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README.md	README.md
deploy.yaml	deploy.yaml

PrivateGPT

PrivateGPT is a production-ready AI project that allows you to ask questions about your documents using the power of Large Language Models (LLMs), even in scenarios without an Internet connection. 100% private, no data leaves your execution environment at any point.

GitHub - 3x3cut0r/privategpt - https://github.com/3x3cut0r/docker/tree/main/privategpt
DockerHub - 3x3cut0r/privategpt - https://hub.docker.com/r/3x3cut0r/privategpt

Documentation

GitHub - zylon-ai/private-gpt - https://github.com/zylon-ai/private-gpt
Docs - docs.privategpt.dev - https://docs.privategpt.dev/

Index

Usage
1.1 docker run
1.2 docker-compose.yaml
1.3 docker-compose.yaml with custom model
Environment Variables
Volumes
Ports
Find Me
License

1 Usage

1.1 docker run

docker run -d \
    --name privategpt \
    -p 8080:8080/tcp \
    3x3cut0r/privategpt:latest

1.2 docker-compose.yml

version: '3.9'

services:
  # https://hub.docker.com/r/3x3cut0r/privategpt
  privategpt:
    image: 3x3cut0r/privategpt:latest
    container_name: privategpt
    ports:
      - 8080:8080/tcp

1.3 docker-compose.yml with custom model

version: '3.9'

services:
  # https://hub.docker.com/r/3x3cut0r/privategpt
  privategpt:
    image: 3x3cut0r/privategpt:latest
    container_name: privategpt
    environment:
      LLAMACPP_LLM_HF_REPO_ID: "lmstudio-community/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf"
      LLAMACPP_LLM_HF_MODEL_FILE: "Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf"
      HUGGINGFACE_EMBEDDING_HF_MODEL_NAME: "nomic-ai/nomic-embed-text-v1.5"
      EMBEDDING_INGEST_MODE: "parallel"
      EMBEDDING_COUNT_WORKERS: "4"
    volumes:
      - /path/to/your/model/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf:/home/worker/app/models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf
    ports:
      - 8080:8080/tcp

2 Environment Variables

you can adjust all values inside the settings.yaml with environment variables

Server

ENV_NAME - Name of the environment (prod, staging, local...) - Default: prod
PORT - Port of PrivateGPT FastAPI server - Default: 8080
KEEP_FILES - Specifies if the server should keep uploaded files after restarting the container (lowercase true or false)- Default: true
RUN_SETUP - Set to true, to run poetry setup again. Do it only once to download models and set it to false afterwards - Default: false

Cors

CORS_ENABLED - Flag indicating if CORS headers are set or not. If set to True, the CORS headers will be set to allow all origins, methods and headers - Default: false
CORS_ALLOW_CREDENTIALS - Indicate that cookies should be supported for cross-origin requests - Default: false
CORS_ALLOW_ORIGINS - A list of origins that should be permitted to make cross-origin requests - Default: *
CORS_ALLOW_ORIGIN_REGEX - A regex string to match against origins that should be permitted to make cross-origin requests - **Default: **
CORS_ALLOW_METHODS - A list of HTTP methods that should be allowed for cross-origin request - Default: *
CORS_ALLOW_HEADERS - A list of HTTP request headers that should be supported for cross-origin requests - Default: *

Auth

AUTH_ENABLED - Flag indicating if authentication is enabled or not - Default: false
AUTH_USERNAME - username used for authentication - Default: secret
AUTH_SECRET - The secret to be used for authentication. It can be any non-blank string. For HTTP basic authentication, this value should be the whole 'Authorization' header that is expected. - Default: Basic c2VjcmV0OmtleQ==

# python -c 'import base64; print("Basic " + base64.b64encode("secret:key".encode()).decode())'
# 'secret' is the username and 'key' is the password for basic auth by default
# If the auth is enabled, this value must be set in the "Authorization" header of the request.
secret: "Basic c2VjcmV0OmtleQ=="

Data

DATA_LOCAL_DATA_FOLDER - Path to local storage. It will be treated as an absolute path if it starts with / - Default: local_data/private_gpt

UI

UI_ENABLED - Enable or Disable the user interface - Default: true
UI_PATH - Set the path for the user interface - Default: /
UI_DEFAULT_CHAT_SYSTEM_PROMPT - The default system prompt to use for the chat mode - Default: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible and follow ALL given instructions. Do not speculate or make up information. Do not reference any given instructions or context.
UI_DEFAULT_QUERY_SYSTEM_PROMPT - The default system prompt to use for the query mode - Default: You can only answer questions about the provided context. If you know the answer but it is not based in the provided context, don't provide the answer, just state the answer is not in the context provided.
UI_DELETE_FILE_BUTTON_ENABLED - If the button to delete a file is enabled or not. - Default: True
UI_DELETE_ALL_FILES_BUTTON_ENABLED - If the button to delete all files is enabled or not. - Default: True

Logo

LOGO_BG_COLOR - Specifies the logo background color - Default: #C7BAFF
LOGO_HEIGHT - Specifies the logo height - Default: 25%
LOGO_SVG_BASE64 - Specifies the logo file (.svg) in base64 format. Provide your own file (.svg) in base64 format using an image to base64 converter - Default: <privategpt svg logo>

LLM

LLM_MODE - The mode to use for the chat engine. - Default: llamacpp
- llamacpp: provide LLAMACPP_PROMPT_STYLE, LLAMACPP_PGPT_HF_MODEL_FILE and HF_EMBEDDING_HF_MODEL_NAME
- openai: provide OPENAI_API_KEY and OPENAI_MODEL
- openailike: provide OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_MODEL
- azopenai: provide AZOPENAI_API_BASE, AZOPENAI_API_KEY and AZOPENAI_MODEL
- gemini: provide GEMINI_API_KEY, GEMINI_MODEL and GEMINI_EMBEDDING_MODEL
- sagemaker: provide SAGEMAKER_LLM_ENDPOINT_NAME and SAGEMAKER_EMBEDDING_ENDPOINT_NAME
- mock: (not supported by this container)
- ollama: provide OLLAMA_API_BASE and OLLAMA_LLM_MODEL
LLM_MAX_NEW_TOKENS - The maximum number of token that the LLM is authorized to generate in one completion - Default: 265
LLM_CONTEXT_WINDOW - The maximum number of context tokens for the model - Default: 3900
LLM_TOKENIZER - Specifies the model from Huggingface.co which is used as tokenizer - Default: meta-llama/Meta-Llama-3.1-8B-Instruct
LLM_TEMPERATURE - The temperature of the model. Increasing the temperature will make the model answer more creatively. A value of 0.1 would be more factual - Default: 0.1

Rag Settings

RAG_SIMILARITY_TOP_K - This value controls the number of documents returned by the RAG pipeline - Default: 2
RAG_SIMILARITY_VALUE - If set, any documents retrieved from the RAG must meet a certain match score. Acceptable values are between 0 and 1. - Default: 0.45
RAG_RERANK_ENABLED - This value controls whether a reranker should be included in the RAG pipeline. - Default: false
RAG_RERANK_MODEL - Rerank model to use. Limited to SentenceTransformer cross-encoder models. - Default: cross-encoder/ms-marco-MiniLM-L-2-v2
RAG_RERANK_TOP_N - This value controls the number of documents returned by the RAG pipeline. - Default: 1

Summarize Settings

SUMMARIZE_USE_ASYNC - If set to True, the summarization will be done asynchronously. - Default: true

llamacpp

LLAMACPP_PROMPT_STYLE - The prompt style to use for the chat engine. - Default: llama3
- default: use the default prompt style from the llama_index. It should look like role: message
- llama2: use the llama2 prompt style from the llama_index. Based on <s>, [INST] and <<SYS>>
- llama3: use the llama3 prompt style from the llama_index.
- tag: use the tag prompt style. It should look like <|role|>: message
- mistral: use the mistral prompt style. It should look like <s>[INST] {System Prompt} [/INST]</s>[INST] { UserInstructions } [/INST] - chatml
LLAMACPP_LLM_HF_REPO_ID - Name of the HuggingFace model to use for chat - Default: lmstudio-community/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf
LLAMACPP_LLM_HF_MODEL_FILE - Specifies the llm model file. Can be a llm model name from the HuggingFace repo or a local file that you mounted via volume to /home/worker/app/models - Default: Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf
LLAMACPP_TFS_Z - Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. - Default: 1.0
LLAMACPP_TOP_K - Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. - Default: 40
LLAMACPP_TOP_P - Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9) - Default: 0.9
LLAMACPP_REPEAT_PENALTY - Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. - Default: 1.1

Embedding

EMBEDDING_MODE - The mode to use for the embedding engine. (see MODE) - Default: huggingface you can additionally use huggingface
EMBEDDING_INGEST_MODE - The ingest mode to use for the embedding engine. - Default: simple
- simple: ingest files sequentially and one by one. It is the historic behaviour.
- batch: if multiple files, parse all the files in parallel, and send them in batch to the embedding model``.
- parallel: parse the files in parallel using multiple cores, and embedd them in parallel. (fastest mode for local setup) - pipeline: the Embedding engine is kept as busy as possible
EMBEDDING_COUNT_WORKERS - The number of workers to use for file ingestion. Do not go too high with this number, as it might cause memory issues. (especially in parallel mode). Do not set it higher than your number of threads of your CPU. - Default: 2
- for simple mode: this number has no effect in simple mode.
- for batch mode: this is the number of workers used to parse the files.
- for parallel mode: this is the number of workers used to parse the files and embed them. - for pipeline mode: this is the number of workers that can perform embeddings.
EMBEDDING_EMBED_DIM - The dimension of the embeddings stored in the Postgres database. - Default: 384
Specify the model used for embedding with HUGGINGFACE_EMBEDDING_HF_MODEL_NAME

HuggingFace

HUGGINGFACE_EMBEDDING_HF_MODEL_NAME - Name of the HuggingFace model to use for embeddings - Default: BAAI/bge-small-en-v1.5
HUGGINGFACE_TOKEN - Huggingface access token, required to download some models - Default: None
HUGGINGFACE_TRUST_REMOTE_CODE - If set to True, the code from the remote model will be trusted and executed. - Default: true

Vectorstore

VECTORSTORE_DATABASE - Specifies the vectorstore database being used. - select one of: chroma, qdrant, postgres .Default: qdrant

Nodestore

NODESTORE_DATABASE - Specifies the nodestore database being used. - select one of: simple, postgres .Default: simple

qdrant

QDRANT_PATH - Persistence path for QdrantLocal - Default: local_data/private_gpt/qdrant

milvus

MILVUS_URI - The URI of the Milvus instance. For example: 'local_data/private_gpt/milvus/milvus_local.db' for Milvus Lite. - Default: local_data/private_gpt/milvus/milvus_local.db
MILVUS_TOKEN - A valid access token to access the specified Milvus instance. This can be used as a recommended alternative to setting user and password separately. - Default: milvus-1234
MILVUS_COLLECTION_NAME - The name of the collection in Milvus. Default is 'make_this_parameterizable_per_api_call'. - Default: milvus_db
MILVUS_OVERWRITE - Overwrite the previous collection schema if it exists. - Default: false

Clickhouse

CLICKHOUSE_HOST - The server hosting the ClickHouse database - Default: localhost
CLICKHOUSE_PORT - The port on which the ClickHouse database is accessible - Default: 8443
CLICKHOUSE_USERNAME - The username to use to connect to the ClickHouse database - Default: admin
CLICKHOUSE_PASSWORD - The password to use to connect to the ClickHouse database - Default: clickhouse
CLICKHOUSE_DATABASE - The default database to use for connections - Default: embeddings
CLICKHOUSE_SECURE - Use https/TLS for secure connection to the server - Default: False
CLICKHOUSE_INTERFACE - Must be either 'http' or 'https'. Determines the protocol to use for the connection - Default:
CLICKHOUSE_SETTINGS - Specific ClickHouse server settings to be used with the session - Default:
CLICKHOUSE_CONNECT_TIMEOUT - Timeout in seconds for establishing a connection - Default:
CLICKHOUSE_SEND_RECEIVE_TIMEOUT - Read timeout in seconds for http connection - Default:
CLICKHOUSE_VERIFY - Verify the server certificate in secure/https mode - Default:
CLICKHOUSE_CA_CERT - Path to Certificate Authority root certificate (.pem format) - Default:
CLICKHOUSE_CLIENT_CERT - Path to TLS Client certificate (.pem format) - Default:
CLICKHOUSE_CLIENT_CERT_KEY - Path to the private key for the TLS Client certificate - Default:
CLICKHOUSE_HTTP_PROXY - HTTP proxy address - Default:
CLICKHOUSE_HTTPS_PROXY - HTTPS proxy address - Default:
CLICKHOUSE_SERVER_HOST_NAME - Server host name to be checked against the TLS certificate - Default:

Postgres

POSTGRES_HOST - the postgres host address - Default: postgres
POSTGRES_PORT - the postgres port - Default: 5432
POSTGRES_DATABASE - the postgres database name - Default: postgres
POSTGRES_USER - the postgres username - Default: postgres
POSTGRES_PASSWORD - the postgres usernames password - Default: admin
POSTGRES_SCHEMA_NAME - the postgres schema name - Default: private_gpt

Sagemaker

SAGEMAKER_LLM_ENDPOINT_NAME - Default: huggingface-pytorch-tgi-inference-2023-09-25-19-53-32-140
SAGEMAKER_EMBEDDING_ENDPOINT_NAME - Default: huggingface-pytorch-inference-2023-11-03-07-41-36-479

OpenAI

OPENAI_API_BASE - Base URL of OpenAI API. Example: https://api.openai.com/v1 - Default: https://api.openai.com/v1
OPENAI_API_KEY - Your API Key for the OpenAI API. Example: sk-1234 - Default: sk-1234
OPENAI_MODEL - OpenAI Model to use. (see OpenAI Models Overview). Example: gpt-4 - Default: gpt-3.5-turbo
OPENAI_REQUEST_TIMEOUT - Time elapsed until openailike server times out the request. Default is 120s. Format is float. - Default: 120.0
OPENAI_EMBEDDING_API_BASE - Base URL of OpenAI API. Example: https://api.openai.com/v1 - Default: same as OPENAI_API_BASE
OPENAI_EMBEDDING_API_KEY - Your API Key for the OpenAI Embedding API. Example: sk-1234. - Default: same as OPENAI_API_KEY
OPENAI_EMBEDDING_MODEL - OpenAI embedding Model to use. Example: text-embedding-3-large - Default: text-embedding-3-small

Gemini

GEMINI_API_KEY - Your Google API Key for the Gemini API. Example: AI1234 - Default: AI1234
GEMINI_MODEL - Google Model to use. Example: models/gemini-pro - Default: models/gemini-pro
GEMINI_EMBEDDING_MODEL - Google Embedding Model to use. Example: models/embedding-001 - Default: models/embedding-001

Ollama

OLLAMA_API_BASE - Base URL of Ollama API. Example: http://192.168.1.100:11434 - Default: http://localhost:11434
OLLAMA_EMBEDDING_API_BASE - Base URL of Ollama Embedding API. Example: http://192.168.1.100:11434 - Default: same as OLLAMA_API_BASE
OLLAMA_LLM_MODEL - Ollama model to use. (see Ollama Library). Example: 'llama2-uncensored' - Default: llama3.1:latest
OLLAMA_EMBEDDING_MODEL - Model to use. Example: 'nomic-embed-text'. - Default: nomic-embed-text
OLLAMA_KEEP_ALIVE - Time the model will stay loaded in memory after a request. examples: 5m, 5h, '-1' - Default: 5m
OLLAMA_TFS_Z - Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. - Default: 1.0
OLLAMA_NUM_PREDICT - Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context) - Default: 128
OLLAMA_TOP_K - Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. - Default: 40
OLLAMA_TOP_P - Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. - Default: 0.9
OLLAMA_REPEAT_LAST_N - Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) - Default: 64
OLLAMA_REPEAT_PENALTY - Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. - Default: 1.1
OLLAMA_REQUEST_TIMEOUT - Time elapsed until ollama times out the request. Default is 120s. Format is float. - Default: 120.0

Azure OpenAI

AZOPENAI_API_KEY - Your API Key for the OpenAI API. Example: sk-1234 - Default: sk-1234
AZOPENAI_ENDPOINT - Base URL of Azure OpenAI Endpoint. Example: https://api.myazure.com/v1 - Default: https://api.myazure.com/v1
AZOPENAI_API_VERSION - The API version to use for this operation. This follows the YYYY-MM-DD format. - Default: 2023_05_15
AZOPENAI_EMBEDDING_DEPLOYMENT_NAME - embedding deployment name in str format - Default: None
AZOPENAI_EMBEDDING_MODEL - OpenAI Model to use. Example: 'text-embedding-ada-002'. - Default: text-embedding-3-small
AZOPENAI_LLM_DEPLOYMENT_NAME - llm deployment name in str format - Default: None
AZOPENAI_LLM_MODEL - OpenAI Model to use. (see OpenAI Models Overview). Example: gpt-4 - Default: gpt-4

3 Volumes

/home/worker/app/local_data - Directory for uploaded files. contains private data! Will be deleted after every restart if KEEP_FILES=false
/home/worker/app/models - Directory for custom llm models. Mount your own model here and set environment variable LLAMACPP_LLM_HF_MODEL_FILE

4 Ports

8080/tcp - HTTP Port

5 Find Me

6 License

- This project is licensed under the GNU General Public License - see the gpl-3.0 for details. ``

Files

privategpt-cpu

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