LangChain integration (langchain-nexevo)

# pip install langchain-nexevo
# Optional: pip install "langchain-nexevo[langgraph]"  for the checkpoint saver
from langchain_nexevo import ChatNexevo
from langchain_core.messages import HumanMessage, SystemMessage

# 1) Drop-in chat — replaces ChatOpenAI in any LangChain app
chat = ChatNexevo(
    model="nexevo/balanced",   # 4-layer smart routing (capability + bandit + ELO + cascade)
    api_key="sk-...",          # or set NEXEVO_API_KEY env var
    temperature=0.2,
)
resp = chat.invoke([
    SystemMessage("You are concise."),
    HumanMessage("Capital of France?"),
])
print(resp.content)                              # → "Paris"
print(resp.response_metadata["trace_id"])        # → "abcd...32hex" — for /v1/feedback

# 2) Streaming + tool calling work out of the box (inherited from ChatOpenAI)
for chunk in chat.stream([HumanMessage("Stream please")]):
    print(chunk.content, end="", flush=True)

# 3) LangGraph + persistent checkpoints (instead of MemorySaver / Postgres)
from langgraph.graph import StateGraph
from langchain_nexevo import NexevoCheckpointSaver

saver = NexevoCheckpointSaver(api_key="sk-...")
# graph = StateGraph(...).compile(checkpointer=saver)
# State persists in /v1/conversations — multi-pod safe + admin auditable.
#   thread_id ↔ conversation_id auto-mapped
#   resume across process restarts

# 4) Embeddings (same OpenAI-compat surface)
from langchain_nexevo import NexevoEmbeddings
emb = NexevoEmbeddings(model="nexevo-embed-balanced")
vec = emb.embed_query("hello world")    # 1536-dim by default