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docker/.env
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docker/.env
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# LLM Config
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LLM_API_ENDPOINT=https://api.llama.cpp.imixs.com/
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LLM_API_KEY=7fdb663fa87ea16731c1e0ece8ad3214e358423eabf2403188110c731ce07321
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55
docker/keys/imixs-llm.xml
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docker/keys/imixs-llm.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<!--
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imixs-llm.xml - LLM Model Registry
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======================================
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Place this file in your deployment so it is available on the classpath,
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e.g. in src/main/resources/ for a Maven project.
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Each <model> element defines one logical LLM service endpoint.
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A model is either a completion model OR an embedding model - never both.
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The BPMN configuration references them separately by their id:
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<imixs-ai name="CONDITION">
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<endpoint>my-llm</endpoint>
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...
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</imixs-ai>
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<imixs-ai name="RAG_INDEX">
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<endpoint-completion>my-llm</endpoint-completion>
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<endpoint-embeddings>my-embeddings</endpoint-embeddings>
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...
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</imixs-ai>
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Environment variable placeholders are supported via ${env.VAR_NAME}.
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-->
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<imixs-llm>
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<!--
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Completion model – used for chat completions, conditions, analysis etc.
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This example connects to a local llama.cpp server.
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-->
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<endpoint id="api.llama.cpp.imixs.com">
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<url>https://api.llama.cpp.imixs.com/</url>
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<apikey>7fdb663fa87ea16731c1e0ece8ad3214e358423eabf2403188110c731ce07321</apikey>
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<options>{
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"temperature": 0.2,
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"max_tokens": 1024
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}
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</options>
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</endpoint>
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<!--
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Embedding model – used for RAG indexing and retrieval.
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This example connects to a separate embedding server.
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No apikey needed for a locally hosted instance.
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-->
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<endpoint id="embeddings.llama.cpp.imixs.com">
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<url>https://embeddings.llama.cpp.imixs.com/</url>
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<apikey>7fdb663fa87ea16731c1e0ece8ad3214e358423eabf2403188110c731ce07321</apikey>
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<options>{
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"max_tokens": 512
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}
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</options>
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</endpoint>
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</imixs-llm>
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5
src/test/resources/.env
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src/test/resources/.env
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# secrets used by junit tests
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TENANT_ID=51e2f038-0a96-41be-801d-bb4118aa018e
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CLIENT_ID=39e9eec5-6939-47dc-9729-3438a9898e63
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CLIENT_SECRET=0lG8Q~UigK0f7n-cR8SSiSXhVChkBD3Qm1CqvbID
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