Merge branch 'form_io' of github.com:logspace-ai/langflow into form_io

src/backend/langflow/api/v1/base.py

@@ -77,7 +77,7 @@ def validate_prompt(template: str):
 def check_input_variables(input_variables: list):
     invalid_chars = []
     fixed_variables = []
-    wrong_variables = set()
+    wrong_variables = []
     empty_variables = []
     for variable in input_variables:
         new_var = variable
@@ -92,17 +92,14 @@ def check_input_variables(input_variables: list):
         if variable[0].isdigit():
             invalid_chars.append(variable[0])
             new_var = new_var.replace(variable[0], "")
-            wrong_variables.add(variable)
-
-        for char in INVALID_CHARACTERS:
-            if char in variable:
-                invalid_chars.append(char)
-                new_var = new_var.replace(char, "")
-                wrong_variables.add(variable)
+            wrong_variables.append(variable)
+        else:
+            for char in INVALID_CHARACTERS:
+                if char in variable:
+                    invalid_chars.append(char)
+                    new_var = new_var.replace(char, "")
+                    wrong_variables.append(variable)
         fixed_variables.append(new_var)
-        # if new_var != variable and new_var not in input_variables:
-        #     input_variables.remove(variable)
-        #     input_variables.append(new_var)
     # If any of the input_variables is not in the fixed_variables, then it means that
     # there are invalid characters in the input_variables
 
@@ -122,17 +119,20 @@ def build_error_message(
     input_variables, invalid_chars, wrong_variables, fixed_variables, empty_variables
 ):
     input_variables_str = ", ".join([f"'{var}'" for var in input_variables])
-    error_string = f"Invalid input variables: {input_variables_str}."
+    error_string = f"Invalid input variables: {input_variables_str}. "
 
     if wrong_variables and invalid_chars:
-        ", ".join([f"'{var}'" for var in wrong_variables])
-        invalid_chars_str = ", ".join([f"'{char}'" for char in invalid_chars])
-        error_string += (
-            f" Please, remove the invalid characters: {invalid_chars_str}"
-            " from the variables: {wrong_variables_str}."
-        )
+        # fix the wrong variables replacing invalid chars and find them in the fixed variables
+        error_string_vars = "You can fix them by replacing the invalid characters: "
+        wvars = wrong_variables.copy()
+        for i, wrong_var in enumerate(wvars):
+            for char in invalid_chars:
+                wrong_var = wrong_var.replace(char, "")
+            if wrong_var in fixed_variables:
+                error_string_vars += f"'{wrong_variables[i]}' -> '{wrong_var}'"
+        error_string += error_string_vars
     elif empty_variables:
         error_string += f" There are {len(empty_variables)} empty variable{'s' if len(empty_variables) > 1 else ''}."
     elif len(set(fixed_variables)) != len(fixed_variables):
-        error_string += " There are duplicate variables."
+        error_string += "There are duplicate variables."
     return error_string

src/backend/langflow/api/v1/callback.py

@@ -1,22 +1,107 @@
 import asyncio
-from typing import Any
 
 from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
 
 from langflow.api.v1.schemas import ChatResponse
 
 
+from typing import Any, Dict, List, Union
+from fastapi import WebSocket
+
+
+from langchain.schema import AgentAction, LLMResult, AgentFinish
+
+
 # https://github.com/hwchase17/chat-langchain/blob/master/callback.py
 class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
     """Callback handler for streaming LLM responses."""
 
-    def __init__(self, websocket):
+    def __init__(self, websocket: WebSocket):
         self.websocket = websocket
 
     async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
         resp = ChatResponse(message=token, type="stream", intermediate_steps="")
         await self.websocket.send_json(resp.dict())
 
+    async def on_llm_start(
+        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+    ) -> Any:
+        """Run when LLM starts running."""
+
+    async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:
+        """Run when LLM ends running."""
+
+    async def on_llm_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> Any:
+        """Run when LLM errors."""
+
+    async def on_chain_start(
+        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+    ) -> Any:
+        """Run when chain starts running."""
+
+    async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> Any:
+        """Run when chain ends running."""
+
+    async def on_chain_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> Any:
+        """Run when chain errors."""
+
+    async def on_tool_start(
+        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
+    ) -> Any:
+        """Run when tool starts running."""
+        resp = ChatResponse(
+            message="",
+            type="stream",
+            intermediate_steps=f"Tool input: {input_str}",
+        )
+        await self.websocket.send_json(resp.dict())
+
+    async def on_tool_end(self, output: str, **kwargs: Any) -> Any:
+        """Run when tool ends running."""
+        resp = ChatResponse(
+            message="",
+            type="stream",
+            intermediate_steps=f"Tool output: {output}",
+        )
+        await self.websocket.send_json(resp.dict())
+
+    async def on_tool_error(
+        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
+    ) -> Any:
+        """Run when tool errors."""
+
+    async def on_text(self, text: str, **kwargs: Any) -> Any:
+        """Run on arbitrary text."""
+        # This runs when first sending the prompt
+        # to the LLM, adding it will send the final prompt
+        # to the frontend
+
+    async def on_agent_action(self, action: AgentAction, **kwargs: Any):
+        log = f"Thought: {action.log}"
+        # if there are line breaks, split them and send them
+        # as separate messages
+        if "\n" in log:
+            logs = log.split("\n")
+            for log in logs:
+                resp = ChatResponse(message="", type="stream", intermediate_steps=log)
+                await self.websocket.send_json(resp.dict())
+        else:
+            resp = ChatResponse(message="", type="stream", intermediate_steps=log)
+            await self.websocket.send_json(resp.dict())
+
+    async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
+        """Run on agent end."""
+        resp = ChatResponse(
+            message="",
+            type="stream",
+            intermediate_steps=finish.log,
+        )
+        await self.websocket.send_json(resp.dict())
+
 
 class StreamingLLMCallbackHandler(BaseCallbackHandler):
     """Callback handler for streaming LLM responses."""

