# SGR Search Log: infinigen_seed
Total time: 10.6s
---
## Step 1: start (+0.0s)
**question:**
```
Find the init.py file where apply_scene_seed function sets numpy random seed
```
**repo_path:**
```
/Users/mix0z/PycharmProjects/Semantic-Search-MCP/data/issues/infinigen/repo
```
**expected_files:**
```
[
"infinigen/core/init.py"
]
```
## Step 2: analyze_repo_start (+0.0s)
**repo_path:**
```
/Users/mix0z/PycharmProjects/Semantic-Search-MCP/data/issues/infinigen/repo
```
## Step 3: log (+0.0s)
**message:**
```
Step 0: Analyzing repository context (smart)...
```
## Step 4: log (+0.0s)
**message:**
```
[Tool] analyze_repo_smart: scanning repository...
```
## Step 5: log (+0.0s)
**message:**
```
Languages found: {'Python': 878, 'C++': 46, 'C': 45}
```
## Step 6: log (+0.0s)
**message:**
```
Primary (detected): Python
```
## Step 7: log (+0.0s)
**message:**
```
Framework (detected): None
```
## Step 8: log (+0.0s)
**message:**
```
Top dirs: ['docs', 'infinigen', 'infinigen_examples', 'scripts', 'tests']
```
## Step 9: log (+0.0s)
**message:**
```
Manifests: ['requirements.txt', 'pyproject.toml', 'setup.py']
```
## Step 10: log (+0.0s)
**message:**
```
[Tool] list_dir: .
```
## Step 11: llm_call_start (+0.0s)
**response_format:**
```
RepoContext
```
**messages:**
```
[
{
"role": "system",
"content": "You are a code repository analyst. Analyze the repository structure to understand:\n\n1. PRIMARY LANGUAGE: What programming language is mainly used?\n - Look at file extensions: .py (Python), .go (Go), .java (Java), .ts/.js (JavaScript/TypeScript)\n - Check for package files: requirements.txt, go.mod, pom.xml, package.json\n\n2. FRAMEWORK: What framework or library is this?\n - Python web: Flask, FastAPI, Tornado, Pyramid\n - JavaScript: Express, Koa, NestJS, React, Vue, Angular\n - Go: Standard library, Echo, Fiber, Chi\n - Java: Spring, Micronaut, Quarkus, Vert.x\n - Or is it a standalone library/tool?\n\n3. PROJECT TYPE:\n - web_framework: HTTP server, REST API\n - library: Reusable package/SDK\n - cli_tool: Command-line application\n - desktop_app: GUI application\n - data_science: ML/Data processing\n - game_engine: Graphics/Game\n - other\n\n4. KEY DIRECTORIES: Important folders for searching\n - Look for:
...
```
## Step 12: log (+0.9s)
**message:**
```
Gemini call took 905ms
```
## Step 13: llm_response (+0.9s)
**response_format:**
```
RepoContext
```
**result:**
```
{
"primary_language": "Python",
"languages": {},
"framework": null,
"project_type": "other",
"key_directories": [],
"file_patterns": [],
"naming_conventions": ""
}
```
## Step 14: log (+0.9s)
**message:**
```
[Smart] Key dirs by source count: ['infinigen/', 'tests/', 'infinigen_examples/']...
```
## Step 15: log (+0.9s)
**message:**
```
Final Language: Python
```
## Step 16: log (+0.9s)
**message:**
```
Final Languages: {'Python': 878, 'C++': 46, 'C': 45}
```
## Step 17: log (+0.9s)
**message:**
```
Final Framework: None
```
## Step 18: log (+0.9s)
**message:**
```
Final Type: other
```
## Step 19: log (+0.9s)
**message:**
```
Final Key dirs: ['infinigen/', 'tests/', 'infinigen_examples/']
```
## Step 20: analyze_repo_result (+0.9s)
**primary_language:**
```
Python
```
**framework:**
```
None
```
**project_type:**
```
other
```
**key_directories:**
```
[
"infinigen/",
"tests/",
"infinigen_examples/"
]
```
## Step 21: analyze_question_start (+0.9s)
**query:**
```
Find the init.py file where apply_scene_seed function sets numpy random seed
```
**repo_hint:**
```
repo
```
## Step 22: log (+0.9s)
**message:**
```
Step 1: Analyzing question concepts...
```
## Step 23: llm_call_start (+0.9s)
**response_format:**
```
QuestionAnalysis
```
**messages:**
```
[
{
"role": "system",
"content": "You are a code search expert analyzing a question about a codebase.\n\nYour task: Extract HIGH-LEVEL CONCEPTS from the question.\nDo NOT generate grep patterns yet - that's the next step.\n\nFocus on understanding:\n\n1. What CONCEPTS is the user asking about?\n Examples of concepts (abstract ideas, not code):\n - \"authentication flow\" - how users log in\n - \"caching mechanism\" - how data is cached\n - \"event handling\" - how events are processed\n - \"error propagation\" - how errors bubble up\n - \"connection pooling\" - how connections are reused\n - \"state management\" - how state is stored/updated\n - \"request lifecycle\" - how requests are processed\n \n2. What PROBLEM is the user trying to solve?\n - Summarize their goal in 1-2 sentences\n\n3. What CODE do they need to find?\n - Be specific: \"the code that handles X\"\n\n4. What TYPE of question is this?\n - how_it_works: Understanding implementation\n
...
```
## Step 24: log (+1.9s)
**message:**
```
Gemini call took 915ms
```
## Step 25: llm_response (+1.9s)
**response_format:**
```
QuestionAnalysis
```
**result:**
```
{
"concepts": [
"numpy random seed",
"apply_scene_seed function",
"init.py file"
],
"user_problem": "The user wants to locate the init.py file that contains the apply_scene_seed function, specifically where the numpy random seed is set.",
"what_to_find": "The init.py file where the apply_scene_seed function sets the numpy random seed",
"question_type": "where_is_it"
}
```
## Step 26: log (+1.9s)
**message:**
```
Concepts: ['numpy random seed', 'apply_scene_seed function', 'init.py file']
```
## Step 27: log (+1.9s)
**message:**
```
User problem: The user wants to locate the init.py file that contains the apply_scene_seed function, specifically where the numpy random seed is set.
```
## Step 28: log (+1.9s)
**message:**
```
What to find: The init.py file where the apply_scene_seed function sets the numpy random seed
```
## Step 29: log (+1.9s)
**message:**
```
Question type: where_is_it
```
## Step 30: analyze_question_result (+1.9s)
**question_type:**
```
where_is_it
```
**concepts:**
```
[
"numpy random seed",
"apply_scene_seed function",
"init.py file"
]
```
**user_problem:**
```
The user wants to locate the init.py file that contains the apply_scene_seed function, specifically where the numpy random seed is set.
```
**what_to_find:**
```
The init.py file where the apply_scene_seed function sets the numpy random seed
```
## Step 31: log (+1.9s)
**message:**
```
Step 2: Extracting technical terms...
```
## Step 32: log (+1.9s)
**message:**
```
Exploring actual directory structure...
```
## Step 33: log (+1.9s)
**message:**
```
[Tool] list_dir: .
```
## Step 34: log (+1.9s)
**message:**
```
[Tool] list_dir: infinigen/
```
## Step 35: log (+1.9s)
**message:**
```
[Tool] list_dir: tests/
```
## Step 36: log (+1.9s)
**message:**
```
[Tool] list_dir: infinigen_examples/
```
## Step 37: log (+1.9s)
**message:**
```
Explored 3 key directories
```
## Step 38: llm_call_start (+1.9s)
**response_format:**
```
TechnicalTerms
```
**messages:**
```
[
{
"role": "system",
"content": "You are a code search expert generating search terms.\n\nYou have TWO sources of information:\n1. The question concepts (what user wants to find)\n2. ACTUAL directory/file structure from the repository\n\nYOUR JOB: Match question concepts to REAL files/modules you see in the structure.\n\n=== HOW TO EXTRACT TERMS ===\n\nLook at the ACTUAL directory listing and extract terms from it:\n\n1. FROM FOLDER NAMES:\n - See \"auth/\" \u2192 use \"auth\" as search term\n - See \"cache/\" \u2192 use \"cache\" as search term\n - See \"handlers/\" \u2192 use \"handler\" as search term\n - See \"services/\" \u2192 use \"service\" as search term\n\n2. FROM FILE NAMES:\n - See \"user_manager.py\" \u2192 use \"user_manager\", \"Manager\"\n - See \"connection_pool.go\" \u2192 use \"connection_pool\", \"Pool\"\n - See \"EventBus.java\" \u2192 use \"EventBus\", \"Bus\"\n - See \"rate_limiter.ts\" \u2192 use \"rate_limiter\", \"limiter\"\n\n3. FRO
...
```
## Step 39: log (+4.5s)
**message:**
```
Gemini call took 2669ms
```
## Step 40: llm_response (+4.5s)
**response_format:**
```
TechnicalTerms
```
**result:**
```
{
"primary_terms": [
{
"term": "apply_scene_seed",
"term_type": "function_name",
"confidence": "high",
"reasoning": "Directly mentioned in the user query as the function to find."
},
{
"term": "init.py",
"term_type": "file_name",
"confidence": "high",
"reasoning": "Specifically requested by the user."
},
{
"term": "numpy",
"term_type": "class_name",
"confidence": "high",
"reasoning": "Mentioned in the user query and is a well-known library, likely to be imported."
},
{
"term": "seed",
"term_type": "variable_name",
"confidence": "medium",
"reasoning": "Related to 'random seed' in the user query, likely to be a variable name."
},
{
"term": "random",
"term_type": "variable_name",
"confidence": "medium",
"reasoning": "Related to 'random seed' in the user query, likely to be a variable name."
},
{
"term": "infinigen",
...
```
## Step 41: log (+4.5s)
**message:**
```
Primary terms: ['apply_scene_seed', 'init.py', 'numpy', 'seed', 'random', 'infinigen', 'core']
```
## Step 42: log (+4.5s)
**message:**
```
Secondary terms: ['apply', 'scene']
```
## Step 43: log (+4.5s)
**message:**
```
Likely files: ['infinigen/core/init.py', 'infinigen_examples/__init__.py']
```
## Step 44: log (+4.6s)
**message:**
```
Step 2.5: Reading 2 likely files...
```
## Step 45: log (+4.6s)
**message:**
```
[Tool] read_file: infinigen/core/init.py
```
## Step 46: log (+4.6s)
**message:**
```
[Read] infinigen/core/init.py: 8 definitions found
```
## Step 47: log (+4.6s)
**message:**
```
[Tool] read_file: infinigen_examples/__init__.py
```
## Step 48: log (+4.6s)
**message:**
```
Extracted 8 findings from likely files
```
## Step 49: log (+4.6s)
**message:**
```
Step 3: Creating search plan...
```
## Step 50: llm_call_start (+4.6s)
**response_format:**
```
TypedSearchPlan
```
**messages:**
```
[
{
"role": "system",
"content": "Create a search plan using the technical terms identified.\n\nYou MUST create actions with ALL REQUIRED FIELDS filled in.\n\n=== GREP ACTIONS ===\nSearch file contents. \"pattern\" is REQUIRED.\nFields:\n- pattern: REQUIRED - regex to search (e.g., \"QuerySet\", \"class.*Handler\")\n- path: optional - directory to search in\n- glob: optional - file filter (e.g., \"*.py\")\n- purpose: what you're looking for\n\n=== READ_FILE ACTIONS ===\nRead specific file. \"file_path\" is REQUIRED.\nFields:\n- file_path: REQUIRED - path to file (e.g., \"django/db/models/query.py\")\n- start_line/end_line: optional - line range\n- purpose: why reading this file\n\n=== LIST_DIR ACTIONS ===\nExplore directory structure.\nFields:\n- path: directory to list (default \".\")\n- max_depth: depth limit (default 2)\n- purpose: what structure to discover\n\n=== GLOB_SEARCH ACTIONS ===\nFind files by name. \"pattern\" is REQUIRED.\nFields:\n- pattern: REQUIRED - glob pa
...
```
## Step 51: log (+6.3s)
**message:**
```
Gemini call took 1781ms
```
## Step 52: llm_response (+6.3s)
**response_format:**
```
TypedSearchPlan
```
**result:**
```
{
"strategy": "keyword_hunt",
"grep_actions": [
{
"purpose": "Find the apply_scene_seed function definition within init.py",
"expected_result": "Find relevant code",
"tool": "grep",
"pattern": "def apply_scene_seed",
"path": "infinigen/core",
"glob": "init.py",
"case_insensitive": false,
"context_lines": 3
},
{
"purpose": "Find where numpy random seed is set within init.py",
"expected_result": "Find relevant code",
"tool": "grep",
"pattern": "np.random.seed",
"path": "infinigen/core",
"glob": "init.py",
"case_insensitive": false,
"context_lines": 3
}
],
"read_file_actions": [
{
"purpose": "Read the init.py file to understand the context of apply_scene_seed and numpy seed setting",
"expected_result": "Find relevant code",
"tool": "read_file",
"file_path": "infinigen/core/init.py",
"start_line": null,
"end_line": null
}
],
...
```
## Step 53: log (+6.3s)
**message:**
```
TypedSearchPlan: 2 grep, 1 read, 0 list, 0 glob
```
## Step 54: log (+6.3s)
**message:**
```
Strategy: keyword_hunt
```
## Step 55: log (+6.3s)
**message:**
```
Actions: 3 (from TypedSearchPlan)
```
## Step 56: log (+6.3s)
**message:**
```
1. grep: def apply_scene_seed - Find the apply_scene_seed function definition with
```
## Step 57: log (+6.3s)
**message:**
```
2. grep: np.random.seed - Find where numpy random seed is set within init.py
```
## Step 58: log (+6.3s)
**message:**
```
3. read_file: infinigen/core/init.py - Read the init.py file to understand the context of
```
## Step 59: log (+6.3s)
**message:**
```
Executing 3 initial actions in parallel...
```
## Step 60: log (+6.3s)
**message:**
```
[Tool] grep: rg --json -C 3 --glob init.py -m 50 np.random.seed /Users/mix0z/PycharmProjects/Semantic-Search-MCP/data/issues/infinigen/repo/infinigen/core
```
## Step 61: log (+6.3s)
**message:**
```
[Tool] grep: rg --json -C 3 --glob init.py -m 50 def apply_scene_seed /Users/mix0z/PycharmProjects/Semantic-Search-MCP/data/issues/infinigen/repo/infinigen/core
```
## Step 62: log (+6.4s)
**message:**
```
[Tool] read_file: infinigen/core/init.py
```
## Step 63: log (+6.4s)
**message:**
```
[Tool] (cache hit)
```
## Step 64: log (+6.4s)
**message:**
```
Step 3: Interpreting result...
```
## Step 65: llm_call_start (+6.4s)
**response_format:**
```
SearchResultInterpretation
```
**messages:**
```
[
{
"role": "system",
"content": "Interpret the search result and decide next steps.\n\nAnalyze:\n1. Did we find anything relevant to the question?\n2. What are the key findings (files, functions, classes)?\n3. What new search directions emerged?\n4. How confident are we this is the right direction?\n5. Do we need more searches or are we ready to answer?\n\nEXTRACTING FINDINGS:\n- Note COMPLETE file paths exactly as shown in the result (e.g., 'src/core/handlers/base.py' not just 'base.py')\n- Note class/function names found\n- Note any imports or references to other files\n\nCONFIDENCE LEVELS:\n- HIGH: Found the exact implementation, clear answer\n- MEDIUM: Found related code, need to verify\n- LOW: Found nothing or unrelated results\n\nCRITICAL PATH REQUIREMENT:\n- ALWAYS use the FULL file path including all directory prefixes\n- NEVER shorten paths: use 'bin/hive-mind/memory.js' NOT 'memory.js'\n- Copy the path EXACTLY as it appears in the search result\n- Format findings a
...
```
## Step 66: log (+7.3s)
**message:**
```
Gemini call took 910ms
```
## Step 67: llm_response (+7.3s)
**response_format:**
```
SearchResultInterpretation
```
**result:**
```
{
"action_executed": "grep",
"found_relevant": true,
"key_findings": [
"infinigen/core/init.py:71: def apply_scene_seed(seed, task=None):"
],
"new_leads": [],
"confidence": "high",
"next_action_needed": false
}
```
## Step 68: log (+7.3s)
**message:**
```
Found relevant: True
```
## Step 69: log (+7.3s)
**message:**
```
Key findings: ['infinigen/core/init.py:71: def apply_scene_seed(seed, task=None):']
```
## Step 70: log (+7.3s)
**message:**
```
Confidence: high
```
## Step 71: log (+7.3s)
**message:**
```
Need more: False
```
## Step 72: log (+7.3s)
**message:**
```
Step 3: Interpreting result...
```
## Step 73: llm_call_start (+7.3s)
**response_format:**
```
SearchResultInterpretation
```
**messages:**
```
[
{
"role": "system",
"content": "Interpret the search result and decide next steps.\n\nAnalyze:\n1. Did we find anything relevant to the question?\n2. What are the key findings (files, functions, classes)?\n3. What new search directions emerged?\n4. How confident are we this is the right direction?\n5. Do we need more searches or are we ready to answer?\n\nEXTRACTING FINDINGS:\n- Note COMPLETE file paths exactly as shown in the result (e.g., 'src/core/handlers/base.py' not just 'base.py')\n- Note class/function names found\n- Note any imports or references to other files\n\nCONFIDENCE LEVELS:\n- HIGH: Found the exact implementation, clear answer\n- MEDIUM: Found related code, need to verify\n- LOW: Found nothing or unrelated results\n\nCRITICAL PATH REQUIREMENT:\n- ALWAYS use the FULL file path including all directory prefixes\n- NEVER shorten paths: use 'bin/hive-mind/memory.js' NOT 'memory.js'\n- Copy the path EXACTLY as it appears in the search result\n- Format findings a
...
```
## Step 74: log (+8.0s)
**message:**
```
Gemini call took 704ms
```
## Step 75: llm_response (+8.0s)
**response_format:**
```
SearchResultInterpretation
```
**result:**
```
{
"action_executed": "grep",
"found_relevant": true,
"key_findings": [
"infinigen/core/init.py:76: np.random.seed(scene_seed)"
],
"new_leads": [],
"confidence": "high",
"next_action_needed": false
}
```
## Step 76: log (+8.0s)
**message:**
```
Found relevant: True
```
## Step 77: log (+8.0s)
**message:**
```
Key findings: ['infinigen/core/init.py:76: np.random.seed(scene_seed)']
```
## Step 78: log (+8.0s)
**message:**
```
Confidence: high
```
## Step 79: log (+8.0s)
**message:**
```
Need more: False
```
## Step 80: log (+8.0s)
**message:**
```
Step 3: Interpreting result...
```
## Step 81: llm_call_start (+8.0s)
**response_format:**
```
SearchResultInterpretation
```
**messages:**
```
[
{
"role": "system",
"content": "Interpret the search result and decide next steps.\n\nAnalyze:\n1. Did we find anything relevant to the question?\n2. What are the key findings (files, functions, classes)?\n3. What new search directions emerged?\n4. How confident are we this is the right direction?\n5. Do we need more searches or are we ready to answer?\n\nEXTRACTING FINDINGS:\n- Note COMPLETE file paths exactly as shown in the result (e.g., 'src/core/handlers/base.py' not just 'base.py')\n- Note class/function names found\n- Note any imports or references to other files\n\nCONFIDENCE LEVELS:\n- HIGH: Found the exact implementation, clear answer\n- MEDIUM: Found related code, need to verify\n- LOW: Found nothing or unrelated results\n\nCRITICAL PATH REQUIREMENT:\n- ALWAYS use the FULL file path including all directory prefixes\n- NEVER shorten paths: use 'bin/hive-mind/memory.js' NOT 'memory.js'\n- Copy the path EXACTLY as it appears in the search result\n- Format findings a
...
```
## Step 82: log (+8.6s)
**message:**
```
Gemini call took 633ms
```
## Step 83: llm_response (+8.6s)
**response_format:**
```
SearchResultInterpretation
```
**result:**
```
{
"action_executed": "read_file",
"found_relevant": true,
"key_findings": [
"infinigen/core/init.py:71: def apply_scene_seed(seed, task=None):",
"infinigen/core/init.py:76: np.random.seed(scene_seed)"
],
"new_leads": [],
"confidence": "high",
"next_action_needed": false
}
```
## Step 84: log (+8.6s)
**message:**
```
Found relevant: True
```
## Step 85: log (+8.6s)
**message:**
```
Key findings: ['infinigen/core/init.py:71: def apply_scene_seed(seed, task=None):', 'infinigen/core/init.py:76: np.random.seed(scene_seed)']
```
## Step 86: log (+8.6s)
**message:**
```
Confidence: high
```
## Step 87: log (+8.6s)
**message:**
```
Need more: False
```
## Step 88: log (+8.6s)
**message:**
```
High confidence with relevant findings - preparing answer
```
## Step 89: log (+8.6s)
**message:**
```
Step 5: Generating final answer...
```
## Step 90: llm_call_start (+8.6s)
**response_format:**
```
FinalAnswer
```
**messages:**
```
[
{
"role": "system",
"content": "Generate a final answer based on the search results.\n\nInclude:\n1. SUMMARY: Clear answer to the question (what code does what, where it is)\n2. CODE LOCATIONS: Specific file paths with relevant functions/classes\n3. REASONING CHAIN: Steps taken to find the answer\n4. CONFIDENCE: How confident you are\n5. POTENTIAL GAPS: What might have been missed\n\nCRITICAL for CODE LOCATIONS (file_path field):\n- Use COMPLETE file paths exactly as shown in the findings\n- NEVER use just the filename: 'memory.js' is WRONG\n- ALWAYS include directory prefix: 'bin/hive-mind/memory.js' is CORRECT\n- Copy paths EXACTLY from the findings - do not shorten or modify them\n- Only include files that were actually found in searches\n- Include the relevant class/function names within each file\n- Don't invent file paths that weren't in the findings\n\nExample of CORRECT file_path: \"src/core/handlers/base.py\"\nExample of WRONG file_path: \"base.py\" "
},
{
...
```
## Step 91: log (+10.6s)
**message:**
```
Gemini call took 1953ms
```
## Step 92: llm_response (+10.6s)
**response_format:**
```
FinalAnswer
```
**result:**
```
{
"summary": "The `apply_scene_seed` function in `infinigen/core/init.py` sets the numpy random seed. Specifically, line 76, `np.random.seed(scene_seed)`, is responsible for setting the seed using a variable named `scene_seed`. The function definition starts on line 71.",
"code_locations": [
{
"file_path": "infinigen/core/init.py",
"relevant_elements": [
"apply_scene_seed"
],
"line_range": "71-76",
"relevance": ""
}
],
"reasoning_chain": [
"Searched for the definition of `apply_scene_seed` within `init.py`.",
"Found the function definition in `infinigen/core/init.py` on line 71.",
"Searched for where the numpy random seed is set within `init.py`.",
"Found the line `np.random.seed(scene_seed)` on line 76, which is within the `apply_scene_seed` function.",
"Confirmed that `apply_scene_seed` sets the numpy random seed."
],
"confidence": "high",
"potential_gaps": []
}
```
## Step 93: log (+10.6s)
**message:**
```
Summary: The `apply_scene_seed` function in `infinigen/core/init.py` sets the numpy random seed. Specifically...
```
## Step 94: log (+10.6s)
**message:**
```
Code locations: 1
```
## Step 95: log (+10.6s)
**message:**
```
Confidence: high
```
## Step 96: log (+10.6s)
**message:**
```
[Tool] read_file: infinigen/core/init.py
```
## Step 97: log (+10.6s)
**message:**
```
[Tool] read_file: infinigen/core/init.py
```
## Step 98: log (+10.6s)
**message:**
```
[Tool] (cache hit)
```
## Step 99: log (+10.6s)
**message:**
```
Search complete:
```
## Step 100: log (+10.6s)
**message:**
```
Items found: 1
```
## Step 101: log (+10.6s)
**message:**
```
Total iterations: 3
```
## Step 102: log (+10.6s)
**message:**
```
Total time: 10560ms
```
## Step 103: log (+10.6s)
**message:**
```
LLM time: 10537ms
```
## Step 104: log (+10.6s)
**message:**
```
Tool time: 23ms
```
## Step 105: log (+10.6s)
**message:**
```
Tools used: {'grep': 2, 'read_file': 1}
```
## Step 106: log (+10.6s)
**message:**
```
Confidence progression: high → high → high
```
## Step 107: log (+10.6s)
**message:**
```
High confidence reached at iteration: 1
```
## Step 108: final_result (+10.6s)
**found_files:**
```
[
"infinigen/core/init.py"
]
```
**patterns_used:**
```
[
"def apply_scene_seed",
"np.random.seed",
"{'file_path': 'infinigen/core/init.py'}"
]
```
**total_time_ms:**
```
10559.879779815674
```
**error:**
```
None
```
