Merge pull request #39 from ggozad/feat/pydantic

Use pydantic AI

README.md

@@ -8,7 +8,7 @@ Retrieval-Augmented Generation (RAG) library on SQLite.
 
 - **Local SQLite**: No external servers required
 - **Multiple embedding providers**: Ollama, VoyageAI, OpenAI
-- **Multiple QA providers**: Ollama, OpenAI, Anthropic
+- **Multiple QA providers**: Any provider/model supported by Pydantic AI
 - **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

docs/configuration.md

@@ -44,13 +44,7 @@ VOYAGE_API_KEY="your-api-key"
 ```
 
 ### OpenAI
-If you want to use OpenAI embeddings you will need to install `haiku.rag` with the VoyageAI extras,
-
-```bash
-uv pip install haiku.rag[openai]
-```
-
-and set environment variables.
+OpenAI embeddings are included in the default installation. Simply set environment variables:
 
 ```bash
 EMBEDDINGS_PROVIDER="openai"
@@ -61,48 +55,50 @@ OPENAI_API_KEY="your-api-key"
 
 ## Question Answering Providers
 
-Configure which LLM provider to use for question answering.
+Configure which LLM provider to use for question answering using the `provider:model` format. Any provider and model supported by [Pydantic AI](https://ai.pydantic.dev/models/) can be used.
 
 ### Ollama (Default)
 
 ```bash
-QA_PROVIDER="ollama"
-QA_MODEL="qwen3"
+QA_PROVIDER="ollama:qwen3"
 OLLAMA_BASE_URL="http://localhost:11434"
 ```
 
 ### OpenAI
 
-For OpenAI QA, you need to install haiku.rag with OpenAI extras:
+OpenAI QA is included in the default installation. Simply configure:
 
 ```bash
-uv pip install haiku.rag[openai]
-```
-
-Then configure:
-
-```bash
-QA_PROVIDER="openai"
-QA_MODEL="gpt-4o-mini"  # or gpt-4, gpt-3.5-turbo, etc.
+QA_PROVIDER="openai:gpt-4o-mini"  # or openai:gpt-4, openai:gpt-3.5-turbo, etc.
 OPENAI_API_KEY="your-api-key"
 ```
 
 ### Anthropic
 
-For Anthropic QA, you need to install haiku.rag with Anthropic extras:
+Anthropic QA is included in the default installation. Simply configure:
 
 ```bash
-uv pip install haiku.rag[anthropic]
-```
-
-Then configure:
-
-```bash
-QA_PROVIDER="anthropic"
-QA_MODEL="claude-3-5-haiku-20241022"  # or claude-3-5-sonnet-20241022, etc.
+QA_PROVIDER="anthropic:claude-3-5-haiku-20241022"  # or anthropic:claude-3-5-sonnet-20241022, etc.
 ANTHROPIC_API_KEY="your-api-key"
 ```
 
+### Other Providers
+
+Any provider supported by Pydantic AI can be used. Examples include:
+
+```bash
+# Google Gemini
+QA_PROVIDER="gemini:gemini-1.5-flash"
+
+# Groq
+QA_PROVIDER="groq:llama-3.3-70b-versatile"
+
+# Mistral
+QA_PROVIDER="mistral:mistral-small-latest"
+```
+
+See the [Pydantic AI documentation](https://ai.pydantic.dev/models/) for the complete list of supported providers and models.
+
 ## Reranking
 
 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.
@@ -144,13 +140,7 @@ RERANK_MODEL="mixedbread-ai/mxbai-rerank-base-v2"
 
 ### Cohere
 
-For Cohere reranking, install with Cohere extras:
-
-```bash
-uv pip install haiku.rag[cohere]
-```
-
-Then configure:
+Cohere reranking is included in the default installation. Simply configure:
 
 ```bash
 RERANK_PROVIDER="cohere"

docs/installation.md

@@ -6,11 +6,15 @@
 uv pip install haiku.rag
 ```
 
-By default, Ollama (with the `mxbai-embed-large` model) is used for embeddings.
+This includes support for:
+- **Ollama** (default embedding provider using `mxbai-embed-large`)
+- **OpenAI** (GPT models for QA and embeddings)
+- **Anthropic** (Claude models for QA)
+- **Cohere** (reranking models)
 
 ## Provider-Specific Installation
 
-For other embedding providers, install with extras:
+For additional embedding providers, install with extras:
 
 ### VoyageAI
 
@@ -18,16 +22,10 @@ For other embedding providers, install with extras:
 uv pip install haiku.rag[voyageai]
 ```
 
-### OpenAI
+### MixedBread AI Reranking
 
 ```bash
-uv pip install haiku.rag[openai]
-```
-
-### Anthropic
-
-```bash
-uv pip install haiku.rag[anthropic]
+uv pip install haiku.rag[mxbai]
 ```
 
 ## Requirements

pyproject.toml

@@ -27,6 +27,7 @@ dependencies = [
     "httpx>=0.28.1",
     "ollama>=0.5.3",
     "pydantic>=2.11.7",
+    "pydantic-ai>=0.7.2",
     "python-dotenv>=1.1.0",
     "rich>=14.0.0",
     "sqlite-vec>=0.1.6",
@@ -37,9 +38,6 @@ dependencies = [
 
 [project.optional-dependencies]
 voyageai = ["voyageai>=0.3.2"]
-openai = ["openai>=1.0.0"]
-anthropic = ["anthropic>=0.56.0"]
-cohere = ["cohere>=5.16.1"]
 mxbai = ["mxbai-rerank>=0.1.6"]
 
 [project.scripts]

src/haiku/rag/config.py

@@ -22,8 +22,7 @@ class AppConfig(BaseModel):
     RERANK_PROVIDER: str = "ollama"
     RERANK_MODEL: str = "qwen3"
 
-    QA_PROVIDER: str = "ollama"
-    QA_MODEL: str = "qwen3"
+    QA_PROVIDER: str = "ollama:qwen3"
 
     CHUNK_SIZE: int = 256
     CONTEXT_CHUNK_RADIUS: int = 0

src/haiku/rag/embeddings/__init__.py

@@ -17,20 +17,14 @@ def get_embedder() -> EmbedderBase:
         except ImportError:
             raise ImportError(
                 "VoyageAI embedder requires the 'voyageai' package. "
-                "Please install haiku.rag with the 'voyageai' extra:"
+                "Please install haiku.rag with the 'voyageai' extra: "
                 "uv pip install haiku.rag[voyageai]"
             )
         return VoyageAIEmbedder(Config.EMBEDDINGS_MODEL, Config.EMBEDDINGS_VECTOR_DIM)
 
     if Config.EMBEDDINGS_PROVIDER == "openai":
-        try:
-            from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
-        except ImportError:
-            raise ImportError(
-                "OpenAI embedder requires the 'openai' package. "
-                "Please install haiku.rag with the 'openai' extra:"
-                "uv pip install haiku.rag[openai]"
-            )
+        from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
+
         return OpenAIEmbedder(Config.EMBEDDINGS_MODEL, Config.EMBEDDINGS_VECTOR_DIM)
 
     raise ValueError(f"Unsupported embedding provider: {Config.EMBEDDINGS_PROVIDER}")

src/haiku/rag/embeddings/openai.py

@@ -1,16 +1,13 @@
-try:
-    from openai import AsyncOpenAI
+from openai import AsyncOpenAI
 
-    from haiku.rag.embeddings.base import EmbedderBase
+from haiku.rag.embeddings.base import EmbedderBase
 
-    class Embedder(EmbedderBase):
-        async def embed(self, text: str) -> list[float]:
-            client = AsyncOpenAI()
-            response = await client.embeddings.create(
-                model=self._model,
-                input=text,
-            )
-            return response.data[0].embedding
 
-except ImportError:
-    pass
+class Embedder(EmbedderBase):
+    async def embed(self, text: str) -> list[float]:
+        client = AsyncOpenAI()
+        response = await client.embeddings.create(
+            model=self._model,
+            input=text,
+        )
+        return response.data[0].embedding

src/haiku/rag/qa/__init__.py

@@ -1,44 +1,13 @@
 from haiku.rag.client import HaikuRAG
 from haiku.rag.config import Config
-from haiku.rag.qa.base import QuestionAnswerAgentBase
-from haiku.rag.qa.ollama import QuestionAnswerOllamaAgent
+from haiku.rag.qa.agent import QuestionAnswerAgent
 
 
-def get_qa_agent(
-    client: HaikuRAG, model: str = "", use_citations: bool = False
-) -> QuestionAnswerAgentBase:
-    """
-    Factory function to get the appropriate QA agent based on the configuration.
-    """
-    if Config.QA_PROVIDER == "ollama":
-        return QuestionAnswerOllamaAgent(
-            client, model or Config.QA_MODEL, use_citations
-        )
+def get_qa_agent(client: HaikuRAG, use_citations: bool = False) -> QuestionAnswerAgent:
+    provider_model = Config.QA_PROVIDER
 
-    if Config.QA_PROVIDER == "openai":
-        try:
-            from haiku.rag.qa.openai import QuestionAnswerOpenAIAgent
-        except ImportError:
-            raise ImportError(
-                "OpenAI QA agent requires the 'openai' package. "
-                "Please install haiku.rag with the 'openai' extra:"
-                "uv pip install haiku.rag[openai]"
-            )
-        return QuestionAnswerOpenAIAgent(
-            client, model or Config.QA_MODEL, use_citations
-        )
-
-    if Config.QA_PROVIDER == "anthropic":
-        try:
-            from haiku.rag.qa.anthropic import QuestionAnswerAnthropicAgent
-        except ImportError:
-            raise ImportError(
-                "Anthropic QA agent requires the 'anthropic' package. "
-                "Please install haiku.rag with the 'anthropic' extra:"
-                "uv pip install haiku.rag[anthropic]"
-            )
-        return QuestionAnswerAnthropicAgent(
-            client, model or Config.QA_MODEL, use_citations
-        )
-
-    raise ValueError(f"Unsupported QA provider: {Config.QA_PROVIDER}")
+    return QuestionAnswerAgent(
+        client=client,
+        provider_model=provider_model,
+        use_citations=use_citations,
+    )

src/haiku/rag/qa/agent.py

@@ -0,0 +1,80 @@
+from pydantic import BaseModel, Field
+from pydantic_ai import Agent, RunContext
+from pydantic_ai.models.openai import OpenAIModel
+from pydantic_ai.providers.ollama import OllamaProvider
+
+from haiku.rag.client import HaikuRAG
+from haiku.rag.config import Config
+from haiku.rag.qa.prompts import SYSTEM_PROMPT, SYSTEM_PROMPT_WITH_CITATIONS
+
+
+class SearchResult(BaseModel):
+    content: str = Field(description="The document text content")
+    score: float = Field(description="Relevance score (higher is more relevant)")
+    document_uri: str = Field(description="Source URI/path of the document")
+
+
+class Dependencies(BaseModel):
+    model_config = {"arbitrary_types_allowed": True}
+    client: HaikuRAG
+
+
+class QuestionAnswerAgent:
+    def __init__(
+        self,
+        client: HaikuRAG,
