[RAG] Operations
[RAG] Adhoc Load Document
The Rag adhoc load document
operation retrieves information based on a plain text prompt and file from embedding and LLM.
Input Fields
Module Configuration
This refers to the MAC Einstein AI configuration set up in the getting started section.
General Operation Fields
- Prompt: What is the file query or prompt or question.
- File Path: This field contains a full file path for the document to be ingested into the embedding store. Ensure the file path is accessible. You can also use a DataWeave expression for this field, such as
mule.home ++ "/apps/" ++ app.name ++ "/customer-service.pdf"
.
Additional Properties
- Model Name: The model name to be used (default is
OpenAI Ada 002
). - File Type: This field contains the type of the document to be ingested into the embedding store. Currently, four file types are supported:
- text: any type of text files (json, xml, txt, csv, etc.),
- pdf: only system-generated,
- csv: comma-separated values,
- url: only a single URL supported.
- Option Type: This field defines how the document is going to be split prior to ingestion into the embedding/vector database.
- Model Name: The model name to be used (default is
OpenAI GPT 3.5 Turbo
). - Probability: The model's probability to stay accurate (default is
0.8
). - Locale: Localization information, which can include the default locale, input locale(s), and expected output locale(s) (default is
en_US
).
XML Configuration
Below is the XML configuration for this operation:
<mac-einstein:rag-adhoc-load-document
doc:name="Rag adhoc load document"
doc:id="edaea124-a8aa-4d4a-8f85-0f32ee4c9858"
config-ref="Einstein_AI"
prompt="#[payload.prompt]"
filePath="#[payload.filePath]"
optionType="PARAGRAPH"
/>
Output Field
This operation responds with a json
payload.
Example Use Cases
The Rag adhoc load document
operation can be used in various scenarios, such as:
- Customer Service: Quickly retrieve relevant information from documents to answer customer queries accurately.
- Legal Teams: Load legal documents and query specific sections to find relevant case information.
- Research: Access specific sections of large research documents based on queries to support ongoing studies.