File size: 3,768 Bytes
7ff7119 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | """agent_node — LLM bind_tools, the heart of the ReAct loop.
The node calls the LLM with the full message history + the system prompt.
If the LLM emits a tool_call, the downstream ``tools_condition`` routes to
the ToolNode; otherwise it routes to the synthesizer.
``build_agent_node(llm_with_tools)`` is a factory returning a closure with
the bound LLM. The graph receives the chat model at compile time.
"""
from __future__ import annotations
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
from config import settings
from graph.states.chat_state import ChatState
from nodes.chat._prompts import AGENTIC_SYSTEM_PROMPT
# Friendly-error message prefixes — these are filtered out of the LLM history
# so they don't pollute follow-up reasoning. (Mirrors the parity behavior of
# the original system's ``_filter_history``.)
_ERROR_MESSAGE_PREFIXES: tuple[str, ...] = (
"Missing",
"Your API balance",
"You exceeded",
"The LLM service",
"Network error",
"Could not load PDF",
"The file is too large",
# Multilingual fallback (HU)
"Hianyzo",
"Az API szamladon",
"Tullepted",
"Az LLM szolgaltatas",
"Halozati hiba",
"Nem sikerult a PDF",
"A fajl tul nagy",
)
def _filter_history(messages: list[BaseMessage]) -> list[BaseMessage]:
"""Drop error-flavored AIMessages from the history.
Friendly-error outputs (e.g. "Your API balance is insufficient") would
confuse follow-up reasoning, so we exclude them when building the LLM input.
"""
cleaned: list[BaseMessage] = []
for m in messages:
if isinstance(m, AIMessage):
content = m.content
if isinstance(content, str) and any(
content.startswith(prefix) for prefix in _ERROR_MESSAGE_PREFIXES
):
continue
cleaned.append(m)
return cleaned
def build_agent_node(llm_with_tools, plan_to_prompt: bool = True):
"""Factory: capture llm_with_tools in a closure.
Args:
llm_with_tools: a ChatModel Runnable already bound with ``bind_tools(...)``
plan_to_prompt: if True, append ``state["plan"]`` to the system prompt
"""
async def agent_node(state: ChatState) -> dict:
messages = state.get("messages") or []
plan = state.get("plan") or []
intent = state.get("intent", "chat")
# Compose the system prompt
system_text = AGENTIC_SYSTEM_PROMPT
if plan_to_prompt and plan:
system_text += (
f"\n\n=== CURRENT PLAN (intent: {intent}) ==="
f"\nSuggested tool order (hint, not mandatory): {' → '.join(plan)}"
)
# Iteration count
iter_count = state.get("iteration_count", 0)
if iter_count >= settings.chat_max_iterations:
# Force-end: synthesize from the existing tool results
return {
"messages": [HumanMessage(
content="Please synthesize an answer from the tool results already collected; do NOT call any more tools."
)],
"iteration_count": iter_count + 1,
"trace": [f"agent: max iter ({iter_count}) → forced synthesis"],
}
# LLM call — error-flavored history is stripped out
cleaned_messages = _filter_history(messages)
full_messages = [SystemMessage(content=system_text)] + cleaned_messages
response = await llm_with_tools.ainvoke(full_messages)
return {
"messages": [response],
"iteration_count": iter_count + 1,
"trace": [f"agent: iter={iter_count + 1}, tool calls={len(getattr(response, 'tool_calls', []) or [])}"],
}
return agent_node
|