title: 'RAG for Contextual Applications'
sidebar_label: 'Applications'
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description: 'Build context-rich AI applications using Spice for Retrieval-Augmented Generation (RAG).'
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Use Spice to access data across various data sources for Retrieval-Augmented-Generation (RAG).
Spice enables developers to combine structured data via SQL queries and unstructured data through built-in vector similarity search. This combined data can then be fed to large language models (LLMs) through a native AI gateway, improving the models' ability to generate accurate and contextually relevant responses.
graph LR
Sources[Data Sources] -->|Embed & Store| Spice[Spice]
Query[User Query] --> Spice
Spice -->|Retrieve Context| Spice
Spice -->|Augmented Prompt| LLM[LLM]
LLM --> Response[Response]
graph LR
Sources[Data Sources] -->|Embed & Store| Spice[Spice]
Query[User Query] --> Spice
Spice -->|Retrieve Context| Spice
Spice -->|Augmented Prompt| LLM[LLM]
LLM --> Response[Response]
Example Configuration
The following spicepod.yaml configures a dataset with vector embeddings and an OpenAI model for RAG:
datasets:
- from: s3://my-bucket/documents/
name: documents
params:
file_format: parquet
columns:
- name: content
embeddings:
- from: openai
acceleration:
enabled: true
embeddings:
- from: openai
name: openai
params:
openai_api_key: ${ env:OPENAI_API_KEY }
models:
- from: openai:gpt-4o
name: rag_model
params:
openai_api_key: ${ env:OPENAI_API_KEY }
tools: auto
datasets:
- from: s3://my-bucket/documents/
name: documents
params:
file_format: parquet
columns:
- name: content
embeddings:
- from: openai
acceleration:
enabled: true
embeddings:
- from: openai
name: openai
params:
openai_api_key: ${ env:OPENAI_API_KEY }
models:
- from: openai:gpt-4o
name: rag_model
params:
openai_api_key: ${ env:OPENAI_API_KEY }
tools: auto
For more details on using vector search, embeddings, and model providers, refer to the following documentation:
