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https://github.com/Mintplex-Labs/anything-llm.git
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5614e2ed30
* feature: Integrate Astra as vectorDBProvider feature: Integrate Astra as vectorDBProvider * Update .env.example * Add env.example to docker example file Update spellcheck fo Astra Update Astra key for vector selection Update order of AstraDB options Resize Astra logo image to 330x330 Update methods of Astra to take in latest vectorDB params like TopN and more Update Astra interface to support default methods and avoid crash errors from 404 collections Update Astra interface to comply to max chunk insertion limitations Update Astra interface to dynamically set dimensionality from chunk 0 size on creation * reset workspaces --------- Co-authored-by: timothycarambat <rambat1010@gmail.com>
136 lines
4.5 KiB
Plaintext
136 lines
4.5 KiB
Plaintext
SERVER_PORT=3001
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STORAGE_DIR="/app/server/storage"
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UID='1000'
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GID='1000'
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# JWT_SECRET="my-random-string-for-seeding" # Only needed if AUTH_TOKEN is set. Please generate random string at least 12 chars long.
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###########################################
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######## LLM API SElECTION ################
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###########################################
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# LLM_PROVIDER='openai'
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# OPEN_AI_KEY=
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# OPEN_MODEL_PREF='gpt-3.5-turbo'
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# LLM_PROVIDER='gemini'
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# GEMINI_API_KEY=
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# GEMINI_LLM_MODEL_PREF='gemini-pro'
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# LLM_PROVIDER='azure'
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# AZURE_OPENAI_ENDPOINT=
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# AZURE_OPENAI_KEY=
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# OPEN_MODEL_PREF='my-gpt35-deployment' # This is the "deployment" on Azure you want to use. Not the base model.
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# 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
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# LLM_PROVIDER='anthropic'
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# ANTHROPIC_API_KEY=sk-ant-xxxx
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# ANTHROPIC_MODEL_PREF='claude-2'
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# LLM_PROVIDER='lmstudio'
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# LMSTUDIO_BASE_PATH='http://your-server:1234/v1'
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# LMSTUDIO_MODEL_TOKEN_LIMIT=4096
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# LLM_PROVIDER='localai'
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# LOCAL_AI_BASE_PATH='http://host.docker.internal:8080/v1'
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# LOCAL_AI_MODEL_PREF='luna-ai-llama2'
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# LOCAL_AI_MODEL_TOKEN_LIMIT=4096
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# LOCAL_AI_API_KEY="sk-123abc"
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# LLM_PROVIDER='ollama'
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# OLLAMA_BASE_PATH='http://host.docker.internal:11434'
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# OLLAMA_MODEL_PREF='llama2'
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# OLLAMA_MODEL_TOKEN_LIMIT=4096
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# LLM_PROVIDER='togetherai'
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# TOGETHER_AI_API_KEY='my-together-ai-key'
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# TOGETHER_AI_MODEL_PREF='mistralai/Mixtral-8x7B-Instruct-v0.1'
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# LLM_PROVIDER='mistral'
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# MISTRAL_API_KEY='example-mistral-ai-api-key'
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# MISTRAL_MODEL_PREF='mistral-tiny'
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###########################################
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######## Embedding API SElECTION ##########
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###########################################
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# Only used if you are using an LLM that does not natively support embedding (openai or Azure)
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# EMBEDDING_ENGINE='openai'
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# OPEN_AI_KEY=sk-xxxx
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# EMBEDDING_ENGINE='azure'
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# AZURE_OPENAI_ENDPOINT=
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# AZURE_OPENAI_KEY=
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# 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
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# EMBEDDING_ENGINE='localai'
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# EMBEDDING_BASE_PATH='http://localhost:8080/v1'
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# EMBEDDING_MODEL_PREF='text-embedding-ada-002'
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# EMBEDDING_MODEL_MAX_CHUNK_LENGTH=1000 # The max chunk size in chars a string to embed can be
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###########################################
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######## Vector Database Selection ########
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###########################################
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# Enable all below if you are using vector database: Chroma.
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# VECTOR_DB="chroma"
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# CHROMA_ENDPOINT='http://host.docker.internal:8000'
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# CHROMA_API_HEADER="X-Api-Key"
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# CHROMA_API_KEY="sk-123abc"
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# Enable all below if you are using vector database: Pinecone.
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# VECTOR_DB="pinecone"
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# PINECONE_API_KEY=
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# PINECONE_INDEX=
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# Enable all below if you are using vector database: LanceDB.
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# VECTOR_DB="lancedb"
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# Enable all below if you are using vector database: Weaviate.
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# VECTOR_DB="weaviate"
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# WEAVIATE_ENDPOINT="http://localhost:8080"
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# WEAVIATE_API_KEY=
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# Enable all below if you are using vector database: Qdrant.
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# VECTOR_DB="qdrant"
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# QDRANT_ENDPOINT="http://localhost:6333"
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# QDRANT_API_KEY=
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# Enable all below if you are using vector database: Milvus.
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# VECTOR_DB="milvus"
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# MILVUS_ADDRESS="http://localhost:19530"
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# MILVUS_USERNAME=
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# MILVUS_PASSWORD=
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# Enable all below if you are using vector database: Zilliz Cloud.
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# VECTOR_DB="zilliz"
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# ZILLIZ_ENDPOINT="https://sample.api.gcp-us-west1.zillizcloud.com"
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# ZILLIZ_API_TOKEN=api-token-here
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# Enable all below if you are using vector database: Astra DB.
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# VECTOR_DB="astra"
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# ASTRA_DB_APPLICATION_TOKEN=
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# ASTRA_DB_ENDPOINT=
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# CLOUD DEPLOYMENT VARIRABLES ONLY
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# AUTH_TOKEN="hunter2" # This is the password to your application if remote hosting.
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###########################################
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######## PASSWORD COMPLEXITY ##############
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###########################################
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# Enforce a password schema for your organization users.
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# Documentation on how to use https://github.com/kamronbatman/joi-password-complexity
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# Default is only 8 char minimum
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# PASSWORDMINCHAR=8
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# PASSWORDMAXCHAR=250
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# PASSWORDLOWERCASE=1
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# PASSWORDUPPERCASE=1
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# PASSWORDNUMERIC=1
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# PASSWORDSYMBOL=1
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# PASSWORDREQUIREMENTS=4
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###########################################
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######## ENABLE HTTPS SERVER ##############
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###########################################
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# By enabling this and providing the path/filename for the key and cert,
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# the server will use HTTPS instead of HTTP.
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#ENABLE_HTTPS="true"
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#HTTPS_CERT_PATH="sslcert/cert.pem"
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#HTTPS_KEY_PATH="sslcert/key.pem"
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