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Native CLI i18n: The TrustGraph CLI has built-in translation support that dynamically loads language strings. You can test and use different languages by simply passing the --lang flag (e.g., --lang es for Spanish, --lang ru for Russian) or by configuring your environment's LANG variable. Automated Docs Translations: This PR introduces autonomously translated Markdown documentation into several target languages, including Spanish, Swahili, Portuguese, Turkish, Hindi, Hebrew, Arabic, Simplified Chinese, and Russian.
99 lines
4.6 KiB
Markdown
99 lines
4.6 KiB
Markdown
---
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layout: default
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title: "Pendekezo la Urekebishaji wa Saraka ya Mfumo"
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parent: "Swahili (Beta)"
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---
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# Pendekezo la Urekebishaji wa Saraka ya Mfumo
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> **Beta Translation:** This document was translated via Machine Learning and as such may not be 100% accurate. All non-English languages are currently classified as Beta.
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## Masuala Yanayoendelea
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1. **Muundo tambarare** - Saraka moja inayokuwa na mifumo yote inafanya iwe ngumu kuelewa uhusiano.
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2. **Mambo mchanganyikano** - Aina za msingi, vitu vya kikoa, na mikatiba ya API yote yamechanganywa.
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3. **Majina yasiyo wazi** - Faili kama vile "object.py", "types.py", "topic.py" hazionyeshi wazi madhumuni yake.
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4. **Hakuna tabaka wazi** - Haiwezekani kuona kwa urahisi nini kinategemea nini.
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## Muundo Ulio Pendekezwa
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```
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trustgraph-base/trustgraph/schema/
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├── __init__.py
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├── core/ # Core primitive types used everywhere
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│ ├── __init__.py
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│ ├── primitives.py # Error, Value, Triple, Field, RowSchema
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│ ├── metadata.py # Metadata record
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│ └── topic.py # Topic utilities
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│
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├── knowledge/ # Knowledge domain models and extraction
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│ ├── __init__.py
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│ ├── graph.py # EntityContext, EntityEmbeddings, Triples
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│ ├── document.py # Document, TextDocument, Chunk
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│ ├── knowledge.py # Knowledge extraction types
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│ ├── embeddings.py # All embedding-related types (moved from multiple files)
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│ └── nlp.py # Definition, Topic, Relationship, Fact types
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│
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└── services/ # Service request/response contracts
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├── __init__.py
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├── llm.py # TextCompletion, Embeddings, Tool requests/responses
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├── retrieval.py # GraphRAG, DocumentRAG queries/responses
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├── query.py # GraphEmbeddingsRequest/Response, DocumentEmbeddingsRequest/Response
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├── agent.py # Agent requests/responses
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├── flow.py # Flow requests/responses
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├── prompt.py # Prompt service requests/responses
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├── config.py # Configuration service
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├── library.py # Librarian service
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└── lookup.py # Lookup service
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```
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## Mabadiliko Muhimu
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1. **Mpangilio wa kimfumo** - Tofauti wazi kati ya aina kuu, modeli za maarifa, na mikatiba ya huduma.
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2. **Majina bora zaidi**:
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`types.py` → `core/primitives.py` (lengo lililoboreshwa)
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`object.py` → Kugawanywa katika faili zinazofaa kulingana na yaliyomo halisi.
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`documents.py` → `knowledge/document.py` (moja, thabiti)
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`models.py` → `services/llm.py` (wazi zaidi ni aina gani ya modeli)
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`prompt.py` → Kugawanywa: sehemu za huduma hadi `services/prompt.py`, aina za data hadi `knowledge/nlp.py`
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3. **Punguzo la mantiki**:
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Aina zote za kuingiza zimeunganishwa katika `knowledge/embeddings.py`
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Mikatiba yote ya huduma inayohusiana na LLM iko katika `services/llm.py`
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Tofauti wazi ya jozi za ombi/jibu katika saraka ya huduma.
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Aina za utoaji wa maarifa zimepangwa pamoja na modeli zingine za uwanja wa maarifa.
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4. **Ufafanuzi wa utegemezi**:
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Aina kuu hazina utegemezi wowote.
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Modeli za maarifa hutegemea tu aina kuu.
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Mikatiba ya huduma inaweza kutegemea aina kuu na modeli za maarifa.
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## Faida za Uhamisho
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1. **Uramaji rahisi** - Wasanidi programu wanaweza kupata haraka kile wanachohitaji.
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2. **Uunganishaji bora zaidi** - Mipaka wazi kati ya masuala tofauti.
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3. **Uingizaji rahisi zaidi** - Njia za uingizaji ambazo ni za angavu zaidi.
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4. **Inaweza kudumu kwa muda mrefu** - Rahisi kuongeza aina mpya za maarifa au huduma bila kusumbua.
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## Mfano wa Mabadiliko ya Uingizaji
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```python
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# Before
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from trustgraph.schema import Error, Triple, GraphEmbeddings, TextCompletionRequest
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# After
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from trustgraph.schema.core import Error, Triple
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from trustgraph.schema.knowledge import GraphEmbeddings
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from trustgraph.schema.services import TextCompletionRequest
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```
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## Maelezo ya Utendaji
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1. Hakikisha utangamano wa zamani kwa kudumisha uingizaji wa faili katika sehemu kuu `__init__.py`
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2. Hamisha faili hatua kwa hatua, na usasishe uingizaji wa faili kama inavyohitajika
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3. Fikiria kuongeza `legacy.py` ambayo huingiza kila kitu kwa kipindi cha mpito
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4. Sasisha nyaraka ili kuonyesha muundo mpya
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<function_calls>
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<invoke name="TodoWrite">
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<parameter name="todos">[{"id": "1", "content": "Fanyia uchunguzi muundo wa sasa wa saraka ya schema", "status": "imekamilika", "priority": "juu"}, {"id": "2", "content": "Changanua faili za schema na madhumuni yao", "status": "imekamilika", "priority": "juu"}, {"id": "3", "content": "Pendekeza jina na muundo uliobora", "status": "imekamilika", "priority": "juu"}]
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