Comprehensive Google ADK integration package for LangDB with enhanced agents, LLM integration, and distributed tracing.
Project description
pylangdb
pylangdb is a Python package for interacting with LangDb APIs. Please find out more at https://langdb.ai/.
Installation
To install pylangdb, use pip:
pip install pylangdb
Usage
Initialize LangDb Client
from pylangdb import LangDb
# Initialize with API key and project ID
client = LangDb(api_key="your_api_key", project_id="your_project_id")
Chat Completions
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Say hello!"}
]
response = client.completion(
model="gemini-1.5-pro-latest",
messages=messages,
temperature=0.7,
max_tokens=100
)
Thread Operations
Get Messages
Retrieve messages from a specific thread:
messages = client.get_messages(thread_id="your_thread_id")
# Access message details
for message in messages:
print(f"Type: {message.type}")
print(f"Content: {message.content}")
if message.tool_calls:
for tool_call in message.tool_calls:
print(f"Tool: {tool_call.function.name}")
Get Thread Cost
Get cost and token usage information for a thread:
usage = client.get_usage(thread_id="your_thread_id")
print(f"Total cost: ${usage.total_cost:.4f}")
print(f"Input tokens: {usage.total_input_tokens}")
print(f"Output tokens: {usage.total_output_tokens}")
Analytics
Get analytics data for specific tags:
# Get raw analytics data
analytics = client.get_analytics(
tags="model1,model2",
start_time_us=None, # Optional: defaults to 24 hours ago
end_time_us=None # Optional: defaults to current time
)
# Get analytics as a pandas DataFrame
df = client.get_analytics_dataframe(
tags="model1,model2",
start_time_us=None,
end_time_us=None
)
Development
Setting up the environment
- Clone the repository
- Create a
.envfile with your credentials:
LANGDB_API_KEY="your_api_key"
LANGDB_PROJECT_ID="your_project_id"
Running Tests
python -m unittest tests/client.py -v
Publishing
poetry build
poetry publish
Project details
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