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Rename model to name under model

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Yiorgis Gozadinos 2025-11-25 12:28:43 +02:00
parent 41ea675964
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17 changed files with 84 additions and 91 deletions
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12
docs/config-index.md
View file

@ -37,13 +37,13 @@ environment: production
embeddings:
model:
provider: ollama
model: qwen3-embedding:4b
name: qwen3-embedding:4b
vector_dim: 2560
qa:
model:
provider: ollama
model: gpt-oss
name: gpt-oss
enable_thinking: false
```
@ -72,18 +72,18 @@ lancedb:
embeddings:
model:
provider: ollama
model: qwen3-embedding:4b
name: qwen3-embedding:4b
vector_dim: 2560
reranking:
model:
provider: "" # Empty to disable, or mxbai, cohere, zeroentropy, vllm
model: ""
name: ""
qa:
model:
provider: ollama
model: gpt-oss
name: gpt-oss
enable_thinking: false
max_sub_questions: 3
max_iterations: 2
@ -92,7 +92,7 @@ qa:
research:
model:
provider: "" # Empty to use qa settings
model: ""
name: ""
enable_thinking: true
max_iterations: 3
confidence_threshold: 0.8

32
docs/providers.md
View file

@ -15,7 +15,7 @@ Configure model behavior for `qa` and `research` workflows. These settings apply
qa:
model:
provider: ollama
model: gpt-oss
name: gpt-oss
temperature: 0.7
max_tokens: 500
```
@ -74,7 +74,7 @@ If you use Ollama, you can use any pulled model that supports embeddings.
embeddings:
model:
provider: ollama
model: mxbai-embed-large
name: mxbai-embed-large
vector_dim: 1024
```
@ -106,7 +106,7 @@ uv pip install haiku.rag-slim[voyageai]
embeddings:
model:
provider: voyageai
model: voyage-3.5
name: voyage-3.5
vector_dim: 1024
```
@ -124,7 +124,7 @@ OpenAI embeddings are included in the default installation:
embeddings:
model:
provider: openai
model: text-embedding-3-small # or text-embedding-3-large
name: text-embedding-3-small # or text-embedding-3-large
vector_dim: 1536
```
@ -142,7 +142,7 @@ For high-performance local inference, you can use vLLM to serve embedding models
embeddings:
model:
provider: vllm
model: mixedbread-ai/mxbai-embed-large-v1
name: mixedbread-ai/mxbai-embed-large-v1
vector_dim: 512
providers:
@ -162,7 +162,7 @@ Configure which LLM provider to use for question answering. Any provider and mod
qa:
model:
provider: ollama
model: gpt-oss
name: gpt-oss
```
The Ollama base URL can be configured via the `OLLAMA_BASE_URL` environment variable, config file, or defaults to `http://localhost:11434`:
@ -187,7 +187,7 @@ OpenAI QA is included in the default installation:
qa:
model:
provider: openai
model: gpt-4o-mini # or gpt-4, gpt-3.5-turbo, etc.
name: gpt-4o-mini # or gpt-4, gpt-3.5-turbo, etc.
```
Set your API key via environment variable:
@ -204,7 +204,7 @@ Anthropic QA is included in the default installation:
qa:
model:
provider: anthropic
model: claude-3-5-haiku-20241022 # or claude-3-5-sonnet-20241022, etc.
name: claude-3-5-haiku-20241022 # or claude-3-5-sonnet-20241022, etc.
```
Set your API key via environment variable:
@ -221,7 +221,7 @@ For high-performance local inference:
qa:
model:
provider: vllm
model: Qwen/Qwen3-4B # Any model with tool support in vLLM
name: Qwen/Qwen3-4B # Any model with tool support in vLLM
providers:
vllm:
@ -239,19 +239,19 @@ Any provider supported by Pydantic AI can be used. Examples:
qa:
model:
provider: gemini
model: gemini-1.5-flash
name: gemini-1.5-flash
# Groq
qa:
model:
provider: groq
model: llama-3.3-70b-versatile
name: llama-3.3-70b-versatile
# Mistral
qa:
model:
provider: mistral
model: mistral-small-latest
name: mistral-small-latest
```
See the [Pydantic AI documentation](https://ai.pydantic.dev/models/) for the complete list of supported providers and models.
@ -276,7 +276,7 @@ Then configure:
reranking:
model:
provider: mxbai
model: mixedbread-ai/mxbai-rerank-base-v2
name: mixedbread-ai/mxbai-rerank-base-v2
```
### Cohere
@ -293,7 +293,7 @@ Then configure:
reranking:
model:
provider: cohere
model: rerank-v3.5
name: rerank-v3.5
```
Set your API key via environment variable:
@ -316,7 +316,7 @@ Then configure:
reranking:
model:
provider: zeroentropy
model: zerank-1 # Currently the only available model
name: zerank-1 # Currently the only available model
```
Set your API key via environment variable:
@ -333,7 +333,7 @@ For high-performance local reranking using dedicated reranking models:
reranking:
model:
provider: vllm
model: mixedbread-ai/mxbai-rerank-base-v2
name: mixedbread-ai/mxbai-rerank-base-v2
providers:
vllm:

