diff --git a/.env.example b/.env.example deleted file mode 100644 index 3817b834b1..0000000000 --- a/.env.example +++ /dev/null @@ -1 +0,0 @@ -OPENAI_API_KEY="your-openai-api-key" diff --git a/.gitignore b/.gitignore index cd2b5930c7..2dfa42cdf2 100644 --- a/.gitignore +++ b/.gitignore @@ -179,3 +179,6 @@ notebooks/*.yaml # cache db *.db + +# local directories for testing +eval/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml deleted file mode 100644 index fb9a68c7b6..0000000000 --- a/.pre-commit-config.yaml +++ /dev/null @@ -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 diff --git a/Makefile b/Makefile index 60b1e2251b..1a04bc78a3 100644 --- a/Makefile +++ b/Makefile @@ -1,46 +1,26 @@ -# 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 @@ -48,9 +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 diff --git a/README.md b/README.md index 4512e3cc8c..9aaa0166a3 100644 --- a/README.md +++ b/README.md @@ -1,125 +1,197 @@ -

- Embedchain Logo -

- -

- - PyPI - - - Downloads - - - Slack - - - Discord - - - Twitter - - - Open in Colab - - - codecov - -

- -
- -## 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() +``` - -

- Embedchain Demo -

+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"] = "" -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 - - - +```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) diff --git a/docs/README.md b/docs/README.md index e322686dc1..4cbe0750c0 100644 --- a/docs/README.md +++ b/docs/README.md @@ -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 diff --git a/docs/favicon.svg b/docs/favicon.svg new file mode 100644 index 0000000000..6a3233265c --- /dev/null +++ b/docs/favicon.svg @@ -0,0 +1,49 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/images/hero-dark.svg b/docs/images/hero-dark.svg new file mode 100644 index 0000000000..e188a0c607 --- /dev/null +++ b/docs/images/hero-dark.svg @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/images/hero-light.svg b/docs/images/hero-light.svg new file mode 100644 index 0000000000..681ad49ede --- /dev/null +++ b/docs/images/hero-light.svg @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/docs/introduction.mdx b/docs/introduction.mdx new file mode 100644 index 0000000000..3ad95bf1b5 --- /dev/null +++ b/docs/introduction.mdx @@ -0,0 +1,191 @@ +--- +title: Introduction +description: 'Welcome to the Mem0 documentation' +--- + +Mem0 is the long-term memory for AI Agents. + +## Installation + +```bash +pip install mem0ai +``` + +## Usage + +### Instantiate + +```python +from mem0 import Memory +m = Memory() +``` + +Mem0 uses Qdrant by default for storing the semantic memories. If you want to use Qdrant in server mode, use the following method to instantiate. + +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) +``` + +### Store a Memory + +```python +m.add("Likes to play cricket over weekend", user_id="deshraj", 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"}) +``` + +### Retrieve all memories + +#### 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' +# } +# } +# ] + +``` +#### 2. Get memories for specific user + +```python +m.get_all(user_id="deshraj") +``` + +#### 3. Get memories for specific agent + +```python +m.get_all(agent_id="agent-x") +``` + +#### 4. Get memories for a user during an agent run + +```python +m.get_all(agent_id="agent-x", user_id="deshraj") +``` + +### Retrieve a Memory + +```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' +# } +# } +``` + +### Search for related memories + +```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") +``` + +### Get history of a Memory + +```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 +# } +# ] +``` + +### Delete a Memory + +```python +m.delete(memory_id="m1") +``` + +### Delete memories of a user or agent + +```python +m.delete_all(user_id="deshraj") +m.delete_all(agent_id="agent-x") +``` + +### Delete all Memories + +```python +m.reset() +``` + +## Contributing + +Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate. + +## License + +[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) diff --git a/docs/logo/dark.svg b/docs/logo/dark.svg index cbd5020944..e188a0c607 100644 --- a/docs/logo/dark.svg +++ b/docs/logo/dark.svg @@ -1,11 +1,8 @@ - - - - - - - - - - + + + + + + + diff --git a/docs/logo/light.svg b/docs/logo/light.svg index cbd5020944..681ad49ede 100644 --- a/docs/logo/light.svg +++ b/docs/logo/light.svg @@ -1,11 +1,8 @@ - - - - - - - - - - + + + + + + + diff --git a/docs/mint.json b/docs/mint.json index 0f70941ac2..f63ab2373a 100644 --- a/docs/mint.json +++ b/docs/mint.json @@ -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/light.svg", "colors": { "primary": "#3B2FC9", "light": "#6673FF", @@ -16,262 +11,40 @@ "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", - "url": "examples" - }, + "topbarLinks": [ { - "name": "API Reference", - "url": "api-reference" + "name": "Support", + "url": "mailto:founders@mem0.ai" } ], "anchors": [ { - "name": "Talk to founders", - "icon": "calendar", - "url": "https://cal.com/taranjeetio/ec" - } - ], - "topbarLinks": [ + "name": "Slack", + "icon": "slack", + "url": "https://mem0.ai/slack/" + }, { - "name": "GitHub", - "url": "https://github.com/embedchain/embedchain" + "name": "Discord", + "icon": "discord", + "url": "https://mem0.ai/discord/" } ], - "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", - "integration/helicone" - ] - } - ] - }, - { - "group": "Use cases", - "pages": [ - "use-cases/introduction", - "use-cases/chatbots", - "use-cases/question-answering", - "use-cases/semantic-search" - ] - }, - { - "group": "Components", - "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" - ] - }, - { - "group": "Deployment", - "pages": [ - "get-started/deployment", - "deployment/fly_io", - "deployment/modal_com", - "deployment/render_com", - "deployment/railway", - "deployment/streamlit_io", - "deployment/gradio_app", - "deployment/huggingface_spaces" - ] - }, - { - "group": "Community", - "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", - "examples/rest-api/create", - "examples/rest-api/get-all-apps", - "examples/rest-api/add-data", - "examples/rest-api/get-data", - "examples/rest-api/query", - "examples/rest-api/deploy", - "examples/rest-api/delete", - "examples/rest-api/check-status" - ] - }, - "examples/full_stack", - "examples/openai-assistant", - "examples/opensource-assistant", - "examples/nextjs-assistant", - "examples/slack-AI" - ] - }, - { - "group": "Chatbots", - "pages": [ - "examples/discord_bot", - "examples/slack_bot", - "examples/telegram_bot", - "examples/whatsapp_bot", - "examples/poe_bot" + "introduction" ] - }, - { - "group": "Showcase", - "pages": ["examples/showcase"] - }, - { - "group": "API Reference", - "pages": [ - "api-reference/app/overview", - { - "group": "App methods", - "pages": [ - "api-reference/app/add", - "api-reference/app/query", - "api-reference/app/chat", - "api-reference/app/search", - "api-reference/app/get", - "api-reference/app/evaluate", - "api-reference/app/deploy", - "api-reference/app/reset", - "api-reference/app/delete" - ] - }, - "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"] } ], "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": { - "apiKey": "phc_PHQDA5KwztijnSojsxJ2c1DuJd52QCzJzT2xnSGvjN2", - "apiHost": "https://app.embedchain.ai/ingest" - }, - "ga4": { - "measurementId": "G-4QK7FJE6T3" - } - }, - "feedback": { - "suggestEdit": true, - "raiseIssue": true, - "thumbsRating": true - }, - "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/" + } +} \ No newline at end of file diff --git a/docs/snippets/snippet-intro.mdx b/docs/snippets/snippet-intro.mdx new file mode 100644 index 0000000000..c57e7c7564 --- /dev/null +++ b/docs/snippets/snippet-intro.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. diff --git a/CITATION.cff b/embedchain/CITATION.cff similarity index 100% rename from CITATION.cff rename to embedchain/CITATION.cff diff --git a/CONTRIBUTING.md b/embedchain/CONTRIBUTING.md similarity index 100% rename from CONTRIBUTING.md rename to embedchain/CONTRIBUTING.md diff --git a/LICENSE b/embedchain/LICENSE similarity index 100% rename from LICENSE rename to embedchain/LICENSE diff --git a/embedchain/Makefile b/embedchain/Makefile new file mode 100644 index 0000000000..60b1e2251b --- /dev/null +++ b/embedchain/Makefile @@ -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 diff --git a/embedchain/README.md b/embedchain/README.md new file mode 100644 index 0000000000..4512e3cc8c --- /dev/null +++ b/embedchain/README.md @@ -0,0 +1,125 @@ +

+ Embedchain Logo +

+ +

+ + PyPI + + + Downloads + + + Slack + + + Discord + + + Twitter + + + Open in Colab + + + codecov + +

+ +
+ +## 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 + + +

+ Embedchain Demo +