src/backend/langflow/chat/utils.py

@@ -23,7 +23,7 @@ async def process_graph(
     try:
         logger.debug("Generating result and thought")
         result, intermediate_steps = await get_result_and_steps(
-            langchain_object, chat_inputs.message or "", websocket=websocket
+            langchain_object, chat_inputs.message, websocket=websocket
         )
         logger.debug("Generated result and intermediate_steps")
         return result, intermediate_steps

src/backend/langflow/graph/vertex/base.py

@@ -226,4 +226,5 @@ class Vertex:
         return id(self)
 
     def _built_object_repr(self):
-        return "Built sucessfully" if self._built_object else "Not built yet"
+        # Add a message with an emoji, stars for sucess,
+        return "Built sucessfully ✨" if self._built_object else "Failed to build 😵‍💫"

src/backend/langflow/graph/vertex/types.py

@@ -201,6 +201,15 @@ class PromptVertex(Vertex):
             self._build()
         return self._built_object
 
+    def _built_object_repr(self):
+        if self.artifacts and hasattr(self._built_object, "format"):
+            # We'll build the prompt with the artifacts
+            # to show the user what the prompt looks like
+            # with the variables filled in
+            return self._built_object.format(**self.artifacts)
+        else:
+            super()._built_object_repr()
+
 
 class OutputParserVertex(Vertex):
     def __init__(self, data: Dict):

src/backend/langflow/interface/initialize/loading.py

@@ -111,6 +111,12 @@ def instantiate_llm(node_type, class_object, params: Dict):
 
 
 def instantiate_memory(node_type, class_object, params):
+    # process input_key and output_key to remove them if
+    # they are empty strings
+    for key in ["input_key", "output_key"]:
+        if key in params and not params[key]:
+            params.pop(key)
+
     try:
         if "retriever" in params and hasattr(params["retriever"], "as_retriever"):
             params["retriever"] = params["retriever"].as_retriever()

src/frontend/src/CustomNodes/GenericNode/index.tsx

@@ -66,7 +66,6 @@ export default function GenericNode({
     deleteNode(data.id);
     return;
   }
-  console.log(data);
   useEffect(() => {}, [closePopUp, data.node.template]);
   return (
     <>
@@ -121,10 +120,11 @@ export default function GenericNode({
                     "Validating..."
                   ) : (
                     <div className="max-h-96 overflow-auto">
-                      {validationStatus.params ||
-                        ""
-                          .split("\n")
-                          .map((line, index) => <div key={index}>{line}</div>)}
+                      {validationStatus.params
+                        ? validationStatus.params
+                            .split("\n")
+                            .map((line, index) => <div key={index}>{line}</div>)
+                        : ""}
                     </div>
                   )
                 }

src/frontend/src/components/chatComponent/buildTrigger/index.tsx

@@ -8,7 +8,6 @@ import { useSSE } from "../../../contexts/SSEContext";
 import { typesContext } from "../../../contexts/typesContext";
 import { alertContext } from "../../../contexts/alertContext";
 import { postBuildInit } from "../../../controllers/API";
-import ShadTooltip from "../../ShadTooltipComponent";
 
 import RadialProgressComponent from "../../RadialProgress";
 import { TabsContext } from "../../../contexts/tabsContext";
@@ -76,7 +75,6 @@ export default function BuildTrigger({
     const eventSource = new EventSource(apiUrl);
 
     eventSource.onmessage = (event) => {
-      console.log(event);
       // If the event is parseable, return
       if (!event.data) {
         return;

src/frontend/src/modals/genericModal/index.tsx

@@ -30,14 +30,7 @@ import {
   varHighlightHTML,
 } from "../../utils";
 import { Badge } from "../../components/ui/badge";
-import {
-  Tooltip,
-  TooltipContent,
-  TooltipProvider,
-  TooltipTrigger,
-} from "../../components/ui/tooltip";
 import ShadTooltip from "../../components/ShadTooltipComponent";
-import { set } from "lodash";
 import DOMPurify from "dompurify";
 
 export default function GenericModal({
@@ -68,7 +61,8 @@ export default function GenericModal({
   const [wordsHighlightInvalid, setWordsHighlightInvalid] = useState([]);
   const [wordsHighlight, setWordsHighlight] = useState([]);
   const { dark } = useContext(darkContext);
-  const { setErrorData, setSuccessData } = useContext(alertContext);
+  const { setErrorData, setSuccessData, setNoticeData } =
+    useContext(alertContext);
   const { closePopUp, setCloseEdit } = useContext(PopUpContext);
   const ref = useRef();
   function setModalOpen(x: boolean) {
@@ -149,9 +143,8 @@ export default function GenericModal({
           let inputVariables = apiReturn.data.input_variables;
           if (inputVariables.length === 0) {
             setIsEdit(true);
-            setErrorData({
-              title:
-                "The template you are attempting to use does not contain any variables for data entry.",
+            setNoticeData({
+              title: "Your template does not have any variables.",
             });
           } else {
             setIsEdit(false);