image: repository: ghcr.io/mintplex-labs/anything-llm pullPolicy: IfNotPresent tag: latest@sha256:0098edd407b003d2004e1065559258854361b605c8b189eaa3f46f330e374a3b securityContext: container: readOnlyRootFilesystem: false runAsUser: 0 runAsGroup: 0 capabilities: add: - SYS_ADMIN service: main: ports: main: protocol: http port: 3001 workload: main: podSpec: containers: main: env: SERVER_PORT: "{{ .Values.service.main.ports.main.port }}" STORAGE_DIR: "{{.Values.persistence.storage.mountPath }}" # forces users to use ingress if https is needed. # keep false. ENABLE_HTTPS: false JWT_SECRET: secretKeyRef: name: anythinglmm-secrets key: JWT_SECRET # LLM_PROVIDER='openai' # OPEN_AI_KEY= # OPEN_MODEL_PREF='gpt-3.5-turbo' # LLM_PROVIDER='gemini' # GEMINI_API_KEY= # GEMINI_LLM_MODEL_PREF='gemini-pro' # LLM_PROVIDER='azure' # AZURE_OPENAI_KEY= # AZURE_OPENAI_ENDPOINT= # OPEN_MODEL_PREF='my-gpt35-deployment' # This is the "deployment" on Azure you want to use. Not the base model. # EMBEDDING_MODEL_PREF='embedder-model' # This is the "deployment" on Azure you want to use for embeddings. Not the base model. Valid base model is text-embedding-ada-002 # LLM_PROVIDER='anthropic' # ANTHROPIC_API_KEY=sk-ant-xxxx # ANTHROPIC_MODEL_PREF='claude-2' # LLM_PROVIDER='lmstudio' # LMSTUDIO_BASE_PATH='http://your-server:1234/v1' # LMSTUDIO_MODEL_TOKEN_LIMIT=4096 # LLM_PROVIDER='localai' # LOCAL_AI_BASE_PATH='http://host.docker.internal:8080/v1' # LOCAL_AI_MODEL_PREF='luna-ai-llama2' # LOCAL_AI_MODEL_TOKEN_LIMIT=4096 # LOCAL_AI_API_KEY="sk-123abc" # LLM_PROVIDER='ollama' # OLLAMA_BASE_PATH='http://host.docker.internal:11434' # OLLAMA_MODEL_PREF='llama2' # OLLAMA_MODEL_TOKEN_LIMIT=4096 # LLM_PROVIDER='togetherai' # TOGETHER_AI_API_KEY='my-together-ai-key' # TOGETHER_AI_MODEL_PREF='mistralai/Mixtral-8x7B-Instruct-v0.1' # LLM_PROVIDER='mistral' # MISTRAL_API_KEY='example-mistral-ai-api-key' # MISTRAL_MODEL_PREF='mistral-tiny' # LLM_PROVIDER='huggingface' # HUGGING_FACE_LLM_ENDPOINT=https://uuid-here.us-east-1.aws.endpoints.huggingface.cloud # HUGGING_FACE_LLM_API_KEY=hf_xxxxxx # HUGGING_FACE_LLM_TOKEN_LIMIT=8000 # EMBEDDING_ENGINE='openai' # OPEN_AI_KEY=sk-xxxx # EMBEDDING_MODEL_PREF='text-embedding-ada-002' # EMBEDDING_ENGINE='azure' # AZURE_OPENAI_ENDPOINT= # AZURE_OPENAI_KEY= # EMBEDDING_MODEL_PREF='my-embedder-model' # This is the "deployment" on Azure you want to use for embeddings. Not the base model. Valid base model is text-embedding-ada-002 # EMBEDDING_ENGINE='localai' # EMBEDDING_BASE_PATH='http://localhost:8080/v1' # EMBEDDING_MODEL_PREF='text-embedding-ada-002' # EMBEDDING_MODEL_MAX_CHUNK_LENGTH=1000 # The max chunk size in chars a string to embed can be # Enable all below if you are using vector database: Chroma. # VECTOR_DB="chroma" # CHROMA_ENDPOINT='http://host.docker.internal:8000' # CHROMA_API_HEADER="X-Api-Key" # CHROMA_API_KEY="sk-123abc" # VECTOR_DB="pinecone" # PINECONE_API_KEY= # PINECONE_INDEX= # VECTOR_DB="lancedb" # VECTOR_DB="weaviate" # WEAVIATE_ENDPOINT="http://localhost:8080" # WEAVIATE_API_KEY= # VECTOR_DB="qdrant" # QDRANT_ENDPOINT="http://localhost:6333" # QDRANT_API_KEY= # VECTOR_DB="milvus" # MILVUS_ADDRESS="http://localhost:19530" # MILVUS_USERNAME= # MILVUS_PASSWORD= # VECTOR_DB="zilliz" # ZILLIZ_ENDPOINT="https://sample.api.gcp-us-west1.zillizcloud.com" # ZILLIZ_API_TOKEN=api-token-here # VECTOR_DB="astra" # ASTRA_DB_APPLICATION_TOKEN= # ASTRA_DB_ENDPOINT= # AUTH_TOKEN="hunter2" # This is the password to your application if remote hosting. # DISABLE_TELEMETRY="false" # Documentation on how to use https://github.com/kamronbatman/joi-password-complexity # Default is only 8 char minimum PASSWORDMINCHAR: 8 PASSWORDMAXCHAR: 250 PASSWORDLOWERCASE: 1 PASSWORDUPPERCASE: 1 PASSWORDNUMERIC: 1 PASSWORDSYMBOL: 1 PASSWORDREQUIREMENTS: 4 persistence: storage: enabled: true mountPath: "/app/server/storage" hotdir: enabled: true mountPath: "/app/collector/hotdir" outputs: enabled: true mountPath: "/app/collector/outputs" portal: open: enabled: true