems.
+
+## Key Features
+
+- **Comprehensive Memory Management**: Easily manage long-term, short-term, semantic, and episodic memories for individual users, agents, and sessions through our robust APIs.
+- **Self-Improving Memory**: Our adaptive system continuously learns from user interactions, refining its understanding over time.
+- **Cross-Platform Consistency**: Ensure a unified user experience across various AI platforms and applications.
+- **Centralized Memory Control**: Store, update, and delete memories effortlessly, taking away the hassle of memory management.
+
+## Common Use Cases
+
+- Personalized Learning Assistants
+- Customer Support AI Agents
+- Healthcare Assistants
+- Virtual Companions
+- Productivity Tools
+- Gaming AI
+
+## Getting Started
+Ready to supercharge your AI application with Mem0? Follow these steps:
+
+1. **Sign Up**: Create your Mem0 account at our platform.
+2. **API Key**: Generate your API key in the dashboard.
+3. **Installation**: Install our Python SDK using pip: `pip install mem0ai`
+4. **Quick Implementation**: Check out our [Quickstart Guide](/platform/quickstart) to start using Mem0 quickly.
+
+## Next Steps
+
+- Explore our API Reference for detailed endpoint documentation.
+- Join our [slack](https://mem0.ai/slack) or [discord](https://mem0.ai/discord) with other developers and get support.
+
+We're excited to see what you'll build with Mem0 Platform. Let's create smarter, more personalized AI experiences together!
@@ -0,0 +1,358 @@
+---
+title: Quickstart
+description: 'Get started with Mem0 Platform in minutes'
+---
+
+## 1. Installation
+
+Install the Mem0 Python package:
+
+```bash
+pip install mem0ai
+```
+
+## 2. API Key Setup
+
+1. Sign in to [Mem0 Platform](https://app.mem0.ai/dashboard/api-keys)
+2. Copy your API Key from the dashboard
+
+![Get API Key from Mem0 Platform](/images/platform/api-key.png)
+
+## 3. Instantiate Client
+
+```python
+from mem0 import MemoryClient
+client = MemoryClient(api_key="your-api-key")
+```
+
+## 4. Memory Operations
+
+We provide a simple yet customizable interface for performing CRUD operations on memory. Here is how you can create and get memories:
+
+
+### 4.1 Create Memories
+
+For users (long-term memory):
+
+```python
+# create long-term memory for users
+client.add("Remember my name is Deshraj Yadav.", user_id="deshraj")
+client.add("I like to eat pizza and go out on weekends.", user_id="deshraj")
+client.add("Oh I am actually allergic to cheese to cannot eat pizza anymore.", user_id="deshraj")
+```
+
+Output:
+```python
+{'message': 'Memory added successfully!'}
+```
+
+You can see all the memory operations happening on the platform itself.
+
+![Mem0 Platform Activity](/images/platform/activity.png)
+
+
+You can also add memories for a particular session or for an AI agent that you are building:
+
+- For user sessions (short-term memory):
+
+```python
+client.add("Deshraj is building Gmail AI agent", user_id="deshraj", session_id="session-1")
+```
+
+- For agents (long-term memory):
+
+```python
+client.add("Return short responses when responding to emails", agent_id="gmail-agent")
+```
+
+### 4.2 Retrieve Memories
+
+
+<CodeGroup>
+
+```python Code
+client.get_all(user_id="deshraj")
+```
+
+```python Output
+[
+  {
+    'id': 'dbce6e06-6adf-40b8-9187-3d30bd13b741',
+    'agent': None,
+    'consumer': {
+      'id': 8,
+      'user_id': 'deshraj',
+      'metadata': None,
+      'created_at': '2024-07-17T16:47:23.899900-07:00',
+      'updated_at': '2024-07-17T16:47:23.899918-07:00'
+    },
+    'app': None,
+    'run': None,
+    'hash': '57288ac8a87c4ac8d3ac7f2075d264ca',
+    'input': 'Remember my name is Deshraj Yadav.',
+    'text': 'My name is Deshraj Yadav.',
+    'metadata': None,
+    'created_at': '2024-07-17T16:47:25.670180-07:00',
+    'updated_at': '2024-07-17T16:47:25.670197-07:00'
+  },
+  {
+    'id': 'f6dec5d1-b5db-45f5-a2fb-3979a0f27d30',
+    'agent': None,
+    'consumer': {
+      'id': 8,
+      'user_id': 'deshraj',
+      # ... other consumer fields ...
