forked from AI_team/Philosophy-RAG-demo
63 lines
1.9 KiB
YAML
63 lines
1.9 KiB
YAML
# Define your application settings here.
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chat_backend: local # Select the primary chat backend (azure, openai, google_vertex, aws, local)
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emb_backend: local # Select the primary embedding backend (azure, openai, google_vertex, aws, local, huggingface)
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use_conditional_graph: false # Use a conditional RAG model with historical chat context, or a non-conditional model without access to the current conversation
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use_reranker: false # Use a LLM to rerank the retrieved context documents
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# --- Provider Specific Settings ---
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azure:
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llm_endpoint: "https://example.openai.azure.com"
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llm_deployment_name: "gpt-4o-mini"
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llm_api_version: "2025-01-01-preview"
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emb_endpoint: "https://example.openai.azure.com"
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emb_deployment_name: "text-embedding-3-large"
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emb_api_version: "2023-05-15"
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openai:
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chat_model: "gpt-4o-mini"
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emb_model: "text-embedding-3-large"
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google_vertex:
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project_id: "your_gcp_project_id"
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location: "europe-west4"
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chat_model: "gemini-pro"
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emb_model: "textembedding-gecko@001"
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aws:
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chat_model: "amazon.titan-llm-v1"
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emb_model: "amazon.titan-embed-text-v1"
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region: "us-east-1"
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local: # Settings for local models (e.g., Ollama)
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chat_model: "llama3.1:8b"
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emb_model: "llama3.1:8b"
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huggingface:
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chat_model: "meta-llama/Llama-2-7b-chat-hf"
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emb_model: "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
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# --- Data Processing Settings ---
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pdf:
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# List of paths to PDF files or folders containing PDFs.
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# Pydantic converts these strings to pathlib.Path objects.
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data:
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- "C:/path/folder"
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unstructured: false # Use the unstructured PDF loader?
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chunk_size: 1000
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chunk_overlap: 200
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add_start_index: false
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web:
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# List of URLs to scrape for data.
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data:
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- "https://www.example.nl/subdomain"
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chunk_size: 200
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chroma_db:
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location: "/app/data/vector_database" # Override default DB path (default: '.chroma_db')
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reset: False # Reset the database on startup? (default: false)
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