update model without structure output

backend/examples/cli_research.py

@@ -21,7 +21,7 @@ def main() -> None:
     )
     parser.add_argument(
         "--reasoning-model",
-        default="GLM-4.5-Air",
+        default="GLM-4.7",
         help="Model for the final answer",
     )
     args = parser.parse_args()

backend/src/agent/configuration.py

@@ -9,21 +9,21 @@ class Configuration(BaseModel):
     """The configuration for the agent."""
 
     query_generator_model: str = Field(
-        default="GLM-4.5-Air",
+        default="GLM-4.7",
         metadata={
             "description": "The name of the language model to use for the agent's query generation."
         },
     )
 
     reflection_model: str = Field(
-        default="GLM-4.5-Air",
+        default="GLM-4.7",
         metadata={
             "description": "The name of the language model to use for the agent's reflection."
         },
     )
 
     answer_model: str = Field(
-        default="GLM-4.5-Air",
+        default="GLM-4.7",
         metadata={
             "description": "The name of the language model to use for the agent's answer."
         },

backend/src/agent/graph.py

@@ -1,7 +1,9 @@
 import os
+from typing import TypeVar
 
 from agent.tools_and_schemas import SearchQueryList, Reflection
 from dotenv import load_dotenv
+from pydantic import BaseModel, ValidationError
 from langchain_core.messages import AIMessage
 from langgraph.types import Send
 from langgraph.graph import StateGraph
@@ -38,6 +40,7 @@ if os.getenv("TAVILY_API_KEY") is None:
     raise ValueError("TAVILY_API_KEY is not set")
 
 tavily_client = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
+SchemaT = TypeVar("SchemaT", bound=BaseModel)
 
 
 def create_llm(model: str, temperature: float) -> ChatOpenAI:
@@ -51,6 +54,63 @@ def create_llm(model: str, temperature: float) -> ChatOpenAI:
     )
 
 
+def _extract_json_payload(content: str) -> str:
+    """Extract a JSON object from a model response."""
+    text = content.strip()
+
+    if text.startswith("```"):
+        lines = text.splitlines()
+        if lines:
+            lines = lines[1:]
+        if lines and lines[-1].strip() == "```":
+            lines = lines[:-1]
+        text = "\n".join(lines).strip()
+
+    if text.startswith("json"):
+        text = text[4:].lstrip()
+
+    return text
+
+
+def _invoke_json_model(
+    llm: ChatOpenAI,
+    prompt: str,
+    schema: type[SchemaT],
+) -> SchemaT:
+    """Invoke the model for JSON text and validate it with Pydantic."""
+    result = llm.invoke(prompt)
+    content = result.content if isinstance(result.content, str) else str(result.content)
+    json_payload = _extract_json_payload(content)
+
+    try:
+        return schema.model_validate_json(json_payload)
+    except ValidationError as exc:
+        raise ValueError(
+            f"Model returned invalid JSON for {schema.__name__}: {json_payload[:500]!r}"
+        ) from exc
+
+
+def _supports_native_structured_output(model: str) -> bool:
+    """Return whether the selected model should use LangChain structured output."""
+    normalized = model.strip().lower()
+    return normalized.startswith("glm-4.7")
+
+
+def _invoke_structured_model(
+    llm: ChatOpenAI,
+    model: str,
+    prompt: str,
+    schema: type[SchemaT],
+) -> SchemaT:
+    """Use native structured output when supported, otherwise fall back to JSON parsing."""
+    if _supports_native_structured_output(model):
+        try:
+            return llm.with_structured_output(schema).invoke(prompt)
+        except ValueError:
+            return _invoke_json_model(llm, prompt, schema)
+    return _invoke_json_model(llm, prompt, schema)
+
+
 # Nodes
 def generate_query(state: OverallState, config: RunnableConfig) -> QueryGenerationState:
     """LangGraph node that generates search queries based on the User's question.
@@ -71,7 +131,6 @@ def generate_query(state: OverallState, config: RunnableConfig) -> QueryGenerati
         state["initial_search_query_count"] = configurable.number_of_initial_queries
 
     llm = create_llm(configurable.query_generator_model, temperature=1.0)
-    structured_llm = llm.with_structured_output(SearchQueryList)
 
     current_date = get_current_date()
     formatted_prompt = query_writer_instructions.format(
@@ -79,7 +138,12 @@ def generate_query(state: OverallState, config: RunnableConfig) -> QueryGenerati
         research_topic=get_research_topic(state["messages"]),
         number_queries=state["initial_search_query_count"],
     )
-    result = structured_llm.invoke(formatted_prompt)
+    result = _invoke_structured_model(
+        llm,
+        configurable.query_generator_model,
+        formatted_prompt,
+        SearchQueryList,
+    )
     return {"search_query": result.query}
 
 
@@ -153,7 +217,12 @@ def reflection(state: OverallState, config: RunnableConfig) -> ReflectionState:
         summaries="\n\n---\n\n".join(state["web_research_result"]),
     )
     llm = create_llm(reasoning_model, temperature=1.0)
-    result = llm.with_structured_output(Reflection).invoke(formatted_prompt)
+    result = _invoke_structured_model(
+        llm,
+        reasoning_model,
+        formatted_prompt,
+        Reflection,
+    )
 
     return {
         "is_sufficient": result.is_sufficient,

backend/src/agent/prompts.py

@@ -15,6 +15,7 @@ Instructions:
 - Queries should be diverse, if the topic is broad, generate more than 1 query.
 - Don't generate multiple similar queries, 1 is enough.
 - Query should ensure that the most current information is gathered. The current date is {current_date}.
+- Return only valid JSON. Do not include markdown fences or any extra text.
 
 Format: 
 - Format your response as a JSON object with ALL two of these exact keys:
@@ -27,7 +28,7 @@ Topic: What revenue grew more last year apple stock or the number of people buyi
 ```json
 {{
     "rationale": "To answer this comparative growth question accurately, we need specific data points on Apple's stock performance and iPhone sales metrics. These queries target the precise financial information needed: company revenue trends, product-specific unit sales figures, and stock price movement over the same fiscal period for direct comparison.",
-    "query": ["Apple total revenue growth fiscal year 2024", "iPhone unit sales growth fiscal year 2024", "Apple stock price growth fiscal year 2024"],
+    "query": ["Apple total revenue growth fiscal year 2024", "iPhone unit sales growth fiscal year 2024", "Apple stock price growth fiscal year 2024"]
 }}
 ```
 
@@ -57,6 +58,7 @@ Instructions:
 
 Requirements:
 - Ensure the follow-up query is self-contained and includes necessary context for web search.
+- Return only valid JSON. Do not include markdown fences or any extra text.
 
 Output Format:
 - Format your response as a JSON object with these exact keys: