retrieved_documents
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
retrieved_documents has 13 facts recorded in Dontopedia across 7 references, with 2 live disagreements.
13 facts·7 predicates·7 sources·2 in dispute
Mostly:rdf:type(5), is output of(2), assigned from(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (17)
Other 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.
producesProduces(2)
- Document Retrieval
ex:document-retrieval - Search Process
ex:search-process
appliedToApplied to(1)
- Language Detection
ex:language-detection
comparesWithCompares With(1)
- True Vector Calculation
ex:true-vector-calculation
containsContains(1)
- Code Snippet
ex:code-snippet
derivedFromDerived From(1)
- True Vector
ex:true-vector
extractsExtracts(1)
- Evaluation Execution
ex:evaluation-execution
generatesGenerates(1)
- Autonomous System
ex:autonomous-system
involvesGeneratingInvolves Generating(1)
- Populate Dataset Step
ex:populate-dataset-step
lengthMatchesLength Matches(1)
- Pred Vector
ex:pred-vector
logged-variableLogged Variable(1)
- Logging
ex:logging
logs-variableLogs Variable(1)
- Logging Info
ex:logging-info
ranksRanks(1)
- Normalized Discounted Cumulative Gain
ex:normalized-discounted-cumulative-gain
returnsReturns(1)
- Process Query
ex:process-query
returnsOnSuccessReturns on Success(1)
- Run Pipeline Method
ex:run-pipeline-method
subsetOfSubset of(1)
- Top 10 Documents
ex:top-10-documents
unpacksRowUnpacks Row(1)
- Evaluation Execution
ex:evaluation-execution
Other facts (12)
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.
12 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Variable | [1] |
| Rdf:type | Document Collection | [2] |
| Rdf:type | Array | [3] |
| Rdf:type | Document Collection | [4] |
| Rdf:type | Data Entity | [7] |
| Is Output of | Process Query | [5] |
| Is Output of | Populate Dataset Step | [7] |
| Assigned From | Retrieve Documents | [1] |
| Has Property | detected-language | [2] |
| Is Split by | Comma | [5] |
| Used for | Y Pred | [6] |
| Result of | Process Query | [6] |
Timeline
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.
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typebeam/b9fc09da-b173-4003-bbaa-2b51be4f7d1d
ex:Variable
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labelbeam/b9fc09da-b173-4003-bbaa-2b51be4f7d1d
retrieved_documents
—
assigned-frombeam/b9fc09da-b173-4003-bbaa-2b51be4f7d1d
ex:retrieve_documents
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typebeam/71bd619f-3a2a-4409-aa90-2bb4c8d66908
ex:DocumentCollection
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hasPropertybeam/71bd619f-3a2a-4409-aa90-2bb4c8d66908
detected-language
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typebeam/3d077be4-0a10-4ccd-bb71-719927d7c95a
ex:Array
—
typebeam/c7de806a-f338-40ff-82dc-3afcd9dc4260
ex:Document-Collection
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isSplitBybeam/34a1dce2-ecc2-4241-ad4a-235e8625b612
ex:comma
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isOutputOfbeam/34a1dce2-ecc2-4241-ad4a-235e8625b612
ex:process-query
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usedForbeam/4cc521bd-2791-4334-88dc-f5e3519e2d92
ex:y_pred
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resultOfbeam/4cc521bd-2791-4334-88dc-f5e3519e2d92
ex:process_query
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typebeam/4b0e94ef-084d-4363-8931-568f755392e6
ex:DataEntity
—
isOutputOfbeam/4b0e94ef-084d-4363-8931-568f755392e6
ex:populate-dataset-step
References (7)
7 references
ctx:claims/beam/b9fc09da-b173-4003-bbaa-2b51be4f7d1dctx:claims/beam/71bd619f-3a2a-4409-aa90-2bb4c8d66908- full textbeam-chunktext/plain1 KB
doc:beam/71bd619f-3a2a-4409-aa90-2bb4c8d66908Show excerpt
4. **Building the Index**: We use Faiss to build an index of the document vectors. The index is optimized for inner product similarity. 5. **Searching and Retrieving**: We encode the query into a vector, normalize it, and search the index t…
ctx:claims/beam/3d077be4-0a10-4ccd-bb71-719927d7c95a- full textbeam-chunktext/plain1 KB
doc:beam/3d077be4-0a10-4ccd-bb71-719927d7c95aShow excerpt
pipeline.add_documents(documents) # Run query query = "What is the meaning of life?" results = pipeline.run_pipeline(query) # Print retrieved documents for doc in results["documents"]: print(f"Document: {doc.content}") ``` ### Explan…
ctx:claims/beam/c7de806a-f338-40ff-82dc-3afcd9dc4260- full textbeam-chunktext/plain1 KB
doc:beam/c7de806a-f338-40ff-82dc-3afcd9dc4260Show excerpt
4. **Rank Documents**: Rank the documents based on the combined score \( S_{combined} \). Higher scores indicate more relevant documents. 5. **Evaluate Relevance Lift**: To achieve an 18% relevance lift, you need to ensure that the combine…
ctx:claims/beam/34a1dce2-ecc2-4241-ad4a-235e8625b612- full textbeam-chunktext/plain1 KB
doc:beam/34a1dce2-ecc2-4241-ad4a-235e8625b612Show excerpt
retrieved_documents = rag_system.process_query(reformulated_query, context) return reformulated_query, retrieved_documents # Apply the function to each row df[['reformulated_query', 'retrieved_documents']] = df.apply( lambda ro…
ctx:claims/beam/4cc521bd-2791-4334-88dc-f5e3519e2d92- full textbeam-chunktext/plain1 KB
doc:beam/4cc521bd-2791-4334-88dc-f5e3519e2d92Show excerpt
2. **Split the Dataset**: Divide the dataset into training and testing sets. 3. **Evaluate Precision and Recall**: Use precision and recall to evaluate the relevance of the retrieved documents. 4. **User Feedback**: Optionally, collect user…
ctx:claims/beam/4b0e94ef-084d-4363-8931-568f755392e6- full textbeam-chunktext/plain1 KB
doc:beam/4b0e94ef-084d-4363-8931-568f755392e6Show excerpt
true_vector = [doc in ground_truth_documents for doc in retrieved_documents] pred_vector = [True] * len(retrieved_documents) y_true.extend(true_vector) y_pred.extend(pred_vector) # Calculate precision and recall precision …
See also
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