RAG system architecture
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RAG system architecture has 5 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
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raw canonical shape-checked rule-derived certifiedInbound mentions (1)
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exemplifiesExemplifies(1)
- Rag System Class
ex:rag-system-class
Other facts (4)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Hybrid Search System | [1] |
| Rdf:type | System Architecture | [2] |
| Uses Vector Search | Milvus | [1] |
| Uses Document Storage | Mongodb | [1] |
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References (2)
ctx:claims/beam/c39988e0-db33-4984-8c77-56ffcecd919a- full textbeam-chunktext/plain1 KB
doc:beam/c39988e0-db33-4984-8c77-56ffcecd919aShow excerpt
# Vector exists but document does not vector_collection.delete([vec_id]) # Run reconciliation periodically reconcile_data() ``` ### Full Example Script Here is the complete script combining all the steps: ```pyth…
ctx:claims/beam/241122f8-dc34-4876-8384-3647f4796af6- full textbeam-chunktext/plain1 KB
doc:beam/241122f8-dc34-4876-8384-3647f4796af6Show excerpt
self.tokenizer = tokenizer def process_query(self, query, context=None): # Reformulate the query reformulated_query = reformulate_query(query, context) # Process the reformulated query (e.g., retrieve r…
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