mirror of
https://github.com/trustgraph-ai/trustgraph.git
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Dual model templates (#263)
* Dual-mode templates * Fixed generate-all so that works * Fix ability to specify Claude model
This commit is contained in:
cybermaggedon
GitHub
parent
1bb1112569
commit
cec9e29222
24 changed files with 725 additions and 360 deletions
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@ -1,47 +1,70 @@
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{
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// Essentials
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"trustgraph-base": import "components/trustgraph.jsonnet",
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"pulsar": import "components/pulsar.jsonnet",
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// LLMs
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"azure": import "components/azure.jsonnet",
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"azure-openai": import "components/azure-openai.jsonnet",
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"bedrock": import "components/bedrock.jsonnet",
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"claude": import "components/claude.jsonnet",
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"cohere": import "components/cohere.jsonnet",
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"document-rag": import "components/document-rag.jsonnet",
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"embeddings-hf": import "components/embeddings-hf.jsonnet",
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"embeddings-ollama": import "components/embeddings-ollama.jsonnet",
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"googleaistudio": import "components/googleaistudio.jsonnet",
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"grafana": import "components/grafana.jsonnet",
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"ollama": import "components/ollama.jsonnet",
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"openai": import "components/openai.jsonnet",
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"vertexai": import "components/vertexai.jsonnet",
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// LLMs for RAG
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"azure-rag": import "components/azure-rag.jsonnet",
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"azure-openai-rag": import "components/azure-openai-rag.jsonnet",
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"bedrock-rag": import "components/bedrock-rag.jsonnet",
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"claude-rag": import "components/claude-rag.jsonnet",
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"cohere-rag": import "components/cohere-rag.jsonnet",
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"googleaistudio-rag": import "components/googleaistudio-rag.jsonnet",
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"ollama-rag": import "components/ollama-rag.jsonnet",
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"openai-rag": import "components/openai-rag.jsonnet",
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"vertexai-rag": import "components/vertexai-rag.jsonnet",
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// Embeddings
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"embeddings-ollama": import "components/embeddings-ollama.jsonnet",
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"embeddings-hf": import "components/embeddings-hf.jsonnet",
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// Processing pipelines
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"graph-rag": import "components/graph-rag.jsonnet",
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"document-rag": import "components/document-rag.jsonnet",
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// Vector stores
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"vector-store-milvus": import "components/milvus.jsonnet",
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"vector-store-qdrant": import "components/qdrant.jsonnet",
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"vector-store-pinecone": import "components/pinecone.jsonnet",
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// Triples stores
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"triple-store-cassandra": import "components/cassandra.jsonnet",
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"triple-store-neo4j": import "components/neo4j.jsonnet",
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"triple-store-falkordb": import "components/falkordb.jsonnet",
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"triple-store-memgraph": import "components/memgraph.jsonnet",
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// Observability support
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"grafana": import "components/grafana.jsonnet",
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// Pulsar manager is a UI for Pulsar. Uses a LOT of memory
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"pulsar-manager": import "components/pulsar-manager.jsonnet",
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"llamafile": import "components/llamafile.jsonnet",
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"ollama": import "components/ollama.jsonnet",
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"openai": import "components/openai.jsonnet",
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"override-recursive-chunker": import "components/chunker-recursive.jsonnet",
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// The prompt manager
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"prompt-template": import "components/prompt-template.jsonnet",
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"prompt-overrides": import "components/prompt-overrides.jsonnet",
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"pulsar": import "components/pulsar.jsonnet",
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"pulsar-manager": import "components/pulsar-manager.jsonnet",
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"trustgraph-base": import "components/trustgraph.jsonnet",
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"vector-store-milvus": import "components/milvus.jsonnet",
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"vector-store-qdrant": import "components/qdrant.jsonnet",
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"vector-store-pinecone": import "components/pinecone.jsonnet",
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"vertexai": import "components/vertexai.jsonnet",
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"workbench-ui": import "components/workbench-ui.jsonnet",
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"null": {},
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// ReAct agent
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"agent-manager-react": import "components/agent-manager-react.jsonnet",
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// FIXME: Dupes
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"cassandra": import "components/cassandra.jsonnet",
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"neo4j": import "components/neo4j.jsonnet",
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"memgraph": import "components/memgraph.jsonnet",
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"qdrant": import "components/qdrant.jsonnet",
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"pinecone": import "components/pinecone.jsonnet",
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"milvus": import "components/milvus.jsonnet",
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"falkordb": import "components/falkordb.jsonnet",
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"trustgraph": import "components/trustgraph.jsonnet",
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// Optional UI
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"workbench-ui": import "components/workbench-ui.jsonnet",
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// Does nothing. But, can be a hack to overwrite parameters
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"null": {},
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}
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61
templates/components/azure-openai-rag.jsonnet
Normal file
61
templates/components/azure-openai-rag.jsonnet
Normal file
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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 prompts = import "prompts/mixtral.jsonnet";
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{
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with:: function(key, value)
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self + {
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["ollama-rag-" + key]:: value,
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},
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"azure-openai-rag-model":: "GPT-3.5-Turbo",
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"azure-openai-rag-max-output-tokens":: 4192,
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"azure-openai-rag-temperature":: 0.0,
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"text-completion-rag" +: {
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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 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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"-x",
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std.toString($["azure-openai-rag-max-output-tokens"]),
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"-t",
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"%0.3f" % $["azure-openai-rag-temperature"],
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"-i",
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"non-persistent://tg/request/text-completion-rag",
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"-o",
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"non-persistent://tg/response/text-completion-rag",
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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 containerSetRag = engine.containers(
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"text-completion-rag", [ containerRag ]
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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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containerSetRag,
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serviceRag,
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])
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},
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} + prompts
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@ -5,6 +5,11 @@ local prompts = import "prompts/mixtral.jsonnet";
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{
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with:: function(key, value)
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self + {
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["azure-openai-" + key]:: value,
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},