+        provider_model: str,
+        use_citations: bool = False,
+        q: float = 0.0,
+    ):
+        self._client = client
+
+        system_prompt = SYSTEM_PROMPT_WITH_CITATIONS if use_citations else SYSTEM_PROMPT
+        model_obj = self._get_model(provider_model)
+
+        self._agent = Agent(
+            model=model_obj,
+            deps_type=Dependencies,
+            system_prompt=system_prompt,
+        )
+
+        @self._agent.tool
+        async def search_documents(
+            ctx: RunContext[Dependencies],
+            query: str,
+            limit: int = 3,
+        ) -> list[SearchResult]:
+            """Search the knowledge base for relevant documents."""
+            search_results = await ctx.deps.client.search(query, limit=limit)
+            expanded_results = await ctx.deps.client.expand_context(search_results)
+
+            return [
+                SearchResult(
+                    content=chunk.content,
+                    score=score,
+                    document_uri=chunk.document_uri or "",
+                )
+                for chunk, score in expanded_results
+            ]
+
+    def _get_model(self, provider_model: str):
+        """Get the appropriate model object for the provider:model format."""
+        if ":" not in provider_model:
+            raise ValueError(f"Invalid provider:model format: {provider_model}")
+
+        provider, model = provider_model.split(":", 1)
+
+        if provider == "ollama":
+            return OpenAIModel(
+                model_name=model,
+                provider=OllamaProvider(base_url=f"{Config.OLLAMA_BASE_URL}/v1"),
+            )
+        else:
+            # For other providers, use the provider:model string directly
+            return provider_model
+
+    async def answer(self, question: str) -> str:
+        """Answer a question using the RAG system."""
+        deps = Dependencies(client=self._client)
+        result = await self._agent.run(question, deps=deps)
+        return result.output

src/haiku/rag/qa/anthropic.py

@@ -1,108 +0,0 @@
-from collections.abc import Sequence
-
-try:
-    from anthropic import AsyncAnthropic  # type: ignore
-    from anthropic.types import (  # type: ignore
-        MessageParam,
-        TextBlock,
-        ToolParam,
-        ToolUseBlock,
-    )
-
-    from haiku.rag.client import HaikuRAG
-    from haiku.rag.qa.base import QuestionAnswerAgentBase
-
-    class QuestionAnswerAnthropicAgent(QuestionAnswerAgentBase):
-        def __init__(
-            self,
-            client: HaikuRAG,
-            model: str = "claude-3-5-haiku-20241022",
-            use_citations: bool = False,
-        ):
-            super().__init__(client, model or self._model, use_citations)
-            self.tools: Sequence[ToolParam] = [
-                ToolParam(
-                    name="search_documents",
-                    description="Search the knowledge base for relevant documents. Returns a JSON array with content, score, and document_uri for each result.",
-                    input_schema={
-                        "type": "object",
-                        "properties": {
-                            "query": {
-                                "type": "string",
-                                "description": "The search query to find relevant documents",
-                            },
-                            "limit": {
-                                "type": "integer",
-                                "description": "Maximum number of results to return",
-                                "default": 3,
-                            },
-                        },
-                        "required": ["query"],
-                    },
-                )
-            ]
-
-        async def answer(self, question: str) -> str:
-            anthropic_client = AsyncAnthropic()
-
-            messages: list[MessageParam] = [{"role": "user", "content": question}]
-
-            max_rounds = 5  # Prevent infinite loops
-
-            for _ in range(max_rounds):
-                response = await anthropic_client.messages.create(
-                    model=self._model,
-                    max_tokens=4096,
-                    system=self._system_prompt,
-                    messages=messages,
-                    tools=self.tools,
-                    temperature=0.0,
-                )
-
-                if response.stop_reason == "tool_use":
-                    messages.append({"role": "assistant", "content": response.content})
-
-                    # Process tool calls
-                    tool_results = []
-                    for content_block in response.content:
-                        if isinstance(content_block, ToolUseBlock):
-                            if content_block.name == "search_documents":
-                                args = content_block.input
-                                query = (
-                                    args.get("query", question)
-                                    if isinstance(args, dict)
-                                    else question
-                                )
-                                limit = (
-                                    int(args.get("limit", 3))
-                                    if isinstance(args, dict)
-                                    else 3
-                                )
-
-                                context = await self._search_and_expand(
-                                    query, limit=limit
-                                )
-
-                                tool_results.append(
-                                    {
-                                        "type": "tool_result",
-                                        "tool_use_id": content_block.id,
-                                        "content": context,
-                                    }
