Merge remote-tracking branch 'origin/dev' into streaming

src/backend/langflow/api/endpoints.py

@@ -5,6 +5,12 @@ from fastapi import APIRouter, HTTPException
 
 from langflow.interface.run import process_graph_cached
 from langflow.interface.types import build_langchain_types_dict
+from langflow.api.schemas import (
+    ExportedFlow,
+    GraphData,
+    PredictRequest,
+    PredictResponse,
+)
 
 # build router
 router = APIRouter()
@@ -16,10 +22,14 @@ def get_all():
     return build_langchain_types_dict()
 
 
-@router.post("/predict")
-def get_load(data: Dict[str, Any]):
+@router.post("/predict", response_model=PredictResponse)
+async def get_load(predict_request: PredictRequest):
     try:
-        return process_graph_cached(data)
+        exported_flow: ExportedFlow = predict_request.exported_flow
+        graph_data: GraphData = exported_flow.data
+        data = graph_data.dict()
+        response = process_graph_cached(data, predict_request.message)
+        return PredictResponse(result=response.get("result", ""))
     except Exception as e:
         # Log stack trace
         logger.exception(e)

src/backend/langflow/api/schemas.py

@@ -1,8 +1,37 @@
-from typing import Any, Union
+from typing import Any, Union, Dict, List
 
 from pydantic import BaseModel, validator
 
 
+class GraphData(BaseModel):
+    """Data inside the exported flow."""
+
+    nodes: List[Dict[str, Any]]
+    edges: List[Dict[str, Any]]
+
+
+class ExportedFlow(BaseModel):
+    """Exported flow from LangFlow."""
+
+    description: str
+    name: str
+    id: str
+    data: GraphData
+
+
+class PredictRequest(BaseModel):
+    """Predict request schema."""
+
+    message: str
+    exported_flow: ExportedFlow
+
+
+class PredictResponse(BaseModel):
+    """Predict response schema."""
+
+    result: str
+
+
 class ChatMessage(BaseModel):
     """Chat message schema."""
 

src/backend/langflow/api/validate.py

@@ -49,5 +49,5 @@ def post_validate_node(node_id: str, data: dict):
             return str(node.params)
         raise Exception(f"Node {node_id} not found")
     except Exception as e:
-        logger.exception(e)
+        logger.error(e)
         raise HTTPException(status_code=500, detail=str(e)) from e

src/backend/langflow/cache/base.py

@@ -48,6 +48,7 @@ def memoize_dict(maxsize=128):
             cache.clear()
 
         wrapper.clear_cache = clear_cache  # type: ignore
+        wrapper.cache = cache  # type: ignore
         return wrapper
 
     return decorator

src/backend/langflow/config.yaml

@@ -51,6 +51,7 @@ tools:
   - BingSearchRun
   - GoogleSearchRun
   - GoogleSearchResults
+  - GoogleSerperRun
   - JsonListKeysTool
   - JsonGetValueTool
   - PythonREPLTool

src/backend/langflow/interface/agents/custom.py

@@ -27,7 +27,7 @@ from langchain.agents.agent_toolkits.vectorstore.prompt import (
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS as SQL_FORMAT_INSTRUCTIONS
 from langchain.base_language import BaseLanguageModel
-from langchain.llms.base import BaseLLM
+
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.sql_database import SQLDatabase
 from langchain.tools.python.tool import PythonAstREPLTool
@@ -63,7 +63,7 @@ class JsonAgent(AgentExecutor):
             llm=llm,
             prompt=prompt,
         )
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)  # type: ignore
         return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
 
     def run(self, *args, **kwargs):
@@ -110,7 +110,7 @@ class CSVAgent(AgentExecutor):
             prompt=partial_prompt,
         )
         tool_names = {tool.name for tool in tools}
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
 
         return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
 
@@ -134,7 +134,7 @@ class VectorStoreAgent(AgentExecutor):
 
     @classmethod
     def from_toolkit_and_llm(
-        cls, llm: BaseLLM, vectorstoreinfo: VectorStoreInfo, **kwargs: Any
+        cls, llm: BaseLanguageModel, vectorstoreinfo: VectorStoreInfo, **kwargs: Any
     ):
         """Construct a vectorstore agent from an LLM and tools."""
 
@@ -147,7 +147,7 @@ class VectorStoreAgent(AgentExecutor):
             prompt=prompt,
         )
         tool_names = {tool.name for tool in tools}
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
         return AgentExecutor.from_agent_and_tools(
             agent=agent, tools=tools, verbose=True
         )
@@ -171,7 +171,9 @@ class SQLAgent(AgentExecutor):
         super().__init__(*args, **kwargs)
 
