Enterprise Support RAG Pipeline
A standard Retrieval-Augmented Generation pipeline optimized for enterprise support. It pairs dense text embeddings with Claude 3.5 Sonnet to deliver context-aware, low-latency streaming responses.
Execution_Steps
- 01
Initialize Search Modalities
Drag an `input` node and connect it directly to an `embedding` node. Open the config for the Embedding node and select "text-embedding-3-large" from OpenAI to ensure high-dimensional semantic capture.
- 02
Configure Vector Retrieval
Add a `vector_store` node. Connect the Embedding node output to the Vector Store input. In the configuration sheet, set your Provider (e.g., Pinecone/Weaviate), define the Index Name, and set Top K to 5.
- 03
Deploy the Reasoning Engine
Attach an `llm_call` node to the Vector Store. Select Anthropic as the provider and "claude-3-5-sonnet" as the model. Context injection is handled inherently by passing the vector store payload into the LLM context window. Allocate 3000 tokens for the system prompt to accommodate context.
- 04
Enable Output Terminals
Connect the LLM Call to a single `output` node. Run Auto Layout [Context Menu -> Auto Layout -> Horizontal] to clean up the graph.
Node_Architecture
Recursive_Read_Next