Merge pull request #17 from ggozad/feat/reranking

Reranking support with mixedbread + cohere

README.md

@@ -10,6 +10,7 @@ Retrieval-Augmented Generation (RAG) library on SQLite.
 - **Multiple embedding providers**: Ollama, VoyageAI, OpenAI
 - **Multiple QA providers**: Ollama, OpenAI, Anthropic
 - **Hybrid search**: Vector + full-text search with Reciprocal Rank Fusion
+- **Reranking**: Default search result reranking with MixedBread AI or Cohere
 - **Question answering**: Built-in QA agents on your documents
 - **File monitoring**: Auto-index files when run as server
 - **40+ file formats**: PDF, DOCX, HTML, Markdown, audio, URLs
@@ -49,7 +50,7 @@ async with HaikuRAG("database.db") as client:
     # Add document
     doc = await client.create_document("Your content")
 
-    # Search
+    # Search (reranking enabled by default)
     results = await client.search("query")
     for chunk, score in results:
         print(f"{score:.3f}: {chunk.content}")

docs/benchmarks.md

@@ -12,17 +12,19 @@ In order to calculate recall, we load the `News Stories` from `repliqa_3` which
 
 The recall obtained is ~0.73 for matching in the top result, raising to ~0.75 for the top 3 results.
 
-| Model                                 | Document in top 1 | Document in top 3 |
-|---------------------------------------|-------------------|-------------------|
-| Ollama / `mxbai-embed-large`          | 0.77              | 0.89              |
-| Ollama / `nomic-embed-text`           | 0.74              | 0.88              |
-| OpenAI / `text-embeddings-3-small`    | 0.75              | 0.88              |
+| Model                                 | Document in top 1 | Document in top 3 | Reranker             |
+|---------------------------------------|-------------------|-------------------|----------------------|
+| Ollama / `mxbai-embed-large`          | 0.77              | 0.89              | None                 |
+| Ollama / `mxbai-embed-large`          | 0.81              | 0.91              | mxbai-rerank-base-v2 |
+| Ollama / `nomic-embed-text`           | 0.74              | 0.88              | None                 |
+| OpenAI / `text-embeddings-3-small`    | 0.75              | 0.88              | None                 |
 
 ## Question/Answer evaluation
 
 Again using the same dataset, we use a QA agent to answer the question. In addition we use an LLM judge (using the Ollama `qwen3`) to evaluate whether the answer is correct or not. The obtained accuracy is as follows:
 
-| Embedding Model              | QA Model                          | Accuracy  |
-|------------------------------|-----------------------------------|-----------|
-| Ollama / `mxbai-embed-large` | Ollama / `qwen3`                  | 0.64      |
-| Ollama / `mxbai-embed-large` | Anthropic / `Claude Sonnet 3.7`   | 0.79      |
+| Embedding Model              | QA Model                          | Accuracy  | Reranker             |
+|------------------------------|-----------------------------------|-----------|----------------------|
+| Ollama / `mxbai-embed-large` | Ollama / `qwen3`                  | 0.64      | 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                 |

docs/configuration.md

@@ -103,6 +103,39 @@ QA_MODEL="claude-3-5-haiku-20241022"  # or claude-3-5-sonnet-20241022, etc.
 ANTHROPIC_API_KEY="your-api-key"
 ```
 
+## Reranking
+
+Reranking is **enabled by default** and 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.
+
+If you use the default reranked (running locally), it can slow down searching significantly. To disable reranking for faster searches:
+
+```bash
+RERANK=false
+```
+
+### MixedBread AI (Default)
+
+```bash
+RERANK_PROVIDER="mxbai"
+RERANK_MODEL="mixedbread-ai/mxbai-rerank-base-v2"
+```
+
+### Cohere
+
+For Cohere reranking, install with Cohere extras:
+
+```bash
+uv pip install haiku.rag --extra cohere
+```
+
+Then configure:
+
+```bash
+RERANK_PROVIDER="cohere"
+RERANK_MODEL="rerank-v3.5"
+COHERE_API_KEY="your-api-key"
+```
+
 ## Other Settings
 
 ### Database and Storage

docs/index.md

@@ -1,13 +1,13 @@
 # haiku.rag
 
-`haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work on SQLite alone without the need for external vector databases. It uses [sqlite-vec](https://github.com/asg017/sqlite-vec) for storing the embeddings and performs semantic (vector) search as well as full-text search combined through Reciprocal Rank Fusion. Both open-source (Ollama) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.
-
+`haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work on SQLite alone without the need for external vector databases. It uses [sqlite-vec](https://github.com/asg017/sqlite-vec) for storing the embeddings and performs semantic (vector) search as well as full-text search combined through Reciprocal Rank Fusion. Both open-source (Ollama, MixedBread AI) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.
 
 ## Features
 
 - **Local SQLite**: No need to run additional servers
 - **Support for various embedding providers**: Ollama, VoyageAI, OpenAI or add your own
 - **Hybrid Search**: Vector search using `sqlite-vec` combined with full-text search `FTS5`, using Reciprocal Rank Fusion
+- **Reranking**: Optional result reranking with MixedBread AI or Cohere
 - **Question Answering**: Built-in QA agents using Ollama, OpenAI, or Anthropic.
 - **File monitoring**: Automatically index files when run as a server
 - **Extended file format support**: Parse 40+ file formats including PDF, DOCX, HTML, Markdown, audio and more. Or add a URL!
@@ -34,7 +34,7 @@ async with HaikuRAG("database.db") as client:
     results = await client.search("query")
 
     # Ask questions
-    answer = await client.ask("Who is the author of haiku.rag?")
+    answer = await client.ask("Who is the author of haiku.rag?", rerank=False)
 ```
 
 Or use the CLI:

docs/python.md

@@ -76,7 +76,9 @@ async for doc_id in client.rebuild_database():
 
 ## Searching Documents
 
-Basic search:
+The search method performs hybrid search (vector + full-text) with **reranking enabled by default** for improved relevance:
+
+Basic search (with reranking):
 ```python
 results = await client.search("machine learning algorithms", limit=5)
 for chunk, score in results:
@@ -90,7 +92,8 @@ With options:
 results = await client.search(
     query="machine learning",
     limit=5,  # Maximum results to return
-    k=60      # RRF parameter for reciprocal rank fusion
+    k=60,     # RRF parameter for reciprocal rank fusion
+    rerank=False  # Disable reranking for faster search
 )
 
 # Process results

pyproject.toml

@@ -25,6 +25,7 @@ dependencies = [
     "fastmcp>=2.8.1",
     "httpx>=0.28.1",
     "markitdown[audio-transcription,docx,pdf,pptx,xlsx]>=0.1.2",
+    "mxbai-rerank>=0.1.6",
     "ollama>=0.5.1",
     "pydantic>=2.11.7",
     "python-dotenv>=1.1.0",
@@ -39,6 +40,7 @@ dependencies = [
 voyageai = ["voyageai>=0.3.2"]
 openai = ["openai>=1.0.0"]
 anthropic = ["anthropic>=0.56.0"]
+cohere = ["cohere>=5.16.1"]
 
 [project.scripts]
 haiku-rag = "haiku.rag.cli:cli"

src/haiku/rag/client.py

@@ -10,6 +10,7 @@ import httpx
 
 from haiku.rag.config import Config
 from haiku.rag.reader import FileReader
+from haiku.rag.reranking import get_reranker
 from haiku.rag.store.engine import Store
 from haiku.rag.store.models.chunk import Chunk
 from haiku.rag.store.models.document import Document
@@ -277,9 +278,9 @@ class HaikuRAG:
         return await self.document_repository.list_all(limit=limit, offset=offset)
 
     async def search(
-        self, query: str, limit: int = 5, k: int = 60
+        self, query: str, limit: int = 3, k: int = 60, rerank=Config.RERANK
     ) -> list[tuple[Chunk, float]]:
-        """Search for relevant chunks using hybrid search (vector similarity + full-text search).
+        """Search for relevant chunks using hybrid search (vector similarity + full-text search) with reranking.
 
         Args:
             query: The search query string.
@@ -289,7 +290,22 @@ class HaikuRAG:
         Returns:
             List of (chunk, score) tuples ordered by relevance.
         """
-        return await self.chunk_repository.search_chunks_hybrid(query, limit, k)
+
+        if not rerank:
+            return await self.chunk_repository.search_chunks_hybrid(query, limit, k)
+
+        # Get more initial results (3X) for reranking
+        search_results = await self.chunk_repository.search_chunks_hybrid(
+            query, limit * 3, k
+        )
+
+        # Apply reranking
+        reranker = get_reranker()
+        chunks = [chunk for chunk, _ in search_results]
+        reranked_results = await reranker.rerank(query, chunks, top_n=limit)
+
+        # Return reranked results with scores from reranker
+        return reranked_results
 
     async def ask(self, question: str) -> str:
         """Ask a question using the configured QA agent.

src/haiku/rag/config.py

@@ -19,6 +19,10 @@ class AppConfig(BaseModel):
     EMBEDDINGS_MODEL: str = "mxbai-embed-large"
     EMBEDDINGS_VECTOR_DIM: int = 1024
 
+    RERANK: bool = True
+    RERANK_PROVIDER: str = "mxbai"
+    RERANK_MODEL: str = "mixedbread-ai/mxbai-rerank-base-v2"
+
     QA_PROVIDER: str = "ollama"
     QA_MODEL: str = "qwen3"
 
@@ -31,6 +35,7 @@ class AppConfig(BaseModel):
     VOYAGE_API_KEY: str = ""
     OPENAI_API_KEY: str = ""
     ANTHROPIC_API_KEY: str = ""
+    COHERE_API_KEY: str = ""
 
     @field_validator("MONITOR_DIRECTORIES", mode="before")
     @classmethod
@@ -52,3 +57,5 @@ if Config.VOYAGE_API_KEY:
     os.environ["VOYAGE_API_KEY"] = Config.VOYAGE_API_KEY
 if Config.ANTHROPIC_API_KEY:
     os.environ["ANTHROPIC_API_KEY"] = Config.ANTHROPIC_API_KEY
+if Config.COHERE_API_KEY:
+    os.environ["CO_API_KEY"] = Config.COHERE_API_KEY

src/haiku/rag/embeddings/base.py

@@ -1,6 +1,9 @@
+from haiku.rag.config import Config
