mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 00:16:23 +02:00
Feature/environment var creds (#116)
- Change templates to interpolate environment variables in docker compose - Change templates to invoke secrets for environment variable credentials in K8s configuration - Update LLMs to pull in credentials from environment variables if not specified
This commit is contained in:
cybermaggedon
GitHub
parent
43756d872b
commit
86288339cf
20 changed files with 327 additions and 271 deletions
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@ -5,7 +5,6 @@ local prompts = import "prompts/mixtral.jsonnet";
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{
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"azure-openai-token":: "${AZURE_OPENAI_TOKEN}",
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"azure-openai-model":: "GPT-3.5-Turbo",
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"azure-openai-max-output-tokens":: 4192,
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"azure-openai-temperature":: 0.0,
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@ -14,6 +13,9 @@ local prompts = import "prompts/mixtral.jsonnet";
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create:: function(engine)
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local envSecrets = engine.envSecrets("azure-openai-credentials")
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.with_env_var("AZURE_TOKEN", "azure-token");
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local container =
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engine.container("text-completion")
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.with_image(images.trustgraph)
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@ -21,8 +23,6 @@ local prompts = import "prompts/mixtral.jsonnet";
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"text-completion-azure-openai",
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"-p",
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url.pulsar,
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"-k",
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$["azure-openai-token"],
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"-m",
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$["azure-openai-model"],
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"-x",
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@ -30,39 +30,17 @@ local prompts = import "prompts/mixtral.jsonnet";
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"-t",
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std.toString($["azure-openai-temperature"]),
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])
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.with_env_var_secrets(envSecrets)
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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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"text-completion", [ container ]
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);
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local service =
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engine.internalService(containerSet)
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.with_port(8000, 8000, "metrics");
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engine.resources([
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containerSet,
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service,
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])
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},
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"text-completion-rag" +: {
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create:: function(engine)
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local container =
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local containerRag =
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engine.container("text-completion-rag")
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.with_image(images.trustgraph)
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.with_command([
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"text-completion-azure",
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"-p",
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url.pulsar,
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"-k",
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$["azure-openai-token"],
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"-e",
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$["azure-openai-model"],
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"-x",
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std.toString($["azure-openai-max-output-tokens"]),
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"-t",
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@ -72,24 +50,35 @@ local prompts = import "prompts/mixtral.jsonnet";
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"-o",
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"non-persistent://tg/response/text-completion-rag-response",
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])
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.with_env_var_secrets(envSecrets)
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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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"text-completion-rag", [ container ]
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"text-completion", [ container ]
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);
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local containerSetRag = engine.containers(
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"text-completion-rag", [ containerRag ]
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);
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local service =
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engine.internalService(containerSet)
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.with_port(8000, 8000, "metrics");
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local serviceRag =
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engine.internalService(containerSetRag)
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.with_port(8000, 8000, "metrics");
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engine.resources([
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envSecrets,
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containerSet,
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containerSetRag,
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service,
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serviceRag,
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])
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}
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},
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} + prompts
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@ -5,8 +5,6 @@ local prompts = import "prompts/mixtral.jsonnet";
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{
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"azure-token":: "${AZURE_TOKEN}",
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"azure-endpoint":: "${AZURE_ENDPOINT}",
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"azure-max-output-tokens":: 4096,
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"azure-temperature":: 0.0,
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@ -14,6 +12,10 @@ local prompts = import "prompts/mixtral.jsonnet";
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create:: function(engine)
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local envSecrets = engine.envSecrets("azure-credentials")
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.with_env_var("AZURE_TOKEN", "azure-token")
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.with_env_var("AZURE_ENDPOINT", "azure-endpoint");
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local container =
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engine.container("text-completion")
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.with_image(images.trustgraph)
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@ -21,48 +23,22 @@ local prompts = import "prompts/mixtral.jsonnet";
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"text-completion-azure",
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"-p",
