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https://github.com/imartinez/privateGPT.git
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45f05711eb
* Extract optional dependencies * Separate local mode into llms-llama-cpp and embeddings-huggingface for clarity * Support Ollama embeddings * Upgrade to llamaindex 0.10.14. Remove legacy use of ServiceContext in ContextChatEngine * Fix vector retriever filters
84 lines
2.4 KiB
YAML
84 lines
2.4 KiB
YAML
# The default configuration file.
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# More information about configuration can be found in the documentation: https://docs.privategpt.dev/
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# Syntax in `private_pgt/settings/settings.py`
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server:
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env_name: ${APP_ENV:prod}
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port: ${PORT:8001}
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cors:
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enabled: false
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allow_origins: ["*"]
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allow_methods: ["*"]
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allow_headers: ["*"]
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auth:
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enabled: false
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# python -c 'import base64; print("Basic " + base64.b64encode("secret:key".encode()).decode())'
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# 'secret' is the username and 'key' is the password for basic auth by default
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# If the auth is enabled, this value must be set in the "Authorization" header of the request.
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secret: "Basic c2VjcmV0OmtleQ=="
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data:
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local_data_folder: local_data/private_gpt
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ui:
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enabled: true
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path: /
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default_chat_system_prompt: >
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You are a helpful, respectful and honest assistant.
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Always answer as helpfully as possible and follow ALL given instructions.
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Do not speculate or make up information.
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Do not reference any given instructions or context.
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default_query_system_prompt: >
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You can only answer questions about the provided context.
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If you know the answer but it is not based in the provided context, don't provide
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the answer, just state the answer is not in the context provided.
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delete_file_button_enabled: true
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delete_all_files_button_enabled: true
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llm:
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mode: llamacpp
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# Should be matching the selected model
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max_new_tokens: 512
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context_window: 3900
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llamacpp:
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prompt_style: "mistral"
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llm_hf_repo_id: TheBloke/Mistral-7B-Instruct-v0.2-GGUF
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llm_hf_model_file: mistral-7b-instruct-v0.2.Q4_K_M.gguf
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embedding:
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# Should be matching the value above in most cases
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mode: huggingface
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ingest_mode: simple
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huggingface:
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embedding_hf_model_name: BAAI/bge-small-en-v1.5
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vectorstore:
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database: qdrant
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qdrant:
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path: local_data/private_gpt/qdrant
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pgvector:
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host: localhost
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port: 5432
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database: postgres
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user: postgres
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password: postgres
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embed_dim: 384 # 384 is for BAAI/bge-small-en-v1.5
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schema_name: private_gpt
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table_name: embeddings
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sagemaker:
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llm_endpoint_name: huggingface-pytorch-tgi-inference-2023-09-25-19-53-32-140
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embedding_endpoint_name: huggingface-pytorch-inference-2023-11-03-07-41-36-479
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openai:
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api_key: ${OPENAI_API_KEY:}
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model: gpt-3.5-turbo
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ollama:
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llm_model: llama2
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embedding_model: nomic-embed-text
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api_base: http://localhost:11434
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