+ +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"] = "" +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). + + + + + +## 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}}, +} +``` diff --git a/configs/anthropic.yaml b/embedchain/configs/anthropic.yaml similarity index 100% rename from configs/anthropic.yaml rename to embedchain/configs/anthropic.yaml diff --git a/configs/azure_openai.yaml b/embedchain/configs/azure_openai.yaml similarity index 100% rename from configs/azure_openai.yaml rename to embedchain/configs/azure_openai.yaml diff --git a/configs/chroma.yaml b/embedchain/configs/chroma.yaml similarity index 100% rename from configs/chroma.yaml rename to embedchain/configs/chroma.yaml diff --git a/configs/chunker.yaml b/embedchain/configs/chunker.yaml similarity index 100% rename from configs/chunker.yaml rename to embedchain/configs/chunker.yaml diff --git a/configs/clarifai.yaml b/embedchain/configs/clarifai.yaml similarity index 100% rename from configs/clarifai.yaml rename to embedchain/configs/clarifai.yaml diff --git a/configs/cohere.yaml b/embedchain/configs/cohere.yaml similarity index 100% rename from configs/cohere.yaml rename to embedchain/configs/cohere.yaml diff --git a/configs/full-stack.yaml b/embedchain/configs/full-stack.yaml similarity index 100% rename from configs/full-stack.yaml rename to embedchain/configs/full-stack.yaml diff --git a/configs/google.yaml b/embedchain/configs/google.yaml similarity index 100% rename from configs/google.yaml rename to embedchain/configs/google.yaml diff --git a/configs/gpt4.yaml b/embedchain/configs/gpt4.yaml similarity index 100% rename from configs/gpt4.yaml rename to embedchain/configs/gpt4.yaml diff --git a/configs/gpt4all.yaml b/embedchain/configs/gpt4all.yaml similarity index 100% rename from configs/gpt4all.yaml rename to embedchain/configs/gpt4all.yaml diff --git a/configs/huggingface.yaml b/embedchain/configs/huggingface.yaml similarity index 100% rename from configs/huggingface.yaml rename to embedchain/configs/huggingface.yaml diff --git a/configs/jina.yaml b/embedchain/configs/jina.yaml similarity index 100% rename from configs/jina.yaml rename to embedchain/configs/jina.yaml diff --git a/configs/llama2.yaml b/embedchain/configs/llama2.yaml similarity index 100% rename from configs/llama2.yaml rename to embedchain/configs/llama2.yaml diff --git a/configs/ollama.yaml b/embedchain/configs/ollama.yaml similarity index 100% rename from configs/ollama.yaml rename to embedchain/configs/ollama.yaml diff --git a/configs/opensearch.yaml b/embedchain/configs/opensearch.yaml similarity index 100% rename from configs/opensearch.yaml rename to embedchain/configs/opensearch.yaml diff --git a/configs/opensource.yaml b/embedchain/configs/opensource.yaml similarity index 100% rename from configs/opensource.yaml rename to embedchain/configs/opensource.yaml diff --git a/configs/pinecone.yaml b/embedchain/configs/pinecone.yaml similarity index 100% rename from configs/pinecone.yaml rename to embedchain/configs/pinecone.yaml diff --git a/configs/pipeline.yaml b/embedchain/configs/pipeline.yaml similarity index 100% rename from configs/pipeline.yaml rename to embedchain/configs/pipeline.yaml diff --git a/configs/together.yaml b/embedchain/configs/together.yaml similarity index 100% rename from configs/together.yaml rename to embedchain/configs/together.yaml diff --git a/configs/vertexai.yaml b/embedchain/configs/vertexai.yaml similarity index 100% rename from configs/vertexai.yaml rename to embedchain/configs/vertexai.yaml diff --git a/configs/vllm.yaml b/embedchain/configs/vllm.yaml similarity index 100% rename from configs/vllm.yaml rename to embedchain/configs/vllm.yaml diff --git a/configs/weaviate.yaml b/embedchain/configs/weaviate.yaml similarity index 100% rename from configs/weaviate.yaml rename to embedchain/configs/weaviate.yaml diff --git a/embedchain/core/db/database.py b/embedchain/core/db/database.py deleted file mode 100644 index 0965ca8ffe..0000000000 --- a/embedchain/core/db/database.py +++ /dev/null @@ -1,88 +0,0 @@ -import os - -from alembic import command -from alembic.config import Config -from sqlalchemy import create_engine -from sqlalchemy.engine.base import Engine -from sqlalchemy.orm import Session as SQLAlchemySession -from sqlalchemy.orm import scoped_session, sessionmaker - -from .models import Base - - -class DatabaseManager: - def __init__(self, echo: bool = False): - self.database_uri = os.environ.get("EMBEDCHAIN_DB_URI") - self.echo = echo - self.engine: Engine = None - self._session_factory = None - - def setup_engine(self) -> None: - """Initializes the database engine and session factory.""" - if not self.database_uri: - raise RuntimeError("Database URI is not set. Set the EMBEDCHAIN_DB_URI environment variable.") - connect_args = {} - if self.database_uri.startswith("sqlite"): - connect_args["check_same_thread"] = False - self.engine = create_engine(self.database_uri, echo=self.echo, connect_args=connect_args) - self._session_factory = scoped_session(sessionmaker(bind=self.engine)) - Base.metadata.bind = self.engine - - def init_db(self) -> None: - """Creates all tables defined in the Base metadata.""" - if not self.engine: - raise RuntimeError("Database engine is not initialized. Call setup_engine() first.") - Base.metadata.create_all(self.engine) - - def get_session(self) -> SQLAlchemySession: - """Provides a session for database operations.""" - if not self._session_factory: - raise RuntimeError("Session factory is not initialized. Call setup_engine() first.") - return self._session_factory() - - def close_session(self) -> None: - """Closes the current session.""" - if self._session_factory: - self._session_factory.remove() - - def execute_transaction(self, transaction_block): - """Executes a block of code within a database transaction.""" - session = self.get_session() - try: - transaction_block(session) - session.commit() - except Exception as e: - session.rollback() - raise e - finally: - self.close_session() - - -# Singleton pattern to use throughout the application -database_manager = DatabaseManager() - - -# Convenience functions for backward compatibility and ease of use -def setup_engine(database_uri: str, echo: bool = False) -> None: - database_manager.database_uri = database_uri - database_manager.echo = echo - database_manager.setup_engine() - - -def alembic_upgrade() -> None: - """Upgrades the database to the latest version.""" - alembic_config_path = os.path.join(os.path.dirname(__file__), "..", "..", "alembic.ini") - alembic_cfg = Config(alembic_config_path) - command.upgrade(alembic_cfg, "head") - - -def init_db() -> None: - alembic_upgrade() - - -def get_session() -> SQLAlchemySession: - return database_manager.get_session() - - -def execute_transaction(transaction_block): - database_manager.execute_transaction(transaction_block) diff --git a/embedchain/core/db/models.py b/embedchain/core/db/models.py deleted file mode 100644 index af77803f7d..0000000000 --- a/embedchain/core/db/models.py +++ /dev/null @@ -1,31 +0,0 @@ -import uuid - -from sqlalchemy import TIMESTAMP, Column, Integer, String, Text, func -from sqlalchemy.orm import declarative_base - -Base = declarative_base() -metadata = Base.metadata - - -class DataSource(Base): - __tablename__ = "ec_data_sources" - - id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4())) - app_id = Column(Text, index=True) - hash = Column(Text, index=True) - type = Column(Text, index=True) - value = Column(Text) - meta_data = Column(Text, name="metadata") - is_uploaded = Column(Integer, default=0) - - -class ChatHistory(Base): - __tablename__ = "ec_chat_history" - - app_id = Column(String, primary_key=True) - id = Column(String, primary_key=True) - session_id = Column(String, primary_key=True, index=True) - question = Column(Text) - answer = Column(Text) - meta_data = Column(Text, name="metadata") - created_at = Column(TIMESTAMP, default=func.current_timestamp(), index=True) diff --git a/docs/Makefile b/embedchain/docs/Makefile similarity index 100% rename from docs/Makefile rename to embedchain/docs/Makefile diff --git a/embedchain/docs/README.md b/embedchain/docs/README.md new file mode 100644 index 0000000000..e322686dc1 --- /dev/null +++ b/embedchain/docs/README.md @@ -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` diff --git a/docs/_snippets/get-help.mdx b/embedchain/docs/_snippets/get-help.mdx similarity index 100% rename from docs/_snippets/get-help.mdx rename to embedchain/docs/_snippets/get-help.mdx diff --git a/docs/_snippets/missing-data-source-tip.mdx b/embedchain/docs/_snippets/missing-data-source-tip.mdx similarity index 100% rename from docs/_snippets/missing-data-source-tip.mdx rename to embedchain/docs/_snippets/missing-data-source-tip.mdx diff --git a/docs/_snippets/missing-llm-tip.mdx 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pathlib-compatible object wrapper for zip files" +optional = false +python-versions = ">=3.8" +files = [ + {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"}, + {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"}, +] + +[package.extras] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] + +[extras] +elasticsearch = ["elasticsearch"] +gmail = ["google-api-core", "google-api-python-client", "google-auth", "google-auth-httplib2", "google-auth-oauthlib", "requests"] +google = ["google-generativeai"] +googledrive = ["google-api-python-client", "google-auth-httplib2", "google-auth-oauthlib"] +lancedb = ["lancedb"] +llama2 = ["replicate"] +milvus = ["pymilvus"] +mistralai = ["langchain-mistralai"] +mysql = ["mysql-connector-python"] +opensearch = ["opensearch-py"] +opensource = ["gpt4all", "sentence-transformers", "torch"] +postgres = ["psycopg", "psycopg-binary", "psycopg-pool"] +qdrant = ["qdrant-client"] +together = ["together"] +vertexai = ["langchain-google-vertexai"] +weaviate = ["weaviate-client"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.9,<=3.13" +content-hash = "afc88f00bafd2b76a954c758a0556cba6d3854e98c444bc5e720319bf472caa8" diff --git a/poetry.toml b/embedchain/poetry.toml similarity index 100% rename from poetry.toml rename to embedchain/poetry.toml diff --git a/embedchain/pyproject.toml b/embedchain/pyproject.toml new file mode 100644 index 0000000000..63521101a1 --- /dev/null +++ b/embedchain/pyproject.toml @@ -0,0 +1,184 @@ +[tool.poetry] +name = "embedchain" +version = "0.1.116" +description = "Simplest open source retrieval (RAG) framework" +authors = [ + "Taranjeet Singh ", + "Deshraj Yadav ", +] +license = "Apache License" +readme = "README.md" +exclude = [ + "db", + "configs", + "notebooks" +] +packages = [ + { include = "embedchain" }, +] + +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.ruff] +select = ["ASYNC", "E", "F"] +ignore = [] +fixable = ["ALL"] +unfixable = [] +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".git-rewrite", + ".hg", + ".mypy_cache", + ".nox", + ".pants.d", + ".pytype", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "venv" +] +line-length = 120 +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" +target-version = "py38" + +[tool.ruff.mccabe] +max-complexity = 10 + +# Ignore `E402` (import violations) in all `__init__.py` files, and in `path/to/file.py`. +[tool.ruff.per-file-ignores] +"embedchain/__init__.py" = ["E401"] + +[tool.black] +line-length = 120 +target-version = ["py38", "py39", "py310", "py311"] +include = '\.pyi?