Hugging Face's logo

Datasets:
NingsenWang
/
misc

Dataset card Files
xet
 Community
misc
File size: 4,851 Bytes
a54fd97
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
"""
Configuration file - System parameters and LLM settings

IMPORTANT:
1. Copy this file to config.py
2. Configure your settings below
3. Never commit config.py to version control (it contains your API key)
"""

# ============================================================================
# LLM Configuration
# ============================================================================

# OpenAI API Key (required)
# Get your key from: https://platform.openai.com/api-keys
OPENAI_API_KEY = "your-api-key-here"

# Custom OpenAI Base URL (optional)
# Set to None to use default OpenAI endpoint
# Examples:
#   - Qwen/Alibaba: "https://dashscope.aliyuncs.com/compatible-mode/v1"
#   - Azure OpenAI: "https://YOUR-RESOURCE.openai.azure.com/openai/deployments/YOUR-DEPLOYMENT"
#   - Local server: "http://localhost:8000/v1"
#   - OpenAI (default): None
OPENAI_BASE_URL = None

# LLM Model name
# Examples: "gpt-4.1-mini", "gpt-4.1", "qwen3-max", "qwen-plus-2025-07-28"
LLM_MODEL = "gpt-4.1-mini"

# Embedding model (local, no API needed)
EMBEDDING_MODEL = "Qwen/Qwen3-Embedding-0.6B"
EMBEDDING_DIMENSION = 1024  # For Qwen3: up to 1024, supports 32-1024
EMBEDDING_CONTEXT_LENGTH = 32768  # Qwen3 supports 32k context


# ============================================================================
# Advanced LLM Features
# ============================================================================

# Enable deep thinking mode (for Qwen and compatible models)
# Adds extra_body={"enable_thinking": True} to API calls
# Set to False for OpenAI models (they don't support this)
ENABLE_THINKING = False

# Enable streaming responses (outputs content as it's generated)
USE_STREAMING = True

# Enable JSON format mode (ensures LLM outputs valid JSON)
# Adds response_format={"type": "json_object"} to API calls
# Helps prevent parsing failures from extra text like ```json
USE_JSON_FORMAT = False


# ============================================================================
# Memory Building Parameters
# ============================================================================

# Number of dialogues per window (for locomo; for other dataset, please finetune it)
WINDOW_SIZE = 40

# Window overlap size (for context continuity)
OVERLAP_SIZE = 2


# ============================================================================
# Retrieval Parameters (can be adjusted to balance between token usage and performance)
# ============================================================================

# Max entries returned by semantic search (vector similarity)
SEMANTIC_TOP_K = 25

# Max entries returned by keyword search (BM25 matching)
KEYWORD_TOP_K = 5

# Max entries returned by structured search (metadata filtering)
STRUCTURED_TOP_K = 5


# ============================================================================
# Database Configuration
# ============================================================================

# Path to LanceDB storage
LANCEDB_PATH = "./lancedb_data"

# Memory table name
MEMORY_TABLE_NAME = "memory_entries"



# ============================================================================
# Parallel Processing Configuration
# ============================================================================

# Memory Building Parallel Processing
ENABLE_PARALLEL_PROCESSING = True
MAX_PARALLEL_WORKERS = 16  # Number of parallel workers for memory building

# Retrieval Parallel Processing  
ENABLE_PARALLEL_RETRIEVAL = True
MAX_RETRIEVAL_WORKERS = 8  # Number of parallel workers for retrieval queries

# Planning and Reflection Configuration
ENABLE_PLANNING = True
ENABLE_REFLECTION = True
MAX_REFLECTION_ROUNDS = 2


# ============================================================================
# LLM-as-Judge Configuration (not used yet)
# ============================================================================

# Judge LLM API Key (optional - if None, uses OPENAI_API_KEY)
JUDGE_API_KEY = "your api-key here"

# Judge LLM Base URL (optional - if None, uses OPENAI_BASE_URL)
# Example: Use cheaper endpoint for evaluation
JUDGE_BASE_URL = "https://api.openai.com/v1/"

# Judge LLM Model (optional - if None, uses LLM_MODEL)
JUDGE_MODEL = "gpt-4.1-mini"

# Judge specific settings
JUDGE_ENABLE_THINKING = False  # Usually false for evaluation tasks
JUDGE_USE_STREAMING = False    # Usually false for evaluation
JUDGE_TEMPERATURE = 0.3        

# Example configurations:
# 1. Use cheaper model for judge evaluation:
#    JUDGE_MODEL = "gpt-4.1-mini"
#
# 2. Use different API provider for judge:
#    JUDGE_API_KEY = "your-judge-api-key"
#    JUDGE_BASE_URL = "https://api.different-provider.com/v1"
#    JUDGE_MODEL = "different-provider-model"
#
# 3. Use Qwen for judge (if available):
#    JUDGE_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
#    JUDGE_MODEL = "qwen-plus-2025-09-11"