     @classmethod
-    def from_toolkit_and_llm(cls, llm: BaseLLM, database_uri: str, **kwargs: Any):
+    def from_toolkit_and_llm(
+        cls, llm: BaseLanguageModel, database_uri: str, **kwargs: Any
+    ):
         """Construct a sql agent from an LLM and tools."""
         db = SQLDatabase.from_uri(database_uri)
         toolkit = SQLDatabaseToolkit(db=db, llm=llm)
@@ -213,7 +215,7 @@ class SQLAgent(AgentExecutor):
             prompt=prompt,
         )
         tool_names = {tool.name for tool in tools}  # type: ignore
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
         return AgentExecutor.from_agent_and_tools(
             agent=agent,
             tools=tools,  # type: ignore
@@ -256,7 +258,7 @@ class VectorStoreRouterAgent(AgentExecutor):
             prompt=prompt,
         )
         tool_names = {tool.name for tool in tools}
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
         return AgentExecutor.from_agent_and_tools(
             agent=agent, tools=tools, verbose=True
         )
@@ -275,7 +277,7 @@ class InitializeAgent(AgentExecutor):
     @classmethod
     def initialize(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         tools: List[Tool],
         agent: str,
         memory: Optional[BaseChatMemory] = None,

src/backend/langflow/interface/agents/prebuilt.py

@@ -33,7 +33,7 @@ class MalfoyAgent(AgentExecutor):
             llm=llm,
             prompt=prompt,
         )
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)  # type: ignore
         return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
 
     def run(self, *args, **kwargs):

src/backend/langflow/interface/importing/utils.py

@@ -7,11 +7,9 @@ from langchain import PromptTemplate
 from langchain.agents import Agent
 from langchain.chains.base import Chain
 from langchain.chat_models.base import BaseChatModel
-from langchain.llms.base import BaseLLM
+from langchain.base_language import BaseLanguageModel
 from langchain.tools import BaseTool
 
-from langflow.interface.tools.base import tool_creator
-
 
 def import_module(module_path: str) -> Any:
     """Import module from module path"""
@@ -100,15 +98,19 @@ def import_agent(agent: str) -> Agent:
     return import_class(f"langchain.agents.{agent}")
 
 
-def import_llm(llm: str) -> BaseLLM:
+def import_llm(llm: str) -> BaseLanguageModel:
     """Import llm from llm name"""
     return import_class(f"langchain.llms.{llm}")
 
 
 def import_tool(tool: str) -> BaseTool:
     """Import tool from tool name"""
+    from langflow.interface.tools.base import tool_creator
 
-    return tool_creator.type_to_loader_dict[tool]["fcn"]
+    if tool in tool_creator.type_to_loader_dict:
+        return tool_creator.type_to_loader_dict[tool]["fcn"]
+
+    return import_class(f"langchain.tools.{tool}")
 
 
 def import_chain(chain: str) -> Type[Chain]:

src/backend/langflow/interface/loading.py

@@ -15,7 +15,7 @@ from langchain.agents.loading import load_agent_from_config
 from langchain.agents.tools import Tool
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.loading import load_chain_from_config
-from langchain.llms.base import BaseLLM
+from langchain.base_language import BaseLanguageModel
 from langchain.llms.loading import load_llm_from_config
 from pydantic import ValidationError
 
@@ -74,12 +74,10 @@ def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
         return loaded_toolkit
     elif base_type == "embeddings":
         # ? Why remove model from params?
-
         try:
             params.pop("model")
         except KeyError:
             pass
-
         # remove all params that are not in class_object.__fields__
         try:
             return class_object(**params)
@@ -188,7 +186,7 @@ def load_langchain_type_from_config(config: Dict[str, Any]):
 
 def load_agent_executor_from_config(
     config: dict,
-    llm: Optional[BaseLLM] = None,
+    llm: Optional[BaseLanguageModel] = None,
     tools: Optional[list[Tool]] = None,
     callback_manager: Optional[BaseCallbackManager] = None,
     **kwargs: Any,

src/backend/langflow/interface/run.py

@@ -101,13 +101,12 @@ def process_graph(data_graph: Dict[str, Any]):
     return {"result": str(result), "thought": thought.strip()}
 
 
-def process_graph_cached(data_graph: Dict[str, Any]):
+def process_graph_cached(data_graph: Dict[str, Any], message: str):
     """
     Process graph by extracting input variables and replacing ZeroShotPrompt
     with PromptTemplate,then run the graph and return the result and thought.
     """
     # Load langchain object
-    message = data_graph.pop("message", "")
     is_first_message = len(data_graph.get("chatHistory", [])) == 0
     langchain_object = load_or_build_langchain_object(data_graph, is_first_message)
     logger.debug("Loaded langchain object")
@@ -120,7 +119,7 @@ def process_graph_cached(data_graph: Dict[str, Any]):
 
     # Generate result and thought
     logger.debug("Generating result and thought")
-    result, thought = get_result_and_steps(langchain_object, message)
+    result, thought = get_result_and_thought(langchain_object, message)
     logger.debug("Generated result and thought")
     return {"result": str(result), "thought": thought.strip()}
 
@@ -247,7 +246,7 @@ async def get_result_and_steps(langchain_object, message: str, **kwargs):
     return result, thought
 