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url.pulsar,
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"-k",
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$["azure-token"],
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"-e",
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$["azure-endpoint"],
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"-x",
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std.toString($["azure-max-output-tokens"]),
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"-t",
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std.toString($["azure-temperature"]),
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])
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.with_env_var_secrets(envSecrets)
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.with_limits("0.5", "128M")
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.with_reservations("0.1", "128M");
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local containerSet = engine.containers(
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"text-completion", [ container ]
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);
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local service =
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engine.internalService(containerSet)
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.with_port(8000, 8000, "metrics");
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engine.resources([
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containerSet,
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service,
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])
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},
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"text-completion-rag" +: {
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create:: function(engine)
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local container =
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local containerRag =
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engine.container("text-completion-rag")
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.with_image(images.trustgraph)
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.with_command([
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"text-completion-azure",
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"-p",
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url.pulsar,
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"-k",
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$["azure-token"],
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"-e",
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$["azure-endpoint"],
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"-x",
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std.toString($["azure-max-output-tokens"]),
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"-t",
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@ -72,22 +48,33 @@ local prompts = import "prompts/mixtral.jsonnet";
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"-o",
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"non-persistent://tg/response/text-completion-rag-response",
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])
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.with_env_var_secrets(envSecrets)
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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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"text-completion-rag", [ container ]
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"text-completion", [ container ]
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);
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local containerSetRag = engine.containers(
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"text-completion-rag", [ containerRag ]
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);
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local service =
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engine.internalService(containerSet)
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.with_port(8000, 8000, "metrics");
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engine.resources([
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containerSet,
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service,
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])
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local serviceRag =
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engine.internalService(containerSetRag)
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.with_port(8000, 8000, "metrics");
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engine.resources([
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envSecrets,
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containerSet,
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containerSetRag,
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service,
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serviceRag,
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])
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}
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@ -6,9 +6,6 @@ local chunker = import "chunker-recursive.jsonnet";
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{
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"aws-id-key":: "${AWS_ID_KEY}",
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"aws-secret-key":: "${AWS_SECRET_KEY}",
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"aws-region":: "us-west-2",
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"bedrock-max-output-tokens":: 4096,
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"bedrock-temperature":: 0.0,
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"bedrock-model":: "mistral.mixtral-8x7b-instruct-v0:1",
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@ -17,6 +14,11 @@ local chunker = import "chunker-recursive.jsonnet";
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create:: function(engine)
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local envSecrets = engine.envSecrets("bedrock-credentials")
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.with_env_var("AWS_ID_KEY", "aws-id-key")
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.with_env_var("AWS_SECRET_KEY", "aws-secret-key")
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.with_env_var("AWS_REGION", "aws-region");
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local container =
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engine.container("text-completion")
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.with_image(images.trustgraph)
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@ -24,12 +26,6 @@ local chunker = import "chunker-recursive.jsonnet";
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"text-completion-bedrock",
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"-p",
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url.pulsar,
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"-z",
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$["aws-id-key"],
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"-k",
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$["aws-secret-key"],
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"-r",
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$["aws-region"],
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"-x",
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std.toString($["bedrock-max-output-tokens"]),
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"-t",
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@ -37,41 +33,17 @@ local chunker = import "chunker-recursive.jsonnet";
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"-m",
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$["bedrock-model"],
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])
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.with_env_var_secrets(envSecrets)
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.with_limits("0.5", "128M")
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.with_reservations("0.1", "128M");
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local containerSet = engine.containers(
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"text-completion", [ container ]