+
+
 class EmbedderBase:
-    _model: str = ""
-    _vector_dim: int = 0
+    _model: str = Config.EMBEDDINGS_MODEL
+    _vector_dim: int = Config.EMBEDDINGS_VECTOR_DIM
 
     def __init__(self, model: str, vector_dim: int):
         self._model = model

src/haiku/rag/embeddings/ollama.py

@@ -5,9 +5,6 @@ from haiku.rag.embeddings.base import EmbedderBase
 
 
 class Embedder(EmbedderBase):
-    _model: str = Config.EMBEDDINGS_MODEL
-    _vector_dim: int = 1024
-
     async def embed(self, text: str) -> list[float]:
         client = AsyncClient(host=Config.OLLAMA_BASE_URL)
         res = await client.embeddings(model=self._model, prompt=text)

src/haiku/rag/embeddings/openai.py

@@ -1,13 +1,9 @@
 try:
     from openai import AsyncOpenAI
 
-    from haiku.rag.config import Config
     from haiku.rag.embeddings.base import EmbedderBase
 
     class Embedder(EmbedderBase):
-        _model: str = Config.EMBEDDINGS_MODEL
-        _vector_dim: int = 1536
-
         async def embed(self, text: str) -> list[float]:
             client = AsyncOpenAI()
             response = await client.embeddings.create(

src/haiku/rag/embeddings/voyageai.py

@@ -1,13 +1,9 @@
 try:
     from voyageai.client import Client  # type: ignore
 
-    from haiku.rag.config import Config
     from haiku.rag.embeddings.base import EmbedderBase
 
     class Embedder(EmbedderBase):
-        _model: str = Config.EMBEDDINGS_MODEL
-        _vector_dim: int = 1024
-
         async def embed(self, text: str) -> list[float]:
             client = Client()
             res = client.embed([text], model=self._model, output_dtype="float")

src/haiku/rag/qa/prompts.py

@@ -6,7 +6,7 @@ Your process:
 2. Search with specific keywords and phrases from the user's question
 3. Review the search results and their relevance scores
 4. If you need additional context, perform follow-up searches with different keywords
-5. Provide a comprehensive answer based only on the retrieved documents
+5. Provide a short and to the point comprehensive answer based only on the retrieved documents
 
 Guidelines:
 - Base your answers strictly on the provided document content
@@ -15,6 +15,7 @@ Guidelines:
 - Indicate when information is incomplete or when you need to search for additional context
 - If the retrieved documents don't contain sufficient information, clearly state: "I cannot find enough information in the knowledge base to answer this question."
 - For complex questions, consider breaking them down and performing multiple searches
+- Stick to the answer, do not ellaborate or provde context unless asked for it.
 
 Be concise, and always maintain accuracy over completeness. Prefer short, direct answers that are well-supported by the documents.
 """

src/haiku/rag/reranking/__init__.py

@@ -0,0 +1,37 @@
+from haiku.rag.config import Config
+from haiku.rag.reranking.base import RerankerBase
+
+try:
+    from haiku.rag.reranking.cohere import CohereReranker
+except ImportError:
+    pass
+
+_reranker: RerankerBase | None = None
+
+
+def get_reranker() -> RerankerBase:
+    """
+    Factory function to get the appropriate reranker based on the configuration.
+    """
+    global _reranker
+    if _reranker is not None:
+        return _reranker
+    if Config.RERANK_PROVIDER == "mxbai":
+        from haiku.rag.reranking.mxbai import MxBAIReranker
+
+        _reranker = MxBAIReranker()
+        return _reranker
+
+    if Config.RERANK_PROVIDER == "cohere":
+        try:
+            from haiku.rag.reranking.cohere import CohereReranker
+        except ImportError:
+            raise ImportError(
+                "Cohere reranker requires the 'cohere' package. "
+                "Please install haiku.rag with the 'cohere' extra:"
+                "uv pip install haiku.rag --extra cohere"
+            )
+        _reranker = CohereReranker()
+        return _reranker
+
+    raise ValueError(f"Unsupported reranker provider: {Config.RERANK_PROVIDER}")

src/haiku/rag/reranking/base.py

