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Rag System has 27 facts recorded in Dontopedia across 4 references, with 5 live disagreements.
Mostly:has method(5), rdf:type(4), has attribute(4)
rdfs:labelhasAttributeinstantiationParameterinstantiationpurposeintegrationPurposerequiresTokenizerrequiresModeldefinedIninstantiatesTokenizerOther subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. “X motherOf this subject” — and answer questions the forward facts can't. Grouped by predicate.
rdf:typeRdf:type(3)ex:rag-systemex:rag-systemex:rag_systemdefinedInDefined in(2)ex:process_queryex:retrieve_documentsmemberOfMember of(2)ex:process_queryex:selfcontainsClassContains Class(1)ex:section-3demonstratesDemonstrates(1)ex:example_usagehasClassHas Class(1)ex:rag-systemisTypeOfIs Type of(1)ex:rag-systemtargetsUseCaseTargets Use Case(1)ex:mongodb-milvus-integration-guideThe 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 |
|---|---|---|
| Instantiates Model | Self.model | [3] |
| Modifies Query Processing Pipeline | true | [3] |
| Integrates Reformulation | Reformulate Query | [3] |
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
doc:beam/4ace5f5a-184d-4eae-9c13-25918a7725e6reformulated_query = reformulate_query(query, context) # Process the reformulated query (e.g., retrieve relevant documents) # This is a placeholder for the actual retrieval logic retrieved_documents = self.r…
doc:beam/50eac377-aaaf-4822-a440-3716011a2137prompt = f"Reformulate the query '{query}' considering the context: {context}. " else: prompt = f"Reformulate the query '{query}'. " # Add examples if needed examples = "Examples: 'What is the capital of France?…
doc:beam/88a5d8fe-a55a-4e46-9940-4f8c3c39cf8bmodel_name = "t5-small" model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` #### 2. Define the Reformulation Function Next, define the reformulation function that leverages t…
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