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Fixed readme typos

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JackColquitt 2024-09-16 17:53:47 -07:00
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@ -9,7 +9,7 @@
## Introduction
TrustGraph deploys a full E2E (end-to-end) AI solution with native GraphRAG in minutes. TrustGraph is designed for maximimum flexibility and modularity whether it's calling Cloud LLMs or deploying SLMs On-Device. TrustGraph ingests data to build a RDF style knowledge graph to enable accurate and private `RAG` responses using only the knowledge you want, when you want.
TrustGraph deploys a full E2E (end-to-end) AI solution with native GraphRAG in minutes. TrustGraph is designed for maximum flexibility and modularity whether it's calling Cloud LLMs or deploying SLMs On-Device. TrustGraph ingests data to build a RDF style knowledge graph to enable accurate and private `RAG` responses using only the knowledge you want, when you want.
The pipeline processing components are interconnected with a pub/sub engine to maximize modularity for agent integration. The core processing components decode documents, chunk text, perform embeddings, apply an On-Device SLM/LLM, call a LLM API, and generate LM predictions.
@ -72,7 +72,7 @@ The entire architecture, the pub/sub backbone and set of modules, is bundled int
- `text-completion-bedrock` - Send request to AWS Bedrock API
- `text-completion-claude` - Sends request to Anthropic's API
- `text-completion-cohere` - Send request to Cohere's API
- `text-completion-llamafile` - Sends request On-Device Llamafile
- `text-completion-llamafile` - Sends request to On-Device Llamafile
- `text-completion-ollama` - Sends request to On-Device LM running using Ollama
- `text-completion-openai` - Sends request to OpenAI's API
- `text-completion-vertexai` - Sends request to model available through VertexAI API