+    },
+    # ... other fields ...
+    'text': 'I am allergic to cheese so I cannot eat pizza anymore.',
+    # ... remaining fields ...
+  },
+  # ... additional memory entries ...
+]
+```
+</CodeGroup>
+
+Similarly, you can get all memories for an agent:
+
+```python
+agent_memories = client.get_all(agent_id="gmail-agent")
+```
+
+Get specific memory:
+
+<CodeGroup>
+
+```python Code
+memory = client.get(memory_id="dbce6e06-6adf-40b8-9187-3d30bd13b741")
+```
+
+```python Output
+{
+    'id': 'dbce6e06-6adf-40b8-9187-3d30bd13b741',
+    'agent': None,
+    'consumer': {
+      'id': 8,
+      'user_id': 'deshraj',
+      'metadata': None,
+      'created_at': '2024-07-17T16:47:23.899900-07:00',
+      'updated_at': '2024-07-17T16:47:23.899918-07:00'
+    },
+    'app': None,
+    'run': None,
+    'hash': '57288ac8a87c4ac8d3ac7f2075d264ca',
+    'input': 'Remember my name is Deshraj Yadav.',
+    'text': 'My name is Deshraj Yadav.',
+    'metadata': None,
+    'created_at': '2024-07-17T16:47:25.670180-07:00',
+    'updated_at': '2024-07-17T16:47:25.670197-07:00'
+}
+```
+</CodeGroup>
+
+### 4.3 Update Memory
+
+You can also update specific memory by using the following method:
+
+<CodeGroup>
+
+```python Code
+client.update(memory_id, data="Updated name is Deshraj Kumar")
+```
+
+```python Output
+{
+    'id': 'dbce6e06-6adf-40b8-9187-3d30bd13b741',
+    'agent': None,
+    'consumer': {
+      'id': 8,
+      'user_id': 'deshraj',
+      'metadata': None,
+      'created_at': '2024-07-17T16:47:23.899900-07:00',
+      'updated_at': '2024-07-17T16:47:23.899918-07:00'
+    },
+    'app': None,
+    'run': None,
+    'hash': '57288ac8a87c4ac8d3ac7f2075d264ca',
+    'input': 'Updated name is Deshraj Kumar.',
+    'text': 'Name is Deshraj Kumar.',
+    'metadata': None,
+    'created_at': '2024-07-17T16:47:25.670180-07:00',
+    'updated_at': '2024-07-17T16:47:25.670197-07:00'
+}
+```
+</CodeGroup>
+
+
+### 4.4 Memory History
+
+Get history of how a memory has changed over time
+
+<CodeGroup>
+
+```python Code
+history = client.history(memory_id)
+```
+
+```python Output
+[
+    {
+        'id': '51193804-2ee6-4f81-b4e7-497e98b70858',
+        'memory': {
+            'id': 'dbce6e06-6adf-40b8-9187-3d30bd13b741',
+            'agent': None,
+            'consumer': {
+                'id': 8,
+                'user_id': 'deshraj',
+                'metadata': None,
+                'created_at': '2024-07-17T16:47:23.899900-07:00',
+                'updated_at': '2024-07-17T16:47:23.899918-07:00'
+            },
+            'app': None,
+            'run': None,
+            'hash': '57288ac8a87c4ac8d3ac7f2075d264ca',
+            'input': 'Remember my name is Deshraj Yadav.',
+            'text': 'My name is Deshraj Yadav.',
+            'metadata': None,
+            'created_at': '2024-07-17T16:47:25.670180-07:00',
+            'updated_at': '2024-07-17T16:47:25.670197-07:00'
+        },
+        'hash': '57288ac8a87c4ac8d3ac7f2075d264ca',
+        'event': 'ADD',
+        'input': 'Remember my name is Deshraj Yadav.',
+        'previous_text': None,
+        'text': 'My name is Deshraj Yadav.',
+        'metadata': None,
+        'created_at': '2024-07-17T16:47:25.686899-07:00',
+        'updated_at': '2024-07-17T16:47:25.670197-07:00',
+        'change_description': 'Memory ADD event'
+    }
+]
+```
+</CodeGroup>
+
+### 4.5 Search for relevant memories
+
+<CodeGroup>
+
+```python Code
+client.search("What does Deshraj like to eat?", user_id="deshraj", limit=3)
+```
+
+```python Output
+[
+  {
+    "id": "dbce6e06-6adf-40b8-9187-3d30bd13b741",
+    "agent": null,
+    "consumer": {
+      "id": 8,
+      "user_id": "deshraj",
+      "metadata": null,
+      "created_at": "...",
+      "updated_at": "..."