4
docs/qa-research.md
View file

@ -8,7 +8,7 @@ Configure the QA workflow:
qa:
model:
provider: ollama
model: gpt-oss
name: gpt-oss
enable_thinking: false
max_sub_questions: 3 # Maximum sub-questions for deep QA
max_iterations: 2 # Maximum search iterations per sub-question
@ -30,7 +30,7 @@ Configure the multi-agent research workflow:
research:
model:
provider: "" # Empty to use qa settings
model: "" # Empty to use qa model
name: "" # Empty to use qa model
enable_thinking: true
max_iterations: 3
confidence_threshold: 0.8

8
evaluations/evaluations/benchmark.py
View file

@ -44,18 +44,16 @@ def build_experiment_metadata(
"dataset": dataset_key,
"test_cases": test_cases,
"embedder_provider": config.embeddings.model.provider,
"embedder_model": config.embeddings.model.model,
"embedder_model": config.embeddings.model.name,
"embedder_dim": config.embeddings.vector_dim,
"chunk_size": config.processing.chunk_size,
"context_chunk_radius": config.processing.context_chunk_radius,
"rerank_provider": config.reranking.model.provider
if config.reranking.model
else None,
"rerank_model": config.reranking.model.model
if config.reranking.model
else None,
"rerank_model": config.reranking.model.name if config.reranking.model else None,
"qa_provider": config.qa.model.provider,
"qa_model": config.qa.model.model,
"qa_model": config.qa.model.name,
"judge_provider": "ollama",
"judge_model": judge_model,
}

4
examples/ag-ui-research/backend/main.py
View file

@ -47,7 +47,7 @@ if not db_path.exists():
logger.info(f"Initializing research assistant with database: {db_path}")
logger.info(
f"Research Provider: {Config.research.model.provider}, Model: {Config.research.model.model}"
f"Research Provider: {Config.research.model.provider}, Model: {Config.research.model.name}"
)
# Store client reference for proper lifecycle management
@ -154,7 +154,7 @@ async def health_check(_: Request) -> JSONResponse:
"status": "healthy",
"agent_model": str(agent.model),
"research_provider": Config.research.model.provider,
"research_model": Config.research.model.model,
"research_model": Config.research.model.name,
"db_path": str(db_path),
"db_exists": db_path.exists(),
}

15
examples/ag-ui-research/haiku.rag.yaml.example
View file

@ -17,18 +17,21 @@ providers:
base_url: http://host.docker.internal:11434
research:
provider: ollama
model: gpt-oss:latest
model:
provider: ollama
name: gpt-oss:latest
max_iterations: 3
confidence_threshold: 0.8
max_concurrency: 1
# For OpenAI:
# research:
# provider: openai
# model: gpt-4o-mini
# model:
# provider: openai
# name: gpt-4o-mini
# For Anthropic:
# research:
# provider: anthropic
# model: claude-3-5-haiku-20241022
# model:
# provider: anthropic
# name: claude-3-5-haiku-20241022

10
haiku_rag_slim/haiku/rag/config/models.py
View file

@ -11,14 +11,14 @@ class ModelConfig(BaseModel):
Attributes:
provider: Model provider (ollama, openai, anthropic, etc.)
model: Model name/identifier
name: Model name/identifier
enable_thinking: Control reasoning behavior (true/false/None for default)
temperature: Sampling temperature (0.0 to 1.0+)
max_tokens: Maximum tokens to generate
"""
provider: str = "ollama"
model: str = "gpt-oss"
name: str = "gpt-oss"
enable_thinking: bool | None = None
temperature: float | None = None
@ -47,7 +47,7 @@ class EmbeddingsConfig(BaseModel):
model: ModelConfig = Field(
default_factory=lambda: ModelConfig(
provider="ollama",
model="qwen3-embedding:4b",
name="qwen3-embedding:4b",
)
)
vector_dim: int = 2560
@ -61,7 +61,7 @@ class QAConfig(BaseModel):
model: ModelConfig = Field(
default_factory=lambda: ModelConfig(
provider="ollama",
model="gpt-oss",
name="gpt-oss",
enable_thinking=False,
)
)
@ -74,7 +74,7 @@ class ResearchConfig(BaseModel):
model: ModelConfig = Field(
default_factory=lambda: ModelConfig(
provider="ollama",
model="gpt-oss",
name="gpt-oss",
enable_thinking=True,
)
)

8
haiku_rag_slim/haiku/rag/embeddings/__init__.py
View file

@ -16,7 +16,7 @@ def get_embedder(config: AppConfig = Config) -> EmbedderBase:
if config.embeddings.model.provider == "ollama":
return OllamaEmbedder(
config.embeddings.model.model, config.embeddings.vector_dim, config
config.embeddings.model.name, config.embeddings.vector_dim, config
)
if config.embeddings.model.provider == "voyageai":
@ -29,21 +29,21 @@ def get_embedder(config: AppConfig = Config) -> EmbedderBase:
"uv pip install haiku.rag[voyageai]"
)
return VoyageAIEmbedder(
config.embeddings.model.model, config.embeddings.vector_dim, config
config.embeddings.model.name, config.embeddings.vector_dim, config
)
if config.embeddings.model.provider == "openai":
from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
return OpenAIEmbedder(
config.embeddings.model.model, config.embeddings.vector_dim, config
config.embeddings.model.name, config.embeddings.vector_dim, config
)
if config.embeddings.model.provider == "vllm":
from haiku.rag.embeddings.vllm import Embedder as VllmEmbedder
return VllmEmbedder(
config.embeddings.model.model, config.embeddings.vector_dim, config
config.embeddings.model.name, config.embeddings.vector_dim, config
)
raise ValueError(

2
haiku_rag_slim/haiku/rag/embeddings/base.py
View file

@ -4,7 +4,7 @@ from haiku.rag.config import AppConfig, Config
class EmbedderBase:
_model: str = Config.embeddings.model.model
_model: str = Config.embeddings.model.name
_vector_dim: int = Config.embeddings.vector_dim
_config: AppConfig = Config

4
haiku_rag_slim/haiku/rag/reranking/__init__.py
View file

@ -45,7 +45,7 @@ def get_reranker(config: AppConfig = Config) -> RerankerBase | None:
try:
from haiku.rag.reranking.vllm import VLLMReranker
reranker = VLLMReranker(config.reranking.model.model)
reranker = VLLMReranker(config.reranking.model.name)
except ImportError:
reranker = None
@ -54,7 +54,7 @@ def get_reranker(config: AppConfig = Config) -> RerankerBase | None:
from haiku.rag.reranking.zeroentropy import ZeroEntropyReranker
# Use configured model or default to zerank-1
model = config.reranking.model.model or "zerank-1"
model = config.reranking.model.name or "zerank-1"
reranker = ZeroEntropyReranker(model)
except ImportError:
reranker = None

4
haiku_rag_slim/haiku/rag/reranking/base.py
View file

@ -3,9 +3,7 @@ from haiku.rag.store.models.chunk import Chunk
class RerankerBase:
_model: str | None = (
Config.reranking.model.model if Config.reranking.model else None
)
_model: str | None = Config.reranking.model.name if Config.reranking.model else None
async def rerank(
self, query: str, chunks: list[Chunk], top_n: int = 10

2
haiku_rag_slim/haiku/rag/reranking/mxbai.py
View file

@ -8,7 +8,7 @@ from haiku.rag.store.models.chunk import Chunk
class MxBAIReranker(RerankerBase):
def __init__(self):
model_name = (
Config.reranking.model.model
Config.reranking.model.name
if Config.reranking.model
else "mxbai-rerank-base-v2"
)

10
haiku_rag_slim/haiku/rag/utils.py
View file

@ -65,7 +65,7 @@ def get_model(
app_config = Config
provider = model_config.provider
model = model_config.model
model = model_config.name
if provider == "ollama":
model_settings = None
@ -366,13 +366,13 @@ def prefetch_models():
# Collect Ollama models from config
required_models: set[str] = set()
if Config.embeddings.model.provider == "ollama":
required_models.add(Config.embeddings.model.model)
required_models.add(Config.embeddings.model.name)
if Config.qa.model.provider == "ollama":
required_models.add(Config.qa.model.model)
required_models.add(Config.qa.model.name)
if Config.research.model.provider == "ollama":
required_models.add(Config.research.model.model)
required_models.add(Config.research.model.name)
if Config.reranking.model and Config.reranking.model.provider == "ollama":
required_models.add(Config.reranking.model.model)
required_models.add(Config.reranking.model.name)
if not required_models:
return

8
tests/test_embedder_config.py
View file

@ -17,7 +17,7 @@ def test_embedder_uses_config_from_get_embedder():
embeddings=EmbeddingsConfig(
model=ModelConfig(
provider="ollama",
model="custom-model",
name="custom-model",
),
vector_dim=512,
),
@ -42,7 +42,7 @@ def test_vllm_embedder_uses_config():
embeddings=EmbeddingsConfig(
model=ModelConfig(
provider="vllm",
model="custom-vllm-model",
name="custom-vllm-model",
),
vector_dim=768,
),
@ -68,7 +68,7 @@ def test_openai_embedder_uses_config():
embeddings=EmbeddingsConfig(
model=ModelConfig(
provider="openai",
model="text-embedding-3-large",
name="text-embedding-3-large",
),
vector_dim=3072,
),
@ -90,7 +90,7 @@ def test_voyageai_embedder_uses_config():
embeddings=EmbeddingsConfig(
model=ModelConfig(
provider="voyageai",
model="voyage-large-2",
name="voyage-large-2",
),
vector_dim=1536,
),

12
tests/test_qa.py
View file

@ -19,7 +19,7 @@ async def test_qa_ollama(qa_corpus: Dataset, temp_db_path):
"""Test Ollama QA with LLM judge."""
client = HaikuRAG(temp_db_path)
qa = QuestionAnswerAgent(
client, ModelConfig(provider="ollama", model="gpt-oss", enable_thinking=False)
client, ModelConfig(provider="ollama", name="gpt-oss", enable_thinking=False)
)
llm_judge = LLMJudge()
@ -44,9 +44,7 @@ async def test_qa_ollama(qa_corpus: Dataset, temp_db_path):
async def test_qa_openai(qa_corpus: Dataset, temp_db_path):
"""Test OpenAI QA with LLM judge."""
client = HaikuRAG(temp_db_path)
qa = QuestionAnswerAgent(
client, ModelConfig(provider="openai", model="gpt-4o-mini")
)
qa = QuestionAnswerAgent(client, ModelConfig(provider="openai", name="gpt-4o-mini"))
llm_judge = LLMJudge()
doc = qa_corpus[1]
@ -71,7 +69,7 @@ async def test_qa_anthropic(qa_corpus: Dataset, temp_db_path):
"""Test Anthropic QA with LLM judge."""
client = HaikuRAG(temp_db_path)
qa = QuestionAnswerAgent(
client, ModelConfig(provider="anthropic", model="claude-3-5-haiku-20241022")
client, ModelConfig(provider="anthropic", name="claude-3-5-haiku-20241022")
)
llm_judge = LLMJudge()
@ -96,9 +94,7 @@ async def test_qa_anthropic(qa_corpus: Dataset, temp_db_path):
async def test_qa_vllm(qa_corpus: Dataset, temp_db_path):
"""Test vLLM QA with LLM judge."""
client = HaikuRAG(temp_db_path)
qa = QuestionAnswerAgent(
client, ModelConfig(provider="vllm", model="Qwen/Qwen3-4B")
)
qa = QuestionAnswerAgent(client, ModelConfig(provider="vllm", name="Qwen/Qwen3-4B"))
llm_judge = LLMJudge()
doc = qa_corpus[1]

2
tests/test_reranker.py
View file

@ -44,7 +44,7 @@ async def test_mxbai_reranker():
from haiku.rag.reranking.mxbai import MxBAIReranker
Config.reranking.model = ModelConfig(
provider="mxbai", model="mixedbread-ai/mxbai-rerank-base-v2"
provider="mxbai", name="mixedbread-ai/mxbai-rerank-base-v2"
)
reranker = MxBAIReranker()
reranked = await reranker.rerank(

38
tests/test_utils.py
View file

@ -136,16 +136,14 @@ Emoji test: 🚀 ✅ 📝"""
def test_get_model_ollama():
"""Test get_model returns OpenAIChatModel for Ollama."""
model_config = ModelConfig(provider="ollama", model="llama3")
model_config = ModelConfig(provider="ollama", name="llama3")
result = get_model(model_config)
assert isinstance(result, OpenAIChatModel)
def test_get_model_ollama_with_thinking():
def test_get_model_ollama_without_thinking():
"""Test get_model configures thinking for gpt-oss on Ollama."""
model_config = ModelConfig(
provider="ollama", model="gpt-oss", enable_thinking=False
)
model_config = ModelConfig(provider="ollama", name="gpt-oss", enable_thinking=False)
result = get_model(model_config)
assert isinstance(result, OpenAIChatModel)
@ -153,7 +151,7 @@ def test_get_model_ollama_with_thinking():
def test_get_model_ollama_with_settings():
"""Test get_model applies temperature and max_tokens for Ollama."""
model_config = ModelConfig(
provider="ollama", model="llama3", temperature=0.5, max_tokens=100
provider="ollama", name="llama3", temperature=0.5, max_tokens=100
)
result = get_model(model_config)
assert isinstance(result, OpenAIChatModel)
@ -161,14 +159,14 @@ def test_get_model_ollama_with_settings():
def test_get_model_openai():
"""Test get_model returns OpenAIChatModel for OpenAI."""
model_config = ModelConfig(provider="openai", model="gpt-4o")
model_config = ModelConfig(provider="openai", name="gpt-4o")
result = get_model(model_config)
assert isinstance(result, OpenAIChatModel)
def test_get_model_openai_with_thinking():
"""Test get_model configures thinking for OpenAI reasoning models."""
model_config = ModelConfig(provider="openai", model="o1", enable_thinking=True)
model_config = ModelConfig(provider="openai", name="o1", enable_thinking=True)
result = get_model(model_config)
assert isinstance(result, OpenAIChatModel)
@ -178,7 +176,7 @@ def test_get_model_anthropic():
"""Test get_model returns AnthropicModel for Anthropic."""
from pydantic_ai.models.anthropic import AnthropicModel
model_config = ModelConfig(provider="anthropic", model="claude-3-5-sonnet-20241022")
model_config = ModelConfig(provider="anthropic", name="claude-3-5-sonnet-20241022")
result = get_model(model_config)
assert isinstance(result, AnthropicModel)
@ -190,7 +188,7 @@ def test_get_model_anthropic_with_thinking():
model_config = ModelConfig(
provider="anthropic",
model="claude-3-5-sonnet-20241022",
name="claude-3-5-sonnet-20241022",
enable_thinking=True,
)
result = get_model(model_config)
@ -202,7 +200,7 @@ def test_get_model_gemini():
"""Test get_model returns GoogleModel for Gemini."""
from pydantic_ai.models.google import GoogleModel
model_config = ModelConfig(provider="gemini", model="gemini-2.0-flash-exp")
