document retrieval
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
document retrieval has 17 facts recorded in Dontopedia across 8 references, with 3 live disagreements.
17 facts·8 predicates·8 sources·3 in dispute
Mostly:rdf:type(7), takes parameters(2), follows(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (11)
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.
functionFunction(2)
- Retrieval Component
ex:retrieval-component - Retrieval Module
ex:retrieval-module
causesCauses(1)
- Query
ex:query
consistsOfConsists of(1)
- Complete Workflow
ex:complete-workflow
hasPurposeHas Purpose(1)
- Retrieve Documents Method
ex:retrieve-documents-method
implementsImplements(1)
- Retrieve Documents Method
ex:retrieve-documents-method
precedesPrecedes(1)
- Document Ingestion
ex:document-ingestion
purposePurpose(1)
- Text Search System
ex:text-search-system
resultsInResults in(1)
- Vector Comparison
ex:vector-comparison
second-stepSecond Step(1)
- Sequence
ex:sequence
usedForUsed for(1)
- Stored Fields
ex:stored-fields
Other facts (15)
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.
15 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Operation | [2] |
| Rdf:type | Process | [3] |
| Rdf:type | Application | [4] |
| Rdf:type | Search Operation | [5] |
| Rdf:type | Application Domain | [6] |
| Rdf:type | Workflow Step | [7] |
| Rdf:type | Query Processing | [8] |
| Takes Parameters | query | [8] |
| Takes Parameters | context | [8] |
| Follows | Document Ingestion | [1] |
| Handled by | Retrieval Module | [1] |
| Produces | Retrieved Documents | [3] |
| Purpose | Full Document Access | [5] |
| Called on | rag_system | [8] |
| Returns | retrieved_documents | [8] |
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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followsbeam/b9fc09da-b173-4003-bbaa-2b51be4f7d1d
ex:document-ingestion
—
handled-bybeam/b9fc09da-b173-4003-bbaa-2b51be4f7d1d
ex:retrieval-module
—
typebeam/e4c92547-2858-4c88-9e26-9a0fad1000c8
ex:Operation
—
typebeam/3d077be4-0a10-4ccd-bb71-719927d7c95a
ex:Process
—
producesbeam/3d077be4-0a10-4ccd-bb71-719927d7c95a
ex:retrieved-documents
—
typebeam/f9279acb-7fb2-4149-a384-0aa4baa0cf16
ex:Application
—
labelbeam/f9279acb-7fb2-4149-a384-0aa4baa0cf16
document retrieval
—
typebeam/bfa4edb1-68b6-4481-81a3-6acb46a81b73
ex:SearchOperation
—
purposebeam/bfa4edb1-68b6-4481-81a3-6acb46a81b73
ex:full-document-access
—
typebeam/b2fa8237-a2ba-45f1-b609-1096fd02ce18
ex:ApplicationDomain
—
typebeam/241122f8-dc34-4876-8384-3647f4796af6
ex:WorkflowStep
—
labelbeam/241122f8-dc34-4876-8384-3647f4796af6
document retrieval step
—
calledOnbeam/4cc521bd-2791-4334-88dc-f5e3519e2d92
rag_system
—
takesParametersbeam/4cc521bd-2791-4334-88dc-f5e3519e2d92
query
—
takesParametersbeam/4cc521bd-2791-4334-88dc-f5e3519e2d92
context
—
returnsbeam/4cc521bd-2791-4334-88dc-f5e3519e2d92
retrieved_documents
—
typebeam/4cc521bd-2791-4334-88dc-f5e3519e2d92
ex:QueryProcessing
References (8)
8 references
ctx:claims/beam/b9fc09da-b173-4003-bbaa-2b51be4f7d1dctx:claims/beam/e4c92547-2858-4c88-9e26-9a0fad1000c8ctx: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/f9279acb-7fb2-4149-a384-0aa4baa0cf16ctx:claims/beam/bfa4edb1-68b6-4481-81a3-6acb46a81b73ctx:claims/beam/b2fa8237-a2ba-45f1-b609-1096fd02ce18- full textbeam-chunktext/plain1 KB
doc:beam/b2fa8237-a2ba-45f1-b609-1096fd02ce18Show excerpt
vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(documents) query_vector = vectorizer.transform([query]) similarity_scores = (query_vector * tfidf_matrix.T).toarray() return similarity_scores def h…
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…
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…
See also
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