+    },
+    "app": null,
+    "run": null,
+    "hash": "57288ac8a87c4ac8d3ac7f2075d264ca",
+    "input": "Remember my name is Deshraj Yadav.",
+    "text": "My name is Deshraj Yadav.",
+    "metadata": null,
+    "created_at": "2024-07-17T16:47:25.670180-07:00",
+    "updated_at": "..."
+  },
+  {
+    "id": "091dbed6-74b4-4e15-b765-81be2abe0d6b",
+    "agent": null,
+    "consumer": {
+      "id": 8,
+      "user_id": "deshraj",
+      "metadata": null,
+      "created_at": "...",
+      "updated_at": "..."
+    },
+    "app": null,
+    "run": null,
+    "hash": "622a5a24d5ac54136414a22ec12f9520",
+    "input": "Oh I am actually allergic to cheese to cannot eat pizza anymore.",
+    "text": "I like to eat pizza and go out on weekends.",
+    "metadata": null,
+    "created_at": "2024-07-17T16:49:24.276695-07:00",
+    "updated_at": "..."
+  },
+  {
+    "id": "5fb8f85d-3383-4bad-9d46-f171272478a4",
+    "agent": null,
+    "consumer": {
+      "id": 8,
+      "user_id": "deshraj",
+      "metadata": null,
+      "created_at": "...",
+      "updated_at": "..."
+    },
+    "app": null,
+    "run": {
+      "id": 1,
+      "run_id": "session-1",
+      "name": "",
+      "metadata": null,
+      "created_at": "...",
+      "updated_at": "..."
+    },
+    "hash": "179ced9649ac2b85350ece4946b1ee9b",
+    "input": "Deshraj is building Gmail AI agent",
+    "text": "Deshraj is building Gmail AI agent",
+    "metadata": null,
+    "created_at": "2024-07-17T16:52:41.278920-07:00",
+    "updated_at": "..."
+  },
+  {
+    "id": "f6dec5d1-b5db-45f5-a2fb-3979a0f27d30",
+    "agent": null,
+    "consumer": {
+      "id": 8,
+      "user_id": "deshraj",
+      "metadata": null,
+      "created_at": "...",
+      "updated_at": "..."
+    },
+    "app": null,
+    "run": null,
+    "hash": "19248f0766044b5973fc0ef1bf3955ef",
+    "input": "Oh I am actually allergic to cheese to cannot eat pizza anymore.",
+    "text": "I am allergic to cheese so I cannot eat pizza anymore.",
+    "metadata": null,
+    "created_at": "2024-07-17T16:49:38.622084-07:00",
+    "updated_at": "..."
+  }
+]
+```
+</CodeGroup>
+
+
+### 4.6 Delete Memory
+
+Delete specific memory:
+
+<CodeGroup>
+
+```python Code
+client.delete(memory_id)
+```
+
+```python Output
+{'message': 'Memory deleted successfully!'}
+```
+</CodeGroup>
+
+
+Delete all memories of a user:
+
+<CodeGroup>
+
+```python Code
+client.delete_all(user_id="alex")
+```
+
+```python Output
+{'message': 'Memories deleted successfully!'}
+```
+</CodeGroup>
@@ -0,0 +1,210 @@
+---
+title: 🚀 Quickstart
+description: 'Get started with Mem0 quickly!'
