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Python API Client

Glean's Python API client provides a Pythonic interface to Glean's Client API, making it easy to integrate enterprise search and AI capabilities into your Python applications.

glean-api-client

Official Python client for Glean's Client API
info

Authentication Required: You'll need a Client API token to use this library.

Installation​

pip install glean-api-client

Quick Start​

1

Set up environment variables

export GLEAN_INSTANCE="your-company"
export GLEAN_API_TOKEN="your-token-here"
2

Basic usage

from glean.api_client import Glean
import os

with Glean(
    api_token=os.getenv("GLEAN_API_TOKEN"),
    instance=os.getenv("GLEAN_INSTANCE"),
) as client:
    response = client.client.chat.create(
        messages=[{
            "fragments": [{"text": "What are our company values?"}]
        }],
        timeout_millis=30000
    )
    print(response)

Core Features​

Chat API​

Build conversational AI applications:

# Simple chat
response = client.client.chat.create(
    messages=[{"fragments": [{"text": "Explain our Q4 strategy"}]}]
)

# Streaming chat for real-time responses
for chunk in client.client.chat.stream(
    messages=[{"fragments": [{"text": "What are our priorities?"}]}]
):
    print(chunk.text, end="", flush=True)

Search API​

Integrate enterprise search:

results = client.client.search.search(
    query="quarterly business review",
    page_size=10
)

for result in results.results:
    print(f"Title: {result.title}")
    print(f"URL: {result.url}")

Agents API​

Execute pre-built agents:

response = client.client.agents.create_and_wait_run(
    agent_id="your-agent-id",
    inputs={"query": "Analyze sales performance"}
)

Framework Integrations​

FastAPI​

from fastapi import FastAPI
from glean.api_client import Glean
from pydantic import BaseModel

app = FastAPI()

class ChatRequest(BaseModel):
    message: str

@app.post("/chat")
async def chat_endpoint(request: ChatRequest):
    with Glean(
        api_token=os.getenv("GLEAN_API_TOKEN"),
        instance=os.getenv("GLEAN_INSTANCE"),
    ) as client:
        response = client.client.chat.create(
            messages=[{"fragments": [{"text": request.message}]}]
        )
        return {"response": response.text}

Django​

from django.http import JsonResponse
from glean.api_client import Glean
import json

def chat_view(request):
    data = json.loads(request.body)
    message = data.get('message')
    
    with Glean(
        api_token=os.getenv("GLEAN_API_TOKEN"),
        instance=os.getenv("GLEAN_INSTANCE"),
    ) as client:
        response = client.client.chat.create(
            messages=[{"fragments": [{"text": message}]}]
        )
        
    return JsonResponse({'response': response.text})

Streamlit​

import streamlit as st
from glean.api_client import Glean

st.title("Company Knowledge Assistant")

user_input = st.text_input("Ask a question:")

if user_input:
    with Glean(
        api_token=os.getenv("GLEAN_API_TOKEN"),
        instance=os.getenv("GLEAN_INSTANCE"),
    ) as client:
        response = client.client.chat.create(
            messages=[{"fragments": [{"text": user_input}]}]
        )
        st.write(response.text)

Authentication​

client = Glean(
    api_token="your-user-token",
    instance="your-company"
)

Global Tokens with ActAs​

response = client.client.chat.create(
    messages=[{"fragments": [{"text": "Hello"}]}],
    headers={"X-Glean-ActAs": "user@company.com"}
)

Error Handling​

from glean.api_client.exceptions import GleanAPIError

try:
    response = client.client.chat.create(messages=[...])
except GleanAPIError as e:
    print(f"API error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Testing​

Unit Testing with Mocks​

import pytest
from unittest.mock import patch, MagicMock

@pytest.fixture
def mock_glean_client():
    with patch('your_app.Glean') as mock:
        client_instance = MagicMock()
        mock.return_value.__enter__.return_value = client_instance
        yield client_instance

def test_chat_service(mock_glean_client):
    mock_response = MagicMock()
    mock_response.text = "Test response"
    mock_glean_client.client.chat.create.return_value = mock_response
    
    result = send_message("Hello")
    assert result == "Test response"

Additional Resources​

GitHub Repository

Source code and examples

API Reference

Complete endpoint documentation

Authentication Guide

Token creation and management

LangChain Integration

Use with LangChain framework