From 04ca323fb90254998606ccf5f8a06a24665c0c53 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 13:46:20 -0700 Subject: [PATCH 01/19] README WIP --- README.md | 28 ++++++++++++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index af2482a4..6e01c4d6 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@
-## Data-to-AI, Simplified. +## Autonomous Operations 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) @@ -11,6 +11,30 @@
+**TrustGraph transforms AI agents from experimental concepts into a continuous paradigm of autonomous operations within an organization.** + +It provides a robust, scalable, and reliable infrastructure designed for complex environments, complete with a full observability stack. TrustGraph automates the deployment of state-of-the-art RAG pipelines using Knowledge Graphs and Vector Databases and offers a unified interface to interact with all major LLM providers. + +--- + +## Table of Contents + +- ✨ [Key Features](#-key-features) +- 🎯 [Why TrustGraph?](#-why-trustgraph) +- 🚀 [Getting Started](#-getting-started) + - [Prerequisites](#prerequisites) + - [Installation](#installation) + - [Quick Start](#quick-start) +- 🔧 [Configuration](#-configuration) +- [Core Concepts](#-core-concepts) +- 🧩 [Integrations](#-integrations) +- 📊 [Observability & Telemetry](#-observability--telemetry) +- 🤝 [Contributing](#-contributing) +- 📄 [License](#-license) +- 📞 [Support & Community](#-support--community) + +--- + ## The AI App Problem: Everything in Between Building enterprise AI applications is *hard*. You're not just connecting APIs with a protocol - you're wrangling a complex ecosystem: @@ -31,7 +55,7 @@ Building enterprise AI applications is *hard*. You're not just connecting APIs w ![architecture](TG-layer-diagram.svg) -## The Stack Layers +## Key Features - 📄 **Data Ingest**: Bulk ingest documents such as `.pdf`,`.txt`, and `.md` - 📃 **OCR Pipelines**: OCR documents with PDF decode, Tesseract, or Mistral OCR services From da2f833c1e1e5eda9ce2febf802755b16249f4ef Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 13:47:16 -0700 Subject: [PATCH 02/19] README WIP --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6e01c4d6..2af82549 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ It provides a robust, scalable, and reliable infrastructure designed for complex ## Table of Contents -- ✨ [Key Features](#-key-features) +- ✨ [Key Features](#key-features) - 🎯 [Why TrustGraph?](#-why-trustgraph) - 🚀 [Getting Started](#-getting-started) - [Prerequisites](#prerequisites) From c8a25e155230d5ab41554e20e101c15ceb960bc8 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 13:51:05 -0700 Subject: [PATCH 03/19] README WIP --- README.md | 36 +++++++++++++++++------------------- 1 file changed, 17 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 2af82549..7320e9f4 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ It provides a robust, scalable, and reliable infrastructure designed for complex ## Table of Contents -- ✨ [Key Features](#key-features) +- ✨ [Key Features](#-key-features) - 🎯 [Why TrustGraph?](#-why-trustgraph) - 🚀 [Getting Started](#-getting-started) - [Prerequisites](#prerequisites) @@ -55,7 +55,7 @@ Building enterprise AI applications is *hard*. You're not just connecting APIs w ![architecture](TG-layer-diagram.svg) -## Key Features +## ✨ Key Features - 📄 **Data Ingest**: Bulk ingest documents such as `.pdf`,`.txt`, and `.md` - 📃 **OCR Pipelines**: OCR documents with PDF decode, Tesseract, or Mistral OCR services @@ -77,14 +77,21 @@ Building enterprise AI applications is *hard*. You're not just connecting APIs w - 🖥️ **Configuration Builder**: Build the `YAML` configuration with drop down menus and selectable parameters - 🕵️ **Test Suite**: A simple UI to fully test TrustGraph performance -## Why Use TrustGraph? +## 🎯 Why TrustGraph? -* **Accelerate Development:** TrustGraph instantly connects your data and app, keeping you laser focused on your users. -* **Reduce Complexity:** Eliminate the pain of integrating disparate tools and technologies. -* **Focus on Innovation:** Spend your time building your core AI logic, not managing infrastructure. -* **Improve Data Relevance:** Ensure your LLM has access to the *right* data, at the *right* time. -* **Scale with Confidence:** Deploy and scale your AI applications reliably and efficiently. -* **Full RAG Solution:** Focus on optimizing your respones not building RAG