do
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
do has 17 facts recorded in Dontopedia across 10 references, with 2 live disagreements.
Mostly:rdf:type(10), returns(1), executes query(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedRdf:typein disputerdf:type
- Execution Method[1]all time · 70bbc43a 27da 4ee6 Abde 0b83af52d874
- Method[2]all time · 5649feba 310c 425b 9ed5 Db5583522d98
- Execution Method[3]all time · E3b0d393 Cb26 4e01 B5f0 47981803de05
- Method Call[4]sourceall time · Cbaeb875 E16f 44dd Bc0f 36b3945d0935
- Method[5]all time · F80d8de8 0d2a 446e Ac9c Fc4672dce4f0
- Execution Method[6]all time · 131a150d 00ba 472b Bdc7 209aa22bc91d
- Execution Method[7]all time · Df58a3ab 2df5 43d0 A3c7 D866e2d0138c
- Method Call[8]all time · Ea34a816 3421 425e 97a9 50206b2c6248
- Method Call[9]sourceall time · 7930b608 9757 4a86 9aa2 C6ca10571913
- Query Execution Method[10]all time · 9d96f8cb 54e9 48bd A699 50a1796601b9
Inbound 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.
chainedMethodChained Method(2)
- Query Get Call
ex:query-get-call - Vector Search Call
ex:vector-search-call
callsDoCalls Do(1)
- Query Operation
ex:query-operation
callsMethodCalls Method(1)
- Query Weaviate Function
ex:query-weaviate-function
chainedWithChained With(1)
- Query Operation
ex:query-operation
chainedWithDoMethodChained With Do Method(1)
- Query Get Method
ex:query-get-method
executesQueryExecutes Query(1)
- Weaviate Search
ex:weaviate-search
executesWithExecutes With(1)
- Query Operation
ex:query-operation
hasMethodHas Method(1)
- Query Builder Object
ex:query-builder-object
invokesDoMethodInvokes Do Method(1)
- Query Get Method
ex:query-get-method
usesMethodUses Method(1)
- Vector Search Example
ex:vector-search-example
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 |
|---|---|---|
| Returns | Query Result Object | [1] |
| Executes Query | Query Operation | [3] |
| Executes | Query Operation | [8] |
| Invoked on | Query Builder | [9] |
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.
References (10)
ctx:claims/beam/70bbc43a-27da-4ee6-abde-0b83af52d874ctx:claims/beam/5649feba-310c-425b-9ed5-db5583522d98- full textbeam-chunktext/plain1 KB
doc:beam/5649feba-310c-425b-9ed5-db5583522d98Show excerpt
client.data_object.create(data[0], "MyClass") print("Data inserted successfully.") except Exception as e: print(f"Failed to insert data: {e}") ``` #### 4. Check Query Implementation Ensure the query is correctly implemented and…
ctx:claims/beam/e3b0d393-cb26-4e01-b5f0-47981803de05- full textbeam-chunktext/plain1 KB
doc:beam/e3b0d393-cb26-4e01-b5f0-47981803de05Show excerpt
client = weaviate.Client("http://localhost:8080") # Define the schema schema = { "class": "MyClass", "properties": [ {"name": "my_text_property", "dataType": ["text"]}, {"name": "my_vector_property", "dataType": ["v…
ctx:claims/beam/cbaeb875-e16f-44dd-bc0f-36b3945d0935- full textbeam-chunktext/plain1 KB
doc:beam/cbaeb875-e16f-44dd-bc0f-36b3945d0935Show excerpt
print("Query successful:") print(result) ``` ### Example with Vector Search If you want to perform a vector search and retrieve both text and vector data, you can use the `nearVector` filter: ```python # Perform a vector search query_vec…
ctx:claims/beam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0- full textbeam-chunktext/plain1 KB
doc:beam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0Show excerpt
# Create the schema in Weaviate client.schema.create_class(schema) print("Schema created successfully.") ``` #### Inserting Data When inserting data, you can specify which vector property to use based on the vector size. ```python # Add …
ctx:claims/beam/131a150d-00ba-472b-bdc7-209aa22bc91dctx:claims/beam/df58a3ab-2df5-43d0-a3c7-d866e2d0138c- full textbeam-chunktext/plain1 KB
doc:beam/df58a3ab-2df5-43d0-a3c7-d866e2d0138cShow excerpt
.with_near_vector(near_vector_128) .with_limit(10) .do() ) print("Vector search query successful (size 128):") print(result_128) query_vector_256 = [0.5, 0.6, 0.7, 0.8] * 64 # Example query vector of size 256 near_vector_256 …
ctx:claims/beam/ea34a816-3421-425e-97a9-50206b2c6248ctx:claims/beam/7930b608-9757-4a86-9aa2-c6ca10571913- full textbeam-chunktext/plain1 KB
doc:beam/7930b608-9757-4a86-9aa2-c6ca10571913Show excerpt
self.name = name self.vector = vector # Add some test data test_data = [ TestData("Test 1", [0.1, 0.2, 0.3]), TestData("Test 2", [0.4, 0.5, 0.6]), ] # Upload the test data to Weaviate for data in test_data: cli…
ctx:claims/beam/9d96f8cb-54e9-48bd-a699-50a1796601b9
See also
Keep researching
Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.