## Agentic Orchestration Platform [![PyPI version](https://img.shields.io/pypi/v/trustgraph.svg)](https://pypi.org/project/trustgraph/) [![Discord](https://img.shields.io/discord/1251652173201149994 )](https://discord.gg/sQMwkRz5GX) 🚀 [Getting Started](https://trustgraph.ai/docs/getstarted) 📚 [YouTube](https://www.youtube.com/@TrustGraphAI?sub_confirmation=1) 🧠 [Cognitive Cores](https://github.com/trustgraph-ai/catalog/tree/master/v3) ⚙ïļ [API Docs](docs/apis/README.md) 🧑‍ðŸ’ŧ [CLI Docs](https://trustgraph.ai/docs/running/cli) 💎 [Discord](https://discord.gg/sQMwkRz5GX) 📖 [Blog](https://blog.trustgraph.ai/subscribe)
TrustGraph empowers you to deploy reasoning AI Agents in minutes. Our Agentic Graph RAG platform allows you to leverage modular cognitive cores for complex reasoning and information retrieval, all within a scalable and reliable infrastructure. Forget lengthy development cycles – TrustGraph delivers instant reasoning. ## Key Features - 📄 **Document Extraction**: Bulk ingest documents such as `.pdf`,`.txt`, and `.md` - 🊓 **Adjustable Chunking**: Choose your chunking algorithm and parameters - 🔁 **No-code LLM Integration**: **Anthropic**, **AWS Bedrock**, **AzureAI**, **AzureOpenAI**, **Cohere**, **Google AI Studio**, **Google VertexAI**, **Llamafiles**, **Ollama**, and **OpenAI** - ☁ïļ **Cloud Deployments**: **AWS** and **Google Cloud** - 📖 **Entity, Topic, and Relationship Knowledge Graphs** - ðŸ”Ē **Mapped Vector Embeddings** - ❔**No-code Graph RAG Queries**: Automatically perform a semantic similiarity search and subgraph extraction for the context of LLM generative responses - 🧠 **Cognitive Cores**: Modular data sets with semantic relationships that can saved and quickly loaded on demand - ðŸĪ– **Agent Flow**: Define custom tools used by a ReAct style Agent Manager that fully controls the response flow including the ability to perform Graph RAG requests - 📚 **Multiple Knowledge Graph Options**: Full integration with **Memgraph**, **FalkorDB**, **Neo4j**, or **Cassandra** - ðŸ§Ū **Multiple VectorDB Options**: Full integration with **Pinecone**, **Qdrant**, or **Milvus** - 🎛ïļ **Production-Grade** reliability, scalability, and accuracy - 🔍 **Observability**: get insights into system performance with Prometheus and Grafana - ðŸŠī **Customizable and Extensible**: Tailor for your data and use cases - ðŸ–Ĩïļ **Configuration Portal**: Build the `YAML` configuration with drop down menus and selectable parameters - ðŸ•ĩïļ **Data Workbench**: Explore your data with a 3D semantic visualizer ## Quickstart Guide 🚀 - [Install the CLI](#install-the-trustgraph-cli) - [Configuration Portal](#configuration-portal) - [System Restarts](#system-restarts) - [Data Workbench](#data-workbench) - [Example Notebooks](#example-trustgraph-notebooks) ## Developer APIs and CLI - [**REST API**](docs/apis/README.md#rest-apis) - [**Websocket API**](docs/apis/README.md#websocket-api) - [**Python SDK**](https://trustgraph.ai/docs/api/apistarted) - [**TrustGraph CLI**](https://trustgraph.ai/docs/running/cli) See the [API Developer's Guide](#api-documentation) for more information. For users, **TrustGraph** has the following interfaces: - [**Configuration Portal**](#configuration-portal) - [**Data Workbench**](#data-workbench) The `TrustGraph CLI` installs the commands for interacting with TrustGraph while running along with the Python SDK. The `Configuration Portal` enables customization of TrustGraph deployments prior to launching. The **REST API** can be accessed through port `8088` of the TrustGraph host machine with JSON request and response bodies. ### Install the TrustGraph CLI ``` pip3 install trustgraph-cli==0.20.9 ``` > [!NOTE] > The `TrustGraph CLI` version must match the desired `TrustGraph` release version. ## Configuration Portal TrustGraph is endlessly customizable by editing the `YAML` launch files. The `Configuration Portal` provides a quick and intuitive tool for building a custom configuration that deploys with Docker, Podman, Minikube, or Google Cloud. There is a `Configuration Portal` for the both the lastest and stable `TrustGraph` releases. - [**Configuration Portal** (Stable 0.20.9) 🚀](https://config-ui.demo.trustgraph.ai/) - [**Configuration Portal** (Latest 0.20.11) 🚀](https://dev.config-ui.demo.trustgraph.ai/) The `Configuration Portal` has 4 important sections: - **Component Selection** ✅: Choose from the available deployment platforms, LLMs, graph store, VectorDB, chunking algorithm, chunking parameters, and LLM parameters - **Customization** 🧰: Customize the prompts for the LLM System, Data Extraction Agents, and Agent Flow - **Data Workbench** ðŸ•ĩïļ: Add the **Data Workbench** to the configuration available on port `8888` - **Finish Deployment** 🚀: Download the launch `YAML` files with deployment instructions The `Configuration Portal` will generate the `YAML` files in `deploy.zip`. Once `deploy.zip` has been downloaded and unzipped, launching TrustGraph is as simple as navigating to the `deploy` directory and running: ``` docker compose up -d ``` > [!TIP] > Docker is the recommended container orchestration platform for first getting started with TrustGraph. When finished, shutting down TrustGraph is as simple as: ``` docker compose down -v ``` ## System Restarts The `-v` flag will destroy all data on shut down. To restart the system, it's necessary to keep the volumes. To keep the volumes, shut down without the `-v` flag: ``` docker compose down ``` With the volumes preserved, restarting the system is as simple as: ``` docker compose up -d ``` All data previously in TrustGraph will be saved and usable on restart. ## Data Workbench If added to the build in the `Configuration Portal`, the `Data Workbench` will be available at port `8888`. The `Data Workbench` has the following capabilities: - **Chat** 💎: Graph RAG queries in a chat interface - **Search** 🔎: Semantic similarity search with cosine similarity scores - **Explorer** ðŸ•ĩïļ: See semantic relationships in a list structure - **Visualizer** 🌐: Visualize semantic relationships in **3D** - **Load** 📂: Directly load `.pdf`, `.txt`, or `.md` into the system with document metadata ## Example TrustGraph Notebooks - [**REST API Notebooks**](https://github.com/trustgraph-ai/example-notebooks/tree/master/api-examples) - [**Python SDK Notebooks**](https://github.com/trustgraph-ai/example-notebooks/tree/master/api-library) ## Prebuilt Configuration Files TrustGraph `YAML` files are available [here](https://github.com/trustgraph-ai/trustgraph/releases). Download `deploy.zip` for the desired release version. | Release Type | Release Version | | ------------ | --------------- | | Latest | [0.20.11](https://github.com/trustgraph-ai/trustgraph/releases/download/v0.20.11/deploy.zip) | | Stable | [0.20.9](https://github.com/trustgraph-ai/trustgraph/releases/download/v0.20.9/deploy.zip) | TrustGraph is fully containerized and is launched with a `YAML` configuration file. Unzipping the `deploy.zip` will add the `deploy` directory with the following subdirectories: - `docker-compose` - `minikube-k8s` - `gcp-k8s` > [!NOTE] > As more integrations have been added, the number of possible combinations of configurations has become quite large. It is recommended to use the `Configuration Portal` to build your deployment configuration. Each directory contains `YAML` configuration files for the default component selections. **Docker**: ``` docker compose -f up -d ``` **Kubernetes**: ``` kubectl apply -f ``` ## Architecture ![architecture](TG-layer-diagram.svg) TrustGraph is designed to be modular to support as many LLMs and environments as possible. A natural fit for a modular architecture is to decompose functions into a set of modules connected through a pub/sub backbone. [Apache Pulsar](https://github.com/apache/pulsar/) serves as this pub/sub backbone. Pulsar acts as the data broker managing data processing queues connected to procesing modules. ### Pulsar Workflows - For processing flows, Pulsar accepts the output of a processing module and queues it for input to the next subscribed module. - For services such as LLMs and embeddings, Pulsar provides a client/server model. A Pulsar queue is used as the input to the service. When processed, the output is then delivered to a separate queue where a client subscriber can request that output. ## Data Extraction Agents TrustGraph extracts knowledge documents to an ultra-dense knowledge graph using 3 automonous data extraction agents. These agents focus on individual elements needed to build the knowledge graph. The agents are: - Topic Extraction Agent - Entity Extraction Agent - Relationship Extraction Agent The agent prompts are built through templates, enabling customized data extraction agents for a specific use case. The data extraction agents are launched automatically with the loader commands. PDF file: ``` tg-load-pdf ``` Text or Markdown file: ``` tg-load-text ``` ## Graph RAG Queries Once the knowledge graph and embeddings have been built or a cognitive core has been loaded, RAG queries are launched with a single line: ``` tg-invoke-graph-rag -q "What are the top 3 takeaways from the document?" ``` ## Agent Flow Invoking the Agent Flow will use a ReAct style approach the combines Graph RAG and text completion requests to think through a problem solution. ``` tg-invoke-agent -v -q "Write a blog post on the top 3 takeaways from the document." ``` > [!TIP] > Adding `-v` to the agent request will return all of the agent manager's thoughts and observations that led to the final response. ## API Documentation [Developing on TrustGraph using APIs](docs/apis/README.md) ## Deploy and Manage TrustGraph [🚀🙏 Full Deployment Guide 🚀🙏](https://trustgraph.ai/docs/getstarted) ## TrustGraph Developer's Guide [Developing for TrustGraph](docs/README.development.md)