pipelines. +Traditional operations often involve manual intervention, siloed tools, and reactive problem-solving. While AI agents show promise, integrating them into reliable, continuous enterprise workflows presents significant challenges: + +1. **Scalability & Reliability:** Standalone agent scripts don't scale or offer the robustness required for business-critical operations. +2. **Contextual Understanding:** Agents need deep, relevant context (often locked in enterprise data) to perform complex tasks effectively. RAG is powerful but complex to set up and manage. +3. **Integration Hell:** Connecting agents to diverse enterprise systems, data sources, and various LLMs is difficult and time-consuming. +4. **Lack of Oversight:** Monitoring, debugging, and understanding the behavior of multiple autonomous agents in production is critical but often overlooked. + +**TrustGraph addresses these challenges by providing:** + +* A **platform**, not just a library, for managing the lifecycle of autonomous operations. +* **Automated, best-practice RAG deployments** that combine the strengths of semantic vector search and structured knowledge graph traversal. +* A **standardized layer** for LLM interaction and enterprise system integration. +* **Built-in observability** to ensure you can trust and manage your autonomous systems. ## Quickstart Guide 🚀 - [Install the CLI](#install-the-trustgraph-cli) @@ -123,7 +130,7 @@ pip3 install trustgraph-cli==0.21.17 TrustGraph is endlessly customizable by editing the `YAML` launch files. The `Configuration Builder` provides a quick and intuitive tool for building a custom configuration that deploys with Docker, Podman, Minikube, or Google Cloud. There is a `Configuration Builder` for the both the lastest and stable `TrustGraph` releases. - [**Configuration Builder** (Stable 0.21.17) 🚀](https://config-ui.demo.trustgraph.ai/) -- [**Configuration Builder** (Latest 0.21.17) 🚀](https://dev.config-ui.demo.trustgraph.ai/) +- [**Configuration Builder** (Latest 0.22.5) 🚀](https://dev.config-ui.demo.trustgraph.ai/) The `Configuration Builder` has 4 important sections: @@ -175,15 +182,6 @@ If added to the build in the `Configuration Builder`, the `Test Suite` will be a - [**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 | [0.21.17](https://github.com/trustgraph-ai/trustgraph/releases/download/v0.21.17/deploy.zip) | -| Stable | [0.21.17](https://github.com/trustgraph-ai/trustgraph/releases/download/v0.21.17/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` From 48d7be331bd41a298bff70591c91a6c73fe14c2d Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 13:57:34 -0700 Subject: [PATCH 04/19] README WIP --- README.md | 24 +++++------------------- 1 file changed, 5 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 7320e9f4..6ed3f7c4 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ It provides a robust, scalable, and reliable infrastructure designed for complex - [Prerequisites](#prerequisites) - [Installation](#installation) - [Quick Start](#quick-start) -- 🔧 [Configuration](#-configuration) +- 🔧 [Configuration Builder](#-configuration-builder) - [Core Concepts](#-core-concepts) - 🧩 [Integrations](#-integrations) - 📊 [Observability & Telemetry](#-observability--telemetry) @@ -35,20 +35,6 @@ It provides a robust, scalable, and reliable infrastructure designed for complex --- -## The AI App Problem: Everything in Between - -Building enterprise AI applications is *hard*. You're not just connecting APIs with a protocol - you're wrangling a complex ecosystem: - -* **Data Silos:** Connecting to and managing data from various sources (databases, APIs, files) is a nightmare. -* **LLM Integration:** Choosing, integrating, and managing different LLMs adds another layer of complexity. -* **Deployment Headaches:** Deploying, scaling, and monitoring your AI application is a constant challenge. -* **Knowledge Graph Construction:** Taking raw knowledge and structuring it so it can be efficiently retrieved. -* **Vector Database Juggling:** Setting up and optimizing a vector database for efficient data retrieval is crucial but complex. -* **Data Pipelines:** Building robust ETL pipelines to prepare and transform your data is time-consuming. -* **Data Management:** As your app grows, so does the data meaning storage and retreival becomes much more complex. -* **Prompt Engineering:** Building, testing, and deploying prompts for specific use cases. -* **Reliability:** With every new connection, the complexity ramps up meaning any simple error can bring the entire system crashing down. - ## What is TrustGraph? **TrustGraph removes the biggest headache of building an AI app: connecting and managing all the data, deployments, and models.