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@ -20,13 +20,13 @@ TrustGraph is a fully agentic AI system for complex unstructured data. Extract y
- 🔁 **No-code LLM Integration**: **Anthropic**, **AWS Bedrock**, **AzureAI**, **AzureOpenAI**, **Cohere**, **Google AI Studio**, **Google VertexAI**, **Llamafiles**, **Ollama**, and **OpenAI**
- 📖 **Entity, Topic, and Relationship Knowledge Graphs**
- 🔢 **Mapped Vector Embeddings**
- ❔**No-code GraphRAG Queries**: Automatically perform a semantic similiarity search and subgraph extraction for the context of LLM generative responses
- 🤖 **Agent Flow**: Define custom tools used by a ReAct style Agent Manager that fully controls the response flow including the ability to perform GraphRAG requests
- ❔**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
- 🤖 **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**
- 🧮 **Multiple VectorDB Options**: Full integration with **Pinecone**, **Qdrant**, or **Milvus**
- 🎛️ **Production-Grade** reliability, scalability, and accuracy
- 🔍 **Observability**: get insights into system performance with Prometheus and Grafana
- 🗄️ **AI Powered Data Warehouse**: Load only the subgraph and vector embeddings you use most often
- 🪴 **Customizable and Extensible**: Tailor for your data and use cases
- 🖥️ **Configuration Portal**: Build the `YAML` configuration with drop down menus and selectable parameters
- 🕵️ **Data Workbench**: Explore your data with a 3D semantic visualizer
@ -109,7 +109,7 @@ All data previously in TrustGraph will be saved and usable on restart.
If added to the build in the `Configuration Portal`, the `Data Workbench` will be available at port `8888`. The `Data Workbench` has 4 capabilities:
- **System Chat** 💬: GraphRAG queries in a chat interface
- **System Chat** 💬: Graph RAG queries in a chat interface
- **Data Explorer** 🕵️: See semantic relationships in a list structure
- **Data Visualizer** 🌐: Visualize semantic relationships in **3D**
- **Data Loader** 📂: Directly load `.pdf`, `.txt`, or `.md` into the system
@ -178,9 +178,9 @@ Text or Markdown file:
tg-load-text <document.txt>
```
## GraphRAG Queries
## Graph RAG Queries
Once the knowledge graph and embeddings have been built or a knowledge core has been loaded, RAG queries are launched with a single line:
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?"
@ -188,7 +188,7 @@ 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 GraphRAG and text completion requests to think through a problem solution.
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."