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* Update README.md text --------- Co-authored-by: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com>
293 lines
14 KiB
Markdown
293 lines
14 KiB
Markdown
<img src="TG-horizon-repo.svg" width=100% />
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<div align="center">
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## Autonomous Operations Platform
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[](https://pypi.org/project/trustgraph/) [](https://discord.gg/sQMwkRz5GX)
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📑 [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)
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</div>
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**Transform AI agents from experimental concepts into a new paradigm of continuous operations.**
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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.
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---
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- ✨ [**Key Features**](#-key-features)
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- 🎯 [**Why TrustGraph?**](#-why-trustgraph)
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- 🚀 [**Getting Started**](#-getting-started)
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- 🔧 [**Configuration Builder**](#-configuration-builder)
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- 🧠 [**Knowledge Cores**](#-knowledge-cores)
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- 📐 [**Architecture**](#-architecture)
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- 🧩 [**Integrations**](#-integrations)
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- 📊 [**Observability & Telemetry**](#-observability--telemetry)
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- 🤝 [**Contributing**](#-contributing)
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- 📄 [**License**](#-license)
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- 📞 [**Support & Community**](#-support--community)
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---
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## ✨ Key Features
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- 📄 **Data Ingest**: Bulk ingest documents such as `.pdf`,`.txt`, and `.md`
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- 📃 **OCR Pipelines**: OCR documents with PDF decode, Tesseract, or Mistral OCR services
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- 🪓 **Adjustable Chunking**: Choose your chunking algorithm and parameters
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- 🔁 **No-code LLM Integration**: **Anthropic**, **AWS Bedrock**, **AzureAI**, **AzureOpenAI**, **Cohere**, **Google AI Studio**, **Google VertexAI**, **Llamafiles**, **LM Studio**, **Mistral**, **Ollama**, and **OpenAI**
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- 📖 **Automated Knowledge Graph Building**: No need for complex ontologies and manual graph building
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- 🔢 **Knowledge Graph to Vector Embeddings Mappings**: Connect knowledge graph enhanced data directly to vector embeddings
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- ❔**Natural Language Data Retrieval**: Automatically perform a semantic similiarity search and subgraph extraction for the context of LLM generative responses
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- 🧠 **Knowledge Cores**: Modular data sets with semantic relationships that can saved and quickly loaded on demand
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- 🤖 **Agent Manager**: Define custom tools used by a ReAct style Agent Manager that fully controls the response flow including the ability to perform Graph RAG requests
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- 📚 **Multiple Knowledge Graph Options**: Full integration with **Memgraph**, **FalkorDB**, **Neo4j**, or **Cassandra**
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- 🧮 **Multiple VectorDB Options**: Full integration with **Qdrant**, **Pinecone**, or **Milvus**
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- 🎛️ **Production-Grade** Reliability, scalability, and accuracy
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- 📊 **Observability and Telemetry**: Get insights into system performance with **Prometheus** and **Grafana**
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- 🎻 **Orchestration**: Fully containerized with **Docker** or **Kubernetes**
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- 🥞 **Stack Manager**: Control and scale the stack with confidence with **Apache Pulsar**
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- ☁️ **Cloud Deployments**: **AWS**, **Azure**, **Google Cloud**, and **Scaleway**
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- 🪴 **Customizable and Extensible**: Tailor for your data and use cases
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- 🖥️ **Configuration Builder**: Build the `YAML` configuration with drop down menus and selectable parameters
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- 🕵️ **Test Suite**: A simple UI to fully test TrustGraph performance
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## 🎯 Why TrustGraph?
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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:
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1. **Scalability & Reliability:** Standalone agents don't scale or offer the robustness required for business-critical operations.
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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.
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3. **Integration Nightmare:** Connecting agents to diverse systems, data sources, and various LLMs is difficult and time-consuming.
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4. **Lack of Oversight:** Monitoring, debugging, and understanding the behavior of multiple autonomous agents in production is critical but often overlooked.
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**TrustGraph addresses these challenges by providing:**
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* A **platform**, not just a library, for managing the lifecycle of autonomous operations.
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* **Automated, best-practice RAG deployments** that combine the strengths of semantic vector search and structured knowledge graph traversal.
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* A **standardized layer** for LLM interaction and enterprise system integration.
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* **Built-in observability** to ensure you can trust and manage your autonomous systems.
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## 🚀 Getting Started
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- [Install the CLI](#install-the-trustgraph-cli)
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- [Configuration Builder](#-configuration-builder)
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- [Platform Restarts](#platform-restarts)
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- [Test Suite](#test-suite)
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- [Example Notebooks](#example-trustgraph-notebooks)
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### Developer APIs and CLI
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- [**REST API**](docs/apis/README.md#rest-apis)
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- [**Websocket API**](docs/apis/README.md#websocket-api)
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- [**Python SDK**](https://trustgraph.ai/docs/api/apistarted)
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- [**TrustGraph CLI**](https://trustgraph.ai/docs/running/cli)
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See the [API Developer's Guide](#api-documentation) for more information.
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For users, **TrustGraph** has the following interfaces:
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- [**Configuration Builder**](#-configuration-builder)
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- [**Test Suite**](#test-suite)
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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.
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### Install the TrustGraph CLI
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```
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pip3 install trustgraph-cli==0.21.17
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```
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> [!NOTE]
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> The `TrustGraph CLI` version must match the desired `TrustGraph` release version.
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## 🔧 Configuration Builder
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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, AWS, Azure, Google Cloud, or Scaleway. There is a `Configuration Builder` for the both the lastest and stable `TrustGraph` releases.
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- [**Configuration Builder** (Stable 0.21.17) 🚀](https://config-ui.demo.trustgraph.ai/)
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- [**Configuration Builder** (Latest 0.22.5) 🚀](https://dev.config-ui.demo.trustgraph.ai/)
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The `Configuration Builder` has 4 important sections:
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- **Component Selection** ✅: Choose from the available deployment platforms, LLMs, graph store, VectorDB, chunking algorithm, chunking parameters, and LLM parameters
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- **Customization** 🧰: Customize the prompts for the LLM System, Data Extraction Agents, and Agent Flow
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- **Test Suite** 🕵️: Add the **Test Suite** to the configuration available on port `8888`
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- **Finish Deployment** 🚀: Download the launch `YAML` files with deployment instructions
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The `Configuration Builder` 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:
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```
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docker compose up -d
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```
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> [!TIP]
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> Docker is the recommended container orchestration platform for first getting started with TrustGraph.
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When finished, shutting down TrustGraph is as simple as:
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```
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docker compose down -v
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```
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### Platform Restarts
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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:
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```
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docker compose down
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```
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With the volumes preserved, restarting the system is as simple as:
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```
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docker compose up -d
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```
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All data previously in TrustGraph will be saved and usable on restart.
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### Test Suite
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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:
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- **Graph RAG Chat** 💬: Graph RAG queries in a chat interface
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- **Vector Search** 🔎: Semantic similarity search with cosine similarity scores
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- **Semantic Relationships** 🕵️: See semantic relationships in a list structure
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- **Graph Visualizer** 🌐: Visualize semantic relationships in **3D**
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- **Data Loader** 📂: Directly load `.pdf`, `.txt`, or `.md` into the system with document metadata
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### Example TrustGraph Notebooks
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- [**REST API Notebooks**](https://github.com/trustgraph-ai/example-notebooks/tree/master/api-examples)
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- [**Python SDK Notebooks**](https://github.com/trustgraph-ai/example-notebooks/tree/master/api-library)
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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:
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- `docker-compose`
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- `minikube-k8s`
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- `gcp-k8s`
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> [!NOTE]
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> As more integrations have been added, the number of possible combinations of configurations has become quite large. It is recommended to use the `Configuration Builder` to build your deployment configuration. Each directory contains `YAML` configuration files for the default component selections.
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**Docker**:
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```
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docker compose -f <launch-file.yaml> up -d
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```
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**Kubernetes**:
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```
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kubectl apply -f <launch-file.yaml>
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```
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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.
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## 🧠 Knowledge Cores
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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.
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A Knowledge Core has two components:
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- Set of Graph Edges
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- Set of mapped Vector Embeddings
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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.
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## 📐 Architecture
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As a full-stack platform, TrustGraph provides all the stack layers needed to connect the data layer to the app layer for autonomous operations.
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## 🧩 Integrations
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TrustGraph seamlessly integrates API services, data stores, observability, telemetry, and control flow for a unified platform experience.
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- LLM Providers: **Anthropic**, **AWS Bedrock**, **AzureAI**, **AzureOpenAI**, **Cohere**, **Google AI Studio**, **Google VertexAI**, **Llamafiles**, **LM Studio**, **Mistral**, **Ollama**, and **OpenAI**
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- Vector Databases: **Qdrant**, **Pinecone**, and **Milvus**
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- Knowledge Graphs: **Memgraph**, **Neo4j**, and **FalkorDB**
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- Data Stores: **Apache Cassandra**
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- Observability: **Prometheus** and **Grafana**
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- Control Flow: **Apache Pulsar**
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### Pulsar Control Flows
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- For control flows, Pulsar accepts the output of a processing module and queues it for input to the next subscribed module.
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- 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.
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### Document Extraction Agents
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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:
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- Topic Extraction Agent
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- Entity Extraction Agent
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- Relationship Extraction Agent
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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.
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PDF file:
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```
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tg-load-pdf <document.pdf>
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```
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Text or Markdown file:
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```
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tg-load-text <document.txt>
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```
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### Graph RAG Queries
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Once the knowledge graph and embeddings have been built or a cognitive core has been loaded, RAG queries are launched with a single line:
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```
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tg-invoke-graph-rag -q "What are the top 3 takeaways from the document?"
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```
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### Agent Flow
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Invoking the Agent Flow will use a ReAct style approach the combines Graph RAG and text completion requests to think through a problem solution.
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```
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tg-invoke-agent -v -q "Write a blog post on the top 3 takeaways from the document."
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```
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> [!TIP]
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> Adding `-v` to the agent request will return all of the agent manager's thoughts and observations that led to the final response.
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## 📊 Observability & Telemetry
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Once the platform is running, access the Grafana dashboard at:
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```
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http://localhost:3000
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```
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Default credentials are:
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```
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user: admin
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password: admin
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```
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The default Grafana dashboard tracks the following:
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- LLM Latency
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- Error Rate
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- Service Request Rates
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- Queue Backlogs
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- Chunking Histogram
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- Error Source by Service
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- Rate Limit Events
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- CPU usage by Service
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- Memory usage by Service
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- Models Deployed
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- Token Throughput (Tokens/second)
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- Cost Throughput (Cost/second)
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## 🤝 Contributing
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[Developing for TrustGraph](docs/README.development.md)
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## 📄 License
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**TrustGraph** is licensed under [AGPL-3.0](https://www.gnu.org/licenses/agpl-3.0.en.html).
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## 📞 Support & Community
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- Bug Reports & Feature Requests: [Discord](https://discord.gg/sQMwkRz5GX)
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- Discussions & Questions: [Discord](https://discord.gg/sQMwkRz5GX)
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- Documentation: [Docs](https://trustgraph.ai/docs/getstarted)
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