ChatSeekrFlow

Seekr provides AI-powered solutions for structured, explainable, and transparent AI interactions.

This guide provides a quick overview for getting started with Seekr chat models. For detailed documentation of all ChatSeekrFlow features and configurations, head to the API reference.

Overview

ChatSeekrFlow class wraps a chat model endpoint hosted on SeekrFlow, enabling seamless integration with LangChain applications.

Integration Details

Class	Package	Local	Serializable	Downloads	Version
ChatSeekrFlow	seekrai	❌	beta

Model Features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	✅	❌	❌	❌	✅	❌	✅	❌

Supported Methods

ChatSeekrFlow supports all methods of ChatModel, except async APIs.

Endpoint Requirements

The serving endpoint ChatSeekrFlow wraps must have OpenAI-compatible chat input/output format. It can be used for:

1. Fine-tuned Seekr models
2. Custom SeekrFlow models
3. RAG-enabled models using Seekr’s retrieval system

For async usage, please refer to AsyncChatSeekrFlow (coming soon).

Getting Started with ChatSeekrFlow in LangChain

This notebook covers how to use SeekrFlow as a chat model in LangChain.

Setup

Ensure you have the necessary dependencies installed:

pip install seekrai langchain langchain-community

You must also have an API key from Seekr to authenticate requests.

# Standard library
import getpass
import os

# Third-party
from langchain.prompts import ChatPromptTemplate
from langchain.schema import HumanMessage
from langchain_core.runnables import RunnableSequence

# OSS SeekrFlow integration
from langchain_seekrflow import ChatSeekrFlow
from seekrai import SeekrFlow

API Key Setup

You’ll need to set your API key as an environment variable to authenticate requests.

Run the below cell.

Or manually assign it before running queries:

SEEKR_API_KEY = "your-api-key-here"

os.environ["SEEKR_API_KEY"] = getpass.getpass("Enter your Seekr API key:")

Instantiation

os.environ["SEEKR_API_KEY"]
seekr_client = SeekrFlow(api_key=SEEKR_API_KEY)

llm = ChatSeekrFlow(
    client=seekr_client, model_name="meta-llama/Meta-Llama-3-8B-Instruct"
)

Invocation

response = llm.invoke([HumanMessage(content="Hello, Seekr!")])
print(response.content)

Hello there! I'm Seekr, nice to meet you! What brings you here today? Do you have a question, or are you looking for some help with something? I'm all ears (or rather, all text)!

Chaining

prompt = ChatPromptTemplate.from_template("Translate to French: {text}")

chain: RunnableSequence = prompt | llm
result = chain.invoke({"text": "Good morning"})
print(result)

content='The translation of "Good morning" in French is:\n\n"Bonne journée"' additional_kwargs={} response_metadata={}

def test_stream():
    """Test synchronous invocation in streaming mode."""
    print("\n🔹 Testing Sync `stream()` (Streaming)...")

    for chunk in llm.stream([HumanMessage(content="Write me a haiku.")]):
        print(chunk.content, end="", flush=True)


# ✅ Ensure streaming is enabled
llm = ChatSeekrFlow(
    client=seekr_client,
    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
    streaming=True,  # ✅ Enable streaming
)

# ✅ Run sync streaming test
test_stream()

🔹 Testing Sync `stream()` (Streaming)...
Here is a haiku:

Golden sunset fades
Ripples on the quiet lake
Peaceful evening sky

Error Handling & Debugging

# Define a minimal mock SeekrFlow client
class MockSeekrClient:
    """Mock SeekrFlow API client that mimics the real API structure."""

    class MockChat:
        """Mock Chat object with a completions method."""

        class MockCompletions:
            """Mock Completions object with a create method."""

            def create(self, *args, **kwargs):
                return {
                    "choices": [{"message": {"content": "Mock response"}}]
                }  # Mimic API response

        completions = MockCompletions()

    chat = MockChat()


def test_initialization_errors():
    """Test that invalid ChatSeekrFlow initializations raise expected errors."""

    test_cases = [
        {
            "name": "Missing Client",
            "args": {"client": None, "model_name": "seekrflow-model"},
            "expected_error": "SeekrFlow client cannot be None.",
        },
        {
            "name": "Missing Model Name",
            "args": {"client": MockSeekrClient(), "model_name": ""},
            "expected_error": "A valid model name must be provided.",
        },
    ]

    for test in test_cases:
        try:
            print(f"Running test: {test['name']}")
            faulty_llm = ChatSeekrFlow(**test["args"])

            # If no error is raised, fail the test
            print(f"❌ Test '{test['name']}' failed: No error was raised!")
        except Exception as e:
            error_msg = str(e)
            assert test["expected_error"] in error_msg, f"Unexpected error: {error_msg}"
            print(f"✅ Expected Error: {error_msg}")


# Run test
test_initialization_errors()

Running test: Missing Client
✅ Expected Error: SeekrFlow client cannot be None.
Running test: Missing Model Name
✅ Expected Error: A valid model name must be provided.

API reference

• ChatSeekrFlow class: langchain_seekrflow.ChatSeekrFlow
• PyPI package: langchain-seekrflow