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chatbot-canifa-feedback
Commit 3baf7736 authored by Hoanganhvu123's avatar Hoanganhvu123
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feat: integrate Claude API via zunef gateway with JSON parsing 

Cherry-pick commit 88341cc4 with conflict resolution in graph.py.
Replace with_structured_output with manual JSON parsing for Claude API.
Co-Authored-By: 's avatarClaude Sonnet 4.6 <noreply@anthropic.com>
parent 7659d548
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Showing with 484 additions and 227 deletions
backend/agent/lead_stage_agent/graph.py
View file @ 3baf7736
......@@ -269,13 +269,15 @@ class LeadStageGraph:
"add_to_cart": add_to_cart,
}
# AI 1: Classifier — structured output, NHẸ (streaming=False)
# AI 1: Classifier — structured output bypass for Claude API (zunef gateway)
_classifier_base = create_llm(model_name=self.model_name, streaming=False)
self.classifier_llm = _classifier_base.with_structured_output(ClassifierOutput, method="function_calling")
self.classifier_llm = _classifier_base
# Note: Claude models bypass structured output, parse JSON manually
# AI 2: Stylist — structured output, NẶNG (streaming=False vì structured)
# AI 2: Stylist — structured output bypass for Claude API (zunef gateway)
_stylist_base = create_llm(model_name=self.model_name, streaming=False)
self.stylist_llm = _stylist_base.with_structured_output(StylistOutput, method="function_calling")
self.stylist_llm = _stylist_base
# Note: Claude models bypass structured output, parse JSON manually
self._compiled = None
logger.info(f"✅ LeadStageGraph initialized | model: {self.model_name}")
......@@ -386,7 +388,33 @@ class LeadStageGraph:
start = time.time()
try:
output: ClassifierOutput = await self.classifier_llm.ainvoke(classifier_messages, config=config)
response = await self.classifier_llm.ainvoke(
classifier_messages, config=config
)
# Parse JSON from text response (Claude models return text, not structured output)
import json as json_lib
text = response.content if hasattr(response, "content") else str(response)
logger.info(f"[CLASSIFIER] Raw response repr: {repr(text[:500])}")
# Extract JSON from text (may have extra text)
text = text.strip()
if text.startswith('```json'):
text = text[7:]
if text.startswith('```'):
text = text[3:]
if text.endswith('```'):
text = text[:-3]
text = text.strip()
try:
output = json_lib.loads(text)
except json_lib.JSONDecodeError as je:
logger.error(f"[CLASSIFIER] JSON parse error: {je}. Text was: {repr(text[:500])}")
raise
# Convert dict to object-like access
class DictToObj:
def __init__(self, data):
self.__dict__.update(data)
output = DictToObj(output)
except Exception as e:
logger.error(f"❌ Classifier error: {e}")
return {
......@@ -403,7 +431,13 @@ class LeadStageGraph:
# ── Resolve tool_args: typed lead_search_args hoac raw dict ──
if tool_name == "lead_search_tool" and output.lead_search_args:
tool_args = output.lead_search_args.model_dump(exclude_none=False)
# Handle both dict and Pydantic model cases
if isinstance(output.lead_search_args, dict):
tool_args = output.lead_search_args
elif hasattr(output.lead_search_args, 'model_dump'):
tool_args = output.lead_search_args.model_dump(exclude_none=False)
else:
tool_args = dict(output.lead_search_args) if output.lead_search_args else {}
elif tool_name == "lead_search_tool" and output.tool_args:
# fallback: model dung tool_args thay vi lead_search_args
tool_args = output.tool_args
......@@ -557,13 +591,39 @@ class LeadStageGraph:
HumanMessage(content="=== KHÔNG CÓ TOOL RESULT === (Khách chào hỏi hoặc câu hỏi chung)")
)
# ── LLM Call: Structured Output ──
# ── LLM Call: Parse JSON from text (Claude returns text, not structured) ──
# ── LLM Call: Parse JSON from text (Claude returns text, not structured) ──
start = time.time()
try:
output: StylistOutput = await self.stylist_llm.ainvoke(stylist_messages, config=config)
response = await self.stylist_llm.ainvoke(
stylist_messages, config=config
)
# Parse JSON from text response
text = response.content if hasattr(response, "content") else str(response)
logger.info(f"[STYLIST] Raw response text (first 500 chars): {repr(text[:500])}")
text = text.strip()
if text.startswith('```json'):
text = text[7:]
if text.startswith('```'):
text = text[3:]
if text.endswith('```'):
text = text[:-3]
text = text.strip()
output = json.loads(text)
# Convert dict to object-like access
class DictToObj:
def __init__(self, data):
self.__dict__.update(data)
def __getattr__(self, name):
return self.__dict__.get(name)
output = DictToObj(output)
# Handle nested user_insight dict
if hasattr(output, "user_insight") and isinstance(output.user_insight, dict):
output.user_insight = DictToObj(output.user_insight)
except Exception as e:
logger.error(f"❌ Stylist structured output error: {e}")
# Fallback: trả response text thuần
logger.error(f"❌ Stylist JSON parse error: {e}")
fallback = "Dạ bạn cho mình hỏi thêm để tư vấn chính xác hơn nhé!"