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);
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local service =
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engine.internalService(containerSet)
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.with_port(8000, 8000, "metrics");
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engine.resources([
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containerSet,
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service,
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])
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},
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"text-completion-rag" +: {
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create:: function(engine)
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local container =
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local containerRag =
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engine.container("text-completion-rag")
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.with_image(images.trustgraph)
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.with_command([
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"text-completion-bedrock",
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"-p",
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url.pulsar,
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"-z",
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$["aws-id-key"],
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"-k",
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$["aws-secret-key"],
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"-r",
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$["aws-region"],
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"-x",
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std.toString($["bedrock-max-output-tokens"]),
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"-t",
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@ -83,24 +55,35 @@ local chunker = import "chunker-recursive.jsonnet";
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"-o",
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"non-persistent://tg/response/text-completion-rag-response",
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])
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.with_env_var_secrets(envSecrets)
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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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"text-completion-rag", [ container ]
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"text-completion", [ container ]
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);
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local containerSetRag = engine.containers(
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"text-completion-rag", [ containerRag ]
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);
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local service =
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engine.internalService(containerSet)
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.with_port(8000, 8000, "metrics");
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local serviceRag =
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engine.internalService(containerSetRag)
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.with_port(8000, 8000, "metrics");
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engine.resources([
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envSecrets,
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containerSet,
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containerSetRag,
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service,
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serviceRag,
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])
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}
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},
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} + prompts + chunker
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@ -5,7 +5,6 @@ local prompts = import "prompts/mixtral.jsonnet";
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{
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"claude-key":: "${CLAUDE_KEY}",
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"claude-max-output-tokens":: 4096,
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"claude-temperature":: 0.0,
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@ -13,6 +12,9 @@ local prompts = import "prompts/mixtral.jsonnet";
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create:: function(engine)
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local envSecrets = engine.envSecrets("claude-credentials")
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.with_env_var("CLAUDE_KEY_TOKEN", "claude-key");
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local container =
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engine.container("text-completion")
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.with_image(images.trustgraph)
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@ -20,44 +22,22 @@ local prompts = import "prompts/mixtral.jsonnet";
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"text-completion-claude",
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"-p",
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url.pulsar,
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"-k",
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$["claude-key"],
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"-x",
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std.toString($["claude-max-output-tokens"]),
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"-t",
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std.toString($["claude-temperature"]),
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])
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.with_env_var_secrets(envSecrets)
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.with_limits("0.5", "128M")
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.with_reservations("0.1", "128M");
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local containerSet = engine.containers(
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"text-completion", [ container ]
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);
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local service =
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engine.internalService(containerSet)
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.with_port(8000, 8000, "metrics");
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engine.resources([
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containerSet,
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service,
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])
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},
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"text-completion-rag" +: {
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create:: function(engine)
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local container =
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local containerRag =
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engine.container("text-completion-rag")
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.with_image(images.trustgraph)
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.with_command([
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"text-completion-claude",
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"-p",
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url.pulsar,
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"-k",
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$["claude-key"],
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"-x",
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std.toString($["claude-max-output-tokens"]),
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"-t",
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|
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@ -67,24 +47,35 @@ local prompts = import "prompts/mixtral.jsonnet";
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"-o",
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"non-persistent://tg/response/text-completion-rag-response",
|
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])
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.with_env_var_secrets(envSecrets)
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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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"text-completion-rag", [ container ]
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"text-completion", [ container ]
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);
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local containerSetRag = engine.containers(
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"text-completion-rag", [ containerRag ]
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);
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local service =
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engine.internalService(containerSet)
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.with_port(8000, 8000, "metrics");
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local serviceRag =
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engine.internalService(containerSetRag)
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.with_port(8000, 8000, "metrics");
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|
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engine.resources([
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envSecrets,
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containerSet,
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containerSetRag,
|
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service,
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serviceRag,
|
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])
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}
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},
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} + prompts
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|
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@ -9,13 +9,15 @@ local prompts = import "prompts/mixtral.jsonnet";
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"chunk-size":: 150,
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"chunk-overlap":: 10,
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"cohere-key":: "${COHERE_KEY}",
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"cohere-temperature":: 0.0,
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"text-completion" +: {
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create:: function(engine)
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local envSecrets = engine.envSecrets("cohere-credentials")
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.with_env_var("COHERE_KEY", "cohere-key");
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local container =
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engine.container("text-completion")
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.with_image(images.trustgraph)
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|
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@ -23,42 +25,19 @@ local prompts = import "prompts/mixtral.jsonnet";
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"text-completion-cohere",
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"-p",
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url.pulsar,
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"-k",
|
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$["cohere-key"],
|
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"-t",
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std.toString($["cohere-temperature"]),
|
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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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|
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local containerSet = engine.containers(
|
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"text-completion", [ container ]
|
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);
|
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|
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local service =
|
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engine.internalService(containerSet)
|
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.with_port(8000, 8000, "metrics");
|
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|
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engine.resources([
|
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containerSet,
|
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service,
|
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])
|
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|
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},
|
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|
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"text-completion-rag" +: {
|
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|
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create:: function(engine)
|
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|
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local container =
|
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local containerRag =
|
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engine.container("text-completion-rag")
|
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.with_image(images.trustgraph)
|
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.with_command([
|
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"text-completion-cohere",
|
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"-p",
|
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url.pulsar,
|
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"-k",
|
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$["cohere-key"],
|
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"-t",
|
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std.toString($["cohere-temperature"]),
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"-i",
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|
|
@ -70,20 +49,30 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
.with_reservations("0.1", "128M");
|
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|
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local containerSet = engine.containers(
|
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"text-completion-rag", [ container ]
|
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"text-completion", [ container ]
|
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);
|
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|
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local containerSetRag = engine.containers(
|
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"text-completion-rag", [ containerRag ]
|
||||
);
|
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|
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local service =
|
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engine.internalService(containerSet)
|
||||
.with_port(8000, 8000, "metrics");
|
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|
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local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSet,
|
||||
containerSetRag,
|
||||
service,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
|
||||
}
|
||||
},
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
|
|||
|
|
@ -6,12 +6,14 @@ local prompts = import "prompts/slm.jsonnet";
|
|||
{
|
||||
|
||||
"llamafile-model":: "LLaMA_CPP",
|
||||
"llamafile-url":: "${LLAMAFILE_URL}",
|
||||
|
||||
"text-completion" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local envSecrets = engine.envSecrets("llamafile-credentials")
|
||||
.with_env_var("LLAMAFILE_URL", "llamafile-url");
|
||||
|
||||
local container =
|
||||
engine.container("text-completion")
|
||||
.with_image(images.trustgraph)
|
||||
|
|
@ -21,27 +23,12 @@ local prompts = import "prompts/slm.jsonnet";
|
|||
url.pulsar,
|
||||
"-m",
|
||||
$["llamafile-model"],
|
||||
"-r",
|
||||
$["llamafile-url"],
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion", [ container ]
|
||||
);
|
||||
|
||||
engine.resources([
|
||||
containerSet,
|
||||
])
|
||||
|
||||
},
|
||||
|
||||
"text-completion-rag" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local container =
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
|
|
@ -50,26 +37,40 @@ local prompts = import "prompts/slm.jsonnet";
|
|||
url.pulsar,
|
||||
"-m",
|
||||
$["llamafile-model"],
|
||||
"-r",
|
||||
$["llamafile-url"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag-response",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion-rag", [ container ]
|
||||
"text-completion", [ container ]
|
||||
);
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local service =
|
||||
engine.internalService(containerSet)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSet,
|
||||
containerSetRag,
|
||||
service,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
|
||||
}
|
||||
},
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
|
|||
|
|
@ -6,12 +6,14 @@ local prompts = import "prompts/slm.jsonnet";
|
|||
{
|
||||
|
||||
"ollama-model":: "gemma2:9b",
|
||||
"ollama-url":: "${OLLAMA_HOST}",
|
||||
|
||||
"text-completion" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local envSecrets = engine.envSecrets("ollama-credentials")
|
||||
.with_env_var("OLLAMA_HOST", "ollama-host");
|
||||
|
||||
local container =
|
||||
engine.container("text-completion")
|
||||
.with_image(images.trustgraph)
|
||||
|
|
@ -21,32 +23,12 @@ local prompts = import "prompts/slm.jsonnet";
|
|||
url.pulsar,
|
||||
"-m",
|
||||
$["ollama-model"],
|
||||
"-r",
|
||||
$["ollama-url"],
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion", [ container ]
|
||||
);
|
||||
|
||||
local service =
|
||||
engine.internalService(containerSet)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
engine.resources([
|
||||
containerSet,
|
||||
service,
|
||||
])
|
||||
|
||||
},
|
||||
|
||||
"text-completion-rag" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local container =
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
|
|
@ -55,31 +37,40 @@ local prompts = import "prompts/slm.jsonnet";
|
|||
url.pulsar,
|
||||
"-m",
|
||||
$["ollama-model"],
|
||||
"-r",
|
||||
$["ollama-url"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag-response",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion-rag", [ container ]
|
||||
"text-completion", [ container ]
|
||||
);
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local service =
|
||||
engine.internalService(containerSet)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSet,
|
||||
containerSetRag,
|
||||
service,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
|
||||
}
|
||||
},
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
|
||||
{
|
||||
|
||||
"openai-key":: "${OPENAI_KEY}",
|
||||
"openai-max-output-tokens":: 4096,
|
||||
"openai-temperature":: 0.0,
|
||||
"openai-model":: "GPT-3.5-Turbo",
|
||||
|
|
@ -14,6 +13,9 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
|
||||
create:: function(engine)
|
||||
|
||||
local envSecrets = engine.envSecrets("openai-credentials")
|
||||
.with_env_var("OPENAI_TOKEN", "openai-token");
|
||||
|
||||
local container =
|
||||
engine.container("text-completion")
|
||||
.with_image(images.trustgraph)
|
||||
|
|
@ -21,8 +23,6 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
"text-completion-openai",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-k",
|
||||
$["openai-key"],
|
||||
"-x",
|
||||
std.toString($["openai-max-output-tokens"]),
|
||||
"-t",
|
||||
|
|
@ -30,37 +30,17 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
"-m",
|
||||
$["openai-model"],
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion", [ container ]
|
||||
);
|
||||
|
||||
local service =
|
||||
engine.internalService(containerSet)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
engine.resources([
|
||||
containerSet,
|
||||
service,
|
||||
])
|
||||
|
||||
},
|
||||
|
||||
"text-completion-rag" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local container =
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-openai",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-k",
|
||||
$["openai-key"],
|
||||
"-x",
|
||||
std.toString($["openai-max-output-tokens"]),
|
||||
"-t",
|
||||
|
|
@ -72,24 +52,35 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag-response",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion-rag", [ container ]
|
||||
"text-completion", [ container ]
|
||||
);
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local service =
|
||||
engine.internalService(containerSet)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSet,
|
||||
containerSetRag,
|
||||
service,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
|
||||
}
|
||||
},
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
|
|||
|
|
@ -18,12 +18,15 @@
|
|||
reservations: {},
|
||||
ports: [],
|
||||
volumes: [],
|
||||
environment: {},
|
||||
|
||||
with_image:: function(x) self + { image: x },
|
||||
|
||||
with_command:: function(x) self + { command: x },
|
||||
|
||||
with_environment:: function(x) self + { environment: x },
|
||||
with_environment:: function(x) self + {
|
||||
environment: super.environment + x,
|
||||
},
|
||||
|
||||
with_limits:: function(c, m) self + { limits: { cpus: c, memory: m } },
|
||||
|
||||
|
|
@ -45,6 +48,16 @@
|
|||
]
|
||||
},
|
||||
|
||||
with_env_var_secrets::
|
||||
function(vars)
|
||||
std.foldl(
|
||||
function(obj, x) obj.with_environment(
|
||||
{ [x]: "${" + x + "}" }
|
||||
),
|
||||
vars.variables,
|
||||
self
|
||||
),
|
||||
|
||||
add:: function() {
|
||||
services +: {
|
||||
[container.name]: {
|
||||
|
|
@ -62,7 +75,7 @@
|
|||
{ command: container.command }
|
||||
else {}) +
|
||||
|
||||
(if std.objectHas(container, "environment") then
|
||||
(if ! std.isEmpty(container.environment) then
|
||||
{ environment: container.environment }
|
||||
else {}) +
|
||||
|
||||
|
|
@ -170,6 +183,27 @@
|
|||
|
||||
},
|
||||
|
||||
envSecrets:: function(name)
|
||||
{
|
||||
|
||||
local volume = self,
|
||||
|
||||
name: name,
|
||||
|
||||
volid:: name,
|
||||
|
||||
variables:: [],
|
||||
|
||||
with_env_var::
|
||||
function(name, key) self + {
|
||||
variables: super.variables + [name],
|
||||
},
|
||||
|
||||
add:: function() {
|
||||
}
|
||||
|
||||
},
|
||||
|
||||
containers:: function(name, containers)
|
||||
{
|
||||
|
||||
|
|
|
|||
|
|
@ -10,12 +10,20 @@
|
|||
reservations: {},
|
||||
ports: [],
|
||||
volumes: [],
|
||||
environment: [],
|
||||
|
||||
with_image:: function(x) self + { image: x },
|
||||
|
||||
with_command:: function(x) self + { command: x },
|
||||
|
||||
with_environment:: function(x) self + { environment: x },
|
||||
with_environment:: function(x) self + {
|
||||
environment: super.environment + [
|
||||
{
|
||||
name: v.key, value: v.value
|
||||
}
|
||||
for v in std.objectKeysValues(x)
|
||||
],
|
||||
},
|
||||
|
||||
with_limits:: function(c, m) self + { limits: { cpu: c, memory: m } },
|
||||
|
||||
|
|
@ -37,6 +45,24 @@
|
|||
]
|
||||
},
|
||||
|
||||
with_env_var_secrets::
|
||||
function(vars)
|
||||
std.foldl(
|
||||
function(obj, x) obj + {
|
||||
environment: super.environment + [{
|
||||
name: x,
|
||||
valueFrom: {
|
||||
secretKeyRef: {
|
||||
name: vars.name,
|
||||
key: vars.keyMap[x],
|
||||
}
|
||||
}
|
||||
}]
|
||||
},
|
||||
vars.variables,
|
||||
self
|
||||
),
|
||||
|
||||
add:: function() [
|
||||
|
||||
{
|
||||
|
|
@ -97,16 +123,11 @@
|
|||
(if std.objectHas(container, "command") then
|
||||
{ command: container.command }
|
||||
else {}) +
|
||||
(if std.objectHas(container, "environment") then
|
||||
{ env: [ {
|
||||
name: e.key, value: e.value
|
||||
}
|
||||
for e in
|
||||
std.objectKeysValues(
|
||||
container.environment
|
||||
)
|
||||
]
|
||||
}
|
||||
|
||||
(if ! std.isEmpty(container.environment) then
|
||||
{
|
||||
env: container.environment,
|
||||
}
|
||||
else {}) +
|
||||
|
||||
(if std.length(container.volumes) > 0 then
|
||||
|
|
@ -283,6 +304,34 @@
|
|||
|
||||
},
|
||||
|
||||
envSecrets:: function(name)
|
||||
{
|
||||
|
||||
local volume = self,
|
||||
|
||||
name: name,
|
||||
|
||||
variables: [],
|
||||
keyMap: {},
|
||||
|
||||
with_size:: function(size) self + { size: size },
|
||||
|
||||
add:: function() [
|
||||
],
|
||||
|
||||
volRef:: function() {
|
||||
name: volume.name,
|
||||
secret: { secretName: volume.name },
|
||||
},
|
||||
|
||||
with_env_var::
|
||||
function(name, key) self + {
|
||||
variables: super.variables + [name],
|
||||
keyMap: super.keyMap + { [name]: key },
|
||||
},
|
||||
|
||||
},
|
||||
|
||||
containers:: function(name, containers)
|
||||
{
|
||||
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ Input is prompt, output is response. Mistral is default.
|
|||
import boto3
|
||||
import json
|
||||
from prometheus_client import Histogram
|
||||
import os
|
||||
|
||||
from .... schema import TextCompletionRequest, TextCompletionResponse, Error
|
||||
from .... schema import text_completion_request_queue
|
||||
|
|
@ -21,10 +22,11 @@ default_input_queue = text_completion_request_queue
|
|||
default_output_queue = text_completion_response_queue
|
||||
default_subscriber = module
|
||||
default_model = 'mistral.mistral-large-2407-v1:0'
|
||||
default_region = 'us-west-2'
|
||||
default_region = os.getenv("AWS_REGION", 'us-west-2')
|
||||
default_temperature = 0.0
|
||||
default_max_output = 2048
|
||||
|
||||
default_aws_id = os.getenv("AWS_ID_KEY")
|
||||
default_aws_secret = os.getenv("AWS_SECRET_KEY")
|
||||
|
||||
class Processor(ConsumerProducer):
|
||||
|
||||
|
|
@ -34,12 +36,18 @@ class Processor(ConsumerProducer):
|
|||
output_queue = params.get("output_queue", default_output_queue)
|
||||
subscriber = params.get("subscriber", default_subscriber)
|
||||
model = params.get("model", default_model)
|
||||
aws_id = params.get("aws_id_key")
|
||||
aws_secret = params.get("aws_secret")
|
||||
aws_id = params.get("aws_id_key", default_aws_id)
|
||||
aws_secret = params.get("aws_secret", default_aws_secret)
|
||||
aws_region = params.get("aws_region", default_region)
|
||||
temperature = params.get("temperature", default_temperature)
|
||||
max_output = params.get("max_output", default_max_output)
|
||||
|
||||
if aws_id is None:
|
||||
raise RuntimeError("AWS ID not specified")
|
||||
|
||||
if aws_secret is None:
|
||||
raise RuntimeError("AWS secret not specified")
|
||||
|
||||
super(Processor, self).__init__(
|
||||
**params | {
|
||||
"input_queue": input_queue,
|
||||
|
|
@ -299,7 +307,7 @@ class Processor(ConsumerProducer):
|
|||
|
||||
parser.add_argument(
|
||||
'-r', '--aws-region',
|
||||
help=f'AWS Region (default: us-west-2)'
|
||||
help=f'AWS Region'
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
|
|
@ -320,4 +328,3 @@ def run():
|
|||
|
||||
Processor.start(module, __doc__)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ serverless endpoint service. Input is prompt, output is response.
|
|||
import requests
|
||||
import json
|
||||
from prometheus_client import Histogram
|
||||
import os
|
||||
|
||||
from .... schema import TextCompletionRequest, TextCompletionResponse, Error
|
||||
from .... schema import text_completion_request_queue
|
||||
|
|
@ -23,6 +24,8 @@ default_subscriber = module
|
|||
default_temperature = 0.0
|
||||
default_max_output = 4192
|
||||
default_model = "AzureAI"
|
||||
default_endpoint = os.getenv("AZURE_ENDPOINT")
|
||||
default_token = os.getenv("AZURE_TOKEN")
|
||||
|
||||
class Processor(ConsumerProducer):
|
||||
|
||||
|
|
@ -31,12 +34,18 @@ class Processor(ConsumerProducer):
|
|||
input_queue = params.get("input_queue", default_input_queue)
|
||||
output_queue = params.get("output_queue", default_output_queue)
|
||||
subscriber = params.get("subscriber", default_subscriber)
|
||||
endpoint = params.get("endpoint")
|
||||
token = params.get("token")
|
||||
endpoint = params.get("endpoint", default_endpoint)
|
||||
token = params.get("token", default_token)
|
||||
temperature = params.get("temperature", default_temperature)
|
||||
max_output = params.get("max_output", default_max_output)
|
||||
model = default_model
|
||||
|
||||
if endpoint is None:
|
||||
raise RuntimeError("Azure endpoint not specified")
|
||||
|
||||
if token is None:
|
||||
raise RuntimeError("Azure token not specified")
|
||||
|
||||
super(Processor, self).__init__(
|
||||
**params | {
|
||||
"input_queue": input_queue,
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ import requests
|
|||
import json
|
||||
from prometheus_client import Histogram
|
||||
from openai import AzureOpenAI
|
||||
import os
|
||||
|
||||
from .... schema import TextCompletionRequest, TextCompletionResponse, Error
|
||||
from .... schema import text_completion_request_queue
|
||||
|
|
@ -24,6 +25,8 @@ default_subscriber = module
|
|||
default_temperature = 0.0
|
||||
default_max_output = 4192
|
||||
default_api = "2024-02-15-preview"
|
||||
default_endpoint = os.getenv("AZURE_ENDPOINT")
|
||||
default_token = os.getenv("AZURE_TOKEN")
|
||||
|
||||
class Processor(ConsumerProducer):
|
||||
|
||||
|
|
@ -32,13 +35,19 @@ class Processor(ConsumerProducer):
|
|||
input_queue = params.get("input_queue", default_input_queue)
|
||||
output_queue = params.get("output_queue", default_output_queue)
|
||||
subscriber = params.get("subscriber", default_subscriber)
|
||||
endpoint = params.get("endpoint")
|
||||
token = params.get("token")
|
||||
endpoint = params.get("endpoint", default_endpoint)
|
||||
token = params.get("token", default_token)
|
||||
temperature = params.get("temperature", default_temperature)
|
||||
max_output = params.get("max_output", default_max_output)
|
||||
model = params.get("model")
|
||||
api = params.get("api_version", default_api)
|
||||
|
||||
if endpoint is None:
|
||||
raise RuntimeError("Azure endpoint not specified")
|
||||
|
||||
if token is None:
|
||||
raise RuntimeError("Azure token not specified")
|
||||
|
||||
super(Processor, self).__init__(
|
||||
**params | {
|
||||
"input_queue": input_queue,
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ Input is prompt, output is response.
|
|||
|
||||
import anthropic
|
||||
from prometheus_client import Histogram
|
||||
import os
|
||||
|
||||
from .... schema import TextCompletionRequest, TextCompletionResponse, Error
|
||||
from .... schema import text_completion_request_queue
|
||||
|
|
@ -22,6 +23,7 @@ default_subscriber = module
|
|||
default_model = 'claude-3-5-sonnet-20240620'
|
||||
default_temperature = 0.0
|
||||
default_max_output = 8192
|
||||
default_api_key = os.getenv("CLAUDE_KEY")
|
||||
|
||||
class Processor(ConsumerProducer):
|
||||
|
||||
|
|
@ -31,10 +33,13 @@ class Processor(ConsumerProducer):
|
|||
output_queue = params.get("output_queue", default_output_queue)
|
||||
subscriber = params.get("subscriber", default_subscriber)
|
||||
model = params.get("model", default_model)
|
||||
api_key = params.get("api_key")
|
||||
api_key = params.get("api_key", default_api_key)
|
||||
temperature = params.get("temperature", default_temperature)
|
||||
max_output = params.get("max_output", default_max_output)
|
||||
|
||||
if api_key is None:
|
||||
raise RuntimeError("Claude API key not specified")
|
||||
|
||||
super(Processor, self).__init__(
|
||||
**params | {
|
||||
"input_queue": input_queue,
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ Input is prompt, output is response.
|
|||
|
||||
import cohere
|
||||
from prometheus_client import Histogram
|
||||
import os
|
||||
|
||||
from .... schema import TextCompletionRequest, TextCompletionResponse, Error
|
||||
from .... schema import text_completion_request_queue
|
||||
|
|
@ -21,6 +22,7 @@ default_output_queue = text_completion_response_queue
|
|||
default_subscriber = module
|
||||
default_model = 'c4ai-aya-23-8b'
|
||||
default_temperature = 0.0
|
||||
default_api_key = os.getenv("COHERE_KEY")
|
||||
|
||||
class Processor(ConsumerProducer):
|
||||
|
||||
|
|
@ -30,9 +32,12 @@ class Processor(ConsumerProducer):
|
|||
output_queue = params.get("output_queue", default_output_queue)
|
||||
subscriber = params.get("subscriber", default_subscriber)
|
||||
model = params.get("model", default_model)
|
||||
api_key = params.get("api_key")
|
||||
api_key = params.get("api_key", default_api_key)
|
||||
temperature = params.get("temperature", default_temperature)
|
||||
|
||||
if api_key is None:
|
||||
raise RuntimeError("Cohere API key not specified")
|
||||
|
||||
super(Processor, self).__init__(
|
||||
**params | {
|
||||
"input_queue": input_queue,
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ Input is prompt, output is response.
|
|||
import google.generativeai as genai
|
||||
from google.generativeai.types import HarmCategory, HarmBlockThreshold
|
||||
from prometheus_client import Histogram
|
||||
import os
|
||||
|
||||
from .... schema import TextCompletionRequest, TextCompletionResponse, Error
|
||||
from .... schema import text_completion_request_queue
|
||||
|
|
@ -23,6 +24,7 @@ default_subscriber = module
|
|||
default_model = 'gemini-1.5-flash-002'
|
||||
default_temperature = 0.0
|
||||
default_max_output = 8192
|
||||
default_api_key = os.getenv("GOOGLE_AI_STUDIO_KEY")
|
||||
|
||||
class Processor(ConsumerProducer):
|
||||
|
||||
|
|
@ -32,10 +34,13 @@ class Processor(ConsumerProducer):
|
|||
output_queue = params.get("output_queue", default_output_queue)
|
||||
subscriber = params.get("subscriber", default_subscriber)
|
||||
model = params.get("model", default_model)
|
||||
api_key = params.get("api_key")
|
||||
api_key = params.get("api_key", default_api_key)
|
||||
temperature = params.get("temperature", default_temperature)
|
||||
max_output = params.get("max_output", default_max_output)
|
||||
|
||||
if api_key is None:
|
||||
raise RuntimeError("Google AI Studio API key not specified")
|
||||
|
||||
super(Processor, self).__init__(
|
||||
**params | {
|
||||
"input_queue": input_queue,
|
||||
|
|
@ -211,4 +216,4 @@ def run():
|
|||
|
||||
Processor.start(module, __doc__)
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ default_input_queue = text_completion_request_queue
|
|||
default_output_queue = text_completion_response_queue
|
||||
default_subscriber = module
|
||||
default_model = 'LLaMA_CPP'
|
||||
default_llamafile = 'http://localhost:8080/v1'
|
||||
default_llamafile = os.getenv("LLAMAFILE_URL", "http://localhost:8080/v1")
|
||||
default_temperature = 0.0
|
||||
default_max_output = 4096
|
||||
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ Input is prompt, output is response.
|
|||
|
||||
from ollama import Client
|
||||
from prometheus_client import Histogram, Info
|
||||
import os
|
||||
|
||||
from .... schema import TextCompletionRequest, TextCompletionResponse, Error
|
||||
from .... schema import text_completion_request_queue
|
||||
|
|
@ -19,8 +20,8 @@ module = ".".join(__name__.split(".")[1:-1])
|
|||
default_input_queue = text_completion_request_queue
|
||||
default_output_queue = text_completion_response_queue
|
||||
default_subscriber = module
|
||||
default_model = 'gemma2'
|
||||
default_ollama = 'http://localhost:11434'
|
||||
default_model = 'gemma2:9b'
|
||||
default_ollama = os.getenv("OLLAMA_HOST", 'http://localhost:11434')
|
||||
|
||||
class Processor(ConsumerProducer):
|
||||
|
||||
|
|
@ -152,7 +153,7 @@ class Processor(ConsumerProducer):
|
|||
parser.add_argument(
|
||||
'-m', '--model',
|
||||
default="gemma2",
|
||||
help=f'LLM model (default: gemma2)'
|
||||
help=f'LLM model (default: {default_model})'
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ Input is prompt, output is response.
|
|||
|
||||
from openai import OpenAI
|
||||
from prometheus_client import Histogram
|
||||
import os
|
||||
|
||||
from .... schema import TextCompletionRequest, TextCompletionResponse, Error
|
||||
from .... schema import text_completion_request_queue
|
||||
|
|
@ -22,6 +23,7 @@ default_subscriber = module
|
|||
default_model = 'gpt-3.5-turbo'
|
||||
default_temperature = 0.0
|
||||
default_max_output = 4096
|
||||
default_api_key = os.getenv("OPENAI_KEY")
|
||||
|
||||
class Processor(ConsumerProducer):
|
||||
|
||||
|
|
@ -31,10 +33,13 @@ class Processor(ConsumerProducer):
|
|||
output_queue = params.get("output_queue", default_output_queue)
|
||||
subscriber = params.get("subscriber", default_subscriber)
|
||||
model = params.get("model", default_model)
|
||||
api_key = params.get("api_key")
|
||||
api_key = params.get("api_key", default_api_key)
|
||||
temperature = params.get("temperature", default_temperature)
|
||||
max_output = params.get("max_output", default_max_output)
|
||||
|
||||
if api_key is None:
|
||||
raise RuntimeError("OpenAI API key not specified")
|
||||
|
||||
super(Processor, self).__init__(
|
||||
**params | {
|
||||
"input_queue": input_queue,
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ Google Cloud. Input is prompt, output is response.
|
|||
import vertexai
|
||||
import time
|
||||
from prometheus_client import Histogram
|
||||
import os
|
||||
|
||||
from google.oauth2 import service_account
|
||||
import google
|
||||
|
|
@ -38,6 +39,7 @@ default_model = 'gemini-1.0-pro-001'
|
|||
default_region = 'us-central1'
|
||||
default_temperature = 0.0
|
||||
default_max_output = 8192
|
||||
default_private_key = os.getenv("VERTEXAI_KEY")
|
||||
|
||||
class Processor(ConsumerProducer):
|
||||
|
||||
|
|
@ -48,10 +50,13 @@ class Processor(ConsumerProducer):
|
|||
subscriber = params.get("subscriber", default_subscriber)
|
||||
region = params.get("region", default_region)
|
||||
model = params.get("model", default_model)
|
||||
private_key = params.get("private_key")
|
||||
private_key = params.get("private_key", default_private_key)
|
||||
temperature = params.get("temperature", default_temperature)
|
||||
max_output = params.get("max_output", default_max_output)
|
||||
|
||||
if private_key is None:
|
||||
raise RuntimeError("Private key file not specified")
|
||||
|
||||
super(Processor, self).__init__(
|
||||
**params | {
|
||||
"input_queue": input_queue,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue