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

templates/components/azure-openai.jsonnet

@@ -5,7 +5,6 @@ local prompts = import "prompts/mixtral.jsonnet";
 
 {
 
-    "azure-openai-token":: "${AZURE_OPENAI_TOKEN}",
     "azure-openai-model":: "GPT-3.5-Turbo",
     "azure-openai-max-output-tokens":: 4192,
     "azure-openai-temperature":: 0.0,
@@ -14,6 +13,9 @@ local prompts = import "prompts/mixtral.jsonnet";
     
         create:: function(engine)
 
+            local envSecrets = engine.envSecrets("azure-openai-credentials")
+                .with_env_var("AZURE_TOKEN", "azure-token");
+
             local container =
                 engine.container("text-completion")
                     .with_image(images.trustgraph)
@@ -21,8 +23,6 @@ local prompts = import "prompts/mixtral.jsonnet";
                         "text-completion-azure-openai",
                         "-p",
                         url.pulsar,
-                        "-k",
-                        $["azure-openai-token"],
                         "-m",
                         $["azure-openai-model"],
                         "-x",
@@ -30,39 +30,17 @@ local prompts = import "prompts/mixtral.jsonnet";
                         "-t",
                         std.toString($["azure-openai-temperature"]),
                     ])
+                    .with_env_var_secrets(envSecrets)
                     .with_limits("0.5", "128M")
                     .with_reservations("0.1", "128M");
 
-            local containerSet = engine.containers(
+            local containerRag =
-                "text-completion", [ container ]
-            );
-
-            local service =
-                engine.internalService(containerSet)
-                .with_port(8000, 8000, "metrics");
-
-            engine.resources([
-                containerSet,
-                service,
-            ])
-
-    },
-
-    "text-completion-rag" +: {
-    
-        create:: function(engine)
-
-            local container =
                 engine.container("text-completion-rag")
                     .with_image(images.trustgraph)
                     .with_command([
                         "text-completion-azure",
                         "-p",
                         url.pulsar,
-                        "-k",
-                        $["azure-openai-token"],
-                        "-e",
-                        $["azure-openai-model"],
                         "-x",
                         std.toString($["azure-openai-max-output-tokens"]),
                         "-t",
@@ -72,24 +50,35 @@ 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(8000, 8000, "metrics");
 
+            local serviceRag =
+                engine.internalService(containerSetRag)
+                .with_port(8000, 8000, "metrics");
+
             engine.resources([
+                envSecrets,
                 containerSet,
+                containerSetRag,
                 service,
+                serviceRag,
             ])
 
-
+    },
-    }
 
 } + prompts
 

templates/components/azure.jsonnet

@@ -5,8 +5,6 @@ local prompts = import "prompts/mixtral.jsonnet";
 
 {
 
-    "azure-token":: "${AZURE_TOKEN}",
-    "azure-endpoint":: "${AZURE_ENDPOINT}",
     "azure-max-output-tokens":: 4096,
     "azure-temperature":: 0.0,
 
@@ -14,6 +12,10 @@ local prompts = import "prompts/mixtral.jsonnet";
     
         create:: function(engine)
 
+            local envSecrets = engine.envSecrets("azure-credentials")
+                .with_env_var("AZURE_TOKEN", "azure-token")
+                .with_env_var("AZURE_ENDPOINT", "azure-endpoint");
+
             local container =
                 engine.container("text-completion")
                     .with_image(images.trustgraph)
@@ -21,48 +23,22 @@ local prompts = import "prompts/mixtral.jsonnet";
                         "text-completion-azure",
                         "-p",
                         url.pulsar,
-                        "-k",
-                        $["azure-token"],
-                        "-e",
-                        $["azure-endpoint"],
                         "-x",
                         std.toString($["azure-max-output-tokens"]),
                         "-t",
                         std.toString($["azure-temperature"]),
                     ])
+                    .with_env_var_secrets(envSecrets)
                     .with_limits("0.5", "128M")
                     .with_reservations("0.1", "128M");
 
-            local containerSet = engine.containers(
+            local containerRag =
-                "text-completion", [ container ]
-            );
-
-            local service =
-                engine.internalService(containerSet)
-                .with_port(8000, 8000, "metrics");
-
-            engine.resources([
-                containerSet,
-                service,
-            ])
-
-    },
-
-    "text-completion-rag" +: {
-    
-        create:: function(engine)
-
-            local container =
                 engine.container("text-completion-rag")
                     .with_image(images.trustgraph)
                     .with_command([
                         "text-completion-azure",
                         "-p",
                         url.pulsar,
-                        "-k",
-                        $["azure-token"],
-                        "-e",
-                        $["azure-endpoint"],
                         "-x",
                         std.toString($["azure-max-output-tokens"]),
                         "-t",
@@ -72,22 +48,33 @@ 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(8000, 8000, "metrics");
 
-            engine.resources([
+            local serviceRag =
-                containerSet,
+                engine.internalService(containerSetRag)
-                service,
+                .with_port(8000, 8000, "metrics");
-            ])
 
+            engine.resources([
+                envSecrets,
+                containerSet,
+                containerSetRag,
+                service,
+                serviceRag,
+            ])
 
     }
 

templates/components/bedrock.jsonnet

@@ -6,9 +6,6 @@ local chunker = import "chunker-recursive.jsonnet";
 
 {
 
-    "aws-id-key":: "${AWS_ID_KEY}",
-    "aws-secret-key":: "${AWS_SECRET_KEY}",
-    "aws-region":: "us-west-2",
     "bedrock-max-output-tokens":: 4096,
     "bedrock-temperature":: 0.0,
     "bedrock-model":: "mistral.mixtral-8x7b-instruct-v0:1",
@@ -17,6 +14,11 @@ local chunker = import "chunker-recursive.jsonnet";
     
         create:: function(engine)
 
+            local envSecrets = engine.envSecrets("bedrock-credentials")
+                .with_env_var("AWS_ID_KEY", "aws-id-key")
+                .with_env_var("AWS_SECRET_KEY", "aws-secret-key")
+                .with_env_var("AWS_REGION", "aws-region");
+
             local container =
                 engine.container("text-completion")
                     .with_image(images.trustgraph)
@@ -24,12 +26,6 @@ local chunker = import "chunker-recursive.jsonnet";
                         "text-completion-bedrock",
                         "-p",
                         url.pulsar,
-                        "-z",
-                        $["aws-id-key"],
-                        "-k",
-                        $["aws-secret-key"],
-                        "-r",
-                        $["aws-region"],
                         "-x",
                         std.toString($["bedrock-max-output-tokens"]),
                         "-t",
@@ -37,41 +33,17 @@ local chunker = import "chunker-recursive.jsonnet";
                         "-m",
                         $["bedrock-model"],
               	    ])
+                    .with_env_var_secrets(envSecrets)
                     .with_limits("0.5", "128M")
                     .with_reservations("0.1", "128M");
 
-            local containerSet = engine.containers(
+            local containerRag =
-                "text-completion", [ container ]
-            );
-
-            local service =
-                engine.internalService(containerSet)
-                .with_port(8000, 8000, "metrics");
-
-            engine.resources([
-                containerSet,
-                service,
-            ])
-
-    },
-
-    "text-completion-rag" +: {
-    
-        create:: function(engine)
-
-            local container =
                 engine.container("text-completion-rag")
                     .with_image(images.trustgraph)
                     .with_command([
                         "text-completion-bedrock",
                         "-p",
                         url.pulsar,
-                        "-z",
-                        $["aws-id-key"],
-                        "-k",
-                        $["aws-secret-key"],
-                        "-r",
-                        $["aws-region"],
                         "-x",
                         std.toString($["bedrock-max-output-tokens"]),
                         "-t",
@@ -83,24 +55,35 @@ local chunker = import "chunker-recursive.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(8000, 8000, "metrics");
 
+            local serviceRag =
+                engine.internalService(containerSetRag)
+                .with_port(8000, 8000, "metrics");
+
             engine.resources([
+                envSecrets,
                 containerSet,
+                containerSetRag,
                 service,
+                serviceRag,
             ])
 
-
+    },
-    }
 
 } + prompts + chunker
 

templates/components/claude.jsonnet

@@ -5,7 +5,6 @@ local prompts = import "prompts/mixtral.jsonnet";
 
 {
 
-    "claude-key":: "${CLAUDE_KEY}",
     "claude-max-output-tokens":: 4096,
     "claude-temperature":: 0.0,
 
@@ -13,6 +12,9 @@ local prompts = import "prompts/mixtral.jsonnet";
     
         create:: function(engine)
 
+            local envSecrets = engine.envSecrets("claude-credentials")
+                .with_env_var("CLAUDE_KEY_TOKEN", "claude-key");
+
             local container =
                 engine.container("text-completion")
                     .with_image(images.trustgraph)
@@ -20,44 +22,22 @@ local prompts = import "prompts/mixtral.jsonnet";
                         "text-completion-claude",
                         "-p",
                         url.pulsar,
-                        "-k",
-                        $["claude-key"],
                         "-x",
                         std.toString($["claude-max-output-tokens"]),
                         "-t",
                         std.toString($["claude-temperature"]),
                     ])
+                    .with_env_var_secrets(envSecrets)
                     .with_limits("0.5", "128M")
                     .with_reservations("0.1", "128M");
 
-            local containerSet = engine.containers(
+            local containerRag =
-                "text-completion", [ container ]
-            );
-
-            local service =
-                engine.internalService(containerSet)
-                .with_port(8000, 8000, "metrics");
-
-            engine.resources([
-                containerSet,
-                service,
-            ])
-
-    },
-
-    "text-completion-rag" +: {
-    
-        create:: function(engine)
-
-            local container =
                 engine.container("text-completion-rag")
                     .with_image(images.trustgraph)
                     .with_command([
                         "text-completion-claude",
                         "-p",
                         url.pulsar,
-                        "-k",
-                        $["claude-key"],
                         "-x",
                         std.toString($["claude-max-output-tokens"]),
                         "-t",
@@ -67,24 +47,35 @@ 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(8000, 8000, "metrics");
 
+            local serviceRag =
+                engine.internalService(containerSetRag)
+                .with_port(8000, 8000, "metrics");
+
             engine.resources([
+                envSecrets,
                 containerSet,
+                containerSetRag,
                 service,
+                serviceRag,
             ])
 
-
+    },
-    }
 
 } + prompts
 

templates/components/cohere.jsonnet

@@ -9,13 +9,15 @@ local prompts = import "prompts/mixtral.jsonnet";
     "chunk-size":: 150,
     "chunk-overlap":: 10,
 
-    "cohere-key":: "${COHERE_KEY}",
     "cohere-temperature":: 0.0,
 
     "text-completion" +: {
     
         create:: function(engine)
 
+            local envSecrets = engine.envSecrets("cohere-credentials")
+                .with_env_var("COHERE_KEY", "cohere-key");
+
             local container =
                 engine.container("text-completion")
                     .with_image(images.trustgraph)
@@ -23,42 +25,19 @@ local prompts = import "prompts/mixtral.jsonnet";
                         "text-completion-cohere",
                         "-p",
                         url.pulsar,
-                        "-k",
-                        $["cohere-key"],
                         "-t",
                         std.toString($["cohere-temperature"]),
                     ])
                     .with_limits("0.5", "128M")
                     .with_reservations("0.1", "128M");
 
-            local containerSet = engine.containers(
+            local containerRag =
-                "text-completion", [ container ]
-            );
-
-            local service =
-                engine.internalService(containerSet)
-                .with_port(8000, 8000, "metrics");
-
-            engine.resources([
-                containerSet,
-                service,
-            ])
-
-    },
-
-    "text-completion-rag" +: {
-    
-        create:: function(engine)
-
-            local container =
                 engine.container("text-completion-rag")
                     .with_image(images.trustgraph)
                     .with_command([
                         "text-completion-cohere",
                         "-p",
                         url.pulsar,
-                        "-k",
-                        $["cohere-key"],
                         "-t",
                         std.toString($["cohere-temperature"]),
                         "-i",
@@ -70,20 +49,30 @@ local prompts = import "prompts/mixtral.jsonnet";
                     .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(8000, 8000, "metrics");
 
+            local serviceRag =
+                engine.internalService(containerSetRag)
+                .with_port(8000, 8000, "metrics");
+
             engine.resources([
+                envSecrets,
                 containerSet,
+                containerSetRag,
                 service,
+                serviceRag,
             ])
 
-
+    },
-    }
 
 } + prompts
 

templates/components/llamafile.jsonnet

@@ -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(
+            local containerRag =
-                "text-completion", [ container ]
-            );
-
-            engine.resources([
-                containerSet,
-            ])
-
-    },
-
-    "text-completion-rag" +: {
-    
-        create:: function(engine)
-
-            local container =
                 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
 

templates/components/ollama.jsonnet

@@ -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(
+            local containerRag =
-                "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 =
                 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
 

templates/components/openai.jsonnet

@@ -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(
+            local containerRag =
-                "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 =
                 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
 

templates/engine/docker-compose.jsonnet

@@ -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)
     {
 

templates/engine/k8s.jsonnet

@@ -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: [ {
+                                    (if ! std.isEmpty(container.environment) then
-                                        name: e.key, value: e.value
+                                    {
-                                        }
+                                        env: container.environment,
-                                        for e in 
+                                    }
-                                        std.objectKeysValues(
-                                            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)
     {
 

trustgraph-bedrock/trustgraph/model/text_completion/bedrock/llm.py

@@ -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_id = params.get("aws_id_key", default_aws_id)
-        aws_secret = params.get("aws_secret")
+        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__)
 
-    

trustgraph-flow/trustgraph/model/text_completion/azure/llm.py

@@ -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")
+        endpoint = params.get("endpoint", default_endpoint)
-        token = params.get("token")
+        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,

trustgraph-flow/trustgraph/model/text_completion/azure_openai/llm.py

@@ -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")
+        endpoint = params.get("endpoint", default_endpoint)
-        token = params.get("token")
+        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,

trustgraph-flow/trustgraph/model/text_completion/claude/llm.py

@@ -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,

trustgraph-flow/trustgraph/model/text_completion/cohere/llm.py

@@ -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,

trustgraph-flow/trustgraph/model/text_completion/googleaistudio/llm.py

@@ -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,

trustgraph-flow/trustgraph/model/text_completion/llamafile/llm.py

@@ -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
 

trustgraph-flow/trustgraph/model/text_completion/ollama/llm.py

@@ -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_model = 'gemma2:9b'
-default_ollama = 'http://localhost:11434'
+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(

trustgraph-flow/trustgraph/model/text_completion/openai/llm.py

@@ -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,

trustgraph-vertexai/trustgraph/model/text_completion/vertexai/llm.py

@@ -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,