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Retrieval-Augmented Generation

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Retrieval-Augmented Generation is RAG (Retrieval-Augmented Generation) system integration.

6 facts·3 predicates·2 sources·2 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Other facts (5)

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5 facts
PredicateValueRef
Rdf:typeSystem Context[1]
Rdf:typeApplication Domain[2]
Includes ComponentMongodb Connection[1]
Includes ComponentMilvus Connection[1]
DescriptionRAG (Retrieval-Augmented Generation) system integration[1]

Timeline

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6 undated
—
typebeam/830f9da6-6442-415f-b959-4e810c077604
ex:SystemContext
—
descriptionbeam/830f9da6-6442-415f-b959-4e810c077604
RAG (Retrieval-Augmented Generation) system integration
—
includesComponentbeam/830f9da6-6442-415f-b959-4e810c077604
ex:mongodb-connection
—
includesComponentbeam/830f9da6-6442-415f-b959-4e810c077604
ex:milvus-connection
—
typebeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
ex:ApplicationDomain
—
labelbeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
Retrieval-Augmented Generation

References (2)

2 references
  1. [1]830f9da6 6442 415f B959 4e810c0776044 facts
    ctx:claims/beam/830f9da6-6442-415f-b959-4e810c077604
    • full textbeam-chunk
      text/plain1 KBdoc:beam/830f9da6-6442-415f-b959-4e810c077604
      Show excerpt
      First, define the structure of your data. For simplicity, let's assume you have documents with text content and associated vectors. ```python import pandas as pd from pymongo import MongoClient from pymilvus import connections, FieldSchema…
  2. [2]D4c82979 1650 4b89 A2fa A0ec5b37bb692 facts
    ctx:claims/beam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
    • full textbeam-chunk
      text/plain1 KBdoc:beam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
      Show excerpt
      FieldSchema(name="id", dtype=DataType.INT64, is_primary=True), FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=3) ] schema = CollectionSchema(fields, "RAG Vector Collection") collection = Collection("rag_vectors", schema…

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