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Migrate QA agent to Pydantic AI.

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Yiorgis Gozadinos 2025-08-13 17:27:58 +02:00
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9 changed files with 596 additions and 427 deletions
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1
pyproject.toml
View file

@ -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",

50
src/haiku/rag/qa/__init__.py
View file

@ -1,44 +1,18 @@
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 = Config.QA_PROVIDER
model_name = Config.QA_MODEL
if provider in ("openai", "anthropic", "ollama"):
return QuestionAnswerAgent(
client=client,
provider=provider,
model=model_name,
use_citations=use_citations,
)
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}")
raise ValueError(f"Unsupported QA provider: {provider}")

79
src/haiku/rag/qa/agent.py Normal file
View file

@ -0,0 +1,79 @@
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: str,
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: str, model: str):
"""Get the appropriate model object for the provider."""
if provider == "openai":
return f"openai:{model}"
elif provider == "anthropic":
return f"anthropic:{model}"
elif provider == "ollama":
return OpenAIModel(
model_name=model,
provider=OllamaProvider(base_url=f"{Config.OLLAMA_BASE_URL}/v1"),
)
else:
raise ValueError(f"Unsupported provider: {provider}")
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

108
src/haiku/rag/qa/anthropic.py
View file

@ -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

89
src/haiku/rag/qa/base.py
View file

@ -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",
},
},
},
},
},
},
}
]

60
src/haiku/rag/qa/ollama.py
View file

@ -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 ""

97
src/haiku/rag/qa/openai.py
View file

@ -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

34
tests/test_qa.py
View file

@ -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, "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, "openai", "gpt-4o-mini")
llm_judge = LLMJudge()
doc = qa_corpus[1]
@ -73,9 +61,9 @@ 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, "anthropic", "claude-3-5-haiku-20241022")
llm_judge = LLMJudge()
doc = qa_corpus[1]

505
uv.lock
View file

@ -28,6 +28,18 @@ wheels = [
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@ -137,7 +149,7 @@ wheels = [
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@ -148,9 +160,9 @@ dependencies = [
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{ name = "typing-extensions" },
]
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