** As a full-stack platform, TrustGraph simplifies the development and deployment of data-driven AI applications. TrustGraph is a complete solution, handling everything from data ingestion to deployment, so you can focus on building innovative AI experiences. @@ -93,9 +79,9 @@ Traditional operations often involve manual intervention, siloed tools, and reac * A **standardized layer** for LLM interaction and enterprise system integration. * **Built-in observability** to ensure you can trust and manage your autonomous systems. -## Quickstart Guide 🚀 +## 🚀 Getting Started - [Install the CLI](#install-the-trustgraph-cli) -- [Configuration Builder](#configuration-builder) +- [Configuration Builder](#-configuration-builder) - [System Restarts](#system-restarts) - [Test Suite](#test-suite) - [Example Notebooks](#example-trustgraph-notebooks) @@ -111,7 +97,7 @@ See the [API Developer's Guide](#api-documentation) for more information. For users, **TrustGraph** has the following interfaces: -- [**Configuration Builder**](#configuration-builder) +- [**Configuration Builder**](#-configuration-builder) - [**Test Suite**](#test-suite) The `TrustGraph CLI` installs the commands for interacting with TrustGraph while running along with the Python SDK. The `Configuration Builder` 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. @@ -125,7 +111,7 @@ pip3 install trustgraph-cli==0.21.17 > [!NOTE] > The `TrustGraph CLI` version must match the desired `TrustGraph` release version. -## Configuration Builder +## 🔧 Configuration Builder TrustGraph is endlessly customizable by editing the `YAML` launch files. The `Configuration Builder` provides a quick and intuitive tool for building a custom configuration that deploys with Docker, Podman, Minikube, or Google Cloud. There is a `Configuration Builder` for the both the lastest and stable `TrustGraph` releases. From b110e44aeaaec6395e99c1b8733abda94e558f54 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 14:02:23 -0700 Subject: [PATCH 05/19] README WIP --- README.md | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 6ed3f7c4..500f8946 100644 --- a/README.md +++ b/README.md @@ -22,11 +22,8 @@ It provides a robust, scalable, and reliable infrastructure designed for complex - ✨ [Key Features](#-key-features) - 🎯 [Why TrustGraph?](#-why-trustgraph) - 🚀 [Getting Started](#-getting-started) - - [Prerequisites](#prerequisites) - - [Installation](#installation) - - [Quick Start](#quick-start) - 🔧 [Configuration Builder](#-configuration-builder) -- [Core Concepts](#-core-concepts) +- 📐 [Architecture](#-architecture) - 🧩 [Integrations](#-integrations) - 📊 [Observability & Telemetry](#-observability--telemetry) - 🤝 [Contributing](#-contributing) @@ -35,12 +32,6 @@ It provides a robust, scalable, and reliable infrastructure designed for complex --- -## What is TrustGraph? - -**TrustGraph removes the biggest headache of building an AI app: connecting and managing all the data, deployments, and models.** As a full-stack platform, TrustGraph simplifies the development and deployment of data-driven AI applications. TrustGraph is a complete solution, handling everything from data ingestion to deployment, so you can focus on building innovative AI experiences. - -![architecture](TG-layer-diagram.svg) - ## ✨ Key Features - 📄 **Data Ingest**: Bulk ingest documents such as `.pdf`,`.txt`, and `.md` @@ -189,6 +180,12 @@ kubectl apply -f 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. +## 📐 Architecture + +**TrustGraph removes the biggest headache of building an AI app: connecting and managing all the data, deployments, and models.** As a full-stack platform, TrustGraph simplifies the development and deployment of data-driven AI applications. TrustGraph is a complete solution, handling everything from data ingestion to deployment, so you can focus on building innovative AI experiences. + +![architecture](TG-layer-diagram.svg) + ### Pulsar Workflows - For processing flows, Pulsar accepts the output of a processing module and queues it for input to the next subscribed module. From 721ba311835bfedc4ee0274fd8fe441d5c15c6ad Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 14:16:45 -0700 Subject: [PATCH 06/19] README WIP --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 500f8946..9126ebd0 100644 --- a/README.md +++ b/README.md @@ -11,9 +11,9 @@ -**TrustGraph transforms AI agents from experimental concepts into a continuous paradigm of autonomous operations within an organization.** +**TrustGraph transforms AI agents from experimental concepts into a new paradigm of continuous autonomous operations.** -It provides a robust, scalable, and reliable infrastructure designed for complex environments, complete with a full observability stack. TrustGraph automates the deployment of state-of-the-art RAG pipelines using Knowledge Graphs and Vector Databases and offers a unified interface to interact with all major LLM providers. +The platform provides a robust, scalable, and reliable infrastructure designed for complex environments, complete with a full observability stack. TrustGraph automates the deployment of state-of-the-art RAG pipelines using Knowledge Graphs and Vector Databases and offers a unified interface to interact with all major LLM providers. --- @@ -73,11 +73,11 @@ Traditional operations often involve manual intervention, siloed tools, and reac ## 🚀 Getting Started - [Install the CLI](#install-the-trustgraph-cli) - [Configuration Builder](#-configuration-builder) -- [System Restarts](#system-restarts) +- [Platform Restarts](#platform-restarts) - [Test Suite](#test-suite) - [Example Notebooks](#example-trustgraph-notebooks) -## Developer APIs and CLI +### Developer APIs and CLI - [**REST API**](docs/apis/README.md#rest-apis) - [**Websocket API**](docs/apis/README.md#websocket-api) @@ -130,7 +130,7 @@ When finished, shutting down TrustGraph is as simple as: docker compose down -v ``` -## System Restarts +## Platform 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: ``` From 53fc500757257b231f98b7e61b78e246f854ccb0 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 14:19:56 -0700 Subject: [PATCH 07/19] README WIP --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9126ebd0..49e5e280 100644 --- a/README.md +++ b/README.md @@ -11,9 +11,9 @@ -**TrustGraph transforms AI agents from experimental concepts into a new paradigm of continuous autonomous operations.** +**TrustGraph transforms agents from experimental concepts into a new paradigm of continuous operations.** -The platform provides a robust, scalable, and reliable infrastructure designed for complex environments, complete with a full observability stack. TrustGraph automates the deployment of state-of-the-art RAG pipelines using Knowledge Graphs and Vector Databases and offers a unified interface to interact with all major LLM providers. +The platform provides a robust, scalable, and reliable infrastructure designed for complex environments, complete with a full observability stack. **TrustGraph** automates the deployment in local and cloud environments of state-of-the-art RAG pipelines using Knowledge Graphs and Vector Databases with a unified interface to all major LLM providers. --- From 04a3efe0f38a5bd2cc5a2ba4739377362a6dded4 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 14:28:45 -0700 Subject: [PATCH 08/19] README WIP --- README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/README.md b/README.md index 49e5e280..91c38738 100644 --- a/README.md +++ b/README.md @@ -186,6 +186,15 @@ TrustGraph is designed to be modular to support as many LLMs and environments as ![architecture](TG-layer-diagram.svg) +## 🧩 Integrations +TrustGraph aims to integrate seamlessly with your existing ecosystem. + +- LLM Providers: **Anthropic**, **AWS Bedrock**, **AzureAI**, **AzureOpenAI**, **Cohere**, **Google AI Studio**, **Google VertexAI**, **Llamafiles**, **LM Studio**, **Mistral**, **Ollama**, and **OpenAI** +- Vector Databases: **Qdrant**, **Pinecone**, and **Milvus** +- Knowledge Graphs: Memgraph, Neo4j, and FalkorDB +- Data Stores: Apache Cassandra +- Observability: Prometheus and Grafana + ### Pulsar Workflows - For processing flows, Pulsar accepts the output of a processing module and queues it for input to the next subscribed module. From d208795c9b5a0af4daff8b26d0a3021ac74c7fcd Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 15:18:35 -0700 Subject: [PATCH 09/19] README WIP --- README.md | 25 +++++++++++++------------ 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 91c38738..9defd659 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ [![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) 🧠 [Knowledge Cores](https://github.com/trustgraph-ai/catalog/tree/master/v3) ⚙️ [API Docs](docs/apis/README.md) 🧑‍💻 [CLI Docs](https://trustgraph.ai/docs/running/cli) 💬 [Discord](https://discord.gg/sQMwkRz5GX) 📖 [Blog](https://blog.trustgraph.ai/subscribe) +📑 [Docs](https://trustgraph.ai/docs/getstarted) 📺 [YouTube](https://www.youtube.com/@TrustGraphAI?sub_confirmation=1) 🧠 [Knowledge Cores](https://github.com/trustgraph-ai/catalog/tree/master/v3) ⚙️ [API Docs](docs/apis/README.md) 🧑‍💻 [CLI Docs](https://trustgraph.ai/docs/running/cli) 💬 [Discord](https://discord.gg/sQMwkRz5GX) 📖 [Blog](https://blog.trustgraph.ai/subscribe) @@ -194,10 +194,11 @@ TrustGraph aims to integrate seamlessly with your existing ecosystem. - Knowledge Graphs: Memgraph, Neo4j, and FalkorDB - Data Stores: Apache Cassandra - Observability: Prometheus and Grafana +- Control Flow: Apache Pulsar -### Pulsar Workflows +## Pulsar Control Flows -- For processing flows, Pulsar accepts the output of a processing module and queues it for input to the next subscribed module. +- For control 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 @@ -239,14 +240,14 @@ tg-invoke-agent -v -q "Write a blog post on the top 3 takeaways from the documen > [!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 +## 🤝 Contributing [Developing for TrustGraph](docs/README.development.md) + +## 📄 License +**TrustGraph** is licensed under [AGPL-3.0](https://www.gnu.org/licenses/agpl-3.0.en.html). + +## 📞 Support & Community +- Bug Reports & Feature Requests: [Discord](https://discord.gg/sQMwkRz5GX) +- Discussions & Questions: [Discord](https://discord.gg/sQMwkRz5GX) +- Documentation: [Docs](https://trustgraph.ai/docs/getstarted) From 62672c5eabc69b09ed00d0f638a0bcac11bd6f22 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 15:24:58 -0700 Subject: [PATCH 10/19] README WIP --- README.md | 44 +++++++++++++++++++++++++++++++++++++------- 1 file changed, 37 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 9defd659..6f8ea34f 100644 --- a/README.md +++ b/README.md @@ -11,9 +11,9 @@ -**TrustGraph transforms agents from experimental concepts into a new paradigm of continuous operations.** +**Transform AI agents from experimental concepts into a new paradigm of continuous operations.** -The platform provides a robust, scalable, and reliable infrastructure designed for complex environments, complete with a full observability stack. **TrustGraph** automates the deployment in local and cloud environments of state-of-the-art RAG pipelines using Knowledge Graphs and Vector Databases with a unified interface to all major LLM providers. +The **TrustGraph** platform provides a robust, scalable, and reliable AI infrastructure designed for complex environments, complete with a full observability and telemetrystack. **TrustGraph** automates the deployment in local and cloud environments of state-of-the-art RAG pipelines using Knowledge Graphs and Vector Databases with a unified interface to all major LLM providers. --- @@ -46,7 +46,7 @@ The platform provides a robust, scalable, and reliable infrastructure designed f - 📚 **Multiple Knowledge Graph Options**: Full integration with **Memgraph**, **FalkorDB**, **Neo4j**, or **Cassandra** - 🧮 **Multiple VectorDB Options**: Full integration with **Qdrant**, **Pinecone**, or **Milvus** - 🎛️ **Production-Grade** Reliability, scalability, and accuracy -- 🔍 **Observability and Telemetry**: Get insights into system performance with **Prometheus** and **Grafana** +- 📊 **Observability and Telemetry**: Get insights into system performance with **Prometheus** and **Grafana** - 🎻 **Orchestration**: Fully containerized with **Docker** or **Kubernetes** - 🥞 **Stack Manager**: Control and scale the stack with confidence with **Apache Pulsar** - ☁️ **Cloud Deployments**: **AWS**, **Azure**, and **Google Cloud** @@ -196,12 +196,12 @@ TrustGraph aims to integrate seamlessly with your existing ecosystem. - Observability: Prometheus and Grafana - Control Flow: Apache Pulsar -## Pulsar Control Flows +### Pulsar Control Flows - For control 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 +### Document 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: @@ -221,7 +221,7 @@ Text or Markdown file: tg-load-text ``` -## Graph RAG Queries +### 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: @@ -229,7 +229,7 @@ Once the knowledge graph and embeddings have been built or a cognitive core has tg-invoke-graph-rag -q "What are the top 3 takeaways from the document?" ``` -## Agent Flow +### 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. @@ -240,6 +240,36 @@ tg-invoke-agent -v -q "Write a blog post on the top 3 takeaways from the documen > [!TIP] > Adding `-v` to the agent request will return all of the agent manager's thoughts and observations that led to the final response. +## 📊 Observability & Telemetry + +Once the platform is running, access the Grafana dashboard at: + +``` +http://localhost:3000 +``` + +Default credentials are: + +``` +user: admin +password: admin +``` + +The default Grafana dashboard tracks the following: + +- LLM Latency +- Error Rate +- Service Request Rates +- Queue Backlogs +- Chunking Histogram +- Error Source by Service +- Rate Limit Events +- CPU usage by Service +- Memory usage by Service +- Models Deployed +- Token Throughput (Tokens/second) +- Cost Throughput (Cost/second) + ## 🤝 Contributing [Developing for TrustGraph](docs/README.development.md) From e9a040ee84b8d6a7b77e876a494993d88801538d Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 15:25:27 -0700 Subject: [PATCH 11/19] README WIP --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6f8ea34f..cbbb344a 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ **Transform AI agents from experimental concepts into a new paradigm of continuous operations.** -The **TrustGraph** platform provides a robust, scalable, and reliable AI infrastructure designed for complex environments, complete with a full observability and telemetrystack. **TrustGraph** automates the deployment in local and cloud environments of state-of-the-art RAG pipelines using Knowledge Graphs and Vector Databases with a unified interface to all major LLM providers. +The **TrustGraph** platform provides a robust, scalable, and reliable AI infrastructure designed for complex environments, complete with a full observability and telemetry stack. **TrustGraph** automates the deployment in local and cloud environments of state-of-the-art RAG pipelines using Knowledge Graphs and Vector Databases with a unified interface to all major LLM providers. --- From 28aa72c5d8863cd0df9b1bf7fbde23108f385bfd Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 16:03:53 -0700 Subject: [PATCH 12/19] README WIP --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index cbbb344a..0766d707 100644 --- a/README.md +++ b/README.md @@ -130,7 +130,7 @@ When finished, shutting down TrustGraph is as simple as: docker compose down -v ``` -## Platform Restarts +### Platform 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: ``` @@ -144,7 +144,7 @@ docker compose up -d All data previously in TrustGraph will be saved and usable on restart. -## Test Suite +### Test Suite If added to the build in the `Configuration Builder`, the `Test Suite` will be available at port `8888`. The `Test Suite` has the following capabilities: @@ -154,7 +154,7 @@ If added to the build in the `Configuration Builder`, the `Test Suite` will be a - **Graph Visualizer** 🌐: Visualize semantic relationships in **3D** - **Data Loader** 📂: Directly load `.pdf`, `.txt`, or `.md` into the system with document metadata -## Example TrustGraph Notebooks +### 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) @@ -187,7 +187,7 @@ TrustGraph is designed to be modular to support as many LLMs and environments as ![architecture](TG-layer-diagram.svg) ## 🧩 Integrations -TrustGraph aims to integrate seamlessly with your existing ecosystem. +TrustGraph seamlessly integrates API services, data stores, OTel, and control flow for a unified platform experience. - LLM Providers: **Anthropic**, **AWS Bedrock**, **AzureAI**, **AzureOpenAI**, **Cohere**, **Google AI Studio**, **Google VertexAI**, **Llamafiles**, **LM Studio**, **Mistral**, **Ollama**, and **OpenAI** - Vector Databases: **Qdrant**, **Pinecone**, and **Milvus** From d3ed373dc6f05eb2eec344b34b441bb9b368787d Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 16:09:02 -0700 Subject: [PATCH 13/19] README WIP --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 0766d707..20572591 100644 --- a/README.md +++ b/README.md @@ -56,11 +56,11 @@ The **TrustGraph** platform provides a robust, scalable, and reliable AI infrast ## 🎯 Why TrustGraph? -Traditional operations often involve manual intervention, siloed tools, and reactive problem-solving. While AI agents show promise, integrating them into reliable, continuous enterprise workflows presents significant challenges: +Traditional operations involve manual intervention, siloed tools, and reactive problem-solving. While AI agents show promise, integrating them into reliable, continuous enterprise operations presents significant challenges: -1. **Scalability & Reliability:** Standalone agent scripts don't scale or offer the robustness required for business-critical operations. -2. **Contextual Understanding:** Agents need deep, relevant context (often locked in enterprise data) to perform complex tasks effectively. RAG is powerful but complex to set up and manage. -3. **Integration Hell:** Connecting agents to diverse enterprise systems, data sources, and various LLMs is difficult and time-consuming. +1. **Scalability & Reliability:** Standalone agents don't scale or offer the robustness required for business-critical operations. +2. **Contextual Understanding:** Agents need deep, relevant context (often locked in sensitive enterprise data) to perform complex tasks effectively. RAG is powerful but complex to set up and manage. +3. **Integration Nightmare:** Connecting agents to diverse enterprise systems, data sources, and various LLMs is difficult and time-consuming. 4. **Lack of Oversight:** Monitoring, debugging, and understanding the behavior of multiple autonomous agents in production is critical but often overlooked. **TrustGraph addresses these challenges by providing:** From b8a98e63740fc8e63c454305748339a629af7dd7 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 17:21:05 -0700 Subject: [PATCH 14/19] README WIP --- README.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/README.md b/README.md index 20572591..1dd4bec0 100644 --- a/README.md +++ b/README.md @@ -23,6 +23,7 @@ The **TrustGraph** platform provides a robust, scalable, and reliable AI infrast - 🎯 [Why TrustGraph?](#-why-trustgraph) - 🚀 [Getting Started](#-getting-started) - 🔧 [Configuration Builder](#-configuration-builder) +- 🧠 [Knowledge Cores](#-knowledge-cores) - 📐 [Architecture](#-architecture) - 🧩 [Integrations](#-integrations) - 📊 [Observability & Telemetry](#-observability--telemetry) @@ -180,6 +181,17 @@ kubectl apply -f 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. +## 🧠 Knowledge Cores + +One of the biggest challenges currently facing RAG architectures is the ability to quickly reuse and integrate knowledge sets. **TrustGraph** solves this problem by storing the results of the document ingestion process in reusable Knowledge Cores. Being able to store and reuse the Knowledge Cores means the process has to be run only once for a set of documents. These reusable Knowledge Cores can be loaded back into **TrustGraph** and used for RAG. + +A Knowledge Core has two components: + +- Set of Graph Edges +- Set of mapped Vector Embeddings + +When a Knowledge Core is loaded into TrustGraph, the corresponding graph edges and vector embeddings are queued and loaded into the chosen graph and vector stores. + ## 📐 Architecture **TrustGraph removes the biggest headache of building an AI app: connecting and managing all the data, deployments, and models.** As a full-stack platform, TrustGraph simplifies the development and deployment of data-driven AI applications. TrustGraph is a complete solution, handling everything from data ingestion to deployment, so you can focus on building innovative AI experiences. From 0a0469813356a7e7737ac87321535fc3f2eb3566 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 17:23:27 -0700 Subject: [PATCH 15/19] README WIP --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1dd4bec0..c4cfb906 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ **Transform AI agents from experimental concepts into a new paradigm of continuous operations.