From 333284a75e9f6676cc86fd58e1139ca471154203 Mon Sep 17 00:00:00 2001 From: Deshraj Yadav Date: Wed, 10 Jul 2024 14:54:33 -0700 Subject: [PATCH 1/5] Move embedchain code to mem0-rag directory --- mem0-rag/CITATION.cff | 8 + mem0-rag/CONTRIBUTING.md | 78 + mem0-rag/LICENSE | 201 + mem0-rag/Makefile | 56 + mem0-rag/README.md | 125 + mem0-rag/configs/anthropic.yaml | 8 + mem0-rag/configs/azure_openai.yaml | 19 + mem0-rag/configs/chroma.yaml | 24 + mem0-rag/configs/chunker.yaml | 4 + mem0-rag/configs/clarifai.yaml | 12 + mem0-rag/configs/cohere.yaml | 7 + mem0-rag/configs/full-stack.yaml | 40 + mem0-rag/configs/google.yaml | 13 + mem0-rag/configs/gpt4.yaml | 8 + mem0-rag/configs/gpt4all.yaml | 11 + mem0-rag/configs/huggingface.yaml | 8 + mem0-rag/configs/jina.yaml | 7 + mem0-rag/configs/llama2.yaml | 8 + mem0-rag/configs/ollama.yaml | 14 + mem0-rag/configs/opensearch.yaml | 33 + mem0-rag/configs/opensource.yaml | 25 + mem0-rag/configs/pinecone.yaml | 6 + mem0-rag/configs/pipeline.yaml | 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"Singh" + given-names: "Taranjeet" +title: "Embedchain" +date-released: 2023-06-20 +url: "https://github.com/embedchain/embedchain" \ No newline at end of file diff --git a/mem0-rag/CONTRIBUTING.md b/mem0-rag/CONTRIBUTING.md new file mode 100644 index 0000000000..3b7b79f00a --- /dev/null +++ b/mem0-rag/CONTRIBUTING.md @@ -0,0 +1,78 @@ +# Contributing to embedchain + +Let us make contribution easy, collaborative and fun. + +## Submit your Contribution through PR + +To make a contribution, follow these steps: + +1. Fork and clone this repository +2. Do the changes on your fork with dedicated feature branch `feature/f1` +3. If you modified the code (new feature or bug-fix), please add tests for it +4. Include proper documentation / docstring and examples to run the feature +5. Check the linting +6. Ensure that all tests pass +7. Submit a pull request + +For more details about pull requests, please read [GitHub's guides](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request). + + +### 📦 Package manager + +We use `poetry` as our package manager. You can install poetry by following the instructions [here](https://python-poetry.org/docs/#installation). + +Please DO NOT use pip or conda to install the dependencies. Instead, use poetry: + +```bash +poetry install --all-extras +or +poetry install --with dev + +#activate + +poetry shell +``` + +### 📌 Pre-commit + +To ensure our standards, make sure to install pre-commit before starting to contribute. + +```bash +pre-commit install +``` + +### 🧹 Linting + +We use `ruff` to lint our code. You can run the linter by running the following command: + +```bash +make lint +``` + +Make sure that the linter does not report any errors or warnings before submitting a pull request. + +### Code Formatting with `black` + +We use `black` to reformat the code by running the following command: + +```bash +make format +``` + +### 🧪 Testing + +We use `pytest` to test our code. You can run the tests by running the following command: + +```bash +poetry run pytest +``` + + +Several packages have been removed from Poetry to make the package lighter. Therefore, it is recommended to run `make install_all` to install the remaining packages and ensure all tests pass. + + +Make sure that all tests pass before submitting a pull request. + +## 🚀 Release Process + +At the moment, the release process is manual. We try to make frequent releases. Usually, we release a new version when we have a new feature or bugfix. A developer with admin rights to the repository will create a new release on GitHub, and then publish the new version to PyPI. diff --git a/mem0-rag/LICENSE b/mem0-rag/LICENSE new file mode 100644 index 0000000000..d20d5102c3 --- /dev/null +++ b/mem0-rag/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2023] [Taranjeet Singh] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/mem0-rag/Makefile b/mem0-rag/Makefile new file mode 100644 index 0000000000..60b1e2251b --- /dev/null +++ b/mem0-rag/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/mem0-rag/README.md b/mem0-rag/README.md new file mode 100644 index 0000000000..4512e3cc8c --- /dev/null +++ b/mem0-rag/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/mem0-rag/configs/anthropic.yaml b/mem0-rag/configs/anthropic.yaml new file mode 100644 index 0000000000..395125f998 --- /dev/null +++ b/mem0-rag/configs/anthropic.yaml @@ -0,0 +1,8 @@ +llm: + provider: anthropic + config: + model: 'claude-instant-1' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false diff --git a/mem0-rag/configs/azure_openai.yaml b/mem0-rag/configs/azure_openai.yaml new file mode 100644 index 0000000000..50eaff0c8c --- /dev/null +++ b/mem0-rag/configs/azure_openai.yaml @@ -0,0 +1,19 @@ +app: + config: + id: azure-openai-app + +llm: + provider: azure_openai + config: + model: gpt-35-turbo + deployment_name: your_llm_deployment_name + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +embedder: + provider: azure_openai + config: + model: text-embedding-ada-002 + deployment_name: you_embedding_model_deployment_name diff --git a/mem0-rag/configs/chroma.yaml b/mem0-rag/configs/chroma.yaml new file mode 100644 index 0000000000..3d1e4359a9 --- /dev/null +++ b/mem0-rag/configs/chroma.yaml @@ -0,0 +1,24 @@ +app: + config: + id: 'my-app' + +llm: + provider: openai + config: + model: 'gpt-3.5-turbo' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +vectordb: + provider: chroma + config: + collection_name: 'my-app' + dir: db + allow_reset: true + +embedder: + provider: openai + config: + model: 'text-embedding-ada-002' diff --git a/mem0-rag/configs/chunker.yaml b/mem0-rag/configs/chunker.yaml new file mode 100644 index 0000000000..63cf3f82c6 --- /dev/null +++ b/mem0-rag/configs/chunker.yaml @@ -0,0 +1,4 @@ +chunker: + chunk_size: 100 + chunk_overlap: 20 + length_function: 'len' diff --git a/mem0-rag/configs/clarifai.yaml b/mem0-rag/configs/clarifai.yaml new file mode 100644 index 0000000000..0c52ba0078 --- /dev/null +++ b/mem0-rag/configs/clarifai.yaml @@ -0,0 +1,12 @@ +llm: + provider: clarifai + config: + model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct" + model_kwargs: + temperature: 0.5 + max_tokens: 1000 + +embedder: + provider: clarifai + config: + model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15" diff --git a/mem0-rag/configs/cohere.yaml b/mem0-rag/configs/cohere.yaml new file mode 100644 index 0000000000..0edd4e8fd5 --- /dev/null +++ b/mem0-rag/configs/cohere.yaml @@ -0,0 +1,7 @@ +llm: + provider: cohere + config: + model: large + temperature: 0.5 + max_tokens: 1000 + top_p: 1 diff --git a/mem0-rag/configs/full-stack.yaml b/mem0-rag/configs/full-stack.yaml new file mode 100644 index 0000000000..dc3379570f --- /dev/null +++ b/mem0-rag/configs/full-stack.yaml @@ -0,0 +1,40 @@ +app: + config: + id: 'full-stack-app' + +chunker: + chunk_size: 100 + chunk_overlap: 20 + length_function: 'len' + +llm: + provider: openai + config: + model: 'gpt-3.5-turbo' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + prompt: | + Use the following pieces of context to answer the query at the end. + If you don't know the answer, just say that you don't know, don't try to make up an answer. + + $context + + Query: $query + + Helpful Answer: + system_prompt: | + Act as William Shakespeare. Answer the following questions in the style of William Shakespeare. + +vectordb: + provider: chroma + config: + collection_name: 'my-collection-name' + dir: db + allow_reset: true + +embedder: + provider: openai + config: + model: 'text-embedding-ada-002' diff --git a/mem0-rag/configs/google.yaml b/mem0-rag/configs/google.yaml new file mode 100644 index 0000000000..4f6a465537 --- /dev/null +++ b/mem0-rag/configs/google.yaml @@ -0,0 +1,13 @@ +llm: + provider: google + config: + model: gemini-pro + max_tokens: 1000 + temperature: 0.9 + top_p: 1.0 + stream: false + +embedder: + provider: google + config: + model: models/embedding-001 diff --git a/mem0-rag/configs/gpt4.yaml b/mem0-rag/configs/gpt4.yaml new file mode 100644 index 0000000000..e06c60de67 --- /dev/null +++ b/mem0-rag/configs/gpt4.yaml @@ -0,0 +1,8 @@ +llm: + provider: openai + config: + model: 'gpt-4' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false \ No newline at end of file diff --git a/mem0-rag/configs/gpt4all.yaml b/mem0-rag/configs/gpt4all.yaml new file mode 100644 index 0000000000..0482393343 --- /dev/null +++ b/mem0-rag/configs/gpt4all.yaml @@ -0,0 +1,11 @@ +llm: + provider: gpt4all + config: + model: 'orca-mini-3b-gguf2-q4_0.gguf' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +embedder: + provider: gpt4all diff --git a/mem0-rag/configs/huggingface.yaml b/mem0-rag/configs/huggingface.yaml new file mode 100644 index 0000000000..508c9d7787 --- /dev/null +++ b/mem0-rag/configs/huggingface.yaml @@ -0,0 +1,8 @@ +llm: + provider: huggingface + config: + model: 'google/flan-t5-xxl' + temperature: 0.5 + max_tokens: 1000 + top_p: 0.5 + stream: false diff --git a/mem0-rag/configs/jina.yaml b/mem0-rag/configs/jina.yaml new file mode 100644 index 0000000000..11627059b5 --- /dev/null +++ b/mem0-rag/configs/jina.yaml @@ -0,0 +1,7 @@ +llm: + provider: jina + config: + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false diff --git a/mem0-rag/configs/llama2.yaml b/mem0-rag/configs/llama2.yaml new file mode 100644 index 0000000000..61b3b92531 --- /dev/null +++ b/mem0-rag/configs/llama2.yaml @@ -0,0 +1,8 @@ +llm: + provider: llama2 + config: + model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5' + temperature: 0.5 + max_tokens: 1000 + top_p: 0.5 + stream: false diff --git a/mem0-rag/configs/ollama.yaml b/mem0-rag/configs/ollama.yaml new file mode 100644 index 0000000000..7ec5def545 --- /dev/null +++ b/mem0-rag/configs/ollama.yaml @@ -0,0 +1,14 @@ +llm: + provider: ollama + config: + model: 'llama2' + temperature: 0.5 + top_p: 1 + stream: true + base_url: http://localhost:11434 + +embedder: + provider: ollama + config: + model: 'mxbai-embed-large:latest' + base_url: http://localhost:11434 diff --git a/mem0-rag/configs/opensearch.yaml b/mem0-rag/configs/opensearch.yaml new file mode 100644 index 0000000000..4918775e68 --- /dev/null +++ b/mem0-rag/configs/opensearch.yaml @@ -0,0 +1,33 @@ +app: + config: + id: 'my-app' + log_level: 'WARNING' + collect_metrics: true + collection_name: 'my-app' + +llm: + provider: openai + config: + model: 'gpt-3.5-turbo' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +vectordb: + provider: opensearch + config: + opensearch_url: 'https://localhost:9200' + http_auth: + - admin + - admin + vector_dimension: 1536 + collection_name: 'my-app' + use_ssl: false + verify_certs: false + +embedder: + provider: openai + config: + model: 'text-embedding-ada-002' + deployment_name: 'my-app' diff --git a/mem0-rag/configs/opensource.yaml b/mem0-rag/configs/opensource.yaml new file mode 100644 index 0000000000..e2d40c1356 --- /dev/null +++ b/mem0-rag/configs/opensource.yaml @@ -0,0 +1,25 @@ +app: + config: + id: 'open-source-app' + collect_metrics: false + +llm: + provider: gpt4all + config: + model: 'orca-mini-3b-gguf2-q4_0.gguf' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +vectordb: + provider: chroma + config: + collection_name: 'open-source-app' + dir: db + allow_reset: true + +embedder: + provider: gpt4all + config: + deployment_name: 'test-deployment' diff --git a/mem0-rag/configs/pinecone.yaml b/mem0-rag/configs/pinecone.yaml new file mode 100644 index 0000000000..24e33c11a8 --- /dev/null +++ b/mem0-rag/configs/pinecone.yaml @@ -0,0 +1,6 @@ +vectordb: + provider: pinecone + config: + metric: cosine + vector_dimension: 1536 + collection_name: my-pinecone-index diff --git a/mem0-rag/configs/pipeline.yaml b/mem0-rag/configs/pipeline.yaml new file mode 100644 index 0000000000..e34866716b --- /dev/null +++ b/mem0-rag/configs/pipeline.yaml @@ -0,0 +1,26 @@ +pipeline: + config: + name: Example pipeline + id: pipeline-1 # Make sure that id is different every time you create a new pipeline + +vectordb: + provider: chroma + config: + collection_name: pipeline-1 + dir: db + allow_reset: true + +llm: + provider: gpt4all + config: + model: 'orca-mini-3b-gguf2-q4_0.gguf' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +embedding_model: + provider: gpt4all + config: + model: 'all-MiniLM-L6-v2' + deployment_name: null diff --git a/mem0-rag/configs/together.yaml b/mem0-rag/configs/together.yaml new file mode 100644 index 0000000000..b19bc07ffb --- /dev/null +++ b/mem0-rag/configs/together.yaml @@ -0,0 +1,6 @@ +llm: + provider: together + config: + model: mistralai/Mixtral-8x7B-Instruct-v0.1 + temperature: 0.5 + max_tokens: 1000 diff --git a/mem0-rag/configs/vertexai.yaml b/mem0-rag/configs/vertexai.yaml new file mode 100644 index 0000000000..f303654c08 --- /dev/null +++ b/mem0-rag/configs/vertexai.yaml @@ -0,0 +1,6 @@ +llm: + provider: vertexai + config: + model: 'chat-bison' + temperature: 0.5 + top_p: 0.5 diff --git a/mem0-rag/configs/vllm.yaml b/mem0-rag/configs/vllm.yaml new file mode 100644 index 0000000000..536a589a15 --- /dev/null +++ b/mem0-rag/configs/vllm.yaml @@ -0,0 +1,14 @@ +llm: + provider: vllm + config: + model: 'meta-llama/Llama-2-70b-hf' + temperature: 0.5 + top_p: 1 + top_k: 10 + stream: true + trust_remote_code: true + +embedder: + provider: huggingface + config: + model: 'BAAI/bge-small-en-v1.5' diff --git a/mem0-rag/configs/weaviate.yaml b/mem0-rag/configs/weaviate.yaml new file mode 100644 index 0000000000..a27623ab91 --- /dev/null +++ b/mem0-rag/configs/weaviate.yaml @@ -0,0 +1,4 @@ +vectordb: + provider: weaviate + config: + collection_name: my_weaviate_index diff --git a/mem0-rag/docs/Makefile b/mem0-rag/docs/Makefile new file mode 100644 index 0000000000..0db640d0e7 --- /dev/null +++ b/mem0-rag/docs/Makefile @@ -0,0 +1,10 @@ +install: + npm i -g mintlify + +run_local: + mintlify dev + +troubleshoot: + mintlify install + +.PHONY: install run_local troubleshoot diff --git a/mem0-rag/docs/README.md b/mem0-rag/docs/README.md new file mode 100644 index 0000000000..e322686dc1 --- /dev/null +++ b/mem0-rag/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/mem0-rag/docs/_snippets/get-help.mdx b/mem0-rag/docs/_snippets/get-help.mdx new file mode 100644 index 0000000000..6f57e5ce5e --- /dev/null +++ b/mem0-rag/docs/_snippets/get-help.mdx @@ -0,0 +1,11 @@ + + + Schedule a call + + + Join our slack community + + + Join our discord community + + diff --git a/mem0-rag/docs/_snippets/missing-data-source-tip.mdx b/mem0-rag/docs/_snippets/missing-data-source-tip.mdx new file mode 100644 index 0000000000..b0e1895536 --- /dev/null +++ b/mem0-rag/docs/_snippets/missing-data-source-tip.mdx @@ -0,0 +1,19 @@ +

If you can't find the specific data source, please feel free to request through one of the following channels and help us prioritize.

+ + + + Fill out this form + + + Let us know on our slack community + + + Let us know on discord community + + + Open an issue on our GitHub + + + Schedule a call with Embedchain founder + + diff --git a/mem0-rag/docs/_snippets/missing-llm-tip.mdx b/mem0-rag/docs/_snippets/missing-llm-tip.mdx new file mode 100644 index 0000000000..7d2782d388 --- /dev/null +++ b/mem0-rag/docs/_snippets/missing-llm-tip.mdx @@ -0,0 +1,16 @@ +

If you can't find the specific LLM you need, no need to fret. We're continuously expanding our support for additional LLMs, and you can help us prioritize by opening an issue on our GitHub or simply reaching out to us on our Slack or Discord community.

+ + + + Let us know on our slack community + + + Let us know on discord community + + + Open an issue on our GitHub + + + Schedule a call with Embedchain founder + + diff --git a/mem0-rag/docs/_snippets/missing-vector-db-tip.mdx b/mem0-rag/docs/_snippets/missing-vector-db-tip.mdx new file mode 100644 index 0000000000..2edbbe4b0b --- /dev/null +++ b/mem0-rag/docs/_snippets/missing-vector-db-tip.mdx @@ -0,0 +1,18 @@ + + +

If you can't find specific feature or run into issues, please feel free to reach out through one of the following channels.

+ + + + Let us know on our slack community + + + Let us know on discord community + + + Open an issue on our GitHub + + + Schedule a call with Embedchain founder + + diff --git a/mem0-rag/docs/api-reference/advanced/configuration.mdx b/mem0-rag/docs/api-reference/advanced/configuration.mdx new file mode 100644 index 0000000000..f3a4c4815f --- /dev/null +++ b/mem0-rag/docs/api-reference/advanced/configuration.mdx @@ -0,0 +1,260 @@ +--- +title: 'Custom configurations' +--- + +Embedchain offers several configuration options for your LLM, vector database, and embedding model. All of these configuration options are optional and have sane defaults. + +You can configure different components of your app (`llm`, `embedding model`, or `vector database`) through a simple yaml configuration that Embedchain offers. Here is a generic full-stack example of the yaml config: + + + +Embedchain applications are configurable using YAML file, JSON file or by directly passing the config dictionary. Checkout the [docs here](/api-reference/app/overview#usage) on how to use other formats. + + + +```yaml config.yaml +app: + config: + name: 'full-stack-app' + +llm: + provider: openai + config: + model: 'gpt-3.5-turbo' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + api_key: sk-xxx + model_kwargs: + response_format: + type: json_object + api_version: 2024-02-01 + http_client_proxies: http://testproxy.mem0.net:8000 + prompt: | + Use the following pieces of context to answer the query at the end. + If you don't know the answer, just say that you don't know, don't try to make up an answer. + + $context + + Query: $query + + Helpful Answer: + system_prompt: | + Act as William Shakespeare. Answer the following questions in the style of William Shakespeare. + +vectordb: + provider: chroma + config: + collection_name: 'full-stack-app' + dir: db + allow_reset: true + +embedder: + provider: openai + config: + model: 'text-embedding-ada-002' + api_key: sk-xxx + +chunker: + chunk_size: 2000 + chunk_overlap: 100 + length_function: 'len' + min_chunk_size: 0 + +cache: + similarity_evaluation: + strategy: distance + max_distance: 1.0 + config: + similarity_threshold: 0.8 + auto_flush: 50 +``` + +```json config.json +{ + "app": { + "config": { + "name": "full-stack-app" + } + }, + "llm": { + "provider": "openai", + "config": { + "model": "gpt-3.5-turbo", + "temperature": 0.5, + "max_tokens": 1000, + "top_p": 1, + "stream": false, + "prompt": "Use the following pieces of context to answer the query at the end.\nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n$context\n\nQuery: $query\n\nHelpful Answer:", + "system_prompt": "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare.", + "api_key": "sk-xxx", + "model_kwargs": {"response_format": {"type": "json_object"}}, + "api_version": "2024-02-01", + "http_client_proxies": "http://testproxy.mem0.net:8000", + } + }, + "vectordb": { + "provider": "chroma", + "config": { + "collection_name": "full-stack-app", + "dir": "db", + "allow_reset": true + } + }, + "embedder": { + "provider": "openai", + "config": { + "model": "text-embedding-ada-002", + "api_key": "sk-xxx" + } + }, + "chunker": { + "chunk_size": 2000, + "chunk_overlap": 100, + "length_function": "len", + "min_chunk_size": 0 + }, + "cache": { + "similarity_evaluation": { + "strategy": "distance", + "max_distance": 1.0, + }, + "config": { + "similarity_threshold": 0.8, + "auto_flush": 50, + }, + }, +} +``` + +```python config.py +config = { + 'app': { + 'config': { + 'name': 'full-stack-app' + } + }, + 'llm': { + 'provider': 'openai', + 'config': { + 'model': 'gpt-3.5-turbo', + 'temperature': 0.5, + 'max_tokens': 1000, + 'top_p': 1, + 'stream': False, + 'prompt': ( + "Use the following pieces of context to answer the query at the end.\n" + "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n" + "$context\n\nQuery: $query\n\nHelpful Answer:" + ), + 'system_prompt': ( + "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare." + ), + 'api_key': 'sk-xxx', + "model_kwargs": {"response_format": {"type": "json_object"}}, + "http_client_proxies": "http://testproxy.mem0.net:8000", + } + }, + 'vectordb': { + 'provider': 'chroma', + 'config': { + 'collection_name': 'full-stack-app', + 'dir': 'db', + 'allow_reset': True + } + }, + 'embedder': { + 'provider': 'openai', + 'config': { + 'model': 'text-embedding-ada-002', + 'api_key': 'sk-xxx' + } + }, + 'chunker': { + 'chunk_size': 2000, + 'chunk_overlap': 100, + 'length_function': 'len', + 'min_chunk_size': 0 + }, + 'cache': { + 'similarity_evaluation': { + 'strategy': 'distance', + 'max_distance': 1.0, + }, + 'config': { + 'similarity_threshold': 0.8, + 'auto_flush': 50, + }, + }, +} +``` + + +Alright, let's dive into what each key means in the yaml config above: + +1. `app` Section: + - `config`: + - `name` (String): The name of your full-stack application. + - `id` (String): The id of your full-stack application. + Only use this to reload already created apps. We recommend users to not create their own ids. + - `collect_metrics` (Boolean): Indicates whether metrics should be collected for the app, defaults to `True` + - `log_level` (String): The log level for the app, defaults to `WARNING` +2. `llm` Section: + - `provider` (String): The provider for the language model, which is set to 'openai'. You can find the full list of llm providers in [our docs](/components/llms). + - `config`: + - `model` (String): The specific model being used, 'gpt-3.5-turbo'. + - `temperature` (Float): Controls the randomness of the model's output. A higher value (closer to 1) makes the output more random. + - `max_tokens` (Integer): Controls how many tokens are used in the response. + - `top_p` (Float): Controls the diversity of word selection. A higher value (closer to 1) makes word selection more diverse. + - `stream` (Boolean): Controls if the response is streamed back to the user (set to false). + - `online` (Boolean): Controls whether to use internet to get more context for answering query (set to false). + - `token_usage` (Boolean): Controls whether to use token usage for the querying models (set to false). + - `prompt` (String): A prompt for the model to follow when generating responses, requires `$context` and `$query` variables. + - `system_prompt` (String): A system prompt for the model to follow when generating responses, in this case, it's set to the style of William Shakespeare. + - `number_documents` (Integer): Number of documents to pull from the vectordb as context, defaults to 1 + - `api_key` (String): The API key for the language model. + - `model_kwargs` (Dict): Keyword arguments to pass to the language model. Used for `aws_bedrock` provider, since it requires different arguments for each model. + - `http_client_proxies` (Dict | String): The proxy server settings used to create `self.http_client` using `httpx.Client(proxies=http_client_proxies)` + - `http_async_client_proxies` (Dict | String): The proxy server settings for async calls used to create `self.http_async_client` using `httpx.AsyncClient(proxies=http_async_client_proxies)` +3. `vectordb` Section: + - `provider` (String): The provider for the vector database, set to 'chroma'. You can find the full list of vector database providers in [our docs](/components/vector-databases). + - `config`: + - `collection_name` (String): The initial collection name for the vectordb, set to 'full-stack-app'. + - `dir` (String): The directory for the local database, set to 'db'. + - `allow_reset` (Boolean): Indicates whether resetting the vectordb is allowed, set to true. + - `batch_size` (Integer): The batch size for docs insertion in vectordb, defaults to `100` + We recommend you to checkout vectordb specific config [here](https://docs.embedchain.ai/components/vector-databases) +4. `embedder` Section: + - `provider` (String): The provider for the embedder, set to 'openai'. You can find the full list of embedding model providers in [our docs](/components/embedding-models). + - `config`: + - `model` (String): The specific model used for text embedding, 'text-embedding-ada-002'. + - `vector_dimension` (Integer): The vector dimension of the embedding model. [Defaults](https://github.com/embedchain/embedchain/blob/main/embedchain/models/vector_dimensions.py) + - `api_key` (String): The API key for the embedding model. + - `endpoint` (String): The endpoint for the HuggingFace embedding model. + - `deployment_name` (String): The deployment name for the embedding model. + - `title` (String): The title for the embedding model for Google Embedder. + - `task_type` (String): The task type for the embedding model for Google Embedder. + - `model_kwargs` (Dict): Used to pass extra arguments to embedders. +5. `chunker` Section: + - `chunk_size` (Integer): The size of each chunk of text that is sent to the language model. + - `chunk_overlap` (Integer): The amount of overlap between each chunk of text. + - `length_function` (String): The function used to calculate the length of each chunk of text. In this case, it's set to 'len'. You can also use any function import directly as a string here. + - `min_chunk_size` (Integer): The minimum size of each chunk of text that is sent to the language model. Must be less than `chunk_size`, and greater than `chunk_overlap`. +6. `cache` Section: (Optional) + - `similarity_evaluation` (Optional): The config for similarity evaluation strategy. If not provided, the default `distance` based similarity evaluation strategy is used. + - `strategy` (String): The strategy to use for similarity evaluation. Currently, only `distance` and `exact` based similarity evaluation is supported. Defaults to `distance`. + - `max_distance` (Float): The bound of maximum distance. Defaults to `1.0`. + - `positive` (Boolean): If the larger distance indicates more similar of two entities, set it `True`, otherwise `False`. Defaults to `False`. + - `config` (Optional): The config for initializing the cache. If not provided, sensible default values are used as mentioned below. + - `similarity_threshold` (Float): The threshold for similarity evaluation. Defaults to `0.8`. + - `auto_flush` (Integer): The number of queries after which the cache is flushed. Defaults to `20`. +7. `memory` Section: (Optional) + - `api_key` (String): The API key of mem0. + - `top_k` (Integer): The number of top-k results to return. Defaults to `10`. + + If you provide a cache section, the app will automatically configure and use a cache to store the results of the language model. This is useful if you want to speed up the response time and save inference cost of your app. + +If you have questions about the configuration above, please feel free to reach out to us using one of the following methods: + + \ No newline at end of file diff --git a/mem0-rag/docs/api-reference/app/add.mdx b/mem0-rag/docs/api-reference/app/add.mdx new file mode 100644 index 0000000000..4586889843 --- /dev/null +++ b/mem0-rag/docs/api-reference/app/add.mdx @@ -0,0 +1,44 @@ +--- +title: '📊 add' +--- + +`add()` method is used to load the data sources from different data sources to a RAG pipeline. You can find the signature below: + +### Parameters + + + The data to embed, can be a URL, local file or raw content, depending on the data type.. You can find the full list of supported data sources [here](/components/data-sources/overview). + + + Type of data source. It can be automatically detected but user can force what data type to load as. + + + Any metadata that you want to store with the data source. Metadata is generally really useful for doing metadata filtering on top of semantic search to yield faster search and better results. + + +## Usage + +### Load data from webpage + +```python Code example +from embedchain import App + +app = App() +app.add("https://www.forbes.com/profile/elon-musk") +# Inserting batches in chromadb: 100%|███████████████| 1/1 [00:00<00:00, 1.19it/s] +# Successfully saved https://www.forbes.com/profile/elon-musk (DataType.WEB_PAGE). New chunks count: 4 +``` + +### Load data from sitemap + +```python Code example +from embedchain import App + +app = App() +app.add("https://python.langchain.com/sitemap.xml", data_type="sitemap") +# Loading pages: 100%|█████████████| 1108/1108 [00:47<00:00, 23.17it/s] +# Inserting batches in chromadb: 100%|█████████| 111/111 [04:41<00:00, 2.54s/it] +# Successfully saved https://python.langchain.com/sitemap.xml (DataType.SITEMAP). New chunks count: 11024 +``` + +You can find complete list of supported data sources [here](/components/data-sources/overview). diff --git a/mem0-rag/docs/api-reference/app/chat.mdx b/mem0-rag/docs/api-reference/app/chat.mdx new file mode 100644 index 0000000000..40c3de3634 --- /dev/null +++ b/mem0-rag/docs/api-reference/app/chat.mdx @@ -0,0 +1,171 @@ +--- +title: '💬 chat' +--- + +`chat()` method allows you to chat over your data sources using a user-friendly chat API. You can find the signature below: + +### Parameters + + + Question to ask + + + Configure different llm settings such as prompt, temprature, number_documents etc. + + + The purpose is to test the prompt structure without actually running LLM inference. Defaults to `False` + + + A dictionary of key-value pairs to filter the chunks from the vector database. Defaults to `None` + + + Session ID of the chat. This can be used to maintain chat history of different user sessions. Default value: `default` + + + Return citations along with the LLM answer. Defaults to `False` + + +### Returns + + + If `citations=False`, return a stringified answer to the question asked.
