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Merge branch 'main' into docs-windows-and-tui-restart

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Mendon Kissling 2025-10-31 09:22:55 -04:00 • committed by GitHub
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README.md
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@ -13,7 +13,7 @@
OpenRAG is a comprehensive Retrieval-Augmented Generation platform that enables intelligent document search and AI-powered conversations. Users can upload, process, and query documents through a chat interface backed by large language models and semantic search capabilities. The system utilizes Langflow for document ingestion, retrieval workflows, and intelligent nudges, providing a seamless RAG experience. Built with [Starlette](https://github.com/Kludex/starlette) and [Next.js](https://github.com/vercel/next.js). Powered by [OpenSearch](https://github.com/opensearch-project/OpenSearch), [Langflow](https://github.com/langflow-ai/langflow), and [Docling](https://github.com/docling-project/docling). OpenRAG is a comprehensive Retrieval-Augmented Generation platform that enables intelligent document search and AI-powered conversations. Users can upload, process, and query documents through a chat interface backed by large language models and semantic search capabilities. The system utilizes Langflow for document ingestion, retrieval workflows, and intelligent nudges, providing a seamless RAG experience. Built with [Starlette](https://github.com/Kludex/starlette) and [Next.js](https://github.com/vercel/next.js). Powered by [OpenSearch](https://github.com/opensearch-project/OpenSearch), [Langflow](https://github.com/langflow-ai/langflow), and [Docling](https://github.com/docling-project/docling).
<a href="https://deepwiki.com/phact/openrag"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a> <a href="https://deepwiki.com/langflow-ai/openrag"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>
</div> </div>
<div align="center"> <div align="center">

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src/services/models_service.py
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@ -1,5 +1,6 @@
import httpx import httpx
from typing import Dict, List from typing import Dict, List
from api.provider_validation import test_embedding
from utils.container_utils import transform_localhost_url from utils.container_utils import transform_localhost_url
from utils.logging_config import get_logger from utils.logging_config import get_logger
@ -9,18 +10,6 @@ logger = get_logger(__name__)
class ModelsService: class ModelsService:
"""Service for fetching available models from different AI providers""" """Service for fetching available models from different AI providers"""
OLLAMA_EMBEDDING_MODELS = [
"nomic-embed-text",
"mxbai-embed-large",
"snowflake-arctic-embed",
"all-minilm",
"bge-m3",
"bge-large",
"paraphrase-multilingual",
"granite-embedding",
"jina-embeddings-v2-base-en",
]
OPENAI_TOOL_CALLING_MODELS = [ OPENAI_TOOL_CALLING_MODELS = [
"gpt-5", "gpt-5",
"gpt-5-mini", "gpt-5-mini",
@ -171,10 +160,12 @@ class ModelsService:
has_tools = TOOL_CALLING_CAPABILITY in capabilities has_tools = TOOL_CALLING_CAPABILITY in capabilities
# Check if it's an embedding model # Check if it's an embedding model
is_embedding = any( try:
embed_model in model_name.lower() await test_embedding("ollama", endpoint=endpoint, embedding_model=model_name)
for embed_model in self.OLLAMA_EMBEDDING_MODELS is_embedding = True
) except Exception as e:
logger.warning(f"Failed to test embedding for model {model_name}: {str(e)}")
is_embedding = False
if is_embedding: if is_embedding:
# Embedding models only need completion capability # Embedding models only need completion capability