+---
+
+> Welcome to the Mem0 quickstart guide. This guide will help you get up and running with Mem0 in no time.
+
+## Installation
+
+To install Mem0, you can use pip. Run the following command in your terminal:
+
+```bash
+pip install mem0ai
+```
+
+## Basic Usage
+
+### Initialize Mem0
+
+<Tabs>
+  <Tab title="Basic">
+```python
+from mem0 import Memory
+m = Memory()
+```
+  </Tab>
+  <Tab title="Advanced">
+If you want to run Mem0 in production, initialize using the following method:
+
+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
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "host": "localhost",
+            "port": 6333,
+        }
+    },
+}
+
+m = Memory.from_config(config)
+```
+  </Tab>
+</Tabs>
+
+
+### Store a Memory
+
+```python
+# For a user
+result = m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+print(result)
+```
+
+Output:
+```python
+[
+  {
+    'id': 'm1',
+    'event': 'add',
+    'data': 'Likes to play cricket on weekends'
+  }
+]
+```
+
+### Retrieve Memories
+
+```python
+# Get all memories
+all_memories = m.get_all()
+print(all_memories)
+```
+
+Output:
+
+```python
+[
+  {
+    'id': 'm1',
+    'text': 'Likes to play cricket on weekends',
+    'metadata': {
+      'data': 'Likes to play cricket on weekends',
+      'category': 'hobbies'
+    }
+  },
+  # ... other memories ...
+]
+```
+
+```python
+# Get a single memory by ID
+specific_memory = m.get("m1")
+print(specific_memory)
+```
+
+Output:
+```python
+{
+  'id': 'm1',
+  'text': 'Likes to play cricket on weekends',
+  'metadata': {
+    'data': 'Likes to play cricket on weekends',
+    'category': 'hobbies'
+  }
+}
+```
+
+### Search Memories
+
+```python
+related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
+print(related_memories)
+```
+
+Output:
+
+```python
+[
+  {
+    'id': 'm1',
+    'text': 'Likes to play cricket on weekends',
+    'metadata': {
+      'data': 'Likes to play cricket on weekends',
+      'category': 'hobbies'
+    },
+    'score': 0.85  # Similarity score
+  },
+  # ... other related memories ...
+]
+```
+
+### Update a Memory
+
+```python
+result = m.update(memory_id="m1", data="Likes to play tennis on weekends")
+print(result)
+```
+
+Output:
+
+```python
+{
+  'id': 'm1',
+  'event': 'update',
+  'data': 'Likes to play tennis on weekends'
+}
+```
+
+### Memory History
+
+```python
+history = m.history(memory_id="m1")
+print(history)
+```
+Output:
+```python
+[
+  {
+    'id': 'h1',
+    'memory_id': 'm1',
+    'prev_value': None,
+    'new_value': 'Likes to play cricket on weekends',
+    'event': 'add',
+    'timestamp': '2024-07-14 10:00:54.466687',
+    'is_deleted': 0
+  },
+  {
+    'id': 'h2',
+    'memory_id': 'm1',
+    'prev_value': 'Likes to play cricket on weekends',
+    'new_value': 'Likes to play tennis on weekends',
+    'event': 'update',
+    'timestamp': '2024-07-14 10:15:17.230943',
+    'is_deleted': 0
+  }
+]
+```
+
+### Delete Memory
+
+```python
+m.delete(memory_id="m1") # Delete a memory
+
+m.delete_all(user_id="alice") # Delete all memories
+```
+
+### Reset Memory
+
+```python
+m.reset() # Reset all memories
+```
+
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />
@@ -0,0 +1,4 @@
+One of the core principles of software development is DRY (Don't Repeat
+Yourself). This is a principle that apply to documentation as
+well. If you find yourself repeating the same content in multiple places, you
+should consider creating a custom snippet to keep your content in sync.
@@ -0,0 +1,56 @@
+# 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
+
+install_milvus:
+	poetry install --extras milvus
+
+shell:
+	poetry shell
+
+py_shell:
+	poetry run python
+
+format:
+	$(PYTHON) -m black .
+	$(PYTHON) -m isort .
+
+clean:
+	rm -rf dist build *.egg-info
+
+lint:
+	poetry run ruff .
