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| import hashlib | |
| import json | |
| import re | |
| import types | |
| import numpy as np | |
| import pytest | |
| class FakeEmbedder: | |
| """Deterministic bag-of-words hashing embedder, so similarity reflects shared words (no model download).""" | |
| def encode(self, texts, convert_to_numpy=True, normalize_embeddings=True, show_progress_bar=False): | |
| out = np.zeros((len(texts), 256), dtype="float32") | |
| for i, text in enumerate(texts): | |
| for word in re.findall(r"[a-z]+", text.lower()): | |
| word = re.sub(r"(ing|s)$", "", word) # crude stemming: drone/drones, monitor/monitoring | |
| out[i, int(hashlib.md5(word.encode()).hexdigest(), 16) % 256] += 1 | |
| if normalize_embeddings: | |
| out /= np.maximum(np.linalg.norm(out, axis=1, keepdims=True), 1e-9) | |
| return out | |
| WEB = { | |
| "solar": [ | |
| {"title": "Agrivoltaics explained", "url": "https://a.example/agri", | |
| "content": "Agrivoltaics combines solar panels with crops on the same farmland. It improves land use and farm income. " * 10}, | |
| ], | |
| "drone": [ | |
| {"title": "Farm drones", "url": "https://b.example/drones", | |
| "content": "Drones monitor crops on farmland and help farmers detect plant disease early using imaging. " * 10}, | |
| {"title": "Cooking pasta", "url": "https://c.example/pasta", | |
| "content": "Boil water, add salt, cook pasta for ten minutes and serve with sauce. " * 8}, | |
| ], | |
| } | |
| def fake_searcher(question, max_results=4, search_depth="basic"): | |
| results = [] | |
| for key, items in WEB.items(): | |
| if key in question.lower(): | |
| results += items | |
| return results or WEB["solar"] | |
| class FakeLLM: | |
| """Scripted stand-in for Groq. Role is detected from the system prompt.""" | |
| def __init__(self, bad_claim="The moon is made of cheese"): | |
| self.bad_claim = bad_claim | |
| self.calls = [] | |
| def complete(self, messages, role="fast", temperature=0.0, json_mode=False): | |
| system, user = messages[0]["content"], messages[-1]["content"] | |
| self.calls.append(system.split(".")[0]) | |
| if "Planner" in system: | |
| return json.dumps({"sub_questions": ["solar panels on farmland", "drone crop monitoring"]}) | |
| if "fact-checker" in system: | |
| results = [] | |
| for match in re.finditer(r"CLAIM (\d+): (.*)", user): | |
| verdict = "unsupported" if self.bad_claim.lower() in match.group(2).lower() else "supported" | |
| results.append({"id": int(match.group(1)), "verdict": verdict, "reason": "scripted"}) | |
| return json.dumps({"results": results}) | |
| if "Reviser" in system: | |
| report = user.split("A fact-checker flagged")[0].replace("REPORT:\n", "") | |
| return "\n".join(l for l in report.split("\n") if self.bad_claim.lower() not in l.lower()) | |
| if "translator" in system: | |
| return "[UR] " + user.split("\n\n", 1)[1] | |
| raise AssertionError("unexpected prompt: " + system) | |
| def stream(self, messages, role="writer", temperature=0.3): | |
| self.calls.append("writer") | |
| text = ( | |
| "## Key Findings\n" | |
| "Solar panels and crops can share the same farmland [1]. " | |
| "Drones help farmers detect plant disease early [2][9].\n" | |
| f"{self.bad_claim} [1].\n" | |
| "## Conclusion\n" | |
| "Both technologies improve how farmland is used [1, 2].\n" | |
| ) | |
| for i in range(0, len(text), 25): | |
| yield text[i : i + 25] | |
| def embedder(): | |
| return FakeEmbedder() | |
| def fake_llm(): | |
| llm = FakeLLM() | |
| return types.SimpleNamespace(complete=llm.complete, stream=llm.stream, raw=llm) | |