Connect LangChain Chains and Agents to Portrix Gateway

pip install langchain langchain-openai

import os
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage

llm = ChatOpenAI(
    model_name="anthropic/claude-3-5-sonnet",
    openai_api_key=os.environ["PORTRIX_API_KEY"],
    openai_api_base="https://api.portrix.ai/v1",
    temperature=0.7,
)

messages = [
    SystemMessage(content="You are a concise technical writer."),
    HumanMessage(content="What is retrieval-augmented generation?"),
]

response = llm.invoke(messages)
print(response.content)

import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(
    model_name="openai/gpt-4o",
    openai_api_key=os.environ["PORTRIX_API_KEY"],
    openai_api_base="https://api.portrix.ai/v1",
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant. Answer in {language}."),
    ("human", "{question}"),
])

chain = prompt | llm | StrOutputParser()

answer = chain.invoke({
    "language": "English",
    "question": "What are the main benefits of a unified AI gateway?",
})

print(answer)

import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage, SystemMessage } from "@langchain/core/messages";

const llm = new ChatOpenAI({
  model: "anthropic/claude-3-5-sonnet",
  temperature: 0.7,
  configuration: {
    baseURL: "https://api.portrix.ai/v1",
    apiKey: process.env.PORTRIX_API_KEY,
  },
});

const response = await llm.invoke([
  new SystemMessage("You are a concise technical writer."),
  new HumanMessage("Explain what a vector database is."),
]);

console.log(response.content);

import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

base_config = {
    "openai_api_key": os.environ["PORTRIX_API_KEY"],
    "openai_api_base": "https://api.portrix.ai/v1",
}

# Fast, cost-efficient model for triage/classification
classifier_llm = ChatOpenAI(
    model_name="google/gemini-2.0-flash",
    temperature=0.0,
    **base_config,
)

# Powerful model for final generation
writer_llm = ChatOpenAI(
    model_name="anthropic/claude-3-5-sonnet",
    temperature=0.7,
    **base_config,
)

classify_prompt = ChatPromptTemplate.from_template(
    "Classify this support ticket in one word (billing/technical/general): {ticket}"
)

respond_prompt = ChatPromptTemplate.from_template(
    "You are a support agent. Write a helpful reply to this {category} ticket: {ticket}"
)

classify_chain = classify_prompt | classifier_llm | StrOutputParser()
respond_chain = respond_prompt | writer_llm | StrOutputParser()

ticket = "I was charged twice for my subscription this month."
category = classify_chain.invoke({"ticket": ticket})
reply = respond_chain.invoke({"ticket": ticket, "category": category})

print(f"Category: {category}")
print(f"Reply: {reply}")

import os
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.tools import tool

@tool
def get_word_count(text: str) -> int:
    """Count the number of words in the given text."""
    return len(text.split())

llm = ChatOpenAI(
    model_name="openai/gpt-4o",
    openai_api_key=os.environ["PORTRIX_API_KEY"],
    openai_api_base="https://api.portrix.ai/v1",
)

tools = [get_word_count]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant with access to tools."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

result = executor.invoke({
    "input": "How many words are in the phrase 'the quick brown fox'?"
})
print(result["output"])