Ollama API Drop-in Replacement: Integrating Fikra API

Overview

Ollama is a local model runner that exposes an OpenAI-compatible API. Because Fikra API also implements an OpenAI-style chat-completions interface, you can configure Ollama (or any OpenAI-compatible client) to point to Fikra API as a remote backend. This guide demonstrates how to set up this integration using the openai Python SDK, which is the standard way to interact with Ollama's API surface.

Note: Ollama itself is a local inference engine. It does not natively proxy requests to external APIs like Fikra. Instead, you use an OpenAI-compatible client (like the openai Python SDK) and configure it to use Fikra's base URL and API key. This effectively replaces the local Ollama endpoint with Fikra's cloud endpoint.

Prerequisites

1. A Fikra API account with an active API key (fk_live_...).
2. Python 3.8+ installed.
3. The openai Python package installed (pip install openai).

Environment Variable Setup

Store your Fikra API key in an environment variable to avoid hardcoding secrets.

export FIKRA_API_KEY="fk_live_your_key_here"

Python Example

The following example uses the openai Python SDK to call Fikra API. The base_url is set to Fikra's endpoint, and the api_key is read from the environment variable.

import os
from openai import OpenAI

# Initialize the client with Fikra's base URL and API key
client = OpenAI(
    base_url="https://api.fikraapi.co.ke/v1",
    api_key=os.getenv("FIKRA_API_KEY")
)

# Make a chat completion request
response = client.chat.completions.create(
    model="fikra-flash",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of Kenya?"}
    ]
)

print(response.choices[0].message.content)

Streaming Guidance

Fikra API supports streaming via Server-Sent Events (SSE). To enable streaming, set stream=True in the request. The response will be an iterator of chunks.

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.fikraapi.co.ke/v1",
    api_key=os.getenv("FIKRA_API_KEY")
)

# Enable streaming
stream = client.chat.completions.create(
    model="fikra-flash",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Common Errors

Production Checklist

• [ ] API Key Security: Store FIKRA_API_KEY in a secure environment variable or secret manager. Never commit it to version control.
• [ ] Rate Limiting: Implement client-side rate limiting and exponential backoff to handle 429 responses gracefully.
• [ ] Error Handling: Catch and log specific HTTP errors (400, 401, 402, 429, 503) to provide meaningful feedback to users.
• [ ] Streaming Timeout: Set appropriate timeouts for streaming requests to avoid hanging connections.
• [ ] Model Availability: Verify that the model alias (e.g., fikra-flash) is available in your account by checking the Fikra dashboard or model registry.
• [ ] Wallet Monitoring: Monitor your token-credit wallet balance to avoid 402 errors during peak usage.
• [ ] Logging: Log request IDs and response metadata for debugging and auditing purposes.

By following this guide, you can seamlessly integrate Fikra API into your existing OpenAI-compatible workflows, replacing local Ollama endpoints with Fikra's cloud-based models.