langchain-seekrflow

langchain-seekrflow is an open-source integration package that wraps SeekrFlow’s chat model endpoints for seamless use with LangChain. This package provides a simple interface—via the ChatSeekrFlow class—to incorporate SeekrFlow's AI-powered chat models into your LangChain applications.

Note: This integration package is maintained independently from the core LangChain repository. Documentation for LangChain integrations continues to be maintained in the LangChain docs.

Features

• LangChain Compatibility: Implements the LangChain BaseChatModel interface.
• Streaming Support: Enables token-level streaming of responses.
• Flexible Input Handling: Accepts strings, lists of messages, or dictionary inputs.
• Easy Integration: Designed to work seamlessly with other LangChain components such as prompt templates and chain runners.

Installation

Ensure you have Python 3.10 (or compatible) installed. Then install via Poetry or pip:

Using pip (if published to PyPI)

pip install langchain-seekrflow

Using Poetry

Clone the repository and install dependencies:

git clone https://github.com/yourusername/langchain-seekrflow.git
cd langchain-seekrflow
poetry install

Usage

Below is a quick example demonstrating how to use ChatSeekrFlow:

from langchain.prompts import ChatPromptTemplate
from langchain.schema import HumanMessage
from langchain_core.runnables import RunnableSequence
from langchain_seekrflow import ChatSeekrFlow
from seekrai import SeekrFlow

# Set your Seekr API key
SEEKR_API_KEY = "your-api-key-here"

# Initialize the SeekrFlow client (from seekrai)
seekr_client = SeekrFlow(api_key=SEEKR_API_KEY)

# Instantiate the ChatSeekrFlow model (for non-streaming mode)
llm = ChatSeekrFlow(
    client=seekr_client, 
    model_name="meta-llama/Meta-Llama-3-8B-Instruct"
)

# Synchronous invocation example:
response = llm.invoke([HumanMessage(content="Hello, Seekr!")])
print("Response:", response.content)

# Chaining example:
prompt = ChatPromptTemplate.from_template("Translate to French: {text}")
chain: RunnableSequence = prompt | llm
result = chain.invoke({"text": "Good morning"})
print("Chained response:", result)

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

Configuration & Requirements

• API Key: You must have a valid Seekr API key to authenticate requests. Set it as an environment variable or pass it to the SeekrFlow client.
• Model Endpoint: Ensure your model endpoint is compatible with OpenAI’s chat format. ChatSeekrFlow can be used with both fine-tuned and custom SeekrFlow models.
• Dependencies: This package depends on langchain, seekrai, and other libraries specified in the pyproject.toml.

Contributing

Contributions are welcome! Please open issues or pull requests on GitHub to help improve the package. For guidelines, see our Contributing Guide.

License

This project is licensed under the MIT License. See the LICENSE file for details.

API Reference

LangChain maintains documentation for community integrations separately. Once this integration is added to their docs, you’ll be able to find ChatSeekrFlow in the LangChain integrations section.

Until then, refer to this repo’s code and examples for usage.

Happy coding!