@@ -0,0 +1,13 @@
+from haiku.rag.config import Config
+from haiku.rag.store.models.chunk import Chunk
+
+
+class RerankerBase:
+    _model: str = Config.RERANK_MODEL
+
+    async def rerank(
+        self, query: str, chunks: list[Chunk], top_n: int = 10
+    ) -> list[tuple[Chunk, float]]:
+        raise NotImplementedError(
+            "Reranker is an abstract class. Please implement the rerank method in a subclass."
+        )

src/haiku/rag/reranking/cohere.py

@@ -0,0 +1,34 @@
+from haiku.rag.config import Config
+from haiku.rag.reranking.base import RerankerBase
+from haiku.rag.store.models.chunk import Chunk
+
+try:
+    import cohere
+except ImportError as e:
+    raise ImportError(
+        "cohere is not installed. Please install it with `pip install cohere` or use the cohere optional dependency."
+    ) from e
+
+
+class CohereReranker(RerankerBase):
+    def __init__(self):
+        self._client = cohere.ClientV2(api_key=Config.COHERE_API_KEY)
+
+    async def rerank(
+        self, query: str, chunks: list[Chunk], top_n: int = 10
+    ) -> list[tuple[Chunk, float]]:
+        if not chunks:
+            return []
+
+        documents = [chunk.content for chunk in chunks]
+
+        response = self._client.rerank(
+            model=self._model, query=query, documents=documents, top_n=top_n
+        )
+
+        reranked_chunks = []
+        for result in response.results:
+            original_chunk = chunks[result.index]
+            reranked_chunks.append((original_chunk, result.relevance_score))
+
+        return reranked_chunks

src/haiku/rag/reranking/mxbai.py

@@ -0,0 +1,28 @@
+from mxbai_rerank import MxbaiRerankV2
+
+from haiku.rag.config import Config
+from haiku.rag.reranking.base import RerankerBase
+from haiku.rag.store.models.chunk import Chunk
+
+
+class MxBAIReranker(RerankerBase):
+    def __init__(self):
+        self._client = MxbaiRerankV2(
+            Config.RERANK_MODEL, disable_transformers_warnings=True
+        )
+
+    async def rerank(
+        self, query: str, chunks: list[Chunk], top_n: int = 10
+    ) -> list[tuple[Chunk, float]]:
+        if not chunks:
+            return []
+
+        documents = [chunk.content for chunk in chunks]
+
+        results = self._client.rank(query=query, documents=documents, top_k=top_n)
+        reranked_chunks = []
+        for result in results:
+            original_chunk = chunks[result.index]
+            reranked_chunks.append((original_chunk, result.score))
+
+        return reranked_chunks

tests/llm_judge.py

@@ -35,23 +35,35 @@ class LLMJudge:
             - score: str rating from 1-5
         """
 
-        prompt = f"""
-        You are an expert judge evaluating the equivalence of two answers to the same question.
+        prompt = f"""You are an expert evaluator determining whether two answers to the same question are semantically equivalent.
 
-        Question: {question}
+QUESTION: {question}
 
-        Generated Answer: {answer}
+GENERATED ANSWER: {answer}
 
-        Expected Answer: {expected_answer}
+EXPECTED ANSWER: {expected_answer}
 
-        Your task is to determine if these two answers are equivalent in meaning and both correctly answer the question. Consider:
+EVALUATION CRITERIA:
+Rate as EQUIVALENT (true) if:
+✓ Both answers contain the same core factual information
+✓ Both directly address the question asked
+✓ The key claims and conclusions are consistent
+✓ Any additional detail in one answer doesn't contradict the other
 
-        1. Do both answers provide the same answer?
-        2. Do both answers directly address the question asked?
-        3. Minor differences in wording or style are acceptable if the meaning of the answer is the same.
-        4. If one answer is more detailed but the other is correct, they can still be considered equivalent.
+Rate as NOT EQUIVALENT (false) if:
+✗ Factual contradictions exist between the answers
+✗ One answer fails to address the core question
+✗ Key information is missing from one answer that changes the meaning
+✗ The answers lead to different conclusions or implications
 
-        Be strict but fair in your evaluation. Focus on factual correctness and whether both answers would satisfy someone asking the question."""
+GUIDELINES:
+- Ignore minor differences in phrasing, style, or formatting
+- Focus on semantic meaning rather than exact wording
+- Consider both answers correct if they convey the same essential information
+- Be tolerant of different levels of detail if the core answer is preserved
+- Evaluate based on what a person asking this question would need to know
+
+Respond with JSON containing only: {{"equivalent": true}} or {{"equivalent": false}}"""
 
         response = await self.client.chat(
             model=self.model,

tests/test_reranker.py

@@ -0,0 +1,56 @@
+import pytest
+
+from haiku.rag.reranking.base import RerankerBase
+from haiku.rag.reranking.mxbai import MxBAIReranker
+from haiku.rag.store.models.chunk import Chunk
+
+chunks = [
+    Chunk(content=content, document_id=i)
+    for i, content in enumerate(
+        [
+            "To Kill a Mockingbird is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.",
+            "The novel Moby-Dick was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.",
+            "Harper Lee, an American novelist widely known for her novel To Kill a Mockingbird, was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.",
+            "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.",
+            "The Harry Potter series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.",
+            "The Great Gatsby, a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan.",
+        ]
+    )
+]
+
+
+@pytest.mark.asyncio
+async def test_reranker_base():
+    reranker = RerankerBase()
+    assert reranker._model == "mixedbread-ai/mxbai-rerank-base-v2"
+
+    with pytest.raises(NotImplementedError):
+        await reranker.rerank("query", [])
+
+
+@pytest.mark.asyncio
+async def test_mxbai_reranker():
+    reranker = MxBAIReranker()
+    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)
+
+
+@pytest.mark.asyncio
+async def test_cohere_reranker():
+    try:
+        from haiku.rag.reranking.cohere import CohereReranker
+
+        reranker = CohereReranker()
+        reranker._model = "rerank-v3.5"
+
+        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)
+
+    except ImportError:
+        pytest.skip("Cohere package not installed")

uv.lock

@@ -8,6 +8,25 @@ resolution-markers = [
     "python_full_version < '3.11'",
 ]
 
+[[package]]
+name = "accelerate"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "safetensors" },
+    { name = "torch" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c4/25/969456a95a90ed38f73f68d0f0915bdf1d76145d05054c59ad587b171150/accelerate-1.9.0.tar.gz", hash = "sha256:0e8c61f81af7bf37195b6175a545ed292617dd90563c88f49020aea5b6a0b47f", size = 383234, upload-time = "2025-07-16T16:24:54.526Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9f/1c/a17fb513aeb684fb83bef5f395910f53103ab30308bbdd77fd66d6698c46/accelerate-1.9.0-py3-none-any.whl", hash = "sha256:c24739a97ade1d54af4549a65f8b6b046adc87e2b3e4d6c66516e32c53d5a8f1", size = 367073, upload-time = "2025-07-16T16:24:52.957Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -259,6 +278,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/41/ff/392bff89415399a979be4a65357a41d92729ae8580a66073d8ec8d810f98/backrefs-5.9-py39-none-any.whl", hash = "sha256:f48ee18f6252b8f5777a22a00a09a85de0ca931658f1dd96d4406a34f3748c60", size = 380265, upload-time = "2025-06-22T19:34:12.405Z" },
 ]
 
+[[package]]
+name = "batched"
+version = "0.1.5"
+source = { registry = "https://pypi.org/simple" }
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+    { name = "mxbai-rerank" },
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@@ -836,6 +922,9 @@ dependencies = [
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+cohere = [
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