-                                )
-
-                    if tool_results:
-                        messages.append({"role": "user", "content": tool_results})
-                else:
-                    # No tool use, return the response
-                    if response.content:
-                        first_content = response.content[0]
-                        if isinstance(first_content, TextBlock):
-                            return first_content.text
-                    return ""
-
-            # If we've exhausted max rounds, return empty string
-            return ""
-
-except ImportError:
-    pass

src/haiku/rag/qa/base.py

@@ -1,89 +0,0 @@
-import json
-
-from haiku.rag.client import HaikuRAG
-from haiku.rag.qa.prompts import SYSTEM_PROMPT, SYSTEM_PROMPT_WITH_CITATIONS
-
-
-class QuestionAnswerAgentBase:
-    _model: str = ""
-    _system_prompt: str = SYSTEM_PROMPT
-
-    def __init__(self, client: HaikuRAG, model: str = "", use_citations: bool = False):
-        self._model = model
-        self._client = client
-        self._system_prompt = (
-            SYSTEM_PROMPT_WITH_CITATIONS if use_citations else SYSTEM_PROMPT
-        )
-
-    async def answer(self, question: str) -> str:
-        raise NotImplementedError(
-            "QABase is an abstract class. Please implement the answer method in a subclass."
-        )
-
-    async def _search_and_expand(self, query: str, limit: int = 3) -> str:
-        """Search for documents and expand context, then format as JSON"""
-        search_results = await self._client.search(query, limit=limit)
-        expanded_results = await self._client.expand_context(search_results)
-        return self._format_search_results(expanded_results)
-
-    def _format_search_results(self, search_results) -> str:
-        """Format search results as JSON list of {content, score, document_uri}"""
-        formatted_results = []
-        for chunk, score in search_results:
-            formatted_results.append(
-                {
-                    "content": chunk.content,
-                    "score": score,
-                    "document_uri": chunk.document_uri,
-                }
-            )
-        return json.dumps(formatted_results, indent=2)
-
-    tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "search_documents",
-                "description": "Search the knowledge base for relevant documents. Returns a JSON array of search results.",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "query": {
-                            "type": "string",
-                            "description": "The search query to find relevant documents",
-                        },
-                        "limit": {
-                            "type": "integer",
-                            "description": "Maximum number of results to return",
-                            "default": 3,
-                        },
-                    },
-                    "required": ["query"],
-                },
-                "returns": {
-                    "type": "string",
-                    "description": "JSON array of search results",
-                    "schema": {
-                        "type": "array",
-                        "items": {
-                            "type": "object",
-                            "properties": {
-                                "content": {
-                                    "type": "string",
-                                    "description": "The document text content",
-                                },
-                                "score": {
-                                    "type": "number",
-                                    "description": "Relevance score (higher is more relevant)",
-                                },
-                                "document_uri": {
-                                    "type": "string",
-                                    "description": "Source URI/path of the document",
-                                },
-                            },
-                        },
-                    },
-                },
-            },
-        }
-    ]

src/haiku/rag/qa/ollama.py

@@ -1,60 +0,0 @@
-from ollama import AsyncClient
-
-from haiku.rag.client import HaikuRAG
-from haiku.rag.config import Config
-from haiku.rag.qa.base import QuestionAnswerAgentBase
-
-OLLAMA_OPTIONS = {"temperature": 0.0, "seed": 42, "num_ctx": 16384}
-
-
-class QuestionAnswerOllamaAgent(QuestionAnswerAgentBase):
-    def __init__(
-        self,
-        client: HaikuRAG,
-        model: str = Config.QA_MODEL,
-        use_citations: bool = False,
-    ):
-        super().__init__(client, model or self._model, use_citations)
-
-    async def answer(self, question: str) -> str:
-        ollama_client = AsyncClient(host=Config.OLLAMA_BASE_URL)
-
-        messages = [
-            {"role": "system", "content": self._system_prompt},
-            {"role": "user", "content": question},
-        ]
-
-        max_rounds = 5  # Prevent infinite loops
-
-        for _ in range(max_rounds):
-            response = await ollama_client.chat(
-                model=self._model,
-                messages=messages,
-                tools=self.tools,
-                options=OLLAMA_OPTIONS,
-                think=False,
-            )
-
-            if response.get("message", {}).get("tool_calls"):
-                messages.append(response["message"])
-
-                for tool_call in response["message"]["tool_calls"]:
-                    if tool_call["function"]["name"] == "search_documents":
-                        args = tool_call["function"]["arguments"]
-                        query = args.get("query", question)
-                        limit = int(args.get("limit", 3))
-
-                        context = await self._search_and_expand(query, limit=limit)
