diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 5a94a09e7..a807555c4 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -6,7 +6,7 @@ on: pull_request: env: - POETRY_VERSION: "1.3.1" + POETRY_VERSION: "1.4.0" jobs: build: diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index e48b80203..3955f2bb5 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -10,7 +10,7 @@ on: - "pyproject.toml" env: - POETRY_VERSION: "1.3.1" + POETRY_VERSION: "1.4.0" jobs: if_release: diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 000000000..52109f944 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,33 @@ +name: test + +on: + push: + branches: [main] + pull_request: + branches: [dev] + +env: + POETRY_VERSION: "1.4.0" + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: + - "3.10" + - "3.11" + steps: + - uses: actions/checkout@v3 + - name: Install poetry + run: pipx install poetry==$POETRY_VERSION + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: "poetry" + - name: Install dependencies + run: poetry install + - name: Run unit tests + run: | + make test diff --git a/poetry.lock b/poetry.lock index 46aa8b702..73235eb67 100644 --- a/poetry.lock +++ b/poetry.lock @@ -220,14 +220,14 @@ files = [ [[package]] name = "beautifulsoup4" -version = "4.11.2" +version = "4.12.0" description = "Screen-scraping library" category = "main" optional = false python-versions = ">=3.6.0" files = [ - {file = "beautifulsoup4-4.11.2-py3-none-any.whl", hash = "sha256:0e79446b10b3ecb499c1556f7e228a53e64a2bfcebd455f370d8927cb5b59e39"}, - {file = "beautifulsoup4-4.11.2.tar.gz", hash = "sha256:bc4bdda6717de5a2987436fb8d72f45dc90dd856bdfd512a1314ce90349a0106"}, + {file = "beautifulsoup4-4.12.0-py3-none-any.whl", hash = "sha256:2130a5ad7f513200fae61a17abb5e338ca980fa28c439c0571014bc0217e9591"}, + {file = "beautifulsoup4-4.12.0.tar.gz", hash = "sha256:c5fceeaec29d09c84970e47c65f2f0efe57872f7cff494c9691a26ec0ff13234"}, ] [package.dependencies] @@ -502,21 +502,23 @@ files = [ [[package]] name = "comm" -version = "0.1.2" +version = "0.1.3" description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc." category = "dev" optional = false python-versions = ">=3.6" files = [ - 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packages" category = "main" optional = false python-versions = ">=3.7" files = [ - {file = "setuptools-67.6.0-py3-none-any.whl", hash = "sha256:b78aaa36f6b90a074c1fa651168723acbf45d14cb1196b6f02c0fd07f17623b2"}, - {file = "setuptools-67.6.0.tar.gz", hash = "sha256:2ee892cd5f29f3373097f5a814697e397cf3ce313616df0af11231e2ad118077"}, + {file = "setuptools-67.6.1-py3-none-any.whl", hash = "sha256:e728ca814a823bf7bf60162daf9db95b93d532948c4c0bea762ce62f60189078"}, + {file = "setuptools-67.6.1.tar.gz", hash = "sha256:257de92a9d50a60b8e22abfcbb771571fde0dbf3ec234463212027a4eeecbe9a"}, ] [package.extras] @@ -2214,14 +2403,41 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6. 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return process_data_graph(data) + return process_graph(data) + except Exception as e: + return HTTPException(status_code=500, detail=str(e)) + + +@router.post("/validate", status_code=200, response_model=ValidationResponse) +def post_validate_code(code: Code): + try: + errors = validate_code(code.code) + return ValidationResponse( + imports=errors.get("imports", {}), + function=errors.get("function", {}), + ) except Exception as e: return HTTPException(status_code=500, detail=str(e)) diff --git a/src/backend/langflow/config.yaml b/src/backend/langflow/config.yaml index 9b03aaeb1..301d89a34 100644 --- a/src/backend/langflow/config.yaml +++ b/src/backend/langflow/config.yaml @@ -20,6 +20,8 @@ tools: - PAL-MATH - Calculator - Serper Search + - Tool + - PythonFunction memories: # - ConversationBufferMemory diff --git a/src/backend/langflow/custom/customs.py b/src/backend/langflow/custom/customs.py index cea81ac1a..877a06387 100644 --- a/src/backend/langflow/custom/customs.py +++ b/src/backend/langflow/custom/customs.py @@ -1,42 +1,12 @@ -from langchain.agents.mrkl import prompt +from langflow.template import nodes -def get_custom_prompts(): - """Get custom prompts.""" +CUSTOM_NODES = { + "prompts": {**nodes.ZeroShotPromptNode().to_dict()}, + "tools": {**nodes.PythonFunctionNode().to_dict(), **nodes.ToolNode().to_dict()}, +} - return { - "ZeroShotPrompt": { - "template": { - "_type": "zero_shot", - "prefix": { - "type": "str", - "required": False, - "placeholder": "", - "list": False, - "show": True, - "multiline": True, - "value": prompt.PREFIX, - }, - "suffix": { - "type": "str", - "required": True, - "placeholder": "", - "list": False, - "show": True, - "multiline": True, - "value": prompt.SUFFIX, - }, - "format_instructions": { - "type": "str", - "required": False, - "placeholder": "", - "list": False, - "show": True, - "multiline": True, - "value": prompt.FORMAT_INSTRUCTIONS, - }, - }, - "description": "Prompt template for Zero Shot Agent.", - "base_classes": ["BasePromptTemplate"], - } - } + +def get_custom_nodes(node_type: str): + """Get custom nodes.""" + return CUSTOM_NODES.get(node_type, {}) diff --git a/src/backend/langflow/graph/__init__.py b/src/backend/langflow/graph/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/graph/graph.py b/src/backend/langflow/graph/graph.py new file mode 100644 index 000000000..8e3c06ef5 --- /dev/null +++ b/src/backend/langflow/graph/graph.py @@ -0,0 +1,387 @@ +# Description: Graph class for building a graph of nodes and edges +# Insights: +# - Defer prompts building to the last moment or when they have all the tools +# - Build each inner agent first, then build the outer agent + +from copy import deepcopy +import types +from typing import Any, Dict, List, Optional, Union +from langflow.utils import payload +from langflow.interface.listing import ALL_TYPES_DICT, ALL_TOOLS_NAMES, TOOLS_DICT +from langflow.interface import loading + + +class Node: + def __init__(self, data: Dict): + self.id: str = data["id"] + self._data = data + self.edges: List[Edge] = [] + self._parse_data() + self._built_object = None + self._built = False + + def _parse_data(self) -> None: + self.data = self._data["data"] + self.output = self.data["node"]["base_classes"] + template_dicts = { + key: value + for key, value in self.data["node"]["template"].items() + if isinstance(value, dict) + } + + self.required_inputs = [ + template_dicts[key]["type"] + for key, value in template_dicts.items() + if value["required"] + ] + self.optional_inputs = [ + template_dicts[key]["type"] + for key, value in template_dicts.items() + if not value["required"] + ] + + template_dict = self.data["node"]["template"] + self.node_type = ( + self.data["type"] if "Tool" not in self.output else template_dict["_type"] + ) + + def _build_params(self): + # Some params are required, some are optional + # but most importantly, some params are python base classes + # like str and others are LangChain objects like LLMChain, BasePromptTemplate + # so we need to be able to distinguish between the two + + # The dicts with "type" == "str" are the ones that are python base classes + # and most likely have a "value" key + + # So for each key besides "_type" in the template dict, we have a dict + # with a "type" key. If the type is not "str", then we need to get the + # edge that connects to that node and get the Node with the required data + # and use that as the value for the param + # If the type is "str", then we need to get the value of the "value" key + # and use that as the value for the param + template_dict = { + key: value + for key, value in self.data["node"]["template"].items() + if isinstance(value, dict) + } + params = {} + for key, value in template_dict.items(): + if key == "_type": + continue + # If the type is not transformable to a python base class + # then we need to get the edge that connects to this node + if value["type"] not in ["str", "bool", "code"]: + # Get the edge that connects to this node + edge = next( + ( + edge + for edge in self.edges + if edge.target == self and edge.matched_type in value["type"] + ), + None, + ) + # Get the output of the node that the edge connects to + # if the value['list'] is True, then there will be more + # than one time setting to params[key] + # so we need to append to a list if it exists + # or create a new list if it doesn't + if edge is None and value["required"]: + # break line + raise ValueError( + f"Required input {key} for module {self.node_type} not found" + ) + if value["list"]: + if key in params: + params[key].append(edge.source) + else: + params[key] = [edge.source] + elif value["required"] or edge is not None: + params[key] = edge.source + elif value["required"] or value.get("value"): + params[key] = value["value"] + + # Add _type to params + self.params = params + + def _build(self): + # The params dict is used to build the module + # it contains values and keys that point to nodes which + # have their own params dict + # When build is called, we iterate through the params dict + # and if the value is a node, we call build on that node + # and use the output of that build as the value for the param + # if the value is not a node, then we use the value as the param + # and continue + # Another aspect is that the node_type is the class that we need to import + # and instantiate with these built params + + # Build each node in the params dict + for key, value in self.params.items(): + # Check if Node or list of Nodes + if isinstance(value, Node): + result = value.build() + # If the key is "func", then we need to use the run method + if key == "func" and not isinstance(result, types.FunctionType): + # func can be PythonFunction(code='\ndef upper_case(text: str) -> str:\n return text.upper()\n') + # so we need to check if there is an attribute called run + if hasattr(result, "run"): + result = result.run # type: ignore + elif hasattr(result, "get_function"): + result = result.get_function() # type: ignore + self.params[key] = result + elif isinstance(value, list) and all( + isinstance(node, Node) for node in value + ): + self.params[key] = [node.build() for node in value] # type: ignore + + # Get the class from LANGCHAIN_TYPES_DICT + # and instantiate it with the params + # and return the instance + for base_type, value in ALL_TYPES_DICT.items(): + if base_type == "tools": + value = TOOLS_DICT + + if self.node_type in value: + self._built_object = loading.instantiate_class( + node_type=self.node_type, + base_type=base_type, + params=self.params, + ) + break + + if self._built_object is None: + raise ValueError(f"Node type {self.node_type} not found") + + self._built = True + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._build() + return deepcopy(self._built_object) + + def add_edge(self, edge: "Edge") -> None: + self.edges.append(edge) + + def __repr__(self) -> str: + return f"Node(id={self.id}, data={self.data})" + + def __eq__(self, __o: object) -> bool: + return self.id == __o.id if isinstance(__o, Node) else False + + def __hash__(self) -> int: + return id(self) + + +class AgentNode(Node): + def __init__(self, data: Dict): + super().__init__(data) + self.tools: List[ToolNode] = [] + self.chains: List[ChainNode] = [] + + def _set_tools_and_chains(self) -> None: + for edge in self.edges: + source_node = edge.source + if isinstance(source_node, ToolNode): + self.tools.append(source_node) + elif isinstance(source_node, ChainNode): + self.chains.append(source_node) + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._set_tools_and_chains() + # First, build the tools + for tool_node in self.tools: + tool_node.build() + + # Next, build the chains and the rest + for chain_node in self.chains: + chain_node.build(tools=self.tools) + + self._build() + return deepcopy(self._built_object) + + +class Edge: + def __init__(self, source: "Node", target: "Node"): + self.source: "Node" = source + self.target: "Node" = target + self.validate_edge() + + def validate_edge(self) -> None: + # Validate that the outputs of the source node are valid inputs + # for the target node + self.source_types = self.source.output + self.target_reqs = self.target.required_inputs + self.target.optional_inputs + # Both lists contain strings and sometimes a string contains the value we are + # looking for e.g. comgin_out=["Chain"] and target_reqs=["LLMChain"] + # so we need to check if any of the strings in source_types is in target_reqs + self.valid = any( + output in target_req + for output in self.source_types + for target_req in self.target_reqs + ) + # Get what type of input the target node is expecting + + self.matched_type = next( + ( + output + for output in self.source_types + for target_req in self.target_reqs + if output in target_req + ), + None, + ) + + def __repr__(self) -> str: + return ( + f"Edge(source={self.source.id}, target={self.target.id}, valid={self.valid}" + f", matched_type={self.matched_type})" + ) + + +class ToolNode(Node): + def __init__(self, data: Dict): + super().__init__(data) + + def build(self, force: bool = False) -> Any: + if not self._built or force: + self._build() + return deepcopy(self._built_object) + + +class PromptNode(Node): + def __init__(self, data: Dict): + super().__init__(data) + + def build( + self, + force: bool = False, + tools: Optional[Union[List[Node], List[ToolNode]]] = None, + ) -> Any: + if not self._built or force: + # Check if it is a ZeroShotPrompt and needs a tool + if self.node_type == "ZeroShotPrompt": + tools = ( + [tool_node.build() for tool_node in tools] + if tools is not None + else [] + ) + self.params["tools"] = tools + + self._build() + return deepcopy(self._built_object) + + +class ChainNode(Node): + def __init__(self, data: Dict): + super().