mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 00:16:23 +02:00
24bbe94136
- Schema - ChunkEmbeddings now uses chunk_id: str instead of chunk: bytes
- Schema - DocumentEmbeddingsResponse now returns chunk_ids: list[str]
instead of chunks
- Translators - Updated to serialize/deserialize chunk_id
- Clients - DocumentEmbeddingsClient.query() returns chunk_ids
- SDK/API - flow.py, socket_client.py, bulk_client.py updated
- Document embeddings service - Stores chunk_id (document ID) instead
of chunk text
- Storage writers - Qdrant, Milvus, Pinecone store chunk_id in payload
- Query services - Return chunk_id from vector store searches
- Gateway dispatchers - Serialize chunk_id in API responses
- Document RAG - Added librarian client to fetch chunk content from
Garage using chunk_ids
- CLI tools - Updated all three tools:
- invoke_document_embeddings.py - displays chunk_ids, removed
max_chunk_length
- save_doc_embeds.py - exports chunk_id
- load_doc_embeds.py - imports chunk_id
97 lines
No EOL
3.1 KiB
Python
97 lines
No EOL
3.1 KiB
Python
from dataclasses import dataclass, field
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from ..core.primitives import Error, Term, Triple
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from ..core.topic import topic
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############################################################################
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# Graph embeddings query
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@dataclass
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class GraphEmbeddingsRequest:
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vectors: list[list[float]] = field(default_factory=list)
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limit: int = 0
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user: str = ""
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collection: str = ""
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@dataclass
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class GraphEmbeddingsResponse:
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error: Error | None = None
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entities: list[Term] = field(default_factory=list)
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############################################################################
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# Graph triples query
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@dataclass
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class TriplesQueryRequest:
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user: str = ""
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collection: str = ""
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s: Term | None = None
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p: Term | None = None
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o: Term | None = None
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g: str | None = None # Graph IRI. None=default graph, "*"=all graphs
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limit: int = 0
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@dataclass
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class TriplesQueryResponse:
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error: Error | None = None
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triples: list[Triple] = field(default_factory=list)
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############################################################################
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# Doc embeddings query
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@dataclass
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class DocumentEmbeddingsRequest:
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vectors: list[list[float]] = field(default_factory=list)
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limit: int = 0
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user: str = ""
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collection: str = ""
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@dataclass
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class DocumentEmbeddingsResponse:
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error: Error | None = None
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chunk_ids: list[str] = field(default_factory=list)
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document_embeddings_request_queue = topic(
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"document-embeddings-request", qos='q0', tenant='trustgraph', namespace='flow'
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)
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document_embeddings_response_queue = topic(
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"document-embeddings-response", qos='q0', tenant='trustgraph', namespace='flow'
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)
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############################################################################
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# Row embeddings query - for semantic/fuzzy matching on row index values
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@dataclass
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class RowIndexMatch:
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"""A single matching row index from a semantic search"""
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index_name: str = "" # The indexed field(s)
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index_value: list[str] = field(default_factory=list) # The index values
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text: str = "" # The text that was embedded
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score: float = 0.0 # Similarity score
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@dataclass
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class RowEmbeddingsRequest:
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"""Request for row embeddings semantic search"""
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vectors: list[list[float]] = field(default_factory=list) # Query vectors
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limit: int = 10 # Max results to return
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user: str = "" # User/keyspace
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collection: str = "" # Collection name
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schema_name: str = "" # Schema name to search within
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index_name: str | None = None # Optional: filter to specific index
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@dataclass
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class RowEmbeddingsResponse:
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"""Response from row embeddings semantic search"""
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error: Error | None = None
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matches: list[RowIndexMatch] = field(default_factory=list)
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row_embeddings_request_queue = topic(
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"row-embeddings-request", qos='q0', tenant='trustgraph', namespace='flow'
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)
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row_embeddings_response_queue = topic(
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"row-embeddings-response", qos='q0', tenant='trustgraph', namespace='flow'
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) |