-                        messages.append(
-                            {
-                                "role": "tool",
-                                "content": context,
-                                "tool_call_id": tool_call.get("id", "search_tool"),
-                            }
-                        )
-            else:
-                # No tool calls, return the response
-                return response["message"]["content"]
-
-        # If we've exhausted max rounds, return empty string
-        return ""

src/haiku/rag/qa/openai.py

@@ -1,97 +0,0 @@
-from collections.abc import Sequence
-
-try:
-    from openai import AsyncOpenAI  # type: ignore
-    from openai.types.chat import (  # type: ignore
-        ChatCompletionAssistantMessageParam,
-        ChatCompletionMessageParam,
-        ChatCompletionSystemMessageParam,
-        ChatCompletionToolMessageParam,
-        ChatCompletionUserMessageParam,
-    )
-    from openai.types.chat.chat_completion_tool_param import (  # type: ignore
-        ChatCompletionToolParam,
-    )
-
-    from haiku.rag.client import HaikuRAG
-    from haiku.rag.qa.base import QuestionAnswerAgentBase
-
-    class QuestionAnswerOpenAIAgent(QuestionAnswerAgentBase):
-        def __init__(
-            self,
-            client: HaikuRAG,
-            model: str = "gpt-4o-mini",
-            use_citations: bool = False,
-        ):
-            super().__init__(client, model or self._model, use_citations)
-            self.tools: Sequence[ChatCompletionToolParam] = [
-                ChatCompletionToolParam(tool) for tool in self.tools
-            ]
-
-        async def answer(self, question: str) -> str:
-            openai_client = AsyncOpenAI()
-
-            messages: list[ChatCompletionMessageParam] = [
-                ChatCompletionSystemMessageParam(
-                    role="system", content=self._system_prompt
-                ),
-                ChatCompletionUserMessageParam(role="user", content=question),
-            ]
-
-            max_rounds = 5  # Prevent infinite loops
-
-            for _ in range(max_rounds):
-                response = await openai_client.chat.completions.create(
-                    model=self._model,
-                    messages=messages,
-                    tools=self.tools,
-                    temperature=0.0,
-                )
-
-                response_message = response.choices[0].message
-
-                if response_message.tool_calls:
-                    messages.append(
-                        ChatCompletionAssistantMessageParam(
-                            role="assistant",
-                            content=response_message.content,
-                            tool_calls=[
-                                {
-                                    "id": tc.id,
-                                    "type": "function",
-                                    "function": {
-                                        "name": tc.function.name,
-                                        "arguments": tc.function.arguments,
-                                    },
-                                }
-                                for tc in response_message.tool_calls
-                            ],
-                        )
-                    )
-
-                    for tool_call in response_message.tool_calls:
-                        if tool_call.function.name == "search_documents":
-                            import json
-
-                            args = json.loads(tool_call.function.arguments)
-                            query = args.get("query", question)
-                            limit = int(args.get("limit", 3))
-
-                            context = await self._search_and_expand(query, limit=limit)
-
-                            messages.append(
-                                ChatCompletionToolMessageParam(
-                                    role="tool",
-                                    content=context,
-                                    tool_call_id=tool_call.id,
-                                )
-                            )
-                else:
-                    # No tool calls, return the response
-                    return response_message.content or ""
-
-            # If we've exhausted max rounds, return empty string
-            return ""
-
-except ImportError:
-    pass

src/haiku/rag/qa/prompts.py

@@ -18,6 +18,7 @@ Guidelines:
 - Stick to the answer, do not ellaborate or provide context unless explicitly asked for it.
 
 Be concise, and always maintain accuracy over completeness. Prefer short, direct answers that are well-supported by the documents.
+/no_think
 """
 
 SYSTEM_PROMPT_WITH_CITATIONS = """
@@ -55,4 +56,5 @@ Citations:
 - /path/to/document2.pdf: "The manual provides guidance on military procedures and..."
 
 Be concise, and always maintain accuracy over completeness. Prefer short, direct answers that are well-supported by the documents.
+/no_think
 """

src/haiku/rag/reranking/ollama.py

@@ -1,14 +1,12 @@
-import json
-
-from ollama import AsyncClient
 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
 
-OLLAMA_OPTIONS = {"temperature": 0.0, "seed": 42, "num_ctx": 16384}
-
 
 class RerankResult(BaseModel):
     """Individual rerank result with index and relevance score."""
@@ -26,7 +24,28 @@ class RerankResponse(BaseModel):
 class OllamaReranker(RerankerBase):
     def __init__(self, model: str = Config.RERANK_MODEL):
         self._model = model
-        self._client = AsyncClient(host=Config.OLLAMA_BASE_URL)
+
+        # 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
@@ -38,15 +57,6 @@ class OllamaReranker(RerankerBase):
         for i, chunk in enumerate(chunks):
             documents.append({"index": i, "content": chunk.content})
 
-        # Create the prompt for reranking
-        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."""
-
         documents_text = ""
         for doc in documents:
             documents_text += f"Index {doc['index']}: {doc['content']}\n\n"
@@ -56,27 +66,14 @@ Only return the top documents up to the requested limit, ordered by decreasing r
 Documents to rerank:
 {documents_text.strip()}
 
-Please rank these documents by relevance to the query and return the top {top_n} results as JSON."""
-
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_prompt},
-        ]
+Rank these documents by relevance to the query and return the top {top_n} results as JSON."""
 
         try:
-            response = await self._client.chat(
-                model=self._model,
-                messages=messages,
-                format=RerankResponse.model_json_schema(),
-                options=OLLAMA_OPTIONS,
-            )
+            result = await self._agent.run(user_prompt)
 
-            content = response["message"]["content"]
-
-            parsed_response = RerankResponse.model_validate(json.loads(content))
             return [
-                (chunks[result.index], result.relevance_score)
-                for result in parsed_response.results[:top_n]
+                (chunks[result_item.index], result_item.relevance_score)
+                for result_item in result.output.results[:top_n]
             ]
 
         except Exception:

tests/llm_judge.py

@@ -1,21 +1,67 @@
-import json
-
-from ollama import AsyncClient
 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
 
+# Shared rubric/prompt for answer equivalence evaluation
+ANSWER_EQUIVALENCE_RUBRIC = """You are evaluating whether two answers to the same question are semantically equivalent.
+
+EVALUATION CRITERIA:
+Rate as EQUIVALENT 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
+
+Rate as NOT EQUIVALENT if:
+✗ Factual contradictions exist between the answers
+✗ One answer fails to address the core question
+✗ Key information is missing that changes the meaning
+✗ The answers lead to different conclusions or implications
+
+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
+/no_think"""
+
 
 class LLMJudgeResponseSchema(BaseModel):
     equivalent: bool
 
 
 class LLMJudge:
-    """LLM-as-judge for evaluating answer equivalence using Ollama."""
+    """LLM-as-judge for evaluating answer equivalence using Pydantic AI."""
 
-    def __init__(self, model: str = Config.QA_MODEL):
-        self.model = model
-        self.client = AsyncClient(host=Config.OLLAMA_BASE_URL)
+    def __init__(self, provider_model: str = Config.QA_PROVIDER):
+        self.provider_model = provider_model
+
+        # Parse provider:model format
+        if ":" not in provider_model:
+            raise ValueError(f"Invalid provider:model format: {provider_model}")
+
+        provider, model = provider_model.split(":", 1)
+
+        if provider == "ollama":
+            # Create Ollama model
+            ollama_model = OpenAIModel(
+                model_name=model,
+                provider=OllamaProvider(base_url=f"{Config.OLLAMA_BASE_URL}/v1"),
+            )
+        else:
+            # For other providers, use the provider:model string directly
+            ollama_model = provider_model
+
+        # Create Pydantic AI agent
+        self._agent = Agent(
+            model=ollama_model,
+            output_type=LLMJudgeResponseSchema,
+            system_prompt=ANSWER_EQUIVALENCE_RUBRIC,
+        )
 
     async def judge_answers(
         self, question: str, answer: str, expected_answer: str
@@ -29,53 +75,14 @@ class LLMJudge:
             expected_answer: The reference/expected answer
 
         Returns:
-            Dictionary with judgment result:
-            - equivalent: bool indicating if answers are equivalent
-            - explanation: str explaining the reasoning
-            - score: str rating from 1-5
+            bool indicating if answers are equivalent
         """
 
-        prompt = f"""You are an expert evaluator determining whether two answers to the same question are semantically equivalent.
-
-QUESTION: {question}
+        prompt = f"""QUESTION: {question}
 
 GENERATED ANSWER: {answer}
 
-EXPECTED ANSWER: {expected_answer}
+EXPECTED ANSWER: {expected_answer}"""
 
-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
-
-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
-
-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,
-            messages=[{"role": "user", "content": prompt}],
-            format=LLMJudgeResponseSchema.model_json_schema(),
-            think=False,
-        )
-
-        answer = response["message"]["content"].strip()
-        try:
-            res = json.loads(answer)
-            assert "equivalent" in res, "Response must contain 'equivalent' key"
-            return res["equivalent"]
-        except json.JSONDecodeError:
-            assert False, "Response is not valid JSON"
+        result = await self._agent.run(prompt)
+        return result.output.equivalent

tests/test_embedder.py

@@ -1,19 +1,26 @@
 import numpy as np
 import pytest
 
-from haiku.rag.embeddings import get_embedder
+from haiku.rag.config import Config
+from haiku.rag.embeddings.ollama import Embedder as OllamaEmbedder
+from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
+
+OPENAI_AVAILABLE = bool(Config.OPENAI_API_KEY)
+VOYAGEAI_AVAILABLE = bool(Config.VOYAGE_API_KEY)
+
+
+# Calculate cosine similarity
+def similarities(embeddings, test_embedding):
+    return [
+        np.dot(embedding, test_embedding)
+        / (np.linalg.norm(embedding) * np.linalg.norm(test_embedding))
+        for embedding in embeddings
+    ]
 
 
 @pytest.mark.asyncio
-async def test_embedder():
-    embedder = get_embedder()
-    embedding = await embedder.embed("hello world")
-    assert len(embedding) == embedder._vector_dim
-
-
-@pytest.mark.asyncio
-async def test_similarity():
-    embedder = get_embedder()
+async def test_ollama_embedder():
+    embedder = OllamaEmbedder("mxbai-embed-large", 1024)
     phrases = [
         "I enjoy eating great food.",
         "Python is my favorite programming language.",
@@ -21,14 +28,6 @@ async def test_similarity():
     ]
     embeddings = [np.array(await embedder.embed(phrase)) for phrase in phrases]
 
-    # Calculate cosine similarity
-    def similarities(embeddings, test_embedding):
-        return [
-            np.dot(embedding, test_embedding)
-            / (np.linalg.norm(embedding) * np.linalg.norm(test_embedding))
-            for embedding in embeddings
-        ]
-
     test_phrase = "I am going for a camping trip."
     test_embedding = await embedder.embed(test_phrase)
 
@@ -49,80 +48,66 @@ async def test_similarity():
 
 
 @pytest.mark.asyncio
-async def test_openai_embedder(monkeypatch):
-    monkeypatch.setenv("EMBEDDINGS_PROVIDER", "openai")
-    monkeypatch.setenv("EMBEDDINGS_MODEL", "text-embedding-3-small")
+@pytest.mark.skipif(not OPENAI_AVAILABLE, reason="OpenAI API key not available")
+async def test_openai_embedder():
+    embedder = OpenAIEmbedder("text-embedding-3-small", 1536)
+    phrases = [
+        "I enjoy eating great food.",
+        "Python is my favorite programming language.",
+        "I love to travel and see new places.",
+    ]
+    embeddings = [np.array(await embedder.embed(phrase)) for phrase in phrases]
 
-    try:
-        from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
+    test_phrase = "I am going for a camping trip."
+    test_embedding = await embedder.embed(test_phrase)
 
-        embedder = OpenAIEmbedder("text-embedding-3-small", 1536)
+    sims = similarities(embeddings, test_embedding)
+    assert max(sims) == sims[2]
 
-        # Mock the OpenAI client
-        class MockEmbeddingData:
-            def __init__(self, embedding):
-                self.embedding = embedding
+    test_phrase = "When is dinner ready?"
+    test_embedding = await embedder.embed(test_phrase)
 
-        class MockResponse:
-            def __init__(self, embedding):
-                self.data = [MockEmbeddingData(embedding)]
+    sims = similarities(embeddings, test_embedding)
+    assert max(sims) == sims[0]
 
-        class MockAsyncOpenAI:
-            class MockEmbeddings:
-                async def create(self, model, input):
-                    return MockResponse([0.1] * 1536)
+    test_phrase = "I work as a software developer."
+    test_embedding = await embedder.embed(test_phrase)
 
-            def __init__(self):
-                self.embeddings = self.MockEmbeddings()
-
-        # Patch the AsyncOpenAI import
-        import haiku.rag.embeddings.openai
-
-        original_client = haiku.rag.embeddings.openai.AsyncOpenAI
-        haiku.rag.embeddings.openai.AsyncOpenAI = MockAsyncOpenAI
-
-        try:
-            embedding = await embedder.embed("test text")
-            assert len(embedding) == 1536
-            assert all(isinstance(x, float) for x in embedding)
-        finally:
-            haiku.rag.embeddings.openai.AsyncOpenAI = original_client
-
-    except ImportError:
-        pytest.skip("OpenAI package not installed")
+    sims = similarities(embeddings, test_embedding)
+    assert max(sims) == sims[1]
 
 
 @pytest.mark.asyncio
-async def test_voyageai_embedder(monkeypatch):
-    monkeypatch.setenv("EMBEDDINGS_PROVIDER", "voyageai")
-    monkeypatch.setenv("EMBEDDINGS_MODEL", "voyage-3.5")
-
+@pytest.mark.skipif(not VOYAGEAI_AVAILABLE, reason="VoyageAI API key not available")
+async def test_voyageai_embedder():
     try:
         from haiku.rag.embeddings.voyageai import Embedder as VoyageAIEmbedder
 
         embedder = VoyageAIEmbedder("voyage-3.5", 1024)
+        phrases = [
+            "I enjoy eating great food.",
+            "Python is my favorite programming language.",
+            "I love to travel and see new places.",
+        ]
+        embeddings = [np.array(await embedder.embed(phrase)) for phrase in phrases]
 
-        # Mock the VoyageAI client
-        class MockEmbeddings:
-            def __init__(self, embeddings):
-                self.embeddings = embeddings
+        test_phrase = "I am going for a camping trip."
+        test_embedding = await embedder.embed(test_phrase)
 
-        class MockClient:
-            def embed(self, texts, model, output_dtype):
-                return MockEmbeddings([[0.1] * 1024])
+        sims = similarities(embeddings, test_embedding)
+        assert max(sims) == sims[2]
 
-        # Patch the Client import
-        import haiku.rag.embeddings.voyageai
+        test_phrase = "When is dinner ready?"
+        test_embedding = await embedder.embed(test_phrase)
 
-        original_client = haiku.rag.embeddings.voyageai.Client
-        haiku.rag.embeddings.voyageai.Client = MockClient
+        sims = similarities(embeddings, test_embedding)
+        assert max(sims) == sims[0]
 
-        try:
-            embedding = await embedder.embed("test text")
-            assert len(embedding) == 1024
-            assert all(isinstance(x, float) for x in embedding)
-        finally:
-            haiku.rag.embeddings.voyageai.Client = original_client
+        test_phrase = "I work as a software developer."
+        test_embedding = await embedder.embed(test_phrase)
+
+        sims = similarities(embeddings, test_embedding)
+        assert max(sims) == sims[1]
 
     except ImportError:
         pytest.skip("VoyageAI package not installed")

tests/test_qa.py

@@ -2,32 +2,20 @@ import pytest
 from datasets import Dataset
 
 from haiku.rag.client import HaikuRAG
-from haiku.rag.qa.ollama import QuestionAnswerOllamaAgent
-
-try:
-    from haiku.rag.qa.openai import QuestionAnswerOpenAIAgent
-
-    OPENAI_AVAILABLE = True
-except ImportError:
-    QuestionAnswerOpenAIAgent = None
-    OPENAI_AVAILABLE = False
-
-try:
-    from haiku.rag.qa.anthropic import QuestionAnswerAnthropicAgent
-
-    ANTHROPIC_AVAILABLE = True
-except ImportError:
-    QuestionAnswerAnthropicAgent = None
-    ANTHROPIC_AVAILABLE = False
+from haiku.rag.config import Config
+from haiku.rag.qa.agent import QuestionAnswerAgent
 
 from .llm_judge import LLMJudge
 
+OPENAI_AVAILABLE = bool(Config.OPENAI_API_KEY)
+ANTHROPIC_AVAILABLE = bool(Config.ANTHROPIC_API_KEY)
+
 
 @pytest.mark.asyncio
 async def test_qa_ollama(qa_corpus: Dataset):
-    """Test QA with actual question from the dataset using LLM judge."""
+    """Test Ollama QA with LLM judge."""
     client = HaikuRAG(":memory:")
-    qa = QuestionAnswerOllamaAgent(client)
+    qa = QuestionAnswerAgent(client, provider_model="ollama:qwen3")
     llm_judge = LLMJudge()
 
     doc = qa_corpus[1]
@@ -49,9 +37,9 @@ async def test_qa_ollama(qa_corpus: Dataset):
 @pytest.mark.asyncio
 @pytest.mark.skipif(not OPENAI_AVAILABLE, reason="OpenAI not available")
 async def test_qa_openai(qa_corpus: Dataset):
-    """Test OpenAI QA basic functionality."""
+    """Test OpenAI QA with LLM judge."""
     client = HaikuRAG(":memory:")
-    qa = QuestionAnswerOpenAIAgent(client)  # type: ignore
+    qa = QuestionAnswerAgent(client, provider_model="openai:gpt-4o-mini")
     llm_judge = LLMJudge()
 
     doc = qa_corpus[1]
@@ -73,9 +61,11 @@ async def test_qa_openai(qa_corpus: Dataset):
 @pytest.mark.asyncio
 @pytest.mark.skipif(not ANTHROPIC_AVAILABLE, reason="Anthropic not available")
 async def test_qa_anthropic(qa_corpus: Dataset):
-    """Test Anthropic QA basic functionality."""
+    """Test Anthropic QA with LLM judge."""
     client = HaikuRAG(":memory:")
-    qa = QuestionAnswerAnthropicAgent(client)  # type: ignore
+    qa = QuestionAnswerAgent(
+        client, provider_model="anthropic:claude-3-5-haiku-20241022"
+    )
     llm_judge = LLMJudge()
 
     doc = qa_corpus[1]

tests/test_reranker.py

@@ -1,8 +1,11 @@
 import pytest
 
+from haiku.rag.config import Config
 from haiku.rag.reranking.base import RerankerBase
 from haiku.rag.store.models.chunk import Chunk
 
+COHERE_AVAILABLE = bool(Config.COHERE_API_KEY)
+
 chunks = [
     Chunk(content=content, document_id=i)
     for i, content in enumerate(
@@ -43,6 +46,7 @@ async def test_mxbai_reranker():
 
 
 @pytest.mark.asyncio
+@pytest.mark.skipif(not COHERE_AVAILABLE, reason="Cohere API key not available")
 async def test_cohere_reranker():
     try:
         from haiku.rag.reranking.cohere import CohereReranker

uv.lock

@@ -28,6 +28,18 @@ 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 = "ag-ui-protocol"
+version = "0.1.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pydantic" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/27/de/0bddf7f26d5f38274c99401735c82ad59df9cead6de42f4bb2ad837286fe/ag_ui_protocol-0.1.8.tar.gz", hash = "sha256:eb745855e9fc30964c77e953890092f8bd7d4bbe6550d6413845428dd0faac0b", size = 5323, upload-time = "2025-07-15T10:55:36.389Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c4/00/40c6b0313c25d1ab6fac2ecba1cd5b15b1cd3c3a71b3d267ad890e405889/ag_ui_protocol-0.1.8-py3-none-any.whl", hash = "sha256:1567ccb067b7b8158035b941a985e7bb185172d660d4542f3f9c6fff77b55c6e", size = 7066, upload-time = "2025-07-15T10:55:35.075Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -137,7 +149,7 @@ wheels = [
 
 [[package]]
 name = "anthropic"
-version = "0.56.0"
+version = "0.63.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -148,9 +160,9 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/4d/40/0c4eb5728466849803782c8a86eb315af1a6eb0efea6a751de120ab845c9/anthropic-0.56.0.tar.gz", hash = "sha256:56fa9eb61afa004a1664bc85eed071e77b96c579b77395e9cc893097e599f72e", size = 421538, upload-time = "2025-07-01T19:39:10.805Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/80/6e/3bedbd1c932cce98495e007b6d8007a139cf46adc5c889d700ec75ddd7f3/anthropic-0.63.0.tar.gz", hash = "sha256:d75ecfff17a0b96d845be3cbd93e06a48ea95aaa27add586748772fa5b926994", size = 427391, upload-time = "2025-08-12T16:59:58.079Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e5/90/7f4d4084f9c35c3ea3e784646ec12f9b2c8cf8743b2bb5489252659b5bda/anthropic-0.56.0-py3-none-any.whl", hash = "sha256:91f1f74abdcf0958d3296b657304588cc244b1107b89f973ff6f511afdacfc56", size = 289603, upload-time = "2025-07-01T19:39:08.794Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/a1/83bdb1a8be76fbb3ceedae9dfe1b515cff56dcfbbc388b53070a27ce341f/anthropic-0.63.0-py3-none-any.whl", hash = "sha256:d1849fe1635ae4277f45a0e4365979ed69e6264b73350ce8a99fee701d347745", size = 296637, upload-time = "2025-08-12T16:59:56.841Z" },
 ]
 
 [[package]]
@@ -167,6 +179,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916, upload-time = "2025-03-17T00:02:52.713Z" },
 ]
 
+[[package]]
+name = "argcomplete"
+version = "3.6.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/16/0f/861e168fc813c56a78b35f3c30d91c6757d1fd185af1110f1aec784b35d0/argcomplete-3.6.2.tar.gz", hash = "sha256:d0519b1bc867f5f4f4713c41ad0aba73a4a5f007449716b16f385f2166dc6adf", size = 73403, upload-time = "2025-04-03T04:57:03.52Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/31/da/e42d7a9d8dd33fa775f467e4028a47936da2f01e4b0e561f9ba0d74cb0ca/argcomplete-3.6.2-py3-none-any.whl", hash = "sha256:65b3133a29ad53fb42c48cf5114752c7ab66c1c38544fdf6460f450c09b42591", size = 43708, upload-time = "2025-04-03T04:57:01.591Z" },
+]
+
 [[package]]
 name = "attrs"
 version = "25.3.0"
@@ -233,6 +254,43 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/50/cd/30110dc0ffcf3b131156077b90e9f60ed75711223f306da4db08eff8403b/beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b", size = 187285, upload-time = "2025-04-15T17:05:12.221Z" },
 ]
 
+[[package]]
+name = "boto3"
+version = "1.40.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "botocore" },
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