Current Query
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Current Query has 12 facts recorded in Dontopedia across 6 references, with 1 live disagreement.
12 facts·6 predicates·6 sources·1 in dispute
Mostly:rdf:type(6), topic(1), has initial weight(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (13)
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.
hasComponentHas Component(2)
- Context Components
ex:context-components - Context Weights
ex:context-weights
hasMemberHas Member(2)
- Context Components
ex:context-components - Context Components
ex:context-components
assignedToAssigned to(1)
- Current Query Weight
ex:current-query-weight
containsContains(1)
- Context Components
ex:context-components
containsValueContains Value(1)
- Query
ex:query
hasExampleHas Example(1)
- Step 1
ex:step-1
hasKeyHas Key(1)
- Weights
ex:weights
hasPropertyHas Property(1)
- Query
ex:query
includesIncludes(1)
- Context Components
ex:context-components
mentionsMentions(1)
- Session History
ex:session-history
retrievesKeyRetrieves Key(1)
- Current Query Weight
ex:current-query-weight
Other facts (11)
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.
11 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Query | [1] |
| Rdf:type | Context Component | [2] |
| Rdf:type | Context Component | [3] |
| Rdf:type | Context Component | [4] |
| Rdf:type | Context Component | [5] |
| Rdf:type | Concept | [6] |
| Topic | RAG systems best practices | [1] |
| Has Initial Weight | 0.4 | [2] |
| Has Weight | Current Query Weight | [4] |
| Is Member of | Context Components | [4] |
| Is Component of | Context Components | [5] |
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/757b9e40-fb47-4dfe-8d07-ef4b75f69515
ex:Query
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topicbeam/757b9e40-fb47-4dfe-8d07-ef4b75f69515
RAG systems best practices
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typebeam/c8578409-db7a-4511-babf-7af22c569322
ex:ContextComponent
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hasInitialWeightbeam/c8578409-db7a-4511-babf-7af22c569322
0.4
—
typebeam/c0dac4b7-a8bf-4fc4-b8c0-172938ac7e75
ex:ContextComponent
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typebeam/a71afa78-3ac4-4931-987f-ad0a5b6a3f57
ex:ContextComponent
—
labelbeam/a71afa78-3ac4-4931-987f-ad0a5b6a3f57
Current Query
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hasWeightbeam/a71afa78-3ac4-4931-987f-ad0a5b6a3f57
ex:current-query-weight
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isMemberOfbeam/a71afa78-3ac4-4931-987f-ad0a5b6a3f57
ex:context-components
—
typebeam/17359c4f-ce82-472f-b0cd-20671ade934f
ex:ContextComponent
—
isComponentOfbeam/17359c4f-ce82-472f-b0cd-20671ade934f
ex:context-components
—
typebeam/c0f9060d-f921-4339-a9ab-df94ea7f7bbb
ex:Concept
References (6)
6 references
ctx:claims/beam/757b9e40-fb47-4dfe-8d07-ef4b75f69515- full textbeam-chunktext/plain1 KB
doc:beam/757b9e40-fb47-4dfe-8d07-ef4b75f69515Show excerpt
{"query": "What are the best practices for RAG systems?", "context": "Previous query was about performance optimization."}, {"query": "Can you explain the retrieval mechanism?", "context": "Previous query was about context-aware ret…
ctx:claims/beam/c8578409-db7a-4511-babf-7af22c569322- full textbeam-chunktext/plain1 KB
doc:beam/c8578409-db7a-4511-babf-7af22c569322Show excerpt
For each combination of weights, evaluate the performance using your test queries and measure the intent precision. ### Example Implementation Here's an example of how you might structure your experiments: ```python import itertools impo…
ctx:claims/beam/c0dac4b7-a8bf-4fc4-b8c0-172938ac7e75- full textbeam-chunktext/plain1 KB
doc:beam/c0dac4b7-a8bf-4fc4-b8c0-172938ac7e75Show excerpt
[Turn 10470] User: I'm trying to optimize the intent precision of my LLM prompts, and I've been experimenting with different context weights. Currently, I'm achieving 88% intent precision on 2,500 test queries, but I want to improve it furt…
ctx:claims/beam/a71afa78-3ac4-4931-987f-ad0a5b6a3f57- full textbeam-chunktext/plain1 KB
doc:beam/a71afa78-3ac4-4931-987f-ad0a5b6a3f57Show excerpt
Identify the different components of your context and assign initial weights. For example: - `user_history` - `current_query` - `system_state` - `external_data_sources` ### Step 2: Generate Weight Combinations Use a systematic approach t…
ctx:claims/beam/17359c4f-ce82-472f-b0cd-20671ade934f- full textbeam-chunktext/plain1 KB
doc:beam/17359c4f-ce82-472f-b0cd-20671ade934fShow excerpt
``` Replace the placeholder functions with your actual logic to evaluate the intent precision. Would you like to proceed with these steps or do you have any specific questions about any part of the process? [Turn 10474] User: Sure, let's…
ctx:claims/beam/c0f9060d-f921-4339-a9ab-df94ea7f7bbb- full textbeam-chunktext/plain1 KB
doc:beam/c0f9060d-f921-4339-a9ab-df94ea7f7bbbShow excerpt
### Different Scenarios Here are a few scenarios where contextual query reformulation can be applied: 1. **Location-Based Search**: - Reformulate queries to include the user's location, such as "restaurants near me." 2. **Time-Base…
See also
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