+
+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
@@ -0,0 +1,125 @@
+<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
+
+```bash
+pip install embedchain
+```
+
+## ✨ Live demo
+
+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).
+
+## 🔍 Usage
+
+<!-- Demo GIF or Image -->
+<p align="center">
+  <img src="docs/images/cover.gif" width="900px" alt="Embedchain Demo">
+</p>
+
+For example, you can create an Elon Musk bot using the following code:
+
+```python
+import os
+from embedchain import App
+
+# Create a bot instance
+os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>"
+app = App()
+
+# Embed online resources
+app.add("https://en.wikipedia.org/wiki/Elon_Musk")
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# 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.
+```
+
+You can also try it in your browser with Google Colab:
+
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing)
+
+## 📖 Documentation
+Comprehensive guides and API documentation are available to help you get the most out of Embedchain:
+
+- [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)
+
+## 🔗 Join the Community
+
+* Connect with fellow developers by joining our [Slack Community](https://embedchain.ai/slack) or [Discord Community](https://embedchain.ai/discord).
+
+* Dive into [GitHub Discussions](https://github.com/embedchain/embedchain/discussions), ask questions, or share your experiences.
+
+## 🤝 Schedule a 1-on-1 Session
+
+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.
+
+## 🌐 Contributing
+
+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).
+
+For more reference, please go through [Development Guide](https://docs.embedchain.ai/contribution/dev) and [Documentation Guide](https://docs.embedchain.ai/contribution/docs).
+
+<a href="https://github.com/embedchain/embedchain/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=embedchain/embedchain" />
+</a>
+
+## Anonymous Telemetry
+
+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.
+
+## Citation
+
+If you utilize this repository, please consider citing it with:
+
+```
+@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}},
+}
+```
@@ -0,0 +1,25 @@
+# Contributing to embedchain docs
+
+
+### 👩‍💻 Development
+
+Install the [Mintlify CLI](https://www.npmjs.com/package/mintlify) to preview the documentation changes locally. To install, use the following command
+
+```
+npm i -g mintlify
+```
+
+Run the following command at the root of your documentation (where mint.json is)
+
+```
+mintlify dev
+```
+
+### 😎 Publishing Changes
+
+Changes will be deployed to production automatically after your PR is merged to the main branch.
+
+#### Troubleshooting
+
+- Mintlify dev isn't running - Run `mintlify install` it'll re-install dependencies.
+- Page loads as a 404 - Make sure you are running in a folder with `mint.json`
@@ -0,0 +1,11 @@
+<CardGroup cols={3}>
+  <Card title="Talk to founders" icon="calendar" href="https://cal.com/taranjeetio/ec">
+  Schedule a call
+  </Card>
+  <Card title="Slack" icon="slack" href="https://embedchain.ai/slack" color="#4A154B">
+    Join our slack community
+  </Card>
+  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
+    Join our discord community
+  </Card>
+</CardGroup>
@@ -30,6 +30,7 @@ llm:
      response_format: 
        type: json_object
    api_version: 2024-02-01
+    http_client_proxies: http://testproxy.mem0.net:8000
    prompt: |
      Use the following pieces of context to answer the query at the end.
      If you don't know the answer, just say that you don't know, don't try to make up an answer.
@@ -89,7 +90,8 @@ cache:
      "system_prompt": "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare.",
      "api_key": "sk-xxx",
      "model_kwargs": {"response_format": {"type": "json_object"}},
-      "api_version": "2024-02-01"
+      "api_version": "2024-02-01",
+      "http_client_proxies": "http://testproxy.mem0.net:8000",
    }
  },
  "vectordb": {
@@ -150,7 +152,8 @@ config = {
                "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare."
            ),
            'api_key': 'sk-xxx',
-            "model_kwargs": {"response_format": {"type": "json_object"}}
+            "model_kwargs": {"response_format": {"type": "json_object"}},
+            "http_client_proxies": "http://testproxy.mem0.net:8000",
        }
    },
    'vectordb': {
@@ -206,17 +209,21 @@ Alright, let's dive into what each key means in the yaml config above:
        - `top_p` (Float): Controls the diversity of word selection. A higher value (closer to 1) makes word selection more diverse.
