deploy: 90b926c2ce

includes/llms.txt

@@ -1,6 +1,6 @@
 Plano Docs v0.4.18
 llms.txt (auto-generated)
-Generated (UTC): 2026-04-14T02:31:14.825020+00:00
+Generated (UTC): 2026-04-15T23:42:11.682797+00:00
 
 Table of contents
 - Agents (concepts/agents)
@@ -3760,9 +3760,9 @@ response = client.chat.completions.create(
 
 
 
-Preference-aligned routing (Arch-Router)
+Preference-aligned routing (Plano-Orchestrator)
 
-Preference-aligned routing uses the Arch-Router model to pick the best LLM based on domain, action, and your configured preferences instead of hard-coding a model.
+Preference-aligned routing uses the Plano-Orchestrator model to pick the best LLM based on domain, action, and your configured preferences instead of hard-coding a model.
 
 Domain: High-level topic of the request (e.g., legal, healthcare, programming).
 
@@ -3770,7 +3770,7 @@ Action: What the user wants to do (e.g., summarize, generate code, translate).
 
 Routing preferences: Your mapping from (domain, action) to preferred models.
 
-Arch-Router analyzes each prompt to infer domain and action, then applies your preferences to select a model. This decouples routing policy (how to choose) from model assignment (what to run), making routing transparent, controllable, and easy to extend as you add or swap models.
+Plano-Orchestrator analyzes each prompt to infer domain and action, then applies your preferences to select a model. This decouples routing policy (how to choose) from model assignment (what to run), making routing transparent, controllable, and easy to extend as you add or swap models.
 
 Configuration
 
@@ -3810,20 +3810,20 @@ Client usage
 
 Clients can let the router decide or still specify aliases:
 
-# Let Arch-Router choose based on content
+# Let Plano-Orchestrator choose based on content
 response = client.chat.completions.create(
     messages=[{"role": "user", "content": "Write a creative story about space exploration"}]
     # No model specified - router will analyze and choose claude-sonnet-4-5
 )
 
-Arch-Router
+Plano-Orchestrator
 
-The Arch-Router is a state-of-the-art preference-based routing model specifically designed to address the limitations of traditional LLM routing. This compact 1.5B model delivers production-ready performance with low latency and high accuracy while solving key routing challenges.
+Plano-Orchestrator is a preference-based routing model specifically designed to address the limitations of traditional LLM routing. It delivers production-ready performance with low latency and high accuracy while solving key routing challenges.
 
 Addressing Traditional Routing Limitations:
 
 Human Preference Alignment
-Unlike benchmark-driven approaches, Arch-Router learns to match queries with human preferences by using domain-action mappings that capture subjective evaluation criteria, ensuring routing decisions align with real-world user needs.
+Unlike benchmark-driven approaches, Plano-Orchestrator learns to match queries with human preferences by using domain-action mappings that capture subjective evaluation criteria, ensuring routing decisions align with real-world user needs.
 
 Flexible Model Integration
 The system supports seamlessly adding new models for routing without requiring retraining or architectural modifications, enabling dynamic adaptation to evolving model landscapes.
@@ -3831,15 +3831,15 @@ The system supports seamlessly adding new models for routing without requiring r
 Preference-Encoded Routing
 Provides a practical mechanism to encode user preferences through domain-action mappings, offering transparent and controllable routing decisions that can be customized for specific use cases.
 
-To support effective routing, Arch-Router introduces two key concepts:
+To support effective routing, Plano-Orchestrator introduces two key concepts:
 
 Domain – the high-level thematic category or subject matter of a request (e.g., legal, healthcare, programming).
 
 Action – the specific type of operation the user wants performed (e.g., summarization, code generation, booking appointment, translation).
 
-Both domain and action configs are associated with preferred models or model variants. At inference time, Arch-Router analyzes the incoming prompt to infer its domain and action using semantic similarity, task indicators, and contextual cues. It then applies the user-defined routing preferences to select the model best suited to handle the request.
+Both domain and action configs are associated with preferred models or model variants. At inference time, Plano-Orchestrator analyzes the incoming prompt to infer its domain and action using semantic similarity, task indicators, and contextual cues. It then applies the user-defined routing preferences to select the model best suited to handle the request.
 
-In summary, Arch-Router demonstrates:
+In summary, Plano-Orchestrator demonstrates:
 
 Structured Preference Routing: Aligns prompt request with model strengths using explicit domain–action mappings.
 
@@ -3849,9 +3849,9 @@ Flexible and Adaptive: Supports evolving user needs, model updates, and new doma
 
 Production-Ready Performance: Optimized for low-latency, high-throughput applications in multi-model environments.
 
-Self-hosting Arch-Router
+Self-hosting Plano-Orchestrator
 
-By default, Plano uses a hosted Arch-Router endpoint. To run Arch-Router locally, you can serve the model yourself using either Ollama or vLLM.
+By default, Plano uses a hosted Plano-Orchestrator endpoint. To run Plano-Orchestrator locally, you can serve the model yourself using either Ollama or vLLM.
 