+ If `citations=True`, returns a tuple with answer and citations respectively. + + +## Usage + +### With citations + +If you want to get the answer to question and return both answer and citations, use the following code snippet: + +```python With Citations +from embedchain import App + +# Initialize app +app = App() + +# Add data source +app.add("https://www.forbes.com/profile/elon-musk") + +# Get relevant answer for your query +answer, sources = app.chat("What is the net worth of Elon?", citations=True) +print(answer) +# Answer: The net worth of Elon Musk is $221.9 billion. + +print(sources) +# [ +# ( +# 'Elon Musk PROFILEElon MuskCEO, Tesla$247.1B$2.3B (0.96%)Real Time Net Worthas of 12/7/23 ...', +# { +# 'url': 'https://www.forbes.com/profile/elon-musk', +# 'score': 0.89, +# ... +# } +# ), +# ( +# '74% of the company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH BY YEARForbes ...', +# { +# 'url': 'https://www.forbes.com/profile/elon-musk', +# 'score': 0.81, +# ... +# } +# ), +# ( +# 'founded in 2002, is worth nearly $150 billion after a $750 million tender offer in June 2023 ...', +# { +# 'url': 'https://www.forbes.com/profile/elon-musk', +# 'score': 0.73, +# ... +# } +# ) +# ] +``` + + +When `citations=True`, note that the returned `sources` are a list of tuples where each tuple has two elements (in the following order): +1. source chunk +2. dictionary with metadata about the source chunk + - `url`: url of the source + - `doc_id`: document id (used for book keeping purposes) + - `score`: score of the source chunk with respect to the question + - other metadata you might have added at the time of adding the source + + + +### Without citations + +If you just want to return answers and don't want to return citations, you can use the following example: + +```python Without Citations +from embedchain import App + +# Initialize app +app = App() + +# Add data source +app.add("https://www.forbes.com/profile/elon-musk") + +# Chat on your data using `.chat()` +answer = app.chat("What is the net worth of Elon?") +print(answer) +# Answer: The net worth of Elon Musk is $221.9 billion. +``` + +### With session id + +If you want to maintain chat sessions for different users, you can simply pass the `session_id` keyword argument. See the example below: + +```python With session id +from embedchain import App + +app = App() +app.add("https://www.forbes.com/profile/elon-musk") + +# Chat on your data using `.chat()` +app.chat("What is the net worth of Elon Musk?", session_id="user1") +# 'The net worth of Elon Musk is $250.8 billion.' +app.chat("What is the net worth of Bill Gates?", session_id="user2") +# "I don't know the current net worth of Bill Gates." +app.chat("What was my last question", session_id="user1") +# 'Your last question was "What is the net worth of Elon Musk?"' +``` + +### With custom context window + +If you want to customize the context window that you want to use during chat (default context window is 3 document chunks), you can do using the following code snippet: + +```python with custom chunks size +from embedchain import App +from embedchain.config import BaseLlmConfig + +app = App() +app.add("https://www.forbes.com/profile/elon-musk") + +query_config = BaseLlmConfig(number_documents=5) +app.chat("What is the net worth of Elon Musk?", config=query_config) +``` + +### With Mem0 to store chat history + +Mem0 is a cutting-edge long-term memory for LLMs to enable personalization for the GenAI stack. It enables LLMs to remember past interactions and provide more personalized responses. + +Follow these steps to use Mem0 to enable memory for personalization in your apps: +- Install the [`mem0`](https://docs.mem0.ai/) package using `pip install memzero`. +- Get the api_key from [Mem0 Platform](https://app.mem0.ai/). +- Provide api_key in config under `memory`, refer [Configurations](docs/api-reference/advanced/configuration.mdx). + +```python with mem0 +from embedchain import App + +config = { + "memory": { + "api_key": "m0-xxx", + "top_k": 5 + } +} + +app = App.from_config(config=config) +app.add("https://www.forbes.com/profile/elon-musk") + +app.chat("What is the net worth of Elon Musk?") +``` \ No newline at end of file diff --git a/mem0-rag/docs/api-reference/app/delete.mdx b/mem0-rag/docs/api-reference/app/delete.mdx new file mode 100644 index 0000000000..d1f2ceda4b --- /dev/null +++ b/mem0-rag/docs/api-reference/app/delete.mdx @@ -0,0 +1,48 @@ +--- +title: 🗑 delete +--- + +## Delete Document + +`delete()` method allows you to delete a document previously added to the app. + +### Usage + +```python +from embedchain import App + +app = App() + +forbes_doc_id = app.add("https://www.forbes.com/profile/elon-musk") +wiki_doc_id = app.add("https://en.wikipedia.org/wiki/Elon_Musk") + +app.delete(forbes_doc_id) # deletes the forbes document +``` + + + If you do not have the document id, you can use `app.db.get()` method to get the document and extract the `hash` key from `metadatas` dictionary object, which serves as the document id. + + + +## Delete Chat Session History + +`delete_session_chat_history()` method allows you to delete all previous messages in a chat history. + +### Usage + +```python +from embedchain import App + +app = App() + +app.add("https://www.forbes.com/profile/elon-musk") + +app.chat("What is the net worth of Elon Musk?") + +app.delete_session_chat_history() +``` + + + `delete_session_chat_history(session_id="session_1")` method also accepts `session_id` optional param for deleting chat history of a specific session. + It assumes the default session if no `session_id` is provided. + \ No newline at end of file diff --git a/mem0-rag/docs/api-reference/app/deploy.mdx b/mem0-rag/docs/api-reference/app/deploy.mdx new file mode 100644 index 0000000000..7cb8ff5e83 --- /dev/null +++ b/mem0-rag/docs/api-reference/app/deploy.mdx @@ -0,0 +1,5 @@ +--- +title: 🚀 deploy +--- + +The `deploy()` method is currently available on an invitation-only basis. To request access, please submit your information via the provided [Google Form](https://forms.gle/vigN11h7b4Ywat668). We will review your request and respond promptly. diff --git a/mem0-rag/docs/api-reference/app/evaluate.mdx b/mem0-rag/docs/api-reference/app/evaluate.mdx new file mode 100644 index 0000000000..64cb612ca4 --- /dev/null +++ b/mem0-rag/docs/api-reference/app/evaluate.mdx @@ -0,0 +1,41 @@ +--- +title: '📝 evaluate' +--- + +`evaluate()` method is used to evaluate the performance of a RAG app. You can find the signature below: + +### Parameters + + + A question or a list of questions to evaluate your app on. + + + The metrics to evaluate your app on. Defaults to all metrics: `["context_relevancy", "answer_relevancy", "groundedness"]` + + + Specify the number of threads to use for parallel processing. + + +### Returns + + + Returns the metrics you have chosen to evaluate your app on as a dictionary. + + +## Usage + +```python +from embedchain import App + +app = App() + +# add data source +app.add("https://www.forbes.com/profile/elon-musk") + +# run evaluation +app.evaluate("what is the net worth of Elon Musk?") +# {'answer_relevancy': 0.958019958036268, 'context_relevancy': 0.12903225806451613} + +# or +# app.evaluate(["what is the net worth of Elon Musk?", "which companies does Elon Musk own?"]) +``` diff --git a/mem0-rag/docs/api-reference/app/get.mdx b/mem0-rag/docs/api-reference/app/get.mdx new file mode 100644 index 0000000000..252c785080 --- /dev/null +++ b/mem0-rag/docs/api-reference/app/get.mdx @@ -0,0 +1,33 @@ +--- +title: 📄 get +--- + +## Get data sources + +`get_data_sources()` returns a list of all the data sources added in the app. + + +### Usage + +```python +from embedchain import App + +app = App() + +app.add("https://www.forbes.com/profile/elon-musk") +app.add("https://en.wikipedia.org/wiki/Elon_Musk") + +data_sources = app.get_data_sources() +# [ +# { +# 'data_type': 'web_page', +# 'data_value': 'https://en.wikipedia.org/wiki/Elon_Musk', +# 'metadata': 'null' +# }, +# { +# 'data_type': 'web_page', +# 'data_value': 'https://www.forbes.com/profile/elon-musk', +# 'metadata': 'null' +# } +# ] +``` \ No newline at end of file diff --git a/mem0-rag/docs/api-reference/app/overview.mdx b/mem0-rag/docs/api-reference/app/overview.mdx new file mode 100644 index 0000000000..8c369cbf84 --- /dev/null +++ b/mem0-rag/docs/api-reference/app/overview.mdx @@ -0,0 +1,130 @@ +--- +title: "App" +--- + +Create a RAG app object on Embedchain. This is the main entrypoint for a developer to interact with Embedchain APIs. An app configures the llm, vector database, embedding model, and retrieval strategy of your choice. + +### Attributes + + + App ID + + + Name of the app + + + Configuration of the app + + + Configured LLM for the RAG app + + + Configured vector database for the RAG app + + + Configured embedding model for the RAG app + + + Chunker configuration + + + Client object (used to deploy an app to Embedchain platform) + + + Logger object + + +## Usage + +You can create an app instance using the following methods: + +### Default setting + +```python Code Example +from embedchain import App +app = App() +``` + + +### Python Dict + +```python Code Example +from embedchain import App + +config_dict = { + 'llm': { + 'provider': 'gpt4all', + 'config': { + 'model': 'orca-mini-3b-gguf2-q4_0.gguf', + 'temperature': 0.5, + 'max_tokens': 1000, + 'top_p': 1, + 'stream': False + } + }, + 'embedder': { + 'provider': 'gpt4all' + } +} + +# load llm configuration from config dict +app = App.from_config(config=config_dict) +``` + +### YAML Config + + + +```python main.py +from embedchain import App + +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: gpt4all + config: + model: 'orca-mini-3b-gguf2-q4_0.gguf' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +embedder: + provider: gpt4all +``` + + + +### JSON Config + + + +```python main.py +from embedchain import App + +# load llm configuration from config.json file +app = App.from_config(config_path="config.json") +``` + +```json config.json +{ + "llm": { + "provider": "gpt4all", + "config": { + "model": "orca-mini-3b-gguf2-q4_0.gguf", + "temperature": 0.5, + "max_tokens": 1000, + "top_p": 1, + "stream": false + } + }, + "embedder": { + "provider": "gpt4all" + } +} +``` + + diff --git a/mem0-rag/docs/api-reference/app/query.mdx b/mem0-rag/docs/api-reference/app/query.mdx new file mode 100644 index 0000000000..f1d94aa8f0 --- /dev/null +++ b/mem0-rag/docs/api-reference/app/query.mdx @@ -0,0 +1,109 @@ +--- +title: '❓ query' +--- + +`.query()` method empowers developers to ask questions and receive relevant answers through a user-friendly query API. Function signature is given below: + +### Parameters + + + Question to ask + + + Configure different llm settings such as prompt, temprature, number_documents etc. + + + The purpose is to test the prompt structure without actually running LLM inference. Defaults to `False` + + + A dictionary of key-value pairs to filter the chunks from the vector database. Defaults to `None` + + + Return citations along with the LLM answer. Defaults to `False` + + +### Returns + + + If `citations=False`, return a stringified answer to the question asked.
+ If `citations=True`, returns a tuple with answer and citations respectively. + + +## Usage + +### With citations + +If you want to get the answer to question and return both answer and citations, use the following code snippet: + +```python With Citations +from embedchain import App + +# Initialize app +app = App() + +# Add data source +app.add("https://www.forbes.com/profile/elon-musk") + +# Get relevant answer for your query +answer, sources = app.query("What is the net worth of Elon?", citations=True) +print(answer) +# Answer: The net worth of Elon Musk is $221.9 billion. + +print(sources) +# [ +# ( +# 'Elon Musk PROFILEElon MuskCEO, Tesla$247.1B$2.3B (0.96%)Real Time Net Worthas of 12/7/23 ...', +# { +# 'url': 'https://www.forbes.com/profile/elon-musk', +# 'score': 0.89, +# ... +# } +# ), +# ( +# '74% of the company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH BY YEARForbes ...', +# { +# 'url': 'https://www.forbes.com/profile/elon-musk', +# 'score': 0.81, +# ... +# } +# ), +# ( +# 'founded in 2002, is worth nearly $150 billion after a $750 million tender offer in June 2023 ...', +# { +# 'url': 'https://www.forbes.com/profile/elon-musk', +# 'score': 0.73, +# ... +# } +# ) +# ] +``` + + +When `citations=True`, note that the returned `sources` are a list of tuples where each tuple has two elements (in the following order): +1. source chunk +2. dictionary with metadata about the source chunk + - `url`: url of the source + - `doc_id`: document id (used for book keeping purposes) + - `score`: score of the source chunk with respect to the question + - other metadata you might have added at the time of adding the source + + +### Without citations + +If you just want to return answers and don't want to return citations, you can use the following example: + +```python Without Citations +from embedchain import App + +# Initialize app +app = App() + +# Add data source +app.add("https://www.forbes.com/profile/elon-musk") + +# Get relevant answer for your query +answer = app.query("What is the net worth of Elon?") +print(answer) +# Answer: The net worth of Elon Musk is $221.9 billion. +``` + diff --git a/mem0-rag/docs/api-reference/app/reset.mdx b/mem0-rag/docs/api-reference/app/reset.mdx new file mode 100644 index 0000000000..07e136d866 --- /dev/null +++ b/mem0-rag/docs/api-reference/app/reset.mdx @@ -0,0 +1,17 @@ +--- +title: 🔄 reset +--- + +`reset()` method allows you to wipe the data from your RAG application and start from scratch. + +## Usage + +```python +from embedchain import App + +app = App() +app.add("https://www.forbes.com/profile/elon-musk") + +# Reset the app +app.reset() +``` \ No newline at end of file diff --git a/mem0-rag/docs/api-reference/app/search.mdx b/mem0-rag/docs/api-reference/app/search.mdx new file mode 100644 index 0000000000..db4eee1b27 --- /dev/null +++ b/mem0-rag/docs/api-reference/app/search.mdx @@ -0,0 +1,111 @@ +--- +title: '🔍 search' +--- + +`.search()` enables you to uncover the most pertinent context by performing a semantic search across your data sources based on a given query. Refer to the function signature below: + +### Parameters + + + Question + + + Number of relevant documents to fetch. Defaults to `3` + + + Key value pair for metadata filtering. + + + Pass raw filter query based on your vector database. + Currently, `raw_filter` param is only supported for Pinecone vector database. + + +### Returns + + + Return list of dictionaries that contain the relevant chunk and their source information. + + +## Usage + +### Basic + +Refer to the following example on how to use the search api: + +```python Code example +from embedchain import App + +app = App() +app.add("https://www.forbes.com/profile/elon-musk") + +context = app.search("What is the net worth of Elon?", num_documents=2) +print(context) +``` + +### Advanced + +#### Metadata filtering using `where` params + +Here is an advanced example of `search()` API with metadata filtering on pinecone database: + +```python +import os + +from embedchain import App + +os.environ["PINECONE_API_KEY"] = "xxx" + +config = { + "vectordb": { + "provider": "pinecone", + "config": { + "metric": "dotproduct", + "vector_dimension": 1536, + "index_name": "ec-test", + "serverless_config": {"cloud": "aws", "region": "us-west-2"}, + }, + } +} + +app = App.from_config(config=config) + +app.add("https://www.forbes.com/profile/bill-gates", metadata={"type": "forbes", "person": "gates"}) +app.add("https://en.wikipedia.org/wiki/Bill_Gates", metadata={"type": "wiki", "person": "gates"}) + +results = app.search("What is the net worth of Bill Gates?", where={"person": "gates"}) +print("Num of search results: ", len(results)) +``` + +#### Metadata filtering using `raw_filter` params + +Following is an example of metadata filtering by passing the raw filter query that pinecone vector database follows: + +```python +import os + +from embedchain import App + +os.environ["PINECONE_API_KEY"] = "xxx" + +config = { + "vectordb": { + "provider": "pinecone", + "config": { + "metric": "dotproduct", + "vector_dimension": 1536, + "index_name": "ec-test", + "serverless_config": {"cloud": "aws", "region": "us-west-2"}, + }, + } +} + +app = App.from_config(config=config) + +app.add("https://www.forbes.com/profile/bill-gates", metadata={"year": 2022, "person": "gates"}) +app.add("https://en.wikipedia.org/wiki/Bill_Gates", metadata={"year": 2024, "person": "gates"}) + +print("Filter with person: gates and year > 2023") +raw_filter = {"$and": [{"person": "gates"}, {"year": {"$gt": 2023}}]} +results = app.search("What is the net worth of Bill Gates?", raw_filter=raw_filter) +print("Num of search results: ", len(results)) +``` diff --git a/mem0-rag/docs/api-reference/overview.mdx b/mem0-rag/docs/api-reference/overview.mdx new file mode 100644 index 0000000000..e69de29bb2 diff --git a/mem0-rag/docs/api-reference/store/ai-assistants.mdx b/mem0-rag/docs/api-reference/store/ai-assistants.mdx new file mode 100644 index 0000000000..09c6122a44 --- /dev/null +++ b/mem0-rag/docs/api-reference/store/ai-assistants.mdx @@ -0,0 +1,54 @@ +--- +title: 'AI Assistant' +--- + +The `AIAssistant` class, an alternative to the OpenAI Assistant API, is designed for those who prefer using large language models (LLMs) other than those provided by OpenAI. It facilitates the creation of AI Assistants with several key benefits: + +- **Visibility into Citations**: It offers transparent access to the sources and citations used by the AI, enhancing the understanding and trustworthiness of its responses. + +- **Debugging Capabilities**: Users have the ability to delve into and debug the AI's processes, allowing for a deeper understanding and fine-tuning of its performance. + +- **Customizable Prompts**: The class provides the flexibility to modify and tailor prompts according to specific needs, enabling more precise and relevant interactions. + +- **Chain of Thought Integration**: It supports the incorporation of a 'chain of thought' approach, which helps in breaking down complex queries into simpler, sequential steps, thereby improving the clarity and accuracy of responses. + +It is ideal for those who value customization, transparency, and detailed control over their AI Assistant's functionalities. + +### Arguments + + + Name for your AI assistant + + + + How the Assistant and model should behave or respond + + + + Load existing AI Assistant. If you pass this, you don't have to pass other arguments. + + + + Existing thread id if exists + + + + Embedchain pipeline config yaml path to use. This will define the configuration of the AI Assistant (such as configuring the LLM, vector database, and embedding model) + + + + Add data sources to your assistant. You can add in the following format: `[{"source": "https://example.com", "data_type": "web_page"}]` + + + + Anonymous telemetry (doesn't collect any user information or user's files). Used to improve the Embedchain package utilization. Default is `True`. + + + +## Usage + +For detailed guidance on creating your own AI Assistant, click the link below. It provides step-by-step instructions to help you through the process: + + + Learn how to build a customized AI Assistant using the `AIAssistant` class. + diff --git a/mem0-rag/docs/api-reference/store/openai-assistant.mdx b/mem0-rag/docs/api-reference/store/openai-assistant.mdx new file mode 100644 index 0000000000..1ab21aa1fe --- /dev/null +++ b/mem0-rag/docs/api-reference/store/openai-assistant.mdx @@ -0,0 +1,45 @@ +--- +title: 'OpenAI Assistant' +--- + +### Arguments + + + Name for your AI assistant + + + + how the Assistant and model should behave or respond + + + + Load existing OpenAI Assistant. If you pass this, you don't have to pass other arguments. + + + + Existing OpenAI thread id if exists + + + + OpenAI model to use + + + + OpenAI tools to use. Default set to `[{"type": "retrieval"}]` + + + + Add data sources to your assistant. You can add in the following format: `[{"source": "https://example.com", "data_type": "web_page"}]` + + + + Anonymous telemetry (doesn't collect any user information or user's files). Used to improve the Embedchain package utilization. Default is `True`. + + +## Usage + +For detailed guidance on creating your own OpenAI Assistant, click the link below. It provides step-by-step instructions to help you through the process: + + + Learn how to build an OpenAI Assistant using the `OpenAIAssistant` class. + diff --git a/mem0-rag/docs/community/connect-with-us.mdx b/mem0-rag/docs/community/connect-with-us.mdx new file mode 100644 index 0000000000..e08dfd1c72 --- /dev/null +++ b/mem0-rag/docs/community/connect-with-us.mdx @@ -0,0 +1,28 @@ +--- +title: 🤝 Connect with Us +--- + +We believe in building a vibrant and supportive community around embedchain. There are various channels through which you can connect with us, stay updated, and contribute to the ongoing discussions: + + + + Follow us on Twitter + + + Join our slack community + + + Join our discord community + + + Connect with us on LinkedIn + + + Schedule a call with Embedchain founder + + + Subscribe to our newsletter + + + +We look forward to connecting with you and seeing how we can create amazing things together! diff --git a/mem0-rag/docs/components/data-sources/audio.mdx b/mem0-rag/docs/components/data-sources/audio.mdx new file mode 100644 index 0000000000..5f2772a71c --- /dev/null +++ b/mem0-rag/docs/components/data-sources/audio.mdx @@ -0,0 +1,25 @@ +--- +title: "🎤 Audio" +--- + + +To use an audio as data source, just add `data_type` as `audio` and pass in the path of the audio (local or hosted). + +We use [Deepgram](https://developers.deepgram.com/docs/introduction) to transcribe the audiot to text, and then use the generated text as the data source. + +You would require an Deepgram API key which is available [here](https://console.deepgram.com/signup?jump=keys) to use this feature. + +### Without customization + +```python +import os +from embedchain import App + +os.environ["DEEPGRAM_API_KEY"] = "153xxx" + +app = App() +app.add("introduction.wav", data_type="audio") +response = app.query("What is my name and how old am I?") +print(response) +# Answer: Your name is Dave and you are 21 years old. +``` diff --git a/mem0-rag/docs/components/data-sources/beehiiv.mdx b/mem0-rag/docs/components/data-sources/beehiiv.mdx new file mode 100644 index 0000000000..5a94cf1fe3 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/beehiiv.mdx @@ -0,0 +1,16 @@ +--- +title: "🐝 Beehiiv" +--- + +To add any Beehiiv data sources to your app, just add the base url as the source and set the data_type to `beehiiv`. + +```python +from embedchain import App + +app = App() + +# source: just add the base url and set the data_type to 'beehiiv' +app.add('https://aibreakfast.beehiiv.com', data_type='beehiiv') +app.query("How much is OpenAI paying developers?") +# Answer: OpenAI is aggressively recruiting Google's top AI researchers with offers ranging between $5 to $10 million annually, primarily in stock options. +``` diff --git a/mem0-rag/docs/components/data-sources/csv.mdx b/mem0-rag/docs/components/data-sources/csv.mdx new file mode 100644 index 0000000000..07663a3b1f --- /dev/null +++ b/mem0-rag/docs/components/data-sources/csv.mdx @@ -0,0 +1,28 @@ +--- +title: '📊 CSV' +--- + +You can load any csv file from your local file system or through a URL. Headers are included for each line, so if you have an `age` column, `18` will be added as `age: 18`. + +## Usage + +### Load from a local file + +```python +from embedchain import App +app = App() +app.add('/path/to/file.csv', data_type='csv') +``` + +### Load from URL + +```python +from embedchain import App +app = App() +app.add('https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv', data_type="csv") +``` + + +There is a size limit allowed for csv file beyond which it can throw error. This limit is set by the LLMs. Please consider chunking large csv files into smaller csv files. + + diff --git a/mem0-rag/docs/components/data-sources/custom.mdx b/mem0-rag/docs/components/data-sources/custom.mdx new file mode 100644 index 0000000000..40a8c75e10 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/custom.mdx @@ -0,0 +1,42 @@ +--- +title: '⚙️ Custom' +--- + +When we say "custom", we mean that you can customize the loader and chunker to your needs. This is done by passing a custom loader and chunker to the `add` method. + +```python +from embedchain import App +import your_loader +from my_module import CustomLoader +from my_module import CustomChunker + +app = App() +loader = CustomLoader() +chunker = CustomChunker() + +app.add("source", data_type="custom", loader=loader, chunker=chunker) +``` + + + The custom loader and chunker must be a class that inherits from the [`BaseLoader`](https://github.com/embedchain/embedchain/blob/main/embedchain/loaders/base_loader.py) and [`BaseChunker`](https://github.com/embedchain/embedchain/blob/main/embedchain/chunkers/base_chunker.py) classes respectively. + + + + If the `data_type` is not a valid data type, the `add` method will fallback to the `custom` data type and expect a custom loader and chunker to be passed by the user. + + +Example: + +```python +from embedchain import App +from embedchain.loaders.github import GithubLoader + +app = App() + +loader = GithubLoader(config={"token": "ghp_xxx"}) + +app.add("repo:embedchain/embedchain type:repo", data_type="github", loader=loader) + +app.query("What is Embedchain?") +# Answer: Embedchain is a Data Platform for Large Language Models (LLMs). It allows users to seamlessly load, index, retrieve, and sync unstructured data in order to build dynamic, LLM-powered applications. There is also a JavaScript implementation called embedchain-js available on GitHub. +``` diff --git a/mem0-rag/docs/components/data-sources/data-type-handling.mdx b/mem0-rag/docs/components/data-sources/data-type-handling.mdx new file mode 100644 index 0000000000..d939537afc --- /dev/null +++ b/mem0-rag/docs/components/data-sources/data-type-handling.mdx @@ -0,0 +1,85 @@ +--- +title: 'Data type handling' +--- + +## Automatic data type detection + +The add method automatically tries to detect the data_type, based on your input for the source argument. So `app.add('https://www.youtube.com/watch?v=dQw4w9WgXcQ')` is enough to embed a YouTube video. + +This detection is implemented for all formats. It is based on factors such as whether it's a URL, a local file, the source data type, etc. + +### Debugging automatic detection + +Set `log_level: DEBUG` in the config yaml to debug if the data type detection is done right or not. Otherwise, you will not know when, for instance, an invalid filepath is interpreted as raw text instead. + +### Forcing a data type + +To omit any issues with the data type detection, you can **force** a data_type by adding it as a `add` method argument. +The examples below show you the keyword to force the respective `data_type`. + +Forcing can also be used for edge cases, such as interpreting a sitemap as a web_page, for reading its raw text instead of following links. + +## Remote data types + + +**Use local files in remote data types** + +Some data_types are meant for remote content and only work with URLs. +You can pass local files by formatting the path using the `file:` [URI scheme](https://en.wikipedia.org/wiki/File_URI_scheme), e.g. `file:///info.pdf`. + + +## Reusing a vector database + +Default behavior is to create a persistent vector db in the directory **./db**. You can split your application into two Python scripts: one to create a local vector db and the other to reuse this local persistent vector db. This is useful when you want to index hundreds of documents and separately implement a chat interface. + +Create a local index: + +```python +from embedchain import App + +config = { + "app": { + "config": { + "id": "app-1" + } + } +} +naval_chat_bot = App.from_config(config=config) +naval_chat_bot.add("https://www.youtube.com/watch?v=3qHkcs3kG44") +naval_chat_bot.add("https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf") +``` + +You can reuse the local index with the same code, but without adding new documents: + +```python +from embedchain import App + +config = { + "app": { + "config": { + "id": "app-1" + } + } +} +naval_chat_bot = App.from_config(config=config) +print(naval_chat_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?")) +``` + +## Resetting an app and vector database + +You can reset the app by simply calling the `reset` method. This will delete the vector database and all other app related files. + +```python +from embedchain import App + +app = App()config = { + "app": { + "config": { + "id": "app-1" + } + } +} +naval_chat_bot = App.from_config(config=config) +app.add("https://www.youtube.com/watch?v=3qHkcs3kG44") +app.reset() +``` diff --git a/mem0-rag/docs/components/data-sources/directory.mdx b/mem0-rag/docs/components/data-sources/directory.mdx new file mode 100644 index 0000000000..33c1e9b73b --- /dev/null +++ b/mem0-rag/docs/components/data-sources/directory.mdx @@ -0,0 +1,41 @@ +--- +title: '📁 Directory/Folder' +--- + +To use an entire directory as data source, just add `data_type` as `directory` and pass in the path of the local directory. + +### Without customization + +```python +import os +from embedchain import App + +os.environ["OPENAI_API_KEY"] = "sk-xxx" + +app = App() +app.add("./elon-musk", data_type="directory") +response = app.query("list all files") +print(response) +# Answer: Files are elon-musk-1.txt, elon-musk-2.pdf. +``` + +### Customization + +```python +import os +from embedchain import App +from embedchain.loaders.directory_loader import DirectoryLoader + +os.environ["OPENAI_API_KEY"] = "sk-xxx" +lconfig = { + "recursive": True, + "extensions": [".txt"] +} +loader = DirectoryLoader(config=lconfig) +app = App() +app.add("./elon-musk", loader=loader) +response = app.query("what are all the files related to?") +print(response) + +# Answer: The files are related to Elon Musk. +``` diff --git a/mem0-rag/docs/components/data-sources/discord.mdx b/mem0-rag/docs/components/data-sources/discord.mdx new file mode 100644 index 0000000000..2c87802100 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/discord.mdx @@ -0,0 +1,28 @@ +--- +title: "💬 Discord" +--- + +To add any Discord channel messages to your app, just add the `channel_id` as the source and set the `data_type` to `discord`. + + + This loader requires a Discord bot token with read messages access. + To obtain the token, follow the instructions provided in this tutorial: + How to Get a Discord Bot Token?. + + +```python +import os +from embedchain import App + +# add your discord "BOT" token +os.environ["DISCORD_TOKEN"] = "xxx" + +app = App() + +app.add("1177296711023075338", data_type="discord") + +response = app.query("What is Joe saying about Elon Musk?") + +print(response) +# Answer: Joe is saying "Elon Musk is a genius". +``` diff --git a/mem0-rag/docs/components/data-sources/discourse.mdx b/mem0-rag/docs/components/data-sources/discourse.mdx new file mode 100644 index 0000000000..4ba0a36ced --- /dev/null +++ b/mem0-rag/docs/components/data-sources/discourse.mdx @@ -0,0 +1,44 @@ +--- +title: '🗨️ Discourse' +--- + +You can now easily load data from your community built with [Discourse](https://discourse.org/). + +## Example + +1. Setup the Discourse Loader with your community url. +```Python +from embedchain.loaders.discourse import DiscourseLoader + +dicourse_loader = DiscourseLoader(config={"domain": "https://community.openai.com"}) +``` + +2. Once you setup the loader, you can create an app and load data using the above discourse loader +```Python +import os +from embedchain.pipeline import Pipeline as App + +os.environ["OPENAI_API_KEY"] = "sk-xxx" + +app = App() + +app.add("openai after:2023-10-1", data_type="discourse", loader=dicourse_loader) + +question = "Where can I find the OpenAI API status page?" +app.query(question) +# Answer: You can find the OpenAI API status page at https:/status.openai.com/. +``` + +NOTE: The `add` function of the app will accept any executable search query to load data. Refer [Discourse API Docs](https://docs.discourse.org/#tag/Search) to learn more about search queries. + +3. We automatically create a chunker to chunk your discourse data, however if you wish to provide your own chunker class. Here is how you can do that: +```Python + +from embedchain.chunkers.discourse import DiscourseChunker +from embedchain.config.add_config import ChunkerConfig + +discourse_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len) +discourse_chunker = DiscourseChunker(config=discourse_chunker_config) + +app.add("openai", data_type='discourse', loader=dicourse_loader, chunker=discourse_chunker) +``` \ No newline at end of file diff --git a/mem0-rag/docs/components/data-sources/docs-site.mdx b/mem0-rag/docs/components/data-sources/docs-site.mdx new file mode 100644 index 0000000000..342bbdc85f --- /dev/null +++ b/mem0-rag/docs/components/data-sources/docs-site.mdx @@ -0,0 +1,14 @@ +--- +title: '📚 Code Docs website' +--- + +To add any code documentation website as a loader, use the data_type as `docs_site`. Eg: + +```python +from embedchain import App + +app = App() +app.add("https://docs.embedchain.ai/", data_type="docs_site") +app.query("What is Embedchain?") +# Answer: Embedchain is a platform that utilizes various components, including paid/proprietary ones, to provide what is believed to be the best configuration available. It uses LLM (Language Model) providers such as OpenAI, Anthpropic, Vertex_AI, GPT4ALL, Azure_OpenAI, LLAMA2, JINA, Ollama, Together and COHERE. Embedchain allows users to import and utilize these LLM providers for their applications.' +``` diff --git a/mem0-rag/docs/components/data-sources/docx.mdx b/mem0-rag/docs/components/data-sources/docx.mdx new file mode 100644 index 0000000000..cc459621fb --- /dev/null +++ b/mem0-rag/docs/components/data-sources/docx.mdx @@ -0,0 +1,18 @@ +--- +title: '📄 Docx file' +--- + +### Docx file + +To add any doc/docx file, use the data_type as `docx`. `docx` allows remote urls and conventional file paths. Eg: + +```python +from embedchain import App + +app = App() +app.add('https://example.com/content/intro.docx', data_type="docx") +# Or add file using the local file path on your system +# app.add('content/intro.docx', data_type="docx") + +app.query("Summarize the docx data?") +``` diff --git a/mem0-rag/docs/components/data-sources/dropbox.mdx b/mem0-rag/docs/components/data-sources/dropbox.mdx new file mode 100644 index 0000000000..bb2800bf87 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/dropbox.mdx @@ -0,0 +1,37 @@ +--- +title: '💾 Dropbox' +--- + +To load folders or files from your Dropbox account, configure the `data_type` parameter as `dropbox` and specify the path to the desired file or folder, starting from the root directory of your Dropbox account. + +For Dropbox access, an **access token** is required. Obtain this token by visiting [Dropbox Developer Apps](https://www.dropbox.com/developers/apps). There, create a new app and generate an access token for it. + +Ensure your app has the following settings activated: + +- In the Permissions section, enable `files.content.read` and `files.metadata.read`. + +## Usage + +Install the `dropbox` pypi package: + +```bash +pip install dropbox +``` + +Following is an example of how to use the dropbox loader: + +```python +import os +from embedchain import App + +os.environ["DROPBOX_ACCESS_TOKEN"] = "sl.xxx" +os.environ["OPENAI_API_KEY"] = "sk-xxx" + +app = App() + +# any path from the root of your dropbox account, you can leave it "" for the root folder +app.add("/test", data_type="dropbox") + +print(app.query("Which two celebrities are mentioned here?")) +# The two celebrities mentioned in the given context are Elon Musk and Jeff Bezos. +``` diff --git a/mem0-rag/docs/components/data-sources/excel-file.mdx b/mem0-rag/docs/components/data-sources/excel-file.mdx new file mode 100644 index 0000000000..af8a2cd62b --- /dev/null +++ b/mem0-rag/docs/components/data-sources/excel-file.mdx @@ -0,0 +1,18 @@ +--- +title: '📄 Excel file' +--- + +### Excel file + +To add any xlsx/xls file, use the data_type as `excel_file`. `excel_file` allows remote urls and conventional file paths. Eg: + +```python +from embedchain import App + +app = App() +app.add('https://example.com/content/intro.xlsx', data_type="excel_file") +# Or add file using the local file path on your system +# app.add('content/intro.xls', data_type="excel_file") + +app.query("Give brief information about data.") +``` diff --git a/mem0-rag/docs/components/data-sources/github.mdx b/mem0-rag/docs/components/data-sources/github.mdx new file mode 100644 index 0000000000..14791aca4d --- /dev/null +++ b/mem0-rag/docs/components/data-sources/github.mdx @@ -0,0 +1,52 @@ +--- +title: 📝 Github +--- + +1. Setup the Github loader by configuring the Github account with username and personal access token (PAT). Check out [this](https://docs.github.com/en/enterprise-server@3.6/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens#creating-a-personal-access-token) link to learn how to create a PAT. +```Python +from embedchain.loaders.github import GithubLoader + +loader = GithubLoader( + config={ + "token":"ghp_xxxx" + } + ) +``` + +2. Once you setup the loader, you can create an app and load data using the above Github loader +```Python +import os +from embedchain.pipeline import Pipeline as App + +os.environ["OPENAI_API_KEY"] = "sk-xxxx" + +app = App() + +app.add("repo:embedchain/embedchain type:repo", data_type="github", loader=loader) + +response = app.query("What is Embedchain?") +# Answer: Embedchain is a Data Platform for Large Language Models (LLMs). It allows users to seamlessly load, index, retrieve, and sync unstructured data in order to build dynamic, LLM-powered applications. There is also a JavaScript implementation called embedchain-js available on GitHub. +``` +The `add` function of the app will accept any valid github query with qualifiers. It only supports loading github code, repository, issues and pull-requests. + +You must provide qualifiers `type:` and `repo:` in the query. The `type:` qualifier can be a combination of `code`, `repo`, `pr`, `issue`, `branch`, `file`. The `repo:` qualifier must be a valid github repository name. + + + + - `repo:embedchain/embedchain type:repo` - to load the repository + - `repo:embedchain/embedchain type:branch name:feature_test` - to load the branch of the repository + - `repo:embedchain/embedchain type:file path:README.md` - to load the specific file of the repository + - `repo:embedchain/embedchain type:issue,pr` - to load the issues and pull-requests of the repository + - `repo:embedchain/embedchain type:issue state:closed` - to load the closed issues of the repository + + +3. We automatically create a chunker to chunk your GitHub data, however if you wish to provide your own chunker class. Here is how you can do that: +```Python +from embedchain.chunkers.common_chunker import CommonChunker +from embedchain.config.add_config import ChunkerConfig + +github_chunker_config = ChunkerConfig(chunk_size=2000, chunk_overlap=0, length_function=len) +github_chunker = CommonChunker(config=github_chunker_config) + +app.add(load_query, data_type="github", loader=loader, chunker=github_chunker) +``` diff --git a/mem0-rag/docs/components/data-sources/gmail.mdx b/mem0-rag/docs/components/data-sources/gmail.mdx new file mode 100644 index 0000000000..aaaf002ed1 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/gmail.mdx @@ -0,0 +1,34 @@ +--- +title: '📬 Gmail' +--- + +To use GmailLoader you must install the extra dependencies with `pip install --upgrade embedchain[gmail]`. + +The `source` must be a valid Gmail search query, you can refer `https://support.google.com/mail/answer/7190?hl=en` to build a query. + +To load Gmail messages, you MUST use the data_type as `gmail`. Otherwise the source will be detected as simple `text`. + +To use this you need to save `credentials.json` in the directory from where you will run the loader. Follow these steps to get the credentials + +1. Go to the [Google Cloud Console](https://console.cloud.google.com/apis/credentials). +2. Create a project if you don't have one already. +3. Create an `OAuth Consent Screen` in the project. You may need to select the `external` option. +4. Make sure the consent screen is published. +5. Enable the [Gmail API](https://console.cloud.google.com/apis/api/gmail.googleapis.com) +6. Create credentials from the `Credentials` tab. +7. Select the type `OAuth Client ID`. +8. Choose the application type `Web application`. As a name you can choose `embedchain` or any other name as per your use case. +9. Add an authorized redirect URI for `http://localhost:8080/`. +10. You can leave everything else at default, finish the creation. +11. When you are done, a modal opens where you can download the details in `json` format. +12. Put the `.json` file in your current directory and rename it to `credentials.json` + +```python +from embedchain import App + +app = App() + +gmail_filter = "to: me label:inbox" +app.add(gmail_filter, data_type="gmail") +app.query("Summarize my email conversations") +``` \ No newline at end of file diff --git a/mem0-rag/docs/components/data-sources/google-drive.mdx b/mem0-rag/docs/components/data-sources/google-drive.mdx new file mode 100644 index 0000000000..5dcf4e45f9 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/google-drive.mdx @@ -0,0 +1,28 @@ +--- +title: 'Google Drive' +--- + +To use GoogleDriveLoader you must install the extra dependencies with `pip install --upgrade embedchain[googledrive]`. + +The data_type must be `google_drive`. Otherwise, it will be considered a regular web page. + +Google Drive requires the setup of credentials. This can be done by following the steps below: + +1. Go to the [Google Cloud Console](https://console.cloud.google.com/apis/credentials). +2. Create a project if you don't have one already. +3. Enable the [Google Drive API](https://console.cloud.google.com/flows/enableapi?apiid=drive.googleapis.com) +4. [Authorize credentials for desktop app](https://developers.google.com/drive/api/quickstart/python#authorize_credentials_for_a_desktop_application) +5. When done, you will be able to download the credentials in `json` format. Rename the downloaded file to `credentials.json` and save it in `~/.credentials/credentials.json` +6. Set the environment variable `GOOGLE_APPLICATION_CREDENTIALS=~/.credentials/credentials.json` + +The first time you use the loader, you will be prompted to enter your Google account credentials. + + +```python +from embedchain import App + +app = App() + +url = "https://drive.google.com/drive/u/0/folders/xxx-xxx" +app.add(url, data_type="google_drive") +``` diff --git a/mem0-rag/docs/components/data-sources/image.mdx b/mem0-rag/docs/components/data-sources/image.mdx new file mode 100644 index 0000000000..b79043660f --- /dev/null +++ b/mem0-rag/docs/components/data-sources/image.mdx @@ -0,0 +1,45 @@ +--- +title: "🖼️ Image" +--- + + +To use an image as data source, just add `data_type` as `image` and pass in the path of the image (local or hosted). + +We use [GPT4 Vision](https://platform.openai.com/docs/guides/vision) to generate meaning of the image using a custom prompt, and then use the generated text as the data source. + +You would require an OpenAI API key with access to `gpt-4-vision-preview` model to use this feature. + +### Without customization + +```python +import os +from embedchain import App + +os.environ["OPENAI_API_KEY"] = "sk-xxx" + +app = App() +app.add("./Elon-Musk.webp", data_type="image") +response = app.query("Describe the man in the image.") +print(response) +# Answer: The man in the image is dressed in formal attire, wearing a dark suit jacket and a white collared shirt. He has short hair and is standing. He appears to be gazing off to the side with a reflective expression. The background is dark with faint, warm-toned vertical lines, possibly from a lit environment behind the individual or reflections. The overall atmosphere is somewhat moody and introspective. +``` + +### Customization + +```python +import os +from embedchain import App +from embedchain.loaders.image import ImageLoader + +image_loader = ImageLoader( + max_tokens=100, + api_key="sk-xxx", + prompt="Is the person looking wealthy? Structure your thoughts around what you see in the image.", +) + +app = App() +app.add("./Elon-Musk.webp", data_type="image", loader=image_loader) +response = app.query("Describe the man in the image.") +print(response) +# Answer: The man in the image appears to be well-dressed in a suit and shirt, suggesting that he may be in a professional or formal setting. His composed demeanor and confident posture further indicate a sense of self-assurance. Based on these visual cues, one could infer that the man may have a certain level of economic or social status, possibly indicating wealth or professional success. +``` diff --git a/mem0-rag/docs/components/data-sources/json.mdx b/mem0-rag/docs/components/data-sources/json.mdx new file mode 100644 index 0000000000..4d38a0a552 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/json.mdx @@ -0,0 +1,44 @@ +--- +title: '📃 JSON' +--- + +To add any json file, use the data_type as `json`. Headers are included for each line, so for example if you have a json like `{"age": 18}`, then it will be added as `age: 18`. + +Here are the supported sources for loading `json`: + +``` +1. URL - valid url to json file that ends with ".json" extension. +2. Local file - valid url to local json file that ends with ".json" extension. +3. String - valid json string (e.g. - app.add('{"foo": "bar"}')) +``` + + +If you would like to add other data structures (e.g. list, dict etc.), convert it to a valid json first using `json.dumps()` function. + + +## Example + + + +```python python +from embedchain import App + +app = App() + +# Add json file +app.add("temp.json") + +app.query("What is the net worth of Elon Musk as of October 2023?") +# As of October 2023, Elon Musk's net worth is $255.2 billion. +``` + + +```json temp.json +{ + "question": "What is your net worth, Elon Musk?", + "answer": "As of October 2023, Elon Musk's net worth is $255.2 billion, making him one of the wealthiest individuals in the world." +} +``` + + + diff --git a/mem0-rag/docs/components/data-sources/mdx.mdx b/mem0-rag/docs/components/data-sources/mdx.mdx new file mode 100644 index 0000000000..c59569e507 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/mdx.mdx @@ -0,0 +1,14 @@ +--- +title: '📝 Mdx file' +--- + +To add any `.mdx` file to your app, use the data_type (first argument to `.add()` method) as `mdx`. Note that this supports support mdx file present on machine, so this should be a file path. Eg: + +```python +from embedchain import App + +app = App() +app.add('path/to/file.mdx', data_type='mdx') + +app.query("What are the docs about?") +``` diff --git a/mem0-rag/docs/components/data-sources/mysql.mdx b/mem0-rag/docs/components/data-sources/mysql.mdx new file mode 100644 index 0000000000..e3ee9a48ee --- /dev/null +++ b/mem0-rag/docs/components/data-sources/mysql.mdx @@ -0,0 +1,47 @@ +--- +title: '🐬 MySQL' +--- + +1. Setup the MySQL loader by configuring the SQL db. +```Python +from embedchain.loaders.mysql import MySQLLoader + +config = { + "host": "host", + "port": "port", + "database": "database", + "user": "username", + "password": "password", +} + +mysql_loader = MySQLLoader(config=config) +``` + +For more details on how to setup with valid config, check MySQL [documentation](https://dev.mysql.com/doc/connector-python/en/connector-python-connectargs.html). + +2. Once you setup the loader, you can create an app and load data using the above MySQL loader +```Python +from embedchain.pipeline import Pipeline as App + +app = App() + +app.add("SELECT * FROM table_name;", data_type='mysql', loader=mysql_loader) +# Adds `(1, 'What is your net worth, Elon Musk?', "As of October 2023, Elon Musk's net worth is $255.2 billion.")` + +response = app.query(question) +# Answer: As of October 2023, Elon Musk's net worth is $255.2 billion. +``` + +NOTE: The `add` function of the app will accept any executable query to load data. DO NOT pass the `CREATE`, `INSERT` queries in `add` function. + +3. We automatically create a chunker to chunk your SQL data, however if you wish to provide your own chunker class. Here is how you can do that: +``Python + +from embedchain.chunkers.mysql import MySQLChunker +from embedchain.config.add_config import ChunkerConfig + +mysql_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len) +mysql_chunker = MySQLChunker(config=mysql_chunker_config) + +app.add("SELECT * FROM table_name;", data_type='mysql', loader=mysql_loader, chunker=mysql_chunker) +``` \ No newline at end of file diff --git a/mem0-rag/docs/components/data-sources/notion.mdx b/mem0-rag/docs/components/data-sources/notion.mdx new file mode 100644 index 0000000000..d6c616df8b --- /dev/null +++ b/mem0-rag/docs/components/data-sources/notion.mdx @@ -0,0 +1,20 @@ +--- +title: '📓 Notion' +--- + +To use notion you must install the extra dependencies with `pip install --upgrade embedchain[community]`. + +To load a notion page, use the data_type as `notion`. Since it is hard to automatically detect, it is advised to specify the `data_type` when adding a notion document. +The next argument must **end** with the `notion page id`. The id is a 32-character string. Eg: + +```python +from embedchain import App + +app = App() + +app.add("cfbc134ca6464fc980d0391613959196", data_type="notion") +app.add("my-page-cfbc134ca6464fc980d0391613959196", data_type="notion") +app.add("https://www.notion.so/my-page-cfbc134ca6464fc980d0391613959196", data_type="notion") + +app.query("Summarize the notion doc") +``` diff --git a/mem0-rag/docs/components/data-sources/openapi.mdx b/mem0-rag/docs/components/data-sources/openapi.mdx new file mode 100644 index 0000000000..84bc966b2e --- /dev/null +++ b/mem0-rag/docs/components/data-sources/openapi.mdx @@ -0,0 +1,22 @@ +--- +title: 🙌 OpenAPI +--- + +To add any OpenAPI spec yaml file (currently the json file will be detected as JSON data type), use the data_type as 'openapi'. 'openapi' allows remote urls and conventional file paths. + +```python +from embedchain import App + +app = App() + +app.add("https://github.com/openai/openai-openapi/blob/master/openapi.yaml", data_type="openapi") +# Or add using the local file path +# app.add("configs/openai_openapi.yaml", data_type="openapi") + +app.query("What can OpenAI API endpoint do? Can you list the things it can learn from?") +# Answer: The OpenAI API endpoint allows users to interact with OpenAI's models and perform various tasks such as generating text, answering questions, summarizing documents, translating languages, and more. The specific capabilities and tasks that the API can learn from may vary depending on the models and features provided by OpenAI. For more detailed information, it is recommended to refer to the OpenAI API documentation at https://platform.openai.com/docs/api-reference. +``` + + +The yaml file added to the App must have the required OpenAPI fields otherwise the adding OpenAPI spec will fail. Please refer to [OpenAPI Spec Doc](https://spec.openapis.org/oas/v3.1.0) + \ No newline at end of file diff --git a/mem0-rag/docs/components/data-sources/overview.mdx b/mem0-rag/docs/components/data-sources/overview.mdx new file mode 100644 index 0000000000..66f5948a33 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/overview.mdx @@ -0,0 +1,43 @@ +--- +title: Overview +--- + +Embedchain comes with built-in support for various data sources. We handle the complexity of loading unstructured data from these data sources, allowing you to easily customize your app through a user-friendly interface. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + diff --git a/mem0-rag/docs/components/data-sources/pdf-file.mdx b/mem0-rag/docs/components/data-sources/pdf-file.mdx new file mode 100644 index 0000000000..9cc45910a4 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/pdf-file.mdx @@ -0,0 +1,43 @@ +--- +title: '📰 PDF' +--- + +You can load any pdf file from your local file system or through a URL. + +## Usage + +### Load from a local file + +```python +from embedchain import App +app = App() +app.add('/path/to/file.pdf', data_type='pdf_file') +``` + +### Load from URL + +```python +from embedchain import App +app = App() +app.add('https://arxiv.org/pdf/1706.03762.pdf', data_type='pdf_file') +app.query("What is the paper 'attention is all you need' about?", citations=True) +# Answer: The paper "Attention Is All You Need" proposes a new network architecture called the Transformer, which is based solely on attention mechanisms. It suggests that complex recurrent or convolutional neural networks can be replaced with a simpler architecture that connects the encoder and decoder through attention. The paper discusses how this approach can improve sequence transduction models, such as neural machine translation. +# Contexts: +# [ +# ( +# 'Provided proper attribution is ...', +# { +# 'page': 0, +# 'url': 'https://arxiv.org/pdf/1706.03762.pdf', +# 'score': 0.3676220203221626, +# ... +# } +# ), +# ] +``` + +We also store the page number under the key `page` with each chunk that helps understand where the answer is coming from. You can fetch the `page` key while during retrieval (refer to the example given above). + + +Note that we do not support password protected pdf files. + diff --git a/mem0-rag/docs/components/data-sources/postgres.mdx b/mem0-rag/docs/components/data-sources/postgres.mdx new file mode 100644 index 0000000000..9cb5d0e6e7 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/postgres.mdx @@ -0,0 +1,64 @@ +--- +title: '🐘 Postgres' +--- + +1. Setup the Postgres loader by configuring the postgres db. +```Python +from embedchain.loaders.postgres import PostgresLoader + +config = { + "host": "host_address", + "port": "port_number", + "dbname": "database_name", + "user": "username", + "password": "password", +} + +""" +config = { + "url": "your_postgres_url" +} +""" + +postgres_loader = PostgresLoader(config=config) + +``` + +You can either setup the loader by passing the postgresql url or by providing the config data. +For more details on how to setup with valid url and config, check postgres [documentation](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING:~:text=34.1.1.%C2%A0Connection%20Strings-,%23,-Several%20libpq%20functions). + +NOTE: if you provide the `url` field in config, all other fields will be ignored. + +2. Once you setup the loader, you can create an app and load data using the above postgres loader +```Python +import os +from embedchain.pipeline import Pipeline as App + +os.environ["OPENAI_API_KEY"] = "sk-xxx" + +app = App() + +question = "What is Elon Musk's networth?" +response = app.query(question) +# Answer: As of September 2021, Elon Musk's net worth is estimated to be around $250 billion, making him one of the wealthiest individuals in the world. However, please note that net worth can fluctuate over time due to various factors such as stock market changes and business ventures. + +app.add("SELECT * FROM table_name;", data_type='postgres', loader=postgres_loader) +# Adds `(1, 'What is your net worth, Elon Musk?', "As of October 2023, Elon Musk's net worth is $255.2 billion.")` + +response = app.query(question) +# Answer: As of October 2023, Elon Musk's net worth is $255.2 billion. +``` + +NOTE: The `add` function of the app will accept any executable query to load data. DO NOT pass the `CREATE`, `INSERT` queries in `add` function as they will result in not adding any data, so it is pointless. + +3. We automatically create a chunker to chunk your postgres data, however if you wish to provide your own chunker class. Here is how you can do that: +```Python + +from embedchain.chunkers.postgres import PostgresChunker +from embedchain.config.add_config import ChunkerConfig + +postgres_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len) +postgres_chunker = PostgresChunker(config=postgres_chunker_config) + +app.add("SELECT * FROM table_name;", data_type='postgres', loader=postgres_loader, chunker=postgres_chunker) +``` \ No newline at end of file diff --git a/mem0-rag/docs/components/data-sources/qna.mdx b/mem0-rag/docs/components/data-sources/qna.mdx new file mode 100644 index 0000000000..3efaa47ff4 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/qna.mdx @@ -0,0 +1,13 @@ +--- +title: '❓💬 Question and answer pair' +--- + +QnA pair is a local data type. To supply your own QnA pair, use the data_type as `qna_pair` and enter a tuple. Eg: + +```python +from embedchain import App + +app = App() + +app.add(("Question", "Answer"), data_type="qna_pair") +``` diff --git a/mem0-rag/docs/components/data-sources/sitemap.mdx b/mem0-rag/docs/components/data-sources/sitemap.mdx new file mode 100644 index 0000000000..96b47ef1c5 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/sitemap.mdx @@ -0,0 +1,13 @@ +--- +title: '🗺️ Sitemap' +--- + +Add all web pages from an xml-sitemap. Filters non-text files. Use the data_type as `sitemap`. Eg: + +```python +from embedchain import App + +app = App() + +app.add('https://example.com/sitemap.xml', data_type='sitemap') +``` \ No newline at end of file diff --git a/mem0-rag/docs/components/data-sources/slack.mdx b/mem0-rag/docs/components/data-sources/slack.mdx new file mode 100644 index 0000000000..7b879fd6d7 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/slack.mdx @@ -0,0 +1,71 @@ +--- +title: '🤖 Slack' +--- + +## Pre-requisite +- Download required packages by running `pip install --upgrade "embedchain[slack]"`. +- Configure your slack bot token as environment variable `SLACK_USER_TOKEN`. + - Find your user token on your [Slack Account](https://api.slack.com/authentication/token-types) + - Make sure your slack user token includes [search](https://api.slack.com/scopes/search:read) scope. + +## Example + +### Get Started + +This will automatically retrieve data from the workspace associated with the user's token. + +```python +import os +from embedchain import App + +os.environ["SLACK_USER_TOKEN"] = "xoxp-xxx" +app = App() + +app.add("in:general", data_type="slack") + +result = app.query("what are the messages in general channel?") + +print(result) +``` + + +### Customize your SlackLoader +1. Setup the Slack loader by configuring the Slack Webclient. +```Python +from embedchain.loaders.slack import SlackLoader + +os.environ["SLACK_USER_TOKEN"] = "xoxp-*" + +config = { + 'base_url': slack_app_url, + 'headers': web_headers, + 'team_id': slack_team_id, +} + +loader = SlackLoader(config) +``` + +NOTE: you can also pass the `config` with `base_url`, `headers`, `team_id` to setup your SlackLoader. + +2. Once you setup the loader, you can create an app and load data using the above slack loader +```Python +import os +from embedchain.pipeline import Pipeline as App + +app = App() + +app.add("in:random", data_type="slack", loader=loader) +question = "Which bots are available in the slack workspace's random channel?" +# Answer: The available bot in the slack workspace's random channel is the Embedchain bot. +``` + +3. We automatically create a chunker to chunk your slack data, however if you wish to provide your own chunker class. Here is how you can do that: +```Python +from embedchain.chunkers.slack import SlackChunker +from embedchain.config.add_config import ChunkerConfig + +slack_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len) +slack_chunker = SlackChunker(config=slack_chunker_config) + +app.add(slack_chunker, data_type="slack", loader=loader, chunker=slack_chunker) +``` \ No newline at end of file diff --git a/mem0-rag/docs/components/data-sources/substack.mdx b/mem0-rag/docs/components/data-sources/substack.mdx new file mode 100644 index 0000000000..dd10a9e7d7 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/substack.mdx @@ -0,0 +1,16 @@ +--- +title: "📝 Substack" +--- + +To add any Substack data sources to your app, just add the main base url as the source and set the data_type to `substack`. + +```python +from embedchain import App + +app = App() + +# source: for any substack just add the root URL +app.add('https://www.lennysnewsletter.com', data_type='substack') +app.query("Who is Brian Chesky?") +# Answer: Brian Chesky is the co-founder and CEO of Airbnb. +``` diff --git a/mem0-rag/docs/components/data-sources/text-file.mdx b/mem0-rag/docs/components/data-sources/text-file.mdx new file mode 100644 index 0000000000..14b48c0052 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/text-file.mdx @@ -0,0 +1,14 @@ +--- +title: '📄 Text file' +--- + +To add a .txt file, specify the data_type as `text_file`. The URL provided in the first parameter of the `add` function, should be a local path. Eg: + +```python +from embedchain import App + +app = App() +app.add('path/to/file.txt', data_type="text_file") + +app.query("Summarize the information of the text file") +``` \ No newline at end of file diff --git a/mem0-rag/docs/components/data-sources/text.mdx b/mem0-rag/docs/components/data-sources/text.mdx new file mode 100644 index 0000000000..0fda6f5736 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/text.mdx @@ -0,0 +1,17 @@ +--- +title: '📝 Text' +--- + +### Text + +Text is a local data type. To supply your own text, use the data_type as `text` and enter a string. The text is not processed, this can be very versatile. Eg: + +```python +from embedchain import App + +app = App() + +app.add('Seek wealth, not money or status. Wealth is having assets that earn while you sleep. Money is how we transfer time and wealth. Status is your place in the social hierarchy.', data_type='text') +``` + +Note: This is not used in the examples because in most cases you will supply a whole paragraph or file, which did not fit. diff --git a/mem0-rag/docs/components/data-sources/web-page.mdx b/mem0-rag/docs/components/data-sources/web-page.mdx new file mode 100644 index 0000000000..f4a50a9238 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/web-page.mdx @@ -0,0 +1,13 @@ +--- +title: '🌐 HTML Web page' +--- + +To add any web page, use the data_type as `web_page`. Eg: + +```python +from embedchain import App + +app = App() + +app.add('a_valid_web_page_url', data_type='web_page') +``` diff --git a/mem0-rag/docs/components/data-sources/xml.mdx b/mem0-rag/docs/components/data-sources/xml.mdx new file mode 100644 index 0000000000..afe9a41249 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/xml.mdx @@ -0,0 +1,17 @@ +--- +title: '🧾 XML file' +--- + +### XML file + +To add any xml file, use the data_type as `xml`. Eg: + +```python +from embedchain import App + +app = App() + +app.add('content/data.xml') +``` + +Note: Only the text content of the xml file will be added to the app. The tags will be ignored. diff --git a/mem0-rag/docs/components/data-sources/youtube-channel.mdx b/mem0-rag/docs/components/data-sources/youtube-channel.mdx new file mode 100644 index 0000000000..d9f037ff02 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/youtube-channel.mdx @@ -0,0 +1,22 @@ +--- +title: '📽️ Youtube Channel' +--- + +## Setup + +Make sure you have all the required packages installed before using this data type. You can install them by running the following command in your terminal. + +```bash +pip install -U "embedchain[youtube]" +``` + +## Usage + +To add all the videos from a youtube channel to your app, use the data_type as `youtube_channel`. + +```python +from embedchain import App + +app = App() +app.add("@channel_name", data_type="youtube_channel") +``` diff --git a/mem0-rag/docs/components/data-sources/youtube-video.mdx b/mem0-rag/docs/components/data-sources/youtube-video.mdx new file mode 100644 index 0000000000..01ac524061 --- /dev/null +++ b/mem0-rag/docs/components/data-sources/youtube-video.mdx @@ -0,0 +1,22 @@ +--- +title: '📺 Youtube Video' +--- + +## Setup + +Make sure you have all the required packages installed before using this data type. You can install them by running the following command in your terminal. + +```bash +pip install -U "embedchain[youtube]" +``` + +## Usage + +To add any youtube video to your app, use the data_type as `youtube_video`. Eg: + +```python +from embedchain import App + +app = App() +app.add('a_valid_youtube_url_here', data_type='youtube_video') +``` diff --git a/mem0-rag/docs/components/embedding-models.mdx b/mem0-rag/docs/components/embedding-models.mdx new file mode 100644 index 0000000000..5de69c7ba3 --- /dev/null +++ b/mem0-rag/docs/components/embedding-models.mdx @@ -0,0 +1,438 @@ +--- +title: 🧩 Embedding models +--- + +## Overview + +Embedchain supports several embedding models from the following providers: + + + + + + + + + + + + + + +## OpenAI + +To use OpenAI embedding function, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys). + +Once you have obtained the key, you can use it like this: + + + +```python main.py +import os +from embedchain import App + +os.environ['OPENAI_API_KEY'] = 'xxx' + +# load embedding model configuration from config.yaml file +app = App.from_config(config_path="config.yaml") + +app.add("https://en.wikipedia.org/wiki/OpenAI") +app.query("What is OpenAI?") +``` + +```yaml config.yaml +embedder: + provider: openai + config: + model: 'text-embedding-3-small' +``` + + + +* OpenAI announced two new embedding models: `text-embedding-3-small` and `text-embedding-3-large`. Embedchain supports both these models. Below you can find YAML config for both: + + + +```yaml text-embedding-3-small.yaml +embedder: + provider: openai + config: + model: 'text-embedding-3-small' +``` + +```yaml text-embedding-3-large.yaml +embedder: + provider: openai + config: + model: 'text-embedding-3-large' +``` + + + +## Google AI + +To use Google AI embedding function, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey) + + +```python main.py +import os +from embedchain import App + +os.environ["GOOGLE_API_KEY"] = "xxx" + +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +embedder: + provider: google + config: + model: 'models/embedding-001' + task_type: "retrieval_document" + title: "Embeddings for Embedchain" +``` + +
