sample.env

DJANGO_ADMIN_URL="79b780ab0b79492ebe031eafe9de0af4/"
# wildcard for subdomain is: ".domain.tld" (without the quotes, but do not add the '*')
DJANGO_ALLOWED_HOSTS="127.0.0.1,localhost,0.0.0.0,.domain.tld"
DJANGO_DEBUG="False"
DJANGO_SECRET_KEY="f4894ca8-07c8-4c57-ad50-5c9f186b873f"
DJANGO_SERVER_EMAIL="application@chatgpt.local"
DJANGO_SETTINGS_MODULE="llm_portal.settings.production"
OPENAI_API_BASE_URL="https://api.openai.com"
OPENAI_API_KEY="*********************************************"
AZURE_OPENAI_ENDPOINT="https://RESOURCE.openai.azure.com"
AZURE_OPENAI_API_KEY="***************************************************"
AZURE_OPENAI_API_VERSION="2023-12-01-preview"
# USED BY DOCKER / NGINX !
HOST="localhost"
PORT="8000"
NGINX_PORT=443
WEB_CONCURRENCY="2"

# doc conversion
LIBRE_OFFICE_PATH="/Applications/LibreOffice.app/Contents/MacOS/soffice"

## APP stuff
APP_NAME="test"
# Leave commented out if you have the passwords set in the fixtures
PASSWORD_INIT_USERS="1234"

# you can set here the list of vector stores you want to use 
# this allow the indexing phase to mutualize the received embeddings and store them in multiple stores at once ...
# default , simple (=default), qdrant , pgvector, chroma, elastic
RAG_VECTOR_STORES="pgvector,qdrant,elastic"
RAG_VECTOR_STORE="pgvector"
# 'mongodb' or 'postgres'
RAG_DOCUMENT_STORE="postgres"
# 'mongodb' or 'postgres'
RAG_INDEX_STORE="postgres"


PGVECTOR_DB="pgvectordb"
PGVECTOR_HOST="127.0.0.1"
PGVECTOR_PORT="5432"
PGVECTOR_USER="pgvectoruser"
PGVECTOR_PASSWORD="GOODPASSWORD_PGVECTOR"
PGVECTOR_TEXT_SEARCH_CONFIG="english"

# FOR NOW, the docker-compose use the same server for both docstore and INDEXSTORE_DB
# make sure they are the same host,user,password and only the DB name changes

DOCSTORE_DB="mongodocstoredb"
DOCSTORE_HOST="127.0.0.1"
DOCSTORE_PORT="27017"
DOCSTORE_USER="mongouser"
DOCSTORE_PASSWORD="GOODPASSWORD_DOC"

INDEXSTORE_DB="mongoindexstoredb"
INDEXSTORE_HOST="127.0.0.1"
INDEXSTORE_PORT="27017"
INDEXSTORE_USER="mongouser"
INDEXSTORE_PASSWORD="GOODPASSWORD_INDEX"

QDRANT_HOST="localhost"

ELASTIC_SEARCH_PORT=9200
ELASTIC_SEARCH_PASSWORD=YOUR_PASSWORD

# used for production when switching from sqlite3 to a proper DB (if specified)
DB_ENGINE="django.db.backends.postgresql"
DB_HOST="localhost"
DB_NAME="pgsqldb"
DB_PORT=5333
DB_USER="pgsql"
DB_PASSWORD="GOODPASSWORD_PGSQL"

PGBOUNCER_PORT=6432
PGBOUNCER_POOL_PREFIX="pgbouncer_"

# NOTE:
# if you get Error code: 404 - {'error': {'code': '404', 'message': 'Resource not found'}}
# check https://learn.microsoft.com/en-us/azure/ai-services/openai/reference
# it is often the version that is wrong: https://stackoverflow.com/questions/75172116/why-im-getting-404-resource-not-found-to-my-newly-azure-openai-deployment
#
# FORMAT:
# provider:model@deployment or provider:model
#
# LLM_MODEL examples:
# "azure_openai:gpt-4@DEPLOYMENT"
# "openai:gpt-3.5-turbo-1106"
# "openai:gpt-4-1106-preview" 
# "ollama:mistral"
# "vllm:mistralai/Mistral-7B-Instruct-v0.1"
# ... 
# LLM_EMBEDDINGS examples:
# "local:local"
# "azure_openai:text-embedding-ada-002@km-ada002"
# "openai:text-embedding-ada-002"
# "hugging_face:..."
# ...
LLM_MODEL="azure_openai:gpt-4@km-gpt4-1106"
# DEPRECATED
# LLM_EMBEDDING="azure_openai:text-embedding-ada-002@km-ada002"
LLM_SYSTEM_MESSAGE="You are a helpful assistant. You will respond in a cheerful, friendly tone with a succint style. Your responses should aim to be three paragraphs or less."
LLM_TEMPERATURE=0.15
LLM_TOKEN_COUNTER="True"

PORTAL_REGISTER_DISABLED=1
PORTAL_IMAGE="portal_image.svg"
PORTAL_COLOR_HEADER=


LOCUST_USER_ADMIN="admin@company.com"
LOCUST_USER_NORMAL="team1@company.com"
LOCUST_PWD_ADMIN="1234"
LOCUST_PWD_NORMAL="1234"
LOCUST_AUTH="HTTP_AUTH_USER:HTTP_AUTH_PASSWORD"


# will use scripts/libraries that have been pre-downloaded 
OFFLINE_MODE="False"
# tells the system if the conversations should be preloaded or fetched when clicked
CONVERSATIONS_LOAD_ALL="False"

# Uncomment and set value if you want to change to a static file server
#STATIC_URL=https://assets.mydomain.com/
#STATIC_ROOT="/home/www-data/staticfiles/"

# Continue to deliver static file even in production (non-debug)
STATIC_SERVE_PRODUCTION="False"


# MS Graph stuff
# MS_GRAPH_TENANT_ID=“****”
# MS_GRAPH_APP_ID=“****”
# MS_GRAPH_SECRET="******"


# LEGACY STUFF to be able to roll back
# -------------------------------------------------------------------------------------------
# LLM_MODEL="openai:gpt-4-1106-preview"
# LLM_EMBEDDINGS="openai:text-embedding-ada-002"
# LLM_MODEL="openai:gpt-4-1106-preview"
# LLM_EMBEDDINGS="openai:text-embedding-ada-002"
# OPENAI_SYSTEM_MESSAGE="You are a helpful assistant. You will respond in a cheerful, friendly tone with a succint style. Your responses should aim to be three paragraphs or less."
# OPENAI_MODEL="gpt-4-1106-preview"
# OPENAI_TEMPERATURE=0.3
# STORE="simple"
# LLAMA_INDEX_VECTOR_STORES="pgvector,qdrant"
# LLAMA_INDEX_VECTOR_STORE="pgvector"
# LLAMA_INDEX_DOCUMENT_STORE="mongodb"
# LLAMA_INDEX_INDEX_STORE="mongodb"
# LLAMA_INDEX_INDEX="vector"
# PORTAL_NAME="Knowledge Portal"
# APP_AUTO_LANGUAGES="en,fr"
# default loaders
# RAG_LOADING_STRATEGIES="default_v1"
# # static|semantic|dynamic{ ... parameters_json_like ...} 
# # !! for 'parameters_json_like', replace ", USE ` and not ' !!!!
# RAG_CHUNKING_STRATEGIES="static|static{`chunk_size`:800,`chunk_overlap`:100}"
# # indexing filters to filter out some combinations of parameters
# # RAG_INDEXING_CONFIGURATIONS_FILTER="___.*default.*static.*|___.*advanced.*semantic.*|en_.*default.*semantic.*"
# # 'vector', 'tree', 'summary'
# RAG_INDEXES="vector"
# RAG_EMBEDDING_MODELS="azure_openai:text-embedding-ada-002@km-ada002|hugging_face:Salesforce/SFR-Embedding-Mistral"
# RAG_VECTOR_STORE_HYBRID="True"