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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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@ -34,48 +39,18 @@ local prompts = import "prompts/mixtral.jsonnet";
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.with_limits("0.5", "128M")
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.with_reservations("0.1", "128M");
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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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"-x",
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std.toString($["azure-openai-max-output-tokens"]),
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"-t",
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"%0.3f" % $["azure-openai-temperature"],
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"-i",
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"non-persistent://tg/request/text-completion-rag",
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"-o",
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"non-persistent://tg/response/text-completion-rag",
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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 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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60
templates/components/azure-rag.jsonnet
Normal file
60
templates/components/azure-rag.jsonnet
Normal file
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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 prompts = import "prompts/mixtral.jsonnet";
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{
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with:: function(key, value)
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self + {
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["azure-rag-" + key]:: value,
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},
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"azure-rag-max-output-tokens":: 4096,
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"azure-rag-temperature":: 0.0,
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"text-completion-rag" +: {
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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 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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"-x",
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std.toString($["azure-rag-max-output-tokens"]),
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"-t",
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"%0.3f" % $["azure-rag-temperature"],
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"-i",
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"non-persistent://tg/request/text-completion-rag",
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"-o",
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"non-persistent://tg/response/text-completion-rag",
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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 containerSetRag = engine.containers(
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"text-completion-rag", [ containerRag ]
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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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containerSetRag,
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serviceRag,
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])
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}
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} + prompts
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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 prompts = import "prompts/mixtral.jsonnet";
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{
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with:: function(key, value)
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self + {
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["azure-" + key]:: value,
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},
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"azure-max-output-tokens":: 4096,
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"azure-temperature":: 0.0,
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@ -32,48 +36,18 @@ local prompts = import "prompts/mixtral.jsonnet";
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.with_limits("0.5", "128M")
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.with_reservations("0.1", "128M");
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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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"-x",
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std.toString($["azure-max-output-tokens"]),
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"-t",
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"%0.3f" % $["azure-temperature"],
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"-i",
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"non-persistent://tg/request/text-completion-rag",
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"-o",
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"non-persistent://tg/response/text-completion-rag",
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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 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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66
templates/components/bedrock-rag.jsonnet
Normal file
66
templates/components/bedrock-rag.jsonnet
Normal file
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@ -0,0 +1,66 @@
