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5a4f8cb321
- Update all demo config files to version v0.3.0
- Rename llm_providers → model_providers across all configs
- Modernize listener format from object to array style
(e.g. listeners: [{type: model, name:, port:}])
- Replace chatbot_ui with AnythingLLM in 6 docker-compose files
- Remove prometheus/grafana services from llm_gateway and
preference_based_routing docker-compose files
- Add tracing: random_sampling: 100 to configs that lacked it
- Update 3 CLI init templates (coding_agent_routing,
preference_aware_routing, conversational_state_v1_responses)
- Fix test_init.py assertion for model_providers key
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
56 lines
1.7 KiB
YAML
56 lines
1.7 KiB
YAML
version: v0.3.0
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listeners:
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- type: prompt
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name: prompt_listener
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port: 10000
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endpoints:
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rag_energy_source_agent:
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endpoint: host.docker.internal:18083
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connect_timeout: 0.005s
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model_providers:
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- access_key: $OPENAI_API_KEY
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model: openai/gpt-4o-mini
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default: true
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system_prompt: |
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You are a helpful assistant and can offer information about energy sources.
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You will get a JSON object with energy_source and consideration fields. Focus on answering the querstion using those fields.
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Keep your responses to just three main points to make it easy for the reader to digest the information
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prompt_targets:
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- name: get_info_for_energy_source
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description: get information about an energy source
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parameters:
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- name: energy_source
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type: str
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description: a source of energy
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required: true
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enum: [renewable, fossil]
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- name: consideration
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type: str
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description: a specific type of consideration for an energy source
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enum: [cost, economic, technology]
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endpoint:
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name: rag_energy_source_agent
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path: /agent/energy_source_info
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http_method: POST
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- name: default_target
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default: true
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description: This is the default target for all unmatched prompts.
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endpoint:
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name: rag_energy_source_agent
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path: /default_target
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http_method: POST
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system_prompt: |
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You are a helpful assistant! Summarize the user's request and provide a helpful response.
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# if it is set to false arch will send response that it received from this prompt target to the user
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# if true arch will forward the response to the default LLM
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auto_llm_dispatch_on_response: false
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tracing:
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random_sampling: 100
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trace_arch_internal: true
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