{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "project_id": "123e4567-e89b-12d3-a456-426614174000",
  "agent_id": "987e6543-e21b-12d3-a456-426614174000",
  "external_conversation_id": "call-12345",
  "timestamp": "2023-10-30T10:30:00Z",
  "customer_phone": "+1234567890",
  "interaction_mode": "voice",
  "call_duration": 180.5,
  "audio_url": "gs://bucket/path/to/audio.mp3",
  "transcript": "... formatted transcript ...",
  "extra_metadata": {
    "call_type": "inbound",
    "duration": 180,
    "metric_ids": ["metric-uuid-1", "metric-uuid-2"]
  },
  "source": "api",
  "agent": {
    "id": "987e6543-e21b-12d3-a456-426614174000",
    "name": "Customer Support Agent"
  },
  "evaluations": [],
  "builtin_evaluations": [],
  "created_at": "2023-10-30T10:32:00Z",
  "updated_at": "2023-10-30T10:32:00Z"
}

{
  "detail": "Must provide either audio_file or transcript_file."
}

{
  "detail": "Not authenticated"
}

{
  "detail": "Insufficient permissions"
}

{
  "detail": "Project {project_id} not found"
}

{
  "detail": "Failed to ingest conversation: {error message}"
}

curl -X POST "https://api.evalion.ai/api/v1/observability/ingest?project_id=123e4567-e89b-12d3-a456-426614174000&agent_id=987e6543-e21b-12d3-a456-426614174000" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -F "timestamp=1698624000" \
  -F "customer_phone=+1234567890" \
  -F "audio_file=@/path/to/call-recording.mp3" \
  -F "external_conversation_id=call-12345" \
  -F 'extra_metadata={"call_type":"inbound","duration":180}'

import requests
from datetime import datetime

url = "https://api.evalion.ai/api/v1/observability/ingest"

# Your API token
headers = {
    "Authorization": "Bearer YOUR_API_TOKEN"
}

# Query parameters
params = {
    "project_id": "123e4567-e89b-12d3-a456-426614174000",
    "agent_id": "987e6543-e21b-12d3-a456-426614174000"
}

# Form data
files = {
    "audio_file": open("call-recording.mp3", "rb")
}

data = {
    "timestamp": int(datetime.now().timestamp()),
    "customer_phone": "+1234567890",
    "external_conversation_id": "call-12345",
    "extra_metadata": '{"call_type": "inbound", "duration": 180}'
}

response = requests.post(url, headers=headers, params=params, files=files, data=data)

if response.status_code == 201:
    conversation = response.json()
    print(f"Conversation created: {conversation['id']}")
else:
    print(f"Error: {response.status_code} - {response.text}")

[
  {
    "role": "user",
    "content": "Hello, I would like assistance with my account.",
    "start_time": 0.0,
    "end_time": 3.2
  },
  {
    "role": "assistant",
    "content": "Hello! I'd be happy to help you with your account. Can you please provide your account number?",
    "start_time": 3.5,
    "end_time": 8.1
  },
  {
    "role": "user",
    "content": "Sure, it's 123456789.",
    "start_time": 8.5,
    "end_time": 10.2
  },
  {
    "role": "assistant",
    "content": "Thank you. I've found your account. How can I assist you today?",
    "start_time": 10.5,
    "end_time": 14.0
  }
]

curl -X POST "https://api.evalion.ai/api/v1/observability/ingest?project_id=123e4567-e89b-12d3-a456-426614174000&agent_id=987e6543-e21b-12d3-a456-426614174000" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -F "timestamp=1698624000" \
  -F "customer_phone=+1234567890" \
  -F "transcript_file=@/path/to/transcript.json" \
  -F "external_conversation_id=call-12345"

import requests
import json
from datetime import datetime

url = "https://api.evalion.ai/api/v1/observability/ingest"

headers = {
    "Authorization": "Bearer YOUR_API_TOKEN"
}

params = {
    "project_id": "123e4567-e89b-12d3-a456-426614174000",
    "agent_id": "987e6543-e21b-12d3-a456-426614174000"
}

# Create transcript data
transcript_data = [
    {
        "role": "user",
        "content": "Hello, I need help with my order.",
        "start_time": 0.0,
        "end_time": 2.5
    },
    {
        "role": "assistant",
        "content": "Of course! I'd be happy to help. What's your order number?",
        "start_time": 3.0,
        "end_time": 6.5
    }
]

# Save to temporary file
with open("transcript.json", "w") as f:
    json.dump(transcript_data, f)

files = {
    "transcript_file": open("transcript.json", "rb")
}

data = {
    "timestamp": int(datetime.now().timestamp()),
    "customer_phone": "+1234567890",
    "external_conversation_id": "call-12345"
}

response = requests.post(url, headers=headers, params=params, files=files, data=data)

if response.status_code == 201:
    conversation = response.json()
    print(f"Conversation created: {conversation['id']}")
else:
    print(f"Error: {response.status_code} - {response.text}")

import time
import requests

def ingest_conversation_with_retry(url, headers, params, files, data, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = requests.post(url, headers=headers, params=params, files=files, data=data)
            
            if response.status_code == 201:
                return response.json()
            elif response.status_code >= 500:
                # Server error, retry
                if attempt < max_retries - 1:
                    time.sleep(2 ** attempt)  # Exponential backoff
                    continue
            else:
                # Client error, don't retry
                response.raise_for_status()
                
        except requests.exceptions.RequestException as e:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)
                continue
            raise
    
    raise Exception("Max retries exceeded")

import time

def ingest_conversations_batch(conversations, rate_limit_per_second=10):
    results = []
    for i, conv in enumerate(conversations):
        result = ingest_conversation(conv)
        results.append(result)
        
        # Rate limiting
        if (i + 1) % rate_limit_per_second == 0:
            time.sleep(1)
    
    return results

{
  "call_type": "inbound",
  "department": "sales",
  "campaign_id": "summer-2024",
  "customer_sentiment": "positive",
  "resolution_status": "resolved"
}

from fastapi import FastAPI, File, UploadFile
import requests

app = FastAPI()

@app.post("/webhook/call-completed")
async def call_completed_webhook(
    call_id: str,
    timestamp: int,
    customer_phone: str,
    audio_url: str
):
    # Download audio from your system
    audio_response = requests.get(audio_url)
    
    # Ingest to Evalion
    evalion_response = requests.post(
        "https://api.evalion.ai/api/v1/observability/ingest",
        headers={"Authorization": f"Bearer {EVALION_API_TOKEN}"},
        params={
            "project_id": PROJECT_ID,
            "agent_id": AGENT_ID
        },
        files={"audio_file": audio_response.content},
        data={
            "timestamp": timestamp,
            "customer_phone": customer_phone,
            "external_conversation_id": call_id
        }
    )
    
    return {"status": "ingested", "evalion_id": evalion_response.json()["id"]}

import schedule
import time
from datetime import datetime, timedelta

def fetch_and_ingest_recent_calls():
    # Fetch calls from the last hour from your system
    calls = your_system.get_calls(
        start_time=datetime.now() - timedelta(hours=1),
        end_time=datetime.now()
    )
    
    for call in calls:
        # Check if already ingested
        if not already_ingested(call.id):
            ingest_conversation(call)
            mark_as_ingested(call.id)

# Run every hour
schedule.every().hour.do(fetch_and_ingest_recent_calls)

while True:
    schedule.run_pending()
    time.sleep(60)

curl -X GET "https://api.evalion.ai/api/v1/observability/conversations/{conversation_id}" \
  -H "Authorization: Bearer YOUR_API_TOKEN"