return {
"messages": [AIMessage(content=fallback)],
......@@ -582,8 +642,18 @@ class LeadStageGraph:
from_text = _extract_skus_from_text(output.ai_response)
merged_product_ids = _dedupe_preserve_order((output.product_ids or []) + from_text)
# Extract insight
insight_dict = output.user_insight.model_dump()
# Extract insight - handle both dict and object cases
merged_product_ids = _dedupe_preserve_order((output.product_ids or []) + from_text)
# Extract insight - handle both dict and object cases
insight_raw = getattr(output, "user_insight", None)
if isinstance(insight_raw, dict):
insight_dict = insight_raw
elif hasattr(insight_raw, "__dict__"):
insight_dict = insight_raw.__dict__
else:
insight_dict = {}
lead_stage = {
"stage": insight_dict.get("STAGE_NUM", 1),
"stage_name": insight_dict.get("STAGE", "BROWSE"),
......@@ -592,21 +662,24 @@ class LeadStageGraph:
}
stage_injection = format_insight_injection(insight_dict)
ai_response = getattr(output, "ai_response", "Dạ mình chưa hiểu, bạn nói lại nhé!")
product_ids = getattr(output, "product_ids", []) or []
logger.info(
f"💬 Stylist: stage={insight_dict.get('STAGE')} | goal={insight_dict.get('GOAL')} | "
f"response_len={len(output.ai_response)} | products={merged_product_ids} | {elapsed_ms:.0f}ms"
f"response_len={len(ai_response)} | products={merged_product_ids} | {elapsed_ms:.0f}ms"
)
diag = {
"step": "stylist",
"label": "💬 Stylist (Response + Insight)",
"content": output.ai_response[:500],
"content": ai_response[:500],
"elapsed_ms": round(elapsed_ms),
"raw_json": json.dumps(output.model_dump(), ensure_ascii=False, indent=2),
"raw_json": json.dumps(insight_dict, ensure_ascii=False, indent=2),
}
return {
"messages": [AIMessage(content=output.ai_response)],
"messages": [AIMessage(content=ai_response)],
"updated_insight": insight_dict,
"lead_stage": lead_stage,
"stage_injection": stage_injection,
......
backend/agent/lead_stage_agent/prompts.py
View file @ 3baf7736
......@@ -225,6 +225,18 @@ Query: "combo gì cho ngày 2/9"
→ inferred.tags: ["Màu đỏ", "Đi chơi / dạo phố"]
→ inferred.keywords: ["quốc khánh", "2/9", "lễ lớn"]
```
**QUAN TRỌNG: BẮT BUỘC OUTPUT FORMAT**
Bạn PHẢI trả về DUY NHẤT một JSON object hợp lệ, KHÔNG text thêm thắt.
JSON schema:
{
"reasoning": "lý luận ngắn gọn",
"tool_name": null hoặc "lead_search_tool",
"lead_search_args": {"literal": {"raw_text": "..."}, "inferred": {"product_line_vn": [], "gender_by_product": null, ...}, "magento_ref_code": null, "reasoning": "..."},
"tool_args": null,
"ai_response": null hoặc "câu chào hỏi",
"product_ids": []
}
"""
# ═══════════════════════════════════════════════
......@@ -420,6 +432,15 @@ Cập nhật 12 trường dưới đây sau mỗi turn. Cộng dồn thông tin.
