-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtest_complete_app.py
More file actions
177 lines (152 loc) · 6.17 KB
/
test_complete_app.py
File metadata and controls
177 lines (152 loc) · 6.17 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
"""
Test complete ACE RAG application functionality without API calls.
"""
from ace_rag.config import Config, GeminiConfig, VectorStoreConfig, DocumentProcessorConfig, ACEConfig
from ace_rag.playbook import Playbook
from ace_rag.ace_generator import ACEGenerator
from ace_rag.ace_reflector import ACEReflector
from ace_rag.ace_curator import ACECurator
from ace_rag.vector_store import VectorStore
from ace_rag.document_processor import DocumentProcessor
from ace_rag.models import Document, Chunk
import numpy as np
print("=" * 70)
print("ACE RAG Application - Complete Functionality Test")
print("=" * 70)
# 1. Configuration
print("\n1. Creating configuration...")
config = Config.from_env()
print(f"✓ Config created")
print(f" - Embedding model: {config.gemini.embedding_model}")
print(f" - Vector dimension: {config.vector_store.dimension}")
print(f" - ACE trajectories: {config.ace.num_trajectories}")
# 2. Playbook
print("\n2. Initializing Playbook...")
playbook = Playbook(config.ace)
print(f"✓ Playbook initialized: {len(playbook.strategies)} strategies")
# Add strategies
strategy1 = playbook.add_strategy(
name="Precise Search",
description="Low temperature for precise results",
parameters={"temperature": 0.3, "top_k": 3, "fusion_method": "mean"}
)
print(f"✓ Added strategy: {strategy1.name}")
strategy2 = playbook.add_strategy(
name="Exploratory Search",
description="High temperature for diverse results",
parameters={"temperature": 0.9, "top_k": 7, "fusion_method": "weighted"}
)
print(f"✓ Added strategy: {strategy2.name}")
print(f" Total strategies: {len(playbook.strategies)}")
# 3. Gemini Client
print("\n3. Initializing Gemini Client...")
from ace_rag.gemini_client import GeminiClient
gemini_client = GeminiClient(config.gemini)
print(f"✓ Gemini client initialized")
# 4. Vector Store
print("\n4. Testing Vector Store...")
vector_store = VectorStore(config.vector_store)
# Add test chunks with embeddings
test_chunks = [
Chunk(
id=f"chunk{i}",
document_id="doc1",
content=f"Test content {i}",
position=i,
embedding=np.random.rand(768).tolist()
)
for i in range(5)
]
for chunk in test_chunks:
vector_store.add_chunk(chunk)
print(f"✓ Added {len(test_chunks)} chunks to vector store")
print(f" Total chunks: {vector_store.get_stats()['total_chunks']}")
# Test search
query_embedding = np.random.rand(768).tolist()
results = vector_store.search(query_embedding, top_k=3)
print(f"✓ Search successful: found {len(results)} results")
# 5. ACE Generator
print("\n5. Testing ACE Generator...")
generator = ACEGenerator(config.ace, gemini_client, vector_store, playbook)
trajectories = generator.generate_trajectories("What is machine learning?")
print(f"✓ Generated {len(trajectories)} trajectories")
for i, traj in enumerate(trajectories, 1):
print(f" {i}. {traj.expanded_query[:50]}...")
print(f" Temperature: {traj.temperature}, Fusion: {traj.fusion_method}")
# 6. Document Processor
print("\n6. Testing Document Processor...")
doc_processor = DocumentProcessor(config.document_processor, gemini_client)
test_doc = Document(
id="test_doc",
content="This is a test document. " * 50, # Make it long enough to chunk
metadata={"source": "test"}
)
chunks = doc_processor.chunk_document(test_doc)
print(f"✓ Processed document into {len(chunks)} chunks")
print(f" Chunk size: {config.document_processor.chunk_size}")
print(f" Overlap: {config.document_processor.chunk_overlap}")
# 7. ACE Reflector
print("\n7. Testing ACE Reflector...")
reflector = ACEReflector(config.ace, gemini_client)
# Create mock trajectories with results
from ace_rag.models import QueryTrajectory, FusionMethod, RetrievalResult
mock_trajectories = [
QueryTrajectory(
id=f"traj{i}",
original_query="test query",
expanded_query=f"expanded test query {i}",
temperature=0.5 + i * 0.2,
fusion_method=FusionMethod.MEAN,
results=[
RetrievalResult(chunk=test_chunks[j], score=0.9 - j * 0.1, rank=j)
for j in range(3)
],
quality_score=0.8 + i * 0.05
)
for i in range(3)
]
insights = reflector.reflect(mock_trajectories, "test query")
print(f"✓ Analyzed {len(mock_trajectories)} trajectories")
print(f" Total insights: {len(insights)}")
# 8. ACE Curator
print("\n8. Testing ACE Curator...")
curator = ACECurator(config.ace, playbook, gemini_client)
if insights:
curator.curate(insights)
print(f"✓ Curated {len(insights)} insights")
stats = curator.get_stats()
print(f" Total deltas: {stats.get('total_deltas', 0)}")
# 9. Playbook Evolution
print("\n9. Testing Playbook Evolution...")
playbook.record_usage(strategy1.id, success=True, metrics={"latency": 0.5})
playbook.record_usage(strategy1.id, success=True, metrics={"latency": 0.4})
playbook.record_usage(strategy2.id, success=False, metrics={"latency": 0.8})
stats = playbook.get_stats()
print(f"✓ Recorded strategy usage")
print(f" Total strategies: {stats['total_strategies']}")
print(f" Average success rate: {stats['avg_success_rate']:.3f}")
print(f" Total usage: {stats['total_usage']}")
# 10. Top Strategies
print("\n10. Getting Top Strategies...")
top_strategies = playbook.get_top_strategies(n=2)
print(f"✓ Retrieved {len(top_strategies)} top strategies")
for i, params in enumerate(top_strategies, 1):
print(f" {i}. Temperature: {params.get('temperature')}, Top-K: {params.get('top_k')}")
# 11. System Statistics
print("\n11. Complete System Statistics:")
print(f" Vector Store: {vector_store.get_stats()}")
print(f" Playbook: {playbook.get_stats()}")
print(f" Document Processor: chunk_size={config.document_processor.chunk_size}")
print("\n" + "=" * 70)
print("✅ ALL COMPONENTS WORKING CORRECTLY!")
print("=" * 70)
print("\nApplication Status:")
print(" ✅ Configuration management")
print(" ✅ Playbook with delta updates")
print(" ✅ ACE Generator (trajectory generation)")
print(" ✅ ACE Reflector (insight extraction)")
print(" ✅ ACE Curator (knowledge synthesis)")
print(" ✅ Vector Store (FAISS integration)")
print(" ✅ Document Processor (chunking)")
print(" ✅ Strategy evolution tracking")
print("\n🎉 Ready for deployment with valid Gemini API key!")