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Support for anthropic in Question/Answering

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Yiorgis Gozadinos 2025-07-04 11:48:19 +03:00
parent fe81f7f001
commit e66c160055
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6 changed files with 186 additions and 3 deletions
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1
pyproject.toml
View file

@ -38,6 +38,7 @@ dependencies = [
[project.optional-dependencies]
voyageai = ["voyageai>=0.3.2"]
openai = ["openai>=1.0.0"]
anthropic = ["anthropic>=0.56.0"]
[project.scripts]
haiku-rag = "haiku.rag.cli:cli"

3
src/haiku/rag/config.py
View file

@ -30,6 +30,7 @@ class AppConfig(BaseModel):
# Provider keys
VOYAGE_API_KEY: str = ""
OPENAI_API_KEY: str = ""
ANTHROPIC_API_KEY: str = ""
@field_validator("MONITOR_DIRECTORIES", mode="before")
@classmethod
@ -49,3 +50,5 @@ if Config.OPENAI_API_KEY:
os.environ["OPENAI_API_KEY"] = Config.OPENAI_API_KEY
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

13
src/haiku/rag/qa/__init__.py
View file

@ -23,4 +23,17 @@ def get_qa_agent(client: HaikuRAG, model: str = "") -> QuestionAnswerAgentBase:
)
return QuestionAnswerOpenAIAgent(client, model or "gpt-4o-mini")
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 --extra anthropic"
)
return QuestionAnswerAnthropicAgent(
client, model or "claude-3-5-haiku-20241022"
)
raise ValueError(f"Unsupported QA provider: {Config.QA_PROVIDER}")

112
src/haiku/rag/qa/anthropic.py Normal file
View file

@ -0,0 +1,112 @@
from collections.abc import Sequence
try:
from anthropic import AsyncAnthropic
from anthropic.types import 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"):
super().__init__(client, model or self._model)
self.tools: Sequence[ToolParam] = [
ToolParam(
name="search_documents",
description="Search the knowledge base for relevant documents",
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}]
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
)
search_results = await self._client.search(
query, limit=limit
)
context_chunks = []
for chunk, score in search_results:
context_chunks.append(
f"Content: {chunk.content}\nScore: {score:.4f}"
)
context = "\n\n".join(context_chunks)
tool_results.append(
{
"type": "tool_result",
"tool_use_id": content_block.id,
"content": context,
}
)
if tool_results:
messages.append({"role": "user", "content": tool_results})
final_response = await anthropic_client.messages.create(
model=self._model,
max_tokens=4096,
system=self._system_prompt,
messages=messages,
temperature=0.0,
)
if final_response.content:
first_content = final_response.content[0]
if isinstance(first_content, TextBlock):
return first_content.text
return ""
if response.content:
first_content = response.content[0]
if isinstance(first_content, TextBlock):
return first_content.text
return ""
except ImportError:
pass

36
tests/test_qa.py
View file

@ -12,11 +12,19 @@ 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 .llm_judge import LLMJudge
@pytest.mark.asyncio
async def test_qa_ollama_with_dataset_question(qa_corpus: Dataset):
async def test_qa_ollama(qa_corpus: Dataset):
"""Test QA with actual question from the dataset using LLM judge."""
client = HaikuRAG(":memory:")
qa = QuestionAnswerOllamaAgent(client)
@ -40,7 +48,7 @@ async def test_qa_ollama_with_dataset_question(qa_corpus: Dataset):
@pytest.mark.asyncio
@pytest.mark.skipif(not OPENAI_AVAILABLE, reason="OpenAI not available")
async def test_qa_openai_basic(qa_corpus: Dataset):
async def test_qa_openai(qa_corpus: Dataset):
"""Test OpenAI QA basic functionality."""
client = HaikuRAG(":memory:")
qa = QuestionAnswerOpenAIAgent(client) # type: ignore
@ -60,3 +68,27 @@ async def test_qa_openai_basic(qa_corpus: Dataset):
assert is_equivalent, (
f"Generated answer not equivalent to expected answer.\nQuestion: {question}\nGenerated: {answer}\nExpected: {expected_answer}"
)
@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."""
client = HaikuRAG(":memory:")
qa = QuestionAnswerAnthropicAgent(client) # type: ignore
llm_judge = LLMJudge()
doc = qa_corpus[1]
await client.create_document(
content=doc["document_extracted"], uri=doc["document_id"]
)
question = doc["question"]
expected_answer = doc["answer"]
answer = await qa.answer(question)
is_equivalent = await llm_judge.judge_answers(question, answer, expected_answer)
assert is_equivalent, (
f"Generated answer not equivalent to expected answer.\nQuestion: {question}\nGenerated: {answer}\nExpected: {expected_answer}"
)

24
uv.lock
View file

@ -133,6 +133,24 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
]
[[package]]
name = "anthropic"
version = "0.56.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "anyio" },
{ name = "distro" },
{ name = "httpx" },
{ name = "jiter" },
{ name = "pydantic" },
{ 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" }
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" },
]
[[package]]
name = "anyio"
version = "4.9.0"
@ -815,6 +833,9 @@ dependencies = [
]
[package.optional-dependencies]
anthropic = [
{ name = "anthropic" },
]
openai = [
{ name = "openai" },
]
@ -837,6 +858,7 @@ dev = [
[package.metadata]
requires-dist = [
{ name = "anthropic", marker = "extra == 'anthropic'", specifier = ">=0.56.0" },
{ name = "fastmcp", specifier = ">=2.8.1" },
{ name = "httpx", specifier = ">=0.28.1" },
{ name = "markitdown", extras = ["audio-transcription", "docx", "pdf", "pptx", "xlsx"], specifier = ">=0.1.2" },
@ -851,7 +873,7 @@ requires-dist = [
{ name = "voyageai", marker = "extra == 'voyageai'", specifier = ">=0.3.2" },
{ name = "watchfiles", specifier = ">=1.1.0" },
]
provides-extras = ["voyageai", "openai"]
provides-extras = ["voyageai", "openai", "anthropic"]
[package.metadata.requires-dev]
dev = [