 Using Ollama (recommended for local development)
 
@@ -3859,14 +3859,14 @@ Install Ollama
 
 Download and install from ollama.ai.
 
-Pull and serve Arch-Router
+Pull and serve the routing model
 
 ollama pull hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M
 ollama serve
 
 This downloads the quantized GGUF model from HuggingFace and starts serving on http://localhost:11434.
 
-Configure Plano to use local Arch-Router
+Configure Plano to use local routing model
 
 overrides:
   llm_routing_model: plano/hf.co/katanemo/Arch-Router-1.5B.gguf:Q4_K_M
@@ -3919,7 +3919,7 @@ vllm serve ${SNAPSHOT_DIR}Arch-Router-1.5B-Q4_K_M.gguf \
     --load-format gguf \
     --chat-template ${SNAPSHOT_DIR}template.jinja \
     --tokenizer katanemo/Arch-Router-1.5B \
-    --served-model-name Arch-Router \
+    --served-model-name Plano-Orchestrator \
     --gpu-memory-utilization 0.3 \
     --tensor-parallel-size 1 \
     --enable-prefix-caching
@@ -3927,10 +3927,10 @@ vllm serve ${SNAPSHOT_DIR}Arch-Router-1.5B-Q4_K_M.gguf \
 Configure Plano to use the vLLM endpoint
 
 overrides:
-  llm_routing_model: plano/Arch-Router
+  llm_routing_model: plano/Plano-Orchestrator
 
 model_providers:
-  - model: plano/Arch-Router
+  - model: plano/Plano-Orchestrator
     base_url: http://<your-server-ip>:10000
 
   - model: openai/gpt-5.2
@@ -3950,16 +3950,16 @@ curl http://localhost:10000/v1/models
 
 Using vLLM on Kubernetes (GPU nodes)
 
-For teams running Kubernetes, Arch-Router and Plano can be deployed as in-cluster services.
+For teams running Kubernetes, Plano-Orchestrator and Plano can be deployed as in-cluster services.
 The demos/llm_routing/model_routing_service/ directory includes ready-to-use manifests:
 
-vllm-deployment.yaml — Arch-Router served by vLLM, with an init container to download
+vllm-deployment.yaml — Plano-Orchestrator served by vLLM, with an init container to download
 the model from HuggingFace
 
-plano-deployment.yaml — Plano proxy configured to use the in-cluster Arch-Router
+plano-deployment.yaml — Plano proxy configured to use the in-cluster Plano-Orchestrator
 
 config_k8s.yaml — Plano config with llm_routing_model pointing at
-http://arch-router:10000 instead of the default hosted endpoint
+http://plano-orchestrator:10000 instead of the default hosted endpoint
 
 Key things to know before deploying:
 
@@ -4092,7 +4092,7 @@ Let the router decide: No model specified, router analyzes content
 
 Example Use Cases
 
-Here are common scenarios where Arch-Router excels:
+Here are common scenarios where Plano-Orchestrator excels:
 
 Coding Tasks: Distinguish between code generation requests (“write a Python function”), debugging needs (“fix this error”), and code optimization (“make this faster”), routing each to appropriately specialized models.
 
@@ -4134,13 +4134,13 @@ Best practices
 
 Unsupported Features
 
-The following features are not supported by the Arch-Router model:
+The following features are not supported by the Plano-Orchestrator routing model:
 
 Multi-modality: The model is not trained to process raw image or audio inputs. It can handle textual queries about these modalities (e.g., “generate an image of a cat”), but cannot interpret encoded multimedia data directly.
 
-Function calling: Arch-Router is designed for semantic preference matching, not exact intent classification or tool execution. For structured function invocation, use models in the Plano Function Calling collection instead.
+Function calling: Plano-Orchestrator is designed for semantic preference matching, not exact intent classification or tool execution. For structured function invocation, use models in the Plano Function Calling collection instead.
 
-System prompt dependency: Arch-Router routes based solely on the user’s conversation history. It does not use or rely on system prompts for routing decisions.
+System prompt dependency: Plano-Orchestrator routes based solely on the user’s conversation history. It does not use or rely on system prompts for routing decisions.
 
 ---
 
@@ -6455,7 +6455,7 @@ model_providers:
 
   # routing_preferences: tags a model with named capabilities so Plano's LLM router
   # can select the best model for each request based on intent. Requires the
-  # Arch-Router model (or equivalent) to be configured in overrides.llm_routing_model.
+  # Plano-Orchestrator model (or equivalent) to be configured in overrides.llm_routing_model.
   # Each preference has a name (short label) and a description (used for intent matching).
   - model: groq/llama-3.3-70b-versatile
     access_key: $GROQ_API_KEY
@@ -6591,7 +6591,7 @@ overrides:
   # Path to the trusted CA bundle for upstream TLS verification
   upstream_tls_ca_path: /etc/ssl/certs/ca-certificates.crt
   # Model used for intent-based LLM routing (must be listed in model_providers)
-  llm_routing_model: Arch-Router
+  llm_routing_model: Plano-Orchestrator
   # Model used for agent orchestration (must be listed in model_providers)
   agent_orchestration_model: Plano-Orchestrator