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fix: Update README.md #170

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100 changes: 9 additions & 91 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
This is the Python client package for Javelin.

For more information about Javelin, see https://getjavelin.com

Javelin Documentation: https://docs.getjavelin.io

### Development
Expand Down Expand Up @@ -64,96 +65,6 @@ poetry build
pip install dist/javelin_sdk-<version>-py3-none-any.whl
```

### Direct OpenAI-Compatible Usage

```python
from openai import OpenAI

# Initialize client with Javelin endpoint
client = OpenAI(
base_url="https://api.javelin.live/v1/query/your_route",
api_key="your_api_key"
)

# Make requests using standard OpenAI format
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
]
)
```

### Using Javelin SDK

```python
import os
from openai import OpenAI
import dotenv
dotenv.load_dotenv()
# Configure regular route with Javelin headers
javelin_api_key = os.getenv("JAVELIN_API_KEY")
llm_api_key = os.getenv("OPENAI_API_KEY")
javelin_headers = {
"x-api-key": javelin_api_key,
}

client = OpenAI(
base_url="https://api-dev.javelin.live/v1/query/<route>",
default_headers=javelin_headers
)

response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "user", "content": "hello"}
],
)

print(response.model_dump_json(indent=2))
```

### Using Universal Endpoints in OpenAI-Compatible Format

```python
from javelin_sdk import JavelinClient, JavelinConfig

# Setup client configuration
config = JavelinConfig(
base_url="https://api.javelin.live",
javelin_api_key="your_javelin_api_key"
)

client = JavelinClient(config)

# Set headers for universal endpoint
custom_headers = {
"Content-Type": "application/json",
"x-javelin-route": "univ_bedrock" # Change route as needed (univ_azure, univ_bedrock, univ_gemini)
}
client.set_headers(custom_headers)

# Make requests using OpenAI format
response = client.chat.completions.create(
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What are the three primary colors?"}
],
temperature=0.7,
max_tokens=150,
model="amazon.titan-text-express-v1" # Use appropriate model for your endpoint
)
```

## Additional Integration Patterns

For more detailed examples and integration patterns, check out:

- [Azure OpenAI Integration](https://docs.getjavelin.io/docs/javelin-core/integration#2-azure-openai-api-endpoints)
- [AWS Bedrock Integration](https://docs.getjavelin.io/docs/javelin-core/integration#2-azure-openai-api-endpoints)
- [Supported Language Models](https://docs.getjavelin.io/docs/javelin-core/supported-llms)

## [Universal Endpoints](https://docs.getjavelin.io/docs/javelin-core/integration#unified-endpoints)

Javelin provides universal endpoints that allow you to use a consistent interface across different LLM providers. Here are the main patterns:
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@gemini-code-assist gemini-code-assist Bot Apr 4, 2025

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medium

The heading level here is inconsistent with the rest of the document. It was previously ### and now it's ##. Consider using ### to maintain consistency.

Suggested change
### Direct OpenAI-Compatible Usage
```python
from openai import OpenAI
# Initialize client with Javelin endpoint
client = OpenAI(
base_url="https://api.javelin.live/v1/query/your_route",
api_key="your_api_key"
)
# Make requests using standard OpenAI format
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
]
)
```
### Using Javelin SDK
```python
import os
from openai import OpenAI
import dotenv
dotenv.load_dotenv()
# Configure regular route with Javelin headers
javelin_api_key = os.getenv("JAVELIN_API_KEY")
llm_api_key = os.getenv("OPENAI_API_KEY")
javelin_headers = {
"x-api-key": javelin_api_key,
}
client = OpenAI(
base_url="https://api-dev.javelin.live/v1/query/<route>",
default_headers=javelin_headers
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "user", "content": "hello"}
],
)
print(response.model_dump_json(indent=2))
```
### Using Universal Endpoints in OpenAI-Compatible Format
```python
from javelin_sdk import JavelinClient, JavelinConfig
# Setup client configuration
config = JavelinConfig(
base_url="https://api.javelin.live",
javelin_api_key="your_javelin_api_key"
)
client = JavelinClient(config)
# Set headers for universal endpoint
custom_headers = {
"Content-Type": "application/json",
"x-javelin-route": "univ_bedrock" # Change route as needed (univ_azure, univ_bedrock, univ_gemini)
}
client.set_headers(custom_headers)
# Make requests using OpenAI format
response = client.chat.completions.create(
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What are the three primary colors?"}
],
temperature=0.7,
max_tokens=150,
model="amazon.titan-text-express-v1" # Use appropriate model for your endpoint
)
```
## Additional Integration Patterns
For more detailed examples and integration patterns, check out:
- [Azure OpenAI Integration](https://docs.getjavelin.io/docs/javelin-core/integration#2-azure-openai-api-endpoints)
- [AWS Bedrock Integration](https://docs.getjavelin.io/docs/javelin-core/integration#2-azure-openai-api-endpoints)
- [Supported Language Models](https://docs.getjavelin.io/docs/javelin-core/supported-llms)
## [Universal Endpoints](https://docs.getjavelin.io/docs/javelin-core/integration#unified-endpoints)
Javelin provides universal endpoints that allow you to use a consistent interface across different LLM providers. Here are the main patterns:
### [Universal Endpoints](https://docs.getjavelin.io/docs/javelin-core/integration#unified-endpoints)

Expand All @@ -173,7 +84,6 @@ Javelin provides universal endpoints that allow you to use a consistent interfac
- [Universal endpoint implementation](examples/gemini/javelin_gemini_univ_endpoint.py)
- [OpenAI-compatible interface](examples/gemini/openai_compatible_univ_gemini.py)


### Agent Examples
- [CrewAI integration](examples/agents/crewai_javelin.ipynb)
- [LangGraph integration](examples/agents/langgraph_javelin.ipynb)
Expand All @@ -186,3 +96,11 @@ Javelin provides universal endpoints that allow you to use a consistent interfac
### Advanced Examples
- [Document processing](examples/gemini/document_processing.py)
- [RAG implementation](examples/rag/javelin_rag_embeddings_demo.ipynb)

## Additional Integration Patterns

For more detailed examples and integration patterns, check out:

- [Azure OpenAI Integration](https://docs.getjavelin.io/docs/javelin-core/integration#2-azure-openai-api-endpoints)
- [AWS Bedrock Integration](https://docs.getjavelin.io/docs/javelin-core/integration#2-azure-openai-api-endpoints)
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Copilot AI Apr 4, 2025

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The URL for AWS Bedrock Integration appears to be the same as for Azure OpenAI Integration. Please verify and update the AWS link to point to the correct documentation.

Suggested change
- [AWS Bedrock Integration](https://docs.getjavelin.io/docs/javelin-core/integration#2-azure-openai-api-endpoints)
- [AWS Bedrock Integration](https://docs.getjavelin.io/docs/javelin-core/integration#3-aws-bedrock-api-endpoints)

Copilot uses AI. Check for mistakes.
- [Supported Language Models](https://docs.getjavelin.io/docs/javelin-core/supported-llms)
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