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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 prompts = import "prompts/mixtral.jsonnet";
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local chunker = import "chunker-recursive.jsonnet";
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{
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with:: function(key, value)
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self + {
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["bedrock-rag-" + key]:: value,
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},
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"bedrock-rag-max-output-tokens":: 4096,
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"bedrock-rag-temperature":: 0.0,
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"bedrock-rag-model":: "mistral.mixtral-8x7b-instruct-v0:1",
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"text-completion-rag" +: {
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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", "aws-secret")
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.with_env_var("AWS_REGION", "aws-region");
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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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"-x",
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std.toString($["bedrock-rag-max-output-tokens"]),
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"-t",
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"%0.3f" % $["bedrock-rag-temperature"],
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"-m",
|
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$["bedrock-rag-model"],
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"-i",
|
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"non-persistent://tg/request/text-completion-rag",
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"-o",
|
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"non-persistent://tg/response/text-completion-rag",
|
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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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|
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local containerSetRag = engine.containers(
|
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"text-completion-rag", [ containerRag ]
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);
|
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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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|
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engine.resources([
|
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envSecrets,
|
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containerSetRag,
|
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serviceRag,
|
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])
|
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|
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},
|
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|
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} + prompts + chunker
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|
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|
|
@ -6,6 +6,11 @@ local chunker = import "chunker-recursive.jsonnet";
|
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|
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{
|
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|
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with:: function(key, value)
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self + {
|
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["bedrock-" + key]:: value,
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},
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|
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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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|
|
@ -37,50 +42,18 @@ local chunker = import "chunker-recursive.jsonnet";
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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 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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"-x",
|
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std.toString($["bedrock-max-output-tokens"]),
|
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"-t",
|
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"%0.3f" % $["bedrock-temperature"],
|
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"-m",
|
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$["bedrock-model"],
|
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"-i",
|
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"non-persistent://tg/request/text-completion-rag",
|
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"-o",
|
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"non-persistent://tg/response/text-completion-rag",
|
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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(
|
||||
"text-completion", [ container ]
|
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);
|
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|
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local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
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|
||||
local service =
|
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engine.internalService(containerSet)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSet,
|
||||
containerSetRag,
|
||||