 
-def async_get_result_and_steps(langchain_object, message: str):
+def get_result_and_thought(langchain_object, message: str):
     """Get result and thought from extracted json"""
     try:
         if hasattr(langchain_object, "verbose"):
@@ -302,34 +301,6 @@ def async_get_result_and_steps(langchain_object, message: str):
     return result, thought
 
 
-def get_result_and_thought(extracted_json: Dict[str, Any], message: str):
-    """Get result and thought from extracted json"""
-    try:
-        langchain_object = loading.load_langchain_type_from_config(
-            config=extracted_json
-        )
-        with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer):
-            output = langchain_object(message)
-            intermediate_steps = (
-                output.get("intermediate_steps", []) if isinstance(output, dict) else []
-            )
-            result = (
-                output.get(langchain_object.output_keys[0])
-                if isinstance(output, dict)
-                else output
-            )
-
-            if intermediate_steps:
-                thought = format_intermediate_steps(intermediate_steps)
-            else:
-                thought = output_buffer.getvalue()
-
-    except Exception as e:
-        result = f"Error: {str(e)}"
-        thought = ""
-    return result, thought
-
-
 def format_intermediate_steps(intermediate_steps):
     formatted_chain = "> Entering new AgentExecutor chain...\n"
     for step in intermediate_steps:

src/backend/langflow/interface/tools/base.py

@@ -29,7 +29,9 @@ TOOL_INPUTS = {
         placeholder="",
         value="",
     ),
-    "llm": TemplateField(field_type="BaseLLM", required=True, is_list=False, show=True),
+    "llm": TemplateField(
+        field_type="BaseLanguageModel", required=True, is_list=False, show=True
+    ),
     "func": TemplateField(
         field_type="function",
         required=True,
@@ -65,6 +67,7 @@ class ToolCreator(LangChainTypeCreator):
     def type_to_loader_dict(self) -> Dict:
         if self.tools_dict is None:
             all_tools = {}
+
             for tool, tool_fcn in ALL_TOOLS_NAMES.items():
                 tool_params = get_tool_params(tool_fcn)
                 tool_name = tool_params.get("name", tool)

src/backend/langflow/interface/tools/constants.py

@@ -1,3 +1,4 @@
+from langchain import tools
 from langchain.agents import Tool
 from langchain.agents.load_tools import (
     _BASE_TOOLS,
@@ -5,50 +6,16 @@ from langchain.agents.load_tools import (
     _EXTRA_OPTIONAL_TOOLS,
     _LLM_TOOLS,
 )
-from langchain.tools.bing_search.tool import BingSearchRun
-from langchain.tools.google_search.tool import GoogleSearchResults, GoogleSearchRun
-from langchain.tools.json.tool import JsonGetValueTool, JsonListKeysTool, JsonSpec
-from langchain.tools.python.tool import PythonAstREPLTool, PythonREPLTool
-from langchain.tools.requests.tool import (
-    RequestsDeleteTool,
-    RequestsGetTool,
-    RequestsPatchTool,
-    RequestsPostTool,
-    RequestsPutTool,
-)
-from langchain.tools.sql_database.tool import (
-    InfoSQLDatabaseTool,
-    ListSQLDatabaseTool,
-    QueryCheckerTool,
-    QuerySQLDataBaseTool,
-)
-from langchain.tools.wikipedia.tool import WikipediaQueryRun
-from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
+from langchain.tools.json.tool import JsonSpec
 
+from langflow.interface.importing.utils import import_class
 from langflow.interface.tools.custom import PythonFunction
 
 FILE_TOOLS = {"JsonSpec": JsonSpec}
 CUSTOM_TOOLS = {"Tool": Tool, "PythonFunction": PythonFunction}
-OTHER_TOOLS = {
-    "QuerySQLDataBaseTool": QuerySQLDataBaseTool,
-    "InfoSQLDatabaseTool": InfoSQLDatabaseTool,
-    "ListSQLDatabaseTool": ListSQLDatabaseTool,
-    "QueryCheckerTool": QueryCheckerTool,
-    "BingSearchRun": BingSearchRun,
-    "GoogleSearchRun": GoogleSearchRun,
-    "GoogleSearchResults": GoogleSearchResults,
-    "JsonListKeysTool": JsonListKeysTool,
-    "JsonGetValueTool": JsonGetValueTool,
-    "PythonREPLTool": PythonREPLTool,
-    "PythonAstREPLTool": PythonAstREPLTool,
-    "RequestsGetTool": RequestsGetTool,
-    "RequestsPostTool": RequestsPostTool,
-    "RequestsPatchTool": RequestsPatchTool,
-    "RequestsPutTool": RequestsPutTool,
-    "RequestsDeleteTool": RequestsDeleteTool,
-    "WikipediaQueryRun": WikipediaQueryRun,
-    "WolframAlphaQueryRun": WolframAlphaQueryRun,
-}
+
+OTHER_TOOLS = {tool: import_class(f"langchain.tools.{tool}") for tool in tools.__all__}
+
 ALL_TOOLS_NAMES = {
     **_BASE_TOOLS,
     **_LLM_TOOLS,  # type: ignore