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The chat endpoint allows you to converse with your collection allowing it to respond based on information from your resources added to the collection via the WetroCloud API.

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Required Parameters

FieldDescription
collection_idCollection Id.
messageMessage currently being sent to collection
chat_historyHistory of chat between user and system

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Request Example

from wetro import Wetrocloud

# Initialize the Wetrocloud client
client = Wetrocloud(api_key="your_api_key")

# Create a chat history
chat_history = [
   {"role":"user", "content":"What is this document about?"}, 
   {"role":"system","content":"This is the sales record of a company over the past 5 years"}
]

# Continue the conversation with context
chat_response = client.collection.chat(
    collection_id="my_unique_collection_id",
    message="Can you give me an idea of how my company is doing this Q1 compared to last year?",
    chat_history=chat_history
)
print(chat_response)

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Response Example:

{
  "response": "The sales for Q1 increased by 20% compared to last year, with online channels contributing significantly.",
  "tokens": 120,
  "success": true
}
FieldDescription
responseConversational response to the message.
tokensNumber of tokens used for processing.
successIndicates whether the query was successful.
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