- KHÔNG dán link URL
- KHÔNG tự bịa tính năng không có trong tool results
- Mở đầu bằng 1 câu đồng cảm dựa trên GOAL
**QUAN TRỌNG: BẮT BUỘC OUTPUT FORMAT**
Bạn PHẢI trả về DUY NHẤT một JSON object hợp lệ, KHÔNG text thêm thắt.
JSON schema:
{
"ai_response": "câu trả lời cho khách (max 250 từ)",
"product_ids": [],
"user_insight": {"USER": "...", "TARGET": "...", "GOAL": "...", ...}
}
"""
# ═══════════════════════════════════════════════
......
backend/common/llm_factory.py
View file @ 3baf7736
"""
LLM Factory - Multi-provider LLM creation with caching.
Supports OpenAI, Groq, and Google Gemini models.
"""
import contextlib
import logging
from langchain_core.language_models import BaseChatModel
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from config import GOOGLE_API_KEY, GROQ_API_KEY, OPENAI_API_KEY
logger = logging.getLogger(__name__)
class LLMFactory:
"""Singleton factory for managing LLM instances with caching (OpenAI, Groq, Gemini)."""
COMMON_MODELS: list[str] = [
"gemini-3.1-flash-lite-preview",
]
def __init__(self):
"""Initialize LLM factory with empty cache."""
self._cache: dict[tuple[str, bool, bool, str | None], BaseChatModel] = {}
def get_model(
self,
model_name: str,
streaming: bool = True,
json_mode: bool = False,
api_key: str | None = None,
) -> BaseChatModel:
"""
Get or create an LLM instance from cache.
Args:
model_name: Model identifier (e.g., "gpt-4o-mini", "gemini-2.0-flash-lite-preview-02-05")
streaming: Enable streaming responses
json_mode: Enable JSON output format
api_key: Optional API key override
Returns:
Configured LLM instance
"""
clean_model = model_name.split("/")[-1] if "/" in model_name else model_name
cache_key = (clean_model, streaming, json_mode, api_key)
if cache_key in self._cache:
logger.debug(f"♻️ Using cached model: {clean_model}")
return self._cache[cache_key]
logger.info(f"Creating new LLM instance: {model_name}")
return self._create_instance(model_name, streaming, json_mode, api_key)
def _create_instance(
self,
model_name: str,
streaming: bool = False,
json_mode: bool = False,
api_key: str | None = None,
) -> BaseChatModel:
"""Create and cache a new LLM instance (auto-detect provider)."""
try:
# Auto-detect: Gemini models use Google API
is_gemini = "gemini" in model_name.lower()
if is_gemini:
llm = self._create_gemini(model_name, streaming, json_mode, api_key)
else:
llm = self._create_openai(model_name, streaming, json_mode, api_key)
cache_key = (model_name, streaming, json_mode, api_key)
self._cache[cache_key] = llm
return llm
except Exception as e:
logger.error(f"❌ Failed to create model {model_name}: {e}")
raise
def _create_gemini(self, model_name: str, streaming: bool, json_mode: bool, api_key: str | None) -> BaseChatModel:
"""Create Google Gemini model instance. Uses GOOGLE_API_KEY if available, otherwise falls back to Gemini CLI (OAuth)."""
key = GOOGLE_API_KEY
if not key:
# Fallback: dùng Gemini CLI (OAuth local) thay vì crash
logger.info("🔄 No GOOGLE_API_KEY — falling back to ChatGeminiCLI (OAuth)")
from common.gemini_cli_wrapper import ChatGeminiCLI
llm = ChatGeminiCLI(
model=model_name,
temperature=0,
max_output_tokens=1500,
streaming=streaming,
)
return llm
llm_kwargs = {
"model": model_name,
"google_api_key": key,
"temperature": 0,
"max_output_tokens": 1500,
"streaming": streaming,
}
if json_mode:
llm_kwargs["response_mime_type"] = "application/json"
logger.info(f"⚙️ Initializing Gemini in JSON mode: {model_name}")
logger.warning(f"🔍 DEBUG: provider=Gemini | model={model_name} | key={key[:10]}...")
llm = ChatGoogleGenerativeAI(**llm_kwargs)
logger.info(f"✅ Created Gemini: {model_name} | Streaming: {streaming}")
return llm
def _create_openai(self, model_name: str, streaming: bool, json_mode: bool, api_key: str | None) -> BaseChatModel:
"""Create OpenAI-compatible model instance (OpenAI or Groq)."""
# --- Auto-detect provider ---
is_groq = any(kw in model_name.lower() for kw in ("gpt-oss", "llama", "mixtral", "gemma", "qwen", "deepseek"))
# Also detect openai/ prefix used by Groq (e.g. "openai/gpt-oss-120b")
if model_name.startswith("openai/"):
is_groq = True
if is_groq:
# Always use GROQ_API_KEY for Groq models (ignore api_key param which may be OpenAI key)
key = GROQ_API_KEY
base_url = "https://api.groq.com/openai/v1"
if not key:
raise ValueError("GROQ_API_KEY is required for Groq models")
else:
key = api_key or OPENAI_API_KEY
base_url = None # default OpenAI
if not key:
raise ValueError("OPENAI_API_KEY is required")
# Models that require /v1/responses API instead of /v1/chat/completions
needs_responses_api = "codex" in model_name.lower()
llm_kwargs = {
"model": model_name,
"streaming": streaming,
"api_key": key,
"temperature": 0,
"max_tokens": 1500,
}
if base_url:
llm_kwargs["base_url"] = base_url
if needs_responses_api:
llm_kwargs["use_responses_api"] = True
logger.info(f"🔄 Using Responses API for model: {model_name}")
if json_mode:
llm_kwargs["model_kwargs"] = {"response_format": {"type": "json_object"}}
logger.info(f"⚙️ Initializing OpenAI in JSON mode: {model_name}")
provider = "Groq" if is_groq else "OpenAI"
logger.warning(f"🔍 DEBUG: provider={provider} | model={model_name} | base_url={base_url} | key={key[:10]}... | is_groq={is_groq}")
llm = ChatOpenAI(**llm_kwargs)
logger.info(f"✅ Created {provider}: {model_name} | Streaming: {streaming}")
return llm
def _enable_json_mode(self, llm: BaseChatModel, model_name: str) -> BaseChatModel:
"""Enable JSON mode for the LLM."""
try:
llm = llm.bind(response_format={"type": "json_object"})
logger.debug(f"⚙️ JSON mode enabled for {model_name}")
except Exception as e:
logger.warning(f"⚠️ JSON mode not supported: {e}")
return llm
def initialize(self, skip_warmup: bool = True) -> None:
"""
Pre-initialize common models.
Args:
skip_warmup: Skip initialization if True
"""
if skip_warmup or self._cache:
return
logger.info("🔥 Warming up LLM Factory...")
for model_name in self.COMMON_MODELS:
with contextlib.suppress(Exception):
self.get_model(model_name, streaming=True)
# --- Singleton Instance & Public API ---
_factory = LLMFactory()
def create_llm(
model_name: str,
streaming: bool = True,
json_mode: bool = False,
api_key: str | None = None,
) -> BaseChatModel:
"""Create or get cached LLM instance."""
return _factory.get_model(model_name, streaming=streaming, json_mode=json_mode, api_key=api_key)
def init_llm_factory(skip_warmup: bool = True) -> None:
"""Initialize the LLM factory."""
_factory.initialize(skip_warmup)
def create_embedding_model() -> OpenAIEmbeddings:
"""Create OpenAI embeddings model."""
return OpenAIEmbeddings(model="text-embedding-3-small", api_key=OPENAI_API_KEY)
"""
LLM Factory - Multi-provider LLM creation with caching.
Supports OpenAI, Groq, and Google Gemini models.
"""
import contextlib
import logging
from langchain_core.language_models import BaseChatModel
from langchain_core.language_models import BaseChatModel
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from config import CLAUDE_BASE_URL, DS2API_API_KEY, DS2API_BASE_URL, GOOGLE_API_KEY, GROQ_API_KEY, OPENAI_API_KEY
logger = logging.getLogger(__name__)
class LLMFactory:
"""Singleton factory for managing LLM instances with caching (OpenAI, Groq, Gemini)."""
COMMON_MODELS: list[str] = [
"gemini-3.1-flash-lite-preview",
]
def __init__(self):
"""Initialize LLM factory with empty cache."""
self._cache: dict[tuple[str, bool, bool, str | None], BaseChatModel] = {}
def get_model(
self,
model_name: str,
streaming: bool = True,
json_mode: bool = False,
api_key: str | None = None,
) -> BaseChatModel:
"""
Get or create an LLM instance from cache.
Args:
model_name: Model identifier (e.g., "gpt-4o-mini", "gemini-2.0-flash-lite-preview-02-05")
streaming: Enable streaming responses
json_mode: Enable JSON output format
api_key: Optional API key override
Returns:
Configured LLM instance
"""
clean_model = model_name.split("/")[-1] if "/" in model_name else model_name
cache_key = (clean_model, streaming, json_mode, api_key)
if cache_key in self._cache:
logger.debug(f"♻️ Using cached model: {clean_model}")
return self._cache[cache_key]
logger.info(f"Creating new LLM instance: {model_name}")
return self._create_instance(model_name, streaming, json_mode, api_key)
def _create_instance(
self,
model_name: str,
streaming: bool = False,
json_mode: bool = False,
api_key: str | None = None,
) -> BaseChatModel:
"""Create and cache a new LLM instance (auto-detect provider)."""
try:
# Auto-detect: Claude models
is_claude = "claude" in model_name.lower()
if is_claude and CLAUDE_BASE_URL:
llm = self._create_claude(model_name, streaming, json_mode, api_key)
else:
# Auto-detect: Gemini models use Google API
is_gemini = "gemini" in model_name.lower()
if is_gemini:
llm = self._create_gemini(model_name, streaming, json_mode, api_key)
else:
llm = self._create_openai(model_name, streaming, json_mode, api_key)
cache_key = (model_name, streaming, json_mode, api_key)
self._cache[cache_key] = llm
return llm
except Exception as e:
logger.error(f"❌ Failed to create model {model_name}: {e}")
raise
def _create_gemini(self, model_name: str, streaming: bool, json_mode: bool, api_key: str | None) -> BaseChatModel:
"""Create Google Gemini model instance. Uses GOOGLE_API_KEY if available, otherwise falls back to Gemini CLI (OAuth)."""
# Override: use DS2API if configured
if DS2API_BASE_URL:
logger.info(f"🔄 Gemini model {model_name} -> using DS2API wrapper (OpenAI-compatible)")
# Treat as OpenAI-compatible via DS2API
key = DS2API_API_KEY or api_key or "dummy-key"
llm_kwargs = {
"model": model_name,
"streaming": streaming,
"api_key": key,
"temperature": 0,
"max_tokens": 1500,
"base_url": DS2API_BASE_URL,
}
if json_mode:
llm_kwargs["model_kwargs"] = {"response_mime_type": "application/json"}
llm = ChatOpenAI(**llm_kwargs)
logger.info(f"✅ Created DS2API (from Gemini): {model_name} | Streaming: {streaming}")
return llm
key = GOOGLE_API_KEY
if not key:
# Fallback: dùng Gemini CLI (OAuth local) thay vì crash
logger.info("🔄 No GOOGLE_API_KEY — falling back to ChatGeminiCLI (OAuth)")
from common.gemini_cli_wrapper import ChatGeminiCLI
llm = ChatGeminiCLI(
model=model_name,
temperature=0,
max_output_tokens=1500,
streaming=streaming,
)
return llm
llm_kwargs = {
"model": model_name,
"google_api_key": key,
"temperature": 0,
"max_output_tokens": 1500,
"streaming": streaming,
}
if json_mode:
llm_kwargs["response_mime_type"] = "application/json"
logger.info(f"⚙️ Initializing Gemini in JSON mode: {model_name}")
logger.warning(f"🔍 DEBUG: provider=Gemini | model={model_name} | key={key[:10]}...")
llm = ChatGoogleGenerativeAI(**llm_kwargs)
logger.info(f"✅ Created Gemini: {model_name} | Streaming: {streaming}")
return llm
def _create_openai(self, model_name: str, streaming: bool, json_mode: bool, api_key: str | None) -> BaseChatModel:
"""Create OpenAI-compatible model instance (OpenAI, Groq, or DS2API)."""
# --- Check DS2API first (override) ---
if DS2API_BASE_URL:
# Use DS2API as OpenAI-compatible endpoint
key = DS2API_API_KEY or api_key or OPENAI_API_KEY or "dummy-key"
base_url = DS2API_BASE_URL
logger.info(f"🔄 Using DS2API wrapper: {base_url} | model={model_name}")
llm_kwargs = {
"model": model_name,
"streaming": streaming,
"api_key": key,
"temperature": 0,
"max_tokens": 1500,
"base_url": base_url,
}
if json_mode:
llm_kwargs["model_kwargs"] = {"response_format": {"type": "json_object"}}
logger.info(f"⚙️ DS2API in JSON mode: {model_name}")
if "codex" in model_name.lower():
llm_kwargs["use_responses_api"] = True
llm = ChatOpenAI(**llm_kwargs)
logger.info(f"✅ DS2API created: {model_name} | Streaming: {streaming}")
return llm
# --- Auto-detect provider ---
is_groq = any(kw in model_name.lower() for kw in ("gpt-oss", "llama", "mixtral", "gemma", "qwen"))
# Also detect openai/ prefix used by Groq (e.g. "openai/gpt-oss-120b")
if model_name.startswith("openai/"):
is_groq = True
if is_groq:
# Always use GROQ_API_KEY for Groq models (ignore api_key param which may be OpenAI key)
key = GROQ_API_KEY
base_url = "https://api.groq.com/openai/v1"
if not key:
raise ValueError("GROQ_API_KEY is required for Groq models")
else:
key = api_key or OPENAI_API_KEY
base_url = None # default OpenAI
if not key:
raise ValueError("OPENAI_API_KEY is required")
# Models that require /v1/responses API instead of /v1/chat/completions
needs_responses_api = "codex" in model_name.lower()
llm_kwargs = {
"model": model_name,
"streaming": streaming,
"api_key": key,
"temperature": 0,
"max_tokens": 1500,
}
if base_url:
llm_kwargs["base_url"] = base_url
if needs_responses_api:
llm_kwargs["use_responses_api"] = True
logger.info(f"🔄 Using Responses API for model: {model_name}")
if json_mode:
llm_kwargs["model_kwargs"] = {"response_format": {"type": "json_object"}}
logger.info(f"⚙️ Initializing OpenAI in JSON mode: {model_name}")
provider = "Groq" if is_groq else "OpenAI"
logger.warning(f"🔍 DEBUG: provider={provider} | model={model_name} | base_url={base_url} | key={key[:10]}... | is_groq={is_groq}")
llm = ChatOpenAI(**llm_kwargs)
logger.info(f"✅ Created {provider}: {model_name} | Streaming: {streaming}")
return llm
def _create_claude(self, model_name: str, streaming: bool, json_mode: bool, api_key: str | None) -> BaseChatModel:
"""Create Claude model via requests library (bypassing LangChain/anthropic SDK to avoid Cloudflare blocks)."""
import requests, json as json_lib
key = api_key or OPENAI_API_KEY
base_url = CLAUDE_BASE_URL
logger.info(f"🔄 Using Claude API (requests): {base_url} | model={model_name}")
# Return a wrapper that mimics LangChain's BaseChatModel interface
class AnthropicWrapper:
def __init__(self, key, base_url, model_name, streaming):
self.key = key
self.base_url = base_url
self.model_name = model_name
self.streaming = streaming
self._output_schema = None
def with_structured_output(self, output_schema, **kwargs):
self._output_schema = output_schema
return self
def _convert_messages(self, messages):
anthropic_messages = []
system_prompt = None
for msg in (messages if isinstance(messages, list) else [messages]):
if hasattr(msg, 'content') and hasattr(msg, 'type'):
if msg.type == 'system':
system_prompt = msg.content
else:
role = 'user' if msg.type == 'human' else 'assistant'
anthropic_messages.append({'role': role, 'content': msg.content})
elif isinstance(msg, dict):
anthropic_messages.append(msg)
elif isinstance(msg, str):
anthropic_messages.append({'role': 'user', 'content': msg})
return anthropic_messages, system_prompt
def _call_api(self, messages_list, system_prompt=None):
url = f"{self.base_url}/messages"
headers = {
'x-api-key': self.key,
'Content-Type': 'application/json'
}
payload = {
'model': self.model_name,
'max_tokens': 1500,
'temperature': 0,
'messages': messages_list
}
# Handle system prompt - ALWAYS inject JSON format requirement
json_instruction = "\n\nIMPORTANT: You MUST output ONLY valid JSON. No text before or after the JSON object."
if system_prompt:
payload['system'] = system_prompt + json_instruction
logger.info(f"[CLAUDE API] System prompt set (first 200 chars): {payload['system'][:200]}")
elif self._output_schema:
schema_str = str(self._output_schema.model_json_schema() if hasattr(self._output_schema, 'model_json_schema') else self._output_schema)
payload['system'] = f"You must output valid JSON matching this schema: {schema_str}. Output ONLY the JSON, no other text."
