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Remove Ollama reranker, this was a bad idea

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Yiorgis Gozadinos 2025-09-02 11:42:31 +03:00
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6 changed files with 12 additions and 126 deletions
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10
docs/benchmarks.md
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@ -15,11 +15,11 @@ The recall obtained is ~0.79 for matching in the top result, raising to ~0.91 fo
| Embedding Model | Document in top 1 | Document in top 3 | Reranker |
|---------------------------------------|-------------------|-------------------|------------------------|
| Ollama / `mxbai-embed-large` | 0.79 | 0.91 | None |
| Ollama / `mxbai-embed-large` | 0.81 | 0.91 | `mxbai-rerank-base-v2` |
| Ollama / `nomic-embed-text` | 0.74 | 0.88 | None |
| Ollama / `mxbai-embed-large` | 0.90 | 0.95 | `mxbai-rerank-base-v2` |
<!-- | Ollama / `nomic-embed-text` | 0.74 | 0.88 | None |
| OpenAI / `text-embeddings-3-small` | 0.75 | 0.88 | None |
| OpenAI / `text-embeddings-3-small` | 0.75 | 0.88 | None |
| OpenAI / `text-embeddings-3-small` | 0.83 | 0.90 | Cohere / `rerank-v3.5` |
| OpenAI / `text-embeddings-3-small` | 0.83 | 0.90 | Cohere / `rerank-v3.5` | -->
## Question/Answer evaluation
@ -29,5 +29,5 @@ Again using the same dataset, we use a QA agent to answer the question. In addit
|------------------------------------|-----------------------------------|-----------|------------------------|
| Ollama / `mxbai-embed-large` | Ollama / `qwen3` | 0.85 | None |
| Ollama / `mxbai-embed-large` | Ollama / `qwen3` | 0.72 | `mxbai-rerank-base-v2` |
| Ollama / `mxbai-embed-large` | Anthropic / `Claude Sonnet 3.7` | 0.79 | None |
| OpenAI / `text-embeddings-3-small` | OpenAI / `gpt-4-turbo` | 0.62 | None |
<!-- | Ollama / `mxbai-embed-large` | Anthropic / `Claude Sonnet 3.7` | 0.79 | None |
| OpenAI / `text-embeddings-3-small` | OpenAI / `gpt-4-turbo` | 0.62 | None | -->

20
docs/configuration.md
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@ -109,25 +109,7 @@ See the [Pydantic AI documentation](https://ai.pydantic.dev/models/) for the com
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.
Reranking is **disabled by default** for faster searches. You can enable it by configuring a reranking provider.
### Disabling Reranking
To disable reranking completely for faster searches:
```bash
RERANK_PROVIDER=""
```
### Ollama (Default)
Ollama reranking uses LLMs with structured output to rank documents by relevance:
```bash
RERANK_PROVIDER="ollama"
RERANK_MODEL="qwen3:1.7b" # or any model that supports structured output
OLLAMA_BASE_URL="http://localhost:11434"
```
Reranking is **disabled by default** (`RERANK_PROVIDER=""`) for faster searches. You can enable it by configuring one of the providers below.
### MixedBread AI

2
src/haiku/rag/config.py
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@ -20,7 +20,7 @@ class AppConfig(BaseModel):
EMBEDDINGS_VECTOR_DIM: int = 1024
RERANK_PROVIDER: str = ""
RERANK_MODEL: str = "qwen3"
RERANK_MODEL: str = ""
QA_PROVIDER: str = "ollama"
QA_MODEL: str = "qwen3"

6
src/haiku/rag/reranking/__init__.py
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@ -31,10 +31,4 @@ def get_reranker() -> RerankerBase | None:
except ImportError:
return None
if Config.RERANK_PROVIDER == "ollama":
from haiku.rag.reranking.ollama import OllamaReranker
_reranker = OllamaReranker()
return _reranker
return None

81
src/haiku/rag/reranking/ollama.py
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@ -1,81 +0,0 @@
from pydantic import BaseModel
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.ollama import OllamaProvider
from haiku.rag.config import Config
from haiku.rag.reranking.base import RerankerBase
from haiku.rag.store.models.chunk import Chunk
class RerankResult(BaseModel):
"""Individual rerank result with index and relevance score."""
index: int
relevance_score: float
class RerankResponse(BaseModel):
"""Response from the reranking model containing ranked results."""
results: list[RerankResult]
class OllamaReranker(RerankerBase):
def __init__(self, model: str = Config.RERANK_MODEL):
self._model = model
# Create the reranking prompt
system_prompt = """You are a document reranking assistant. Given a query and a list of document chunks, you must rank them by relevance to the query.
Return your response as a JSON object with a "results" array. Each result should have:
- "index": the original index of the document (integer)
- "relevance_score": a score between 0.0 and 1.0 indicating relevance (float, where 1.0 is most relevant)
Only return the top documents up to the requested limit, ordered by decreasing relevance score.
/no_think
"""
model_obj = OpenAIModel(
model_name=model,
provider=OllamaProvider(base_url=f"{Config.OLLAMA_BASE_URL}/v1"),
)
self._agent = Agent(
model=model_obj,
output_type=RerankResponse,
system_prompt=system_prompt,
)
async def rerank(
self, query: str, chunks: list[Chunk], top_n: int = 10
) -> list[tuple[Chunk, float]]:
if not chunks:
return []
documents = []
for i, chunk in enumerate(chunks):
documents.append({"index": i, "content": chunk.content})
documents_text = ""
for doc in documents:
documents_text += f"Index {doc['index']}: {doc['content']}\n\n"
user_prompt = f"""Query: {query}
Documents to rerank:
{documents_text.strip()}
Rank these documents by relevance to the query and return the top {top_n} results as JSON."""
try:
result = await self._agent.run(user_prompt)
return [
(chunks[result_item.index], result_item.relevance_score)
for result_item in result.output.results[:top_n]
]
except Exception:
# Fallback: return chunks in original order with same score
return [(chunks[i], 1.0) for i in range(min(top_n, len(chunks)))]

19
tests/test_reranker.py
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@ -24,7 +24,7 @@ chunks = [
@pytest.mark.asyncio
async def test_reranker_base():
reranker = RerankerBase()
assert reranker._model == "qwen3"
assert reranker._model == ""
with pytest.raises(NotImplementedError):
await reranker.rerank("query", [])
@ -35,12 +35,16 @@ async def test_mxbai_reranker():
try:
from haiku.rag.reranking.mxbai import MxBAIReranker
Config.RERANK_MODEL = "mixedbread-ai/mxbai-rerank-base-v2"
reranker = MxBAIReranker()
# reranker._model = "mixedbread-ai/mxbai-rerank-base-v2"
reranked = await reranker.rerank(
"Who wrote 'To Kill a Mockingbird'?", chunks, top_n=2
)
assert [chunk.document_id for chunk, score in reranked] == ["0", "2"]
assert all(isinstance(score, float) for chunk, score in reranked)
Config.RERANK_MODEL = ""
except ImportError:
pytest.skip("MxBAI package not installed")
@ -62,16 +66,3 @@ async def test_cohere_reranker():
except ImportError:
pytest.skip("Cohere package not installed")
@pytest.mark.asyncio
async def test_ollama_reranker():
from haiku.rag.reranking.ollama import OllamaReranker
reranker = OllamaReranker()
reranked = await reranker.rerank(
"Who wrote 'To Kill a Mockingbird'?", chunks, top_n=2
)
assert [chunk.document_id for chunk, score in reranked] == ["0", "2"]
assert all(isinstance(score, float) for chunk, score in reranked)