__init__(data) + + def build( + self, + force: bool = False, + tools: Optional[Union[List[Node], List[ToolNode]]] = None, + ) -> Any: + if not self._built or force: + # Check if the chain requires a PromptNode + for key, value in self.params.items(): + if isinstance(value, PromptNode): + # Build the PromptNode, passing the tools if available + self.params[key] = value.build(tools=tools, force=force) + + self._build() + return deepcopy(self._built_object) + + +class Graph: + def __init__( + self, + nodes: List[Dict[str, Union[str, Dict[str, Union[str, List[str]]]]]], + edges: List[Dict[str, str]], + ) -> None: + self._nodes = nodes + self._edges = edges + self._build_graph() + + def _build_graph(self) -> None: + self.nodes = self._build_nodes() + self.edges = self._build_edges() + for edge in self.edges: + edge.source.add_edge(edge) + edge.target.add_edge(edge) + + for node in self.nodes: + node._build_params() + + def get_node(self, node_id: str) -> Union[None, Node]: + return next((node for node in self.nodes if node.id == node_id), None) + + def get_nodes_with_target(self, node: Node) -> List[Node]: + connected_nodes: List[Node] = [ + edge.source for edge in self.edges if edge.target == node + ] + return connected_nodes + + def build(self) -> List[Node]: + # Get root node + root_node = payload.get_root_node(self) + return root_node.build() + + def get_node_neighbors(self, node: Node) -> Dict[Node, int]: + neighbors: Dict[Node, int] = {} + for edge in self.edges: + if edge.source == node: + neighbor = edge.target + if neighbor not in neighbors: + neighbors[neighbor] = 0 + neighbors[neighbor] += 1 + elif edge.target == node: + neighbor = edge.source + if neighbor not in neighbors: + neighbors[neighbor] = 0 + neighbors[neighbor] += 1 + return neighbors + + def _build_edges(self) -> List[Edge]: + # Edge takes two nodes as arguments, so we need to build the nodes first + # and then build the edges + # if we can't find a node, we raise an error + + edges: List[Edge] = [] + for edge in self._edges: + source = self.get_node(edge["source"]) + target = self.get_node(edge["target"]) + if source is None: + raise ValueError(f"Source node {edge['source']} not found") + if target is None: + raise ValueError(f"Target node {edge['target']} not found") + edges.append(Edge(source, target)) + return edges + + def _build_nodes(self) -> List[Node]: + nodes: List[Node] = [] + for node in self._nodes: + node_data = node["data"] + node_type: str = node_data["type"] # type: ignore + node_lc_type: str = node_data["node"]["template"]["_type"] # type: ignore + + if node_type in {"ZeroShotPrompt", "PromptTemplate"}: + nodes.append(PromptNode(node)) + elif "agent" in node_type.lower(): + nodes.append(AgentNode(node)) + elif "chain" in node_type.lower(): + nodes.append(ChainNode(node)) + elif "tool" in node_type.lower() or node_lc_type in ALL_TOOLS_NAMES: + nodes.append(ToolNode(node)) + else: + nodes.append(Node(node)) + return nodes + + def get_children_by_node_type(self, node: Node, node_type: str) -> List[Node]: + children = [] + node_types = [node.data["type"]] + if "node" in node.data: + node_types += node.data["node"]["base_classes"] + if node_type in node_types: + children.append(node) + return children diff --git a/src/backend/langflow/graph/utils.py b/src/backend/langflow/graph/utils.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/custom_types.py b/src/backend/langflow/interface/custom_types.py new file mode 100644 index 000000000..05d77fd1d --- /dev/null +++ b/src/backend/langflow/interface/custom_types.py @@ -0,0 +1,35 @@ +from typing import Callable, Optional +from langflow.utils import validate +from pydantic import BaseModel, validator + + +class Function(BaseModel): + code: str + function: Optional[Callable] = None + imports: Optional[str] = None + + # Eval code and store the function + def __init__(self, **data): + super().__init__(**data) + + # Validate the function + @validator("code") + def validate_func(cls, v): + try: + validate.eval_function(v) + except Exception as e: + raise e + + return v + + def get_function(self): + """Get the function""" + function_name = validate.extract_function_name(self.code) + + return validate.create_function(self.code, function_name) + + +class PythonFunction(Function): + """Python function""" + + code: str diff --git a/src/backend/langflow/interface/importing/__init__.py b/src/backend/langflow/interface/importing/__init__.py new file mode 100644 index 000000000..317849f8e --- /dev/null +++ b/src/backend/langflow/interface/importing/__init__.py @@ -0,0 +1,7 @@ +from langflow.interface.importing.utils import import_by_type # noqa: F401 + +# This module is used to import any langchain class by name. + +ALL = [ + "import_by_type", +] diff --git a/src/backend/langflow/interface/importing/utils.py b/src/backend/langflow/interface/importing/utils.py new file mode 100644 index 000000000..5524e2d32 --- /dev/null +++ b/src/backend/langflow/interface/importing/utils.py @@ -0,0 +1,63 @@ +# This module is used to import any langchain class by name. + +import importlib +from typing import Any + +from langchain import PromptTemplate +from langchain.agents import Agent +from langchain.chains.base import Chain +from langchain.llms.base import BaseLLM +from langchain.tools import BaseTool +from langflow.utils.util import get_tool_by_name + + +def import_module(module_path: str) -> Any: + """Import module from module path""" + return importlib.import_module(module_path) + + +def import_by_type(_type: str, name: str) -> Any: + """Import class by type and name""" + func_dict = { + "agents": import_agent, + "prompts": import_prompt, + "llms": import_llm, + "tools": import_tool, + "chains": import_chain, + } + return func_dict[_type](name) + + +def import_class(class_path: str) -> Any: + """Import class from class path""" + module_path, class_name = class_path.rsplit(".", 1) + module = import_module(module_path) + return getattr(module, class_name) + + +def import_prompt(prompt: str) -> PromptTemplate: + """Import prompt from prompt name""" + if prompt == "ZeroShotPrompt": + return import_class("langchain.prompts.PromptTemplate") + return import_class(f"langchain.prompts.{prompt}") + + +def import_agent(agent: str) -> Agent: + """Import agent from agent name""" + return import_class(f"langchain.agents.{agent}") + + +def import_llm(llm: str) -> BaseLLM: + """Import llm from llm name""" + return import_class(f"langchain.llms.{llm}") + + +def import_tool(tool: str) -> BaseTool: + """Import tool from tool name""" + + return get_tool_by_name(tool) + + +def import_chain(chain: str) -> Chain: + """Import chain from chain name""" + return import_class(f"langchain.chains.{chain}") diff --git a/src/backend/langflow/interface/listing.py b/src/backend/langflow/interface/listing.py index 21e763de4..1e668b2b5 100644 --- a/src/backend/langflow/interface/listing.py +++ b/src/backend/langflow/interface/listing.py @@ -1,5 +1,4 @@ from langchain import agents, chains, prompts -from langchain.agents.load_tools import get_all_tool_names from langflow.custom import customs from langflow.interface.custom_lists import ( @@ -8,18 +7,30 @@ from langflow.interface.custom_lists import ( ) from langflow.settings import settings from langflow.utils import util +from langchain.agents.load_tools import get_all_tool_names +from langchain.agents import Tool +from langflow.interface.custom_types import PythonFunction -def list_type(object_type: str): - """List all components""" +CUSTOM_TOOLS = {"Tool": Tool, "PythonFunction": PythonFunction} +TOOLS_DICT = util.get_tools_dict() +ALL_TOOLS_NAMES = set(get_all_tool_names() + list(CUSTOM_TOOLS.keys())) + + +def get_type_dict(): return { "chains": list_chain_types, "agents": list_agents, "prompts": list_prompts, "llms": list_llms, - "memories": list_memories, "tools": list_tools, - }.get(object_type, lambda: "Invalid type")() + "memories": list_memories, + } + + +def list_type(object_type: str): + """List all components""" + return get_type_dict().get(object_type, lambda: None)() def list_agents(): @@ -33,7 +44,7 @@ def list_agents(): def list_prompts(): """List all prompt types""" - custom_prompts = customs.get_custom_prompts() + custom_prompts = customs.get_custom_nodes("prompts") library_prompts = [ prompt.__annotations__["return"].__name__ for prompt in prompts.loading.type_to_loader_dict.values() @@ -47,12 +58,14 @@ def list_tools(): tools = [] - for tool in get_all_tool_names(): - tool_params = util.get_tool_params(util.get_tools_dict(tool)) - if tool_params and tool_params["name"] in settings.tools or settings.dev: + for tool in ALL_TOOLS_NAMES: + tool_params = util.get_tool_params(util.get_tool_by_name(tool)) + if tool_params and tool_params.get("name") in settings.tools or settings.dev: tools.append(tool_params["name"]) - return tools + # Add Tool + custom_tools = customs.get_custom_nodes("tools") + return tools + list(custom_tools.keys()) def list_llms(): @@ -80,3 +93,15 @@ def list_memories(): for memory in memory_type_to_cls_dict.values() if memory.