** -The **TrustGraph** platform provides a robust, scalable, and reliable AI infrastructure designed for complex environments, complete with a full observability and telemetry stack. **TrustGraph** automates the deployment in local and cloud environments of state-of-the-art RAG pipelines using Knowledge Graphs and Vector Databases with a unified interface to all major LLM providers. +The **TrustGraph** platform provides a robust, scalable, and reliable AI infrastructure designed for complex environments, complete with a full observability and telemetry stack. **TrustGraph** automates the deployment of state-of-the-art RAG pipelines using both Knowledge Graphs and Vector Databases in local and cloud environments with a unified interface to all major LLM providers. --- From 36ebfaaffddc86d769e327643a164e553cdcbdbf Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 17:25:34 -0700 Subject: [PATCH 16/19] README WIP --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index c4cfb906..4f724037 100644 --- a/README.md +++ b/README.md @@ -17,8 +17,6 @@ The **TrustGraph** platform provides a robust, scalable, and reliable AI infrast --- -## Table of Contents - - ✨ [Key Features](#-key-features) - 🎯 [Why TrustGraph?](#-why-trustgraph) - 🚀 [Getting Started](#-getting-started) From 1db1a596c28d4956c2a68662bc50c84ef248e743 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 17:27:45 -0700 Subject: [PATCH 17/19] README WIP --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 4f724037..c80859a9 100644 --- a/README.md +++ b/README.md @@ -55,11 +55,11 @@ The **TrustGraph** platform provides a robust, scalable, and reliable AI infrast ## 🎯 Why TrustGraph? -Traditional operations involve manual intervention, siloed tools, and reactive problem-solving. While AI agents show promise, integrating them into reliable, continuous enterprise operations presents significant challenges: +Traditional operations involve manual intervention, siloed tools, and reactive problem-solving. While AI agents show promise, integrating them into reliable, continuous operations presents significant challenges: 1. **Scalability & Reliability:** Standalone agents don't scale or offer the robustness required for business-critical operations. -2. **Contextual Understanding:** Agents need deep, relevant context (often locked in sensitive enterprise data) to perform complex tasks effectively. RAG is powerful but complex to set up and manage. -3. **Integration Nightmare:** Connecting agents to diverse enterprise systems, data sources, and various LLMs is difficult and time-consuming. +2. **Contextual Understanding:** Agents need deep, relevant context (often locked in sensitive and protectec data) to perform complex tasks effectively. RAG is powerful but complex to deploy and manage. +3. **Integration Nightmare:** Connecting agents to diverse systems, data sources, and various LLMs is difficult and time-consuming. 4. **Lack of Oversight:** Monitoring, debugging, and understanding the behavior of multiple autonomous agents in production is critical but often overlooked. **TrustGraph addresses these challenges by providing:** From d46cf483fa4ea0a6c5c9bb1dc52871512256c4e0 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 17:30:13 -0700 Subject: [PATCH 18/19] README WIP --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c80859a9..54ac8a18 100644 --- a/README.md +++ b/README.md @@ -197,7 +197,7 @@ When a Knowledge Core is loaded into TrustGraph, the corresponding graph edges a ![architecture](TG-layer-diagram.svg) ## 🧩 Integrations -TrustGraph seamlessly integrates API services, data stores, OTel, and control flow for a unified platform experience. +TrustGraph seamlessly integrates API services, data stores, observability, telemetry, and control flow for a unified platform experience. - LLM Providers: **Anthropic**, **AWS Bedrock**, **AzureAI**, **AzureOpenAI**, **Cohere**, **Google AI Studio**, **Google VertexAI**, **Llamafiles**, **LM Studio**, **Mistral**, **Ollama**, and **OpenAI** - Vector Databases: **Qdrant**, **Pinecone**, and **Milvus** From 64b5d3484d1baa677b6ad624f44fae913ab26c06 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 7 Apr 2025 17:33:02 -0700 Subject: [PATCH 19/19] README WIP --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 54ac8a18..7fb9eeb2 100644 --- a/README.md +++ b/README.md @@ -192,7 +192,7 @@ When a Knowledge Core is loaded into TrustGraph, the corresponding graph edges a ## 📐 Architecture -**TrustGraph removes the biggest headache of building an AI app: connecting and managing all the data, deployments, and models.** As a full-stack platform, TrustGraph simplifies the development and deployment of data-driven AI applications. TrustGraph is a complete solution, handling everything from data ingestion to deployment, so you can focus on building innovative AI experiences. +As a full-stack platform, TrustGraph provides all the stack layers needed to connect the data layer to the app layer for autonomous operations. ![architecture](TG-layer-diagram.svg)