logger.info(f"[CLAUDE API] Output schema prompt set (first 200 chars): {payload['system'][:200]}")
logger.info(f"[CLAUDE API] Payload messages count: {len(messages_list)}")
resp = requests.post(url, headers=headers, json=payload, timeout=30, stream=True)
resp.raise_for_status()
# Parse SSE response
full_text = ""
for line in resp.iter_lines():
if line:
line_str = line.decode('utf-8') if isinstance(line, bytes) else line
if line_str.startswith('data: '):
data_str = line_str[6:].strip()
if data_str == '[DONE]':
break
try:
data = json_lib.loads(data_str)
if data.get('type') == 'content_block_delta':
delta = data.get('delta', {})
if delta.get('type') == 'text_delta':
full_text += delta.get('text', '')
except Exception:
pass
return full_text
def invoke(self, messages, **kwargs):
result = self._convert_messages(messages)
messages_list = result[0]
system_prompt = result[1]
text = self._call_api(messages_list, system_prompt)
from langchain_core.messages import AIMessage
return AIMessage(content=text)
async def ainvoke(self, messages, **kwargs):
import asyncio
loop = asyncio.get_event_loop()
result = self._convert_messages(messages)
messages_list = result[0]
system_prompt = result[1]
text = await loop.run_in_executor(None, self._call_api, messages_list, system_prompt)
from langchain_core.messages import AIMessage
return AIMessage(content=text)
llm = AnthropicWrapper(key, base_url, model_name, streaming)
logger.info(f"✅ Claude API (requests) created: {model_name} | Streaming: {streaming}")
return llm
def _enable_json_mode(self, llm: BaseChatModel, model_name: str) -> BaseChatModel:
"""Enable JSON mode for the LLM."""
try:
llm = llm.bind(response_format={"type": "json_object"})
logger.debug(f"⚙️ JSON mode enabled for {model_name}")
except Exception as e:
logger.warning(f"⚠️ JSON mode not supported: {e}")
return llm
def initialize(self, skip_warmup: bool = True) -> None:
"""
Pre-initialize common models.
Args:
skip_warmup: Skip initialization if True
"""
if skip_warmup or self._cache:
return
logger.info("🔥 Warming up LLM Factory...")
for model_name in self.COMMON_MODELS:
with contextlib.suppress(Exception):
self.get_model(model_name, streaming=True)
# --- Singleton Instance & Public API ---
_factory = LLMFactory()
def create_llm(
model_name: str,
streaming: bool = True,
json_mode: bool = False,
api_key: str | None = None,
) -> BaseChatModel:
"""Create or get cached LLM instance."""
return _factory.get_model(model_name, streaming=streaming, json_mode=json_mode, api_key=api_key)
def init_llm_factory(skip_warmup: bool = True) -> None:
"""Initialize the LLM factory."""
_factory.initialize(skip_warmup)
def create_embedding_model() -> OpenAIEmbeddings:
"""Create OpenAI embeddings model."""
return OpenAIEmbeddings(model="text-embedding-3-small", api_key=OPENAI_API_KEY)
backend/config.py
View file @ 3baf7736
......@@ -81,7 +81,14 @@ OPENAI_API_KEY: str | None = os.getenv("OPENAI_API_KEY")
GOOGLE_API_KEY: str | None = os.getenv("GOOGLE_API_KEY")
GROQ_API_KEY: str | None = os.getenv("GROQ_API_KEY")
DEFAULT_MODEL: str = os.getenv("DEFAULT_MODEL", "gemini-3.1-flash-lite-preview")
# DS2API (DeepSeek via OpenAI-compatible wrapper)
DS2API_BASE_URL: str | None = os.getenv("DS2API_BASE_URL")
DS2API_API_KEY: str | None = os.getenv("DS2API_API_KEY")
# Claude API (via zunef gateway)
CLAUDE_BASE_URL: str | None = os.getenv("CLAUDE_BASE_URL", "https://claude-api.zunef.com/v1/ai")
DEFAULT_MODEL: str = os.getenv("DEFAULT_MODEL", "claude-sonnet-4-6")
# DEFAULT_MODEL: str = os.getenv("DEFAULT_MODEL", "gpt-5.1-codex-mini")
# ====================== JWT CONFIGURATION ======================
......
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