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XFEstudio/gpt4free

fix: Improve tool response handling and thought signature restoration in Antigravity and GeminiCLI providers

f41007cc

hlohaus <hlohaus@users.noreply.github.com>

提交于 2026-04-04 01:29

父提交 03e7726

代码差异

2 个文件 +56 -52

Modified g4f/Provider/needs_auth/Antigravity.py +34 -33

@@ -955,35 +955,42 @@ class AntigravityProvider:

955 955 "Could not discover project ID. Ensure authentication or set ANTIGRAVITY_PROJECT_ID."

956 956 )

957 957

958

958 959 @staticmethod

959 960 def _messages_to_gemini_format(messages: list, media: MediaListType) -> List[Dict[str, Any]]:

960 """Convert OpenAI-style messages to Gemini format."""

961 961 format_messages = []

962 962 for msg in messages:

963 # Convert a ChatMessage dict to GeminiFormattedMessage dict

963 964 role = "model" if msg["role"] == "assistant" else "user"

964 965

965 content = msg.get("content")

966

967 966 # Handle tool role (OpenAI style)

967 # Group consecutive tool responses into a single user turn so that

968 # the number of functionResponse parts equals the number of functionCall parts.

968 969 if msg["role"] == "tool":

969 parts = [

970 {

971 "functionResponse": {

972 "name": msg.get("tool_call_id", "unknown_function"),

973 "response": {

974 "result": (

975 content

976 if isinstance(content, str)

977 else json.dumps(content)

978 )

979 },

980 }

970 tool_result = msg.get("content", "")

971 func_response_part = {

972 "functionResponse": {

973 "name": msg.get("tool_call_id", "unknown_function"),

974 "response": {

975 "result": (

976 tool_result

977 if isinstance(tool_result, str)

978 else json.dumps(tool_result)

979 )

980 },

981 981 }

982 ]

982 }

983 if (format_messages and format_messages[-1]["role"] == "user"

984 and any("functionResponse" in p for p in format_messages[-1]["parts"])):

985 format_messages[-1]["parts"].append(func_response_part)

986 else:

987 format_messages.append({"role": "user", "parts": [func_response_part]})

988 continue

983 989

984 990 # Handle assistant messages with tool calls

985 991 elif msg["role"] == "assistant" and msg.get("tool_calls"):

986 992 parts = []

993 content = msg.get("content")

987 994 if isinstance(content, str) and content.strip():

988 995 parts.append({"text": content})

989 996 for tool_call in msg["tool_calls"]:

@@ -994,24 +1001,21 @@ class AntigravityProvider:

994 1001 }

995 1002 # Restore thought_signature for Gemini thinking models when available

996 1003

                        thought_sig = tool_call.get("extra_content", {}).get("google", {}).get("thought_signature", "skip_thought_signature_validator")

997

                        if idx == 0:  # Only add skip_thought_signature_validator for the first tool call if no signature is present

998 parts.append({"functionCall": func_call, "thoughtSignature": thought_sig})

999 else:

1000 parts.append({"functionCall": func_call})

1004 parts.append({"functionCall": func_call, "thoughtSignature": thought_sig})

1001 1005

1002 1006 # Handle string content

1003 elif isinstance(content, str):

1004 parts = [{"text": content}]

1007 elif isinstance(msg["content"], str):

1008 parts = [{"text": msg["content"]}]

1005 1009

1006 1010 # Handle array content (possibly multimodal)

1007 elif isinstance(content, list):

1011 elif isinstance(msg["content"], list):

1008 1012 parts = []

1009 for item in content:

1010 ctype = item.get("type")

1013 for content in msg["content"]:

1014 ctype = content.get("type")

1011 1015 if ctype == "text":

1012 parts.append({"text": item["text"]})

1016 parts.append({"text": content["text"]})

1013 1017 elif ctype == "image_url":

1014 image_url = item.get("image_url", {}).get("url")

1018 image_url = content.get("image_url", {}).get("url")

1015 1019 if not image_url:

1016 1020 continue

1017 1021 if image_url.startswith("data:"):

@@ -1023,7 +1027,7 @@ class AntigravityProvider:

1023 1027 parts.append(

1024 1028 {

1025 1029 "fileData": {

1026 "mimeType": "image/jpeg",

1030 "mimeType": "image/jpeg", # Could improve by validation

1027 1031 "fileUri": image_url,

1028 1032 }

1029 1033 }

@@ -1034,8 +1038,6 @@ class AntigravityProvider:

1034 1038 parts = []

1035 1039

1036 1040 format_messages.append({"role": role, "parts": parts})

1037

1038 # Handle media attachments

1039 1041 if media:

1040 1042 if not format_messages:

1041 1043 format_messages.append({"role": "user", "parts": []})

@@ -1048,7 +1050,7 @@ class AntigravityProvider:

1048 1050 {

1049 1051 "fileData": {

1050 1052 "mimeType": f"image/{extension}",

1051 "fileUri": media_data,

1053 "fileUri": image_url,

1052 1054 }

1053 1055 }

1054 1056 )

@@ -1060,7 +1062,6 @@ class AntigravityProvider:

1060 1062 "data": base64.b64encode(media_data).decode()

1061 1063 }

1062 1064 })

1063

1064 1065 return format_messages

1065 1066

1066 1067 async def stream_content(

@@ -1291,7 +1292,7 @@ class AntigravityProvider:

1291 1292 if "thoughtSignature" in part:

1292 1293 tool_call_obj["extra_content"] = {

1293 1294 "google": {

1294 "thought_signature": tc["thought_signature"]

1295 "thought_signature": part["thoughtSignature"]

1295 1296 }

1296 1297 }

1297 1298 openai_tool_calls.append(tool_call_obj)

Modified g4f/Provider/needs_auth/GeminiCLI.py +22 -19

@@ -571,22 +571,28 @@ class GeminiCLIProvider():

571 571 role = "model" if msg["role"] == "assistant" else "user"

572 572

573 573 # Handle tool role (OpenAI style)

574 # Group consecutive tool responses into a single user turn so that

575 # the number of functionResponse parts equals the number of functionCall parts.

574 576 if msg["role"] == "tool":

575 577 tool_result = msg.get("content", "")

576 parts = [

577 {

578 "functionResponse": {

579 "name": msg.get("tool_call_id", "unknown_function"),

580 "response": {

581 "result": (

582 tool_result

583 if isinstance(tool_result, str)

584 else json.dumps(tool_result)

585 )

586 },

587 }

578 func_response_part = {

579 "functionResponse": {

580 "name": msg.get("tool_call_id", "unknown_function"),

581 "response": {

582 "result": (

583 tool_result

584 if isinstance(tool_result, str)

585 else json.dumps(tool_result)

586 )

587 },

588 588 }

589 ]

589 }

590 if (format_messages and format_messages[-1]["role"] == "user"

591 and any("functionResponse" in p for p in format_messages[-1]["parts"])):

592 format_messages[-1]["parts"].append(func_response_part)

593 else:

594 format_messages.append({"role": "user", "parts": [func_response_part]})

595 continue

590 596

591 597 # Handle assistant messages with tool calls

592 598 elif msg["role"] == "assistant" and msg.get("tool_calls"):

@@ -594,18 +600,15 @@ class GeminiCLIProvider():

594 600 content = msg.get("content")

595 601 if isinstance(content, str) and content.strip():

596 602 parts.append({"text": content})

597 for idx, tool_call in enumerate(msg["tool_calls"]):

603 for tool_call in msg["tool_calls"]:

598 604 if tool_call.get("type") == "function":

599 605 func_call = {

600 606 "name": tool_call["function"]["name"],

601 607 "args": json.loads(tool_call["function"]["arguments"]),

602 608 }

603 # Restore thought_signature required by Gemini thinking models

609 # Restore thought_signature for Gemini thinking models when available

604 610

                        thought_sig = tool_call.get("extra_content", {}).get("google", {}).get("thought_signature", "skip_thought_signature_validator")

605

                        if idx == 0:  # Only add skip_thought_signature_validator for the first tool call if no signature is present

606 parts.append({"functionCall": func_call, "thoughtSignature": thought_sig})

607 else:

608 parts.append({"functionCall": func_call})

611 parts.append({"functionCall": func_call, "thoughtSignature": thought_sig})

609 612

610 613 # Handle string content

611 614 elif isinstance(msg["content"], str):