feat(providers): add Mistral embedding provider, route registry through dynaconf
Adds a hosted Mistral embedding option (mistral-embed, 1024-dim) alongside the existing Bedrock / OpenAI / Ollama / Simple providers. Implementation mirrors OpenAIProvider: lazy dimension detection with a known-models lookup, chunked batch requests, defensive index sort, and a 429-aware retry decorator. In the same change, ProviderRegistry switches from os.getenv to the dynaconf-backed Settings dataclass so all five providers share a single configuration path. config.py gains the previously-uncovered Bedrock keys, the new Mistral keys, the missing OPENAI_GENERATION_MODEL / OLLAMA_GENERATION_MODEL, and SIMPLE_EMBEDDING_DIMENSION. Auto-detection priority: Bedrock → OpenAI → Mistral → Ollama → Simple. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Chris Coutinho
co-authored by
Claude Opus 4.7
Claude Opus 4.7
parent
61cadf7935
commit
3268a13d11
@@ -1,10 +1,11 @@
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"""Provider registry and factory for auto-detection and instantiation."""
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import logging
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import os
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from ..config import get_settings
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from .base import Provider
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from .bedrock import BedrockProvider
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from .mistral import MistralProvider
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from .ollama import OllamaProvider
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from .openai import OpenAIProvider
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from .simple import SimpleProvider
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@@ -16,117 +17,112 @@ class ProviderRegistry:
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"""
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Registry for provider auto-detection and instantiation.
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Checks environment variables in priority order and creates appropriate provider:
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1. Bedrock (AWS_REGION + BEDROCK_*_MODEL)
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2. OpenAI (OPENAI_API_KEY)
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3. Ollama (OLLAMA_BASE_URL)
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4. Simple (fallback for testing/development)
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Reads configuration via dynaconf-backed Settings (see ``config.py``).
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Checks provider settings in priority order and creates the appropriate
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provider:
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1. Bedrock (``AWS_REGION`` or ``BEDROCK_*_MODEL``)
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2. OpenAI (``OPENAI_API_KEY``)
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3. Mistral (``MISTRAL_API_KEY``)
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4. Ollama (``OLLAMA_BASE_URL``)
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5. Simple (fallback for testing/development)
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"""
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@staticmethod
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def create_provider() -> Provider:
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"""
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Auto-detect and create provider based on environment variables.
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Auto-detect and create provider based on configured settings.
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Settings are sourced via :func:`nextcloud_mcp_server.config.get_settings`,
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which reads from settings files and environment variables (env vars
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always win, see ADR-024/025).
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Priority order:
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1. Bedrock - if AWS_REGION or BEDROCK_EMBEDDING_MODEL is set
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2. OpenAI - if OPENAI_API_KEY is set
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3. Ollama - if OLLAMA_BASE_URL is set
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4. Simple - fallback for testing/development
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1. Bedrock - if ``aws_region`` or ``bedrock_embedding_model`` is set
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2. OpenAI - if ``openai_api_key`` is set
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3. Mistral - if ``mistral_api_key`` is set
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4. Ollama - if ``ollama_base_url`` is set
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5. Simple - fallback for testing/development
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Returns:
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Provider instance
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Environment Variables:
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Bedrock:
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- AWS_REGION: AWS region (e.g., "us-east-1")
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- AWS_ACCESS_KEY_ID: AWS access key (optional, uses credential chain)
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- AWS_SECRET_ACCESS_KEY: AWS secret key (optional)
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- BEDROCK_EMBEDDING_MODEL: Model ID for embeddings (e.g., "amazon.titan-embed-text-v2:0")
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- BEDROCK_GENERATION_MODEL: Model ID for text generation (e.g., "anthropic.claude-3-sonnet-20240229-v1:0")
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OpenAI:
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- OPENAI_API_KEY: OpenAI API key (or GITHUB_TOKEN for GitHub Models)
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- OPENAI_BASE_URL: Base URL override (e.g., "https://models.github.ai/inference")
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- OPENAI_EMBEDDING_MODEL: Model for embeddings (default: "text-embedding-3-small")
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- OPENAI_GENERATION_MODEL: Model for text generation (e.g., "gpt-4o-mini")
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Ollama:
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- OLLAMA_BASE_URL: Ollama API base URL (e.g., "http://localhost:11434")
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- OLLAMA_EMBEDDING_MODEL: Model for embeddings (default: "nomic-embed-text")
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- OLLAMA_GENERATION_MODEL: Model for text generation (e.g., "llama3.2:1b")
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- OLLAMA_VERIFY_SSL: Verify SSL certificates (default: "true")
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Simple (no configuration needed, fallback):
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- SIMPLE_EMBEDDING_DIMENSION: Embedding dimension (default: 384)
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"""
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# 1. Check for Bedrock
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aws_region = os.getenv("AWS_REGION")
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bedrock_embedding_model = os.getenv("BEDROCK_EMBEDDING_MODEL")
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bedrock_generation_model = os.getenv("BEDROCK_GENERATION_MODEL")
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settings = get_settings()
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if aws_region or bedrock_embedding_model or bedrock_generation_model:
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# 1. Bedrock
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if (
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settings.aws_region
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or settings.bedrock_embedding_model
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or settings.bedrock_generation_model
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):
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logger.info(
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f"Using Bedrock provider: region={aws_region}, "
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f"embedding_model={bedrock_embedding_model}, "
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f"generation_model={bedrock_generation_model}"
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"Using Bedrock provider: region=%s, embedding_model=%s, "
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"generation_model=%s",
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settings.aws_region,
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settings.bedrock_embedding_model,
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settings.bedrock_generation_model,
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)
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return BedrockProvider(
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region_name=aws_region,
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embedding_model=bedrock_embedding_model,
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generation_model=bedrock_generation_model,
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aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
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aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
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region_name=settings.aws_region,
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embedding_model=settings.bedrock_embedding_model,
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generation_model=settings.bedrock_generation_model,
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aws_access_key_id=settings.aws_access_key_id,
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aws_secret_access_key=settings.aws_secret_access_key,
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)
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# 2. Check for OpenAI
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openai_api_key = os.getenv("OPENAI_API_KEY")
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if openai_api_key:
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base_url = os.getenv("OPENAI_BASE_URL")
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embedding_model = os.getenv(
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"OPENAI_EMBEDDING_MODEL", "text-embedding-3-small"
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)
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generation_model = os.getenv("OPENAI_GENERATION_MODEL")
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# 2. OpenAI
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if settings.openai_api_key:
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logger.info(
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f"Using OpenAI provider: base_url={base_url or 'default'}, "
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f"embedding_model={embedding_model}, "
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f"generation_model={generation_model}"
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"Using OpenAI provider: base_url=%s, embedding_model=%s, "
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"generation_model=%s",
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settings.openai_base_url or "default",
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settings.openai_embedding_model,
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settings.openai_generation_model,
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)
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return OpenAIProvider(
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api_key=openai_api_key,
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base_url=base_url,
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embedding_model=embedding_model,
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generation_model=generation_model,
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api_key=settings.openai_api_key,
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base_url=settings.openai_base_url,
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embedding_model=settings.openai_embedding_model,
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generation_model=settings.openai_generation_model,
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)
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# 3. Check for Ollama (local LLM)
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ollama_url = os.getenv("OLLAMA_BASE_URL")
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if ollama_url:
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embedding_model = os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text")
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generation_model = os.getenv("OLLAMA_GENERATION_MODEL")
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verify_ssl = os.getenv("OLLAMA_VERIFY_SSL", "true").lower() == "true"
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# 3. Mistral
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if settings.mistral_api_key:
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logger.info(
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f"Using Ollama provider: {ollama_url}, "
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f"embedding_model={embedding_model}, "
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f"generation_model={generation_model}"
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"Using Mistral provider: base_url=%s, embedding_model=%s",
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settings.mistral_base_url or "default",
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settings.mistral_embedding_model,
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)
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return MistralProvider(
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api_key=settings.mistral_api_key,
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base_url=settings.mistral_base_url,
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embedding_model=settings.mistral_embedding_model,
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)
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# 4. Ollama
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if settings.ollama_base_url:
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logger.info(
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"Using Ollama provider: %s, embedding_model=%s, generation_model=%s",
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settings.ollama_base_url,
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settings.ollama_embedding_model,
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settings.ollama_generation_model,
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)
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return OllamaProvider(
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base_url=ollama_url,
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embedding_model=embedding_model,
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generation_model=generation_model,
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verify_ssl=verify_ssl,
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base_url=settings.ollama_base_url,
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embedding_model=settings.ollama_embedding_model,
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generation_model=settings.ollama_generation_model,
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verify_ssl=settings.ollama_verify_ssl,
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)
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# 4. Fallback to Simple provider for development/testing
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dimension = int(os.getenv("SIMPLE_EMBEDDING_DIMENSION", "384"))
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# 5. Simple (fallback)
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logger.warning(
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"No provider configured (AWS_REGION, OPENAI_API_KEY, OLLAMA_BASE_URL not set). "
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"No provider configured (AWS_REGION, OPENAI_API_KEY, "
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"MISTRAL_API_KEY, OLLAMA_BASE_URL not set). "
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"Using SimpleProvider for testing/development. "
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"For production, configure Bedrock, OpenAI, or Ollama."
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"For production, configure Bedrock, OpenAI, Mistral, or Ollama."
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)
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return SimpleProvider(dimension=dimension)
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return SimpleProvider(dimension=settings.simple_embedding_dimension)
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# Singleton instance
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