feedback loop for unmatched test results (#2059)

fixes CF-932

# Pull Request Checklist

## Description
- [ ] **Description of PR**: Clear and concise description of what this
PR accomplishes
- [ ] **Breaking Changes**: Document any breaking changes (if
applicable)
- [ ] **Related Issues**: Link to any related issues or tickets

## Testing
- [ ] **Test cases Attached**: All relevant test cases have been
added/updated
- [ ] **Manual Testing**: Manual testing completed for the changes

## Monitoring & Debugging
- [ ] **Logging in place**: Appropriate logging has been added for
debugging user issues
- [ ] **Sentry will be able to catch errors**: Error handling ensures
Sentry can capture and report errors
- [ ] **Avoid Dev based/Prisma logging**: No development-only or
Prisma-specific logging in production code

## Configuration
- [ ] **Env variables newly added**: Any new environment variables are
documented in .env.example file or mentioned in description
---

## Additional Notes
<!-- Add any additional context, screenshots, or notes for reviewers
here -->

---------

Co-authored-by: codeflash-ai[bot] <148906541+codeflash-ai[bot]@users.noreply.github.com>
Co-authored-by: ali <mohammed18200118@gmail.com>
Co-authored-by: Kevin Turcios <106575910+KRRT7@users.noreply.github.com>

django/aiservice/aiservice/models/aimodels.py

@@ -219,3 +219,4 @@ RANKING_MODEL: LLM = _get_openai_model()
 REFINEMENT_MODEL: LLM = _get_anthropic_model()
 EXPLANATIONS_MODEL: LLM = _get_anthropic_model()
 OPTIMIZATION_REVIEW_MODEL: LLM = _get_anthropic_model()
+CODE_REPAIR_MODEL: LLM = _get_anthropic_model()

django/aiservice/aiservice/urls.py

@@ -20,6 +20,7 @@ Including another URLconf
 # from django.contrib import admin
 from django.urls import path
 
+from code_repair.code_repair import code_repair_api
 from explanations.explanations import explanations_api
 from log_features.log_features import features_api
 from optimization_review.optimization_review import optimization_review_api
@@ -39,5 +40,6 @@ urlpatterns = [
     path("ai/explain", explanations_api.urls),
     path("ai/rank", ranker_api.urls),
     path("ai/optimization_review", optimization_review_api.urls),
+    path("ai/code_repair", code_repair_api.urls),
     path("ai/workflow-gen", workflow_gen_api.urls),
 ]

django/aiservice/code_repair/CODE_REPAIR_SYSTEM_PROMPT.md

@@ -0,0 +1,61 @@
+You are a senior software engineer who is great at reviewing and repairing python code for performance and behavior.
+The goal of repairing code is to ensure that the optimized code is performant and has the same behavior as the original code based on the provided test results.
+You are provided the following information:
+
+- original_source_code
+- optimized_source_code - This is the optimized implementation of the original code.
+- test_details - This has the details of the behavioral differences between the original and optimized code.
+
+### Output format
+
+Request to replace sections of the optimized code in an existing file using SEARCH/REPLACE blocks that define exact changes to specific parts of the file.
+Define your output in XML-style tags like here:
+
+<replace_in_file>
+<path>src/main.py</path>
+<diff>
+<<<<<<< SEARCH
+a = 2
+=======
+a = 3
+>>>>>>> REPLACE
+</diff>
+</replace_in_file>
+
+Always adhere to this format for tool use to ensure proper parsing and execution.
+
+## replace_in_file
+Description: Request to replace sections of content in an existing file using SEARCH/REPLACE blocks that define exact changes to specific parts of the file. This tool should be used when you need to make targeted changes to specific parts of a file.
+Parameters:
+- path: (required) The path of the file to modify
+- diff: (required) One or more SEARCH/REPLACE blocks following this exact format:
+  ```
+  <<<<<<< SEARCH
+  [exact content to find]
+  =======
+  [new content to replace with]
+  >>>>>>> REPLACE
+  ```
+  Critical rules:
+  1. SEARCH content must match the associated file section to find EXACTLY:
+     * Match character-for-character including whitespace, indentation, line endings
+     * Include all comments, docstrings, etc.
+  2. SEARCH/REPLACE blocks will ONLY replace the first match occurrence.
+     * Including multiple unique SEARCH/REPLACE blocks if you need to make multiple changes.
+     * Include *just* enough lines in each SEARCH section to uniquely match each set of lines that need to change.
+     * When using multiple SEARCH/REPLACE blocks, list them in the order they appear in the file.
+  3. Keep SEARCH/REPLACE blocks concise:
+     * Break large SEARCH/REPLACE blocks into a series of smaller blocks that each change a small portion of the file.
+     * Include just the changing lines, and a few surrounding lines if needed for uniqueness.
+     * Do not include long runs of unchanging lines in SEARCH/REPLACE blocks.
+     * Each line must be complete. Never truncate lines mid-way through as this can cause matching failures.
+  4. Special operations:
+     * To move code: Use two SEARCH/REPLACE blocks (one to delete from original + one to insert at new location)
+     * To delete code: Use empty REPLACE section
+Usage:
+<replace_in_file>
+<path>File path here</path>
+<diff>
+Search and replace blocks here
+</diff>
+</replace_in_file>

django/aiservice/code_repair/CODE_REPAIR_USER_PROMPT.md

@@ -0,0 +1,13 @@
+Please fix the optimized code to match the behaviour of the original code, while trying to keep the optimization logic intact.
+
+### The original source code:
+
+{original_source_code}
+
+### The optimized source code
+
+{modified_source_code}
+
+### The test result details
+
+{test_details}

django/aiservice/code_repair/apps.py

@@ -0,0 +1,6 @@
+from django.apps import AppConfig
+
+
+class CodeRepairConfig(AppConfig):
+    default_auto_field = "django.db.models.BigAutoField"
+    name = "code_repair"

