Remove vllm and lmstudio custom configs, they can now use the openai base

CHANGELOG.md

@@ -21,6 +21,7 @@
 ### Removed
 
 - Deleted obsolete embedder implementations: `ollama.py`, `openai.py`, `vllm.py`, `lm_studio.py`, `base.py`
+- Removed `VLLMConfig` and `LMStudioConfig` from configuration (use `base_url` in model config instead)
 
 ## [0.22.0] - 2025-12-19
 

docs/configuration/providers.md

@@ -378,10 +378,7 @@ reranking:
   model:
     provider: vllm
     name: mixedbread-ai/mxbai-rerank-base-v2
-
+    base_url: http://localhost:8001
-providers:
-  vllm:
-    rerank_base_url: http://localhost:8001
 ```
 
-**Note:** vLLM reranking uses the `/rerank` API endpoint. You need to run a vLLM server separately with a reranking model loaded. Consult the specific model's documentation for proper vLLM serving configuration.
+**Note:** vLLM reranking uses the `/v1/rerank` API endpoint. You need to run a vLLM server separately with a reranking model loaded.

haiku_rag_slim/haiku/rag/config/__init__.py

@@ -12,7 +12,6 @@ from haiku.rag.config.models import (
     EmbeddingModelConfig,
     EmbeddingsConfig,
     LanceDBConfig,
-    LMStudioConfig,
     ModelConfig,
     MonitorConfig,
     OllamaConfig,
@@ -22,7 +21,6 @@ from haiku.rag.config.models import (
     RerankingConfig,
     ResearchConfig,
     StorageConfig,
-    VLLMConfig,
 )
 
 __all__ = [
@@ -33,7 +31,6 @@ __all__ = [
     "EmbeddingModelConfig",
     "EmbeddingsConfig",
     "LanceDBConfig",
-    "LMStudioConfig",
     "ModelConfig",
     "MonitorConfig",
     "OllamaConfig",
@@ -43,7 +40,6 @@ __all__ = [
     "RerankingConfig",
     "ResearchConfig",
     "StorageConfig",
-    "VLLMConfig",
     "find_config_file",
     "generate_default_config",
     "get_config",

haiku_rag_slim/haiku/rag/config/models.py

@@ -142,27 +142,14 @@ class OllamaConfig(BaseModel):
     )
 
 
-class VLLMConfig(BaseModel):
-    embeddings_base_url: str = ""
-    rerank_base_url: str = ""
-    qa_base_url: str = ""
-    research_base_url: str = ""
-
-
 class DoclingServeConfig(BaseModel):
     base_url: str = "http://localhost:5001"
     api_key: str = ""
     timeout: int = 300
 
 
-class LMStudioConfig(BaseModel):
-    base_url: str = "http://localhost:1234"
-
-
 class ProvidersConfig(BaseModel):
     ollama: OllamaConfig = Field(default_factory=OllamaConfig)
-    vllm: VLLMConfig = Field(default_factory=VLLMConfig)
-    lm_studio: LMStudioConfig = Field(default_factory=LMStudioConfig)
     docling_serve: DoclingServeConfig = Field(default_factory=DoclingServeConfig)
 
 

haiku_rag_slim/haiku/rag/reranking/__init__.py

@@ -45,7 +45,10 @@ def get_reranker(config: AppConfig = Config) -> RerankerBase | None:
         try:
             from haiku.rag.reranking.vllm import VLLMReranker
 
-            reranker = VLLMReranker(config.reranking.model.name)
+            base_url = config.reranking.model.base_url
+            if not base_url:
+                raise ValueError("vLLM reranker requires base_url in reranking.model")
+            reranker = VLLMReranker(config.reranking.model.name, base_url)
         except ImportError:
             reranker = None
 

haiku_rag_slim/haiku/rag/reranking/vllm.py

@@ -1,14 +1,13 @@
 import httpx
 
-from haiku.rag.config import Config
 from haiku.rag.reranking.base import RerankerBase
 from haiku.rag.store.models.chunk import Chunk
 
 
 class VLLMReranker(RerankerBase):  # pragma: no cover
-    def __init__(self, model: str):
+    def __init__(self, model: str, base_url: str):
         self._model = model
-        self._base_url = Config.providers.vllm.rerank_base_url
+        self._base_url = base_url
 
     async def rerank(
         self, query: str, chunks: list[Chunk], top_n: int = 10

tests/test_qa.py

@@ -5,13 +5,11 @@ from datasets import Dataset
 from evaluations.evaluators import LLMJudge
 
 from haiku.rag.client import HaikuRAG
-from haiku.rag.config import Config
 from haiku.rag.config.models import ModelConfig
 from haiku.rag.qa.agent import QuestionAnswerAgent
 
 OPENAI_AVAILABLE = bool(os.getenv("OPENAI_API_KEY"))
 ANTHROPIC_AVAILABLE = bool(os.getenv("ANTHROPIC_API_KEY"))
-VLLM_QA_AVAILABLE = bool(Config.providers.vllm.qa_base_url)
 
 
 @pytest.mark.asyncio
@@ -87,26 +85,3 @@ async def test_qa_anthropic(qa_corpus: Dataset, temp_db_path):
     assert is_equivalent, (
         f"Generated answer not equivalent to expected answer.\nQuestion: {question}\nGenerated: {answer}\nExpected: {expected_answer}"
     )
-
-
-@pytest.mark.asyncio
-@pytest.mark.skipif(not VLLM_QA_AVAILABLE, reason="vLLM QA server not configured")
-async def test_qa_vllm(qa_corpus: Dataset, temp_db_path):
-    """Test vLLM QA with LLM judge."""
-    client = HaikuRAG(temp_db_path, create=True)
-    qa = QuestionAnswerAgent(client, ModelConfig(provider="vllm", name="Qwen/Qwen3-4B"))
-    llm_judge = LLMJudge()
-
-    doc = qa_corpus[1]
-    await client.create_document(
-        content=doc["document_extracted"], uri=doc["document_id"]
-    )
-
-    question = doc["question"]
-    expected_answer = doc["answer"]
-    answer, _ = await qa.answer(question)
-    is_equivalent = await llm_judge.judge_answers(question, answer, expected_answer)
-
-    assert is_equivalent, (
-        f"Generated answer not equivalent to expected answer.\nQuestion: {question}\nGenerated: {answer}\nExpected: {expected_answer}"
-    )

tests/test_reranker.py

@@ -2,13 +2,12 @@ import os
 
 import pytest
 
-from haiku.rag.config import Config
 from haiku.rag.reranking.base import RerankerBase
 from haiku.rag.reranking.vllm import VLLMReranker
 from haiku.rag.store.models.chunk import Chunk
 
 COHERE_AVAILABLE = bool(os.getenv("CO_API_KEY"))
-VLLM_RERANK_AVAILABLE = bool(Config.providers.vllm.rerank_base_url)
+VLLM_RERANK_BASE_URL = os.getenv("VLLM_RERANK_BASE_URL", "")
 ZEROENTROPY_AVAILABLE = bool(os.getenv("ZEROENTROPY_API_KEY"))
 
 chunks = [
@@ -41,6 +40,7 @@ async def test_reranker_base():
 @pytest.mark.asyncio
 async def test_mxbai_reranker():
     try:
+        from haiku.rag.config import Config
         from haiku.rag.config.models import ModelConfig
         from haiku.rag.reranking.mxbai import MxBAIReranker
 
@@ -80,11 +80,13 @@ async def test_cohere_reranker():
 
 @pytest.mark.asyncio
 @pytest.mark.skipif(
-    not VLLM_RERANK_AVAILABLE, reason="vLLM rerank server not configured"
+    not VLLM_RERANK_BASE_URL, reason="vLLM rerank server not configured"
 )
 async def test_vllm_reranker():
     try:
-        reranker = VLLMReranker("mixedbread-ai/mxbai-rerank-base-v2")
+        reranker = VLLMReranker(
+            "mixedbread-ai/mxbai-rerank-base-v2", VLLM_RERANK_BASE_URL
+        )
 
         reranked = await reranker.rerank(
             "Who wrote 'To Kill a Mockingbird'?", chunks, top_n=2