StreamChat
Bot API allows you to try out different Large Language Models (LLMs) from providers like Mistral, OpenAI, and Claude.
For more details on the supported LLM models and their capabilities, please refer to the ChatBot.
Stream chat with LLM
POST
/stream_chat
Headers
Content-Type
application/json
Authorization
Bearer <token>
Body
llm
string
-> The llm
parameter specifies the type of Large Language Model (LLM) to be used. The supported values are:
ClaudeAI
OpenAI
GeminiAI
OpenRouterAI
llm_model
string
-> The llm_model
parameter specifies the name of the Large Language Model (LLM) to be used. The supported values for this parameter depend on the llm
you have selected.
For example, if the llm
is OpenAI
, the supported llm_model
values include:
"gpt-4.1-mini-2025-04-14"
"gpt-4.1-2025-04-14"
Similarly, if the llm
is ClaudeAI
, the supported llm_model
values include:
"claude-3-7-sonnet-latest"
"claude-opus-4-20250514"
Ensure that the llm_model
value you provide is compatible with the selected model_type
.
-> Supported LLMs and their Models are :
GeminiAI
gemini-2.5-pro
gemini-2.5-flash
ClaudeAI
claude-3-7-sonnet-latest
claude-opus-4-20250514
claude-sonnet-4-20250514
OpenAI
gpt-4.1-mini-2025-04-14
gpt-4o-mini
gpt-4.1-2025-04-14
o3-mini
o1
o3
o4-mini-2025-04-16 (Note: Must be configured with
temperature=1
,while runninng this model)
OpenRouterAI
x-ai/grok-3-beta
x-ai/grok-3-mini-beta
perplexity/sonar-pro
perplexity/sonar-reasoning-pro
perplexity/sonar-reasoning
perplexity/sonar-deep-research
deepseek/deepseek-chat
deepseek/deepseek-r1
history
List of Dictionary (Python) or map (C++)
-> The history
parameter allows you to provide the previous chat history and the last user message. The history should follow a specific pattern:
The history should start with a user message.
After each user message, there should be a response from the assistant.
The last message in the history should be a user message or query.
-> The history should be provided as a list of dictionaries, where each dictionary represents a message. The dictionary should have the following structure:
{
"role": "user",
"content": {
"text": "please give 50 word story in English",
"image_data": [
{
"url": "string",
"details": "low"
}
]
}
}
The image_data
parameter is a list that can contain up to 5 image URLs. The details
parameter is optional and can be used to specify the level of detail required for the image analysis (only for OpenAI models).
Here's an example of the history
parameter:
"history": [
{
"role": "user",
"content": {
"text": "hey",
"image_data": []
}
},
{
"role": "assistant",
"content": {
"text": "Hello! How can I assist you today?",
"image_data": []
}
},
{
"role": "user",
"content": {
"text": "Please analyze this image",
"image_data": [
{
"url": "https://res.cloudinary.com/qolaba/image/upload/v1695690455/kxug1tmiolt1dtsvv5br.jpg",
"details": "low"
}
]
}
}
]
temperature
float
-> The temperature
parameter accepts a float value between 0 and 1. This parameter helps control the level of determinism in the output from the Large Language Model (LLM).
-> Higher temperature values (closer to 1) will result in more diverse and creative output, while lower values (closer to 0) will lead to more deterministic and conservative responses.
image_analyze
bool
-> If you are passing image URLs and want the model to analyze the images, set the image_analyze
parameter to true
.
enable_tool
bool
-> To use the tools supported by the Chat API, enable the enable_tool
parameter. The Chat API currently supports two tools:
Vector Search
Internet Search
-> After enabling the enable_tool
parameter, you can provide the details of the tool you want to use in the tools
parameter.
system_msg
string
-> The system_msg
parameter allows you to set a system message for the Large Language Model (LLM). This message can be used to provide context or instructions to the model, which can influence the tone and behavior of the generated responses.
tools
dictionary or map
The tools
parameter allows you to configure the capabilities available to the assistant during a conversation. It defines which tools are enabled, how much past context to consider, and specifies settings for embeddings, PDFs, and image generation. This is useful for tailoring the assistant’s functionality to your use case.
🔹 Structure
jsonCopyEdit"tools": {
"tool_list": {
"image_generation": false,
"image_generation1": false,
"image_editing": false,
"search_doc": false,
"internet_search": false,
"python_code_execution_tool": false,
"csv_analysis": false
},
"number_of_context": 3,
"pdf_references": [],
"embedding_model": [
"text-embedding-3-large"
],
"image_generation_parameters": {}
}
🔹 Field Descriptions
tool_list: An object with boolean flags to enable or disable specific tools. Setting a value to
true
activates that feature.image_generation
: Enables image creation from text prompts.image_generation1
: Optional alternate switch for image generation (if supported).image_editing
: Enables uploading and editing images.search_doc
: Allows document-based searching.internet_search
: Enables real-time web searches.python_code_execution_tool
: Allows execution of Python code within a safe sandbox.csv_analysis
: Enables processing and analysis of uploaded CSV files.
number_of_context: An integer defining how many previous user-assistant interactions to retain in memory. Higher values help preserve conversation flow.
pdf_references: A list of PDFs that the assistant can reference for contextual information should be stored in vector store database. Each entry includes an unique id which comes from the output of vector store API. If you're adding more than one document using
pdf_references
, make sure you add the same number of entries in theembedding_model
array.
embedding_model: we support
"text-embedding-3-large"
as the embedding model.image_generation_parameters: An object to define settings like size or style for image generation. Leave it empty for default behavior.
token
string
Authentication token needed for the request.
orgID
string
Identifies the organization associated with the request.
function_call_list
string
Lists the functions called during the process.
systemId
string
Specifies the system identifier linked to the request.
last_user_query
string
Provides the last user query made in the chat.
Example of Input body parameters :
data '{
"llm": "GeminiAI",
"llm_model": "gemini-2.5-flash",
"history": [
{
"role": "user",
"content": {
"text": "please give 50 word story in English",
"image_data": [
{
"url": "string",
"details": "low"
}
]
}
}
],
"temperature": 0.7,
"image_analyze": false,
"enable_tool": false,
"system_msg": "You are a helpful assistant.",
"tools": {
"tool_list": {
"image_generation": false,
"image_generation1": false,
"image_editing": false,
"search_doc": false,
"internet_search": false,
"python_code_execution_tool": false,
"csv_analysis": false
},
"number_of_context": 3,
"pdf_references": [
"output vectorstore1",
"output vectorstore2"
],
"embedding_model": [
"text-embedding-3-large",
"text-embedding-3-large"
],
"image_generation_parameters": {}
},
"token": "123",
"orgID": "string",
"function_call_list": [],
"systemId": "string",
"last_user_query": "string"
}'
After passing the necessary parameters and executing the Chat API, you will receive a stream response. A successful response will look similar to the following:
Response
{
"output": null,
"error": null,
"error_data": null
}
The Chat API response is a streaming response, which means you will receive the output in chunks as the model generates the response. The response will continue to stream until the generation is complete.
The response will contain the following elements:
output
: This object contains the generated text output from the model.
The text output from the LLM can be obtained from the output
parameter. When the response is complete, the final chunk will contain a null
value in the output
parameter, indicating the end of the stream.
Run the API
To test this API, please use the following link:
Last updated
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