> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.
# ChatSeekrFlow integration
> Integrate with the ChatSeekrFlow chat model using LangChain Python.
> [Seekr](https://www.seekr.com/) provides AI-powered solutions for structured, explainable, and transparent AI interactions.
This guide provides a quick overview for getting started with `ChatSeekrFlow` [chat models](/oss/python/langchain/models).
## Overview
`ChatSeekrFlow` class wraps a chat model endpoint hosted on SeekrFlow, enabling seamless integration with LangChain applications.
### Integration details
| Class | Package | Serializable | Downloads | Version |
| :-------------- | :------------------------------------------------------------------------- | :----------: | :--------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------: |
| `ChatSeekrFlow` | [seekrai](https://python.langchain.com/docs/integrations/providers/seekr/) | beta |  |  |
### Model features
| [Tool calling](/oss/python/langchain/tools/) | [Structured output](/oss/python/langchain/structured-output) | [Image input](/oss/python/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/python/langchain/streaming/) | Native async | [Token usage](/oss/python/langchain/models#token-usage) | [Logprobs](/oss/python/langchain/models#log-probabilities) |
| :------------------------------------------: | :----------------------------------------------------------: | :------------------------------------------------------: | :---------: | :---------: | :-------------------------------------------------------: | :----------: | :-----------------------------------------------------: | :--------------------------------------------------------: |
| ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ |
### 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:
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install seekrai langchain langchain-community
```
The `langchain-community` package is no longer maintained. Examples that import from `langchain_community` may be outdated or broken. Use with caution.
You must also have an API key from Seekr to authenticate requests.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 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:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
SEEKR_API_KEY = "your-api-key-here"
```
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["SEEKR_API_KEY"] = getpass.getpass("Enter your Seekr API key:")
```
## Instantiation
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
response = llm.invoke([HumanMessage(content="Hello, Seekr!")])
print(response.content)
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
prompt = ChatPromptTemplate.from_template("Translate to French: {text}")
chain: RunnableSequence = prompt | llm
result = chain.invoke({"text": "Good morning"})
print(result)
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
content='The translation of "Good morning" in French is:\n\n"Bonne journée"' additional_kwargs={} response_metadata={}
```
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
def test_stream():
"""Test synchronous invocation in streaming mode."""
print("\n🔹 Testing Sync `stream()` (Streaming)...")
stream = llm.stream_events([HumanMessage(content="Write me a haiku.")], version="v3")
for token in stream.text:
print(token, 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()
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
🔹 Testing Sync `stream()` (Streaming)...
Here is a haiku:
Golden sunset fades
Ripples on the quiet lake
Peaceful evening sky
```
## Error handling & debugging
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 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()
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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`](https://github.com/benfaircloth/langchain-seekrflow/blob/main/langchain_seekrflow/seekrflow.py)
* PyPI package: [`langchain-seekrflow`](https://pypi.org/project/langchain-seekrflow/)
***
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[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/chat/seekrflow.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).