✨ (frontend): refactor message processing in ChatPage component to handle function calls from chunks or response_data
♻️ (agent.py): refactor async_response, async_langflow, async_chat, async_langflow_chat, and async_langflow_chat_stream functions to return full response object for function calls 🔧 (chat_service.py): update ChatService to include function call data in message_data if present
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cristhianzl
parent
7ff3bfd70b
commit
f83851b259
3 changed files with 121 additions and 13 deletions
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@ -454,12 +454,99 @@ function ChatPage() {
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content: string;
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timestamp?: string;
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response_id?: string;
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}) => ({
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role: msg.role as "user" | "assistant",
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content: msg.content,
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timestamp: new Date(msg.timestamp || new Date()),
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// Add any other necessary properties
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})
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chunks?: any[];
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response_data?: any;
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}) => {
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const message: Message = {
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role: msg.role as "user" | "assistant",
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content: msg.content,
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timestamp: new Date(msg.timestamp || new Date()),
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};
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// Extract function calls from chunks or response_data
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if (msg.role === "assistant" && (msg.chunks || msg.response_data)) {
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const functionCalls: FunctionCall[] = [];
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console.log("Processing assistant message for function calls:", {
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hasChunks: !!msg.chunks,
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chunksLength: msg.chunks?.length,
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hasResponseData: !!msg.response_data,
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});
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// Process chunks (streaming data)
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if (msg.chunks && Array.isArray(msg.chunks)) {
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for (const chunk of msg.chunks) {
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// Handle Langflow format: chunks[].item.tool_call
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if (chunk.item && chunk.item.type === "tool_call") {
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const toolCall = chunk.item;
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console.log("Found Langflow tool call:", toolCall);
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functionCalls.push({
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id: toolCall.id,
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name: toolCall.tool_name,
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arguments: toolCall.inputs || {},
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argumentsString: JSON.stringify(toolCall.inputs || {}),
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result: toolCall.results,
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status: toolCall.status || "completed",
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type: "tool_call",
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});
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}
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// Handle OpenAI format: chunks[].delta.tool_calls
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else if (chunk.delta?.tool_calls) {
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for (const toolCall of chunk.delta.tool_calls) {
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if (toolCall.function) {
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functionCalls.push({
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id: toolCall.id,
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name: toolCall.function.name,
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arguments: toolCall.function.arguments ? JSON.parse(toolCall.function.arguments) : {},
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argumentsString: toolCall.function.arguments,
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status: "completed",
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type: toolCall.type || "function",
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});
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}
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}
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}
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// Process tool call results from chunks
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if (chunk.type === "response.tool_call.result" || chunk.type === "tool_call_result") {
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const lastCall = functionCalls[functionCalls.length - 1];
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if (lastCall) {
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lastCall.result = chunk.result || chunk;
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lastCall.status = "completed";
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}
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}
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}
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}
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// Process response_data (non-streaming data)
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if (msg.response_data && typeof msg.response_data === 'object') {
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// Look for tool_calls in various places in the response data
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const responseData = typeof msg.response_data === 'string' ? JSON.parse(msg.response_data) : msg.response_data;
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if (responseData.tool_calls && Array.isArray(responseData.tool_calls)) {
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for (const toolCall of responseData.tool_calls) {
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functionCalls.push({
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id: toolCall.id,
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name: toolCall.function?.name || toolCall.name,
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arguments: toolCall.function?.arguments || toolCall.arguments,
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argumentsString: typeof (toolCall.function?.arguments || toolCall.arguments) === 'string'
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? toolCall.function?.arguments || toolCall.arguments
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: JSON.stringify(toolCall.function?.arguments || toolCall.arguments),
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result: toolCall.result,
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status: "completed",
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type: toolCall.type || "function",
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});
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}
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}
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}
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if (functionCalls.length > 0) {
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console.log("Setting functionCalls on message:", functionCalls);
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message.functionCalls = functionCalls;
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} else {
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console.log("No function calls found in message");
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}
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}
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return message;
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}
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);
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setMessages(convertedMessages);
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21
src/agent.py
21
src/agent.py
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@ -180,7 +180,7 @@ async def async_response(
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response, "response_id", None
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)
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return response_text, response_id
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return response_text, response_id, response
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# Unified streaming function for both chat and langflow
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@ -211,7 +211,7 @@ async def async_langflow(
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extra_headers: dict = None,
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previous_response_id: str = None,
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):
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response_text, response_id = await async_response(
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response_text, response_id, response_obj = await async_response(
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langflow_client,
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prompt,
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flow_id,
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@ -281,7 +281,7 @@ async def async_chat(
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"Added user message", message_count=len(conversation_state["messages"])
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)
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response_text, response_id = await async_response(
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response_text, response_id, response_obj = await async_response(
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async_client,
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prompt,
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model,
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@ -292,12 +292,13 @@ async def async_chat(
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"Got response", response_preview=response_text[:50], response_id=response_id
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)
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# Add assistant response to conversation with response_id and timestamp
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# Add assistant response to conversation with response_id, timestamp, and full response object
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assistant_message = {
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"role": "assistant",
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"content": response_text,
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"response_id": response_id,
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"timestamp": datetime.now(),
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"response_data": response_obj.model_dump() if hasattr(response_obj, "model_dump") else str(response_obj), # Store complete response for function calls
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}
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conversation_state["messages"].append(assistant_message)
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logger.debug(
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@ -419,7 +420,7 @@ async def async_langflow_chat(
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message_count=len(conversation_state["messages"]),
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)
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response_text, response_id = await async_response(
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response_text, response_id, response_obj = await async_response(
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langflow_client,
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prompt,
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flow_id,
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@ -433,12 +434,13 @@ async def async_langflow_chat(
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response_id=response_id,
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)
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# Add assistant response to conversation with response_id and timestamp
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# Add assistant response to conversation with response_id, timestamp, and full response object
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assistant_message = {
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"role": "assistant",
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"content": response_text,
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"response_id": response_id,
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"timestamp": datetime.now(),
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"response_data": response_obj.model_dump() if hasattr(response_obj, "model_dump") else str(response_obj), # Store complete response for function calls
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}
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conversation_state["messages"].append(assistant_message)
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logger.debug(
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@ -504,6 +506,8 @@ async def async_langflow_chat_stream(
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full_response = ""
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response_id = None
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collected_chunks = [] # Store all chunks for function call data
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async for chunk in async_stream(
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langflow_client,
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prompt,
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@ -517,6 +521,8 @@ async def async_langflow_chat_stream(
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import json
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chunk_data = json.loads(chunk.decode("utf-8"))
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collected_chunks.append(chunk_data) # Collect all chunk data
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if "delta" in chunk_data and "content" in chunk_data["delta"]:
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full_response += chunk_data["delta"]["content"]
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# Extract response_id from chunk
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@ -528,13 +534,14 @@ async def async_langflow_chat_stream(
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pass
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yield chunk
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# Add the complete assistant response to message history with response_id and timestamp
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# Add the complete assistant response to message history with response_id, timestamp, and function call data
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if full_response:
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assistant_message = {
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"role": "assistant",
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"content": full_response,
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"response_id": response_id,
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"timestamp": datetime.now(),
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"chunks": collected_chunks, # Store complete chunk data for function calls
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}
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conversation_state["messages"].append(assistant_message)
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@ -226,6 +226,13 @@ class ChatService:
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}
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if msg.get("response_id"):
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message_data["response_id"] = msg["response_id"]
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# Include function call data if present
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if msg.get("chunks"):
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message_data["chunks"] = msg["chunks"]
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if msg.get("response_data"):
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message_data["response_data"] = msg["response_data"]
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messages.append(message_data)
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if messages: # Only include conversations with actual messages
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@ -305,6 +312,13 @@ class ChatService:
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}
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if msg.get("response_id"):
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message_data["response_id"] = msg["response_id"]
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# Include function call data if present
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if msg.get("chunks"):
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message_data["chunks"] = msg["chunks"]
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if msg.get("response_data"):
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message_data["response_data"] = msg["response_data"]
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messages.append(message_data)
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if messages: # Only include conversations with actual messages
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