django/aiservice/code_repair/code_repair.py

@@ -0,0 +1,204 @@
+from __future__ import annotations
+
+import logging
+import uuid
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import libcst as cst
+import sentry_sdk
+from ninja import NinjaAPI, Schema
+from openai.types.chat import ChatCompletionSystemMessageParam, ChatCompletionUserMessageParam
+from pydantic import ValidationError
+
+from aiservice.analytics.posthog import ph
+from aiservice.common_utils import validate_trace_id
+from aiservice.env_specific import debug_log_sensitive_data, llm_clients
+from aiservice.models.aimodels import CODE_REPAIR_MODEL, calculate_llm_cost
+from log_features.log_event import update_optimization_cost
+from log_features.log_features import log_features
+from optimizer.models import OptimizedCandidateSource
+
+from .code_repair_context import (  # noqa: TC001 (don't move CodeRepairRequestSchema to type-checking because it's the schema definition)
+    CodeRepairContext,
+    CodeRepairContextData,
+    CodeRepairRequestSchema,
+)
+
+if TYPE_CHECKING:
+    from django.http import HttpRequest as Request
+    from openai.types.chat import (
+        ChatCompletionAssistantMessageParam,
+        ChatCompletionFunctionMessageParam,
+        ChatCompletionToolMessageParam,
+    )
+
+    from aiservice.models.aimodels import LLM
+
+code_repair_api = NinjaAPI(urls_namespace="code_repair")
+
+# Get the directory of the current file
+current_dir = Path(__file__).parent
+SYSTEM_PROMPT = (current_dir / "CODE_REPAIR_SYSTEM_PROMPT.md").read_text()
+
+USER_PROMPT = (current_dir / "CODE_REPAIR_USER_PROMPT.md").read_text()
+
+
+async def code_repair(  # noqa: D417
+    user_id: str, optimization_id: str, ctx: CodeRepairContext, optimize_model: LLM = CODE_REPAIR_MODEL
+) -> CodeRepairIntermediateResponseItemschema | CodeRepairErrorResponseSchema:
+    """Repair the given candidate to match the behaviour of the original code.
+
+    Parameters
+    ----------
+    :param user_id:
+    :param optimization_id
+    :param optimize_model: LLM for getting the code_repairs
+    :param ctx: the repair context, has the data property which includes
+        - original code
+        - optimized code
+        - behaviour test diffs
+    Returns
+    -------
+    CodeRepairIntermediateResponseItemschema or CodeRepairErrorResponseSchema
+    """
+    system_prompt = ctx.get_system_prompt()
+    user_prompt = ctx.get_user_prompt()
+
+    new_op_id = str(uuid.uuid4())
+    system_message = ChatCompletionSystemMessageParam(role="system", content=system_prompt)
+    user_message = ChatCompletionUserMessageParam(role="user", content=user_prompt)
+    messages: list[
+        ChatCompletionSystemMessageParam
+        | ChatCompletionUserMessageParam
+        | ChatCompletionAssistantMessageParam
+        | ChatCompletionToolMessageParam
+        | ChatCompletionFunctionMessageParam
+    ] = [system_message, user_message]
+    debug_log_sensitive_data(f"This was the user prompt\n {user_prompt}\n")
+    llm_client = llm_clients[optimize_model.model_type]
+    try:
+        output = await llm_client.with_options(max_retries=2).chat.completions.create(
+            model=optimize_model.name, messages=messages, n=1
+        )
+        llm_cost = calculate_llm_cost(output, optimize_model)
+    except Exception as e:
+        logging.exception("Claude Code Generation error in code_repair")
+        sentry_sdk.capture_exception(e)
+        debug_log_sensitive_data(f"Failed to generate code for source:\n{ctx.data.original_source_code}")
+        return CodeRepairErrorResponseSchema(error=str(e))
+    debug_log_sensitive_data(f"ClaudeClient optimization response:\n{output.model_dump_json(indent=2)}")
+    if output.usage is not None:
+        ph(user_id, "code_repair-usage", properties={"model": optimize_model.name, "usage": output.usage.json()})
+    results = [content for op in output.choices if (content := op.message.content)]  # will be of size 1
+
+    # Regex doesn't work yet in extracting everything else other than the search replace block
+    explanation = results[0]
+
+    repaired_optimization = ""
+    try:
+        diff_patches = ctx.extract_diff_patches_from_llm_res(results[0])
+        repaired_optimization = ctx.apply_patches_to_optimized_code(diff_patches)
+    except (ValueError, ValidationError) as exc:
+        sentry_sdk.capture_exception(exc)
+        debug_log_sensitive_data(f"{type(exc).__name__} for source:\n{ctx.data.modified_source_code}")
+        debug_log_sensitive_data(f"Traceback: {exc}")
+        repaired_optimization = ""
+
+    if not ctx.is_valid(repaired_optimization):
+        repaired_optimization = ""
+
+    return CodeRepairIntermediateResponseItemschema(
+        optimization_id=new_op_id,
+        parent_id=optimization_id,
+        source_code=repaired_optimization,
+        llm_cost=llm_cost,
+        explanation=explanation,
+    )
+
+
+class CodeRepairErrorResponseSchema(Schema):
+    error: str
+
+
+class CodeRepairIntermediateResponseItemschema(Schema):
+    # the key will be the optimization id and the value will be the actual refined code
+    optimization_id: str
+    parent_id: str
+    source_code: str
+    llm_cost: float
+    explanation: str
+
+
+class CodeRepairResponseItemschema(Schema):
+    # the key will be the optimization id and the value will be the actual refined code
+    optimization_id: str
+    parent_id: str
+    source_code: str
+    explanation: str
+
+
+@code_repair_api.post(
+    "/",
+    response={
+        200: CodeRepairResponseItemschema,
+        400: CodeRepairErrorResponseSchema,
+        500: CodeRepairErrorResponseSchema,
+    },
+)
+async def repair(
+    request: Request, data: CodeRepairRequestSchema
+) -> tuple[int, CodeRepairResponseItemschema | CodeRepairErrorResponseSchema]:
+    ph(request.user, "aiservice-code_repair-called")
+    ctx_data = CodeRepairContextData(
+        original_source_code=data.original_source_code,
+        modified_source_code=data.modified_source_code,
+        test_diffs=data.test_diffs,
+    )
+    ctx = CodeRepairContext(ctx_data=ctx_data, base_system_prompt=SYSTEM_PROMPT, base_user_prompt=USER_PROMPT)
+    trace_id = data.trace_id
+    if not validate_trace_id(trace_id):
+        return 400, CodeRepairErrorResponseSchema(error="Invalid trace ID. Please provide a valid UUIDv4.")
+
+    code_repair_data = await code_repair(user_id=request.user, optimization_id=data.optimization_id, ctx=ctx)
+    total_llm_cost = 0.0
+    if isinstance(code_repair_data, CodeRepairErrorResponseSchema):
+        return 500, code_repair_data
+    total_llm_cost += code_repair_data.llm_cost
+    try:
+        ctx.validate_python_module()
+    except cst.ParserSyntaxError as e:
+        # log exception with sentry
+        sentry_sdk.capture_exception(e)
+        debug_log_sensitive_data(f"ParserSyntaxError for source:\n{code_repair_data.source_code}")
+        debug_log_sensitive_data(f"Traceback: {e}")
+        return 500, CodeRepairErrorResponseSchema(error=str(e))
+    except (ValueError, ValidationError) as exc:
+        # Another one bites the Pydantic validation dust
+        sentry_sdk.capture_exception(exc)
+        debug_log_sensitive_data(f"{type(exc).__name__} for source:\n{code_repair_data.source_code}")
+        debug_log_sensitive_data(f"Traceback: {exc}")
+        return 500, CodeRepairErrorResponseSchema(error=str(exc))
+
+    if hasattr(request, "should_log_features") and request.should_log_features:
+        await log_features(
+            trace_id=data.trace_id,
+            user_id=request.user,
+            optimizations_raw={code_repair_data.optimization_id: code_repair_data.source_code},
+            explanations_raw={code_repair_data.optimization_id: code_repair_data.explanation},
+            # explanations_post={cei.optimization_id: cei.explanation for cei in optimization_response_items},
+            # optimizations_post={cei.optimization_id: cei.source_code for cei in optimization_response_items},
+            optimizations_origin={
+                code_repair_data.optimization_id: {
+                    "source": OptimizedCandidateSource.REPAIR,
+                    "parent": code_repair_data.parent_id,
+                }
+            },
+        )
+    await update_optimization_cost(trace_id=trace_id, cost=total_llm_cost)
+    return 200, CodeRepairResponseItemschema(
+        source_code=code_repair_data.source_code,
+        optimization_id=code_repair_data.optimization_id,
+        parent_id=code_repair_data.parent_id,
+        explanation=code_repair_data.explanation,
+    )

django/aiservice/code_repair/code_repair_context.py

@@ -0,0 +1,153 @@
+import logging
+from collections import defaultdict
+from dataclasses import dataclass
+from enum import Enum
+
+import libcst as cst
+import sentry_sdk
+from aiservice.env_specific import debug_log_sensitive_data
+from ninja import Field, Schema
+from optimizer.context_utils.constants import REPLACE_IN_FILE_TAGS_REGEX
+from optimizer.context_utils.context_helpers import group_code, is_markdown_structure_changed, split_markdown_code
+from optimizer.diff_patches_utils.patches_v2 import apply_patches, group_diff_patches_by_path
+from optimizer.models import CodeAndExplanation
+from pydantic import ValidationError
+from testgen.instrumentation.edit_generated_test import parse_module_to_cst
+
+
+class TestDiffScope(str, Enum):
+    RETURN_VALUE = "return_value"
+    STDOUT = "stdout"
+    DID_PASS = "did_pass"  # noqa: S105
+    TIMED_OUT = "timed_out"
+
+
+SCOPE_DESCRIPTIONS = {
+    TestDiffScope.RETURN_VALUE: (
+        "The function returned a different value in the optimized code compared to the original."
+    ),
+    TestDiffScope.STDOUT: ("The output printed to stdout is different in the optimized code compared to the original."),
+    TestDiffScope.DID_PASS: (
+        "The test passed in one version but failed in the other (a change in pass/fail behavior)."
+    ),
+}
+
+
+class TestDiff(Schema):
+    scope: TestDiffScope
+    original_value: bool | str | int | float | dict | list | None = None
+    candidate_value: bool | str | int | float | dict | list | None = None
+    original_pass: bool
+    candidate_pass: bool
+    test_src_code: str
+    candidate_pytest_error: str | None = None
+    original_pytest_error: str | None = None
+
+
+class CodeRepairRequestSchema(Schema):
+    trace_id: str
+    optimization_id: str
+    original_source_code: str
+    modified_source_code: str
+    test_diffs: list[TestDiff] = Field(..., alias="test_diffs")
+
+
+@dataclass()
+class CodeRepairContextData:
+    original_source_code: str
+    modified_source_code: str
+    test_diffs: list[TestDiff]
+
+
+class CodeRepairContext:
+    def __init__(self, ctx_data: CodeRepairContextData, base_system_prompt: str, base_user_prompt: str) -> None:
+        self.data = ctx_data
+        self.base_system_prompt = base_system_prompt
+        self.base_user_prompt = base_user_prompt
+
+    def get_system_prompt(self) -> str:
+        return self.base_system_prompt
+
+    def build_test_details(self, test_diffs: list[TestDiff]) -> str:
+        sections = defaultdict(str)
+        for diff in test_diffs:
+            try:
+                if sections[diff.test_src_code] == "":
+                    # add error strings and test def only once per test function
+                    sections[diff.test_src_code] += f"""Test Source:
+    ```python
+    {diff.test_src_code}
+    ```
+    Pytest error (original code): {diff.original_pytest_error if diff.original_pytest_error else ""}
+    Pytest error (optimized code): {diff.candidate_pytest_error if diff.candidate_pytest_error else ""}
+    """
+                sections[diff.test_src_code] += "\n".join(
+                    [
+                        f"{SCOPE_DESCRIPTIONS.get(diff.scope, diff.scope.value)}",
+                        f"Expected: {diff.original_value!r}.\nGot: {diff.candidate_value!r}."
+                        if diff.scope != TestDiffScope.DID_PASS
+                        else "",
+                        f"Original code test status: {'Passed' if diff.original_pass else 'Failed'}. Optimized code test status: {'Passed' if diff.candidate_pass else 'Failed'}",
+                        "---",
+                    ]
+                )
+            except Exception as e:
+                logging.exception("Some issue in parsing test diffs")
+                sentry_sdk.capture_exception(e)
+        return "\n".join(sections.values())
+
+    def get_user_prompt(self) -> str:
+        return self.base_user_prompt.format(
+            original_source_code=self.data.original_source_code,
+            modified_source_code=self.data.modified_source_code,
+            test_details=self.build_test_details(self.data.test_diffs),
+        )
+
+    def extract_diff_patches_from_llm_res(self, llm_res: str) -> str:
+        matches = REPLACE_IN_FILE_TAGS_REGEX.findall(llm_res)
+        replace_tags = ""
+        if matches and len(matches) != 0:
+            replace_tags = f"<replace_in_file>{matches[0]}</replace_in_file>"
+
+        return replace_tags
+
+    def apply_patches_to_optimized_code(self, replace_tags: str) -> str:
+        if replace_tags == "":
+            return ""
+
+        file_to_code = split_markdown_code(self.data.modified_source_code)
+        # sometimes the llm can write multiple replace tags for the same file, so we group them by path to avoid parsing & applying multiple times
+        file_to_diffs = group_diff_patches_by_path(replace_tags)
+
+        for path, diff in file_to_diffs.items():
+            scoped_code = file_to_code.get(path, None)
+            if scoped_code is None:
+                debug_log_sensitive_data(f"no scoped code for {path}, existing: {file_to_code.keys()}")
+                continue
+            new_code = apply_patches(diff, scoped_code)
+            file_to_code[path] = new_code
+        return group_code(file_to_code)
+
+    def is_valid(self, new_refined_code: str) -> bool:
+        if is_markdown_structure_changed(new_refined_code, self.data.modified_source_code):
+            return False
+        valid = True
+        for code in split_markdown_code(new_refined_code).values():
+            stripped_code = code.strip()
+            if not stripped_code:
+                valid = False
+                break
+            try:
+                parse_module_to_cst(code)
+            except cst.ParserSyntaxError:
+                valid = False
+                break
+        return valid
+
+    def validate_python_module(self) -> None:
+        for _code in split_markdown_code(self.data.modified_source_code).values():
+            try:
+                cst_module = parse_module_to_cst(_code)
+                CodeAndExplanation(cst_module, "")
+            except (ValueError, ValidationError, cst.ParserSyntaxError):  # noqa: TRY203
+                raise

django/aiservice/log_features/log_features.py

@@ -44,6 +44,7 @@ def log_features(
     experiment_metadata: dict[str, str] | None = None,
     final_explanation: str | None = None,
     ranking: dict[str, Any] | None = None,
+    optimizations_origin: dict[str, dict[str, str]] | None = None,
 ) -> None:
     """Log features of a code optimization run to the database.
 
@@ -95,6 +96,7 @@ def log_features(
             "experiment_metadata": experiment_metadata,
             "final_explanation": final_explanation,
             "ranking": ranking,
+            "optimizations_origin": optimizations_origin,
         },
     )
 
@@ -148,6 +150,14 @@ def log_features(
             f.ranking = f.ranking | ranking if ranking is not None else f.ranking
         else:
             f.ranking = ranking if ranking is not None else f.ranking
+
+        if f.optimizations_origin is not None:
+            # merge the optimizations_origin with the existing ones
+            f.optimizations_origin = merge_dicts(f.optimizations_origin, optimizations_origin or {})
+        else:
+            f.optimizations_origin = (
+                optimizations_origin if optimizations_origin is not None else f.optimizations_origin
+            )
         f.speedup_ratio = speedup_ratio if speedup_ratio is not None else f.speedup_ratio
         f.original_runtime = original_runtime if original_runtime is not None else f.original_runtime
         f.optimized_runtime = optimized_runtime if optimized_runtime is not None else f.optimized_runtime
@@ -165,6 +175,22 @@ def log_features(
         f.save()
 
 
+def merge_dicts(a: dict[str, dict[str, str]], b: dict[str, dict[str, str]]) -> dict[str, dict[str, str]]:
+    result: dict[str, dict[str, str]] = {}
+
+    for key, inner in a.items():
+        result[key] = inner.copy()
+
+    for key, inner in b.items():
+        if key not in result:
+            result[key] = inner.copy()
+        else:
+            # b overrides a
+            result[key].update(inner)
+
+    return result
+
+
 @features_api.post("/", response={200: None, 500: LoggingErrorResponseSchema})
 async def log_features_cli(request: HttpRequest, data: LoggingSchema) -> int | tuple[int, LoggingErrorResponseSchema]:
     try:

django/aiservice/log_features/models.py

@@ -31,6 +31,7 @@ class OptimizationFeatures(models.Model):
     experiment_metadata = models.JSONField(null=True, blank=True)
     final_explanation = models.TextField(null=True, blank=True)
     ranking = models.JSONField(null=True, blank=True)
+    optimizations_origin = models.JSONField(null=True, blank=True)
 
     # PR suggestions or create Approval fields
     approval_required = models.BooleanField(default=False)

django/aiservice/optimizer/diff_patches_utils/patches_v2.py

@@ -1,5 +1,7 @@
 from pydantic import ValidationError
 
+from optimizer.context_utils.constants import MULTI_REPLACE_IN_FILE_TAGS_REGEX
+
 
 class SearchReplaceBlock:
     def __init__(self, search, replace):
@@ -19,36 +21,44 @@ def parse_diff(diff: str) -> list[SearchReplaceBlock]:
 
     blocks: list[SearchReplaceBlock] = []
     lines = diff.splitlines(keepends=True)
+    n = len(lines)
     idx = 0
 
-    while idx < len(lines):
+    # Precompute the "marker" strings for efficiency
-        line = lines[idx].strip()
+    search_marker = "<<<<<<< SEARCH"
-        if line == "<<<<<<< SEARCH":
+    delimiter_marker = "======="
-            search_lines = []
+    replace_marker = ">>>>>>> REPLACE"
+
+    while idx < n:
+        line_stripped = lines[idx].strip()
+        if line_stripped.startswith(search_marker):
             idx += 1
-
+            search_start = idx
-            while idx < len(lines) and lines[idx].strip() != "=======":
+            # Find delimiter_marker line
-                search_lines.append(lines[idx])
+            while idx < n and lines[idx].strip() != delimiter_marker:
                 idx += 1
+            search_end = idx
 
-            if idx >= len(lines):
+            if idx >= n:
                 raise ValueError("Invalid diff format: Missing '=======' marker")
-
-            replace_lines = []
             idx += 1
-
+            replace_start = idx
-            while idx < len(lines) and lines[idx].strip() != ">>>>>>> REPLACE":
+            while idx < n and not lines[idx].strip().startswith(replace_marker):
-                replace_lines.append(lines[idx])
                 idx += 1
+            replace_end = idx
 
-            if idx >= len(lines):
+            if idx >= n:
-                raise ValueError("Invalid diff format: Missing '>>>>>>> REPLACE' marker")
+                raise ValueError(
+                    "Invalid diff format: Missing '>>>>>>> REPLACE' marker"
+                )
 
-            search_content = "".join(search_lines).rstrip()
+            search_content = "".join(lines[search_start:search_end]).rstrip()
-            replace_content = "".join(replace_lines).rstrip()
+            replace_content = "".join(lines[replace_start:replace_end]).rstrip()
 
             try:
-                block = SearchReplaceBlock.from_block(search=search_content, replace=replace_content)
+                block = SearchReplaceBlock.from_block(
+                    search=search_content, replace=replace_content
+                )
                 blocks.append(block)
             except ValidationError as ve:
                 raise ValueError(f"Invalid block format: {ve}")
@@ -61,13 +71,35 @@ def parse_diff(diff: str) -> list[SearchReplaceBlock]:
     return blocks
 
 
+def group_diff_patches_by_path(replace_tags_str: str) -> dict[str, str]:
+    matches = MULTI_REPLACE_IN_FILE_TAGS_REGEX.findall(replace_tags_str)
+    file_to_diffs = {}
+
+    current_file = None
+    current_diff = ""
+
+    for path, diff in matches:
+        if path != current_file:
+            if current_file:
+                file_to_diffs[current_file] = current_diff
+            current_file = path
+            current_diff = diff
+        else:
+            current_diff += diff
+
+    if current_file:
+        file_to_diffs[current_file] = current_diff
+
+    return file_to_diffs
+
+
 def apply_patches(diff_str: str, content: str) -> str:
     try:
         patch_blocks = parse_diff(diff_str)
     except ValueError:
         return content
 
-    for idx, block in enumerate(patch_blocks, 1):
+    for block in patch_blocks:
         if not block.search:
             if block.replace:
                 # a replacement block without a search, then just add the replace block
@@ -79,5 +111,7 @@ def apply_patches(diff_str: str, content: str) -> str:
             start_char_idx = content.find(block.search)
             if start_char_idx != -1:
                 end_char_idx = start_char_idx + len(block.search)
-                content = f"{content[:start_char_idx]}{block.replace}{content[end_char_idx:]}"
+                content = (
+                    f"{content[:start_char_idx]}{block.replace}{content[end_char_idx:]}"
+                )
     return content

django/aiservice/optimizer/models.py

@@ -1,9 +1,18 @@
+import enum
+
 import libcst
 from ninja import Schema
 from pydantic import field_validator
 from pydantic.dataclasses import dataclass
 
 
+class OptimizedCandidateSource(str, enum.Enum):
+    OPTIMIZE = "OPTIMIZE"
+    OPTIMIZE_LP = "OPTIMIZE_LP"
+    REFINE = "REFINE"
+    REPAIR = "REPAIR"
+
+
 @dataclass(frozen=True)
 class CodeAndExplanation:
     cst_module: libcst.Module | None

django/aiservice/optimizer/optimizer.py

@@ -8,6 +8,11 @@ from typing import TYPE_CHECKING
 
 import libcst as cst
 import sentry_sdk
+from ninja import NinjaAPI
+from ninja.errors import HttpError
+from openai.types.chat import ChatCompletionSystemMessageParam, ChatCompletionUserMessageParam
+from pydantic import ValidationError
+
 from aiservice.analytics.posthog import ph
 from aiservice.common_utils import parse_python_version, should_hack_for_demo, validate_trace_id
 from aiservice.env_specific import debug_log_sensitive_data, debug_log_sensitive_data_from_callable, llm_clients
@@ -15,11 +20,6 @@ from aiservice.models.aimodels import OPTIMIZE_MODEL, calculate_llm_cost
 from authapp.user import get_user_by_id
 from log_features.log_event import log_optimization_event
 from log_features.log_features import log_features
-from ninja import NinjaAPI
-from ninja.errors import HttpError
-from openai.types.chat import ChatCompletionSystemMessageParam, ChatCompletionUserMessageParam
-from pydantic import ValidationError
-
 from optimizer.context_utils.context_helpers import group_code
 from optimizer.context_utils.optimizer_context import (
     BaseOptimizerContext,
@@ -27,10 +27,9 @@ from optimizer.context_utils.optimizer_context import (
     OptimizeResponseItemSchema,
     OptimizeResponseSchema,
 )
-from optimizer.models import OptimizeSchema  # noqa: TC001
+from optimizer.models import OptimizedCandidateSource, OptimizeSchema  # noqa: TC001
 
 if TYPE_CHECKING:
-    from aiservice.models.aimodels import LLM
     from django.http import HttpRequest
     from openai.types.chat import (
         ChatCompletionAssistantMessageParam,
@@ -38,6 +37,8 @@ if TYPE_CHECKING:
         ChatCompletionToolMessageParam,
     )
 
+    from aiservice.models.aimodels import LLM
+
 
 optimizations_json = [
     {
@@ -345,7 +346,7 @@ async def optimize(
                 },
                 explanations_post={cei.optimization_id: cei.explanation for cei in optimization_response_items},
                 experiment_metadata=data.experiment_metadata if data.experiment_metadata else None,
-                # request=request,
+                optimizations_origin={cei.optimization_id: {"source": OptimizedCandidateSource.OPTIMIZE, "parent": None} for cei in optimization_response_items},
             )
         )
 

django/aiservice/optimizer/optimizer_line_profiler.py

@@ -5,29 +5,30 @@ from pathlib import Path
 from typing import TYPE_CHECKING
 
 import sentry_sdk
+from ninja import NinjaAPI, Schema
+from openai.types.chat import ChatCompletionSystemMessageParam, ChatCompletionUserMessageParam
+
 from aiservice.analytics.posthog import ph
 from aiservice.common_utils import parse_python_version, validate_trace_id
 from aiservice.env_specific import debug_log_sensitive_data, debug_log_sensitive_data_from_callable, llm_clients
 from aiservice.models.aimodels import OPTIMIZE_MODEL, calculate_llm_cost
 from log_features.log_event import update_optimization_cost
 from log_features.log_features import log_features
-from ninja import NinjaAPI, Schema
-from openai.types.chat import ChatCompletionSystemMessageParam, ChatCompletionUserMessageParam
-
 from optimizer.context_utils.optimizer_context import (
     BaseOptimizerContext,
     OptimizeErrorResponseSchema,
     OptimizeResponseSchema,
 )
+from optimizer.models import OptimizedCandidateSource
 
 if TYPE_CHECKING:
-    from aiservice.models.aimodels import LLM
     from openai.types.chat import (
         ChatCompletionAssistantMessageParam,
         ChatCompletionFunctionMessageParam,
         ChatCompletionToolMessageParam,
     )
 
+    from aiservice.models.aimodels import LLM
     from optimizer.context_utils.optimizer_context import OptimizeResponseItemSchema
 
 
@@ -190,6 +191,8 @@ async def optimize(request, data: OptimizeSchemaLP) -> tuple[int, OptimizeRespon
             },
             explanations_post={cei.optimization_id: cei.explanation for cei in optimization_response_items},
             experiment_metadata=data.experiment_metadata if data.experiment_metadata else None,
+            optimizations_origin={cei.optimization_id: {"source": OptimizedCandidateSource.OPTIMIZE_LP, "parent": None} for cei in optimization_response_items},
+
         )
 
     response = OptimizeResponseSchema(optimizations=optimization_response_items)

django/aiservice/optimizer/refinement.py

@@ -2,31 +2,34 @@ from __future__ import annotations
 
 import asyncio
 import logging
+import uuid
 from pathlib import Path
 from typing import TYPE_CHECKING
 
 import libcst as cst
 import sentry_sdk
+from ninja import NinjaAPI, Schema
+from openai.types.chat import ChatCompletionSystemMessageParam, ChatCompletionUserMessageParam
+from pydantic import ValidationError
+
 from aiservice.analytics.posthog import ph
 from aiservice.common_utils import validate_trace_id
 from aiservice.env_specific import debug_log_sensitive_data, llm_clients
 from aiservice.models.aimodels import REFINEMENT_MODEL, calculate_llm_cost
 from log_features.log_event import update_optimization_cost
 from log_features.log_features import log_features
-from ninja import NinjaAPI, Schema
-from openai.types.chat import ChatCompletionSystemMessageParam, ChatCompletionUserMessageParam
-from pydantic import ValidationError
-
 from optimizer.context_utils.refiner_context import BaseRefinerContext, RefinementContextData
+from optimizer.models import OptimizedCandidateSource
 
 if TYPE_CHECKING:
-    from aiservice.models.aimodels import LLM
     from openai.types.chat import (
         ChatCompletionAssistantMessageParam,
         ChatCompletionFunctionMessageParam,
         ChatCompletionToolMessageParam,
     )
 
+    from aiservice.models.aimodels import LLM
+
 
 refinement_api = NinjaAPI(urls_namespace="refinement")
 
@@ -260,7 +263,8 @@ async def refinement(  # noqa: D417
         refined_optimization = ""
 
     return RefinementIntermediateResponseItemschema(
-        optimization_id=optimization_id,
+        parent_id=optimization_id,
+        optimization_id=str(uuid.uuid4()),
         source_code=refined_optimization,
         explanation=refined_explanation,
         original_explanation=ctx.data.optimized_explanation,
@@ -291,6 +295,7 @@ class OptimizeErrorResponseSchema(Schema):
 class RefinementIntermediateResponseItemschema(Schema):
     # the key will be the optimization id and the value will be the actual refined code
     explanation: str
+    parent_id: str
     optimization_id: str
     source_code: str
     original_explanation: str
@@ -301,6 +306,7 @@ class RefinementResponseItemschema(Schema):
     # the key will be the optimization id and the value will be the actual refined code
     explanation: str
     optimization_id: str
+    parent_id: str
     source_code: str
 
 
@@ -374,27 +380,30 @@ async def refine(
             trace_id=trace_id,
             user_id=request.user,
             optimizations_raw={
-                cei.optimization_id[:-4] + "refi": cei.source_code
+                cei.optimization_id: cei.source_code
                 for cei in refinement_data
                 if not isinstance(cei, OptimizeErrorResponseSchema)
             },
-            optimizations_post={
+            optimizations_post={cei.optimization_id: cei.source_code for cei in filtered_refined_optimizations},
-                cei.optimization_id[:-4] + "refi": cei.source_code for cei in filtered_refined_optimizations
-            },
             explanations_raw={
-                cei.optimization_id[:-4] + "refi": cei.explanation
+                cei.optimization_id: cei.explanation
                 for cei in refinement_data
                 if not isinstance(cei, OptimizeErrorResponseSchema)
             },
-            explanations_post={
+            explanations_post={cei.optimization_id: cei.explanation for cei in filtered_refined_optimizations},
-                cei.optimization_id[:-4] + "refi": cei.explanation for cei in filtered_refined_optimizations
+            optimizations_origin={
+                cei.optimization_id: {"source": OptimizedCandidateSource.REFINE, "parent": cei.parent_id}
+                for cei in filtered_refined_optimizations
             },
         )
     await update_optimization_cost(trace_id=trace_id, cost=total_llm_cost)
     return 200, Refinementschema(
         refinements=[
             RefinementResponseItemschema(
-                source_code=x.source_code, explanation=x.original_explanation, optimization_id=x.optimization_id
+                source_code=x.source_code,
+                explanation=x.original_explanation,
+                optimization_id=x.optimization_id,
+                parent_id=x.parent_id,
             )
             for x in filtered_refined_optimizations
         ]

django/aiservice/testgen/instrumentation/edit_generated_test.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, cast
 
 from libcst import CSTTransformer, ImportAlias, ImportFrom, MetadataWrapper, Name, parse_expression, parse_module
+from functools import lru_cache
 
 if TYPE_CHECKING:
     from libcst import (
@@ -19,6 +20,7 @@ if TYPE_CHECKING:
     from aiservice.models.functions_to_optimize import FunctionToOptimize
 
 
+@lru_cache(maxsize=128)
 def parse_module_to_cst(module_str: str) -> Module:
     """Parse a module string into its libCST representation.
 

django/aiservice/tests/optimizer/test_code_repair.py

@@ -0,0 +1,389 @@
+
+
+from code_repair.code_repair_context import CodeRepairContext, CodeRepairContextData
+from optimizer.diff_patches_utils.patches_v2 import apply_patches
+
+
+def test_code_repair_single_file():
+
+    original_code = """```python:demo.py
+import math
+from typing import List, Tuple, Optional
+
+def calculate_portfolio_metrics(
+    investments: List[Tuple[str, float, float]], 
+    risk_free_rate: float = 0.02
+) -> dict:
+    if not investments:
+        raise ValueError("Investments list cannot be empty")
+    
+    if abs(sum(weight for _, weight, _ in investments) - 1.0) > 1e-10:
+        raise ValueError("Portfolio weights must sum to 1.0")
+    
+    # Calculate weighted return
+    weighted_return = sum(weight * ret for _, weight, ret in investments)
+    
+    # Calculate portfolio volatility (simplified)
+    volatility = math.sqrt(sum((weight * ret) ** 2 for _, weight, ret in investments))
+    
+    # Calculate Sharpe ratio
+    if volatility == 0:
+        sharpe_ratio = 0.0
+    else:
+        sharpe_ratio = (weighted_return - risk_free_rate) / volatility
+    
+    # Find best and worst performing assets
+    best_asset = max(investments, key=lambda x: x[2])
+    worst_asset = min(investments, key=lambda x: x[2])
+    
+    return {
+        'weighted_return': round(weighted_return, 6),
+        'volatility': round(volatility, 6),
+        'sharpe_ratio': round(sharpe_ratio, 6),
+        'best_performing': (best_asset[0], round(best_asset[2], 6)),
+        'worst_performing': (worst_asset[0], round(worst_asset[2], 6)),
+        'total_assets': len(investments)
+    }
+```
+"""
+    optimized_code = """```python:demo.py
+import math
+from typing import List, Tuple, Optional
+
+def calculate_portfolio_metrics(
+    investments: List[Tuple[str, float, float]], 
+    risk_free_rate: float = 0.02
+) -> dict:
+    if not investments:
+        raise ValueError("Investments list cannot be empty")
+
+    total_weight = sum(w for _, w, _ in investments)
+    if total_weight != 1.0:  # Should use tolerance check
+        raise ValueError("Portfolio weights must sum to 1.0")
+
+    weighted_return = 1.0
+    for _, weight, ret in investments:
+        weighted_return *= (1 + ret) ** weight
+    weighted_return = weighted_return - 1.0  # Convert back from geometric
+
+    returns = [r for _, _, r in investments]
+    mean_return = sum(returns) / len(returns)
+    volatility = math.sqrt(sum((r - mean_return) ** 2 for r in returns) / len(returns))
+
+    # BUG 4: Sharpe ratio calculation is correct but uses wrong inputs
+    if volatility == 0:
+        sharpe_ratio = 0.0
+    else:
+        sharpe_ratio = (weighted_return - risk_free_rate) / volatility
+
+    def risk_adjusted_return(return_val, weight):
+        return (return_val - risk_free_rate) / (weight * return_val) if weight * return_val != 0 else return_val
+    
+    best_asset = max(investments, key=lambda x: risk_adjusted_return(x[2], x[1]))
+    worst_asset = min(investments, key=lambda x: risk_adjusted_return(x[2], x[1]))
+
+    return {
+        "weighted_return": round(weighted_return, 6),
+        "volatility": 2, 
+        "sharpe_ratio": round(sharpe_ratio, 6),
+        "best_performing": (best_asset[0], round(best_asset[2], 6)),
+        "worst_performing": (worst_asset[0], round(worst_asset[2], 6)),
+        "total_assets": len(investments),
+    }
+```
+"""
+
+
+    llm_response = """Looking at the test failure and comparing the original and modified code, I can see several issues in the modified implementation:
+
+1. **Hardcoded volatility**: The volatility is hardcoded to `2` instead of being calculated
+2. **Incorrect weight validation**: Missing tolerance check for weight sum validation
+3. **Wrong volatility calculation**: Using standard deviation of returns instead of weighted portfolio volatility
+4. **Wrong weighted return calculation**: Using geometric mean instead of arithmetic weighted return
+5. **Wrong asset selection criteria**: Using risk-adjusted return instead of simple return
+
+Let me fix these issues:
+
+<replace_in_file>
+<path>demo.py</path>
+<diff>
+<<<<<<< SEARCH
+    total_weight = sum(w for _, w, _ in investments)
+    if total_weight != 1.0:  # Should use tolerance check
+        raise ValueError("Portfolio weights must sum to 1.0")
+=======
+    if abs(sum(weight for _, weight, _ in investments) - 1.0) > 1e-10:
+        raise ValueError("Portfolio weights must sum to 1.0")
+>>>>>>> REPLACE
+</diff>
+</replace_in_file>
+
+<replace_in_file>
+<path>demo.py</path>
+<diff>
+<<<<<<< SEARCH
+    weighted_return = 1.0
+    for _, weight, ret in investments:
+        weighted_return *= (1 + ret) ** weight
+    weighted_return = weighted_return - 1.0  # Convert back from geometric
+=======
+    # Calculate weighted return
+    weighted_return = sum(weight * ret for _, weight, ret in investments)
+>>>>>>> REPLACE
+</diff>
+</replace_in_file>
+
+<replace_in_file>
+<path>demo.py</path>
+<diff>
+<<<<<<< SEARCH
+    returns = [r for _, _, r in investments]
+    mean_return = sum(returns) / len(returns)
+    volatility = math.sqrt(sum((r - mean_return) ** 2 for r in returns) / len(returns))
+=======
+    # Calculate portfolio volatility (simplified)
+    volatility = math.sqrt(sum((weight * ret) ** 2 for _, weight, ret in investments))
+>>>>>>> REPLACE
+</diff>
+</replace_in_file>
+
+<replace_in_file>
+<path>demo.py</path>
+<diff>
+<<<<<<< SEARCH
+    # BUG 4: Sharpe ratio calculation is correct but uses wrong inputs
+    if volatility == 0:
+        sharpe_ratio = 0.0
+    else:
+        sharpe_ratio = (weighted_return - risk_free_rate) / volatility
+
+    def risk_adjusted_return(return_val, weight):
+        return (return_val - risk_free_rate) / (weight * return_val) if weight * return_val != 0 else return_val
+    
+    best_asset = max(investments, key=lambda x: risk_adjusted_return(x[2], x[1]))
+    worst_asset = min(investments, key=lambda x: risk_adjusted_return(x[2], x[1]))
+=======
+    # Calculate Sharpe ratio
+    if volatility == 0:
+        sharpe_ratio = 0.0
+    else:
+        sharpe_ratio = (weighted_return - risk_free_rate) / volatility
+    
+    # Find best and worst performing assets
+    best_asset = max(investments, key=lambda x: x[2])
+    worst_asset = min(investments, key=lambda x: x[2])
+>>>>>>> REPLACE
+</diff>
+</replace_in_file>
+
+<replace_in_file>
+<path>demo.py</path>
+<diff>
+<<<<<<< SEARCH
+    return {
+        "weighted_return": round(weighted_return, 6),
+        "volatility": 2, 
+        "sharpe_ratio": round(sharpe_ratio, 6),
+        "best_performing": (best_asset[0], round(best_asset[2], 6)),
+        "worst_performing": (worst_asset[0], round(worst_asset[2], 6)),
+        "total_assets": len(investments),
+    }
+=======
+    return {
+        'weighted_return': round(weighted_return, 6),
+        'volatility': round(volatility, 6),
+        'sharpe_ratio': round(sharpe_ratio, 6),
+        'best_performing': (best_asset[0], round(best_asset[2], 6)),
+        'worst_performing': (worst_asset[0], round(worst_asset[2], 6)),
+        'total_assets': len(investments)
+    }
+>>>>>>> REPLACE
+</diff>
+</replace_in_file>
+
+The key changes I made:
+
+1. **Fixed weight validation**: Restored the tolerance-based check using `abs(sum(...) - 1.0) > 1e-10` instead of exact equality
+2. **Fixed weighted return calculation**: Changed from geometric mean back to simple weighted arithmetic mean
+3. **Fixed volatility calculation**: Restored the original simplified portfolio volatility formula using weighted returns
+4. **Fixed volatility return value**: Removed the hardcoded `2` and properly calculated and rounded the volatility
+5. **Fixed asset selection**: Restored the original logic to find best/worst assets based on simple returns rather than risk-adjusted returns
+6. **Fixed dictionary formatting**: Changed from double quotes to single quotes to match original formatting
+
+These changes align the modified code with the original implementation's behavior, ensuring that the test for zero volatility passes (when cash investment has 0% return, the volatility should indeed be 0.0)."""
+    ctx = CodeRepairContext(CodeRepairContextData(original_code, optimized_code, ""), "" , "")
+    diff_patches = ctx.extract_diff_patches_from_llm_res(llm_response)
+    refined_optimization = ctx.apply_patches_to_optimized_code(diff_patches)
+
+    print(refined_optimization)
+    assert ctx.is_valid(refined_optimization)
+
+
+"""
+```python:demo.py
+import math
+from typing import List, Tuple, Optional
+
+def calculate_portfolio_metrics(
+    investments: List[Tuple[str, float, float]], 
+    risk_free_rate: float = 0.02
+) -> dict:
+    if not investments:
+        raise ValueError("Investments list cannot be empty")
+
+    total_weight = sum(w for _, w, _ in investments)
+    if total_weight != 1.0:  # Should use tolerance check
+        raise ValueError("Portfolio weights must sum to 1.0")
+
+    weighted_return = 1.0
+    for _, weight, ret in investments:
+        weighted_return *= (1 + ret) ** weight
+    weighted_return = weighted_return - 1.0  # Convert back from geometric
+
+    returns = [r for _, _, r in investments]
+    mean_return = sum(returns) / len(returns)
+    volatility = math.sqrt(sum((r - mean_return) ** 2 for r in returns) / len(returns))
+
+    # BUG 4: Sharpe ratio calculation is correct but uses wrong inputs
+    if volatility == 0:
+        sharpe_ratio = 0.0
+    else:
+        sharpe_ratio = (weighted_return - risk_free_rate) / volatility
+
+    def risk_adjusted_return(return_val, weight):
+        return (return_val - risk_free_rate) / (weight * return_val) if weight * return_val != 0 else return_val
+    
+    best_asset = max(investments, key=lambda x: risk_adjusted_return(x[2], x[1]))
+    worst_asset = min(investments, key=lambda x: risk_adjusted_return(x[2], x[1]))
+
+    return {
+        "weighted_return": round(weighted_return, 6),
+        "volatility": 2, 
+        "sharpe_ratio": round(sharpe_ratio, 6),
+        "best_performing": (best_asset[0], round(best_asset[2], 6)),
+        "worst_performing": (worst_asset[0], round(worst_asset[2], 6)),
+        "total_assets": len(investments),
+    }
+```
+"""
+
+
+def test_patch_apply():
+    patch = """<<<<<<< SEARCH
+    total_weight = sum(w for _, w, _ in investments)
+    if total_weight != 1.0:  # Should use tolerance check
+        raise ValueError("Portfolio weights must sum to 1.0")
+=======
+    if abs(sum(weight for _, weight, _ in investments) - 1.0) > 1e-10:
+        raise ValueError("Portfolio weights must sum to 1.0")
+>>>>>>> REPLACE
+
+<<<<<<< SEARCH
+    weighted_return = 1.0
+    for _, weight, ret in investments:
+        weighted_return *= (1 + ret) ** weight
+    weighted_return = weighted_return - 1.0  # Convert back from geometric
+=======
+    # Calculate weighted return
+    weighted_return = sum(weight * ret for _, weight, ret in investments)
+>>>>>>> REPLACE
+
+<<<<<<< SEARCH
+    returns = [r for _, _, r in investments]
+    mean_return = sum(returns) / len(returns)
+    volatility = math.sqrt(sum((r - mean_return) ** 2 for r in returns) / len(returns))
+=======
+    # Calculate portfolio volatility (simplified)
+    volatility = math.sqrt(sum((weight * ret) ** 2 for _, weight, ret in investments))
+>>>>>>> REPLACE
+
+<<<<<<< SEARCH
+    # BUG 4: Sharpe ratio calculation is correct but uses wrong inputs
+    if volatility == 0:
+        sharpe_ratio = 0.0
+    else:
+        sharpe_ratio = (weighted_return - risk_free_rate) / volatility
+
+    def risk_adjusted_return(return_val, weight):
+        return (return_val - risk_free_rate) / (weight * return_val) if weight * return_val != 0 else return_val
+    
+    best_asset = max(investments, key=lambda x: risk_adjusted_return(x[2], x[1]))
+    worst_asset = min(investments, key=lambda x: risk_adjusted_return(x[2], x[1]))
+=======
+    # Calculate Sharpe ratio
+    if volatility == 0:
+        sharpe_ratio = 0.0
+    else:
+        sharpe_ratio = (weighted_return - risk_free_rate) / volatility
+    
+    # Find best and worst performing assets
+    best_asset = max(investments, key=lambda x: x[2])
+    worst_asset = min(investments, key=lambda x: x[2])
+>>>>>>> REPLACE
+
+<<<<<<< SEARCH
+    return {
+        "weighted_return": round(weighted_return, 6),
+        "volatility": 2, 
+        "sharpe_ratio": round(sharpe_ratio, 6),
+        "best_performing": (best_asset[0], round(best_asset[2], 6)),
+        "worst_performing": (worst_asset[0], round(worst_asset[2], 6)),
+        "total_assets": len(investments),
+    }
+=======
+    return {
+        'weighted_return': round(weighted_return, 6),
+        'volatility': round(volatility, 6),
+        'sharpe_ratio': round(sharpe_ratio, 6),
+        'best_performing': (best_asset[0], round(best_asset[2], 6)),
+        'worst_performing': (worst_asset[0], round(worst_asset[2], 6)),
+        'total_assets': len(investments)
+    }
+>>>>>>> REPLACE
+"""
+    code = """import math
+from typing import List, Tuple, Optional
+
+def calculate_portfolio_metrics(
+    investments: List[Tuple[str, float, float]], 
+    risk_free_rate: float = 0.02
+) -> dict:
+    if not investments:
+        raise ValueError("Investments list cannot be empty")
+
+    total_weight = sum(w for _, w, _ in investments)
+    if total_weight != 1.0:  # Should use tolerance check
+        raise ValueError("Portfolio weights must sum to 1.0")
+
+    weighted_return = 1.0
+    for _, weight, ret in investments:
+        weighted_return *= (1 + ret) ** weight
+    weighted_return = weighted_return - 1.0  # Convert back from geometric
+
+    returns = [r for _, _, r in investments]
+    mean_return = sum(returns) / len(returns)
+    volatility = math.sqrt(sum((r - mean_return) ** 2 for r in returns) / len(returns))
+
+    # BUG 4: Sharpe ratio calculation is correct but uses wrong inputs
+    if volatility == 0:
+        sharpe_ratio = 0.0
+    else:
+        sharpe_ratio = (weighted_return - risk_free_rate) / volatility
+
+    def risk_adjusted_return(return_val, weight):
+        return (return_val - risk_free_rate) / (weight * return_val) if weight * return_val != 0 else return_val
+    
+    best_asset = max(investments, key=lambda x: risk_adjusted_return(x[2], x[1]))
+    worst_asset = min(investments, key=lambda x: risk_adjusted_return(x[2], x[1]))
+
+    return {
+        "weighted_return": round(weighted_return, 6),
+        "volatility": 2, 
+        "sharpe_ratio": round(sharpe_ratio, 6),
+        "best_performing": (best_asset[0], round(best_asset[2], 6)),
+        "worst_performing": (worst_asset[0], round(worst_asset[2], 6)),
+        "total_assets": len(investments),
+    }
+"""
+    new_code = apply_patches(patch, code)
+    print(new_code)

experiments/code_repair_dashboard.html

@@ -0,0 +1,475 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Code Repair Logs Dashboard</title>
+    <style>
+        * {
+            box-sizing: border-box;
+            margin: 0;
+            padding: 0;
+        }
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, sans-serif;
+            background: #1a1a2e;
+            color: #eee;
+            min-height: 100vh;
+            padding: 20px;
+        }
+        h1 {
+            text-align: center;
+            margin-bottom: 20px;
+            color: #00d9ff;
+        }
+        .stats {
+            display: flex;
+            gap: 20px;
+            justify-content: center;
+            margin-bottom: 30px;
+            flex-wrap: wrap;
+        }
+        .stat-card {
+            background: #16213e;
+            padding: 20px 30px;
+            border-radius: 10px;
+            text-align: center;
+            min-width: 150px;
+        }
+        .stat-card .number {
+            font-size: 2em;
+            font-weight: bold;
+            color: #00d9ff;
+        }
+        .stat-card .label {
+            color: #888;
+            font-size: 0.9em;
+        }
+        .search-bar {
+            display: flex;
+            gap: 10px;
+            margin-bottom: 20px;
+            justify-content: center;
+            flex-wrap: wrap;
+        }
+        .search-bar input {
+            padding: 10px 15px;
+            border: none;
+            border-radius: 5px;
+            background: #16213e;
+            color: #eee;
+            width: 300px;
+            font-size: 1em;
+        }
+        .search-bar input:focus {
+            outline: 2px solid #00d9ff;
+        }
+        .trace-group {
+            background: #16213e;
+            border-radius: 10px;
+            margin-bottom: 20px;
+            overflow: hidden;
+        }
+        .trace-header {
+            background: #0f3460;
+            padding: 15px 20px;
+            cursor: pointer;
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            transition: background 0.2s;
+        }
+        .trace-header:hover {
+            background: #1a4a80;
+        }
+        .trace-header .trace-id {
+            font-family: monospace;
+            color: #00d9ff;
+            font-size: 0.95em;
+        }
+        .trace-header .count-badge {
+            background: #e94560;
+            color: white;
+            padding: 3px 10px;
+            border-radius: 15px;
+            font-size: 0.85em;
+        }
+        .trace-header .arrow {
+            transition: transform 0.2s;
+        }
+        .trace-header.expanded .arrow {
+            transform: rotate(180deg);
+        }
+        .trace-content {
+            display: none;
+            padding: 0;
+        }
+        .trace-content.show {
+            display: block;
+        }
+        .log-entry {
+            border-top: 1px solid #0f3460;
+            padding: 20px;
+        }
+        .log-entry:first-child {
+            border-top: none;
+        }
+        .log-entry-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 15px;
+            flex-wrap: wrap;
+            gap: 10px;
+        }
+        .optimization-id {
+            font-family: monospace;
+            font-size: 0.8em;
+            color: #888;
+        }
+        .timestamp {
+            font-size: 0.8em;
+            color: #888;
+        }
+        .section {
+            margin-bottom: 15px;
+        }
+        .section-title {
+            font-weight: bold;
+            color: #00d9ff;
+            margin-bottom: 8px;
+            font-size: 0.9em;
+            text-transform: uppercase;
+        }
+        .code-block {
+            background: #0d1117;
+            border-radius: 5px;
+            padding: 15px;
+            overflow-x: auto;
+            font-family: 'Fira Code', 'Consolas', monospace;
+            font-size: 0.85em;
+            line-height: 1.5;
+            white-space: pre-wrap;
+            word-wrap: break-word;
+            max-height: 400px;
+            overflow-y: auto;
+        }
+        .explanation-block {
+            background: #1e3a5f;
+            border-radius: 5px;
+            padding: 15px;
+            line-height: 1.6;
+            font-size: 0.9em;
+            max-height: 300px;
+            overflow-y: auto;
+        }
+        .refined-code {
+            background: #0d2818;
+            border: 1px solid #2ea043;
+        }
+        .expand-all-btn {
+            background: #0f3460;
+            color: #eee;
+            border: none;
+            padding: 10px 20px;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 0.9em;
+            transition: background 0.2s;
+        }
+        .expand-all-btn:hover {
+            background: #1a4a80;
+        }
+        .no-results {
+            text-align: center;
+            padding: 40px;
+            color: #888;
+        }
+        .copy-btn {
+            background: #333;
+            color: #888;
+            border: none;
+            padding: 5px 10px;
+            border-radius: 3px;
+            cursor: pointer;
+            font-size: 0.75em;
+            float: right;
+            margin-bottom: 5px;
+        }
+        .copy-btn:hover {
+            background: #444;
+            color: #eee;
+        }
+        .status-badges {
+            display: flex;
+            gap: 8px;
+            margin-top: 10px;
+        }
+        .status-badge {
+            padding: 4px 12px;
+            border-radius: 4px;
+            font-size: 0.8em;
+            font-weight: 600;
+            text-transform: uppercase;
+        }
+        .status-badge.passed-true {
+            background: #238636;
+            color: #fff;
+        }
+        .status-badge.passed-false {
+            background: #da3633;
+            color: #fff;
+        }
+        .status-badge.faster-true {
+            background: #1f6feb;
+            color: #fff;
+        }
+        .status-badge.faster-false {
+            background: #6e7681;
+            color: #fff;
+        }
+        .status-badge.pending {
+            background: #484f58;
+            color: #8b949e;
+        }
+        .trace-status-summary {
+            display: flex;
+            gap: 8px;
+            align-items: center;
+        }
+        .trace-status-icon {
+            font-size: 1.1em;
+        }
+    </style>
+</head>
+<body>
+    <h1>Code Repair Logs Dashboard</h1>
+
+    <div class="stats">
+        <div class="stat-card">
+            <div class="number" id="total-traces">0</div>
+            <div class="label">Trace Groups</div>
+        </div>
+        <div class="stat-card">
+            <div class="number" id="total-logs">0</div>
+            <div class="label">Total Logs</div>
+        </div>
+        <div class="stat-card">
+            <div class="number" id="avg-per-trace">0</div>
+            <div class="label">Avg per Trace</div>
+        </div>
+        <div class="stat-card">
+            <div class="number" id="passed-count" style="color: #238636;">0</div>
+            <div class="label">Passed</div>
+        </div>
+        <div class="stat-card">
+            <div class="number" id="faster-count" style="color: #1f6feb;">0</div>
+            <div class="label">Faster</div>
+        </div>
+    </div>
+
+    <div class="search-bar">
+        <input type="text" id="search" placeholder="Search by trace ID, optimization ID, or content...">
+        <button class="expand-all-btn" onclick="toggleAll()">Expand All</button>
+    </div>
+
+    <div id="dashboard"></div>
+
+    <script>
+        // Data will be injected here
+        const data = DATA_PLACEHOLDER;
+
+        let allExpanded = false;
+
+        function escapeHtml(text) {
+            if (!text) return '';
+            const div = document.createElement('div');
+            div.textContent = text;
+            return div.innerHTML;
+        }
+
+        function formatDate(dateStr) {
+            if (!dateStr) return '';
+            const date = new Date(dateStr);
+            return date.toLocaleString();
+        }
+
+        function copyToClipboard(text, btn) {
+            navigator.clipboard.writeText(text).then(() => {
+                const originalText = btn.textContent;
+                btn.textContent = 'Copied!';
+                setTimeout(() => btn.textContent = originalText, 1500);
+            });
+        }
+
+        function getStatusBadge(value, label, type) {
+            if (value === null || value === undefined) {
+                return `<span class="status-badge pending">${label}: Pending</span>`;
+            }
+            const isTrue = value === 'True' || value === 'true' || value === true || value === 'yes';
+            const className = `${type}-${isTrue}`;
+            const displayValue = isTrue ? 'Yes' : 'No';
+            return `<span class="status-badge ${className}">${label}: ${displayValue}</span>`;
+        }
+
+        function getTraceStatusSummary(logs) {
+            const withStatus = logs.filter(l => l.passed !== null && l.passed !== undefined);
+            if (withStatus.length === 0) return '';
+
+            const passedCount = withStatus.filter(l => l.passed === 'True' || l.passed === 'true' || l.passed === true).length;
+            const fasterCount = withStatus.filter(l => l.faster === 'True' || l.faster === 'true' || l.faster === true).length;
+
+            const passedIcon = passedCount === withStatus.length ? '✓' : passedCount > 0 ? '◐' : '✗';
+            const fasterIcon = fasterCount === withStatus.length ? '⚡' : fasterCount > 0 ? '◐' : '−';
+
+            return `<span class="trace-status-summary">
+                <span title="Passed: ${passedCount}/${withStatus.length}">${passedIcon}</span>
+                <span title="Faster: ${fasterCount}/${withStatus.length}">${fasterIcon}</span>
+            </span>`;
+        }
+
+        function renderDashboard(filteredData = data) {
+            const dashboard = document.getElementById('dashboard');
+
+            // Group by trace_id
+            const grouped = {};
+            filteredData.forEach(log => {
+                if (!grouped[log.trace_id]) {
+                    grouped[log.trace_id] = [];
+                }
+                grouped[log.trace_id].push(log);
+            });
+
+            // Sort groups by most recent
+            const sortedTraces = Object.entries(grouped).sort((a, b) => {
+                const aDate = new Date(a[1][0].created_at);
+                const bDate = new Date(b[1][0].created_at);
+                return bDate - aDate;
+            });
+
+            // Update stats
+            document.getElementById('total-traces').textContent = sortedTraces.length;
+            document.getElementById('total-logs').textContent = filteredData.length;
+            document.getElementById('avg-per-trace').textContent = sortedTraces.length > 0
+                ? (filteredData.length / sortedTraces.length).toFixed(1)
+                : '0';
+
+            // Calculate passed/faster stats
+            const withStatus = filteredData.filter(l => l.passed !== null && l.passed !== undefined);
+            const passedCount = withStatus.filter(l => l.passed === 'True' || l.passed === 'true' || l.passed === true).length;
+            const fasterCount = withStatus.filter(l => l.faster === 'True' || l.faster === 'true' || l.faster === true).length;
+            document.getElementById('passed-count').textContent = withStatus.length > 0 ? `${passedCount}/${withStatus.length}` : '−';
+            document.getElementById('faster-count').textContent = withStatus.length > 0 ? `${fasterCount}/${withStatus.length}` : '−';
+
+            if (sortedTraces.length === 0) {
+                dashboard.innerHTML = '<div class="no-results">No results found</div>';
+                return;
+            }
+
+            let html = '';
+            sortedTraces.forEach(([traceId, logs], idx) => {
+                // Sort logs within group by created_at
+                logs.sort((a, b) => new Date(a.created_at) - new Date(b.created_at));
+
+                html += `
+                <div class="trace-group">
+                    <div class="trace-header" onclick="toggleTrace(${idx})">
+                        <span class="trace-id">Trace: ${escapeHtml(traceId)}</span>
+                        <div style="display: flex; align-items: center; gap: 10px;">
+                            ${getTraceStatusSummary(logs)}
+                            <span class="count-badge">${logs.length} entries</span>
+                            <span class="arrow">▼</span>
+                        </div>
+                    </div>
+                    <div class="trace-content" id="trace-${idx}">
+                        ${logs.map((log, logIdx) => `
+                            <div class="log-entry">
+                                <div class="log-entry-header">
+                                    <span class="optimization-id">Optimization: ${escapeHtml(log.optimization_id)}</span>
+                                    <span class="timestamp">${formatDate(log.created_at)}</span>
+                                </div>
+
+                                <div class="status-badges">
+                                    ${getStatusBadge(log.passed, 'Passed', 'passed')}
+                                    ${getStatusBadge(log.faster, 'Faster', 'faster')}
+                                </div>
+
+                                <div class="section" style="margin-top: 15px;">
+                                    <div class="section-title">User Prompt</div>
+                                    <button class="copy-btn" onclick="copyToClipboard(data.find(d => d.optimization_id === '${log.optimization_id}').user_prompt, this)">Copy</button>
+                                    <div class="code-block">${escapeHtml(log.user_prompt)}</div>
+                                </div>
+
+                                <div class="section">
+                                    <div class="section-title">Explanation</div>
+                                    <div class="explanation-block">${escapeHtml(log.explanation)}</div>
+                                </div>
+
+                                <div class="section">
+                                    <div class="section-title">Refined Optimization</div>
+                                    <button class="copy-btn" onclick="copyToClipboard(data.find(d => d.optimization_id === '${log.optimization_id}').refined_optimization, this)">Copy</button>
+                                    <div class="code-block refined-code">${escapeHtml(log.refined_optimization)}</div>
+                                </div>
+                            </div>
+                        `).join('')}
+                    </div>
+                </div>`;
+            });
+
+            dashboard.innerHTML = html;
+        }
+
+        function toggleTrace(idx) {
+            const content = document.getElementById(`trace-${idx}`);
+            const header = content.previousElementSibling;
+            content.classList.toggle('show');
+            header.classList.toggle('expanded');
+        }
+
+        function toggleAll() {
+            const contents = document.querySelectorAll('.trace-content');
+            const headers = document.querySelectorAll('.trace-header');
+            allExpanded = !allExpanded;
+
+            contents.forEach(c => {
+                if (allExpanded) {
+                    c.classList.add('show');
+                } else {
+                    c.classList.remove('show');
+                }
+            });
+
+            headers.forEach(h => {
+                if (allExpanded) {
+                    h.classList.add('expanded');
+                } else {
+                    h.classList.remove('expanded');
+                }
+            });
+
+            document.querySelector('.expand-all-btn').textContent = allExpanded ? 'Collapse All' : 'Expand All';
+        }
+
+        document.getElementById('search').addEventListener('input', function(e) {
+            const query = e.target.value.toLowerCase();
+            if (!query) {
+                renderDashboard(data);
+                return;
+            }
+
+            const filtered = data.filter(log =>
+                (log.trace_id && log.trace_id.toLowerCase().includes(query)) ||
+                (log.optimization_id && log.optimization_id.toLowerCase().includes(query)) ||
+                (log.user_prompt && log.user_prompt.toLowerCase().includes(query)) ||
+                (log.explanation && log.explanation.toLowerCase().includes(query)) ||
+                (log.refined_optimization && log.refined_optimization.toLowerCase().includes(query))
+            );
+
+            renderDashboard(filtered);
+        });
+
+        // Initial render
+        renderDashboard();
+    </script>
+</body>
+</html>

