LangChain و LangGraph

# pip install -U langchain-openai
import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o-mini",
    base_url="https://gisoo.pro/api/v1",
    api_key=os.environ["GISOO_API_KEY"],
    temperature=0.3,
    max_tokens=1024,
    timeout=120,
    max_retries=2,
)

print(llm.invoke("Explain what an API key is, in two sentences.").content)

# Streaming: chunks arrive as the model writes them
for chunk in llm.stream("Write a two-line welcome message for a bakery website."):
    print(chunk.content, end="", flush=True)

// npm install @langchain/openai @langchain/core
import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
  apiKey: process.env.GISOO_API_KEY,
  configuration: { baseURL: "https://gisoo.pro/api/v1" },
  maxTokens: 1024,
  timeout: 120000,
  maxRetries: 2,
});

const reply = await llm.invoke("Explain what an API key is, in two sentences.");
console.log(reply.content);

for await (const chunk of await llm.stream("Write a two-line welcome message for a bakery website.")) {
  process.stdout.write(String(chunk.content));
}

from langchain_core.tools import tool

@tool
def order_status(order_id: str) -> str:
    """Return the delivery status of an order by its id."""
    return "shipped"                     # call your own service here

agent_llm = ChatOpenAI(model="openrouter/anthropic/claude-sonnet-5.5", base_url="https://gisoo.pro/api/v1", api_key=os.environ["GISOO_API_KEY"])

msg = agent_llm.bind_tools([order_status]).invoke("Where is order 1042?")
print(msg.tool_calls)    # [{'name': 'order_status', 'args': {'order_id': '1042'}, ...}]

import { tool } from "@langchain/core/tools";
import { z } from "zod";

const orderStatus = tool(async ({ orderId }) => "shipped", {
  name: "order_status",
  description: "Return the delivery status of an order by its id.",
  schema: z.object({ orderId: z.string() }),
});

const agentLlm = new ChatOpenAI({ model: "openrouter/anthropic/claude-sonnet-5.5", apiKey: process.env.GISOO_API_KEY, configuration: { baseURL: "https://gisoo.pro/api/v1" } });
const msg = await agentLlm.bindTools([orderStatus]).invoke("Where is order 1042?");
console.log(msg.tool_calls);

# pip install -U langchain langchain-openai   (LangChain 1.x; the agent runs on LangGraph)
from langchain.agents import create_agent

agent = create_agent(model=agent_llm, tools=[order_status], system_prompt="Answer in Persian.")
result = agent.invoke({"messages": [{"role": "user", "content": "Where is order 1042?"}]})
print(result["messages"][-1].content)

from pydantic import BaseModel, Field

class Ticket(BaseModel):
    category: str = Field(description="billing, technical or other")
    urgent: bool

classifier = llm.with_structured_output(Ticket, method="function_calling")
print(classifier.invoke("My card was charged twice and I need it fixed today!"))
# category='billing' urgent=True

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    base_url="https://gisoo.pro/api/v1",
    api_key=os.environ["GISOO_API_KEY"],
)
vectors = embeddings.embed_documents(["first text", "second text"])
query = embeddings.embed_query("what is in the first text?")

# pip install -U langchain-anthropic
from langchain_anthropic import ChatAnthropic

claude = ChatAnthropic(
    model="claude-sonnet-5-5",
    base_url="https://gisoo.pro/api",            # not .../v1 — the Anthropic SDK adds /v1/messages
    api_key=os.environ["GISOO_API_KEY"],
    max_tokens=1024,
)
print(claude.invoke("Hello, Claude").content)