OCI Gen AI Integration

This guide shows how to use Oracle Cloud Infrastructure (OCI) Generative AI models with Ragas for evaluation.

Installation

First, install the OCI dependency:

pip install ragas[oci]

Setup

1. Configure OCI Authentication

Set up your OCI configuration using one of these methods:

Option A: OCI CLI Configuration

oci setup config

Option B: Environment Variables

export OCI_CONFIG_FILE=~/.oci/config
export OCI_PROFILE=DEFAULT

Option C: Manual Configuration

config = {
    "user": "ocid1.user.oc1..example",
    "key_file": "~/.oci/private_key.pem",
    "fingerprint": "your_fingerprint",
    "tenancy": "ocid1.tenancy.oc1..example",
    "region": "us-ashburn-1"
}

2. Get Required IDs

You'll need: - Model ID: The OCI model ID (e.g., cohere.command, meta.llama-3-8b) - Compartment ID: Your OCI compartment OCID - Endpoint ID (optional): If using a custom endpoint

Usage

Basic Usage

from ragas.llms import oci_genai_factory
from ragas import evaluate
from datasets import Dataset

# Initialize OCI Gen AI LLM
llm = oci_genai_factory(
    model_id="cohere.command",
    compartment_id="ocid1.compartment.oc1..example"
)

# Your dataset
dataset = Dataset.from_dict({
    "question": ["What is the capital of France?"],
    "answer": ["Paris"],
    "contexts": [["France is a country in Europe. Its capital is Paris."]],
    "ground_truth": ["Paris"]
})

# Evaluate with OCI Gen AI
result = evaluate(
    dataset,
    llm=llm,
    embeddings=None  # You can use any embedding model
)

Advanced Configuration

from ragas.llms import oci_genai_factory
from ragas.run_config import RunConfig

# Custom OCI configuration
config = {
    "user": "ocid1.user.oc1..example",
    "key_file": "~/.oci/private_key.pem",
    "fingerprint": "your_fingerprint",
    "tenancy": "ocid1.tenancy.oc1..example",
    "region": "us-ashburn-1"
}

# Custom run configuration
run_config = RunConfig(
    timeout=60,
    max_retries=3
)

# Initialize with custom config and endpoint
llm = oci_genai_factory(
    model_id="cohere.command",
    compartment_id="ocid1.compartment.oc1..example",
    config=config,
    endpoint_id="ocid1.endpoint.oc1..example",  # Optional
    run_config=run_config
)

Using with Different Models

# Cohere Command model
llm_cohere = oci_genai_factory(
    model_id="cohere.command",
    compartment_id="ocid1.compartment.oc1..example"
)

# Meta Llama model
llm_llama = oci_genai_factory(
    model_id="meta.llama-3-8b",
    compartment_id="ocid1.compartment.oc1..example"
)

# Using with different endpoints
llm_endpoint = oci_genai_factory(
    model_id="cohere.command",
    compartment_id="ocid1.compartment.oc1..example",
    endpoint_id="ocid1.endpoint.oc1..example"
)

Available Models

OCI Gen AI supports various models including:

Cohere: cohere.command, cohere.command-light
Meta: meta.llama-3-8b, meta.llama-3-70b
Mistral: mistral.mistral-7b-instruct
And more: Check OCI documentation for the latest available models

Error Handling

The OCI Gen AI wrapper includes comprehensive error handling:

try:
    result = evaluate(dataset, llm=llm)
except Exception as e:
    print(f"Evaluation failed: {e}")

Performance Considerations

Rate Limits: OCI Gen AI has rate limits. Use appropriate retry configurations.
Timeout: Set appropriate timeouts for your use case.
Batch Processing: The wrapper supports batch processing for multiple completions.

Troubleshooting

Common Issues

Authentication Errors
```
Error: OCI SDK authentication failed
```
Solution: Verify your OCI configuration and credentials.
Model Not Found
```
Error: Model not found in compartment
```
Solution: Check if the model ID exists in your compartment.
Permission Errors
```
Error: Insufficient permissions
```
Solution: Ensure your user has the necessary IAM policies for Generative AI.

Debug Mode

Enable debug logging to troubleshoot issues:

import logging
logging.basicConfig(level=logging.DEBUG)

# Your OCI Gen AI code here

Examples

Complete Evaluation Example

from ragas import evaluate
from ragas.llms import oci_genai_factory
from ragas.metrics import faithfulness, answer_relevancy, context_precision
from datasets import Dataset

# Initialize OCI Gen AI
llm = oci_genai_factory(
    model_id="cohere.command",
    compartment_id="ocid1.compartment.oc1..example"
)

# Create dataset
dataset = Dataset.from_dict({
    "question": [
        "What is the capital of France?",
        "Who wrote Romeo and Juliet?"
    ],
    "answer": [
        "Paris is the capital of France.",
        "William Shakespeare wrote Romeo and Juliet."
    ],
    "contexts": [
        ["France is a country in Europe. Its capital is Paris."],
        ["Romeo and Juliet is a play by William Shakespeare."]
    ],
    "ground_truth": [
        "Paris",
        "William Shakespeare"
    ]
})

# Evaluate
result = evaluate(
    dataset,
    metrics=[faithfulness, answer_relevancy, context_precision],
    llm=llm
)

print(result)

Custom Metrics with OCI Gen AI

from ragas.metrics import MetricWithLLM

# Create custom metric using OCI Gen AI
class CustomMetric(MetricWithLLM):
    def __init__(self):
        super().__init__()
        self.llm = oci_genai_factory(
            model_id="cohere.command",
            compartment_id="ocid1.compartment.oc1..example"
        )

# Use in evaluation
result = evaluate(
    dataset,
    metrics=[CustomMetric()],
    llm=llm
)

Best Practices

Use Appropriate Models: Choose models based on your evaluation needs.
Monitor Costs: OCI Gen AI usage is billed. Monitor your usage.
Handle Errors: Implement proper error handling for production use.
Use Caching: Enable caching for repeated evaluations.
Batch Operations: Use batch operations when possible for efficiency.

Support

For issues specific to OCI Gen AI integration: - Check OCI documentation: https://docs.oracle.com/en-us/iaas/Content/generative-ai/ - OCI Python SDK: https://docs.oracle.com/en-us/iaas/tools/python/2.160.1/api/generative_ai.html - Ragas GitHub issues: https://github.com/explodinggradients/ragas/issues