Handles both thinking and non thinking models now

agent/agent_runner.py

@@ -16,7 +16,7 @@ from __future__ import annotations
 
 import json
 import logging
-from typing import Any, Dict
+from typing import Any, Dict, Union
 
 from agent.state import AgentState
 from agent.memory import get_memory, SQLBufferMemory
@@ -37,6 +37,23 @@ logger.setLevel(logging.DEBUG)
 # ─────────────────────────────────────────────────────────────
 # Helper – normalise LLM / chain return types
 # ─────────────────────────────────────────────────────────────
+def _stringify_values(d: Dict[str, Any]) -> Dict[str, str]:
+    """Ensure all dict values are plain strings (JSON-encoded if needed)."""
+    out: Dict[str, str] = {}
+    for k, v in d.items():
+        if isinstance(v, str):
+            out[k] = v
+        else:
+            # Fallback – JSON-encode non-string values (e.g. nested dicts)
+            try:
+                out[k] = json.dumps(v, ensure_ascii=False)
+            except TypeError:
+                out[k] = str(v)
+    return out
+
+# ─────────────────────────────────────────────────────────────
+# Helper – normalise LLM / chain return types
+
 def _extract_text(res: Any) -> str:
     if isinstance(res, str):
         return res
@@ -97,7 +114,7 @@ def run_agent_step(state: AgentState, user_input: str, conversation_id: str):
     field_fallback_raw = parsed.get("update_needed_values", {}) or {}
     if field_fallback_raw:
         if isinstance(field_fallback_raw, dict):
-            state.needed_fields.update(field_fallback_raw)
+            state.needed_fields.update(_stringify_values(field_fallback_raw))
         else:
             # Handle cases where the LLM returns a list/tuple of key–value pairs
             try:
@@ -131,7 +148,7 @@ def run_agent_step(state: AgentState, user_input: str, conversation_id: str):
                 new_vals_raw = parsed.get("update_needed_values", {})
                 if new_vals_raw:
                     if isinstance(new_vals_raw, dict):
-                        state.needed_fields.update(new_vals_raw)
+                        state.needed_fields.update(_stringify_values(new_vals_raw))
                     else:
                         try:
                             state.needed_fields.update(dict(new_vals_raw))
@@ -223,7 +240,7 @@ def run_agent_step(state: AgentState, user_input: str, conversation_id: str):
 
                         if follow_parsed:
                             new_vals = follow_parsed.get("update_needed_values", {})
-                            state.needed_fields.update(new_vals)
+                            state.needed_fields.update(_stringify_values(new_vals))
 
                         # We echo the missing description back to the user
                         reply_to_user = check.missing_desc

agent/llm.py

@@ -19,6 +19,7 @@ logger.setLevel(logging.INFO)
 # ────────────────────────────────
 load_dotenv()
 HF_TOKEN = os.getenv("HF_TOKEN")
+print("HF_TOKEN", HF_TOKEN)
 if not HF_TOKEN:
     raise RuntimeError("Missing HF_TOKEN environment variable")
 
@@ -35,8 +36,17 @@ llm_chain: Runnable = ChatOpenAI(
     streaming=True,
 )
 
-#DeepSeekR1-1q1
+#DeepSeekOpenRouter
+# llm_chain: Runnable = ChatOpenAI(
+#     model="deepseek/deepseek-chat-v3-0324:free",  # Optional for HF OpenAI-compatible endpoints, but kept for clarity
+#     openai_api_base="https://openrouter.ai/api/v1",
+#     openai_api_key=os.getenv("OPENROUTER_API_KEY"),
+#     temperature=0.3,
+#     max_tokens=8192,
+#     streaming=True,
+# )
 
+#DeepSeekR1-1q1
 # llm_chain: Runnable = ChatOpenAI(
 #     model="deepseek-chat",  # Optional for HF OpenAI-compatible endpoints, but kept for clarity
 #     openai_api_base="https://qwryad273mlndckn.us-east-1.aws.endpoints.huggingface.cloud/v1/",

agent/state.py

@@ -4,7 +4,9 @@ agent/state.py
 Canonical state shared by all chains & the runner.
 """
 
-from typing import Dict
+import json
+from typing import Dict, Any
+from pydantic import BaseModel, field_validator
 from pydantic import BaseModel
 
 
@@ -18,6 +20,35 @@ class AgentState(BaseModel):
     is_drafted: bool = False                  # True ⇢ draft has been generated
     missing_prompt_count: int = 0             # Times user has been asked for missing details
 
+    # ──────────────────────────────
+    # Validation – ensure string values
+    # ──────────────────────────────
+    @field_validator('needed_fields', mode='before')
+    @classmethod
+    def _coerce_needed_fields(cls, v: Any):
+        """Ensure needed_fields is Dict[str, str] regardless of input shapes."""
+        if v is None:
+            return {}
+        if isinstance(v, dict):
+            str_dict: Dict[str, str] = {}
+            for k, val in v.items():
+                if isinstance(val, str):
+                    str_dict[str(k)] = val
+                else:
+                    try:
+                        str_dict[str(k)] = json.dumps(val, ensure_ascii=False)
+                    except TypeError:
+                        str_dict[str(k)] = str(val)
+            return str_dict
+        # Accept list of pairs etc.
+        try:
+            as_dict = dict(v)
+            return cls._coerce_needed_fields(as_dict)
+        except Exception:
+            # Fallback: ignore invalid format
+            return {}
+
+
     # ──────────────────────────────
     # Internal helpers (not exposed)
     # ──────────────────────────────