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"""Integration tests for the RAG/HAG pipeline using mock encoder and generator."""
import numpy as np
import torch
import torch.nn.functional as F
from hag.config import (
HopfieldConfig,
MemoryBankConfig,
PipelineConfig,
)
from hag.encoder import FakeEncoder
from hag.generator import FakeGenerator
from hag.memory_bank import MemoryBank
from hag.pipeline import RAGPipeline
from hag.retriever_faiss import FAISSRetriever
def _make_memory_bank(n: int = 20, d: int = 64) -> MemoryBank:
"""Create a small memory bank with random embeddings."""
torch.manual_seed(123)
config = MemoryBankConfig(embedding_dim=d, normalize=True)
mb = MemoryBank(config)
embeddings = torch.randn(n, d)
passages = [f"passage {i} content" for i in range(n)]
mb.build_from_embeddings(embeddings, passages)
return mb
def _make_faiss_retriever(n: int = 20, d: int = 64) -> FAISSRetriever:
"""Create a small FAISS retriever with random embeddings."""
np.random.seed(123)
embeddings = np.random.randn(n, d).astype(np.float32)
passages = [f"passage {i} content" for i in range(n)]
retriever = FAISSRetriever(top_k=3)
retriever.build_index(embeddings, passages)
return retriever
class TestPipeline:
"""Integration tests using mock encoder and generator."""
def test_pipeline_runs_end_to_end_hopfield(self) -> None:
"""Mock encoder + mock LLM + Hopfield retriever -> produces an answer."""
config = PipelineConfig(
hopfield=HopfieldConfig(beta=1.0, max_iter=3, top_k=3),
memory=MemoryBankConfig(embedding_dim=64, normalize=True),
retriever_type="hopfield",
)
mb = _make_memory_bank(n=20, d=64)
encoder = FakeEncoder(dim=64)
generator = FakeGenerator()
pipeline = RAGPipeline(
config=config,
encoder=encoder,
generator=generator,
memory_bank=mb,
)
result = pipeline.run("What is the capital of France?")
assert isinstance(result.answer, str)
assert len(result.answer) > 0
assert len(result.retrieved_passages) == 3
def test_pipeline_runs_end_to_end_faiss(self) -> None:
"""Mock encoder + mock LLM + FAISS retriever -> produces an answer."""
config = PipelineConfig(
hopfield=HopfieldConfig(top_k=3),
retriever_type="faiss",
)
faiss_ret = _make_faiss_retriever(n=20, d=64)
encoder = FakeEncoder(dim=64)
generator = FakeGenerator()
pipeline = RAGPipeline(
config=config,
encoder=encoder,
generator=generator,
faiss_retriever=faiss_ret,
)
result = pipeline.run("What is the capital of France?")
assert isinstance(result.answer, str)
assert len(result.answer) > 0
assert len(result.retrieved_passages) == 3
def test_pipeline_retrieval_result_included(self) -> None:
"""PipelineResult should contain retrieval metadata."""
config = PipelineConfig(
hopfield=HopfieldConfig(beta=1.0, max_iter=3, top_k=3),
memory=MemoryBankConfig(embedding_dim=64, normalize=True),
retriever_type="hopfield",
)
mb = _make_memory_bank(n=20, d=64)
encoder = FakeEncoder(dim=64)
generator = FakeGenerator()
pipeline = RAGPipeline(
config=config,
encoder=encoder,
generator=generator,
memory_bank=mb,
)
result = pipeline.run("Test question?")
assert result.retrieval_result is not None
assert result.retrieval_result.scores is not None
assert result.retrieval_result.indices is not None
assert result.question == "Test question?"
def test_pipeline_batch(self) -> None:
"""run_batch should return results for each question."""
config = PipelineConfig(
hopfield=HopfieldConfig(beta=1.0, max_iter=3, top_k=3),
memory=MemoryBankConfig(embedding_dim=64, normalize=True),
retriever_type="hopfield",
)
mb = _make_memory_bank(n=20, d=64)
encoder = FakeEncoder(dim=64)
generator = FakeGenerator()
pipeline = RAGPipeline(
config=config,
encoder=encoder,
generator=generator,
memory_bank=mb,
)
questions = ["Q1?", "Q2?", "Q3?"]
results = pipeline.run_batch(questions)
assert len(results) == 3
assert all(r.answer == "mock answer" for r in results)
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