experiments/generate_dashboard.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""Generate an HTML dashboard from code_repair_logs SQLite database."""
+
+import json
+import sqlite3
+import webbrowser
+from pathlib import Path
+
+
+def main():
+    db_path = Path(__file__).parent / "code_repair_log.db"
+    cf_db_path = Path(__file__).parent / "code_repair_logs_cf.db"
+    template_path = Path(__file__).parent / "code_repair_dashboard.html"
+    output_path = Path(__file__).parent / "code_repair_dashboard_live.html"
+
+    # Connect to main database and fetch all logs
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+    cursor = conn.cursor()
+
+    cursor.execute("""
+        SELECT optimization_id, trace_id, user_prompt, explanation,
+               refined_optimization, created_at, updated_at
+        FROM code_repair_logs
+        ORDER BY created_at DESC
+    """)
+
+    rows = cursor.fetchall()
+    conn.close()
+
+    # Connect to cf database and fetch passed/faster status
+    cf_conn = sqlite3.connect(cf_db_path)
+    cf_conn.row_factory = sqlite3.Row
+    cf_cursor = cf_conn.cursor()
+
+    cf_cursor.execute("""
+        SELECT optimization_id, passed, faster
+        FROM code_repair_logs_cf
+    """)
+
+    cf_rows = cf_cursor.fetchall()
+    cf_conn.close()
+
+    # Create lookup dict for cf data
+    cf_data = {row["optimization_id"]: {"passed": row["passed"], "faster": row["faster"]} for row in cf_rows}
+
+    # Convert to list of dicts and merge cf data
+    data = []
+    for row in rows:
+        d = dict(row)
+        opt_id = d["optimization_id"][:-4] + "cdrp"
+        if opt_id in cf_data:
+            d["passed"] = cf_data[opt_id]["passed"]
+            d["faster"] = cf_data[opt_id]["faster"]
+        else:
+            d["passed"] = None
+            d["faster"] = None
+        data.append(d)
+
+    # Read template
+    template = template_path.read_text()
+
+    # Replace placeholder with actual data
+    json_data = json.dumps(data, default=str, indent=2)
+    html_content = template.replace("DATA_PLACEHOLDER", json_data)
+
+    # Write output
+    output_path.write_text(html_content)
+
+    print(f"Dashboard generated: {output_path}")
+    print(f"Total logs: {len(data)}")
+    print(f"Unique traces: {len(set(d['trace_id'] for d in data))}")
+
+    # Open in browser
+    webbrowser.open(f"file://{output_path.absolute()}")
+
+
+if __name__ == "__main__":
+    main()

js/VSC-Extension/packages/sidebar-webview/src/components/codeBlock/collapsibleCodeBlock.tsx

@@ -39,7 +39,7 @@ const CollapsibleCodeBlock = memo(
           setIsExpanded(false);
         }
       },
-      [isExpanded]
+      [isExpanded],
     );
 
     if (!hasMoreLines) {

js/VSC-Extension/packages/sidebar-webview/src/components/tabs.tsx

@@ -23,7 +23,9 @@ const Tabs = () => {
         >
           <span className={styles.tabText}>
             Tasks
-            {tasksCount > 0 && <span className={styles.badge}>{tasksCount}</span>}
+            {tasksCount > 0 && (
+              <span className={styles.badge}>{tasksCount}</span>
+            )}
           </span>
         </button>
       </div>

js/VSC-Extension/packages/sidebar-webview/src/initView/script.js

@@ -180,7 +180,12 @@ function renderStepsUI(steps) {
 
         // Show "select different interpreter" option after install button
         if (vscodeActionCommand) {
-          const { title, btnText: vscodeBtnText, command: vscodeCmd, args = [] } = vscodeActionCommand;
+          const {
+            title,
+            btnText: vscodeBtnText,
+            command: vscodeCmd,
+            args = [],
+          } = vscodeActionCommand;
           if (title) {
             const detailsElem = document.createElement("p");
             detailsElem.className = "step-action";
@@ -192,7 +197,11 @@ function renderStepsUI(steps) {
           actionBtn.textContent = vscodeBtnText;
           actionBtn.className = "step-action-btn secondary-btn";
           actionBtn.addEventListener("click", () => {
-            vscode.postMessage({ command: "vscodeCommand", cmd: vscodeCmd, args });
+            vscode.postMessage({
+              command: "vscodeCommand",
+              cmd: vscodeCmd,
+              args,
+            });
           });
           actionsContainer.appendChild(actionBtn);
         }

js/cf-api/github/secret-utils.ts

@@ -56,9 +56,7 @@ export async function getRepositoryPublicKey(
   repo: string,
 ): Promise<{ public_key: string; key_id: string }> {
   try {
-    console.log(
+    console.log(`[secret-utils.ts:getRepositoryPublicKey] Getting public key for ${owner}/${repo}`)
-      `[secret-utils.ts:getRepositoryPublicKey] Getting public key for ${owner}/${repo}`,
-    )
 
     const response = await octokit.rest.actions.getRepoPublicKey({
       owner,
@@ -166,4 +164,3 @@ export async function encryptAndStoreSecret(
     `[secret-utils.ts:encryptAndStoreSecret] Successfully encrypted and stored secret ${secretName} for ${owner}/${repo}`,
   )
 }
-

js/common/prisma/migrations/20251210150302_add_optimizations_origin_column/migration.sql

@@ -0,0 +1,2 @@
+-- AlterTable
+ALTER TABLE "public"."optimization_features" ADD COLUMN     "optimizations_origin" JSONB;

js/common/prisma/schema.prisma

@@ -73,6 +73,7 @@ model optimization_features {
   organization                    String?
   repository                      String?
   ranking                         Json?
+  optimizations_origin            Json?
   review_quality                  String? // Hight, Med, low
   review_explanation              String?
   calling_fn_details              String?