+ +For more details regarding the Google AI embedding model, please refer to the [Google AI documentation](https://ai.google.dev/tutorials/python_quickstart#use_embeddings). + + +## Azure OpenAI + +To use Azure OpenAI embedding model, you have to set some of the azure openai related environment variables as given in the code block below: + + + +```python main.py +import os +from embedchain import App + +os.environ["OPENAI_API_TYPE"] = "azure" +os.environ["AZURE_OPENAI_ENDPOINT"] = "https://xxx.openai.azure.com/" +os.environ["AZURE_OPENAI_API_KEY"] = "xxx" +os.environ["OPENAI_API_VERSION"] = "xxx" + +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: azure_openai + config: + model: gpt-35-turbo + deployment_name: your_llm_deployment_name + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +embedder: + provider: azure_openai + config: + model: text-embedding-ada-002 + deployment_name: you_embedding_model_deployment_name +``` + + +You can find the list of models and deployment name on the [Azure OpenAI Platform](https://oai.azure.com/portal). + +## GPT4ALL + +GPT4All supports generating high quality embeddings of arbitrary length documents of text using a CPU optimized contrastively trained Sentence Transformer. + + + +```python main.py +from embedchain import App + +# load embedding model configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: gpt4all + config: + model: 'orca-mini-3b-gguf2-q4_0.gguf' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +embedder: + provider: gpt4all +``` + + + +## Hugging Face + +Hugging Face supports generating embeddings of arbitrary length documents of text using Sentence Transformer library. Example of how to generate embeddings using hugging face is given below: + + + +```python main.py +from embedchain import App + +# load embedding model configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: huggingface + config: + model: 'google/flan-t5-xxl' + temperature: 0.5 + max_tokens: 1000 + top_p: 0.5 + stream: false + +embedder: + provider: huggingface + config: + model: 'sentence-transformers/all-mpnet-base-v2' + model_kwargs: + trust_remote_code: True # Only use if you trust your embedder +``` + + + +## Vertex AI + +Embedchain supports Google's VertexAI embeddings model through a simple interface. You just have to pass the `model_name` in the config yaml and it would work out of the box. + + + +```python main.py +from embedchain import App + +# load embedding model configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: vertexai + config: + model: 'chat-bison' + temperature: 0.5 + top_p: 0.5 + +embedder: + provider: vertexai + config: + model: 'textembedding-gecko' +``` + + + +## NVIDIA AI + +[NVIDIA AI Foundation Endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/) let you quickly use NVIDIA's AI models, such as Mixtral 8x7B, Llama 2 etc, through our API. These models are available in the [NVIDIA NGC catalog](https://catalog.ngc.nvidia.com/ai-foundation-models), fully optimized and ready to use on NVIDIA's AI platform. They are designed for high speed and easy customization, ensuring smooth performance on any accelerated setup. + + +### Usage + +In order to use embedding models and LLMs from NVIDIA AI, create an account on [NVIDIA NGC Service](https://catalog.ngc.nvidia.com/). + +Generate an API key from their dashboard. Set the API key as `NVIDIA_API_KEY` environment variable. Note that the `NVIDIA_API_KEY` will start with `nvapi-`. + +Below is an example of how to use LLM model and embedding model from NVIDIA AI: + + + +```python main.py +import os +from embedchain import App + +os.environ['NVIDIA_API_KEY'] = 'nvapi-xxxx' + +config = { + "app": { + "config": { + "id": "my-app", + }, + }, + "llm": { + "provider": "nvidia", + "config": { + "model": "nemotron_steerlm_8b", + }, + }, + "embedder": { + "provider": "nvidia", + "config": { + "model": "nvolveqa_40k", + "vector_dimension": 1024, + }, + }, +} + +app = App.from_config(config=config) + +app.add("https://www.forbes.com/profile/elon-musk") +answer = app.query("What is the net worth of Elon Musk today?") +# Answer: The net worth of Elon Musk is subject to fluctuations based on the market value of his holdings in various companies. +# As of March 1, 2024, his net worth is estimated to be approximately $210 billion. However, this figure can change rapidly due to stock market fluctuations and other factors. +# Additionally, his net worth may include other assets such as real estate and art, which are not reflected in his stock portfolio. +``` + + + +## Cohere + +To use embedding models and LLMs from COHERE, create an account on [COHERE](https://dashboard.cohere.com/welcome/login?redirect_uri=%2Fapi-keys). + +Generate an API key from their dashboard. Set the API key as `COHERE_API_KEY` environment variable. + +Once you have obtained the key, you can use it like this: + + + +```python main.py +import os +from embedchain import App + +os.environ['COHERE_API_KEY'] = 'xxx' + +# load embedding model configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +embedder: + provider: cohere + config: + model: 'embed-english-light-v3.0' +``` + + + +* Cohere has few embedding models: `embed-english-v3.0`, `embed-multilingual-v3.0`, `embed-multilingual-light-v3.0`, `embed-english-v2.0`, `embed-english-light-v2.0` and `embed-multilingual-v2.0`. Embedchain supports all these models. Below you can find YAML config for all: + + + +```yaml embed-english-v3.0.yaml +embedder: + provider: cohere + config: + model: 'embed-english-v3.0' + vector_dimension: 1024 +``` + +```yaml embed-multilingual-v3.0.yaml +embedder: + provider: cohere + config: + model: 'embed-multilingual-v3.0' + vector_dimension: 1024 +``` + +```yaml embed-multilingual-light-v3.0.yaml +embedder: + provider: cohere + config: + model: 'embed-multilingual-light-v3.0' + vector_dimension: 384 +``` + +```yaml embed-english-v2.0.yaml +embedder: + provider: cohere + config: + model: 'embed-english-v2.0' + vector_dimension: 4096 +``` + +```yaml embed-english-light-v2.0.yaml +embedder: + provider: cohere + config: + model: 'embed-english-light-v2.0' + vector_dimension: 1024 +``` + +```yaml embed-multilingual-v2.0.yaml +embedder: + provider: cohere + config: + model: 'embed-multilingual-v2.0' + vector_dimension: 768 +``` + + + +## Ollama + +Ollama enables the use of embedding models, allowing you to generate high-quality embeddings directly on your local machine. Make sure to install [Ollama](https://ollama.com/download) and keep it running before using the embedding model. + +You can find the list of models at [Ollama Embedding Models](https://ollama.com/blog/embedding-models). + +Below is an example of how to use embedding model Ollama: + + + +```python main.py +import os +from embedchain import App + +# load embedding model configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +embedder: + provider: ollama + config: + model: 'all-minilm:latest' +``` + + + +## Clarifai + +Install related dependencies using the following command: + +```bash +pip install --upgrade 'embedchain[clarifai]' +``` + +set the `CLARIFAI_PAT` as environment variable which you can find in the [security page](https://clarifai.com/settings/security). Optionally you can also pass the PAT key as parameters in LLM/Embedder class. + +Now you are all set with exploring Embedchain. + + + +```python main.py +import os +from embedchain import App + +os.environ["CLARIFAI_PAT"] = "XXX" + +# load llm and embedder configuration from config.yaml file +app = App.from_config(config_path="config.yaml") + +#Now let's add some data. +app.add("https://www.forbes.com/profile/elon-musk") + +#Query the app +response = app.query("what college degrees does elon musk have?") +``` +Head to [Clarifai Platform](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22output_fields%22%2C%22value%22%3A%5B%22embeddings%22%5D%7D%5D) to explore all the State of the Art embedding models available to use. +For passing LLM model inference parameters use `model_kwargs` argument in the config file. Also you can use `api_key` argument to pass `CLARIFAI_PAT` in the config. + +```yaml config.yaml +llm: + provider: clarifai + config: + model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct" + model_kwargs: + temperature: 0.5 + max_tokens: 1000 +embedder: + provider: clarifai + config: + model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15" +``` + \ No newline at end of file diff --git a/mem0-rag/docs/components/evaluation.mdx b/mem0-rag/docs/components/evaluation.mdx new file mode 100644 index 0000000000..c1143d2ecd --- /dev/null +++ b/mem0-rag/docs/components/evaluation.mdx @@ -0,0 +1,275 @@ +--- +title: 🔬 Evaluation +--- + +## Overview + +We provide out-of-the-box evaluation metrics for your RAG application. You can use them to evaluate your RAG applications and compare against different settings of your production RAG application. + +Currently, we provide support for following evaluation metrics: + + + + + + + + +## Quickstart + +Here is a basic example of running evaluation: + +```python example.py +from embedchain import App + +app = App() + +# Add data sources +app.add("https://www.forbes.com/profile/elon-musk") + +# Run evaluation +app.evaluate(["What is the net worth of Elon Musk?", "How many companies Elon Musk owns?"]) +# {'answer_relevancy': 0.9987286412340826, 'groundedness': 1.0, 'context_relevancy': 0.3571428571428571} +``` + +Under the hood, Embedchain does the following: + +1. Runs semantic search in the vector database and fetches context +2. LLM call with question, context to fetch the answer +3. Run evaluation on following metrics: `context relevancy`, `groundedness`, and `answer relevancy` and return result + +## Advanced Usage + +We use OpenAI's `gpt-4` model as default LLM model for automatic evaluation. Hence, we require you to set `OPENAI_API_KEY` as an environment variable. + +### Step-1: Create dataset + +In order to evaluate your RAG application, you have to setup a dataset. A data point in the dataset consists of `questions`, `contexts`, `answer`. Here is an example of how to create a dataset for evaluation: + +```python +from embedchain.utils.eval import EvalData + +data = [ + { + "question": "What is the net worth of Elon Musk?", + "contexts": [ + "Elon Musk PROFILEElon MuskCEO, ...", + "a Twitter poll on whether the journalists' ...", + "2016 and run by Jared Birchall.[335]...", + ], + "answer": "As of the information provided, Elon Musk's net worth is $241.6 billion.", + }, + { + "question": "which companies does Elon Musk own?", + "contexts": [ + "of December 2023[update], ...", + "ThielCofounderView ProfileTeslaHolds ...", + "Elon Musk PROFILEElon MuskCEO, ...", + ], + "answer": "Elon Musk owns several companies, including Tesla, SpaceX, Neuralink, and The Boring Company.", + }, +] + +dataset = [] + +for d in data: + eval_data = EvalData(question=d["question"], contexts=d["contexts"], answer=d["answer"]) + dataset.append(eval_data) +``` + +### Step-2: Run evaluation + +Once you have created your dataset, you can run evaluation on the dataset by picking the metric you want to run evaluation on. + +For example, you can run evaluation on context relevancy metric using the following code: + +```python +from embedchain.evaluation.metrics import ContextRelevance +metric = ContextRelevance() +score = metric.evaluate(dataset) +print(score) +``` + +You can choose a different metric or write your own to run evaluation on. You can check the following links: + +- [Context Relevancy](#context_relevancy) +- [Answer relenvancy](#answer_relevancy) +- [Groundedness](#groundedness) +- [Build your own metric](#custom_metric) + +## Metrics + +### Context Relevancy + +Context relevancy is a metric to determine "how relevant the context is to the question". We use OpenAI's `gpt-4` model to determine the relevancy of the context. We achieve this by prompting the model with the question and the context and asking it to return relevant sentences from the context. We then use the following formula to determine the score: + +``` +context_relevance_score = num_relevant_sentences_in_context / num_of_sentences_in_context +``` + +#### Examples + +You can run the context relevancy evaluation with the following simple code: + +```python +from embedchain.evaluation.metrics import ContextRelevance + +metric = ContextRelevance() +score = metric.evaluate(dataset) # 'dataset' is definted in the create dataset section +print(score) +# 0.27975528364849833 +``` + +In the above example, we used sensible defaults for the evaluation. However, you can also configure the evaluation metric as per your needs using the `ContextRelevanceConfig` class. + +Here is a more advanced example of how to pass a custom evaluation config for evaluating on context relevance metric: + +```python +from embedchain.config.evaluation.base import ContextRelevanceConfig +from embedchain.evaluation.metrics import ContextRelevance + +eval_config = ContextRelevanceConfig(model="gpt-4", api_key="sk-xxx", language="en") +metric = ContextRelevance(config=eval_config) +metric.evaluate(dataset) +``` + +#### `ContextRelevanceConfig` + + + The model to use for the evaluation. Defaults to `gpt-4`. We only support openai's models for now. + + + The openai api key to use for the evaluation. Defaults to `None`. If not provided, we will use the `OPENAI_API_KEY` environment variable. + + + The language of the dataset being evaluated. We need this to determine the understand the context provided in the dataset. Defaults to `en`. + + + The prompt to extract the relevant sentences from the context. Defaults to `CONTEXT_RELEVANCY_PROMPT`, which can be found at `embedchain.config.evaluation.base` path. + + + +### Answer Relevancy + +Answer relevancy is a metric to determine how relevant the answer is to the question. We prompt the model with the answer and asking it to generate questions from the answer. We then use the cosine similarity between the generated questions and the original question to determine the score. + +``` +answer_relevancy_score = mean(cosine_similarity(generated_questions, original_question)) +``` + +#### Examples + +You can run the answer relevancy evaluation with the following simple code: + +```python +from embedchain.evaluation.metrics import AnswerRelevance + +metric = AnswerRelevance() +score = metric.evaluate(dataset) +print(score) +# 0.9505334177461916 +``` + +In the above example, we used sensible defaults for the evaluation. However, you can also configure the evaluation metric as per your needs using the `AnswerRelevanceConfig` class. Here is a more advanced example where you can provide your own evaluation config: + +```python +from embedchain.config.evaluation.base import AnswerRelevanceConfig +from embedchain.evaluation.metrics import AnswerRelevance + +eval_config = AnswerRelevanceConfig( + model='gpt-4', + embedder="text-embedding-ada-002", + api_key="sk-xxx", + num_gen_questions=2 +) +metric = AnswerRelevance(config=eval_config) +score = metric.evaluate(dataset) +``` + +#### `AnswerRelevanceConfig` + + + The model to use for the evaluation. Defaults to `gpt-4`. We only support openai's models for now. + + + The embedder to use for embedding the text. Defaults to `text-embedding-ada-002`. We only support openai's embedders for now. + + + The openai api key to use for the evaluation. Defaults to `None`. If not provided, we will use the `OPENAI_API_KEY` environment variable. + + + The number of questions to generate for each answer. We use the generated questions to compare the similarity with the original question to determine the score. Defaults to `1`. + + + The prompt to extract the `num_gen_questions` number of questions from the provided answer. Defaults to `ANSWER_RELEVANCY_PROMPT`, which can be found at `embedchain.config.evaluation.base` path. + + +## Groundedness + +Groundedness is a metric to determine how grounded the answer is to the context. We use OpenAI's `gpt-4` model to determine the groundedness of the answer. We achieve this by prompting the model with the answer and asking it to generate claims from the answer. We then again prompt the model with the context and the generated claims to determine the verdict on the claims. We then use the following formula to determine the score: + +``` +groundedness_score = (sum of all verdicts) / (total # of claims) +``` + +You can run the groundedness evaluation with the following simple code: + +```python +from embedchain.evaluation.metrics import Groundedness +metric = Groundedness() +score = metric.evaluate(dataset) # dataset from above +print(score) +# 1.0 +``` + +In the above example, we used sensible defaults for the evaluation. However, you can also configure the evaluation metric as per your needs using the `GroundednessConfig` class. Here is a more advanced example where you can configure the evaluation config: + +```python +from embedchain.config.evaluation.base import GroundednessConfig +from embedchain.evaluation.metrics import Groundedness + +eval_config = GroundednessConfig(model='gpt-4', api_key="sk-xxx") +metric = Groundedness(config=eval_config) +score = metric.evaluate(dataset) +``` + + +#### `GroundednessConfig` + + + The model to use for the evaluation. Defaults to `gpt-4`. We only support openai's models for now. + + + The openai api key to use for the evaluation. Defaults to `None`. If not provided, we will use the `OPENAI_API_KEY` environment variable. + + + The prompt to extract the claims from the provided answer. Defaults to `GROUNDEDNESS_ANSWER_CLAIMS_PROMPT`, which can be found at `embedchain.config.evaluation.base` path. + + + The prompt to get verdicts on the claims from the answer from the given context. Defaults to `GROUNDEDNESS_CLAIMS_INFERENCE_PROMPT`, which can be found at `embedchain.config.evaluation.base` path. + + +## Custom + +You can also create your own evaluation metric by extending the `BaseMetric` class. You can find the source code for the existing metrics at `embedchain.evaluation.metrics` path. + + +You must provide the `name` of your custom metric in the `__init__` method of your class. This name will be used to identify your metric in the evaluation report. + + +```python +from typing import Optional + +from embedchain.config.base_config import BaseConfig +from embedchain.evaluation.metrics import BaseMetric +from embedchain.utils.eval import EvalData + +class MyCustomMetric(BaseMetric): + def __init__(self, config: Optional[BaseConfig] = None): + super().__init__(name="my_custom_metric") + + def evaluate(self, dataset: list[EvalData]): + score = 0.0 + # write your evaluation logic here + return score +``` diff --git a/mem0-rag/docs/components/introduction.mdx b/mem0-rag/docs/components/introduction.mdx new file mode 100644 index 0000000000..3f9122b5d2 --- /dev/null +++ b/mem0-rag/docs/components/introduction.mdx @@ -0,0 +1,13 @@ +--- +title: 🧩 Introduction +--- + +## Overview + +You can configure following components + +* [Data Source](/components/data-sources/overview) +* [LLM](/components/llms) +* [Embedding Model](/components/embedding-models) +* [Vector Database](/components/vector-databases) +* [Evaluation](/components/evaluation) diff --git a/mem0-rag/docs/components/llms.mdx b/mem0-rag/docs/components/llms.mdx new file mode 100644 index 0000000000..c2cae52c81 --- /dev/null +++ b/mem0-rag/docs/components/llms.mdx @@ -0,0 +1,901 @@ +--- +title: 🤖 Large language models (LLMs) +--- + +## Overview + +Embedchain comes with built-in support for various popular large language models. We handle the complexity of integrating these models for you, allowing you to easily customize your language model interactions through a user-friendly interface. + + + + + + + + + + + + + + + + + + + + + + +## OpenAI + +To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys). + +Once you have obtained the key, you can use it like this: + +```python +import os +from embedchain import App + +os.environ['OPENAI_API_KEY'] = 'xxx' + +app = App() +app.add("https://en.wikipedia.org/wiki/OpenAI") +app.query("What is OpenAI?") +``` + +If you are looking to configure the different parameters of the LLM, you can do so by loading the app using a [yaml config](https://github.com/embedchain/embedchain/blob/main/configs/chroma.yaml) file. + + + +```python main.py +import os +from embedchain import App + +os.environ['OPENAI_API_KEY'] = 'xxx' + +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: openai + config: + model: 'gpt-3.5-turbo' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false +``` + + +### Function Calling +Embedchain supports OpenAI [Function calling](https://platform.openai.com/docs/guides/function-calling) with a single function. It accepts inputs in accordance with the [Langchain interface](https://python.langchain.com/docs/modules/model_io/chat/function_calling#legacy-args-functions-and-function_call). + + + ```python + from pydantic import BaseModel + + class multiply(BaseModel): + """Multiply two integers together.""" + + a: int = Field(..., description="First integer") + b: int = Field(..., description="Second integer") + ``` + + + + ```python + def multiply(a: int, b: int) -> int: + """Multiply two integers together. + + Args: + a: First integer + b: Second integer + """ + return a * b + ``` + + + ```python + multiply = { + "type": "function", + "function": { + "name": "multiply", + "description": "Multiply two integers together.", + "parameters": { + "type": "object", + "properties": { + "a": { + "description": "First integer", + "type": "integer" + }, + "b": { + "description": "Second integer", + "type": "integer" + } + }, + "required": [ + "a", + "b" + ] + } + } + } + ``` + + +With any of the previous inputs, the OpenAI LLM can be queried to provide the appropriate arguments for the function. + +```python +import os +from embedchain import App +from embedchain.llm.openai import OpenAILlm + +os.environ["OPENAI_API_KEY"] = "sk-xxx" + +llm = OpenAILlm(tools=multiply) +app = App(llm=llm) + +result = app.query("What is the result of 125 multiplied by fifteen?") +``` + +## Google AI + +To use Google AI model, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey) + + +```python main.py +import os +from embedchain import App + +os.environ["GOOGLE_API_KEY"] = "xxx" + +app = App.from_config(config_path="config.yaml") + +app.add("https://www.forbes.com/profile/elon-musk") + +response = app.query("What is the net worth of Elon Musk?") +if app.llm.config.stream: # if stream is enabled, response is a generator + for chunk in response: + print(chunk) +else: + print(response) +``` + +```yaml config.yaml +llm: + provider: google + config: + model: gemini-pro + max_tokens: 1000 + temperature: 0.5 + top_p: 1 + stream: false + +embedder: + provider: google + config: + model: 'models/embedding-001' + task_type: "retrieval_document" + title: "Embeddings for Embedchain" +``` + + +## Azure OpenAI + +To use Azure OpenAI model, you have to set some of the azure openai related environment variables as given in the code block below: + + + +```python main.py +import os +from embedchain import App + +os.environ["OPENAI_API_TYPE"] = "azure" +os.environ["AZURE_OPENAI_ENDPOINT"] = "https://xxx.openai.azure.com/" +os.environ["AZURE_OPENAI_KEY"] = "xxx" +os.environ["OPENAI_API_VERSION"] = "xxx" + +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: azure_openai + config: + model: gpt-3.5-turbo + deployment_name: your_llm_deployment_name + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +embedder: + provider: azure_openai + config: + model: text-embedding-ada-002 + deployment_name: you_embedding_model_deployment_name +``` + + +You can find the list of models and deployment name on the [Azure OpenAI Platform](https://oai.azure.com/portal). + +## Anthropic + +To use anthropic's model, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys). + + + +```python main.py +import os +from embedchain import App + +os.environ["ANTHROPIC_API_KEY"] = "xxx" + +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: anthropic + config: + model: 'claude-instant-1' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false +``` + + + +## Cohere + +Install related dependencies using the following command: + +```bash +pip install --upgrade 'embedchain[cohere]' +``` + +Set the `COHERE_API_KEY` as environment variable which you can find on their [Account settings page](https://dashboard.cohere.com/api-keys). + +Once you have the API key, you are all set to use it with Embedchain. + + + +```python main.py +import os +from embedchain import App + +os.environ["COHERE_API_KEY"] = "xxx" + +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: cohere + config: + model: large + temperature: 0.5 + max_tokens: 1000 + top_p: 1 +``` + + + +## Together + +Install related dependencies using the following command: + +```bash +pip install --upgrade 'embedchain[together]' +``` + +Set the `TOGETHER_API_KEY` as environment variable which you can find on their [Account settings page](https://api.together.xyz/settings/api-keys). + +Once you have the API key, you are all set to use it with Embedchain. + + + +```python main.py +import os +from embedchain import App + +os.environ["TOGETHER_API_KEY"] = "xxx" + +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: together + config: + model: togethercomputer/RedPajama-INCITE-7B-Base + temperature: 0.5 + max_tokens: 1000 + top_p: 1 +``` + + + +## Ollama + +Setup Ollama using https://github.com/jmorganca/ollama + + + +```python main.py +import os +os.environ["OLLAMA_HOST"] = "http://127.0.0.1:11434" +from embedchain import App + +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: ollama + config: + model: 'llama2' + temperature: 0.5 + top_p: 1 + stream: true + base_url: 'http://localhost:11434' +embedder: + provider: ollama + config: + model: znbang/bge:small-en-v1.5-q8_0 + base_url: http://localhost:11434 + +``` + + + + +## vLLM + +Setup vLLM by following instructions given in [their docs](https://docs.vllm.ai/en/latest/getting_started/installation.html). + + + +```python main.py +import os +from embedchain import App + +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: vllm + config: + model: 'meta-llama/Llama-2-70b-hf' + temperature: 0.5 + top_p: 1 + top_k: 10 + stream: true + trust_remote_code: true +``` + + + +## Clarifai + +Install related dependencies using the following command: + +```bash +pip install --upgrade 'embedchain[clarifai]' +``` + +set the `CLARIFAI_PAT` as environment variable which you can find in the [security page](https://clarifai.com/settings/security). Optionally you can also pass the PAT key as parameters in LLM/Embedder class. + +Now you are all set with exploring Embedchain. + + + +```python main.py +import os +from embedchain import App + +os.environ["CLARIFAI_PAT"] = "XXX" + +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") + +#Now let's add some data. +app.add("https://www.forbes.com/profile/elon-musk") + +#Query the app +response = app.query("what college degrees does elon musk have?") +``` +Head to [Clarifai Platform](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22use_cases%22%2C%22value%22%3A%5B%22llm%22%5D%7D%5D) to browse various State-of-the-Art LLM models for your use case. +For passing model inference parameters use `model_kwargs` argument in the config file. Also you can use `api_key` argument to pass `CLARIFAI_PAT` in the config. + +```yaml config.yaml +llm: + provider: clarifai + config: + model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct" + model_kwargs: + temperature: 0.5 + max_tokens: 1000 +embedder: + provider: clarifai + config: + model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15" +``` + + + +## GPT4ALL + +Install related dependencies