$' +exclude = ''' +/( + \.eggs + | \.git + | \.hg + | \.mypy_cache + | \.nox + | \.pants.d + | \.pytype + | \.ruff_cache + | \.svn + | \.tox + | \.venv + | __pypackages__ + | _build + | buck-out + | build + | dist + | node_modules + | venv +)/ +''' + +[tool.black.format] +color = true + +[tool.poetry.dependencies] +python = ">=3.9,<=3.13" +python-dotenv = "^1.0.0" +langchain = ">0.2,<=0.3" +requests = "^2.31.0" +openai = ">=1.1.1" +chromadb = "^0.4.24" +posthog = "^3.0.2" +rich = "^13.7.0" +beautifulsoup4 = "^4.12.2" +pypdf = "^4.0.1" +gptcache = "^0.1.43" +pysbd = "^0.3.4" +memzero = "^0.0.7" +tiktoken = { version = "^0.7.0", optional = true } +sentence-transformers = { version = "^2.2.2", optional = true } +torch = { version = "2.3.0", optional = true } +# Torch 2.0.1 is not compatible with poetry (https://github.com/pytorch/pytorch/issues/100974) +gpt4all = { version = "2.0.2", optional = true } +# 1.0.9 is not working for some users (https://github.com/nomic-ai/gpt4all/issues/1394) +opensearch-py = { version = "2.3.1", optional = true } +elasticsearch = { version = "^8.9.0", optional = true } +cohere = { version = "^5.3", optional = true } +together = { version = "^0.2.8", optional = true } +lancedb = { version = "^0.6.2", optional = true } +weaviate-client = { version = "^3.24.1", optional = true } +qdrant-client = { version = "^1.6.3", optional = true } +pymilvus = { version = "2.4.3", optional = true } +google-cloud-aiplatform = { version = "^1.26.1", optional = true } +replicate = { version = "^0.15.4", optional = true } +schema = "^0.7.5" +psycopg = { version = "^3.1.12", optional = true } +psycopg-binary = { version = "^3.1.12", optional = true } +psycopg-pool = { version = "^3.1.8", optional = true } +mysql-connector-python = { version = "^8.1.0", optional = true } +google-generativeai = { version = "^0.3.0", optional = true } +google-api-python-client = { version = "^2.111.0", optional = true } +google-auth-oauthlib = { version = "^1.2.0", optional = true } +google-auth = { version = "^2.25.2", optional = true } +google-auth-httplib2 = { version = "^0.2.0", optional = true } +google-api-core = { version = "^2.15.0", optional = true } +langchain-mistralai = { version = "^0.1.9", optional = true } +langchain-openai = "^0.1.7" +langchain-google-vertexai = { version = "^1.0.6", optional = true } +sqlalchemy = "^2.0.27" +alembic = "^1.13.1" +langchain-cohere = "^0.1.4" +langchain-community = "^0.2.6" + +[tool.poetry.group.dev.dependencies] +black = "^23.3.0" +pre-commit = "^3.2.2" +ruff = "^0.1.11" +pytest = "^7.3.1" +pytest-mock = "^3.10.0" +pytest-env = "^0.8.1" +click = "^8.1.3" +isort = "^5.12.0" +pytest-cov = "^4.1.0" +responses = "^0.23.3" +mock = "^5.1.0" +pytest-asyncio = "^0.21.1" + +[tool.poetry.extras] +opensource = ["sentence-transformers", "torch", "gpt4all"] +lancedb = ["lancedb"] +elasticsearch = ["elasticsearch"] +opensearch = ["opensearch-py"] +weaviate = ["weaviate-client"] +qdrant = ["qdrant-client"] +together = ["together"] +milvus = ["pymilvus"] +vertexai = ["langchain-google-vertexai"] +llama2 = ["replicate"] +gmail = [ + "requests", + "google-api-python-client", + "google-auth", + "google-auth-oauthlib", + "google-auth-httplib2", + "google-api-core", +] +googledrive = ["google-api-python-client", "google-auth-oauthlib", "google-auth-httplib2"] +postgres = ["psycopg", "psycopg-binary", "psycopg-pool"] +mysql = ["mysql-connector-python"] +google = ["google-generativeai"] +mistralai = ["langchain-mistralai"] + +[tool.poetry.group.docs.dependencies] + +[tool.poetry.scripts] +ec = "embedchain.cli:cli" \ No newline at end of file diff --git a/examples/rest-api/__init__.py b/embedchain/tests/__init__.py similarity index 100% rename from examples/rest-api/__init__.py rename to embedchain/tests/__init__.py diff --git a/tests/chunkers/test_base_chunker.py b/embedchain/tests/chunkers/test_base_chunker.py similarity index 100% rename from tests/chunkers/test_base_chunker.py rename to embedchain/tests/chunkers/test_base_chunker.py diff --git a/tests/chunkers/test_chunkers.py b/embedchain/tests/chunkers/test_chunkers.py similarity index 100% rename from tests/chunkers/test_chunkers.py rename to embedchain/tests/chunkers/test_chunkers.py diff --git a/tests/chunkers/test_text.py b/embedchain/tests/chunkers/test_text.py similarity index 100% rename from tests/chunkers/test_text.py rename to embedchain/tests/chunkers/test_text.py diff --git a/tests/conftest.py b/embedchain/tests/conftest.py similarity index 100% rename from tests/conftest.py rename to embedchain/tests/conftest.py diff --git a/tests/embedchain/test_add.py b/embedchain/tests/embedchain/test_add.py similarity index 100% rename from tests/embedchain/test_add.py rename to embedchain/tests/embedchain/test_add.py diff --git a/tests/embedchain/test_embedchain.py b/embedchain/tests/embedchain/test_embedchain.py similarity index 100% rename from tests/embedchain/test_embedchain.py rename to embedchain/tests/embedchain/test_embedchain.py diff --git a/tests/embedchain/test_utils.py b/embedchain/tests/embedchain/test_utils.py similarity index 100% rename from tests/embedchain/test_utils.py rename to embedchain/tests/embedchain/test_utils.py diff --git a/tests/embedder/test_embedder.py b/embedchain/tests/embedder/test_embedder.py similarity index 100% rename from tests/embedder/test_embedder.py rename to embedchain/tests/embedder/test_embedder.py diff --git a/tests/embedder/test_huggingface_embedder.py b/embedchain/tests/embedder/test_huggingface_embedder.py similarity index 100% rename from tests/embedder/test_huggingface_embedder.py rename to embedchain/tests/embedder/test_huggingface_embedder.py diff --git a/tests/evaluation/test_answer_relevancy_metric.py b/embedchain/tests/evaluation/test_answer_relevancy_metric.py similarity index 100% rename from tests/evaluation/test_answer_relevancy_metric.py rename to embedchain/tests/evaluation/test_answer_relevancy_metric.py diff --git a/tests/evaluation/test_context_relevancy_metric.py b/embedchain/tests/evaluation/test_context_relevancy_metric.py similarity index 100% rename from tests/evaluation/test_context_relevancy_metric.py rename to embedchain/tests/evaluation/test_context_relevancy_metric.py diff --git a/tests/evaluation/test_groundedness_metric.py b/embedchain/tests/evaluation/test_groundedness_metric.py similarity index 100% rename from tests/evaluation/test_groundedness_metric.py rename to embedchain/tests/evaluation/test_groundedness_metric.py diff --git a/tests/helper_classes/test_json_serializable.py b/embedchain/tests/helper_classes/test_json_serializable.py similarity index 100% rename from tests/helper_classes/test_json_serializable.py rename to embedchain/tests/helper_classes/test_json_serializable.py diff --git a/tests/llm/test_anthrophic.py b/embedchain/tests/llm/test_anthrophic.py similarity index 100% rename from tests/llm/test_anthrophic.py rename to embedchain/tests/llm/test_anthrophic.py diff --git a/tests/llm/test_aws_bedrock.py b/embedchain/tests/llm/test_aws_bedrock.py similarity index 100% rename from tests/llm/test_aws_bedrock.py rename to embedchain/tests/llm/test_aws_bedrock.py diff --git a/tests/llm/test_azure_openai.py b/embedchain/tests/llm/test_azure_openai.py similarity index 100% rename from tests/llm/test_azure_openai.py rename to embedchain/tests/llm/test_azure_openai.py diff --git a/tests/llm/test_base_llm.py b/embedchain/tests/llm/test_base_llm.py similarity index 100% rename from tests/llm/test_base_llm.py rename to embedchain/tests/llm/test_base_llm.py diff --git a/tests/llm/test_chat.py b/embedchain/tests/llm/test_chat.py similarity index 100% rename from tests/llm/test_chat.py rename to embedchain/tests/llm/test_chat.py diff --git a/tests/llm/test_clarifai.py b/embedchain/tests/llm/test_clarifai.py similarity index 100% rename from tests/llm/test_clarifai.py rename to embedchain/tests/llm/test_clarifai.py diff --git a/tests/llm/test_cohere.py b/embedchain/tests/llm/test_cohere.py similarity index 100% rename from tests/llm/test_cohere.py rename to embedchain/tests/llm/test_cohere.py diff --git a/tests/llm/test_generate_prompt.py b/embedchain/tests/llm/test_generate_prompt.py similarity index 100% rename from tests/llm/test_generate_prompt.py rename to embedchain/tests/llm/test_generate_prompt.py diff --git a/tests/llm/test_google.py b/embedchain/tests/llm/test_google.py similarity index 100% rename from tests/llm/test_google.py rename to embedchain/tests/llm/test_google.py diff --git a/tests/llm/test_gpt4all.py b/embedchain/tests/llm/test_gpt4all.py similarity index 100% rename from tests/llm/test_gpt4all.py rename to embedchain/tests/llm/test_gpt4all.py diff --git a/tests/llm/test_huggingface.py b/embedchain/tests/llm/test_huggingface.py similarity index 100% rename from tests/llm/test_huggingface.py rename to embedchain/tests/llm/test_huggingface.py diff --git a/tests/llm/test_jina.py b/embedchain/tests/llm/test_jina.py similarity index 100% rename from tests/llm/test_jina.py rename to embedchain/tests/llm/test_jina.py diff --git a/tests/llm/test_llama2.py b/embedchain/tests/llm/test_llama2.py similarity index 100% rename from tests/llm/test_llama2.py rename to embedchain/tests/llm/test_llama2.py diff --git a/tests/llm/test_mistralai.py b/embedchain/tests/llm/test_mistralai.py similarity index 100% rename from tests/llm/test_mistralai.py rename to embedchain/tests/llm/test_mistralai.py diff --git a/tests/llm/test_ollama.py b/embedchain/tests/llm/test_ollama.py similarity index 100% rename from tests/llm/test_ollama.py rename to embedchain/tests/llm/test_ollama.py diff --git a/tests/llm/test_openai.py b/embedchain/tests/llm/test_openai.py similarity index 100% rename from tests/llm/test_openai.py rename to embedchain/tests/llm/test_openai.py diff --git a/tests/llm/test_query.py b/embedchain/tests/llm/test_query.py similarity index 100% rename from tests/llm/test_query.py rename to embedchain/tests/llm/test_query.py diff --git a/tests/llm/test_together.py b/embedchain/tests/llm/test_together.py similarity index 100% rename from tests/llm/test_together.py rename to embedchain/tests/llm/test_together.py diff --git a/tests/llm/test_vertex_ai.py b/embedchain/tests/llm/test_vertex_ai.py similarity index 100% rename from tests/llm/test_vertex_ai.py rename to embedchain/tests/llm/test_vertex_ai.py diff --git a/tests/loaders/test_audio.py b/embedchain/tests/loaders/test_audio.py similarity index 100% rename from tests/loaders/test_audio.py rename to embedchain/tests/loaders/test_audio.py diff --git a/tests/loaders/test_csv.py b/embedchain/tests/loaders/test_csv.py similarity index 100% rename from tests/loaders/test_csv.py rename to embedchain/tests/loaders/test_csv.py diff --git a/tests/loaders/test_discourse.py b/embedchain/tests/loaders/test_discourse.py similarity index 100% rename from tests/loaders/test_discourse.py rename to embedchain/tests/loaders/test_discourse.py diff --git a/tests/loaders/test_docs_site.py b/embedchain/tests/loaders/test_docs_site.py similarity index 100% rename from tests/loaders/test_docs_site.py rename to embedchain/tests/loaders/test_docs_site.py diff --git a/tests/loaders/test_docs_site_loader.py b/embedchain/tests/loaders/test_docs_site_loader.py similarity index 100% rename from tests/loaders/test_docs_site_loader.py rename to embedchain/tests/loaders/test_docs_site_loader.py diff --git a/tests/loaders/test_docx_file.py b/embedchain/tests/loaders/test_docx_file.py similarity index 100% rename from tests/loaders/test_docx_file.py rename to embedchain/tests/loaders/test_docx_file.py diff --git a/tests/loaders/test_dropbox.py b/embedchain/tests/loaders/test_dropbox.py similarity index 100% rename from tests/loaders/test_dropbox.py rename to embedchain/tests/loaders/test_dropbox.py diff --git a/tests/loaders/test_excel_file.py b/embedchain/tests/loaders/test_excel_file.py similarity index 100% rename from tests/loaders/test_excel_file.py rename to embedchain/tests/loaders/test_excel_file.py diff --git a/tests/loaders/test_github.py b/embedchain/tests/loaders/test_github.py similarity index 100% rename from tests/loaders/test_github.py rename to embedchain/tests/loaders/test_github.py diff --git a/tests/loaders/test_gmail.py b/embedchain/tests/loaders/test_gmail.py similarity index 100% rename from tests/loaders/test_gmail.py rename to embedchain/tests/loaders/test_gmail.py diff --git a/tests/loaders/test_google_drive.py b/embedchain/tests/loaders/test_google_drive.py similarity index 100% rename from tests/loaders/test_google_drive.py rename to embedchain/tests/loaders/test_google_drive.py diff --git a/tests/loaders/test_json.py b/embedchain/tests/loaders/test_json.py similarity index 100% rename from tests/loaders/test_json.py rename to embedchain/tests/loaders/test_json.py diff --git a/tests/loaders/test_local_qna_pair.py b/embedchain/tests/loaders/test_local_qna_pair.py similarity index 100% rename from tests/loaders/test_local_qna_pair.py rename to embedchain/tests/loaders/test_local_qna_pair.py diff --git a/tests/loaders/test_local_text.py b/embedchain/tests/loaders/test_local_text.py similarity index 100% rename from tests/loaders/test_local_text.py rename to embedchain/tests/loaders/test_local_text.py diff --git a/tests/loaders/test_mdx.py b/embedchain/tests/loaders/test_mdx.py similarity index 100% rename from tests/loaders/test_mdx.py rename to embedchain/tests/loaders/test_mdx.py diff --git a/tests/loaders/test_mysql.py b/embedchain/tests/loaders/test_mysql.py similarity index 100% rename from tests/loaders/test_mysql.py rename to embedchain/tests/loaders/test_mysql.py diff --git a/tests/loaders/test_notion.py b/embedchain/tests/loaders/test_notion.py similarity index 100% rename from tests/loaders/test_notion.py rename to embedchain/tests/loaders/test_notion.py diff --git a/tests/loaders/test_openapi.py b/embedchain/tests/loaders/test_openapi.py similarity index 100% rename from tests/loaders/test_openapi.py rename to embedchain/tests/loaders/test_openapi.py diff --git a/tests/loaders/test_pdf_file.py b/embedchain/tests/loaders/test_pdf_file.py similarity index 100% rename from tests/loaders/test_pdf_file.py rename to embedchain/tests/loaders/test_pdf_file.py diff --git a/tests/loaders/test_postgres.py b/embedchain/tests/loaders/test_postgres.py similarity index 100% rename from tests/loaders/test_postgres.py rename to embedchain/tests/loaders/test_postgres.py diff --git a/tests/loaders/test_slack.py b/embedchain/tests/loaders/test_slack.py similarity index 100% rename from tests/loaders/test_slack.py rename to embedchain/tests/loaders/test_slack.py diff --git a/tests/loaders/test_web_page.py b/embedchain/tests/loaders/test_web_page.py similarity index 100% rename from tests/loaders/test_web_page.py rename to embedchain/tests/loaders/test_web_page.py diff --git a/tests/loaders/test_xml.py b/embedchain/tests/loaders/test_xml.py similarity index 100% rename from tests/loaders/test_xml.py rename to embedchain/tests/loaders/test_xml.py diff --git a/tests/loaders/test_youtube_video.py b/embedchain/tests/loaders/test_youtube_video.py similarity index 100% rename from tests/loaders/test_youtube_video.py rename to embedchain/tests/loaders/test_youtube_video.py diff --git a/tests/memory/test_chat_memory.py b/embedchain/tests/memory/test_chat_memory.py similarity index 100% rename from tests/memory/test_chat_memory.py rename to embedchain/tests/memory/test_chat_memory.py diff --git a/tests/memory/test_memory_messages.py b/embedchain/tests/memory/test_memory_messages.py similarity index 100% rename from tests/memory/test_memory_messages.py rename to embedchain/tests/memory/test_memory_messages.py diff --git a/tests/models/test_data_type.py b/embedchain/tests/models/test_data_type.py similarity index 100% rename from tests/models/test_data_type.py rename to embedchain/tests/models/test_data_type.py diff --git a/tests/telemetry/test_posthog.py b/embedchain/tests/telemetry/test_posthog.py similarity index 100% rename from tests/telemetry/test_posthog.py rename to embedchain/tests/telemetry/test_posthog.py diff --git a/tests/test_app.py b/embedchain/tests/test_app.py similarity index 100% rename from tests/test_app.py rename to embedchain/tests/test_app.py diff --git a/tests/test_client.py b/embedchain/tests/test_client.py similarity index 100% rename from tests/test_client.py rename to embedchain/tests/test_client.py diff --git a/tests/test_factory.py b/embedchain/tests/test_factory.py similarity index 100% rename from tests/test_factory.py rename to embedchain/tests/test_factory.py diff --git a/tests/test_utils.py b/embedchain/tests/test_utils.py similarity index 100% rename from tests/test_utils.py rename to embedchain/tests/test_utils.py diff --git a/tests/vectordb/test_chroma_db.py b/embedchain/tests/vectordb/test_chroma_db.py similarity index 100% rename from tests/vectordb/test_chroma_db.py rename to embedchain/tests/vectordb/test_chroma_db.py diff --git a/tests/vectordb/test_elasticsearch_db.py b/embedchain/tests/vectordb/test_elasticsearch_db.py similarity index 100% rename from tests/vectordb/test_elasticsearch_db.py rename to embedchain/tests/vectordb/test_elasticsearch_db.py diff --git a/tests/vectordb/test_lancedb.py b/embedchain/tests/vectordb/test_lancedb.py similarity index 100% rename from tests/vectordb/test_lancedb.py rename to embedchain/tests/vectordb/test_lancedb.py diff --git a/tests/vectordb/test_pinecone.py b/embedchain/tests/vectordb/test_pinecone.py similarity index 100% rename from tests/vectordb/test_pinecone.py rename to embedchain/tests/vectordb/test_pinecone.py diff --git a/tests/vectordb/test_qdrant.py b/embedchain/tests/vectordb/test_qdrant.py similarity index 100% rename from tests/vectordb/test_qdrant.py rename to embedchain/tests/vectordb/test_qdrant.py diff --git a/tests/vectordb/test_weaviate.py b/embedchain/tests/vectordb/test_weaviate.py similarity index 100% rename from tests/vectordb/test_weaviate.py rename to embedchain/tests/vectordb/test_weaviate.py diff --git a/tests/vectordb/test_zilliz_db.py b/embedchain/tests/vectordb/test_zilliz_db.py similarity index 100% rename from tests/vectordb/test_zilliz_db.py rename to embedchain/tests/vectordb/test_zilliz_db.py diff --git a/mem0/__init__.py b/mem0/__init__.py new file mode 100644 index 0000000000..10133d3fbd --- /dev/null +++ b/mem0/__init__.py @@ -0,0 +1,5 @@ +import importlib.metadata + +__version__ = importlib.metadata.version(__package__ or __name__) + +from mem0.memory.main import Memory # noqa diff --git a/mem0/configs/__init__.py b/mem0/configs/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/mem0/configs/base.py b/mem0/configs/base.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/mem0/configs/prompts.py b/mem0/configs/prompts.py new file mode 100644 index 0000000000..972d27e540 --- /dev/null +++ b/mem0/configs/prompts.py @@ -0,0 +1,17 @@ +UPDATE_MEMORY_PROMPT = """ +You are an expert at merging, updating, and organizing user memories. When provided with existing memories and new information, your task is to merge and update the memory list to reflect the most accurate and current information. You are also provided with the matching score for each existing memory to the new information. Make sure to leverage this information to make informed decisions about which memories to update or merge. + +Guidelines: +- Eliminate duplicate memories and merge related memories to ensure a concise and updated list. +- If a memory is directly contradicted by new information, critically evaluate both pieces of information: + - If the new memory provides a more recent or accurate update, replace the old memory with new one. + - If the new memory seems inaccurate or less detailed, retain the original and discard the old one. +- Maintain a consistent and clear style throughout all memories, ensuring each entry is concise yet informative. +- If the new memory is a variation or extension of an existing memory, update the existing memory to reflect the new information. + +Here are the details of the task: +- Existing Memories: +{existing_memories} + +- New Memory: {memory} +""" diff --git a/mem0/embeddings/__init__.py b/mem0/embeddings/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/mem0/embeddings/base.py b/mem0/embeddings/base.py new file mode 100644 index 0000000000..388ddf6a22 --- /dev/null +++ b/mem0/embeddings/base.py @@ -0,0 +1,16 @@ +from abc import ABC, abstractmethod + + +class EmbeddingBase(ABC): + @abstractmethod + def embed(self, text): + """ + Get the embedding for the given text. + + Args: + text (str): The text to embed. + + Returns: + list: The embedding vector. + """ + pass diff --git a/mem0/embeddings/huggingface.py b/mem0/embeddings/huggingface.py new file mode 100644 index 0000000000..7e7ca59077 --- /dev/null +++ b/mem0/embeddings/huggingface.py @@ -0,0 +1,19 @@ +from embedding.base import EmbeddingBase +from sentence_transformers import SentenceTransformer + + +class HuggingFaceEmbedding(EmbeddingBase): + def __init__(self, model_name="multi-qa-MiniLM-L6-cos-v1"): + self.model = SentenceTransformer(model_name) + + def get_embedding(self, text): + """ + Get the embedding for the given text using Hugging Face. + + Args: + text (str): The text to embed. + + Returns: + list: The embedding vector. + """ + return self.model.encode(text) diff --git a/mem0/embeddings/ollama.py b/mem0/embeddings/ollama.py new file mode 100644 index 0000000000..649fe1618f --- /dev/null +++ b/mem0/embeddings/ollama.py @@ -0,0 +1,30 @@ +import ollama +from embedding.base import EmbeddingBase + + +class OllamaEmbedding(EmbeddingBase): + def __init__(self, model="nomic-embed-text"): + self.model = model + self._ensure_model_exists() + self.dims = 512 + + def _ensure_model_exists(self): + """ + Ensure the specified model exists locally. If not, pull it from Ollama. + """ + model_list = [m["name"] for m in ollama.list()["models"]] + if not any(m.startswith(self.model) for m in model_list): + ollama.pull(self.model) + + def embed(self, text): + """ + Get the embedding for the given text using Ollama. + + Args: + text (str): The text to embed. + + Returns: + list: The embedding vector. + """ + response = ollama.embeddings(model=self.model, prompt=text) + return response["embedding"] diff --git a/mem0/embeddings/openai.py b/mem0/embeddings/openai.py new file mode 100644 index 0000000000..619683978a --- /dev/null +++ b/mem0/embeddings/openai.py @@ -0,0 +1,27 @@ +from openai import OpenAI + +from mem0.embeddings.base import EmbeddingBase + + +class OpenAIEmbedding(EmbeddingBase): + def __init__(self, model="text-embedding-3-small"): + self.client = OpenAI() + self.model = model + self.dims = 1536 + + def embed(self, text): + """ + Get the embedding for the given text using OpenAI. + + Args: + text (str): The text to embed. + + Returns: + list: The embedding vector. + """ + text = text.replace("\n", " ") + return ( + self.client.embeddings.create(input=[text], model=self.model) + .data[0] + .embedding + ) diff --git a/mem0/llms/__init__.py b/mem0/llms/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/mem0/llms/base.py b/mem0/llms/base.py new file mode 100644 index 0000000000..421bde2026 --- /dev/null +++ b/mem0/llms/base.py @@ -0,0 +1,16 @@ +from abc import ABC, abstractmethod + + +class LLMBase(ABC): + @abstractmethod + def generate_response(self, messages): + """ + Generate a response based on the given messages. + + Args: + messages (list): List of message dicts containing 'role' and 'content'. + + Returns: + str: The generated response. + """ + pass diff --git a/mem0/llms/ollama.py b/mem0/llms/ollama.py new file mode 100644 index 0000000000..3311e2ea8e --- /dev/null +++ b/mem0/llms/ollama.py @@ -0,0 +1,29 @@ +import ollama +from llm.base import LLMBase + + +class OllamaLLM(LLMBase): + def __init__(self, model="llama3"): + self.model = model + self._ensure_model_exists() + + def _ensure_model_exists(self): + """ + Ensure the specified model exists locally. If not, pull it from Ollama. + """ + model_list = [m["name"] for m in ollama.list()["models"]] + if not any(m.startswith(self.model) for m in model_list): + ollama.pull(self.model) + + def generate_response(self, messages): + """ + Generate a response based on the given messages using Ollama. + + Args: + messages (list): List of message dicts containing 'role' and 'content'. + + Returns: + str: The generated response. + """ + response = ollama.chat(model=self.model, messages=messages) + return response["message"]["content"] diff --git a/mem0/llms/openai.py b/mem0/llms/openai.py new file mode 100644 index 0000000000..bf9a38cda2 --- /dev/null +++ b/mem0/llms/openai.py @@ -0,0 +1,41 @@ +from typing import Dict, List, Optional + +from openai import OpenAI + +from mem0.llms.base import LLMBase + + +class OpenAILLM(LLMBase): + def __init__(self, model="gpt-4o"): + self.client = OpenAI() + self.model = model + + def generate_response( + self, + messages: List[Dict[str, str]], + response_format=None, + tools: Optional[List[Dict]] = None, + tool_choice: str = "auto", + ): + """ + Generate a response based on the given messages using OpenAI. + + Args: + messages (list): List of message dicts containing 'role' and 'content'. + response_format (str or object, optional): Format of the response. Defaults to "text". + tools (list, optional): List of tools that the model can call. Defaults to None. + tool_choice (str, optional): Tool choice method. Defaults to "auto". + + Returns: + str: The generated response. + """ + params = {"model": self.model, "messages": messages} + if response_format: + params["response_format"] = response_format + if tools: + params["tools"] = tools + params["tool_choice"] = tool_choice + + response = self.client.chat.completions.create(**params) + return response + # return response.choices[0].message["content"] diff --git a/mem0/llms/utils/__init__.py b/mem0/llms/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/mem0/llms/utils/functions.py b/mem0/llms/utils/functions.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/mem0/llms/utils/tools.py b/mem0/llms/utils/tools.py new file mode 100644 index 0000000000..acb3d2a841 --- /dev/null +++ b/mem0/llms/utils/tools.py @@ -0,0 +1,54 @@ +ADD_MEMORY_TOOL = { + "type": "function", + "function": { + "name": "add_memory", + "description": "Add a memory", + "parameters": { + "type": "object", + "properties": { + "data": {"type": "string", "description": "Data to add to memory"} + }, + "required": ["data"], + }, + }, +} + +UPDATE_MEMORY_TOOL = { + "type": "function", + "function": { + "name": "update_memory", + "description": "Update memory provided ID and data", + "parameters": { + "type": "object", + "properties": { + "memory_id": { + "type": "string", + "description": "memory_id of the memory to update", + }, + "data": { + "type": "string", + "description": "Updated data for the memory", + }, + }, + "required": ["memory_id", "data"], + }, + }, +} + +DELETE_MEMORY_TOOL = { + "type": "function", + "function": { + "name": "delete_memory", + "description": "Delete memory by memory_id", + "parameters": { + "type": "object", + "properties": { + "memory_id": { + "type": "string", + "description": "memory_id of the memory to delete", + } + }, + "required": ["memory_id"], + }, + }, +} diff --git a/mem0/memory/__init__.py b/mem0/memory/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/mem0/memory/base.py b/mem0/memory/base.py new file mode 100644 index 0000000000..2db4d8ec1f --- /dev/null +++ b/mem0/memory/base.py @@ -0,0 +1,63 @@ +from abc import ABC, abstractmethod + + +class MemoryBase(ABC): + @abstractmethod + def get(self, memory_id): + """ + Retrieve a memory by ID. + + Args: + memory_id (str): ID of the memory to retrieve. + + Returns: + dict: Retrieved memory. + """ + pass + + @abstractmethod + def get_all(self): + """ + List all memories. + + Returns: + list: List of all memories. + """ + pass + + @abstractmethod + def update(self, memory_id, data): + """ + Update a memory by ID. + + Args: + memory_id (str): ID of the memory to update. + data (dict): Data to update the memory with. + + Returns: + dict: Updated memory. + """ + pass + + @abstractmethod + def delete(self, memory_id): + """ + Delete a memory by ID. + + Args: + memory_id (str): ID of the memory to delete. + """ + pass + + @abstractmethod + def history(self, memory_id): + """ + Get the history of changes for a memory by ID. + + Args: + memory_id (str): ID of the memory to get history for. + + Returns: + list: List of changes for the memory. + """ + pass diff --git a/mem0/memory/main.py b/mem0/memory/main.py new file mode 100644 index 0000000000..631b55ff87 --- /dev/null +++ b/mem0/memory/main.py @@ -0,0 +1,401 @@ +import json +import logging +import os +import time +import uuid +from typing import Any, Dict, Optional + +from pydantic import BaseModel, Field, ValidationError + +from mem0.embeddings.openai import OpenAIEmbedding +from mem0.llms.openai import OpenAILLM +from mem0.llms.utils.tools import ( + ADD_MEMORY_TOOL, + DELETE_MEMORY_TOOL, + UPDATE_MEMORY_TOOL, +) +from mem0.memory.base import MemoryBase +from mem0.memory.setup import mem0_dir, setup_config +from mem0.memory.storage import SQLiteManager +from mem0.memory.telemetry import capture_event +from mem0.memory.utils import get_update_memory_messages +from mem0.vector_stores.configs import VectorStoreConfig +from mem0.vector_stores.qdrant import Qdrant + +# Setup user config +setup_config() + + +class MemoryItem(BaseModel): + id: str = Field(..., description="The unique identifier for the text data") + text: str = Field(..., description="The text content") + # The metadata value can be anything and not just string. Fix it + metadata: Dict[str, Any] = Field( + default_factory=dict, description="Additional metadata for the text data" + ) + score: Optional[float] = Field( + None, description="The score associated with the text data" + ) + + +class MemoryConfig(BaseModel): + vector_store: VectorStoreConfig = Field( + description="Configuration for the vector store", + default_factory=VectorStoreConfig, + ) + history_db_path: str = Field( + description="Path to the history database", + default=os.path.join(mem0_dir, "history.db"), + ) + collection_name: str = Field(default="mem0", description="Name of the collection") + embedding_model_dims: int = Field( + default=1536, description="Dimensions of the embedding model" + ) + + +class Memory(MemoryBase): + def __init__(self, config: MemoryConfig = MemoryConfig()): + self.config = config + self.embedding_model = OpenAIEmbedding() + # Initialize the appropriate vector store based on the configuration + vector_store_config = self.config.vector_store.config + if self.config.vector_store.provider == "qdrant": + self.vector_store = Qdrant( + host=vector_store_config.host, + port=vector_store_config.port, + path=vector_store_config.path, + url=vector_store_config.url, + api_key=vector_store_config.api_key, + ) + else: + raise ValueError( + f"Unsupported vector store type: {self.config.vector_store_type}" + ) + + self.llm = OpenAILLM() + self.db = SQLiteManager(self.config.history_db_path) + self.collection_name = self.config.collection_name + self.vector_store.create_col( + name=self.collection_name, vector_size=self.embedding_model.dims + ) + self.vector_store.create_col( + name=self.collection_name, vector_size=self.embedding_model.dims + ) + capture_event("mem0.init", self) + + @classmethod + def from_config(cls, config_dict: Dict[str, Any]): + try: + config = MemoryConfig(**config_dict) + except ValidationError as e: + logging.error(f"Configuration validation error: {e}") + raise + return cls(config) + + def add( + self, + data, + user_id=None, + agent_id=None, + run_id=None, + metadata=None, + filters=None, + ): + """ + Create a new memory. + + Args: + data (str): Data to store in the memory. + user_id (str, optional): ID of the user creating the memory. Defaults to None. + agent_id (str, optional): ID of the agent creating the memory. Defaults to None. + run_id (str, optional): ID of the run creating the memory. Defaults to None. + metadata (dict, optional): Metadata to store with the memory. Defaults to None. + filters (dict, optional): Filters to apply to the search. Defaults to None. + + Returns: + str: ID of the created memory. + """ + if metadata is None: + logging.warn("Metadata not provided. Using empty metadata.") + metadata = {} + embeddings = self.embedding_model.embed(data) + + filters = filters or {} + if user_id: + filters["user_id"] = metadata["user_id"] = user_id + if agent_id: + filters["agent_id"] = metadata["agent_id"] = agent_id + if run_id: + filters["run_id"] = metadata["run_id"] = run_id + + existing_memories = self.vector_store.search( + name=self.collection_name, + query=embeddings, + limit=5, + filters=filters, + ) + existing_memories = [ + MemoryItem( + id=mem.id, + score=mem.score, + metadata=mem.payload, + text=mem.payload["data"], + ) + for mem in existing_memories + ] + serialized_existing_memories = [ + item.model_dump(include={"id", "text", "score"}) + for item in existing_memories + ] + logging.info(f"Total existing memories: {len(existing_memories)}") + messages = get_update_memory_messages(serialized_existing_memories, data) + # Add tools for noop, add, update, delete memory. + tools = [ADD_MEMORY_TOOL, UPDATE_MEMORY_TOOL, DELETE_MEMORY_TOOL] + response = self.llm.generate_response(messages=messages, tools=tools) + response_message = response.choices[0].message + tool_calls = response_message.tool_calls + + response = [] + if tool_calls: + # Create a new memory + available_functions = { + "add_memory": self._create_memory_tool, + "update_memory": self._update_memory_tool, + "delete_memory": self._delete_memory_tool, + } + for tool_call in tool_calls: + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + logging.info( + f"[openai_func] func: {function_name}, args: {function_args}" + ) + + # Pass metadata to the function if it requires it + if function_name in ["add_memory", "update_memory"]: + function_args["metadata"] = metadata + + function_result = function_to_call(**function_args) + # Fetch the memory_id from the response + response.append( + { + "id": function_result, + "event": function_name.replace("_memory", ""), + "data": function_args.get("data"), + } + ) + capture_event( + "mem0.add.function_call", + self, + {"memory_id": function_result, "function_name": function_name}, + ) + capture_event("mem0.add", self) + return response + + def get(self, memory_id): + """ + Retrieve a memory by ID. + + Args: + memory_id (str): ID of the memory to retrieve. + + Returns: + dict: Retrieved memory. + """ + capture_event("mem0.get", self, {"memory_id": memory_id}) + memory = self.vector_store.get(name=self.collection_name, vector_id=memory_id) + if not memory: + return None + return MemoryItem( + id=memory.id, + metadata=memory.payload, + text=memory.payload["data"], + ).model_dump(exclude={"score"}) + + def get_all(self, user_id=None, agent_id=None, run_id=None, limit=100): + """ + List all memories. + + Returns: + list: List of all memories. + """ + filters = {} + if user_id: + filters["user_id"] = user_id + if agent_id: + filters["agent_id"] = agent_id + if run_id: + filters["run_id"] = run_id + + capture_event("mem0.get_all", self, {"filters": len(filters), "limit": limit}) + memories = self.vector_store.list( + name=self.collection_name, filters=filters, limit=limit + ) + return [ + MemoryItem( + id=mem.id, + metadata=mem.payload, + text=mem.payload["data"], + ).model_dump(exclude={"score"}) + for mem in memories[0] + ] + + def search( + self, query, user_id=None, agent_id=None, run_id=None, limit=100, filters=None + ): + """ + Search for memories. + + Args: + query (str): Query to search for. + user_id (str, optional): ID of the user to search for. Defaults to None. + agent_id (str, optional): ID of the agent to search for. Defaults to None. + run_id (str, optional): ID of the run to search for. Defaults to None. + limit (int, optional): Limit the number of results. Defaults to 100. + filters (dict, optional): Filters to apply to the search. Defaults to None. + + Returns: + list: List of search results. + """ + filters = filters or {} + if user_id: + filters["user_id"] = user_id + if agent_id: + filters["agent_id"] = agent_id + if run_id: + filters["run_id"] = run_id + + capture_event("mem0.search", self, {"filters": len(filters), "limit": limit}) + embeddings = self.embedding_model.embed(query) + memories = self.vector_store.search( + name=self.collection_name, query=embeddings, limit=limit, filters=filters + ) + return [ + MemoryItem( + id=mem.id, + metadata=mem.payload, + score=mem.score, + text=mem.payload["data"], + ).model_dump() + for mem in memories + ] + + def update(self, memory_id, data): + """ + Update a memory by ID. + + Args: + memory_id (str): ID of the memory to update. + data (dict): Data to update the memory with. + + Returns: + dict: Updated memory. + """ + capture_event("mem0.get_all", self, {"memory_id": memory_id}) + self._update_memory_tool(memory_id, data) + + def delete(self, memory_id): + """ + Delete a memory by ID. + + Args: + memory_id (str): ID of the memory to delete. + """ + capture_event("mem0.delete", self, {"memory_id": memory_id}) + self._delete_memory_tool(memory_id) + + def delete_all(self, user_id=None, agent_id=None, run_id=None): + """ + Delete all memories. + + Args: + user_id (str, optional): ID of the user to delete memories for. Defaults to None. + agent_id (str, optional): ID of the agent to delete memories for. Defaults to None. + run_id (str, optional): ID of the run to delete memories for. Defaults to None. + """ + filters = {} + if user_id: + filters["user_id"] = user_id + if agent_id: + filters["agent_id"] = agent_id + if run_id: + filters["run_id"] = run_id + + if not filters: + raise ValueError( + "At least one filter is required to delete all memories. If you want to delete all memories, use the `reset()` method." + ) + + capture_event("mem0.delete_all", self, {"filters": len(filters)}) + memories = self.vector_store.list(name=self.collection_name, filters=filters)[0] + for memory in memories: + self._delete_memory_tool(memory.id) + + def history(self, memory_id): + """ + Get the history of changes for a memory by ID. + + Args: + memory_id (str): ID of the memory to get history for. + + Returns: + list: List of changes for the memory. + """ + capture_event("mem0.history", self, {"memory_id": memory_id}) + return self.db.get_history(memory_id) + + def _create_memory_tool(self, data, metadata=None): + logging.info(f"Creating memory with {data=}") + embeddings = self.embedding_model.embed(data) + memory_id = str(uuid.uuid4()) + metadata = metadata or {} + metadata["data"] = data + metadata["created_at"] = int(time.time()) + + self.vector_store.insert( + name=self.collection_name, + vectors=[embeddings], + ids=[memory_id], + payloads=[metadata], + ) + self.db.add_history(memory_id, None, data, "add") + return memory_id + + def _update_memory_tool(self, memory_id, data, metadata=None): + existing_memory = self.vector_store.get( + name=self.collection_name, vector_id=memory_id + ) + prev_value = existing_memory.payload.get("data") + + new_metadata = metadata or {} + new_metadata["data"] = data + new_metadata["updated_at"] = int(time.time()) + embeddings = self.embedding_model.embed(data) + self.vector_store.update( + name=self.collection_name, + vector_id=memory_id, + vector=embeddings, + payload=new_metadata, + ) + logging.info(f"Updating memory with ID {memory_id=} with {data=}") + self.db.add_history(memory_id, prev_value, data, "update") + + def _delete_memory_tool(self, memory_id): + logging.info(f"Deleting memory with {memory_id=}") + existing_memory = self.vector_store.get( + name=self.collection_name, vector_id=memory_id + ) + prev_value = existing_memory.payload["data"] + self.vector_store.delete(name=self.collection_name, vector_id=memory_id) + self.db.add_history(memory_id, prev_value, None, "delete", is_deleted=1) + + def reset(self): + """ + Reset the memory store. + """ + self.vector_store.delete_col(name=self.collection_name) + self.db.reset() + capture_event("mem0.reset", self) + + def chat(self, query): + raise NotImplementedError("Chat function not implemented yet.") diff --git a/mem0/memory/setup.py b/mem0/memory/setup.py new file mode 100644 index 0000000000..85ca65e67f --- /dev/null +++ b/mem0/memory/setup.py @@ -0,0 +1,28 @@ +import json +import os +import uuid + +# Set up the directory path +home_dir = os.path.expanduser("~") +mem0_dir = os.path.join(home_dir, ".mem0") +os.makedirs(mem0_dir, exist_ok=True) + + +def setup_config(): + config_path = os.path.join(mem0_dir, "config.json") + if not os.path.exists(config_path): + user_id = str(uuid.uuid4()) + config = {"user_id": user_id} + with open(config_path, "w") as config_file: + json.dump(config, config_file, indent=4) + + +def get_user_id(): + config_path = os.path.join(mem0_dir, "config.json") + if os.path.exists(config_path): + with open(config_path, "r") as config_file: + config = json.load(config_file) + user_id = config.get("user_id") + return user_id + else: + return "anonymous_user" diff --git a/mem0/memory/storage.py b/mem0/memory/storage.py new file mode 100644 index 0000000000..8e762cd9be --- /dev/null +++ b/mem0/memory/storage.py @@ -0,0 +1,71 @@ +import sqlite3 +import uuid +from datetime import datetime + + +class SQLiteManager: + def __init__(self, db_path=":memory:"): + self.connection = sqlite3.connect(db_path, check_same_thread=False) + self._create_history_table() + + def _create_history_table(self): + with self.connection: + self.connection.execute( + """ + CREATE TABLE IF NOT EXISTS history ( + id TEXT PRIMARY KEY, + memory_id TEXT, + prev_value TEXT, + new_value TEXT, + event TEXT, + timestamp DATETIME, + is_deleted INTEGER + ) + """ + ) + + def add_history(self, memory_id, prev_value, new_value, event, is_deleted=0): + with self.connection: + self.connection.execute( + """ + INSERT INTO history (id, memory_id, prev_value, new_value, event, timestamp, is_deleted) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, + ( + str(uuid.uuid4()), + memory_id, + prev_value, + new_value, + event, + datetime.utcnow(), + is_deleted, + ), + ) + + def get_history(self, memory_id): + cursor = self.connection.execute( + """ + SELECT id, memory_id, prev_value, new_value, event, timestamp, is_deleted + FROM history + WHERE memory_id = ? + ORDER BY timestamp ASC + """, + (memory_id,), + ) + rows = cursor.fetchall() + return [ + { + "id": row[0], + "memory_id": row[1], + "prev_value": row[2], + "new_value": row[3], + "event": row[4], + "timestamp": row[5], + "is_deleted": row[6], + } + for row in rows + ] + + def reset(self): + with self.connection: + self.connection.execute("DROP TABLE IF EXISTS history") diff --git a/mem0/memory/telemetry.py b/mem0/memory/telemetry.py new file mode 100644 index 0000000000..804e0be324 --- /dev/null +++ b/mem0/memory/telemetry.py @@ -0,0 +1,61 @@ +import platform +import sys + +from posthog import Posthog + +from mem0.memory.setup import get_user_id, setup_config + + +class AnonymousTelemetry: + def __init__(self, project_api_key, host): + self.posthog = Posthog(project_api_key=project_api_key, host=host) + # Call setup config to ensure that the user_id is generated + setup_config() + self.user_id = get_user_id() + + def capture_event(self, event_name, properties=None): + if properties is None: + properties = {} + properties = { + "python_version": sys.version, + "os": sys.platform, + "os_version": platform.version(), + "os_release": platform.release(), + "processor": platform.processor(), + "machine": platform.machine(), + **properties, + } + self.posthog.capture( + distinct_id=self.user_id, event=event_name, properties=properties + ) + + def identify_user(self, user_id, properties=None): + if properties is None: + properties = {} + self.posthog.identify(distinct_id=user_id, properties=properties) + + def close(self): + self.posthog.shutdown() + + +# Initialize AnonymousTelemetry +telemetry = AnonymousTelemetry( + project_api_key="phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX", + host="https://us.i.posthog.com", +) + + +def capture_event(event_name, memory_instance, additional_data=None): + event_data = { + "collection": memory_instance.collection_name, + "vector_size": memory_instance.embedding_model.dims, + "history_store": "sqlite", + "vector_store": f"{memory_instance.vector_store.__class__.__module__}.{memory_instance.vector_store.__class__.__name__}", + "llm": f"{memory_instance.llm.__class__.__module__}.{memory_instance.llm.__class__.__name__}", + "embedding_model": f"{memory_instance.embedding_model.__class__.__module__}.{memory_instance.embedding_model.__class__.__name__}", + "function": f"{memory_instance.__class__.__module__}.{memory_instance.__class__.__name__}", + } + if additional_data: + event_data.update(additional_data) + + telemetry.capture_event(event_name, event_data) diff --git a/mem0/memory/utils.py b/mem0/memory/utils.py new file mode 100644 index 0000000000..211fa7a987 --- /dev/null +++ b/mem0/memory/utils.py @@ -0,0 +1,14 @@ +from mem0.configs.prompts import UPDATE_MEMORY_PROMPT + + +def get_update_memory_prompt(existing_memories, memory, template=UPDATE_MEMORY_PROMPT): + return template.format(existing_memories=existing_memories, memory=memory) + + +def get_update_memory_messages(existing_memories, memory): + return [ + { + "role": "user", + "content": get_update_memory_prompt(existing_memories, memory), + }, + ] diff --git a/mem0/vector_stores/__init__.py b/mem0/vector_stores/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/mem0/vector_stores/base.py b/mem0/vector_stores/base.py new file mode 100644 index 0000000000..974f120f9c --- /dev/null +++ b/mem0/vector_stores/base.py @@ -0,0 +1,48 @@ +from abc import ABC, abstractmethod + + +class VectorStoreBase(ABC): + @abstractmethod + def create_col(self, name, vector_size, distance): + """Create a new collection.""" + pass + + @abstractmethod + def insert(self, name, vectors, payloads=None, ids=None): + """Insert vectors into a collection.""" + pass + + @abstractmethod + def search(self, name, query, limit=5, filters=None): + """Search for similar vectors.""" + pass + + @abstractmethod + def delete(self, name, vector_id): + """Delete a vector by ID.""" + pass + + @abstractmethod + def update(self, name, vector_id, vector=None, payload=None): + """Update a vector and its payload.""" + pass + + @abstractmethod + def get(self, name, vector_id): + """Retrieve a vector by ID.""" + pass + + @abstractmethod + def list_cols(self): + """List all collections.""" + pass + + @abstractmethod + def delete_col(self, name): + """Delete a collection.""" + pass + + @abstractmethod + def col_info(self, name): + """Get information about a collection.""" + pass diff --git a/mem0/vector_stores/configs.py b/mem0/vector_stores/configs.py new file mode 100644 index 0000000000..37c8072815 --- /dev/null +++ b/mem0/vector_stores/configs.py @@ -0,0 +1,45 @@ +from typing import Optional + +from pydantic import BaseModel, Field, field_validator, model_validator + + +class QdrantConfig(BaseModel): + host: Optional[str] = Field(None, description="Host address for Qdrant server") + port: Optional[int] = Field(None, description="Port for Qdrant server") + path: Optional[str] = Field(None, description="Path for local Qdrant database") + url: Optional[str] = Field(None, description="Full URL for Qdrant server") + api_key: Optional[str] = Field(None, description="API key for Qdrant server") + + @model_validator(mode="before") + def check_host_port_or_path(cls, values): + host, port, path, url, api_key = ( + values.get("host"), + values.get("port"), + values.get("path"), + values.get("url"), + values.get("api_key"), + ) + if not path and not (host and port) and not (url and api_key): + raise ValueError( + "Either 'host' and 'port' or 'url' and 'api_key' or 'path' must be provided." + ) + return values + + +class VectorStoreConfig(BaseModel): + provider: str = Field( + description="Provider of the vector store (e.g., 'qdrant', 'chromadb', 'elasticsearch')", + default="qdrant", + ) + config: QdrantConfig = Field( + description="Configuration for the specific vector store", + default=QdrantConfig(path="/tmp/qdrant"), + ) + + @field_validator("config") + def validate_config(cls, v, values): + provider = values.data.get("provider") + if provider == "qdrant": + return QdrantConfig(**v.model_dump()) + else: + raise ValueError(f"Unsupported vector store provider: {provider}") diff --git a/mem0/vector_stores/qdrant.py b/mem0/vector_stores/qdrant.py new file mode 100644 index 0000000000..8275ba5920 --- /dev/null +++ b/mem0/vector_stores/qdrant.py @@ -0,0 +1,242 @@ +import logging +from typing import Optional + +from pydantic import BaseModel, Field +from qdrant_client import QdrantClient +from qdrant_client.models import ( + Distance, + FieldCondition, + Filter, + MatchValue, + PointIdsList, + PointStruct, + Range, + VectorParams, +) + +from mem0.vector_stores.base import VectorStoreBase + + +class QdrantConfig(BaseModel): + host: Optional[str] = Field(None, description="Host address for Qdrant server") + port: Optional[int] = Field(None, description="Port for Qdrant server") + path: Optional[str] = Field(None, description="Path for local Qdrant database") + + +class Qdrant(VectorStoreBase): + def __init__( + self, + client=None, + host="localhost", + port=6333, + path=None, + url=None, + api_key=None, + ): + """ + Initialize the Qdrant vector store. + + Args: + client (QdrantClient, optional): Existing Qdrant client instance. Defaults to None. + host (str, optional): Host address for Qdrant server. Defaults to "localhost". + port (int, optional): Port for Qdrant server. Defaults to 6333. + path (str, optional): Path for local Qdrant database. Defaults to None. + url (str, optional): Full URL for Qdrant server. Defaults to None. + api_key (str, optional): API key for Qdrant server. Defaults to None. + """ + if client: + self.client = client + else: + params = {} + if path: + params["path"] = path + if api_key: + params["api_key"] = api_key + if url: + params["url"] = url + if host and port: + params["host"] = host + params["port"] = port + self.client = QdrantClient(**params) + + def create_col(self, name, vector_size, distance=Distance.COSINE): + """ + Create a new collection. + + Args: + name (str): Name of the collection. + vector_size (int): Size of the vectors to be stored. + distance (Distance, optional): Distance metric for vector similarity. Defaults to Distance.COSINE. + """ + # Skip creating collection if already exists + response = self.list_cols() + for collection in response.collections: + if collection.name == name: + logging.debug(f"Collection {name} already exists. Skipping creation.") + return + + self.client.create_collection( + collection_name=name, + vectors_config=VectorParams(size=vector_size, distance=distance), + ) + + def insert(self, name, vectors, payloads=None, ids=None): + """ + Insert vectors into a collection. + + Args: + name (str): Name of the collection. + vectors (list): List of vectors to insert. + payloads (list, optional): List of payloads corresponding to vectors. Defaults to None. + ids (list, optional): List of IDs corresponding to vectors. Defaults to None. + """ + points = [ + PointStruct( + id=idx if ids is None else ids[idx], + vector=vector, + payload=payloads[idx] if payloads else {}, + ) + for idx, vector in enumerate(vectors) + ] + self.client.upsert(collection_name=name, points=points) + + def _create_filter(self, filters): + """ + Create a Filter object from the provided filters. + + Args: + filters (dict): Filters to apply. + + Returns: + Filter: The created Filter object. + """ + conditions = [] + for key, value in filters.items(): + if isinstance(value, dict) and "gte" in value and "lte" in value: + conditions.append( + FieldCondition( + key=key, range=Range(gte=value["gte"], lte=value["lte"]) + ) + ) + else: + conditions.append( + FieldCondition(key=key, match=MatchValue(value=value)) + ) + return Filter(must=conditions) if conditions else None + + def search(self, name, query, limit=5, filters=None): + """ + Search for similar vectors. + + Args: + name (str): Name of the collection. + query (list): Query vector. + limit (int, optional): Number of results to return. Defaults to 5. + filters (dict, optional): Filters to apply to the search. Defaults to None. + + Returns: + list: Search results. + """ + query_filter = self._create_filter(filters) if filters else None + hits = self.client.search( + collection_name=name, + query_vector=query, + query_filter=query_filter, + limit=limit, + ) + return hits + + def delete(self, name, vector_id): + """ + Delete a vector by ID. + + Args: + name (str): Name of the collection. + vector_id (int): ID of the vector to delete. + """ + self.client.delete( + collection_name=name, + points_selector=PointIdsList( + points=[vector_id], + ), + ) + + def update(self, name, vector_id, vector=None, payload=None): + """ + Update a vector and its payload. + + Args: + name (str): Name of the collection. + vector_id (int): ID of the vector to update. + vector (list, optional): Updated vector. Defaults to None. + payload (dict, optional): Updated payload. Defaults to None. + """ + point = PointStruct(id=vector_id, vector=vector, payload=payload) + self.client.upsert(collection_name=name, points=[point]) + + def get(self, name, vector_id): + """ + Retrieve a vector by ID. + + Args: + name (str): Name of the collection. + vector_id (int): ID of the vector to retrieve. + + Returns: + dict: Retrieved vector. + """ + result = self.client.retrieve( + collection_name=name, ids=[vector_id], with_payload=True + ) + return result[0] if result else None + + def list_cols(self): + """ + List all collections. + + Returns: + list: List of collection names. + """ + return self.client.get_collections() + + def delete_col(self, name): + """ + Delete a collection. + + Args: + name (str): Name of the collection to delete. + """ + self.client.delete_collection(collection_name=name) + + def col_info(self, name): + """ + Get information about a collection. + + Args: + name (str): Name of the collection. + + Returns: + dict: Collection information. + """ + return self.client.get_collection(collection_name=name) + + def list(self, name, filters=None, limit=100): + """ + List all vectors in a collection. + + Args: + name (str): Name of the collection. + limit (int, optional): Number of vectors to return. 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-[extras] -elasticsearch = ["elasticsearch"] -gmail = ["google-api-core", "google-api-python-client", "google-auth", "google-auth-httplib2", "google-auth-oauthlib", "requests"] -google = ["google-generativeai"] -googledrive = ["google-api-python-client", "google-auth-httplib2", "google-auth-oauthlib"] -lancedb = ["lancedb"] -llama2 = ["replicate"] -milvus = ["pymilvus"] -mistralai = ["langchain-mistralai"] -mysql = ["mysql-connector-python"] -opensearch = ["opensearch-py"] -opensource = ["gpt4all", "sentence-transformers", "torch"] -postgres = ["psycopg", "psycopg-binary", "psycopg-pool"] -qdrant = ["qdrant-client"] -together = ["together"] -vertexai = ["langchain-google-vertexai"] -weaviate = ["weaviate-client"] +brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"] +h2 = ["h2 (>=4,<5)"] +socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] +zstd = ["zstandard (>=0.18.0)"] [metadata] lock-version = "2.0" -python-versions = ">=3.9,<=3.13" -content-hash = "afc88f00bafd2b76a954c758a0556cba6d3854e98c444bc5e720319bf472caa8" +python-versions = "^3.8" +content-hash = "645ed126f59268fc6cc8cae8e4ae1599fa65b462d8629c97ab45b6e14bdc1bc2" diff --git a/pyproject.toml b/pyproject.toml index 63521101a1..6b2b5c0ae0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,184 +1,39 @@ [tool.poetry] -name = "embedchain" -version = "0.1.116" -description = "Simplest open source retrieval (RAG) framework" -authors = [ - "Taranjeet Singh ", - "Deshraj Yadav ", -] -license = "Apache License" -readme = "README.md" +name = "mem0ai" +version = "0.0.2" +description = "Long-term memory for AI Agents" +authors = ["Deshraj Yadav ", "Taranjeet Singh "] exclude = [ "db", "configs", - "notebooks" + "notebooks", + "mem0-rag", ] packages = [ - { include = "embedchain" }, -] - -[build-system] -build-backend = "poetry.core.masonry.api" -requires = ["poetry-core"] - -[tool.ruff] -select = ["ASYNC", "E", "F"] -ignore = [] -fixable = ["ALL"] -unfixable = [] -exclude = [ - ".bzr", - ".direnv", - ".eggs", - ".git", - ".git-rewrite", - ".hg", - ".mypy_cache", - ".nox", - ".pants.d", - ".pytype", - ".ruff_cache", - ".svn", - ".tox", - ".venv", - "__pypackages__", - "_build", - "buck-out", - "build", - "dist", - "node_modules", - "venv" + { include = "mem0" }, ] -line-length = 120 -dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" -target-version = "py38" - -[tool.ruff.mccabe] -max-complexity = 10 - -# Ignore `E402` (import violations) in all `__init__.py` files, and in `path/to/file.py`. -[tool.ruff.per-file-ignores] -"embedchain/__init__.py" = ["E401"] +readme = "README.md" -[tool.black] -line-length = 120 -target-version = ["py38", "py39", "py310", "py311"] -include = '\.pyi?$' -exclude = ''' -/( - 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"requests", - "google-api-python-client", - "google-auth", - "google-auth-oauthlib", - "google-auth-httplib2", - "google-api-core", -] -googledrive = ["google-api-python-client", "google-auth-oauthlib", "google-auth-httplib2"] -postgres = ["psycopg", "psycopg-binary", "psycopg-pool"] -mysql = ["mysql-connector-python"] -google = ["google-generativeai"] -mistralai = ["langchain-mistralai"] -[tool.poetry.group.docs.dependencies] +[tool.poetry.group.optional.dependencies] +ollama = "^0.2.1" -[tool.poetry.scripts] -ec = "embedchain.cli:cli" \ No newline at end of file +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/tests/test_memory.py b/tests/test_memory.py new file mode 100644 index 0000000000..7f3db03bd7 --- /dev/null +++ b/tests/test_memory.py @@ -0,0 +1,61 @@ +import pytest + +from mem0 import Memory + + +@pytest.fixture +def memory_store(): + return Memory() + + +def test_create_memory(memory_store): + data = "Name is John Doe." + memory_id = memory_store.create(data=data) + assert memory_store.get(memory_id) == data + + +def test_get_memory(memory_store): + data = "Name is John Doe." + memory_id = memory_store.create(data=data) + retrieved_data = memory_store.get(memory_id) + assert retrieved_data == data + + +def test_update_memory(memory_store): + data = "Name is John Doe." + memory_id = memory_store.create(data=data) + new_data = "Name is John Kapoor." + updated_memory = memory_store.update(memory_id, new_data) + assert updated_memory == new_data + assert memory_store.get(memory_id) == new_data + + +def test_delete_memory(memory_store): + data = "Name is John Doe." + memory_id = memory_store.create(data=data) + memory_store.delete(memory_id) + assert memory_store.get(memory_id) is None + + +def test_history(memory_store): + data = "I like indian food." + memory_id = memory_store.create(data=data) + history = memory_store.history(memory_id) + assert history == [data] + assert memory_store.get(memory_id) == data + + new_data = "I like italian food." + memory_store.update(memory_id, new_data) + history = memory_store.history(memory_id) + assert history == [data, new_data] + assert memory_store.get(memory_id) == new_data + + +def test_list_memories(memory_store): + data1 = "Name is John Doe." + data2 = "Name is John Doe. I like to code in Python." + memory_store.create(data=data1) + memory_store.create(data=data2) + memories = memory_store.list() + assert data1 in memories + assert data2 in memories