177 lines
4.2 KiB
Markdown
177 lines
4.2 KiB
Markdown
# Configuration
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Configuration is done through the use of environment variables.
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!!! note
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If you create a db with certain settings and later change them, `haiku.rag` will detect incompatibilities (for example, if you change embedding provider) and will exit. You can **rebuild** the database to apply the new settings, see [Rebuild Database](./cli.md#rebuild-database).
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## File Monitoring
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Set directories to monitor for automatic indexing:
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```bash
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# Monitor single directory
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MONITOR_DIRECTORIES="/path/to/documents"
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# Monitor multiple directories
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MONITOR_DIRECTORIES="/path/to/documents,/another_path/to/documents"
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```
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## Embedding Providers
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If you use Ollama, you can use any pulled model that supports embeddings.
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### Ollama (Default)
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```bash
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EMBEDDINGS_PROVIDER="ollama"
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EMBEDDINGS_MODEL="mxbai-embed-large"
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EMBEDDINGS_VECTOR_DIM=1024
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```
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### VoyageAI
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If you want to use VoyageAI embeddings you will need to install `haiku.rag` with the VoyageAI extras,
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```bash
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uv pip install haiku.rag[voyageai]
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```
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```bash
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EMBEDDINGS_PROVIDER="voyageai"
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EMBEDDINGS_MODEL="voyage-3.5"
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EMBEDDINGS_VECTOR_DIM=1024
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VOYAGE_API_KEY="your-api-key"
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```
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### OpenAI
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OpenAI embeddings are included in the default installation. Simply set environment variables:
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```bash
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EMBEDDINGS_PROVIDER="openai"
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EMBEDDINGS_MODEL="text-embedding-3-small" # or text-embedding-3-large
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EMBEDDINGS_VECTOR_DIM=1536
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OPENAI_API_KEY="your-api-key"
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```
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## Question Answering Providers
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Configure which LLM provider to use for question answering. Any provider and model supported by [Pydantic AI](https://ai.pydantic.dev/models/) can be used.
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### Ollama (Default)
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```bash
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QA_PROVIDER="ollama"
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QA_MODEL="qwen3"
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OLLAMA_BASE_URL="http://localhost:11434"
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```
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### OpenAI
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OpenAI QA is included in the default installation. Simply configure:
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```bash
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QA_PROVIDER="openai"
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QA_MODEL="gpt-4o-mini" # or gpt-4, gpt-3.5-turbo, etc.
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OPENAI_API_KEY="your-api-key"
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```
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### Anthropic
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Anthropic QA is included in the default installation. Simply configure:
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```bash
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QA_PROVIDER="anthropic"
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QA_MODEL="claude-3-5-haiku-20241022" # or claude-3-5-sonnet-20241022, etc.
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ANTHROPIC_API_KEY="your-api-key"
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```
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### Other Providers
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Any provider supported by Pydantic AI can be used. Examples include:
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```bash
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# Google Gemini
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QA_PROVIDER="gemini"
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QA_MODEL="gemini-1.5-flash"
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# Groq
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QA_PROVIDER="groq"
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QA_MODEL="llama-3.3-70b-versatile"
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# Mistral
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QA_PROVIDER="mistral"
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QA_MODEL="mistral-small-latest"
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```
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See the [Pydantic AI documentation](https://ai.pydantic.dev/models/) for the complete list of supported providers and models.
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## Reranking
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Reranking improves search quality by re-ordering the initial search results using specialized models. When enabled, the system retrieves more candidates (3x the requested limit) and then reranks them to return the most relevant results.
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Reranking is **automatically enabled** by default using Ollama, or if you install the appropriate reranking provider package.
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### Disabling Reranking
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To disable reranking completely for faster searches:
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```bash
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RERANK_PROVIDER=""
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```
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### Ollama (Default)
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Ollama reranking uses LLMs with structured output to rank documents by relevance:
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```bash
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RERANK_PROVIDER="ollama"
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RERANK_MODEL="qwen3:1.7b" # or any model that supports structured output
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OLLAMA_BASE_URL="http://localhost:11434"
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```
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### MixedBread AI
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For MxBAI reranking, install with mxbai extras:
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```bash
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uv pip install haiku.rag[mxbai]
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```
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Then configure:
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```bash
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RERANK_PROVIDER="mxbai"
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RERANK_MODEL="mixedbread-ai/mxbai-rerank-base-v2"
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```
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### Cohere
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Cohere reranking is included in the default installation. Simply configure:
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```bash
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RERANK_PROVIDER="cohere"
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RERANK_MODEL="rerank-v3.5"
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COHERE_API_KEY="your-api-key"
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```
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## Other Settings
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### Database and Storage
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```bash
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# Default data directory (where SQLite database is stored)
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DEFAULT_DATA_DIR="/path/to/data"
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```
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### Document Processing
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```bash
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# Chunk size for document processing
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CHUNK_SIZE=256
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# Number of adjacent chunks to include before/after retrieved chunks for context
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# 0 = no expansion (default), 1 = include 1 chunk before and after, etc.
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# When expanded chunks overlap or are adjacent, they are automatically merged
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# into single chunks with continuous content to eliminate duplication
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CONTEXT_CHUNK_RADIUS=0
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```
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