service,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
},
|
||||
|
|
|
|||
63
templates/components/claude-rag.jsonnet
Normal file
63
templates/components/claude-rag.jsonnet
Normal file
|
|
@ -0,0 +1,63 @@
|
|||
local base = import "base/base.jsonnet";
|
||||
local images = import "values/images.jsonnet";
|
||||
local url = import "values/url.jsonnet";
|
||||
local prompts = import "prompts/mixtral.jsonnet";
|
||||
|
||||
{
|
||||
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["claude-rag-" + key]:: value,
|
||||
},
|
||||
|
||||
"claude-rag-model":: "claude-3-sonnet-20240229",
|
||||
"claude-rag-max-output-tokens":: 4096,
|
||||
"claude-rag-temperature":: 0.0,
|
||||
|
||||
"text-completion-rag" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local envSecrets = engine.envSecrets("claude-credentials")
|
||||
.with_env_var("CLAUDE_KEY", "claude-key");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-claude",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-x",
|
||||
std.toString($["claude-rag-max-output-tokens"]),
|
||||
"-m",
|
||||
$["claude-rag-model"],
|
||||
"-t",
|
||||
"%0.3f" % $["claude-rag-temperature"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSetRag,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
},
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
@ -5,6 +5,12 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
|
||||
{
|
||||
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["claude-" + key]:: value,
|
||||
},
|
||||
|
||||
"claude-model":: "claude-3-sonnet-20240229",
|
||||
"claude-max-output-tokens":: 4096,
|
||||
"claude-temperature":: 0.0,
|
||||
|
||||
|
|
@ -24,6 +30,8 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
url.pulsar,
|
||||
"-x",
|
||||
std.toString($["claude-max-output-tokens"]),
|
||||
"-m",
|
||||
$["claude-model"],
|
||||
"-t",
|
||||
"%0.3f" % $["claude-temperature"],
|
||||
])
|
||||
|
|
@ -31,48 +39,18 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-claude",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-x",
|
||||
std.toString($["claude-max-output-tokens"]),
|
||||
"-t",
|
||||
"%0.3f" % $["claude-temperature"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion", [ container ]
|
||||
);
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local service =
|
||||
engine.internalService(containerSet)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSet,
|
||||
containerSetRag,
|
||||
service,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
},
|
||||
|
|
|
|||
56
templates/components/cohere-rag.jsonnet
Normal file
56
templates/components/cohere-rag.jsonnet
Normal file
|
|
@ -0,0 +1,56 @@
|
|||
local base = import "base/base.jsonnet";
|
||||
local images = import "values/images.jsonnet";
|
||||
local url = import "values/url.jsonnet";
|
||||
local prompts = import "prompts/mixtral.jsonnet";
|
||||
|
||||
{
|
||||
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["cohere-rag-" + key]:: value,
|
||||
},
|
||||
|
||||
"cohere-rag-temperature":: 0.0,
|
||||
|
||||
"text-completion-rag" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local envSecrets = engine.envSecrets("cohere-credentials")
|
||||
.with_env_var("COHERE_KEY", "cohere-key");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-cohere",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-t",
|
||||
"%0.3f" % $["cohere-rag-temperature"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSetRag,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
},
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
@ -5,9 +5,10 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
|
||||
{
|
||||
|
||||
// Override chunking
|
||||
"chunk-size":: 150,
|
||||
"chunk-overlap":: 10,
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["cohere-" + key]:: value,
|
||||
},
|
||||
|
||||
"cohere-temperature":: 0.0,
|
||||
|
||||
|
|
@ -31,45 +32,18 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-cohere",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-t",
|
||||
"%0.3f" % $["cohere-temperature"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion", [ container ]
|
||||
);
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local service =
|
||||
engine.internalService(containerSet)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSet,
|
||||
containerSetRag,
|
||||
service,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
},
|
||||
|
|
|
|||
65
templates/components/googleaistudio-rag.jsonnet
Normal file
65
templates/components/googleaistudio-rag.jsonnet
Normal file
|
|
@ -0,0 +1,65 @@
|
|||
local base = import "base/base.jsonnet";
|
||||
local images = import "values/images.jsonnet";
|
||||
local url = import "values/url.jsonnet";
|
||||
local prompts = import "prompts/mixtral.jsonnet";
|
||||
|
||||
{
|
||||
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["googleaistudio-rag-" + key]:: value,
|
||||
},
|
||||
|
||||
"googleaistudio-rag-max-output-tokens":: 4096,
|
||||
"googleaistudio-rag-temperature":: 0.0,
|
||||
"googleaistudio-rag-model":: "gemini-1.5-flash-002",
|
||||
|
||||
"text-completion-rag" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local envSecrets = engine.envSecrets("googleaistudio-key")
|
||||
.with_env_var("GOOGLE_AI_STUDIO_KEY", "googleaistudio-key");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-googleaistudio",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-x",
|
||||
std.toString(
|
||||
$["googleaistudio-rag-max-output-tokens"]
|
||||
),
|
||||
"-t",
|
||||
"%0.3f" % $["googleaistudio-rag-temperature"],
|
||||
"-m",
|
||||
$["googleaistudio-rag-model"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSetRag,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
},
