ChatParallel integration

import getpass
import os

if not os.environ.get("PARALLEL_API_KEY"):
    os.environ["PARALLEL_API_KEY"] = getpass.getpass("Parallel API key:\n")

from langchain_parallel import ChatParallel

llm = ChatParallel(
    model="speed",
    # timeout=None,
    # max_retries=2,
    # api_key="...",  # optional if PARALLEL_API_KEY is set
    # base_url="https://api.parallel.ai",  # default
)

messages = [
    ("system", "You are a helpful assistant with access to real-time web information."),
    ("human", "What are the latest developments in AI?"),
]
ai_msg = llm.invoke(messages)
print(ai_msg.content)

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate(
    [
        (
            "system",
            "You are a research assistant with access to real-time web information. "
            "Answer questions about {topic} using current sources.",
        ),
        ("human", "{question}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "topic": "artificial intelligence",
        "question": "What are the most significant AI breakthroughs in 2026?",
    }
)

from pydantic import BaseModel, Field

class Founder(BaseModel):
    name: str = Field(description="Full name of the founder")
    company: str = Field(description="Company they founded")

structured = ChatParallel(model="lite").with_structured_output(Founder)
parsed = structured.invoke([("human", "Who founded SpaceX?")])
print(parsed)

name='Elon Musk' company='SpaceX'

structured = ChatParallel(model="lite").with_structured_output(Founder, include_raw=True)
res = structured.invoke([("human", "Who founded SpaceX?")])
res["parsed"]          # Founder(...) or None
res["parsing_error"]   # Exception or None
res["raw"]             # original AIMessage

cited = ChatParallel(model="lite").invoke([
    ("human", "Who is the current CEO of OpenAI? One sentence."),
])
print(cited.content)
print("\nbasis:", cited.response_metadata.get("basis"))
print("interaction_id:", cited.response_metadata.get("interaction_id"))

stream = llm.stream_events(messages, version="v3")
for token in stream.text:
    print(token, end="", flush=True)

ai_msg = await llm.ainvoke(messages)

stream = await llm.astream_events(messages, version="v3")
async for token in stream.text:
    print(token, end="", flush=True)

ai_msg = llm.invoke(messages)
print(ai_msg.usage_metadata)
# None

ai_msg = llm.invoke(messages)
print(ai_msg.response_metadata)
# {'model_name': 'speed', 'finish_reason': 'stop', 'created': 1764043410}

from langchain_parallel import ChatParallel

try:
    llm = ChatParallel(api_key="invalid-key")
    response = llm.invoke([("human", "Hello")])
except ValueError as e:
    if "Authentication failed" in str(e):
        print("Invalid API key provided")
    elif "Rate limit exceeded" in str(e):
        print("API rate limit exceeded, please try again later")

llm = ChatParallel(
    model="speed",
    # accepted but ignored by Parallel:
    tools=[{"type": "function", "function": {"name": "example"}}],
    tool_choice="auto",
    top_p=1.0,
    frequency_penalty=0.0,
    presence_penalty=0.0,
    logit_bias={},
    seed=42,
    user="user-123",
)

from langchain.messages import HumanMessage, SystemMessage

# Consecutive system messages are automatically merged before the API call.
messages = [
    SystemMessage("You are a helpful assistant."),
    SystemMessage("Always be polite and concise."),
    HumanMessage("What is the weather like today?"),
]

response = llm.invoke(messages)