using the following command: + +```bash +pip install --upgrade 'embedchain[opensource]' +``` + +GPT4all is a free-to-use, locally running, privacy-aware chatbot. No GPU or internet required. You can use this with Embedchain using the following code: + + + +```python main.py +from embedchain import App + +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: gpt4all + config: + model: 'orca-mini-3b-gguf2-q4_0.gguf' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +embedder: + provider: gpt4all +``` + + + +## JinaChat + +First, set `JINACHAT_API_KEY` in environment variable which you can obtain from [their platform](https://chat.jina.ai/api). + +Once you have the key, load the app using the config yaml file: + + + +```python main.py +import os +from embedchain import App + +os.environ["JINACHAT_API_KEY"] = "xxx" +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: jina + config: + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false +``` + + + +## Hugging Face + + +Install related dependencies using the following command: + +```bash +pip install --upgrade 'embedchain[huggingface-hub]' +``` + +First, set `HUGGINGFACE_ACCESS_TOKEN` in environment variable which you can obtain from [their platform](https://huggingface.co/settings/tokens). + +You can load the LLMs from Hugging Face using three ways: + +- [Hugging Face Hub](#hugging-face-hub) +- [Hugging Face Local Pipelines](#hugging-face-local-pipelines) +- [Hugging Face Inference Endpoint](#hugging-face-inference-endpoint) + +### Hugging Face Hub + +To load the model from Hugging Face Hub, use the following code: + + + +```python main.py +import os +from embedchain import App + +os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "xxx" + +config = { + "app": {"config": {"id": "my-app"}}, + "llm": { + "provider": "huggingface", + "config": { + "model": "bigscience/bloom-1b7", + "top_p": 0.5, + "max_length": 200, + "temperature": 0.1, + }, + }, +} + +app = App.from_config(config=config) +``` + + +### Hugging Face Local Pipelines + +If you want to load the locally downloaded model from Hugging Face, you can do so by following the code provided below: + + +```python main.py +from embedchain import App + +config = { + "app": {"config": {"id": "my-app"}}, + "llm": { + "provider": "huggingface", + "config": { + "model": "Trendyol/Trendyol-LLM-7b-chat-v0.1", + "local": True, # Necessary if you want to run model locally + "top_p": 0.5, + "max_tokens": 1000, + "temperature": 0.1, + }, + } +} +app = App.from_config(config=config) +``` + + +### Hugging Face Inference Endpoint + +You can also use [Hugging Face Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index#-inference-endpoints) to access custom endpoints. First, set the `HUGGINGFACE_ACCESS_TOKEN` as above. + +Then, load the app using the config yaml file: + + + +```python main.py +from embedchain import App + +config = { + "app": {"config": {"id": "my-app"}}, + "llm": { + "provider": "huggingface", + "config": { + "endpoint": "https://api-inference.huggingface.co/models/gpt2", + "model_params": {"temprature": 0.1, "max_new_tokens": 100} + }, + }, +} +app = App.from_config(config=config) + +``` + + +Currently only supports `text-generation` and `text2text-generation` for now [[ref](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html?highlight=huggingfaceendpoint#)]. + +See langchain's [hugging face endpoint](https://python.langchain.com/docs/integrations/chat/huggingface#huggingfaceendpoint) for more information. + +## Llama2 + +Llama2 is integrated through [Replicate](https://replicate.com/). Set `REPLICATE_API_TOKEN` in environment variable which you can obtain from [their platform](https://replicate.com/account/api-tokens). + +Once you have the token, load the app using the config yaml file: + + + +```python main.py +import os +from embedchain import App + +os.environ["REPLICATE_API_TOKEN"] = "xxx" + +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: llama2 + config: + model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5' + temperature: 0.5 + max_tokens: 1000 + top_p: 0.5 + stream: false +``` + + +## Vertex AI + +Setup Google Cloud Platform application credentials by following the instruction on [GCP](https://cloud.google.com/docs/authentication/external/set-up-adc). Once setup is done, use the following code to create an app using VertexAI as provider: + + + +```python main.py +from embedchain import App + +# load llm configuration from config.yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: vertexai + config: + model: 'chat-bison' + temperature: 0.5 + top_p: 0.5 +``` + + + +## Mistral AI + +Obtain the Mistral AI api key from their [console](https://console.mistral.ai/). + + + + ```python main.py +os.environ["MISTRAL_API_KEY"] = "xxx" + +app = App.from_config(config_path="config.yaml") + +app.add("https://www.forbes.com/profile/elon-musk") + +response = app.query("what is the net worth of Elon Musk?") +# As of January 16, 2024, Elon Musk's net worth is $225.4 billion. + +response = app.chat("which companies does elon own?") +# Elon Musk owns Tesla, SpaceX, Boring Company, Twitter, and X. + +response = app.chat("what question did I ask you already?") +# You have asked me several times already which companies Elon Musk owns, specifically Tesla, SpaceX, Boring Company, Twitter, and X. +``` + +```yaml config.yaml +llm: + provider: mistralai + config: + model: mistral-tiny + temperature: 0.5 + max_tokens: 1000 + top_p: 1 +embedder: + provider: mistralai + config: + model: mistral-embed +``` + + + +## AWS Bedrock + +### Setup +- Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess). +- You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials) +- You can optionally export an `AWS_REGION` + + +### Usage + + + +```python main.py +import os +from embedchain import App + +os.environ["AWS_ACCESS_KEY_ID"] = "xxx" +os.environ["AWS_SECRET_ACCESS_KEY"] = "xxx" +os.environ["AWS_REGION"] = "us-west-2" + +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +llm: + provider: aws_bedrock + config: + model: amazon.titan-text-express-v1 + # check notes below for model_kwargs + model_kwargs: + temperature: 0.5 + topP: 1 + maxTokenCount: 1000 +``` + + +
+ + The model arguments are different for each providers. Please refer to the [AWS Bedrock Documentation](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/providers) to find the appropriate arguments for your model. + + +
+ +## Groq + +[Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine. + + +### Usage + +In order to use LLMs from Groq, go to their [platform](https://console.groq.com/keys) and get the API key. + +Set the API key as `GROQ_API_KEY` environment variable or pass in your app configuration to use the model as given below in the example. + + + +```python main.py +import os +from embedchain import App + +# Set your API key here or pass as the environment variable +groq_api_key = "gsk_xxxx" + +config = { + "llm": { + "provider": "groq", + "config": { + "model": "mixtral-8x7b-32768", + "api_key": groq_api_key, + "stream": True + } + } +} + +app = App.from_config(config=config) +# Add your data source here +app.add("https://docs.embedchain.ai/sitemap.xml", data_type="sitemap") +app.query("Write a poem about Embedchain") + +# In the realm of data, vast and wide, +# Embedchain stands with knowledge as its guide. +# A platform open, for all to try, +# Building bots that can truly fly. + +# With REST API, data in reach, +# Deployment a breeze, as easy as a speech. +# Updating data sources, anytime, anyday, +# Embedchain's power, never sway. + +# A knowledge base, an assistant so grand, +# Connecting to platforms, near and far. +# Discord, WhatsApp, Slack, and more, +# Embedchain's potential, never a bore. +``` + + +## NVIDIA AI + +[NVIDIA AI Foundation Endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/) let you quickly use NVIDIA's AI models, such as Mixtral 8x7B, Llama 2 etc, through our API. These models are available in the [NVIDIA NGC catalog](https://catalog.ngc.nvidia.com/ai-foundation-models), fully optimized and ready to use on NVIDIA's AI platform. They are designed for high speed and easy customization, ensuring smooth performance on any accelerated setup. + + +### Usage + +In order to use LLMs from NVIDIA AI, create an account on [NVIDIA NGC Service](https://catalog.ngc.nvidia.com/). + +Generate an API key from their dashboard. Set the API key as `NVIDIA_API_KEY` environment variable. Note that the `NVIDIA_API_KEY` will start with `nvapi-`. + +Below is an example of how to use LLM model and embedding model from NVIDIA AI: + + + +```python main.py +import os +from embedchain import App + +os.environ['NVIDIA_API_KEY'] = 'nvapi-xxxx' + +config = { + "app": { + "config": { + "id": "my-app", + }, + }, + "llm": { + "provider": "nvidia", + "config": { + "model": "nemotron_steerlm_8b", + }, + }, + "embedder": { + "provider": "nvidia", + "config": { + "model": "nvolveqa_40k", + "vector_dimension": 1024, + }, + }, +} + +app = App.from_config(config=config) + +app.add("https://www.forbes.com/profile/elon-musk") +answer = app.query("What is the net worth of Elon Musk today?") +# Answer: The net worth of Elon Musk is subject to fluctuations based on the market value of his holdings in various companies. +# As of March 1, 2024, his net worth is estimated to be approximately $210 billion. However, this figure can change rapidly due to stock market fluctuations and other factors. +# Additionally, his net worth may include other assets such as real estate and art, which are not reflected in his stock portfolio. +``` + + +## Token Usage + +You can get the cost of the query by setting `token_usage` to `True` in the config file. This will return the token details: `prompt_tokens`, `completion_tokens`, `total_tokens`, `total_cost`, `cost_currency`. +The list of paid LLMs that support token usage are: +- OpenAI +- Vertex AI +- Anthropic +- Cohere +- Together +- Groq +- Mistral AI +- NVIDIA AI + +Here is an example of how to use token usage: + + +```python main.py +os.environ["OPENAI_API_KEY"] = "xxx" + +app = App.from_config(config_path="config.yaml") + +app.add("https://www.forbes.com/profile/elon-musk") + +response = app.query("what is the net worth of Elon Musk?") +# {'answer': 'Elon Musk's net worth is $209.9 billion as of 6/9/24.', +# 'usage': {'prompt_tokens': 1228, +# 'completion_tokens': 21, +# 'total_tokens': 1249, +# 'total_cost': 0.001884, +# 'cost_currency': 'USD'} +# } + + +response = app.chat("Which companies did Elon Musk found?") +# {'answer': 'Elon Musk founded six companies, including Tesla, which is an electric car maker, SpaceX, a rocket producer, and the Boring Company, a tunneling startup.', +# 'usage': {'prompt_tokens': 1616, +# 'completion_tokens': 34, +# 'total_tokens': 1650, +# 'total_cost': 0.002492, +# 'cost_currency': 'USD'} +# } +``` + +```yaml config.yaml +llm: + provider: openai + config: + model: gpt-3.5-turbo + temperature: 0.5 + max_tokens: 1000 + token_usage: true +``` + + +If a model is missing and you'd like to add it to `model_prices_and_context_window.json`, please feel free to open a PR. + +
+ + diff --git a/mem0-rag/docs/components/retrieval-methods.mdx b/mem0-rag/docs/components/retrieval-methods.mdx new file mode 100644 index 0000000000..e69de29bb2 diff --git a/mem0-rag/docs/components/vector-databases.mdx b/mem0-rag/docs/components/vector-databases.mdx new file mode 100644 index 0000000000..c889e10540 --- /dev/null +++ b/mem0-rag/docs/components/vector-databases.mdx @@ -0,0 +1,20 @@ +--- +title: 🗄️ Vector databases +--- + +## Overview + +Utilizing a vector database alongside Embedchain is a seamless process. All you need to do is configure it within the YAML configuration file. We've provided examples for each supported database below: + + + + + + + + + + + + + diff --git a/mem0-rag/docs/components/vector-databases/chromadb.mdx b/mem0-rag/docs/components/vector-databases/chromadb.mdx new file mode 100644 index 0000000000..783dfe8901 --- /dev/null +++ b/mem0-rag/docs/components/vector-databases/chromadb.mdx @@ -0,0 +1,35 @@ +--- +title: ChromaDB +--- + + + +```python main.py +from embedchain import App + +# load chroma configuration from yaml file +app = App.from_config(config_path="config1.yaml") +``` + +```yaml config1.yaml +vectordb: + provider: chroma + config: + collection_name: 'my-collection' + dir: db + allow_reset: true +``` + +```yaml config2.yaml +vectordb: + provider: chroma + config: + collection_name: 'my-collection' + host: localhost + port: 5200 + allow_reset: true +``` + + + + diff --git a/mem0-rag/docs/components/vector-databases/elasticsearch.mdx b/mem0-rag/docs/components/vector-databases/elasticsearch.mdx new file mode 100644 index 0000000000..0a354e65f8 --- /dev/null +++ b/mem0-rag/docs/components/vector-databases/elasticsearch.mdx @@ -0,0 +1,39 @@ +--- +title: Elasticsearch +--- + +Install related dependencies using the following command: + +```bash +pip install --upgrade 'embedchain[elasticsearch]' +``` + + +You can configure the Elasticsearch connection by providing either `es_url` or `cloud_id`. If you are using the Elasticsearch Service on Elastic Cloud, you can find the `cloud_id` on the [Elastic Cloud dashboard](https://cloud.elastic.co/deployments). + + +You can authorize the connection to Elasticsearch by providing either `basic_auth`, `api_key`, or `bearer_auth`. + + + +```python main.py +from embedchain import App + +# load elasticsearch configuration from yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +vectordb: + provider: elasticsearch + config: + collection_name: 'es-index' + cloud_id: 'deployment-name:xxxx' + basic_auth: + - elastic + - + verify_certs: false +``` + + + diff --git a/mem0-rag/docs/components/vector-databases/lancedb.mdx b/mem0-rag/docs/components/vector-databases/lancedb.mdx new file mode 100644 index 0000000000..97af57dfe2 --- /dev/null +++ b/mem0-rag/docs/components/vector-databases/lancedb.mdx @@ -0,0 +1,100 @@ +--- +title: LanceDB +--- + +## Install Embedchain with LanceDB + +Install Embedchain, LanceDB and related dependencies using the following command: + +```bash +pip install "embedchain[lancedb]" +``` + +LanceDB is a developer-friendly, open source database for AI. From hyper scalable vector search and advanced retrieval for RAG, to streaming training data and interactive exploration of large scale AI datasets. +In order to use LanceDB as vector database, not need to set any key for local use. + +### With OPENAI + + +```python main.py +import os +from embedchain import App + +# set OPENAI_API_KEY as env variable +os.environ["OPENAI_API_KEY"] = "sk-xxx" + +# create Embedchain App and set config +app = App.from_config(config={ + "vectordb": { + "provider": "lancedb", + "config": { + "collection_name": "lancedb-index" + } + } + } +) + +# add data source and start query in +app.add("https://www.forbes.com/profile/elon-musk") + +# query continuously +while(True): + question = input("Enter question: ") + if question in ['q', 'exit', 'quit']: + break + answer = app.query(question) + print(answer) +``` + + + +### With Local LLM + + +```python main.py +from embedchain import Pipeline as App + +# config for Embedchain App +config = { + 'llm': { + 'provider': 'huggingface', + 'config': { + 'model': 'mistralai/Mistral-7B-v0.1', + 'temperature': 0.1, + 'max_tokens': 250, + 'top_p': 0.1, + 'stream': True + } + }, + 'embedder': { + 'provider': 'huggingface', + 'config': { + 'model': 'sentence-transformers/all-mpnet-base-v2' + } + }, + 'vectordb': { + 'provider': 'lancedb', + 'config': { + 'collection_name': 'lancedb-index' + } + } +} + +app = App.from_config(config=config) + +# add data source and start query in +app.add("https://www.tesla.com/ns_videos/2022-tesla-impact-report.pdf") + +# query continuously +while(True): + question = input("Enter question: ") + if question in ['q', 'exit', 'quit']: + break + answer = app.query(question) + print(answer) +``` + + + + + \ No newline at end of file diff --git a/mem0-rag/docs/components/vector-databases/opensearch.mdx b/mem0-rag/docs/components/vector-databases/opensearch.mdx new file mode 100644 index 0000000000..8f68669774 --- /dev/null +++ b/mem0-rag/docs/components/vector-databases/opensearch.mdx @@ -0,0 +1,36 @@ +--- +title: OpenSearch +--- + +Install related dependencies using the following command: + +```bash +pip install --upgrade 'embedchain[opensearch]' +``` + + + +```python main.py +from embedchain import App + +# load opensearch configuration from yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +vectordb: + provider: opensearch + config: + collection_name: 'my-app' + opensearch_url: 'https://localhost:9200' + http_auth: + - admin + - admin + vector_dimension: 1536 + use_ssl: false + verify_certs: false +``` + + + + diff --git a/mem0-rag/docs/components/vector-databases/pinecone.mdx b/mem0-rag/docs/components/vector-databases/pinecone.mdx new file mode 100644 index 0000000000..d21ebfeac6 --- /dev/null +++ b/mem0-rag/docs/components/vector-databases/pinecone.mdx @@ -0,0 +1,109 @@ +--- +title: Pinecone +--- + +## Overview + +Install pinecone related dependencies using the following command: + +```bash +pip install --upgrade 'pinecone-client pinecone-text' +``` + +In order to use Pinecone as vector database, set the environment variable `PINECONE_API_KEY` which you can find on [Pinecone dashboard](https://app.pinecone.io/). + + + +```python main.py +from embedchain import App + +# Load pinecone configuration from yaml file +app = App.from_config(config_path="pod_config.yaml") +# Or +app = App.from_config(config_path="serverless_config.yaml") +``` + +```yaml pod_config.yaml +vectordb: + provider: pinecone + config: + metric: cosine + vector_dimension: 1536 + index_name: my-pinecone-index + pod_config: + environment: gcp-starter + metadata_config: + indexed: + - "url" + - "hash" +``` + +```yaml serverless_config.yaml +vectordb: + provider: pinecone + config: + metric: cosine + vector_dimension: 1536 + index_name: my-pinecone-index + serverless_config: + cloud: aws + region: us-west-2 +``` + + + +
+ +You can find more information about Pinecone configuration [here](https://docs.pinecone.io/docs/manage-indexes#create-a-pod-based-index). +You can also optionally provide `index_name` as a config param in yaml file to specify the index name. If not provided, the index name will be `{collection_name}-{vector_dimension}`. + + +## Usage + +### Hybrid search + +Here is an example of how you can do hybrid search using Pinecone as a vector database through Embedchain. + +```python +import os + +from embedchain import App + +config = { + 'app': { + "config": { + "id": "ec-docs-hybrid-search" + } + }, + 'vectordb': { + 'provider': 'pinecone', + 'config': { + 'metric': 'dotproduct', + 'vector_dimension': 1536, + 'index_name': 'my-index', + 'serverless_config': { + 'cloud': 'aws', + 'region': 'us-west-2' + }, + 'hybrid_search': True, # Remember to set this for hybrid search + } + } +} + +# Initialize app +app = App.from_config(config=config) + +# Add documents +app.add("/path/to/file.pdf", data_type="pdf_file", namespace="my-namespace") + +# Query +app.query("", namespace="my-namespace") + +# Chat +app.chat("", namespace="my-namespace") +``` + +Under the hood, Embedchain fetches the relevant chunks from the documents you added by doing hybrid search on the pinecone index. +If you have questions on how pinecone hybrid search works, please refer to their [offical documentation here](https://docs.pinecone.io/docs/hybrid-search). + + diff --git a/mem0-rag/docs/components/vector-databases/qdrant.mdx b/mem0-rag/docs/components/vector-databases/qdrant.mdx new file mode 100644 index 0000000000..cadb42e929 --- /dev/null +++ b/mem0-rag/docs/components/vector-databases/qdrant.mdx @@ -0,0 +1,23 @@ +--- +title: Qdrant +--- + +In order to use Qdrant as a vector database, set the environment variables `QDRANT_URL` and `QDRANT_API_KEY` which you can find on [Qdrant Dashboard](https://cloud.qdrant.io/). + + +```python main.py +from embedchain import App + +# load qdrant configuration from yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +vectordb: + provider: qdrant + config: + collection_name: my_qdrant_index +``` + + + diff --git a/mem0-rag/docs/components/vector-databases/weaviate.mdx b/mem0-rag/docs/components/vector-databases/weaviate.mdx new file mode 100644 index 0000000000..e5b1d5eda9 --- /dev/null +++ b/mem0-rag/docs/components/vector-databases/weaviate.mdx @@ -0,0 +1,24 @@ +--- +title: Weaviate +--- + + +In order to use Weaviate as a vector database, set the environment variables `WEAVIATE_ENDPOINT` and `WEAVIATE_API_KEY` which you can find on [Weaviate dashboard](https://console.weaviate.cloud/dashboard). + + +```python main.py +from embedchain import App + +# load weaviate configuration from yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +vectordb: + provider: weaviate + config: + collection_name: my_weaviate_index +``` + + + diff --git a/mem0-rag/docs/components/vector-databases/zilliz.mdx b/mem0-rag/docs/components/vector-databases/zilliz.mdx new file mode 100644 index 0000000000..55c0dbaa70 --- /dev/null +++ b/mem0-rag/docs/components/vector-databases/zilliz.mdx @@ -0,0 +1,39 @@ +--- +title: Zilliz +--- + +Install related dependencies using the following command: + +```bash +pip install --upgrade 'embedchain[milvus]' +``` + +Set the Zilliz environment variables `ZILLIZ_CLOUD_URI` and `ZILLIZ_CLOUD_TOKEN` which you can find it on their [cloud platform](https://cloud.zilliz.com/). + + + +```python main.py +import os +from embedchain import App + +os.environ['ZILLIZ_CLOUD_URI'] = 'https://xxx.zillizcloud.com' +os.environ['ZILLIZ_CLOUD_TOKEN'] = 'xxx' + +# load zilliz configuration from yaml file +app = App.from_config(config_path="config.yaml") +``` + +```yaml config.yaml +vectordb: + provider: zilliz + config: + collection_name: 'zilliz_app' + uri: https://xxxx.api.gcp-region.zillizcloud.com + token: xxx + vector_dim: 1536 + metric_type: L2 +``` + + + + diff --git a/mem0-rag/docs/contribution/dev.mdx b/mem0-rag/docs/contribution/dev.mdx new file mode 100644 index 0000000000..3ce71c25ce --- /dev/null +++ b/mem0-rag/docs/contribution/dev.mdx @@ -0,0 +1,45 @@ +--- +title: '👨‍💻 Development' +description: 'Contribute to Embedchain framework development' +--- + +Thank you for your interest in contributing to the EmbedChain project! We welcome your ideas and contributions to help improve the project. Please follow the instructions below to get started: + +1. **Fork the repository**: Click on the "Fork" button at the top right corner of this repository page. This will create a copy of the repository in your own GitHub account. + +2. **Install the required dependencies**: Ensure that you have the necessary dependencies installed in your Python environment. You can do this by running the following command: + +```bash +make install +``` + +3. **Make changes in the code**: Create a new branch in your forked repository and make your desired changes in the codebase. +4. **Format code**: Before creating a pull request, it's important to ensure that your code follows our formatting guidelines. Run the following commands to format the code: + +```bash +make lint format +``` + +5. **Create a pull request**: When you are ready to contribute your changes, submit a pull request to the EmbedChain repository. Provide a clear and descriptive title for your pull request, along with a detailed description of the changes you have made. + +## Team + +### Authors + +- Taranjeet Singh ([@taranjeetio](https://twitter.com/taranjeetio)) +- Deshraj Yadav ([@deshrajdry](https://twitter.com/deshrajdry)) + +### Citation + +If you utilize this repository, please consider citing it with: + +``` +@misc{embedchain, + author = {Taranjeet Singh, Deshraj Yadav}, + title = {Embechain: The Open Source RAG Framework}, + year = {2023}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/embedchain/embedchain}}, +} +``` diff --git a/mem0-rag/docs/contribution/docs.mdx b/mem0-rag/docs/contribution/docs.mdx new file mode 100644 index 0000000000..7aa846ec55 --- /dev/null +++ b/mem0-rag/docs/contribution/docs.mdx @@ -0,0 +1,61 @@ +--- +title: '📝 Documentation' +description: 'Contribute to Embedchain docs' +--- + + + **Prerequisite** You should have installed Node.js (version 18.10.0 or + higher). + + +Step 1. Install Mintlify on your OS: + + + +```bash npm +npm i -g mintlify +``` + +```bash yarn +yarn global add mintlify +``` + + + +Step 2. Go to the `docs/` directory (where you can find `mint.json`) and run the following command: + +```bash +mintlify dev +``` + +The documentation website is now available at `http://localhost:3000`. + +### Custom Ports + +Mintlify uses port 3000 by default. You can use the `--port` flag to customize the port Mintlify runs on. For example, use this command to run in port 3333: + +```bash +mintlify dev --port 3333 +``` + +You will see an error like this if you try to run Mintlify in a port that's already taken: + +```md +Error: listen EADDRINUSE: address already in use :::3000 +``` + +## Mintlify Versions + +Each CLI is linked to a specific version of Mintlify. Please update the CLI if your local website looks different than production. + + + +```bash npm +npm i -g mintlify@latest +``` + +```bash yarn +yarn global upgrade mintlify +``` + + diff --git a/mem0-rag/docs/contribution/guidelines.mdx b/mem0-rag/docs/contribution/guidelines.mdx new file mode 100644 index 0000000000..72659c8d30 --- /dev/null +++ b/mem0-rag/docs/contribution/guidelines.mdx @@ -0,0 +1,4 @@ +--- +title: '📋 Guidelines' +url: https://github.com/embedchain/embedchain/blob/main/CONTRIBUTING.md +--- \ No newline at end of file diff --git a/mem0-rag/docs/contribution/python.mdx b/mem0-rag/docs/contribution/python.mdx new file mode 100644 index 0000000000..47bc84c278 --- /dev/null +++ b/mem0-rag/docs/contribution/python.mdx @@ -0,0 +1,4 @@ +--- +title: '🐍 Python' +url: https://github.com/embedchain/embedchain +--- \ No newline at end of file diff --git a/mem0-rag/docs/deployment/fly_io.mdx b/mem0-rag/docs/deployment/fly_io.mdx new file mode 100644 index 0000000000..ed89929154 --- /dev/null +++ b/mem0-rag/docs/deployment/fly_io.mdx @@ -0,0 +1,101 @@ +--- +title: 'Fly.io' +description: 'Deploy your RAG application to fly.io platform' +--- + +Embedchain has a nice and simple abstraction on top of the [Fly.io](https://fly.io/) tools to let developers deploy RAG application to fly.io platform seamlessly. + +Follow the instructions given below to deploy your first application quickly: + + +## Step-1: Install flyctl command line + + +```bash OSX +brew install flyctl +``` + +```bash Linux +curl -L https://fly.io/install.sh | sh +``` + +```bash Windows +pwsh -Command "iwr https://fly.io/install.ps1 -useb | iex" +``` + + +Once you have installed the fly.io cli tool, signup/login to their platform using the following command: + + +```bash Sign up +fly auth signup +``` + +```bash Sign in +fly auth login +``` + + +In case you run into issues, refer to official [fly.io docs](https://fly.io/docs/hands-on/install-flyctl/). + +## Step-2: Create RAG app + +We provide a command line utility called `ec` in embedchain that inherits the template for `fly.io` platform and help you deploy the app. Follow the instructions to create a fly.io app using the template provided: + +```bash Install embedchain +pip install embedchain +``` + +```bash Create application +mkdir my-rag-app +ec create --template=fly.io +``` + +This will generate a directory structure like this: + +```bash +├── Dockerfile +├── app.py +├── fly.toml +├── .env +├── .env.example +├── embedchain.json +└── requirements.txt +``` + +Feel free to edit the files as required. +- `Dockerfile`: Defines the steps to setup the application +- `app.py`: Contains API app code +- `fly.toml`: fly.io config file +- `.env`: Contains environment variables for production +- `.env.example`: Contains dummy environment variables (can ignore this file) +- `embedchain.json`: Contains embedchain specific configuration for deployment (you don't need to configure this) +- `requirements.txt`: Contains python dependencies for your application + +## Step-3: Test app locally + +You can run the app locally by simply doing: + +```bash Run locally +pip install -r requirements.txt +ec dev +``` + +## Step-4: Deploy to fly.io + +You can deploy to fly.io using the following command: +```bash Deploy app +ec deploy +``` + +Once this step finished, it will provide you with the deployment endpoint where you can access the app live. It will look something like this (Swagger docs): + +You can also check the logs, monitor app status etc on their dashboard by running command `fly dashboard`. + + + +## Seeking help? + +If you run into issues with deployment, please feel free to reach out to us via any of the following methods: + + diff --git a/mem0-rag/docs/deployment/gradio_app.mdx b/mem0-rag/docs/deployment/gradio_app.mdx new file mode 100644 index 0000000000..6c79aa208d --- /dev/null +++ b/mem0-rag/docs/deployment/gradio_app.mdx @@ -0,0 +1,59 @@ +--- +title: 'Gradio.app' +description: 'Deploy your RAG application to gradio.app platform' +--- + +Embedchain offers a Streamlit template to facilitate the development of RAG chatbot applications in just three easy steps. + +Follow the instructions given below to deploy your first application quickly: + +## Step-1: Create RAG app + +We provide a command line utility called `ec` in embedchain that inherits the template for `gradio.app` platform and help you deploy the app. Follow the instructions to create a gradio.app app using the template provided: + +```bash Install embedchain +pip install embedchain +``` + +```bash Create application +mkdir my-rag-app +ec create --template=gradio.app +``` + +This will generate a directory structure like this: + +```bash +├── app.py +├── embedchain.json +└── requirements.txt +``` + +Feel free to edit the files as required. +- `app.py`: Contains API app code +- `embedchain.json`: Contains embedchain specific configuration for deployment (you don't need to configure this) +- `requirements.txt`: Contains python dependencies for your application + +## Step-2: Test app locally + +You can run the app locally by simply doing: + +```bash Run locally +pip install -r requirements.txt +ec dev +``` + +## Step-3: Deploy to gradio.app + +```bash Deploy to gradio.app +ec deploy +``` + +This will run `gradio deploy` which will prompt you questions and deploy your app directly to huggingface spaces. + +gradio app + +## Seeking help? + +If you run into issues with deployment, please feel free to reach out to us via any of the following methods: + + diff --git a/mem0-rag/docs/deployment/huggingface_spaces.mdx b/mem0-rag/docs/deployment/huggingface_spaces.mdx new file mode 100644 index 0000000000..5b8811e41d --- /dev/null +++ b/mem0-rag/docs/deployment/huggingface_spaces.mdx @@ -0,0 +1,103 @@ +--- +title: 'Huggingface.co' +description: 'Deploy your RAG application to huggingface.co platform' +--- + +With Embedchain, you can directly host your apps in just three steps to huggingface spaces where you can view and deploy your app to the world. + +We support two types of deployment to huggingface spaces: + + + + Streamlit.io + + + Gradio.app + + + +## Using streamlit.io + +### Step 1: Create a new RAG app + +Create a new RAG app using the following command: + +```bash +mkdir my-rag-app +ec create --template=hf/streamlit.io # inside my-rag-app directory +``` + +When you run this for the first time, you'll be asked to login to huggingface.co. Once you login, you'll need to create a **write** token. You can create a write token by going to [huggingface.co settings](https://huggingface.co/settings/token). Once you create a token, you'll be asked to enter the token in the terminal. + +This will also create an `embedchain.json` file in your app directory. Add a `name` key into the `embedchain.json` file. This will be the "repo-name" of your app in huggingface spaces. + +```json embedchain.json +{ + "name": "my-rag-app", + "provider": "hf/streamlit.io" +} +``` + +### Step-2: Test app locally + +You can run the app locally by simply doing: + +```bash Run locally +pip install -r requirements.txt +ec dev +``` + +### Step-3: Deploy to huggingface spaces + +```bash Deploy to huggingface spaces +ec deploy +``` + +This will deploy your app to huggingface spaces. You can view your app at `https://huggingface.co/spaces//my-rag-app`. This will get prompted in the terminal once the app is deployed. + +## Using gradio.app + +Similar to streamlit.io, you can deploy your app to gradio.app in just three steps. + +### Step 1: Create a new RAG app + +Create a new RAG app using the following command: + +```bash +mkdir my-rag-app +ec create --template=hf/gradio.app # inside my-rag-app directory +``` + +When you run this for the first time, you'll be asked to login to huggingface.co. Once you login, you'll need to create a **write** token. You can create a write token by going to [huggingface.co settings](https://huggingface.co/settings/token). Once you create a token, you'll be asked to enter the token in the terminal. + +This will also create an `embedchain.json` file in your app directory. Add a `name` key into the `embedchain.json` file. This will be the "repo-name" of your app in huggingface spaces. + +```json embedchain.json +{ + "name": "my-rag-app", + "provider": "hf/gradio.app" +} +``` + +### Step-2: Test app locally + +You can run the app locally by simply doing: + +```bash Run locally +pip install -r requirements.txt +ec dev +``` + +### Step-3: Deploy to huggingface spaces + +```bash Deploy to huggingface spaces +ec deploy +``` + +This will deploy your app to huggingface spaces. You can view your app at `https://huggingface.co/spaces//my-rag-app`. This will get prompted in the terminal once the app is deployed. + +## Seeking help? + +If you run into issues with deployment, please feel free to reach out to us via any of the following methods: + + diff --git a/mem0-rag/docs/deployment/modal_com.mdx b/mem0-rag/docs/deployment/modal_com.mdx new file mode 100644 index 0000000000..e82d367b69 --- /dev/null +++ b/mem0-rag/docs/deployment/modal_com.mdx @@ -0,0 +1,63 @@ +--- +title: 'Modal.com' +description: 'Deploy your RAG application to modal.com platform' +--- + +Embedchain has a nice and simple abstraction on top of the [Modal.com](https://modal.com/) tools to let developers deploy RAG application to modal.com platform seamlessly. + +Follow the instructions given below to deploy your first application quickly: + + +## Step-1 Create RAG application: + +We provide a command line utility called `ec` in embedchain that inherits the template for `modal.com` platform and help you deploy the app. Follow the instructions to create a modal.com app using the template provided: + + +```bash Create application +pip install embedchain[modal] +mkdir my-rag-app +ec create --template=modal.com +``` + +This `create` command will open a browser window and ask you to login to your modal.com account and will generate a directory structure like this: + +```bash +├── app.py +├── .env +├── .env.example +├── embedchain.json +└── requirements.txt +``` + +Feel free to edit the files as required. +- `app.py`: Contains API app code +- `.env`: Contains environment variables for production +- `.env.example`: Contains dummy environment variables (can ignore this file) +- `embedchain.json`: Contains embedchain specific configuration for deployment (you don't need to configure this) +- `requirements.txt`: Contains python dependencies for your FastAPI application + +## Step-2: Test app locally + +You can run the app locally by simply doing: + +```bash Run locally +pip install -r requirements.txt +ec dev +``` + +## Step-3: Deploy to modal.com + +You can deploy to modal.com using the following command: +```bash Deploy app +ec deploy +``` + +Once this step finished, it will provide you with the deployment endpoint where you can access the app live. It will look something like this (Swagger docs): + + + +## Seeking help? + +If you run into issues with deployment, please feel free to reach out to us via any of the following methods: + + diff --git a/mem0-rag/docs/deployment/railway.mdx b/mem0-rag/docs/deployment/railway.mdx new file mode 100644 index 0000000000..ef8a60ab8c --- /dev/null +++ b/mem0-rag/docs/deployment/railway.mdx @@ -0,0 +1,86 @@ +--- +title: 'Railway.app' +description: 'Deploy your RAG application to railway.app' +--- + +It's easy to host your Embedchain-powered apps and APIs on railway. + +Follow the instructions given below to deploy your first application quickly: + +## Step-1: Create RAG app + +```bash Install embedchain +pip install embedchain +``` + + +**Create a full stack app using Embedchain CLI** + +To use your hosted embedchain RAG app, you can easily set up a FastAPI server that can be used anywhere. +To easily set up a FastAPI server, check out [Get started with Full stack](https://docs.embedchain.ai/get-started/full-stack) page. + +Hosting this server on railway is super easy! + + + +## Step-2: Set up your project + +### With Docker + +You can create a `Dockerfile` in the root of the project, with all the instructions. However, this method is sometimes slower in deployment. + +### Without Docker + +By default, Railway uses Python 3.7. Embedchain requires the python version to be >3.9 in order to install. + +To fix this, create a `.python-version` file in the root directory of your project and specify the correct version + +```bash .python-version +3.10 +``` + +You also need to create a `requirements.txt` file to specify the requirements. + +```bash requirements.txt +python-dotenv +embedchain +fastapi==0.108.0 +uvicorn==0.25.0 +embedchain +beautifulsoup4 +sentence-transformers +``` + +## Step-3: Deploy to Railway 🚀 + +1. Go to https://railway.app and create an account. +2. Create a project by clicking on the "Start a new project" button + +### With Github + +Select `Empty Project` or `Deploy from Github Repo`. + +You should be all set! + +### Without Github + +You can also use the railway CLI to deploy your apps from the terminal, if you don't want to connect a git repository. + +To do this, just run this command in your terminal + +```bash Install and set up railway CLI +npm i -g @railway/cli +railway login +railway link [projectID] +``` + +Finally, run `railway up` to deploy your app. +```bash Deploy +railway up +``` + +## Seeking help? + +If you run into issues with deployment, please feel free to reach out to us via any of the following methods: + + diff --git a/mem0-rag/docs/deployment/render_com.mdx b/mem0-rag/docs/deployment/render_com.mdx new file mode 100644 index 0000000000..81ba7f6dff --- /dev/null +++ b/mem0-rag/docs/deployment/render_com.mdx @@ -0,0 +1,93 @@ +--- +title: 'Render.com' +description: 'Deploy your RAG application to render.com platform' +--- + +Embedchain has a nice and simple abstraction on top of the [render.com](https://render.com/) tools to let developers deploy RAG application to render.com platform seamlessly. + +Follow the instructions given below to deploy your first application quickly: + +## Step-1: Install `render` command line + + +```bash OSX +brew tap render-oss/render +brew install render +``` + +```bash Linux +# Make sure you have deno installed -> https://docs.render.com/docs/cli#from-source-unsupported-operating-systems +git clone https://github.com/render-oss/render-cli +cd render-cli +make deps +deno task run +deno compile +``` + +```bash Windows +choco install rendercli +``` + + +In case you run into issues, refer to official [render.com docs](https://docs.render.com/docs/cli). + +## Step-2 Create RAG application: + +We provide a command line utility called `ec` in embedchain that inherits the template for `render.com` platform and help you deploy the app. Follow the instructions to create a render.com app using the template provided: + + +```bash Create application +pip install embedchain +mkdir my-rag-app +ec create --template=render.com +``` + +This `create` command will open a browser window and ask you to login to your render.com account and will generate a directory structure like this: + +```bash +├── app.py +├── .env +├── render.yaml +├── embedchain.json +└── requirements.txt +``` + +Feel free to edit the files as required. +- `app.py`: Contains API app code +- `.env`: Contains environment variables for production +- `render.yaml`: Contains render.com specific configuration for deployment (configure this according to your needs, follow [this](https://docs.render.com/docs/blueprint-spec) for more info) +- `embedchain.json`: Contains embedchain specific configuration for deployment (you don't need to configure this) +- `requirements.txt`: Contains python dependencies for your application + +## Step-3: Test app locally + +You can run the app locally by simply doing: + +```bash Run locally +pip install -r requirements.txt +ec dev +``` + +## Step-4: Deploy to render.com + +Before deploying to render.com, you only have to set up one thing. + +In the render.yaml file, make sure to modify the repo key by inserting the URL of your Git repository where your application will be hosted. You can create a repository from [GitHub](https://github.com) or [GitLab](https://gitlab.com/users/sign_in). + +After that, you're ready to deploy on render.com. + +```bash Deploy app +ec deploy +``` + +When you run this, it should open up your render dashboard and you can see the app being deployed. You can find your hosted link over there only. + +You can also check the logs, monitor app status etc on their dashboard by running command `render dashboard`. + + + +## Seeking help? + +If you run into issues with deployment, please feel free to reach out to us via any of the following methods: + + diff --git a/mem0-rag/docs/deployment/streamlit_io.mdx b/mem0-rag/docs/deployment/streamlit_io.mdx new file mode 100644 index 0000000000..93dde74009 --- /dev/null +++ b/mem0-rag/docs/deployment/streamlit_io.mdx @@ -0,0 +1,62 @@ +--- +title: 'Streamlit.io' +description: 'Deploy your RAG application to streamlit.io platform' +--- + +Embedchain offers a Streamlit template to facilitate the development of RAG chatbot applications in just three easy steps. + +Follow the instructions given below to deploy your first application quickly: + +## Step-1: Create RAG app + +We provide a command line utility called `ec` in embedchain that inherits the template for `streamlit.io` platform and help you deploy the app. Follow the instructions to create a streamlit.io app using the template provided: + +```bash Install embedchain +pip install embedchain +``` + +```bash Create application +mkdir my-rag-app +ec create --template=streamlit.io +``` + +This will generate a directory structure like this: + +```bash +├── .streamlit +│ └── secrets.toml +├── app.py +├── embedchain.json +└── requirements.txt +``` + +Feel free to edit the files as required. +- `app.py`: Contains API app code +- `.streamlit/secrets.toml`: Contains secrets for your application +- `embedchain.json`: Contains embedchain specific configuration for deployment (you don't need to configure this) +- `requirements.txt`: Contains python dependencies for your application + +Add your `OPENAI_API_KEY` in `.streamlit/secrets.toml` file to run and deploy the app. + +## Step-2: Test app locally + +You can run the app locally by simply doing: + +```bash Run locally +pip install -r requirements.txt +ec dev +``` + +## Step-3: Deploy to streamlit.io + +![Streamlit App deploy button](https://github.com/embedchain/embedchain/assets/73601258/90658e28-29e5-4ceb-9659-37ff8b861a29) + +Use the deploy button from the streamlit website to deploy your app. + +You can refer this [guide](https://docs.streamlit.io/streamlit-community-cloud/deploy-your-app) if you run into any problems. + +## Seeking help? + +If you run into issues with deployment, please feel free to reach out to us via any of the following methods: + + diff --git a/mem0-rag/docs/development.mdx b/mem0-rag/docs/development.mdx new file mode 100644 index 0000000000..878300893c --- /dev/null +++ b/mem0-rag/docs/development.mdx @@ -0,0 +1,98 @@ +--- +title: 'Development' +description: 'Learn how to preview changes locally' +--- + + + **Prerequisite** You should have installed Node.js (version 18.10.0 or + higher). + + +Step 1. Install Mintlify on your OS: + + + +```bash npm +npm i -g mintlify +``` + +```bash yarn +yarn global add mintlify +``` + + + +Step 2. Go to the docs are located (where you can find `mint.json`) and run the following command: + +```bash +mintlify dev +``` + +The documentation website is now available at `http://localhost:3000`. + +### Custom Ports + +Mintlify uses port 3000 by default. You can use the `--port` flag to customize the port Mintlify runs on. For example, use this command to run in port 3333: + +```bash +mintlify dev --port 3333 +``` + +You will see an error like this if you try to run Mintlify in a port that's already taken: + +```md +Error: listen EADDRINUSE: address already in use :::3000 +``` + +## Mintlify Versions + +Each CLI is linked to a specific version of Mintlify. Please update the CLI if your local website looks different than production. + + + +```bash npm +npm i -g mintlify@latest +``` + +```bash yarn +yarn global upgrade mintlify +``` + + + +## Deployment + + + Unlimited editors available under the [Startup + Plan](https://mintlify.com/pricing) + + +You should see the following if the deploy successfully went through: + + + + + +## Troubleshooting + +Here's how to solve some common problems when working with the CLI. + + + + Update to Node v18. Run `mintlify install` and try again. + + +Go to the `C:/Users/Username/.mintlify/` directory and remove the `mint` +folder. Then Open the Git Bash in this location and run `git clone +https://github.com/mintlify/mint.git`. + +Repeat step 3. + + + + Try navigating to the root of your device and delete the ~/.mintlify folder. + Then run `mintlify dev` again. + + + +Curious about what changed in a CLI version? [Check out the CLI changelog.](/changelog/command-line) diff --git a/mem0-rag/docs/examples/chat-with-PDF.mdx b/mem0-rag/docs/examples/chat-with-PDF.mdx new file mode 100644 index 0000000000..ad8fb9a5bf --- /dev/null +++ b/mem0-rag/docs/examples/chat-with-PDF.mdx @@ -0,0 +1,32 @@ +### Embedchain Chat with PDF App + +You can easily create and deploy your own `chat-pdf` App using Embedchain. + +Here are few simple steps for you to create and deploy your app: + +1. Fork the embedchain repo from [Github](https://github.com/embedchain/embedchain). + + +If you run into problems with forking, please refer to [github docs](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo) for forking a repo. + + +2. Navigate to `chat-pdf` example app from your forked repo: + +```bash +cd /examples/chat-pdf +``` + +3. Run your app in development environment with simple commands + +```bash +pip install -r requirements.txt +ec dev +``` + +Feel free to improve our simple `chat-pdf` streamlit app and create pull request to showcase your app [here](https://docs.embedchain.ai/examples/showcase) + +4. You can easily deploy your app using Streamlit interface + +Connect your Github account with Streamlit and refer this [guide](https://docs.streamlit.io/streamlit-community-cloud/deploy-your-app) to deploy your app. + +You can also use the deploy button from your streamlit website you see when running `ec dev` command. diff --git a/mem0-rag/docs/examples/community/showcase.mdx b/mem0-rag/docs/examples/community/showcase.mdx new file mode 100644 index 0000000000..d8b5119192 --- /dev/null +++ b/mem0-rag/docs/examples/community/showcase.mdx @@ -0,0 +1,115 @@ +--- +title: '🎪 Community showcase' +--- + +Embedchain community has been super active in creating demos on top of Embedchain. On this page, we showcase all the apps, blogs, videos, and tutorials created by the community. ❤️ + +## Apps + +### Open Source + +- [My GSoC23 bot- Streamlit chat](https://github.com/lucifertrj/EmbedChain_GSoC23_BOT) by Tarun Jain +- [Discord Bot for LLM chat](https://github.com/Reidond/discord_bots_playground/tree/c8b0c36541e4b393782ee506804c4b6962426dd6/python/chat-channel-bot) by Reidond +- [EmbedChain-Streamlit-Docker App](https://github.com/amjadraza/embedchain-streamlit-app) by amjadraza +- [Harry Potter Philosphers Stone Bot](https://github.com/vinayak-kempawad/Harry_Potter_Philosphers_Stone_Bot/) by Vinayak Kempawad, ([LinkedIn post](https://www.linkedin.com/feed/update/urn:li:activity:7080907532155686912/)) +- [LLM bot trained on own messages](https://github.com/Harin329/harinBot) by Hao Wu + +### Closed Source + +- [Taobot.io](https://taobot.io) - chatbot & knowledgebase hybrid by [cachho](https://github.com/cachho) +- [Create Instant ChatBot 🤖 using embedchain](https://databutton.com/v/h3e680h9) by Avra, ([Tweet](https://twitter.com/Avra_b/status/1674704745154641920/)) +- [JOBO 🤖 — The AI-driven sidekick to craft your resume](https://try-jobo.com/) by Enrico Willemse, ([LinkedIn Post](https://www.linkedin.com/posts/enrico-willemse_jobai-gptfun-embedchain-activity-7090340080879374336-ueLB/)) +- [Explore Your Knowledge Base: Interactive chats over various forms of documents](https://chatdocs.dkedar.com/) by Kedar Dabhadkar, ([LinkedIn Post](https://www.linkedin.com/posts/dkedar7_machinelearning-llmops-activity-7092524836639424513-2O3L/)) +- [Chatbot trained on 1000+ videos of Ester hicks the co-author behind the famous book Secret](https://ask-abraham.thoughtseed.repl.co) by Mohan Kumar + + +## Templates + +### Replit +- [Embedchain Chat Bot](https://replit.com/@taranjeet1/Embedchain-Chat-Bot) by taranjeetio +- [Embedchain Memory Chat Bot Template](https://replit.com/@taranjeetio/Embedchain-Memory-Chat-Bot-Template) by taranjeetio +- [Chatbot app to demonstrate question-answering using retrieved information](https://replit.com/@AllisonMorrell/EmbedChainlitPublic) by Allison Morrell, ([LinkedIn Post](https://www.linkedin.com/posts/allison-morrell-2889275a_retrievalbot-screenshots-activity-7080339991754649600-wihZ/)) + +## Posts + +### Blogs + +- [Customer Service LINE Bot](https://www.evanlin.com/langchain-embedchain/) by Evan Lin +- [Chatbot in Under 5 mins using Embedchain](https://medium.com/@ayush.wattal/chatbot-in-under-5-mins-using-embedchain-a4f161fcf9c5) by Ayush Wattal +- [Understanding what the LLM framework embedchain does](https://zenn.dev/hijikix/articles/4bc8d60156a436) by Daisuke Hashimoto +- [In bed with GPT and Node.js](https://dev.to/worldlinetech/in-bed-with-gpt-and-nodejs-4kh2) by Raphaël Semeteys, ([LinkedIn Post](https://www.linkedin.com/posts/raphaelsemeteys_in-bed-with-gpt-and-nodejs-activity-7088113552326029313-nn87/)) +- [Using Embedchain — A powerful LangChain Python wrapper to build Chat Bots even faster!⚡](https://medium.com/@avra42/using-embedchain-a-powerful-langchain-python-wrapper-to-build-chat-bots-even-faster-35c12994a360) by Avra, ([Tweet](https://twitter.com/Avra_b/status/1686767751560310784/)) +- [What is the Embedchain library?](https://jahaniwww.com/%da%a9%d8%aa%d8%a7%d8%a8%d8%ae%d8%a7%d9%86%d9%87-embedchain/) by Ali Jahani, ([LinkedIn Post](https://www.linkedin.com/posts/ajahani_aepaetaeqaexaggahyaeu-aetaexaesabraeaaeqaepaeu-activity-7097605202135904256-ppU-/)) +- [LangChain is Nice, But Have You Tried EmbedChain ?](https://medium.com/thoughts-on-machine-learning/langchain-is-nice-but-have-you-tried-embedchain-215a34421cde) by FS Ndzomga, ([Tweet](https://twitter.com/ndzfs/status/1695583640372035951/)) +- [Simplest Method to Build a Custom Chatbot with GPT-3.5 (via Embedchain)](https://www.ainewsletter.today/p/simplest-method-to-build-a-custom) by Arjun, ([Tweet](https://twitter.com/aiguy_arjun/status/1696393808467091758/)) + +### LinkedIn + +- [What is embedchain](https://www.linkedin.com/posts/activity-7079393104423698432-wRyi/) by Rithesh Sreenivasan +- [Building a chatbot with EmbedChain](https://www.linkedin.com/posts/activity-7078434598984060928-Zdso/) by Lior Sinclair +- [Making chatbot without vs with embedchain](https://www.linkedin.com/posts/kalyanksnlp_llms-chatbots-langchain-activity-7077453416221863936-7N1L/) by Kalyan KS +- [EmbedChain - very intuitive, first you index your data and then query!](https://www.linkedin.com/posts/shubhamsaboo_embedchain-a-framework-to-easily-create-activity-7079535460699557888-ad1X/) by Shubham Saboo +- [EmbedChain - Harnessing power of LLM](https://www.linkedin.com/posts/uditsaini_chatbotrevolution-llmpoweredbots-embedchainframework-activity-7077520356827181056-FjTK/) by Udit S. +- [AI assistant for ABBYY Vantage](https://www.linkedin.com/posts/maximevermeir_llm-github-abbyy-activity-7081658972071424000-fXfZ/) by Maxime V. +- [About embedchain](https://www.linkedin.com/feed/update/urn:li:activity:7080984218914189312/) by Morris Lee +- [How to use Embedchain](https://www.linkedin.com/posts/nehaabansal_github-embedchainembedchain-framework-activity-7085830340136595456-kbW5/) by Neha Bansal +- [Youtube/Webpage summary for Energy Study](https://www.linkedin.com/posts/bar%C4%B1%C5%9F-sanl%C4%B1-34b82715_enerji-python-activity-7082735341563977730-Js0U/) by Barış Sanlı, ([Tweet](https://twitter.com/barissanli/status/1676968784979193857/)) +- [Demo: How to use Embedchain? (Contains Collab Notebook link)](https://www.linkedin.com/posts/liorsinclair_embedchain-is-getting-a-lot-of-traction-because-activity-7103044695995424768-RckT/) by Lior Sinclair + +### Twitter + +- [What is embedchain](https://twitter.com/AlphaSignalAI/status/1672668574450847745) by Lior +- [Building a chatbot with Embedchain](https://twitter.com/Saboo_Shubham_/status/1673537044419686401) by Shubham Saboo +- [Chatbot docker image behind an API with yaml configs with Embedchain](https://twitter.com/tricalt/status/1678411430192730113/) by Vasilije +- [Build AI powered PDF chatbot with just five lines of Python code with Embedchain!](https://twitter.com/Saboo_Shubham_/status/1676627104866156544/) by Shubham Saboo +- [Chatbot against a youtube video using embedchain](https://twitter.com/smaameri/status/1675201443043704834/) by Sami Maameri +- [Highlights of EmbedChain](https://twitter.com/carl_AIwarts/status/1673542204328120321/) by carl_AIwarts +- [Build Llama-2 chatbot in less than 5 minutes](https://twitter.com/Saboo_Shubham_/status/1682168956918833152/) by Shubham Saboo +- [All cool features of embedchain](https://twitter.com/DhravyaShah/status/1683497882438217728/) by Dhravya Shah, ([LinkedIn Post](https://www.linkedin.com/posts/dhravyashah_what-if-i-tell-you-that-you-can-make-an-ai-activity-7089459599287726080-ZIYm/)) +- [Read paid Medium articles for Free using embedchain](https://twitter.com/kumarkaushal_/status/1688952961622585344) by Kaushal Kumar + +## Videos + +- [Embedchain in one shot](https://www.youtube.com/watch?v=vIhDh7H73Ww&t=82s) by AI with Tarun +- [embedChain Create LLM powered bots over any dataset Python Demo Tesla Neurallink Chatbot Example](https://www.youtube.com/watch?v=bJqAn22a6Gc) by Rithesh Sreenivasan +- [Embedchain - NEW 🔥 Langchain BABY to build LLM Bots](https://www.youtube.com/watch?v=qj_GNQ06I8o) by 1littlecoder +- [EmbedChain -- NEW!: Build LLM-Powered Bots with Any Dataset](https://www.youtube.com/watch?v=XmaBezzGHu4) by DataInsightEdge +- [Chat With Your PDFs in less than 10 lines of code! EMBEDCHAIN tutorial](https://www.youtube.com/watch?v=1ugkcsAcw44) by Phani Reddy +- [How To Create A Custom Knowledge AI Powered Bot | Install + How To Use](https://www.youtube.com/watch?v=VfCrIiAst-c) by The Ai Solopreneur +- [Build Custom Chatbot in 6 min with this Framework [Beginner Friendly]](https://www.youtube.com/watch?v=-8HxOpaFySM) by Maya Akim +- [embedchain-streamlit-app](https://www.youtube.com/watch?v=3-9GVd-3v74) by Amjad Raza +- [🤖CHAT with ANY ONLINE RESOURCES using EMBEDCHAIN - a LangChain wrapper, in few lines of code !](https://www.youtube.com/watch?v=Mp7zJe4TIdM) by Avra +- [Building resource-driven LLM-powered bots with Embedchain](https://www.youtube.com/watch?v=IVfcAgxTO4I) by BugBytes +- [embedchain-streamlit-demo](https://www.youtube.com/watch?v=yJAWB13FhYQ) by Amjad Raza +- [Embedchain - create your own AI chatbots using open source models](https://www.youtube.com/shorts/O3rJWKwSrWE) by Dhravya Shah +- [AI ChatBot in 5 lines Python Code](https://www.youtube.com/watch?v=zjWvLJLksv8) by Data Engineering +- [Interview with Karl Marx](https://www.youtube.com/watch?v=5Y4Tscwj1xk) by Alexander Ray Williams +- [Vlog where we try to build a bot based on our content on the internet](https://www.youtube.com/watch?v=I2w8CWM3bx4) by DV, ([Tweet](https://twitter.com/dvcoolster/status/1688387017544261632)) +- [CHAT with ANY ONLINE RESOURCES using EMBEDCHAIN|STREAMLIT with MEMORY |All OPENSOURCE](https://www.youtube.com/watch?v=TqQIHWoWTDQ&pp=ygUKZW1iZWRjaGFpbg%3D%3D) by DataInsightEdge +- [Build POWERFUL LLM Bots EASILY with Your Own Data - Embedchain - Langchain 2.0? (Tutorial)](https://www.youtube.com/watch?v=jE24Y_GasE8) by WorldofAI, ([Tweet](https://twitter.com/intheworldofai/status/1696229166922780737)) +- [Embedchain: An AI knowledge base assistant for customizing enterprise private data, which can be connected to discord, whatsapp, slack, tele and other terminals (with gradio to build a request interface) in Chinese](https://www.youtube.com/watch?v=5RZzCJRk-d0) by AIGC LINK +- [Embedchain Introduction](https://www.youtube.com/watch?v=Jet9zAqyggI) by Fahd Mirza + +## Mentions + +### Github repos + +- [Awesome-LLM](https://github.com/Hannibal046/Awesome-LLM) +- [awesome-chatgpt-api](https://github.com/reorx/awesome-chatgpt-api) +- [awesome-langchain](https://github.com/kyrolabs/awesome-langchain) +- [Awesome-Prompt-Engineering](https://github.com/promptslab/Awesome-Prompt-Engineering) +- [awesome-chatgpt](https://github.com/eon01/awesome-chatgpt) +- [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) +- [awesome-generative-ai](https://github.com/filipecalegario/awesome-generative-ai) +- [awesome-gpt](https://github.com/formulahendry/awesome-gpt) +- [awesome-ChatGPT-repositories](https://github.com/taishi-i/awesome-ChatGPT-repositories) +- [awesome-gpt-prompt-engineering](https://github.com/snwfdhmp/awesome-gpt-prompt-engineering) +- [awesome-chatgpt](https://github.com/awesome-chatgpt/awesome-chatgpt) +- [awesome-llm-and-aigc](https://github.com/sjinzh/awesome-llm-and-aigc) +- [awesome-compbio-chatgpt](https://github.com/csbl-br/awesome-compbio-chatgpt) +- [Awesome-LLM4Tool](https://github.com/OpenGVLab/Awesome-LLM4Tool) + +## Meetups + +- [Dash and ChatGPT: Future of AI-enabled apps 30/08/23](https://go.plotly.com/dash-chatgpt) +- [Pie & AI: Bangalore - Build end-to-end LLM app using Embedchain 01/09/23](https://www.eventbrite.com/e/pie-ai-bangalore-build-end-to-end-llm-app-using-embedchain-tickets-698045722547) diff --git a/mem0-rag/docs/examples/discord_bot.mdx b/mem0-rag/docs/examples/discord_bot.mdx new file mode 100644 index 0000000000..247f3c634a --- /dev/null +++ b/mem0-rag/docs/examples/discord_bot.mdx @@ -0,0 +1,70 @@ +--- +title: "🤖 Discord Bot" +--- + +### 🔑 Keys Setup + +- Set your `OPENAI_API_KEY` in your variables.env file. +- Go to [https://discord.com/developers/applications/](https://discord.com/developers/applications/) and click on `New Application`. +- Enter the name for your bot, accept the terms and click on `Create`. On the resulting page, enter the details of your bot as you like. +- On the left sidebar, click on `Bot`. Under the heading `Privileged Gateway Intents`, toggle all 3 options to ON position. Save your changes. +- Now click on `Reset Token` and copy the token value. Set it as `DISCORD_BOT_TOKEN` in .env file. +- On the left sidebar, click on `OAuth2` and go to `General`. +- Set `Authorization Method` to `In-app Authorization`. Under `Scopes` select `bot`. +- Under `Bot Permissions` allow the following and then click on `Save Changes`. + +```text +Send Messages (under Text Permissions) +``` + +- Now under `OAuth2` and go to `URL Generator`. Under `Scopes` select `bot`. +- Under `Bot Permissions` set the same permissions as above. +- Now scroll down and copy the `Generated URL`. Paste it in a browser window and select the Server where you want to add the bot. +- Click on `Continue` and authorize the bot. +- 🎉 The bot has been successfully added to your server. But it's still offline. + +### Take the bot online + + + + ```bash + docker run --name discord-bot -e OPENAI_API_KEY=sk-xxx -e DISCORD_BOT_TOKEN=xxx -p 8080:8080 embedchain/discord-bot:latest + ``` + + + ```bash + pip install --upgrade "embedchain[discord]" + + python -m embedchain.bots.discord + + # or if you prefer to see the question and not only the answer, run it with + python -m embedchain.bots.discord --include-question + ``` + + + +### 🚀 Usage Instructions + +- Go to the server where you have added your bot. + ![Slash commands interaction with bot](https://github.com/embedchain/embedchain/assets/73601258/bf1414e3-d408-4863-b0d2-ef382a76467e) +- You can add data sources to the bot using the slash command: + +```text +/ec add +``` + +- You can ask your queries from the bot using the slash command: + +```text +/ec query +``` + +- You can chat with the bot using the slash command: + +```text +/ec chat +``` + +📝 Note: To use the bot privately, you can message the bot directly by right clicking the bot and selecting `Message`. + +🎉 Happy Chatting! 🎉 diff --git a/mem0-rag/docs/examples/full_stack.mdx b/mem0-rag/docs/examples/full_stack.mdx new file mode 100644 index 0000000000..6f0bb4fdac --- /dev/null +++ b/mem0-rag/docs/examples/full_stack.mdx @@ -0,0 +1,57 @@ +--- +title: 'Full Stack' +--- + +The Full Stack app example can be found [here](https://github.com/embedchain/embedchain/tree/main/examples/full_stack). + +This guide will help you setup the full stack app on your local machine. + +### 🐳 Docker Setup + +- Create a `docker-compose.yml` file and paste the following code in it. + +```yaml +version: "3.9" + +services: + backend: + container_name: embedchain-backend + restart: unless-stopped + build: + context: backend + dockerfile: Dockerfile + image: embedchain/backend + ports: + - "8000:8000" + + frontend: + container_name: embedchain-frontend + restart: unless-stopped + build: + context: frontend + dockerfile: Dockerfile + image: embedchain/frontend + ports: + - "3000:3000" + depends_on: + - "backend" +``` + +- Run the following command, + +```bash +docker-compose up +``` + +📝 Note: The build command might take a while to install all the packages depending on your system resources. + +![Fullstack App](https://github.com/embedchain/embedchain/assets/73601258/c7c04bbb-9be7-4669-a6af-039e7e972a13) + +### 🚀 Usage Instructions + +- Go to [http://localhost:3000/](http://localhost:3000/) in your browser to view the dashboard. +- Add your `OpenAI API key` 🔑 in the Settings. +- Create a new bot and you'll be navigated to its page. +- Here you can add your data sources and then chat with the bot. + +🎉 Happy Chatting! 🎉 diff --git a/mem0-rag/docs/examples/nextjs-assistant.mdx b/mem0-rag/docs/examples/nextjs-assistant.mdx new file mode 100644 index 0000000000..86f82fb4f5 --- /dev/null +++ b/mem0-rag/docs/examples/nextjs-assistant.mdx @@ -0,0 +1,124 @@ +Fork the Embedchain repo on [Github](https://github.com/embedchain/embedchain) to create your own NextJS discord and slack bot powered by Embedchain. + +If you run into problems with forking, please refer to [github docs](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo) for forking a repo. + +We will work from the `examples/nextjs` folder so change your current working directory by running the command - `cd /examples/nextjs` + +# Installation + +First, lets start by install all the required packages and dependencies. + +- Install all the required python packages by running ```pip install -r requirements.txt``` + +- We will use [Fly.io](https://fly.io/) to deploy our embedchain app, discord and slack bot. Follow the step one to install [Fly.io CLI](https://docs.embedchain.ai/deployment/fly_io#step-1-install-flyctl-command-line) + +# Developement + +## Embedchain App + +First, we need an Embedchain app powered with the knowledge of NextJS. We have already created an embedchain app using FastAPI in `ec_app` folder for you. Feel free to ingest data of your choice to power the App. + + +Navigate to `ec_app` folder and create `.env` file in this folder and set your OpenAI API key as shown in `.env.example` file. If you want to use other open-source models, feel free to use the app config in `app.py`. More details for using custom configuration for Embedchain app is [available here](https://docs.embedchain.ai/api-reference/advanced/configuration). + + +Before running the ec commands to develope the app, open `fly.toml` file and update the `name` variable to something unique. This is important as `fly.io` requires users to provide a globally unique deployment app names. + +Now, we need to launch this application with fly.io. You can see your app on [fly.io dashboard](https://fly.io/dashboard). Run the following command to launch your app on fly.io: +```bash +fly launch --no-deploy +``` + +To run the app in development, run the following command: + +```bash +ec dev +``` + +Run `ec deploy` to deploy your app on Fly.io. Once you deploy your app, save the endpoint on which our discord and slack bot will send requests. + + +## Discord bot + +For discord bot, you will need to create the bot on discord developer portal and get the discord bot token and your discord bot name. + +While keeping in mind the following note, create the discord bot by following the instructions from our [discord bot docs](https://docs.embedchain.ai/examples/discord_bot) and get discord bot token. + + +You do not need to set `OPENAI_API_KEY` to run this discord bot. Follow the remaining instructions to create a discord bot app. We recommend you to give the following sets of bot permissions to run the discord bot without errors: + +``` +(General Permissions) +Read Message/View Channels + +(Text Permissions) +Send Messages +Create Public Thread +Create Private Thread +Send Messages in Thread +Manage Threads +Embed Links +Read Message History +``` + + +Once you have your discord bot token and discord app name. Navigate to `nextjs_discord` folder and create `.env` file and define your discord bot token, discord bot name and endpoint of your embedchain app as shown in `.env.example` file. + +To run the app in development: + +```bash +python app.py +``` + +Before deploying the app, open `fly.toml` file and update the `name` variable to something unique. This is important as `fly.io` requires users to provide a globally unique deployment app names. + +Now, we need to launch this application with fly.io. You can see your app on [fly.io dashboard](https://fly.io/dashboard). Run the following command to launch your app on fly.io: +```bash +fly launch --no-deploy +``` + +Run `ec deploy` to deploy your app on Fly.io. Once you deploy your app, your discord bot will be live! + + +## Slack bot + +For Slack bot, you will need to create the bot on slack developer portal and get the slack bot token and slack app token. + +### Setup + +- Create a workspace on Slack if you don't have one already by clicking [here](https://slack.com/intl/en-in/). +- Create a new App on your Slack account by going [here](https://api.slack.com/apps). +- Select `From Scratch`, then enter the Bot Name and select your workspace. +- Go to `App Credentials` section on the `Basic Information` tab from the left sidebar, create your app token and save it in your `.env` file as `SLACK_APP_TOKEN`. +- Go to `Socket Mode` tab from the left sidebar and enable the socket mode to listen to slack message from your workspace. +- (Optional) Under the `App Home` tab you can change your App display name and default name. +- Navigate to `Event Subscription` tab, and enable the event subscription so that we can listen to slack events. +- Once you enable the event subscription, you will need to subscribe to bot events to authorize the bot to listen to app mention events of the bot. Do that by tapping on `Add Bot User Event` button and select `app_mention`. +- On the left Sidebar, go to `OAuth and Permissions` and add the following scopes under `Bot Token Scopes`: +```text +app_mentions:read +channels:history +channels:read +chat:write +emoji:read +reactions:write +reactions:read +``` +- Now select the option `Install to Workspace` and after it's done, copy the `Bot User OAuth Token` and set it in your `.env` file as `SLACK_BOT_TOKEN`. + +Once you have your slack bot token and slack app token. Navigate to `nextjs_slack` folder and create `.env` file and define your slack bot token, slack app token and endpoint of your embedchain app as shown in `.env.example` file. + +To run the app in development: + +```bash +python app.py +``` + +Before deploying the app, open `fly.toml` file and update the `name` variable to something unique. This is important as `fly.io` requires users to provide a globally unique deployment app names. + +Now, we need to launch this application with fly.io. You can see your app on [fly.io dashboard](https://fly.io/dashboard). Run the following command to launch your app on fly.io: +```bash +fly launch --no-deploy +``` + +Run `ec deploy` to deploy your app on Fly.io. Once you deploy your app, your slack bot will be live! diff --git a/mem0-rag/docs/examples/notebooks-and-replits.mdx b/mem0-rag/docs/examples/notebooks-and-replits.mdx new file mode 100644 index 0000000000..2da7208a4c --- /dev/null +++ b/mem0-rag/docs/examples/notebooks-and-replits.mdx @@ -0,0 +1,138 @@ +--- +title: Notebooks & Replits +--- + +# Explore awesome apps + +Check out the remarkable work accomplished using [Embedchain](https://app.embedchain.ai/custom-gpts/). + +## Collection of Google colab notebook and Replit links for users + +Get started with Embedchain by trying out the examples below. You can run the examples in your browser using Google Colab or Replit. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
LLMGoogle ColabReplit
OpenAIOpen In ColabTry with Replit Badge
AnthropicOpen In ColabTry with Replit Badge
Azure OpenAIOpen In ColabTry with Replit Badge
VertexAIOpen In ColabTry with Replit Badge
CohereOpen In ColabTry with Replit Badge
TogetherOpen In Colab
OllamaOpen In Colab
Hugging FaceOpen In ColabTry with Replit Badge
JinaChatOpen In ColabTry with Replit Badge
GPT4AllOpen In ColabTry with Replit Badge
Llama2Open In ColabTry with Replit Badge
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Embedding modelGoogle ColabReplit
OpenAIOpen In ColabTry with Replit Badge
VertexAIOpen In ColabTry with Replit Badge
GPT4AllOpen In ColabTry with Replit Badge
Hugging FaceOpen In ColabTry with Replit Badge
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Vector DBGoogle ColabReplit
ChromaDBOpen In ColabTry with Replit Badge
ElasticsearchOpen In ColabTry with Replit Badge
OpensearchOpen In ColabTry with Replit Badge
PineconeOpen In ColabTry with Replit Badge
\ No newline at end of file diff --git a/mem0-rag/docs/examples/openai-assistant.mdx b/mem0-rag/docs/examples/openai-assistant.mdx new file mode 100644 index 0000000000..ffd312fa71 --- /dev/null +++ b/mem0-rag/docs/examples/openai-assistant.mdx @@ -0,0 +1,60 @@ +--- +title: 'OpenAI Assistant' +--- + +OpenAI Logo + +Embedchain now supports [OpenAI Assistants API](https://platform.openai.com/docs/assistants/overview) which allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. + +At a high level, an integration of the Assistants API has the following flow: + +1. Create an Assistant in the API by defining custom instructions and picking a model +2. Create a Thread when a user starts a conversation +3. Add Messages to the Thread as the user ask questions +4. Run the Assistant on the Thread to trigger responses. This automatically calls the relevant tools. + +Creating an OpenAI Assistant using Embedchain is very simple 3 step process. + +## Step 1: Create OpenAI Assistant + +Make sure that you have `OPENAI_API_KEY` set in the environment variable. + +```python Initialize +from embedchain.store.assistants import OpenAIAssistant + +assistant = OpenAIAssistant( + name="OpenAI DevDay Assistant", + instructions="You are an organizer of OpenAI DevDay", +) +``` + +If you want to use the existing assistant, you can do something like this: + +```python Initialize +# Load an assistant and create a new thread +assistant = OpenAIAssistant(assistant_id="asst_xxx") + +# Load a specific thread for an assistant +assistant = OpenAIAssistant(assistant_id="asst_xxx", thread_id="thread_xxx") +``` + +## Step-2: Add data to thread + +You can add any custom data source that is supported by Embedchain. Else, you can directly pass the file path on your local system and Embedchain propagates it to OpenAI Assistant. +```python Add data +assistant.add("/path/to/file.pdf") +assistant.add("https://www.youtube.com/watch?v=U9mJuUkhUzk") +assistant.add("https://openai.com/blog/new-models-and-developer-products-announced-at-devday") +``` + +## Step-3: Chat with your Assistant +```python Chat +assistant.chat("How much OpenAI credits were offered to attendees during OpenAI DevDay?") +# Response: 'Every attendee of OpenAI DevDay 2023 was offered $500 in OpenAI credits.' +``` + +You can try it out yourself using the following Google Colab notebook: + + + Open in Colab + diff --git a/mem0-rag/docs/examples/opensource-assistant.mdx b/mem0-rag/docs/examples/opensource-assistant.mdx new file mode 100644 index 0000000000..f4dcaa5214 --- /dev/null +++ b/mem0-rag/docs/examples/opensource-assistant.mdx @@ -0,0 +1,51 @@ +--- +title: 'Open-Source AI Assistant' +--- + +Embedchain also provides support for creating Open-Source AI Assistants (similar to [OpenAI Assistants API](https://platform.openai.com/docs/assistants/overview)) which allows you to build AI assistants within your own applications using any LLM (OpenAI or otherwise). An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. + +At a high level, the Open-Source AI Assistants API has the following flow: + +1. Create an AI Assistant by picking a model +2. Create a Thread when a user starts a conversation +3. Add Messages to the Thread as the user ask questions +4. Run the Assistant on the Thread to trigger responses. This automatically calls the relevant tools. + +Creating an Open-Source AI Assistant is a simple 3 step process. + +## Step 1: Instantiate AI Assistant + +```python Initialize +from embedchain.store.assistants import AIAssistant + +assistant = AIAssistant( + name="My Assistant", + data_sources=[{"source": "https://www.youtube.com/watch?v=U9mJuUkhUzk"}]) +``` + +If you want to use the existing assistant, you can do something like this: + +```python Initialize +# Load an assistant and create a new thread +assistant = AIAssistant(assistant_id="asst_xxx") + +# Load a specific thread for an assistant +assistant = AIAssistant(assistant_id="asst_xxx", thread_id="thread_xxx") +``` + +## Step-2: Add data to thread + +You can add any custom data source that is supported by Embedchain. Else, you can directly pass the file path on your local system and Embedchain propagates it to OpenAI Assistant. + +```python Add data +assistant.add("/path/to/file.pdf") +assistant.add("https://www.youtube.com/watch?v=U9mJuUkhUzk") +assistant.add("https://openai.com/blog/new-models-and-developer-products-announced-at-devday") +``` + +## Step-3: Chat with your AI Assistant + +```python Chat +assistant.chat("How much OpenAI credits were offered to attendees during OpenAI DevDay?") +# Response: 'Every attendee of OpenAI DevDay 2023 was offered $500 in OpenAI credits.' +``` diff --git a/mem0-rag/docs/examples/poe_bot.mdx b/mem0-rag/docs/examples/poe_bot.mdx new file mode 100644 index 0000000000..58e831f22a --- /dev/null +++ b/mem0-rag/docs/examples/poe_bot.mdx @@ -0,0 +1,59 @@ +--- +title: '🔮 Poe Bot' +--- + +### 🚀 Getting started + +1. Install embedchain python package: + +```bash +pip install fastapi-poe==0.0.16 +``` + +2. Create a free account on [Poe](https://www.poe.com?utm_source=embedchain). +3. Click "Create Bot" button on top left. +4. Give it a handle and an optional description. +5. Select `Use API`. +6. Under `API URL` enter your server or ngrok address. You can use your machine's public IP or DNS. Otherwise, employ a proxy server like [ngrok](https://ngrok.com/) to make your local bot accessible. +7. Copy your api key and paste it in `.env` as `POE_API_KEY`. +8. You will need to set `OPENAI_API_KEY` for generating embeddings and using LLM. Copy your OpenAI API key from [here](https://platform.openai.com/account/api-keys) and paste it in `.env` as `OPENAI_API_KEY`. +9. Now create your bot using the following code snippet. + +```bash +# make sure that you have set OPENAI_API_KEY and POE_API_KEY in .env file +from embedchain.bots import PoeBot + +poe_bot = PoeBot() + +# add as many data sources as you want +poe_bot.add("https://en.wikipedia.org/wiki/Adam_D%27Angelo") +poe_bot.add("https://www.youtube.com/watch?v=pJQVAqmKua8") + +# start the bot +# this start the poe bot server on port 8080 by default +poe_bot.start() +``` + +10. You can paste the above in a file called `your_script.py` and then simply do + +```bash +python your_script.py +``` + +Now your bot will start running at port `8080` by default. + +11. You can refer the [Supported Data formats](https://docs.embedchain.ai/advanced/data_types) section to refer the supported data types in embedchain. + +12. Click `Run check` to make sure your machine can be reached. +13. Make sure your bot is private if that's what you want. +14. Click `Create bot` at the bottom to finally create the bot +15. Now your bot is created. + +### 💬 How to use + +- To ask the bot questions, just type your query in the Poe interface: +```text + +``` + +- If you wish to add more data source to the bot, simply update your script and add as many `.add` as you like. You need to restart the server. diff --git a/mem0-rag/docs/examples/rest-api/add-data.mdx b/mem0-rag/docs/examples/rest-api/add-data.mdx new file mode 100644 index 0000000000..05ed37968d --- /dev/null +++ b/mem0-rag/docs/examples/rest-api/add-data.mdx @@ -0,0 +1,22 @@ +--- +openapi: post /{app_id}/add +--- + + + +```bash Request +curl --request POST \ + --url http://localhost:8080/{app_id}/add \ + -d "source=https://www.forbes.com/profile/elon-musk" \ + -d "data_type=web_page" +``` + + + + + +```json Response +{ "response": "fec7fe91e6b2d732938a2ec2e32bfe3f" } +``` + + diff --git a/mem0-rag/docs/examples/rest-api/chat.mdx b/mem0-rag/docs/examples/rest-api/chat.mdx new file mode 100644 index 0000000000..2571bf716f --- /dev/null +++ b/mem0-rag/docs/examples/rest-api/chat.mdx @@ -0,0 +1,3 @@ +--- +openapi: post /{app_id}/chat +--- \ No newline at end of file diff --git a/mem0-rag/docs/examples/rest-api/check-status.mdx b/mem0-rag/docs/examples/rest-api/check-status.mdx new file mode 100644 index 0000000000..0893cba4c2 --- /dev/null +++ b/mem0-rag/docs/examples/rest-api/check-status.mdx @@ -0,0 +1,20 @@ +--- +openapi: get /ping +--- + + + +```bash Request + curl --request GET \ + --url http://localhost:8080/ping +``` + + + + + +```json Response +{ "ping": "pong" } +``` + + diff --git a/mem0-rag/docs/examples/rest-api/create.mdx b/mem0-rag/docs/examples/rest-api/create.mdx new file mode 100644 index 0000000000..6736bf7add --- /dev/null +++ b/mem0-rag/docs/examples/rest-api/create.mdx @@ -0,0 +1,96 @@ +--- +openapi: post /create +--- + + + +```bash Request +curl --request POST \ + --url http://localhost:8080/create?app_id=app1 \ + -F "config=@/path/to/config.yaml" +``` + + + + + +```json Response +{ "response": "App created successfully. App ID: app1" } +``` + + + +By default we will use the opensource **gpt4all** model to get started. You can also specify your own config by uploading a config YAML file. + +For example, create a `config.yaml` file (adjust according to your requirements): + +```yaml +app: + config: + id: "default-app" + +llm: + provider: openai + config: + model: "gpt-3.5-turbo" + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + prompt: | + Use the following pieces of context to answer the query at the end. + If you don't know the answer, just say that you don't know, don't try to make up an answer. + + $context + + Query: $query + + Helpful Answer: + +vectordb: + provider: chroma + config: + collection_name: "rest-api-app" + dir: db + allow_reset: true + +embedder: + provider: openai + config: + model: "text-embedding-ada-002" +``` + +To learn more about custom configurations, check out the [custom configurations docs](https://docs.embedchain.ai/advanced/configuration). To explore more examples of config yamls for embedchain, visit [embedchain/configs](https://github.com/embedchain/embedchain/tree/main/configs). + +Now, you can upload this config file in the request body. + +For example, + +```bash Request +curl --request POST \ + --url http://localhost:8080/create?app_id=my-app \ + -F "config=@/path/to/config.yaml" +``` + +**Note:** To use custom models, an **API key** might be required. Refer to the table below to determine the necessary API key for your provider. + +| Keys | Providers | +| -------------------------- | ------------------------------ | +| `OPENAI_API_KEY ` | OpenAI, Azure OpenAI, Jina etc | +| `OPENAI_API_TYPE` | Azure OpenAI | +| `OPENAI_API_BASE` | Azure OpenAI | +| `OPENAI_API_VERSION` | Azure OpenAI | +| `COHERE_API_KEY` | Cohere | +| `TOGETHER_API_KEY` | Together | +| `ANTHROPIC_API_KEY` | Anthropic | +| `JINACHAT_API_KEY` | Jina | +| `HUGGINGFACE_ACCESS_TOKEN` | Huggingface | +| `REPLICATE_API_TOKEN` | LLAMA2 | + +To add env variables, you can simply run the docker command with the `-e` flag. + +For example, + +```bash +docker run --name embedchain -p 8080:8080 -e OPENAI_API_KEY= embedchain/rest-api:latest +``` \ No newline at end of file diff --git a/mem0-rag/docs/examples/rest-api/delete.mdx b/mem0-rag/docs/examples/rest-api/delete.mdx new file mode 100644 index 0000000000..3aada33981 --- /dev/null +++ b/mem0-rag/docs/examples/rest-api/delete.mdx @@ -0,0 +1,21 @@ +--- +openapi: delete /{app_id}/delete +--- + + + + +```bash Request + curl --request DELETE \ + --url http://localhost:8080/{app_id}/delete +``` + + + + + +```json Response +{ "response": "App with id {app_id} deleted successfully." } +``` + + diff --git a/mem0-rag/docs/examples/rest-api/deploy.mdx b/mem0-rag/docs/examples/rest-api/deploy.mdx new file mode 100644 index 0000000000..b72f91da06 --- /dev/null +++ b/mem0-rag/docs/examples/rest-api/deploy.mdx @@ -0,0 +1,22 @@ +--- +openapi: post /{app_id}/deploy +--- + + + + +```bash Request +curl --request POST \ + --url http://localhost:8080/{app_id}/deploy \ + -d "api_key=ec-xxxx" +``` + + + + + +```json Response +{ "response": "App deployed successfully." } +``` + + diff --git a/mem0-rag/docs/examples/rest-api/get-all-apps.mdx b/mem0-rag/docs/examples/rest-api/get-all-apps.mdx new file mode 100644 index 0000000000..6f603f9a68 --- /dev/null +++ b/mem0-rag/docs/examples/rest-api/get-all-apps.mdx @@ -0,0 +1,33 @@ +--- +openapi: get /apps +--- + + + +```bash Request +curl --request GET \ + --url http://localhost:8080/apps +``` + + + + + +```json Response +{ + "results": [ + { + "config": "config1.yaml", + "id": 1, + "app_id": "app1" + }, + { + "config": "config2.yaml", + "id": 2, + "app_id": "app2" + } + ] +} +``` + + diff --git a/mem0-rag/docs/examples/rest-api/get-data.mdx b/mem0-rag/docs/examples/rest-api/get-data.mdx new file mode 100644 index 0000000000..0c960e6cb9 --- /dev/null +++ b/mem0-rag/docs/examples/rest-api/get-data.mdx @@ -0,0 +1,28 @@ +--- +openapi: get /{app_id}/data +--- + + + +```bash Request +curl --request GET \ + --url http://localhost:8080/{app_id}/data +``` + + + + + +```json Response +{ + "results": [ + { + "data_type": "web_page", + "data_value": "https://www.forbes.com/profile/elon-musk/", + "metadata": "null" + } + ] +} +``` + + diff --git a/mem0-rag/docs/examples/rest-api/getting-started.mdx b/mem0-rag/docs/examples/rest-api/getting-started.mdx new file mode 100644 index 0000000000..5501792b67 --- /dev/null +++ b/mem0-rag/docs/examples/rest-api/getting-started.mdx @@ -0,0 +1,294 @@ +--- +title: "🌍 Getting Started" +--- + +## Quickstart + +To use Embedchain as a REST API service, run the following command: + +```bash +docker run --name embedchain -p 8080:8080 embedchain/rest-api:latest +``` + +Navigate to [http://localhost:8080/docs](http://localhost:8080/docs) to interact with the API. There is a full-fledged Swagger docs playground with all the information about the API endpoints. + +![Swagger Docs Screenshot](https://github.com/embedchain/embedchain/assets/73601258/299d81e5-a0df-407c-afc2-6fa2c4286844) + +## ⚡ Steps to get started + + + + + + ```bash + curl --request POST "http://localhost:8080/create?app_id=my-app" \ + -H "accept: application/json" + ``` + + + ```python + import requests + + url = "http://localhost:8080/create?app_id=my-app" + + payload={} + + response = requests.request("POST", url, data=payload) + + print(response) + ``` + + + ```javascript + const data = fetch("http://localhost:8080/create?app_id=my-app", { + method: "POST", + }).then((res) => res.json()); + + console.log(data); + ``` + + + ```go + package main + + import ( + "fmt" + "net/http" + "io/ioutil" + ) + + func main() { + + url := "http://localhost:8080/create?app_id=my-app" + + payload := strings.NewReader("") + + req, _ := http.NewRequest("POST", url, payload) + + req.Header.Add("Content-Type", "application/json") + + res, _ := http.DefaultClient.Do(req) + + defer res.Body.Close() + body, _ := ioutil.ReadAll(res.Body) + + fmt.Println(res) + fmt.Println(string(body)) + + } + ``` + + + + + + + + ```bash + curl --request POST \ + --url http://localhost:8080/my-app/add \ + -d "source=https://www.forbes.com/profile/elon-musk" \ + -d "data_type=web_page" + ``` + + + ```python + import requests + + url = "http://localhost:8080/my-app/add" + + payload = "source=https://www.forbes.com/profile/elon-musk&data_type=web_page" + headers = {} + + response = requests.request("POST", url, headers=headers, data=payload) + + print(response) + ``` + + + ```javascript + const data = fetch("http://localhost:8080/my-app/add", { + method: "POST", + body: "source=https://www.forbes.com/profile/elon-musk&data_type=web_page", + }).then((res) => res.json()); + + console.log(data); + ``` + + + ```go + package main + + import ( + "fmt" + "strings" + "net/http" + "io/ioutil" + ) + + func main() { + + url := "http://localhost:8080/my-app/add" + + payload := strings.NewReader("source=https://www.forbes.com/profile/elon-musk&data_type=web_page") + + req, _ := http.NewRequest("POST", url, payload) + + req.Header.Add("Content-Type", "application/x-www-form-urlencoded") + + res, _ := http.DefaultClient.Do(req) + + defer res.Body.Close() + body, _ := ioutil.ReadAll(res.Body) + + fmt.Println(res) + fmt.Println(string(body)) + + } + ``` + + + + + + + + ```bash + curl --request POST \ + --url http://localhost:8080/my-app/query \ + -d "query=Who is Elon Musk?" + ``` + + + ```python + import requests + + url = "http://localhost:8080/my-app/query" + + payload = "query=Who is Elon Musk?" + headers = {} + + response = requests.request("POST", url, headers=headers, data=payload) + + print(response) + ``` + + + ```javascript + const data = fetch("http://localhost:8080/my-app/query", { + method: "POST", + body: "query=Who is Elon Musk?", + }).then((res) => res.json()); + + console.log(data); + ``` + + + ```go + package main + + import ( + "fmt" + "strings" + "net/http" + "io/ioutil" + ) + + func main() { + + url := "http://localhost:8080/my-app/query" + + payload := strings.NewReader("query=Who is Elon Musk?") + + req, _ := http.NewRequest("POST", url, payload) + + req.Header.Add("Content-Type", "application/x-www-form-urlencoded") + + res, _ := http.DefaultClient.Do(req) + + defer res.Body.Close() + body, _ := ioutil.ReadAll(res.Body) + + fmt.Println(res) + fmt.Println(string(body)) + + } + ``` + + + + + + + + ```bash + curl --request POST \ + --url http://localhost:8080/my-app/deploy \ + -d "api_key=ec-xxxx" + ``` + + + ```python + import requests + + url = "http://localhost:8080/my-app/deploy" + + payload = "api_key=ec-xxxx" + + response = requests.request("POST", url, data=payload) + + print(response) + ``` + + + ```javascript + const data = fetch("http://localhost:8080/my-app/deploy", { + method: "POST", + body: "api_key=ec-xxxx", + }).then((res) => res.json()); + + console.log(data); + ``` + + + ```go + package main + + import ( + "fmt" + "strings" + "net/http" + "io/ioutil" + ) + + func main() { + + url := "http://localhost:8080/my-app/deploy" + + payload := strings.NewReader("api_key=ec-xxxx") + + req, _ := http.NewRequest("POST", url, payload) + + req.Header.Add("Content-Type", "application/x-www-form-urlencoded") + + res, _ := http.DefaultClient.Do(req) + + defer res.Body.Close() + body, _ := ioutil.ReadAll(res.Body) + + fmt.Println(res) + fmt.Println(string(body)) + + } + ``` + + + + + + +And you're ready! 🎉 + +If you run into issues, please feel free to contact us using below links: + + diff --git a/mem0-rag/docs/examples/rest-api/query.mdx b/mem0-rag/docs/examples/rest-api/query.mdx new file mode 100644 index 0000000000..2d647e5058 --- /dev/null +++ b/mem0-rag/docs/examples/rest-api/query.mdx @@ -0,0 +1,21 @@ +--- +openapi: post /{app_id}/query +--- + + + +```bash Request +curl --request POST \ + --url http://localhost:8080/{app_id}/query \ + -d "query=who is Elon Musk?" +``` + + + + + +```json Response +{ "response": "Net worth of Elon Musk is $218 Billion." } +``` + + diff --git a/mem0-rag/docs/examples/showcase.mdx b/mem0-rag/docs/examples/showcase.mdx new file mode 100644 index 0000000000..d614c3b002 --- /dev/null +++ b/mem0-rag/docs/examples/showcase.mdx @@ -0,0 +1,115 @@ +--- +title: '🎪 Community showcase' +--- + +Embedchain community has been super active in creating demos on top of Embedchain. On this page, we showcase all the apps, blogs, videos, and tutorials created by the community. ❤️ + +## Apps + +### Open Source + +- [My GSoC23 bot- Streamlit chat](https://github.com/lucifertrj/EmbedChain_GSoC23_BOT) by Tarun Jain +- [Discord Bot for LLM chat](https://github.com/Reidond/discord_bots_playground/tree/c8b0c36541e4b393782ee506804c4b6962426dd6/python/chat-channel-bot) by Reidond +- [EmbedChain-Streamlit-Docker App](https://github.com/amjadraza/embedchain-streamlit-app) by amjadraza +- [Harry Potter Philosphers Stone Bot](https://github.com/vinayak-kempawad/Harry_Potter_Philosphers_Stone_Bot/) by Vinayak Kempawad, ([LinkedIn post](https://www.linkedin.com/feed/update/urn:li:activity:7080907532155686912/)) +- [LLM bot trained on own messages](https://github.com/Harin329/harinBot) by Hao Wu + +### Closed Source + +- [Taobot.io](https://taobot.io) - chatbot & knowledgebase hybrid by [cachho](https://github.com/cachho) +- [Create Instant ChatBot 🤖 using embedchain](https://databutton.com/v/h3e680h9) by Avra, ([Tweet](https://twitter.com/Avra_b/status/1674704745154641920/)) +- [JOBO 🤖 — The AI-driven sidekick to craft your resume](https://try-jobo.com/) by Enrico Willemse, ([LinkedIn Post](https://www.linkedin.com/posts/enrico-willemse_jobai-gptfun-embedchain-activity-7090340080879374336-ueLB/)) +- [Explore Your Knowledge Base: Interactive chats over various forms of documents](https://chatdocs.dkedar.com/) by Kedar Dabhadkar, ([LinkedIn Post](https://www.linkedin.com/posts/dkedar7_machinelearning-llmops-activity-7092524836639424513-2O3L/)) +- [Chatbot trained on 1000+ videos of Ester hicks the co-author behind the famous book Secret](https://askabraham.tokenofme.io/) by Mohan Kumar + + +## Templates + +### Replit +- [Embedchain Chat Bot](https://replit.com/@taranjeet1/Embedchain-Chat-Bot) by taranjeetio +- [Embedchain Memory Chat Bot Template](https://replit.com/@taranjeetio/Embedchain-Memory-Chat-Bot-Template) by taranjeetio +- [Chatbot app to demonstrate question-answering using retrieved information](https://replit.com/@AllisonMorrell/EmbedChainlitPublic) by Allison Morrell, ([LinkedIn Post](https://www.linkedin.com/posts/allison-morrell-2889275a_retrievalbot-screenshots-activity-7080339991754649600-wihZ/)) + +## Posts + +### Blogs + +- [Customer Service LINE Bot](https://www.evanlin.com/langchain-embedchain/) by Evan Lin +- [Chatbot in Under 5 mins using Embedchain](https://medium.com/@ayush.wattal/chatbot-in-under-5-mins-using-embedchain-a4f161fcf9c5) by Ayush Wattal +- [Understanding what the LLM framework embedchain does](https://zenn.dev/hijikix/articles/4bc8d60156a436) by Daisuke Hashimoto +- [In bed with GPT and Node.js](https://dev.to/worldlinetech/in-bed-with-gpt-and-nodejs-4kh2) by Raphaël Semeteys, ([LinkedIn Post](https://www.linkedin.com/posts/raphaelsemeteys_in-bed-with-gpt-and-nodejs-activity-7088113552326029313-nn87/)) +- [Using Embedchain — A powerful LangChain Python wrapper to build Chat Bots even faster!⚡](https://medium.com/@avra42/using-embedchain-a-powerful-langchain-python-wrapper-to-build-chat-bots-even-faster-35c12994a360) by Avra, ([Tweet](https://twitter.com/Avra_b/status/1686767751560310784/)) +- [What is the Embedchain library?](https://jahaniwww.com/%da%a9%d8%aa%d8%a7%d8%a8%d8%ae%d8%a7%d9%86%d9%87-embedchain/) by Ali Jahani, ([LinkedIn Post](https://www.linkedin.com/posts/ajahani_aepaetaeqaexaggahyaeu-aetaexaesabraeaaeqaepaeu-activity-7097605202135904256-ppU-/)) +- [LangChain is Nice, But Have You Tried EmbedChain ?](https://medium.com/thoughts-on-machine-learning/langchain-is-nice-but-have-you-tried-embedchain-215a34421cde) by FS Ndzomga, ([Tweet](https://twitter.com/ndzfs/status/1695583640372035951/)) +- [Simplest Method to Build a Custom Chatbot with GPT-3.5 (via Embedchain)](https://www.ainewsletter.today/p/simplest-method-to-build-a-custom) by Arjun, ([Tweet](https://twitter.com/aiguy_arjun/status/1696393808467091758/)) + +### LinkedIn + +- [What is embedchain](https://www.linkedin.com/posts/activity-7079393104423698432-wRyi/) by Rithesh Sreenivasan +- [Building a chatbot with EmbedChain](https://www.linkedin.com/posts/activity-7078434598984060928-Zdso/) by Lior Sinclair +- [Making chatbot without vs with embedchain](https://www.linkedin.com/posts/kalyanksnlp_llms-chatbots-langchain-activity-7077453416221863936-7N1L/) by Kalyan KS +- [EmbedChain - very intuitive, first you index your data and then query!](https://www.linkedin.com/posts/shubhamsaboo_embedchain-a-framework-to-easily-create-activity-7079535460699557888-ad1X/) by Shubham Saboo +- [EmbedChain - Harnessing power of LLM](https://www.linkedin.com/posts/uditsaini_chatbotrevolution-llmpoweredbots-embedchainframework-activity-7077520356827181056-FjTK/) by Udit S. +- [AI assistant for ABBYY Vantage](https://www.linkedin.com/posts/maximevermeir_llm-github-abbyy-activity-7081658972071424000-fXfZ/) by Maxime V. +- [About embedchain](https://www.linkedin.com/feed/update/urn:li:activity:7080984218914189312/) by Morris Lee +- [How to use Embedchain](https://www.linkedin.com/posts/nehaabansal_github-embedchainembedchain-framework-activity-7085830340136595456-kbW5/) by Neha Bansal +- [Youtube/Webpage summary for Energy Study](https://www.linkedin.com/posts/bar%C4%B1%C5%9F-sanl%C4%B1-34b82715_enerji-python-activity-7082735341563977730-Js0U/) by Barış Sanlı, ([Tweet](https://twitter.com/barissanli/status/1676968784979193857/)) +- [Demo: How to use Embedchain? (Contains Collab Notebook link)](https://www.linkedin.com/posts/liorsinclair_embedchain-is-getting-a-lot-of-traction-because-activity-7103044695995424768-RckT/) by Lior Sinclair + +### Twitter + +- [What is embedchain](https://twitter.com/AlphaSignalAI/status/1672668574450847745) by Lior +- [Building a chatbot with Embedchain](https://twitter.com/Saboo_Shubham_/status/1673537044419686401) by Shubham Saboo +- [Chatbot docker image behind an API with yaml configs with Embedchain](https://twitter.com/tricalt/status/1678411430192730113/) by Vasilije +- [Build AI powered PDF chatbot with just five lines of Python code with Embedchain!](https://twitter.com/Saboo_Shubham_/status/1676627104866156544/) by Shubham Saboo +- [Chatbot against a youtube video using embedchain](https://twitter.com/smaameri/status/1675201443043704834/) by Sami Maameri +- [Highlights of EmbedChain](https://twitter.com/carl_AIwarts/status/1673542204328120321/) by carl_AIwarts +- [Build Llama-2 chatbot in less than 5 minutes](https://twitter.com/Saboo_Shubham_/status/1682168956918833152/) by Shubham Saboo +- [All cool features of embedchain](https://twitter.com/DhravyaShah/status/1683497882438217728/) by Dhravya Shah, ([LinkedIn Post](https://www.linkedin.com/posts/dhravyashah_what-if-i-tell-you-that-you-can-make-an-ai-activity-7089459599287726080-ZIYm/)) +- [Read paid Medium articles for Free using embedchain](https://twitter.com/kumarkaushal_/status/1688952961622585344) by Kaushal Kumar + +## Videos + +- [Embedchain in one shot](https://www.youtube.com/watch?v=vIhDh7H73Ww&t=82s) by AI with Tarun +- [embedChain Create LLM powered bots over any dataset Python Demo Tesla Neurallink Chatbot Example](https://www.youtube.com/watch?v=bJqAn22a6Gc) by Rithesh Sreenivasan +- [Embedchain - NEW 🔥 Langchain BABY to build LLM Bots](https://www.youtube.com/watch?v=qj_GNQ06I8o) by 1littlecoder +- [EmbedChain -- NEW!: Build LLM-Powered Bots with Any Dataset](https://www.youtube.com/watch?v=XmaBezzGHu4) by DataInsightEdge +- [Chat With Your PDFs in less than 10 lines of code! EMBEDCHAIN tutorial](https://www.youtube.com/watch?v=1ugkcsAcw44) by Phani Reddy +- [How To Create A Custom Knowledge AI Powered Bot | Install + How To Use](https://www.youtube.com/watch?v=VfCrIiAst-c) by The Ai Solopreneur +- [Build Custom Chatbot in 6 min with this Framework [Beginner Friendly]](https://www.youtube.com/watch?v=-8HxOpaFySM) by Maya Akim +- [embedchain-streamlit-app](https://www.youtube.com/watch?v=3-9GVd-3v74) by Amjad Raza +- [🤖CHAT with ANY ONLINE RESOURCES using EMBEDCHAIN - a LangChain wrapper, in few lines of code !](https://www.youtube.com/watch?v=Mp7zJe4TIdM) by Avra +- [Building resource-driven LLM-powered bots with Embedchain](https://www.youtube.com/watch?v=IVfcAgxTO4I) by BugBytes +- [embedchain-streamlit-demo](https://www.youtube.com/watch?v=yJAWB13FhYQ) by Amjad Raza +- [Embedchain - create your own AI chatbots using open source models](https://www.youtube.com/shorts/O3rJWKwSrWE) by Dhravya Shah +- [AI ChatBot in 5 lines Python Code](https://www.youtube.com/watch?v=zjWvLJLksv8) by Data Engineering +- [Interview with Karl Marx](https://www.youtube.com/watch?v=5Y4Tscwj1xk) by Alexander Ray Williams +- [Vlog where we try to build a bot based on our content on the internet](https://www.youtube.com/watch?v=I2w8CWM3bx4) by DV, ([Tweet](https://twitter.com/dvcoolster/status/1688387017544261632)) +- [CHAT with ANY ONLINE RESOURCES using EMBEDCHAIN|STREAMLIT with MEMORY |All OPENSOURCE](https://www.youtube.com/watch?v=TqQIHWoWTDQ&pp=ygUKZW1iZWRjaGFpbg%3D%3D) by DataInsightEdge +- [Build POWERFUL LLM Bots EASILY with Your Own Data - Embedchain - Langchain 2.0? (Tutorial)](https://www.youtube.com/watch?v=jE24Y_GasE8) by WorldofAI, ([Tweet](https://twitter.com/intheworldofai/status/1696229166922780737)) +- [Embedchain: An AI knowledge base assistant for customizing enterprise private data, which can be connected to discord, whatsapp, slack, tele and other terminals (with gradio to build a request interface) in Chinese](https://www.youtube.com/watch?v=5RZzCJRk-d0) by AIGC LINK +- [Embedchain Introduction](https://www.youtube.com/watch?v=Jet9zAqyggI) by Fahd Mirza + +## Mentions + +### Github repos + +- [Awesome-LLM](https://github.com/Hannibal046/Awesome-LLM) +- [awesome-chatgpt-api](https://github.com/reorx/awesome-chatgpt-api) +- [awesome-langchain](https://github.com/kyrolabs/awesome-langchain) +- [Awesome-Prompt-Engineering](https://github.com/promptslab/Awesome-Prompt-Engineering) +- [awesome-chatgpt](https://github.com/eon01/awesome-chatgpt) +- [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) +- [awesome-generative-ai](https://github.com/filipecalegario/awesome-generative-ai) +- [awesome-gpt](https://github.com/formulahendry/awesome-gpt) +- [awesome-ChatGPT-repositories](https://github.com/taishi-i/awesome-ChatGPT-repositories) +- [awesome-gpt-prompt-engineering](https://github.com/snwfdhmp/awesome-gpt-prompt-engineering) +- [awesome-chatgpt](https://github.com/awesome-chatgpt/awesome-chatgpt) +- [awesome-llm-and-aigc](https://github.com/sjinzh/awesome-llm-and-aigc) +- [awesome-compbio-chatgpt](https://github.com/csbl-br/awesome-compbio-chatgpt) +- [Awesome-LLM4Tool](https://github.com/OpenGVLab/Awesome-LLM4Tool) + +## Meetups + +- [Dash and ChatGPT: Future of AI-enabled apps 30/08/23](https://go.plotly.com/dash-chatgpt) +- [Pie & AI: Bangalore - Build end-to-end LLM app using Embedchain 01/09/23](https://www.eventbrite.com/e/pie-ai-bangalore-build-end-to-end-llm-app-using-embedchain-tickets-698045722547) diff --git a/mem0-rag/docs/examples/slack-AI.mdx b/mem0-rag/docs/examples/slack-AI.mdx new file mode 100644 index 0000000000..7efaba2797 --- /dev/null +++ b/mem0-rag/docs/examples/slack-AI.mdx @@ -0,0 +1,67 @@ +[Embedchain Examples Repo](https://github.com/embedchain/examples) contains code on how to build your own Slack AI to chat with the unstructured data lying in your slack channels. + +![Slack AI Demo](/images/slack-ai.png) + +## Getting started + +Create a Slack AI involves 3 steps + +* Create slack user +* Set environment variables +* Run the app locally + +### Step 1: Create Slack user token + +Follow the steps given below to fetch your slack user token to get data through Slack APIs: + +1. Create a workspace on Slack if you don’t have one already by clicking [here](https://slack.com/intl/en-in/). +2. Create a new App on your Slack account by going [here](https://api.slack.com/apps). +3. Select `From Scratch`, then enter the App Name and select your workspace. +4. Navigate to `OAuth & Permissions` tab from the left sidebar and go to the `scopes` section. Add the following scopes under `User Token Scopes`: + + ``` + # Following scopes are needed for reading channel history + channels:history + channels:read + + # Following scopes are needed to fetch list of channels from slack + groups:read + mpim:read + im:read + ``` + +5. Click on the `Install to Workspace` button under `OAuth Tokens for Your Workspace` section in the same page and install the app in your slack workspace. +6. After installing the app you will see the `User OAuth Token`, save that token as you will need to configure it as `SLACK_USER_TOKEN` for this demo. + +### Step 2: Set environment variables + +Navigate to `api` folder and set your `HUGGINGFACE_ACCESS_TOKEN` and `SLACK_USER_TOKEN` in `.env.example` file. Then rename the `.env.example` file to `.env`. + + + +By default, we use `Mixtral` model from Hugging Face. However, if you prefer to use OpenAI model, then set `OPENAI_API_KEY` instead of `HUGGINGFACE_ACCESS_TOKEN` along with `SLACK_USER_TOKEN` in `.env` file, and update the code in `api/utils/app.py` file to use OpenAI model instead of Hugging Face model. + + +### Step 3: Run app locally + +Follow the instructions given below to run app locally based on your development setup (with docker or without docker): + +#### With docker + +```bash +docker-compose build +ec start --docker +``` + +#### Without docker + +```bash +ec install-reqs +ec start +``` + +Finally, you will have the Slack AI frontend running on http://localhost:3000. You can also access the REST APIs on http://localhost:8000. + +## Credits + +This demo was built using the Embedchain's [full stack demo template](https://docs.embedchain.ai/get-started/full-stack). Follow the instructions [given here](https://docs.embedchain.ai/get-started/full-stack) to create your own full stack RAG application. diff --git a/mem0-rag/docs/examples/slack_bot.mdx b/mem0-rag/docs/examples/slack_bot.mdx new file mode 100644 index 0000000000..034c821d2f --- /dev/null +++ b/mem0-rag/docs/examples/slack_bot.mdx @@ -0,0 +1,50 @@ +--- +title: '💼 Slack Bot' +--- + +### 🖼️ Setup + +1. Create a workspace on Slack if you don't have one already by clicking [here](https://slack.com/intl/en-in/). +2. Create a new App on your Slack account by going [here](https://api.slack.com/apps). +3. Select `From Scratch`, then enter the Bot Name and select your workspace. +4. On the left Sidebar, go to `OAuth and Permissions` and add the following scopes under `Bot Token Scopes`: +```text +app_mentions:read +channels:history +channels:read +chat:write +``` +5. Now select the option `Install to Workspace` and after it's done, copy the `Bot User OAuth Token` and set it in your secrets as `SLACK_BOT_TOKEN`. +6. Run your bot now, + + + ```bash + docker run --name slack-bot -e OPENAI_API_KEY=sk-xxx -e SLACK_BOT_TOKEN=xxx -p 8000:8000 embedchain/slack-bot + ``` + + + ```bash + pip install --upgrade "embedchain[slack]" + python3 -m embedchain.bots.slack --port 8000 + ``` + + +7. Expose your bot to the internet. You can use your machine's public IP or DNS. Otherwise, employ a proxy server like [ngrok](https://ngrok.com/) to make your local bot accessible. +8. On the Slack API website go to `Event Subscriptions` on the left Sidebar and turn on `Enable Events`. +9. In `Request URL`, enter your server or ngrok address. +10. After it gets verified, click on `Subscribe to bot events`, add `message.channels` Bot User Event and click on `Save Changes`. +11. Now go to your workspace, right click on the bot name in the sidebar, click `view app details`, then `add this app to a channel`. + +### 🚀 Usage Instructions + +- Go to the channel where you have added your bot. +- To add data sources to the bot, use the command: +```text +add +``` +- To ask queries from the bot, use the command: +```text +query +``` + +🎉 Happy Chatting! 🎉 diff --git a/mem0-rag/docs/examples/telegram_bot.mdx b/mem0-rag/docs/examples/telegram_bot.mdx new file mode 100644 index 0000000000..14f17e90c3 --- /dev/null +++ b/mem0-rag/docs/examples/telegram_bot.mdx @@ -0,0 +1,51 @@ +--- +title: "📱 Telegram Bot" +--- + +### 🖼️ Template Setup + +- Open the Telegram app and search for the `BotFather` user. +- Start a chat with BotFather and use the `/newbot` command to create a new bot. +- Follow the instructions to choose a name and username for your bot. +- Once the bot is created, BotFather will provide you with a unique token for your bot. + + + + ```bash + docker run --name telegram-bot -e OPENAI_API_KEY=sk-xxx -e TELEGRAM_BOT_TOKEN=xxx -p 8000:8000 embedchain/telegram-bot + ``` + + + If you wish to use **Docker**, you would need to host your bot on a server. + You can use [ngrok](https://ngrok.com/) to expose your localhost to the + internet and then set the webhook using the ngrok URL. + + + + + + Fork **[this](https://replit.com/@taranjeetio/EC-Telegram-Bot-Template?v=1#README.md)** replit template. + + + - Set your `OPENAI_API_KEY` in Secrets. + - Set the unique token as `TELEGRAM_BOT_TOKEN` in Secrets. + + + + + +- Click on `Run` in the replit container and a URL will get generated for your bot. +- Now set your webhook by running the following link in your browser: + +```url +https://api.telegram.org/bot/setWebhook?url= +``` + +- When you get a successful response in your browser, your bot is ready to be used. + +### 🚀 Usage Instructions + +- Open your bot by searching for it using the bot name or bot username. +- Click on `Start` or type `/start` and follow the on screen instructions. + +🎉 Happy Chatting! 🎉 diff --git a/mem0-rag/docs/examples/whatsapp_bot.mdx b/mem0-rag/docs/examples/whatsapp_bot.mdx new file mode 100644 index 0000000000..16a8c504a0 --- /dev/null +++ b/mem0-rag/docs/examples/whatsapp_bot.mdx @@ -0,0 +1,55 @@ +--- +title: '💬 WhatsApp Bot' +--- + +### 🚀 Getting started + +1. Install embedchain python package: + +```bash +pip install --upgrade embedchain +``` + +2. Launch your WhatsApp bot: + + + + ```bash + docker run --name whatsapp-bot -e OPENAI_API_KEY=sk-xxx -p 8000:8000 embedchain/whatsapp-bot + ``` + + + ```bash + python -m embedchain.bots.whatsapp --port 5000 + ``` + + + + +If your bot needs to be accessible online, use your machine's public IP or DNS. Otherwise, employ a proxy server like [ngrok](https://ngrok.com/) to make your local bot accessible. + +3. Create a free account on [Twilio](https://www.twilio.com/try-twilio) + - Set up a WhatsApp Sandbox in your Twilio dashboard. Access it via the left sidebar: `Messaging > Try it out > Send a WhatsApp Message`. + - Follow on-screen instructions to link a phone number for chatting with your bot + - Copy your bot's public URL, add /chat at the end, and paste it in Twilio's WhatsApp Sandbox settings under "When a message comes in". Save the settings. + +- Copy your bot's public url, append `/chat` at the end and paste it under `When a message comes in` under the `Sandbox settings` for Whatsapp in Twilio. Save your settings. + +### 💬 How to use + +- To connect a new number or reconnect an old one in the Sandbox, follow Twilio's instructions. +- To include data sources, use this command: +```text +add +``` + +- To ask the bot questions, just type your query: +```text + +``` + +### Example + +Here is an example of Elon Musk WhatsApp Bot that we created: + + diff --git a/mem0-rag/docs/favicon.png b/mem0-rag/docs/favicon.png new file mode 100644 index 0000000000000000000000000000000000000000..35494d9ea0133aebd23d143480f1311c0339b046 GIT binary patch literal 5121 zcmbtW2Ut_fwjMe}dIymr9aKc5DG)`eDqtv~BfS&4G({0afkPD#q$oiUX(~-bf(;NU zf>c9qN|D}^ko`8t`_8%F{a*XtoypqsXU(iN^RJn`_CQ0>1Yp(I*3$+Q6cj)gt^k^% ze5`ljf}Ih{P+RY^7Tf^pyc_X9C|D^gC zxIF-1PXP2DKk)T0o&Ve4Lr%^CjsQ^5!vj?uef_y2EP$YWEC5tP0K9wfw>{1)vU&G4GhKy}$jR z^H9N0N*5OZ){6mPvH*Ysjy1yS-|hb$Z`k*Ne}EkXz$6@i%bfsZWdk4q&&Og74TESv zLrY7?K!-SV2*Jb*R}OYoR(6gfT-xN1*#)Q|Jj?UX0r1H#WDncXsy(6oBeCoxd3U6CXC14<$7<6*b)f9}3Fruu!p4 z(}>8^vY)?7XYa>x^we#7PR*1jFW)nWDxg-m9Q=C_++wFE#n%of{mJP6Mkwn4#po|W zfAN9vfRTy<-Y_aQfCL+sQUy&|!&HP+!L9#Zo<63>o4f0_OQU-IbMO zzWD+6U9YgKQ(&9DjwH0Sd+LFy+7izAeN0QZu#MMn>Y`F8;B6$gedwgt{t<*;ph&W$~P49uzK~?m`nG)vrHI)xCk>K@XxouzMCP(hRS^9I}@KiQW<`h z1I-|%rN7fq+nT=D6Z!Qb1aL9Z86y3Ck?&$|MpYy|jcFY!Cq}j_LI5#E5-DSAeW`(o~rdfCQb^$kIq8MOLigs`DKx~_O~nB@MZbi~kF?qZU49I?tw z<8bfsPdh1=7x;V|vK3pVyM1L-R)^ire$b#uEtS!TWdvkz#)Gvv878fL&-C^X((QlwMik zkM+t{yE$0Dxa=dE_`UMj?#sze8FithZmU+#o-6F^aXew4hjqPzTKr{JBTV&iN7E%^ zbJVf=r+kPF!I~wvA2{GsgXbdNYjL8#=Y^mj?@E%{Fx55?@P5GdE2(Y7YQ<@ph#RU@ z3&tl5NrD{aV!`yv=k806*Jt>!5BA)MSXFD4E)8s>zr%b>xv~!4fZ0<|3x6rS^n3RC z*Eml}AEJ;oNogGdlYH^*5I}F2Hp%UORjnnbPe6bz(8kh^&7(@k*?&!db@4Elur|dE zx<2CgH8pYfGzc8`L2lX?h2*#5X;PYAW?MwhbBIv#IJXz+bkLq1{GoE$K$a!XVH9zq zEayjd|MvjQlV1j`dHe$3u9_LHp3S8rpI1U|E%A_}WWSKE6z(;#Mx2!xPoGZ7A)Ze~ zZpv&iQL|x(SO~%QBBu)qqIfICU*LF0QobNpeo&B2C+ycw+?A*v=u#Acz-@_?eii8t zvYp7+b9DMsYKI{pOl%?|>>}rWRJ>}Lv$kxN+4M`<{L0m~t3W=#6S>WagFsKG_39OS z&*C>ZNK{@`5_TVvtyTUdKi_`wR=mP|VNAGx zdC8(fd!X~wXQO4*-sFq$K_4NG`{YlbP}_1t_EWj`8YRn29;07c{godD?KwvO3D*}K&93AerhQR4S^K>NWyL{9J{DImi-N> z%_MS8N)R#@{c}%9s7H&7D6(YgLQa#@E( zF!G7YjIPUr?>;i+Om0|i&G#hqzDt_fp6_Qo^cAM($8O0H;cm9&MoJB<>yUA{Hl`_v6z6ZFPcD?W|6YPqPXA@Gsl}mrM0k0IM zFdUexv5w^S%rs;$m%Q8W5Ro8%ZusSRLjNBZ$l9Z#eIQAjpI zS{fNg3?12Cxu1Q7jD4cRl-tlL^+tQBkm?q*VTYE#9mD4E;-N;%v1Pium=mM5^3+QX zLr7`S9JA}B6K9(UR4hcoI7!RFD#EZn-s~-o-yBnYfl*7X<-?wMqCZd0az(c4O+SHG zwQSw7#~Nq$(pQ3t$QSi%g@&~wx`^K0+nvT&8Zw*l;`ob~xwm&N$xu&8wR1+saHw67v#E-aY8WbvwCY9 zS>+*JU6+)S%PgJ0;da0Hf92yH9enB$VTu}K!U6W{7rI@^mWVA8{?(54p#hY!ZKvY# z{-UMuJ%N5zTfw^HWqq}q>L+LfO(IxcH+{j$Rn>CrE*)*T;K?32ovHwVUp3nggcgkV zo5`l@vC)s&NjhmsTi4OAATWLmP8u#1d4eUs=3Nw)&Nt;1I$%S4?8Y>z@bWq6<~-%v z^$7Pc`CB{5x*ADUggBz^nY(yHl`i$tS(doq79`Jb8{XX8a4MBG^5L_|GdZspQpg^s z>;f*A`8SHsiY_u?kH|)^($jrhgTP?_I62W{g~m4zz?teMl0axU$nth)4fnh1_HPvL z=`KVzC+#{D1Tydaii)_nI{MC5+~R7|TaO{3G_%-dU*shFKT=->*6xLZM;PNY*=CFO zIC=5IB5Cp7Zc~`(D(R&6SNw@&s=Pe(3Q}OHIs74_qE=bv5}G<~GhvvNAj!pRwsD=r z^1!V;wnx#lcVMqm|H? z_-54|qlIcF*CGjX`2^NtxflZdKC^99lLqFt@?iefjvoZD{7vUGA6Mp+m?qT^8CUm7 zysb)fr!5YA@<nRCHS@C+2ORc<|g`+@~Q2{lP%ZD01-& zW}iWJQME8irCoR7`I#zKKwA8AteQzqf9+|Ug;k;KiLBr*9<}7P+XF?rFP=y6L?+XZ zvrr!#l6Hj>#*e2zs*K5(^J>|6R7$cIa4m9!Meab*^TWGf!3}Zo^e@zQ*^0L ze_pKmt|5md{^!0+zFPiH$30|u$YAp#W;bgtEiwAx*TD{Lu1#uu5O#qf%uz7xqNBwr zf3`$jlAo>Kn*U&M{?jWEP^*!8Pf%&yDEHT2O>~Z#y%L_9P}yJId_O+Wbuj=Rt3Rm* zBB%0_$hZm!YzCkM&!8D5g!Xu_aJqQZv@g&RjfD&q!g8;U6x4 z(_GKjlbn7lmPm>U*{oF6W{mrY1v=B6UZV>;bxPd9{;znwIq#XApr`BGDT~{WKXl-KmW0lZrDe4dEEiN>$*cAB5qfD z)th#<{eF(a*hmSvxTuQ3!j=KIqw(raGr#Fd;-tK_wta1{kEjAS1>b1*q~2nJ$vCIt zA-=aF;R5KB4H>bB3f}x2!CQlEUeCuvsJ8fu?oHE)a0(fLirv*_UWb$Yj+S~?XR9q6 z_iT1HPGy`z6{}P?E-vDKj&BoiSu@21?-zS$vSnrQz}uB~=&%S@k8kQ7QoRdGUI;z{#r?Q^t=%BaM7q_Hw)Mm(fH>B*k(b=c{Ww#hObB4x&4J6c zqjF4 + + + + + + + + + +## Seeking help? + +If you run into issues with deployment, please feel free to reach out to us via any of the following methods: + + diff --git a/mem0-rag/docs/get-started/faq.mdx b/mem0-rag/docs/get-started/faq.mdx new file mode 100644 index 0000000000..fd00258cbf --- /dev/null +++ b/mem0-rag/docs/get-started/faq.mdx @@ -0,0 +1,191 @@ +--- +title: ❓ FAQs +description: 'Collections of all the frequently asked questions' +--- + + +Yes, it does. Please refer to the [OpenAI Assistant docs page](/examples/openai-assistant). + + +Use the model provided on huggingface: `mistralai/Mistral-7B-v0.1` + +```python main.py +import os +from embedchain import App + +os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "hf_your_token" + +app = App.from_config("huggingface.yaml") +``` +```yaml huggingface.yaml +llm: + provider: huggingface + config: + model: 'mistralai/Mistral-7B-v0.1' + temperature: 0.5 + max_tokens: 1000 + top_p: 0.5 + stream: false + +embedder: + provider: huggingface + config: + model: 'sentence-transformers/all-mpnet-base-v2' +``` + + + +Use the model `gpt-4-turbo` provided my openai. + + +```python main.py +import os +from embedchain import App + +os.environ['OPENAI_API_KEY'] = 'xxx' + +# load llm configuration from gpt4_turbo.yaml file +app = App.from_config(config_path="gpt4_turbo.yaml") +``` + +```yaml gpt4_turbo.yaml +llm: + provider: openai + config: + model: 'gpt-4-turbo' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false +``` + + + + + +```python main.py +import os +from embedchain import App + +os.environ['OPENAI_API_KEY'] = 'xxx' + +# load llm configuration from gpt4.yaml file +app = App.from_config(config_path="gpt4.yaml") +``` + +```yaml gpt4.yaml +llm: + provider: openai + config: + model: 'gpt-4' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false +``` + + + + + + +```python main.py +from embedchain import App + +# load llm configuration from opensource.yaml file +app = App.from_config(config_path="opensource.yaml") +``` + +```yaml opensource.yaml +llm: + provider: gpt4all + config: + model: 'orca-mini-3b-gguf2-q4_0.gguf' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: false + +embedder: + provider: gpt4all + config: + model: 'all-MiniLM-L6-v2' +``` + + + + +You can achieve this by setting `stream` to `true` in the config file. + + +```yaml openai.yaml +llm: + provider: openai + config: + model: 'gpt-3.5-turbo' + temperature: 0.5 + max_tokens: 1000 + top_p: 1 + stream: true +``` + +```python main.py +import os +from embedchain import App + +os.environ['OPENAI_API_KEY'] = 'sk-xxx' + +app = App.from_config(config_path="openai.yaml") + +app.add("https://www.forbes.com/profile/elon-musk") + +response = app.query("What is the net worth of Elon Musk?") +# response will be streamed in stdout as it is generated. +``` + + + + + Set up the app by adding an `id` in the config file. This keeps the data for future use. You can include this `id` in the yaml config or input it directly in `config` dict. + ```python app1.py + import os + from embedchain import App + + os.environ['OPENAI_API_KEY'] = 'sk-xxx' + + app1 = App.from_config(config={ + "app": { + "config": { + "id": "your-app-id", + } + } + }) + + app1.add("https://www.forbes.com/profile/elon-musk") + + response = app1.query("What is the net worth of Elon Musk?") + ``` + ```python app2.py + import os + from embedchain import App + + os.environ['OPENAI_API_KEY'] = 'sk-xxx' + + app2 = App.from_config(config={ + "app": { + "config": { + # this will persist and load data from app1 session + "id": "your-app-id", + } + } + }) + + response = app2.query("What is the net worth of Elon Musk?") + ``` + + + +#### Still have questions? +If docs aren't sufficient, please feel free to reach out to us using one of the following methods: + + diff --git a/mem0-rag/docs/get-started/full-stack.mdx b/mem0-rag/docs/get-started/full-stack.mdx new file mode 100644 index 0000000000..cd45a0b9ef --- /dev/null +++ b/mem0-rag/docs/get-started/full-stack.mdx @@ -0,0 +1,81 @@ +--- +title: '💻 Full stack' +--- + +Get started with full-stack RAG applications using Embedchain's easy-to-use CLI tool. Set up everything with just a few commands, whether you prefer Docker or not. + +## Prerequisites + +Choose your setup method: + +* [Without docker](#without-docker) +* [With Docker](#with-docker) + +### Without Docker + +Ensure these are installed: + +- Embedchain python package (`pip install embedchain`) +- [Node.js](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) and [Yarn](https://classic.yarnpkg.com/lang/en/docs/install/) + +### With Docker + +Install Docker from [Docker's official website](https://docs.docker.com/engine/install/). + +## Quick Start Guide + +### Install the package + +Before proceeding, make sure you have the Embedchain package installed. + +```bash +pip install embedchain -U +``` + +### Setting Up + +For the purpose of the demo, you have to set `OPENAI_API_KEY` to start with but you can choose any llm by changing the configuration easily. + +### Installation Commands + + + +```bash without docker +ec create-app my-app +cd my-app +ec start +``` + +```bash with docker +ec create-app my-app --docker +cd my-app +ec start --docker +``` + + + +### What Happens Next? + +1. Embedchain fetches a full stack template (FastAPI backend, Next.JS frontend). +2. Installs required components. +3. Launches both frontend and backend servers. + +### See It In Action + +Open http://localhost:3000 to view the chat UI. + +![full stack example](/images/fullstack.png) + +### Admin Panel + +Check out the Embedchain admin panel to see the document chunks for your RAG application. + +![full stack chunks](/images/fullstack-chunks.png) + +### API Server + +If you want to access the API server, you can do so at http://localhost:8000/docs. + +![API Server](/images/fullstack-api-server.png) + +You can customize the UI and code as per your requirements. diff --git a/mem0-rag/docs/get-started/integrations.mdx b/mem0-rag/docs/get-started/integrations.mdx new file mode 100644 index 0000000000..e69de29bb2 diff --git a/mem0-rag/docs/get-started/introduction.mdx b/mem0-rag/docs/get-started/introduction.mdx new file mode 100644 index 0000000000..fc7ce22ed6 --- /dev/null +++ b/mem0-rag/docs/get-started/introduction.mdx @@ -0,0 +1,66 @@ +--- +title: 📚 Introduction +--- + +## What is Embedchain? + +Embedchain is an Open Source Framework that 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. + +## Who is Embedchain for? + +Embedchain is designed for a diverse range of users, from AI professionals like Data Scientists and Machine Learning Engineers to those just starting their AI journey, including college students, independent developers, and hobbyists. Essentially, it's for anyone with an interest in AI, regardless of their expertise level. + +Our APIs are user-friendly yet adaptable, enabling beginners to effortlessly create LLM-powered applications with as few as 4 lines of code. At the same time, we offer extensive customization options for every aspect of building a personalized AI application. This includes the choice of LLMs, vector databases, loaders and chunkers, retrieval strategies, re-ranking, and more. + +Our platform's clear and well-structured abstraction layers ensure that users can tailor the system to meet their specific needs, whether they're crafting a simple project or a complex, nuanced AI application. + +## Why Use Embedchain? + +Developing a personalized AI application for production use presents numerous complexities, such as: + +- Integrating and indexing data from diverse sources. +- Determining optimal data chunking methods for each source. +- Synchronizing the RAG pipeline with regularly updated data sources. +- Implementing efficient data storage in a vector store. +- Deciding whether to include metadata with document chunks. +- Handling permission management. +- Configuring Large Language Models (LLMs). +- Selecting effective prompts. +- Choosing suitable retrieval strategies. +- Assessing the performance of your RAG pipeline. +- Deploying the pipeline into a production environment, among other concerns. + +Embedchain is designed to simplify these tasks, offering conventional yet customizable APIs. Our solution handles the intricate processes of loading, chunking, indexing, and retrieving data. This enables you to concentrate on aspects that are crucial for your specific use case or business objectives, ensuring a smoother and more focused development process. + +## How it works? + +Embedchain makes it easy to add data to your RAG pipeline with these straightforward steps: + +1. **Automatic Data Handling**: It automatically recognizes the data type and loads it. +2. **Efficient Data Processing**: The system creates embeddings for key parts of your data. +3. **Flexible Data Storage**: You get to choose where to store this processed data in a vector database. + +When a user asks a question, whether for chatting, searching, or querying, Embedchain simplifies the response process: + +1. **Query Processing**: It turns the user's question into embeddings. +2. **Document Retrieval**: These embeddings are then used to find related documents in the database. +3. **Answer Generation**: The related documents are used by the LLM to craft a precise answer. + +With Embedchain, you don’t have to worry about the complexities of building a personalized AI application. It offers an easy-to-use interface for developing applications with any kind of data. + +## Getting started + +Checkout our [quickstart guide](/get-started/quickstart) to start your first AI application. + +## Support + +Feel free to reach out to us if you have ideas, feedback or questions that we can help out with. + + + +## Contribute + +- [GitHub](https://github.com/embedchain/embedchain) +- [Contribution docs](/contribution/dev) diff --git a/mem0-rag/docs/get-started/quickstart.mdx b/mem0-rag/docs/get-started/quickstart.mdx new file mode 100644 index 0000000000..04d2701919 --- /dev/null +++ b/mem0-rag/docs/get-started/quickstart.mdx @@ -0,0 +1,89 @@ +--- +title: '⚡ Quickstart' +description: '💡 Create an AI app on your own data in a minute' +--- + +## Installation + +First install the Python package: + +```bash +pip install embedchain +``` + +Once you have installed the package, depending upon your preference you can either use: + + + + This includes Open source LLMs like Mistral, Llama, etc.
+ Free to use, and runs locally on your machine. + + + This includes paid LLMs like GPT 4, Claude, etc.
+ Cost money and are accessible via an API. + + + +## Open Source Models + +This section gives a quickstart example of using Mistral as the Open source LLM and Sentence transformers as the Open source embedding model. These models are free and run mostly on your local machine. + +We are using Mistral hosted at Hugging Face, so will you need a Hugging Face token to run this example. Its *free* and you can create one [here](https://huggingface.co/docs/hub/security-tokens). + + +```python huggingface_demo.py +import os +# Replace this with your HF token +os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "hf_xxxx" + +from embedchain import App + +config = { + 'llm': { + 'provider': 'huggingface', + 'config': { + 'model': 'mistralai/Mistral-7B-Instruct-v0.2', + 'top_p': 0.5 + } + }, + 'embedder': { + 'provider': 'huggingface', + 'config': { + 'model': 'sentence-transformers/all-mpnet-base-v2' + } + } +} +app = App.from_config(config=config) +app.add("https://www.forbes.com/profile/elon-musk") +app.add("https://en.wikipedia.org/wiki/Elon_Musk") +app.query("What is the net worth of Elon Musk today?") +# Answer: The net worth of Elon Musk today is $258.7 billion. +``` + + +## Paid Models + +In this section, we will use both LLM and embedding model from OpenAI. + +```python openai_demo.py +import os +from embedchain import App + +# Replace this with your OpenAI key +os.environ["OPENAI_API_KEY"] = "sk-xxxx" + +app = App() +app.add("https://www.forbes.com/profile/elon-musk") +app.add("https://en.wikipedia.org/wiki/Elon_Musk") +app.query("What is the net worth of Elon Musk today?") +# Answer: The net worth of Elon Musk today is $258.7 billion. +``` + +# Next Steps + +Now that you have created your first app, you can follow any of the links: + +* [Introduction](/get-started/introduction) +* [Customization](/components/introduction) +* [Use cases](/use-cases/introduction) +* [Deployment](/get-started/deployment) diff --git a/mem0-rag/docs/images/checks-passed.png b/mem0-rag/docs/images/checks-passed.png new file mode 100644 index 0000000000000000000000000000000000000000..3303c773646ca12fb6852356663540e3ed048115 GIT binary patch literal 160724 zcmeFZ1yEc~*Y68~APG(g?iPZ3kU(&P1`EO6-6g=_?(PJ)Ai*7iyGw9qaCe!xGs*M5 z&->Lmx9Xm%^VK=G>Y-|8HhXvP?&;}X-D~}Sy+c09Nua(UcmV?ggZfcYR1pRSUIqpR zRvifeat6MxKMe*3MbJz{ 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