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https://github.com/trustgraph-ai/trustgraph.git
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a9197d11ee
- Keeps processing in different flows separate so that data can go to different stores / collections etc. - Potentially supports different processing flows - Tidies the processing API with common base-classes for e.g. LLMs, and automatic configuration of 'clients' to use the right queue names in a flow
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
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from . request_response_spec import RequestResponse, RequestResponseSpec
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from .. schema import GraphEmbeddingsRequest, GraphEmbeddingsResponse
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from .. knowledge import Uri, Literal
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def to_value(x):
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if x.is_uri: return Uri(x.value)
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return Literal(x.value)
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class GraphEmbeddingsClient(RequestResponse):
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async def query(self, vectors, limit=20, user="trustgraph",
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collection="default", timeout=30):
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resp = await self.request(
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GraphEmbeddingsRequest(
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vectors = vectors,
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limit = limit,
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user = user,
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collection = collection
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),
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timeout=timeout
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)
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print(resp, flush=True)
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if resp.error:
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raise RuntimeError(resp.error.message)
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return [
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to_value(v)
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for v in resp.entities
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]
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class GraphEmbeddingsClientSpec(RequestResponseSpec):
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def __init__(
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self, request_name, response_name,
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):
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super(GraphEmbeddingsClientSpec, self).__init__(
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request_name = request_name,
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request_schema = GraphEmbeddingsRequest,
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response_name = response_name,
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response_schema = GraphEmbeddingsResponse,
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impl = GraphEmbeddingsClient,
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)
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