#!/bin/bash
set -euo pipefail

FILE="src/llm/backends/openai.py"
if [ ! -f "$FILE" ]; then
  echo "Missing file: $FILE" >&2
  exit 1
fi

echo "=== Function search ==="
rg -n "def _create_structured_response|_create_structured_response" "$FILE"

echo "=== Context around lines ~360-440 ==="
nl -ba "$FILE" | sed -n '340,460p'

echo "=== Search for json_schema / json_object handling ==="
rg -n "json_schema|json_object|response_format" "$FILE" | head -n 200

#!/bin/bash
set -euo pipefail

FILE="src/llm/backends/openai.py"
if [ ! -f "$FILE" ]; then
  echo "Missing file: $FILE" >&2
  exit 1
fi

echo "=== Context around lines ~340-460 ==="
sed -n '340,460p' "$FILE" | awk '{printf "%6d:%s\n", NR+339, $0}'

echo "=== Search for json_schema / json_object handling ==="
rg -n "json_schema|json_object|response_format|BadRequestError" "$FILE"

#!/bin/bash
set -euo pipefail

# Locate the function and inspect around the reported lines
rg -n "def _create_structured_response" -S src/llm/backends/openai.py
nl -ba src/llm/backends/openai.py | sed -n '360,460p'

# Check exception definition for ValidationException and imports in this file
rg -n "ValidationException" -S src/llm/backends/openai.py src/exceptions.py src -S
nl -ba src/exceptions.py | sed -n '1,220p'

# Find all references to "_create_structured_response" to understand how messages are constructed/used
rg -n "_create_structured_response" -S src/llm/backends/openai.py src | head -n 50

# Search for places where multimodal message content is represented as a list/part array
rg -n "\"content\"\s*:\s*\[" -S src/llm | head -n 50
rg -n "type\"\\s*:\\s*\"text\"|\"text\"\\s*:\\s*schema_prompt" -S src/llm/backends/openai.py src/llm | head -n 50

#!/bin/bash
set -euo pipefail

FILE="src/llm/backends/openai.py"

# Show function header and surrounding lines with line numbers
awk 'NR>=340 && NR<=460 {printf "%d:%s\n", NR, $0}' "$FILE"

# Show exact portion around the reported snippet (406-413)
awk 'NR>=390 && NR<=430 {printf "%d:%s\n", NR, $0}' "$FILE"

# Check where ValidationException is defined and how it's imported/used
rg -n "class ValidationException|ValidationException" src/exceptions.py src/llm/backends/openai.py src | head -n 200
awk 'NR>=1 && NR<=260 {printf "%d:%s\n", NR, $0}' src/exceptions.py

# Check for multimodal content conventions in the codebase (OpenAI message parts)
rg -n "\"role\"\\s*:\\s*\"user\"|\"type\"\\s*:\\s*\"text\"|\"content\"\\s*:\\s*\\[" src/llm/backends/openai.py src/llm | head -n 200

#!/bin/bash
set -euo pipefail

# Inspect how multimodal/user message content is represented
FILE_HA="src/llm/history_adapters.py"
awk 'NR>=1 && NR<=140 {printf "%d:%s\n", NR, $0}' "$FILE_HA"

# Inspect tool_loop message construction around where it sets {"role":"user","content": ...}
FILE_TL="src/llm/tool_loop.py"
rg -n "messages\.copy\(\) if messages else|content:\s*prompt|synthesis_prompt" "$FILE_TL"
awk 'NR>=280 && NR<=660 {printf "%d:%s\n", NR, $0}' "$FILE_TL"

# Inspect how OpenAIBackend builds/uses messages and calls _create_structured_response
FILE_OAI="src/llm/backends/openai.py"
rg -n "_create_structured_response" "$FILE_OAI"
awk 'NR>=1 && NR<=260 {printf "%d:%s\n", NR, $0}' "$FILE_OAI"
awk 'NR>=260 && NR<=520 {printf "%d:%s\n", NR, $0}' "$FILE_OAI"

#!/bin/bash
set -euo pipefail

# Search for OpenAI-style multimodal message content arrays (parts)
rg -n "\"content\"\\s*:\\s*\\[" src/llm src | head -n 200

# Search for common multimodal part keys
rg -n "image_url|type\"\\s*:\\s*\"image|input_image|base64" src/llm src | head -n 200

# Search for explicit OpenAI message part shapes
rg -n "\"type\"\\s*:\\s*\"text\"|\"text\"\\s*:\\s*\"" src/llm src | head -n 200

# Look for calls into OpenAIBackend.complete with non-str content usage
rg -n "OpenAIBackend\\(|response_format.*structured|_create_structured_response\\(" src/llm src | head -n 200

#!/bin/bash
set -euo pipefail

rg -n "backend\\.complete\\(" -S src/llm src | head -n 200
rg -n "OpenAIBackend\\(" -S src/llm src

# Find honcho_llm_call_inner usage and how it handles messages for retries
rg -n "def honcho_llm_call_inner|honcho_llm_call_inner\\(" -S src/llm | head -n 200
rg -n "messages=.*copy\\(|messages\\.copy\\(" -S src/llm src | head -n 200

# Inspect llm/api.py around retry logic that might reuse messages
awk 'NR>=250 && NR<=520 {printf "%d:%s\n", NR, $0}' src/llm/api.py

#!/bin/bash
set -euo pipefail

# Find log_reasoning_trace implementation/usage
rg -n "def log_reasoning_trace|log_reasoning_trace\\(" -S src | head -n 50
rg -n "messages=" -S src/llm | head -n 50

# Inspect request_builder / where messages come from for OpenAI calls
rg -n "request_builder|build_messages|messages:" src/llm/request_builder.py src/llm/api.py src/llm/executor.py src/llm/tool_loop.py | head -n 200

# Search for OpenAI multimodal content arrays specifically with role/content list pattern near OpenAIHistoryAdapter usage
rg -n "OpenAIHistoryAdapter|role\"\\s*:\\s*\"user\"\\s*,\\s*\"content\"\\s*:\\s*\\[" -S src/llm | head -n 200

#!/bin/bash
set -euo pipefail

# Inspect message building for OpenAI calls
sed -n '1,220p' src/llm/request_builder.py

# Search for OpenAI multimodal keys/payloads
rg -n "image_url|input_image|base64|data:image" -S src/llm src | head -n 200

# Search for OpenAI-style message content arrays (role/content where content is a list)
rg -n "\"role\"\\s*:\\s*\"(user|assistant|system)\"[\\s\\S]{0,120}\"content\"\\s*:\\s*\\[" -S src/llm src | head -n 200

# Search for generic "content": [ across OpenAI-related adapters
rg -n "OpenAIHistoryAdapter|format_assistant_tool_message" -S src/llm/history_adapters.py

#!/bin/bash
set -euo pipefail

# Find where the history adapter is selected/used
rg -n "HistoryAdapter|history_adapters|OpenAIHistoryAdapter|history_adapter" -S src/llm | head -n 200

# Inspect the relevant parts in llm/api.py and registry
sed -n '1,260p' src/llm/registry.py
sed -n '1,520p' src/llm/api.py

# Inspect conversation assembly in tool_loop for where it calls format_assistant_tool_message / append_tool_results
rg -n "format_assistant_tool_message|append_tool_results|append_tool_results\\(" -S src/llm/tool_loop.py
sed -n '140,260p' src/llm/tool_loop.py
sed -n '260,360p' src/llm/tool_loop.py
sed -n '360,520p' src/llm/tool_loop.py

#!/bin/bash
set -euo pipefail
awk 'NR>=1 && NR<=40 {printf "%d:%s\n", NR, $0}' src/llm/backends/openai.py

#!/bin/bash
set -euo pipefail

rg -n "honcho_llm_call\\([\\s\\S]{0,400}tools=" src | head -n 200
rg -n "honcho_llm_call\\([\\s\\S]{0,400}response_model=" src | head -n 200

# Narrow: response_model together with tools on same call
rg -n "honcho_llm_call\\([\\s\\S]{0,600}tools=[\\s\\S]{0,200}response_model=" src | head -n 200
rg -n "honcho_llm_call\\([\\s\\S]{0,600}response_model=[\\s\\S]{0,200}tools=" src | head -n 200

# Search for execute_tool_loop called with response_model not None
rg -n "execute_tool_loop\\([\\s\\S]{0,400}response_model=" src/llm | head -n 200