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Fix problem with Google Cloud models with reasoning (like gemini) - Additional fix to issue #10474 (#10502)

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### What problem does this PR solve?

Issue #10474  -  Update to PR #10477 

### Type of change

- [X] Bug Fix (non-breaking change which fixes an issue)
This commit is contained in:
Günter Lukas
2025-10-15 08:54:20 +02:00
committed by GitHub
parent fdac4afd10
commit 5037a28e4d
3 changed files with 151 additions and 98 deletions
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9
pyproject.toml
View File

@ -44,7 +44,7 @@ dependencies = [
"groq==0.9.0",
"hanziconv==0.3.2",
"html-text==0.6.2",
"httpx[socks]==0.27.2",
"httpx[socks]>=0.28.1,<0.29.0",
"huggingface-hub>=0.25.0,<0.26.0",
"infinity-sdk==0.6.0",
"infinity-emb>=0.0.66,<0.0.67",
@ -56,7 +56,7 @@ dependencies = [
"mistralai==0.4.2",
"nltk==3.9.1",
"numpy>=1.26.0,<2.0.0",
"ollama==0.2.1",
"ollama>=0.5.0",
"onnxruntime==1.19.2; sys_platform == 'darwin' or platform_machine != 'x86_64'",
"onnxruntime-gpu==1.19.2; sys_platform != 'darwin' and platform_machine == 'x86_64'",
"openai>=1.45.0",
@ -102,7 +102,8 @@ dependencies = [
"tika==2.6.0",
"tiktoken==0.7.0",
"umap_learn==0.5.6",
"vertexai==1.64.0",
"vertexai==1.70.0",
"google-genai>=1.41.0,<2.0.0",
"volcengine==1.0.194",
"voyageai==0.2.3",
"webdriver-manager==4.0.1",
@ -113,7 +114,7 @@ dependencies = [
"xpinyin==0.7.6",
"yfinance==0.2.65",
"zhipuai==2.0.1",
"google-generativeai>=0.8.1,<0.9.0",
"google-generativeai>=0.8.1,<0.9.0", # Needed for cv_model and embedding_model
"python-docx>=1.1.2,<2.0.0",
"pypdf2>=3.0.1,<4.0.0",
"graspologic>=3.4.1,<4.0.0",

187
rag/llm/chat_model.py
View File

@ -1165,15 +1165,13 @@ class GoogleChat(Base):
else:
self.client = AnthropicVertex(region=region, project_id=project_id)
else:
import vertexai.generative_models as glm
from google.cloud import aiplatform
from google import genai
if access_token:
credits = service_account.Credentials.from_service_account_info(access_token)
aiplatform.init(credentials=credits, project=project_id, location=region)
credits = service_account.Credentials.from_service_account_info(access_token, scopes=scopes)
self.client = genai.Client(vertexai=True, project=project_id, location=region, credentials=credits)
else:
aiplatform.init(project=project_id, location=region)
self.client = glm.GenerativeModel(model_name=self.model_name)
self.client = genai.Client(vertexai=True, project=project_id, location=region)
def _clean_conf(self, gen_conf):
if "claude" in self.model_name:
@ -1188,38 +1186,11 @@ class GoogleChat(Base):
del gen_conf[k]
return gen_conf
def _get_thinking_config(self, gen_conf):
"""Extract and create ThinkingConfig from gen_conf.
Default behavior for Vertex AI Generative Models: thinking_budget=0 (disabled)
unless explicitly specified by the user. This does not apply to Claude models.
Users can override by setting thinking_budget in gen_conf/llm_setting:
- 0: Disabled (default)
- 1-24576: Manual budget
- -1: Auto (model decides)
"""
# Claude models don't support ThinkingConfig
if "claude" in self.model_name:
gen_conf.pop("thinking_budget", None)
return None
# For Vertex AI Generative Models, default to thinking disabled
thinking_budget = gen_conf.pop("thinking_budget", 0)
if thinking_budget is not None:
try:
import vertexai.generative_models as glm # type: ignore
return glm.ThinkingConfig(thinking_budget=thinking_budget)
except Exception:
pass
return None
def _chat(self, history, gen_conf={}, **kwargs):
system = history[0]["content"] if history and history[0]["role"] == "system" else ""
thinking_config = self._get_thinking_config(gen_conf)
gen_conf = self._clean_conf(gen_conf)
if "claude" in self.model_name:
gen_conf = self._clean_conf(gen_conf)
response = self.client.messages.create(
model=self.model_name,
messages=[h for h in history if h["role"] != "system"],
@ -1235,28 +1206,63 @@ class GoogleChat(Base):
response["usage"]["input_tokens"] + response["usage"]["output_tokens"],
)
self.client._system_instruction = system
hist = []
# Gemini models with google-genai SDK
# Set default thinking_budget=0 if not specified
if "thinking_budget" not in gen_conf:
gen_conf["thinking_budget"] = 0
thinking_budget = gen_conf.pop("thinking_budget", 0)
gen_conf = self._clean_conf(gen_conf)
# Build GenerateContentConfig
try:
from google.genai.types import GenerateContentConfig, ThinkingConfig, Content, Part
except ImportError as e:
logging.error(f"[GoogleChat] Failed to import google-genai: {e}. Please install: pip install google-genai>=1.41.0")
raise
config_dict = {}
if system:
config_dict["system_instruction"] = system
if "temperature" in gen_conf:
config_dict["temperature"] = gen_conf["temperature"]
if "top_p" in gen_conf:
config_dict["top_p"] = gen_conf["top_p"]
if "max_output_tokens" in gen_conf:
config_dict["max_output_tokens"] = gen_conf["max_output_tokens"]
# Add ThinkingConfig
config_dict["thinking_config"] = ThinkingConfig(thinking_budget=thinking_budget)
config = GenerateContentConfig(**config_dict)
# Convert history to google-genai Content format
contents = []
for item in history:
if item["role"] == "system":
continue
hist.append(deepcopy(item))
item = hist[-1]
if "role" in item and item["role"] == "assistant":
item["role"] = "model"
if "content" in item:
item["parts"] = [
{
"text": item.pop("content"),
}
]
# google-genai uses 'model' instead of 'assistant'
role = "model" if item["role"] == "assistant" else item["role"]
content = Content(
role=role,
parts=[Part(text=item["content"])]
)
contents.append(content)
response = self.client.models.generate_content(
model=self.model_name,
contents=contents,
config=config
)
if thinking_config:
response = self.client.generate_content(hist, generation_config=gen_conf, thinking_config=thinking_config)
else:
response = self.client.generate_content(hist, generation_config=gen_conf)
ans = response.text
return ans, response.usage_metadata.total_token_count
# Get token count from response
try:
total_tokens = response.usage_metadata.total_token_count
except Exception:
total_tokens = 0
return ans, total_tokens
def chat_streamly(self, system, history, gen_conf={}, **kwargs):
if "claude" in self.model_name:
@ -1283,34 +1289,59 @@ class GoogleChat(Base):
yield total_tokens
else:
response = None
total_tokens = 0
self.client._system_instruction = system
thinking_config = self._get_thinking_config(gen_conf)
if "max_tokens" in gen_conf:
gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
del gen_conf["max_tokens"]
for k in list(gen_conf.keys()):
if k not in ["temperature", "top_p", "max_output_tokens"]:
del gen_conf[k]
for item in history:
if "role" in item and item["role"] == "assistant":
item["role"] = "model"
if "content" in item:
item["parts"] = [
{
"text": item.pop("content"),
}
]
# Gemini models with google-genai SDK
ans = ""
total_tokens = 0
# Set default thinking_budget=0 if not specified
if "thinking_budget" not in gen_conf:
gen_conf["thinking_budget"] = 0
thinking_budget = gen_conf.pop("thinking_budget", 0)
gen_conf = self._clean_conf(gen_conf)
# Build GenerateContentConfig
try:
if thinking_config:
response = self.client.generate_content(history, generation_config=gen_conf, thinking_config=thinking_config, stream=True)
else:
response = self.client.generate_content(history, generation_config=gen_conf, stream=True)
for resp in response:
ans = resp.text
total_tokens += num_tokens_from_string(ans)
from google.genai.types import GenerateContentConfig, ThinkingConfig, Content, Part
except ImportError as e:
logging.error(f"[GoogleChat] Failed to import google-genai: {e}. Please install: pip install google-genai>=1.41.0")
raise
config_dict = {}
if system:
config_dict["system_instruction"] = system
if "temperature" in gen_conf:
config_dict["temperature"] = gen_conf["temperature"]
if "top_p" in gen_conf:
config_dict["top_p"] = gen_conf["top_p"]
if "max_output_tokens" in gen_conf:
config_dict["max_output_tokens"] = gen_conf["max_output_tokens"]
# Add ThinkingConfig
config_dict["thinking_config"] = ThinkingConfig(thinking_budget=thinking_budget)
config = GenerateContentConfig(**config_dict)
# Convert history to google-genai Content format
contents = []
for item in history:
# google-genai uses 'model' instead of 'assistant'
role = "model" if item["role"] == "assistant" else item["role"]
content = Content(
role=role,
parts=[Part(text=item["content"])]
)
contents.append(content)
try:
for chunk in self.client.models.generate_content_stream(
model=self.model_name,
contents=contents,
config=config
):
text = chunk.text
ans = text
total_tokens += num_tokens_from_string(text)
yield ans
except Exception as e:

53
uv.lock generated
View File

@ -2017,7 +2017,7 @@ wheels = [
[[package]]
name = "google-cloud-aiplatform"
version = "1.64.0"
version = "1.70.0"
source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
dependencies = [
{ name = "docstring-parser" },
@ -2032,9 +2032,9 @@ dependencies = [
{ name = "pydantic" },
{ name = "shapely" },
]
sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/5e/e3/f86b429d000a9c25f25bcd122e4b6286aeef70a89acfd6ea088324af016c/google-cloud-aiplatform-1.64.0.tar.gz", hash = "sha256:475a612829b283eb8f783e773d37115c30db42e2e50065c8653db0c9bd18b0da", size = 6258492, upload-time = "2024-08-28T01:03:24.573Z" }
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@ -2129,6 +2129,25 @@ wheels = [
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[[package]]
name = "google-genai"
version = "1.43.0"
source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
dependencies = [
{ name = "anyio" },
{ name = "google-auth" },
{ name = "httpx" },
{ name = "pydantic" },
{ name = "requests" },
{ name = "tenacity" },
{ name = "typing-extensions" },
{ name = "websockets" },
]
sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/c1/75/992ca4462682949750709678b8efbc865222c9a16cf34504b69c5459606c/google_genai-1.43.0.tar.gz", hash = "sha256:84eb219d320759c5882bc2cdb4e2ac84544d00f5d12c7892c79fb03d71bfc9a4", size = 236132, upload-time = "2025-10-10T23:16:40.131Z" }
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[[package]]
name = "google-generativeai"
version = "0.8.5"
@ -2472,18 +2491,17 @@ wheels = [
[[package]]
name = "httpx"
version = "0.27.2"
version = "0.28.1"
source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
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{ name = "certifi" },
{ name = "httpcore" },
{ name = "idna" },
{ name = "sniffio" },
]
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[package.optional-dependencies]
@ -3883,14 +3901,15 @@ wheels = [
[[package]]
name = "ollama"
version = "0.2.1"
version = "0.6.0"
source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
dependencies = [
{ name = "httpx" },
{ name = "pydantic" },
]
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[[package]]
@ -5476,6 +5495,7 @@ dependencies = [
{ name = "flask-login" },
{ name = "flask-mail" },
{ name = "flask-session" },
{ name = "google-genai" },
{ name = "google-generativeai" },
{ name = "google-search-results" },
{ name = "graspologic" },
@ -5635,13 +5655,14 @@ requires-dist = [
{ name = "flask-login", specifier = "==0.6.3" },
{ name = "flask-mail", specifier = ">=0.10.0" },
{ name = "flask-session", specifier = "==0.8.0" },
{ name = "google-genai", specifier = ">=1.41.0,<2.0.0" },
{ name = "google-generativeai", specifier = ">=0.8.1,<0.9.0" },
{ name = "google-search-results", specifier = "==2.4.2" },
{ name = "graspologic", specifier = ">=3.4.1,<4.0.0" },
{ name = "groq", specifier = "==0.9.0" },
{ name = "hanziconv", specifier = "==0.3.2" },
{ name = "html-text", specifier = "==0.6.2" },
{ name = "httpx", extras = ["socks"], specifier = "==0.27.2" },
{ name = "httpx", extras = ["socks"], specifier = ">=0.28.1,<0.29.0" },
{ name = "huggingface-hub", specifier = ">=0.25.0,<0.26.0" },
{ name = "infinity-emb", specifier = ">=0.0.66,<0.0.67" },
{ name = "infinity-sdk", specifier = "==0.6.0" },
@ -5660,7 +5681,7 @@ requires-dist = [
{ name = "mistralai", specifier = "==0.4.2" },
{ name = "nltk", specifier = "==3.9.1" },
{ name = "numpy", specifier = ">=1.26.0,<2.0.0" },
{ name = "ollama", specifier = "==0.2.1" },
{ name = "ollama", specifier = ">=0.5.0" },
{ name = "onnxruntime", marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'", specifier = "==1.19.2" },
{ name = "onnxruntime-gpu", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'", specifier = "==1.19.2" },
{ name = "openai", specifier = ">=1.45.0" },
@ -5716,7 +5737,7 @@ requires-dist = [
{ name = "trio", specifier = ">=0.29.0" },
{ name = "umap-learn", specifier = "==0.5.6" },
{ name = "valkey", specifier = "==6.0.2" },
{ name = "vertexai", specifier = "==1.64.0" },
{ name = "vertexai", specifier = "==1.70.0" },
{ name = "volcengine", specifier = "==1.0.194" },
{ name = "voyageai", specifier = "==0.2.3" },
{ name = "webdriver-manager", specifier = "==4.0.1" },
@ -7217,14 +7238,14 @@ wheels = [
[[package]]
name = "vertexai"
version = "1.64.0"
version = "1.70.0"
source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
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[[package]]