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
@ -34,50 +34,18 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-googleaistudio",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-x",
|
||||
std.toString($["googleaistudio-max-output-tokens"]),
|
||||
"-t",
|
||||
"%0.3f" % $["googleaistudio-temperature"],
|
||||
"-m",
|
||||
$["googleaistudio-model"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion", [ container ]
|
||||
);
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local service =
|
||||
engine.internalService(containerSet)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSet,
|
||||
containerSetRag,
|
||||
service,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
},
|
||||
|
|
|
|||
57
templates/components/llamafile-rag.jsonnet
Normal file
57
templates/components/llamafile-rag.jsonnet
Normal file
|
|
@ -0,0 +1,57 @@
|
|||
local base = import "base/base.jsonnet";
|
||||
local images = import "values/images.jsonnet";
|
||||
local url = import "values/url.jsonnet";
|
||||
local prompts = import "prompts/slm.jsonnet";
|
||||
|
||||
{
|
||||
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["llamafile-rag-" + key]:: value,
|
||||
},
|
||||
|
||||
"llamafile-rag-model":: "LLaMA_CPP",
|
||||
|
||||
"text-completion-rag" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local envSecrets = engine.envSecrets("llamafile-credentials")
|
||||
.with_env_var("LLAMAFILE_URL", "llamafile-url");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-llamafile",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-m",
|
||||
$["llamafile-rag-model"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSetRag,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
},
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
@ -5,6 +5,11 @@ local prompts = import "prompts/slm.jsonnet";
|
|||
|
||||
{
|
||||
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["llamafile-" + key]:: value,
|
||||
},
|
||||
|
||||
"llamafile-model":: "LLaMA_CPP",
|
||||
|
||||
"text-completion" +: {
|
||||
|
|
@ -28,46 +33,18 @@ local prompts = import "prompts/slm.jsonnet";
|
|||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-llamafile",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-m",
|
||||
$["llamafile-model"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"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,
|
||||
])
|
||||
|
||||
},
|
||||
|
|
|
|||
57
templates/components/ollama-rag.jsonnet
Normal file
57
templates/components/ollama-rag.jsonnet
Normal file
|
|
@ -0,0 +1,57 @@
|
|||
local base = import "base/base.jsonnet";
|
||||
local images = import "values/images.jsonnet";
|
||||
local url = import "values/url.jsonnet";
|
||||
local prompts = import "prompts/mixtral.jsonnet";
|
||||
|
||||
{
|
||||
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["ollama-rag-" + key]:: value,
|
||||
},
|
||||
|
||||
"ollama-rag-model":: "gemma2:9b",
|
||||
|
||||
"text-completion-rag" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local envSecrets = engine.envSecrets("ollama-credentials")
|
||||
.with_env_var("OLLAMA_HOST", "ollama-host");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-ollama",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-m",
|
||||
$["ollama-rag-model"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSetRag,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
},
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
@ -5,6 +5,11 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
|
||||
{
|
||||
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["ollama-" + key]:: value,
|
||||
},
|
||||
|
||||
"ollama-model":: "gemma2:9b",
|
||||
|
||||
"text-completion" +: {
|
||||
|
|
@ -28,46 +33,18 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-ollama",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-m",
|
||||
$["ollama-model"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion", [ container ]
|
||||
);
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local service =
|
||||
engine.internalService(containerSet)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSet,
|
||||
containerSetRag,
|
||||
service,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
},
|
||||
|
|
|
|||
63
templates/components/openai-rag.jsonnet
Normal file
63
templates/components/openai-rag.jsonnet
Normal file
|
|
@ -0,0 +1,63 @@
|
|||
local base = import "base/base.jsonnet";
|
||||
local images = import "values/images.jsonnet";
|
||||
local url = import "values/url.jsonnet";
|
||||
local prompts = import "prompts/mixtral.jsonnet";
|
||||
|
||||
{
|
||||
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["openai-rag-" + key]:: value,
|
||||
},
|
||||
|
||||
"openai-rag-max-output-tokens":: 4096,
|
||||
"openai-rag-temperature":: 0.0,
|
||||
"openai-rag-model":: "GPT-3.5-Turbo",
|
||||
|
||||
"text-completion-rag" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local envSecrets = engine.envSecrets("openai-credentials")
|
||||
.with_env_var("OPENAI_TOKEN", "openai-token");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-openai",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-x",
|
||||
std.toString($["openai-rag-max-output-tokens"]),
|
||||
"-t",
|
||||
"%0.3f" % $["openai-rag-temperature"],
|
||||
"-m",
|
||||
$["openai-rag-model"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSetRag = engine.containers(
|
||||
"text-completion-rag", [ containerRag ]
|
||||
);
|
||||
|
||||
local serviceRag =
|
||||
engine.internalService(containerSetRag)
|
||||
.with_port(8080, 8080, "metrics");
|
||||
|
||||
engine.resources([
|
||||
envSecrets,
|
||||
containerSetRag,
|
||||
serviceRag,
|
||||
])
|
||||
|
||||
},
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