model_config = ModelConfig(provider="gemini", name="gemini-2.0-flash-exp")
result = get_model(model_config)
assert isinstance(result, GoogleModel)
@ -213,7 +211,7 @@ def test_get_model_gemini_with_thinking():
from pydantic_ai.models.google import GoogleModel
model_config = ModelConfig(
provider="gemini", model="gemini-2.0-flash-thinking-exp", enable_thinking=True
provider="gemini", name="gemini-2.0-flash-thinking-exp", enable_thinking=True
)
result = get_model(model_config)
assert isinstance(result, GoogleModel)
@ -224,7 +222,7 @@ def test_get_model_groq():
"""Test get_model returns GroqModel for Groq."""
from pydantic_ai.models.groq import GroqModel
model_config = ModelConfig(provider="groq", model="llama-3.3-70b-versatile")
model_config = ModelConfig(provider="groq", name="llama-3.3-70b-versatile")
result = get_model(model_config)
assert isinstance(result, GroqModel)
@ -235,7 +233,7 @@ def test_get_model_groq_with_thinking():
from pydantic_ai.models.groq import GroqModel
model_config = ModelConfig(
provider="groq", model="llama-3.3-70b-versatile", enable_thinking=False
provider="groq", name="llama-3.3-70b-versatile", enable_thinking=False
)
result = get_model(model_config)
assert isinstance(result, GroqModel)
@ -247,7 +245,7 @@ def test_get_model_bedrock():
from pydantic_ai.models.bedrock import BedrockConverseModel
model_config = ModelConfig(
provider="bedrock", model="anthropic.claude-3-5-sonnet-20241022-v2:0"
provider="bedrock", name="anthropic.claude-3-5-sonnet-20241022-v2:0"
)
result = get_model(model_config)
assert isinstance(result, BedrockConverseModel)
@ -260,7 +258,7 @@ def test_get_model_bedrock_with_thinking():
model_config = ModelConfig(
provider="bedrock",
model="anthropic.claude-3-5-sonnet-20241022-v2:0",
name="anthropic.claude-3-5-sonnet-20241022-v2:0",
enable_thinking=True,
)
result = get_model(model_config)
@ -269,21 +267,21 @@ def test_get_model_bedrock_with_thinking():
def test_get_model_vllm():
"""Test get_model returns OpenAIChatModel for vLLM."""
model_config = ModelConfig(provider="vllm", model="Qwen/Qwen3-4B")
model_config = ModelConfig(provider="vllm", name="Qwen/Qwen3-4B")
result = get_model(model_config)
assert isinstance(result, OpenAIChatModel)
def test_get_model_vllm_with_thinking():
"""Test get_model configures thinking for gpt-oss on vLLM."""
model_config = ModelConfig(provider="vllm", model="gpt-oss", enable_thinking=False)
model_config = ModelConfig(provider="vllm", name="gpt-oss", enable_thinking=False)
result = get_model(model_config)
assert isinstance(result, OpenAIChatModel)
def test_get_model_unknown_provider():
"""Test get_model returns string format for unknown providers."""
model_config = ModelConfig(provider="mistral", model="mistral-large-latest")
model_config = ModelConfig(provider="mistral", name="mistral-large-latest")
result = get_model(model_config)
assert isinstance(result, str)
assert result == "mistral:mistral-large-latest"
@ -293,7 +291,7 @@ def test_get_model_with_all_settings():
"""Test get_model applies all settings together."""
model_config = ModelConfig(
provider="openai",
model="gpt-4o",
name="gpt-4o",
enable_thinking=False,
temperature=0.7,
max_tokens=500,