        - `stream` (Boolean): Controls if the response is streamed back to the user (set to false).
        - `online` (Boolean): Controls whether to use internet to get more context for answering query (set to false).
+        - `token_usage` (Boolean): Controls whether to use token usage for the querying models (set to false).
        - `prompt` (String): A prompt for the model to follow when generating responses, requires `$context` and `$query` variables.
        - `system_prompt` (String): A system prompt for the model to follow when generating responses, in this case, it's set to the style of William Shakespeare.
        - `number_documents` (Integer): Number of documents to pull from the vectordb as context, defaults to 1
        - `api_key` (String): The API key for the language model.
        - `model_kwargs` (Dict): Keyword arguments to pass to the language model. Used for `aws_bedrock` provider, since it requires different arguments for each model.
+        - `http_client_proxies` (Dict | String): The proxy server settings used to create `self.http_client` using `httpx.Client(proxies=http_client_proxies)`
+        - `http_async_client_proxies` (Dict | String): The proxy server settings for async calls used to create `self.http_async_client` using `httpx.AsyncClient(proxies=http_async_client_proxies)`
 3. `vectordb` Section:
    - `provider` (String): The provider for the vector database, set to 'chroma'. You can find the full list of vector database providers in [our docs](/components/vector-databases).
    - `config`:
        - `collection_name` (String): The initial collection name for the vectordb, set to 'full-stack-app'.
        - `dir` (String): The directory for the local database, set to 'db'.
        - `allow_reset` (Boolean): Indicates whether resetting the vectordb is allowed, set to true.
+        - `batch_size` (Integer): The batch size for docs insertion in vectordb, defaults to `100`
    <Note>We recommend you to checkout vectordb specific config [here](https://docs.embedchain.ai/components/vector-databases)</Note>
 4. `embedder` Section:
    - `provider` (String): The provider for the embedder, set to 'openai'. You can find the full list of embedding model providers in [our docs](/components/embedding-models).
@@ -228,6 +235,7 @@ Alright, let's dive into what each key means in the yaml config above:
        - `deployment_name` (String): The deployment name for the embedding model.
        - `title` (String): The title for the embedding model for Google Embedder.
        - `task_type` (String): The task type for the embedding model for Google Embedder.
+        - `model_kwargs` (Dict): Used to pass extra arguments to embedders.
 5. `chunker` Section:
    - `chunk_size` (Integer): The size of each chunk of text that is sent to the language model.
    - `chunk_overlap` (Integer): The amount of overlap between each chunk of text.
@@ -241,6 +249,8 @@ Alright, let's dive into what each key means in the yaml config above:
    - `config` (Optional): The config for initializing the cache. If not provided, sensible default values are used as mentioned below.
      - `similarity_threshold` (Float): The threshold for similarity evaluation. Defaults to `0.8`.
      - `auto_flush` (Integer): The number of queries after which the cache is flushed. Defaults to `20`.
+7. `memory` Section: (Optional)
+    - `top_k` (Integer): The number of top-k results to return. Defaults to `10`.
    <Note>
    If you provide a cache section, the app will automatically configure and use a cache to store the results of the language model. This is useful if you want to speed up the response time and save inference cost of your app.
    </Note>
@@ -144,3 +144,26 @@ app.add("https://www.forbes.com/profile/elon-musk")
 query_config = BaseLlmConfig(number_documents=5)
 app.chat("What is the net worth of Elon Musk?", config=query_config)
 ```
+
+### With Mem0 to store chat history
+
+Mem0 is a cutting-edge long-term memory for LLMs to enable personalization for the GenAI stack. It enables LLMs to remember past interactions and provide more personalized responses.
+
+In order to use Mem0 to enable memory for personalization in your apps:
+- Install the [`mem0`](https://docs.mem0.ai/) package using `pip install mem0ai`. 
+- Prepare config for `memory`, refer [Configurations](docs/api-reference/advanced/configuration.mdx).
+
+```python with mem0
+from embedchain import App
+
+config = {
+  "memory": {
+    "top_k": 5
+  }
+}
+
+app = App.from_config(config=config)
+app.add("https://www.forbes.com/profile/elon-musk")
+
+app.chat("What is the net worth of Elon Musk?")
+```
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Deshraj Yadav	fb5a3bfd95	Fix CI/CD (#1492 )	2024-07-17 23:40:12 -07:00
Dev Khant	7441f1462d	Change dependency to mem0ai (#1476 )	2024-07-17 22:12:25 -07:00
Deshraj Yadav	c9240e7ca6	User/dyadav/add platform docs (#1491 )	2024-07-17 17:39:57 -07:00
Dev Khant	1e7618dfa4	Add AWS Bedrock support (#1482 )	2024-07-17 14:38:10 -07:00
Deshraj Yadav	4e5d34103f	[Docs] Add multion integration (#1489 )	2024-07-17 10:53:21 -07:00
Dev Khant	da435bc025	add delete and reset in docs (#1488 )	2024-07-17 09:47:16 -07:00
Deshraj Yadav	2a43aa6902	[Docs] Add example for building Personal AI Assistant using Mem0 (#1486 )	2024-07-16 15:20:03 -07:00
Dev Khant	b620f8fae3	Add TogetherAI support (#1485 )	2024-07-16 13:19:18 -07:00
Deshraj Yadav	03f787d5cb	Update mem0 version to 0.0.4 (#1484 )	2024-07-16 11:12:42 -07:00
Dev Khant	19637804b3	Add Groq Support (#1481 )	2024-07-16 11:03:28 -07:00
Dev Khant	80f145fceb	Add model pricing file (#1483 )	2024-07-16 10:55:33 -07:00
Deshraj Yadav	34477d4936	Update README (#1478 )	2024-07-15 00:16:56 -07:00
Deshraj Yadav	4ec51f2dd6	[Mem0] Fix issues and update docs (#1477 )	2024-07-14 22:21:07 -07:00
Taranjeet Singh	f842a92e25	Rename embedchain to mem0 and open sourcing code for long term memory (#1474 ) Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>	2024-07-12 20:21:33 +05:30
Vatsal Rathod	83e8c97295	Refactoring vectordb naming convention in embedchain.config (#1469 )	2024-07-08 16:01:17 -07:00
Dev Khant	1a5d0d236a	Remove unwanted libraries and lighten package (#1391 )	2024-07-08 16:00:16 -07:00
Dev Khant	ebbf90f4aa	Version bump -> 0.1.116 (#1464 )	2024-07-06 21:23:10 -07:00
Stefan Bokarev	4f119692f1	[Docs]: Add Integration for 🧊 Helicone (LLM-Observability for Developers) (#1458 )	2024-07-06 12:27:57 -07:00
Dev Khant	bbe56107fb	Integrate Mem0 (#1462 ) Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>	2024-07-06 12:27:01 -07:00
Parshva Daftari	bd654e7aac	Fixed Docs for the Token Usage (#1461 ) Co-authored-by: parshvadaftari <parshva@192.168.1.2>	2024-07-05 08:54:04 -07:00
Dev Khant	33500a7ce2	Version bump (#1460 )	2024-07-04 14:42:59 -07:00
Dev Khant	4880557d51	Show details for query tokens (#1392 )	2024-07-04 11:40:56 -07:00
Dev Khant	ea09b5f7f0	Version bump (#1457 )	2024-07-02 23:07:52 -07:00
Pranav Puranik	5258fd91ea	http_client and http_async_client bugfix (#1454 )	2024-07-02 16:13:33 -07:00
João Moura	b305d674de	Updating dependencies (#1453 )	2024-07-02 16:12:52 -07:00
Pranav Puranik	7c24601d0f	Adding model_kwargs for huggingface embedders. (#1450 )	2024-06-29 12:37:31 -07:00
Dev Khant	50c0285cb2	Fix batch_size for vectordb (#1449 )	2024-06-28 11:18:22 -07:00
Dev Khant	0a78198bb5	Add batch_size in config for VectorDB (#1448 )	2024-06-27 14:45:58 -07:00
Vatsal Rathod	edaeb78ccf	Refactor openai embedder (#1444 )	2024-06-26 10:58:12 -07:00