@ -5,6 +5,11 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
|
||||
{
|
||||
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["openai-" + key]:: value,
|
||||
},
|
||||
|
||||
"openai-max-output-tokens":: 4096,
|
||||
"openai-temperature":: 0.0,
|
||||
"openai-model":: "GPT-3.5-Turbo",
|
||||
|
|
@ -34,50 +39,18 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerRag =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-openai",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-x",
|
||||
std.toString($["openai-max-output-tokens"]),
|
||||
"-t",
|
||||
"%0.3f" % $["openai-temperature"],
|
||||
"-m",
|
||||
$["openai-model"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_env_var_secrets(envSecrets)
|
||||
.with_limits("0.5", "128M")
|
||||
.with_reservations("0.1", "128M");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"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,
|
||||
])
|
||||
|
||||
},
|
||||
|
|
|
|||
|
|
@ -1,7 +1,3 @@
|
|||
local base = import "base/base.jsonnet";
|
||||
local images = import "values/images.jsonnet";
|
||||
local url = import "values/url.jsonnet";
|
||||
local prompts = import "prompts/mixtral.jsonnet";
|
||||
local default_prompts = import "prompts/default-prompts.jsonnet";
|
||||
|
||||
{
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
local base = import "base/base.jsonnet";
|
||||
local images = import "values/images.jsonnet";
|
||||
local url = import "values/url.jsonnet";
|
||||
local prompt = import "prompt-template.jsonnet";
|
||||
|
||||
{
|
||||
|
||||
|
|
@ -181,5 +180,5 @@ local prompt = import "prompt-template.jsonnet";
|
|||
|
||||
},
|
||||
|
||||
} + prompt
|
||||
}
|
||||
|
||||
|
|
|
|||
74
templates/components/vertexai-rag.jsonnet
Normal file
74
templates/components/vertexai-rag.jsonnet
Normal file
|
|
@ -0,0 +1,74 @@
|
|||
local base = import "base/base.jsonnet";
|
||||
local images = import "values/images.jsonnet";
|
||||
local url = import "values/url.jsonnet";
|
||||
local prompts = import "prompts/mixtral.jsonnet";
|
||||
|
||||
{
|
||||
|
||||
with:: function(key, value)
|
||||
self + {
|
||||
["vertexai-rag-" + key]:: value,
|
||||
},
|
||||
|
||||
"vertexai-rag-model":: "gemini-1.0-pro-001",
|
||||
"vertexai-rag-private-key":: "/vertexai/private.json",
|
||||
"vertexai-rag-region":: "us-central1",
|
||||
"vertexai-rag-max-output-tokens":: 4096,
|
||||
"vertexai-rag-temperature":: 0.0,
|
||||
|
||||
"text-completion-rag" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local cfgVol = engine.secretVolume(
|
||||
"vertexai-creds",
|
||||
"./vertexai",
|
||||
{
|
||||
"private.json": importstr "vertexai/private.json",
|
||||
}
|
||||
);
|
||||
|
||||
local container =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-vertexai",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-k",
|
||||
$["vertexai-rag-private-key"],
|
||||
"-r",
|
||||
$["vertexai-rag-region"],
|
||||
"-x",
|
||||
std.toString($["vertexai-rag-max-output-tokens"]),
|
||||
"-t",
|
||||
"%0.3f" % $["vertexai-rag-temperature"],
|
||||
"-m",
|
||||
$["vertexai-rag-model"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_limits("0.5", "256M")
|
||||
.with_reservations("0.1", "256M")
|
||||
.with_volume_mount(cfgVol, "/vertexai");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion-rag", [ container ]
|
||||
);
|
||||
|
||||
local service =
|
||||
engine.internalService(containerSet)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
engine.resources([
|
||||
cfgVol,
|
||||
containerSet,
|
||||
service,
|
||||
])
|
||||
|
||||
}
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
@ -61,59 +61,5 @@ local prompts = import "prompts/mixtral.jsonnet";
|
|||
|
||||
},
|
||||
|
||||
"text-completion-rag" +: {
|
||||
|
||||
create:: function(engine)
|
||||
|
||||
local cfgVol = engine.secretVolume(
|
||||
"vertexai-creds",
|
||||
"./vertexai",
|
||||
{
|
||||
"private.json": importstr "vertexai/private.json",
|
||||
}
|
||||
);
|
||||
|
||||
local container =
|
||||
engine.container("text-completion-rag")
|
||||
.with_image(images.trustgraph)
|
||||
.with_command([
|
||||
"text-completion-vertexai",
|
||||
"-p",
|
||||
url.pulsar,
|
||||
"-k",
|
||||
$["vertexai-private-key"],
|
||||
"-r",
|
||||
$["vertexai-region"],
|
||||
"-x",
|
||||
std.toString($["vertexai-max-output-tokens"]),
|
||||
"-t",
|
||||
"%0.3f" % $["vertexai-temperature"],
|
||||
"-m",
|
||||
$["vertexai-model"],
|
||||
"-i",
|
||||
"non-persistent://tg/request/text-completion-rag",
|
||||
"-o",
|
||||
"non-persistent://tg/response/text-completion-rag",
|
||||
])
|
||||
.with_limits("0.5", "256M")
|
||||
.with_reservations("0.1", "256M")
|
||||
.with_volume_mount(cfgVol, "/vertexai");
|
||||
|
||||
local containerSet = engine.containers(
|
||||
"text-completion-rag", [ container ]
|
||||
);
|
||||
|
||||
local service =
|
||||
engine.internalService(containerSet)
|
||||
.with_port(8000, 8000, "metrics");
|
||||
|
||||
engine.resources([
|
||||
cfgVol,
|
||||
containerSet,
|
||||
service,
|
||||
])
|
||||
|
||||
}
|
||||
|
||||
} + prompts
|
||||
|
||||
|
|
|
|||
|
|
@ -87,8 +87,18 @@ def full_config_object(
|
|||
):
|
||||
|
||||
return config_object([
|
||||
graph_store, "pulsar", vector_store, embeddings,
|
||||
"graph-rag", "grafana", "trustgraph", llm, "workbench-ui",
|
||||
"triple-store-" + graph_store,
|
||||
"pulsar",
|
||||
"vector-store-" + vector_store,
|
||||
embeddings,
|
||||
"graph-rag",
|
||||
"grafana",
|
||||
"trustgraph-base",
|
||||
llm,
|
||||
llm + "-rag",
|
||||
"workbench-ui",
|
||||
"prompt-template",
|
||||
"agent-manager-react",
|
||||
])
|
||||
|
||||
def generate_config(
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue