mirror of
https://github.com/trustgraph-ai/trustgraph.git
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65d7f6d261
* Switching from docker compose to abstract form - should be easier to k8s later * Text loader util * Recreate templates
91 lines
2.4 KiB
Jsonnet
91 lines
2.4 KiB
Jsonnet
local base = import "base/base.jsonnet";
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local images = import "values/images.jsonnet";
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local url = import "values/url.jsonnet";
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local prompt = import "prompt-template.jsonnet";
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{
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"chunk-size":: 250,
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"chunk-overlap":: 15,
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"chunker" +: {
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create:: function(engine)
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local container =
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engine.container("chunker")
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.with_image(images.trustgraph)
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.with_command([
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"chunker-token",
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"-p",
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url.pulsar,
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"--chunk-size",
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std.toString($["chunk-size"]),
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"--chunk-overlap",
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std.toString($["chunk-overlap"]),
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])
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.with_limits("0.5", "128M")
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.with_reservations("0.1", "128M");
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local containerSet = engine.containers(
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"chunker", [ container ]
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);
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engine.resources([
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containerSet,
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])
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},
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"pdf-decoder" +: {
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create:: function(engine)
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local container =
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engine.container("pdf-decoder")
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.with_image(images.trustgraph)
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.with_command([
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"pdf-decoder",
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"-p",
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url.pulsar,
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])
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.with_limits("0.5", "128M")
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.with_reservations("0.1", "128M");
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local containerSet = engine.containers(
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"pdf-decoder", [ container ]
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);
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engine.resources([
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containerSet,
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])
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},
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"vectorize" +: {
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create:: function(engine)
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local container =
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engine.container("vectorize")
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.with_image(images.trustgraph)
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.with_command([
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"embeddings-vectorize",
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"-p",
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url.pulsar,
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])
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.with_limits("1.0", "512M")
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.with_reservations("0.5", "512M");
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local containerSet = engine.containers(
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"vectorize", [ container ]
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);
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engine.resources([
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containerSet,
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])
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},
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} + prompt
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