__name__ in settings.memories or settings.dev ] + + +LANGCHAIN_TYPES_DICT = { + k: list_function() for k, list_function in get_type_dict().items() +} + +# Now we'll build a dict with Langchain types and ours + +ALL_TYPES_DICT = { + **LANGCHAIN_TYPES_DICT, + "Custom": ["Custom Tool", "Python Function"], +} diff --git a/src/backend/langflow/interface/loading.py b/src/backend/langflow/interface/loading.py index 619264988..1c73368e9 100644 --- a/src/backend/langflow/interface/loading.py +++ b/src/backend/langflow/interface/loading.py @@ -8,6 +8,12 @@ from langchain.agents.load_tools import ( _EXTRA_OPTIONAL_TOOLS, _LLM_TOOLS, ) +from langchain.agents import agent as agent_module + + +from langflow.interface.importing.utils import import_by_type + +from langchain.agents import ZeroShotAgent from langchain.agents.loading import load_agent_from_config from langchain.agents.tools import Tool from langchain.callbacks.base import BaseCallbackManager @@ -16,19 +22,43 @@ from langchain.llms.base import BaseLLM from langchain.llms.loading import load_llm_from_config from langflow.interface.types import get_type_list -from langflow.utils import payload, util +from langflow.utils import payload, util, validate + + +def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any: + """Instantiate class from module type and key, and params""" + class_object = import_by_type(_type=base_type, name=node_type) + if base_type == "agents": + # We need to initialize it differently + allowed_tools = params["allowed_tools"] + llm_chain = params["llm_chain"] + return load_agent_executor(class_object, allowed_tools, llm_chain) + elif base_type == "tools" or node_type != "ZeroShotPrompt": + return class_object(**params) + elif node_type == "PythonFunction": + # If the node_type is "PythonFunction" + # we need to get the function from the params + # which will be a str containing a python function + # and then we need to compile it and return the function + # as the instance + function_string = params["code"] + if isinstance(function_string, str): + return validate.eval_function(function_string) + raise ValueError("Function should be a string") + else: + if "tools" not in params: + params["tools"] = [] + return ZeroShotAgent.create_prompt(**params) def load_flow_from_json(path: str): + # This is done to avoid circular imports + from langflow.graph.graph import Graph + """Load flow from json file""" with open(path, "r") as f: flow_graph = json.load(f) data_graph = flow_graph["data"] - extracted_json = extract_json(data_graph) - return load_langchain_type_from_config(config=extracted_json) - - -def extract_json(data_graph): nodes = data_graph["nodes"] # Substitute ZeroShotPrompt with PromptTemplate nodes = replace_zero_shot_prompt_with_prompt_template(nodes) @@ -36,8 +66,8 @@ def extract_json(data_graph): nodes = payload.extract_input_variables(nodes) # Nodes, edges and root node edges = data_graph["edges"] - root = payload.get_root_node(nodes, edges) - return payload.build_json(root, nodes, edges) + graph = Graph(nodes, edges) + return graph.build() def replace_zero_shot_prompt_with_prompt_template(nodes): @@ -92,6 +122,19 @@ def load_agent_executor_from_config( ) +def load_agent_executor( + agent_class: type[agent_module.Agent], allowed_tools, llm_chain, **kwargs +): + """Load agent executor from agent class, tools and chain""" + tool_names = [tool.name for tool in allowed_tools] + agent = agent_class(allowed_tools=tool_names, llm_chain=llm_chain) + return AgentExecutor.from_agent_and_tools( + agent=agent, + tools=allowed_tools, + **kwargs, + ) + + def load_tools_from_config(tool_list: list[dict]) -> list: """Load tools based on a config list. diff --git a/src/backend/langflow/interface/run.py b/src/backend/langflow/interface/run.py index 0844e2073..f4363562c 100644 --- a/src/backend/langflow/interface/run.py +++ b/src/backend/langflow/interface/run.py @@ -4,20 +4,27 @@ import re from typing import Any, Dict from langflow.interface import loading +from langflow.utils import payload +from langflow.graph.graph import Graph -def process_data_graph(data_graph: Dict[str, Any]): +def process_graph(data_graph: Dict[str, Any]): """ - Process data graph by extracting input variables and replacing ZeroShotPrompt + Process graph by extracting input variables and replacing ZeroShotPrompt with PromptTemplate,then run the graph and return the result and thought. """ + nodes = data_graph["nodes"] + # Add input variables + # ? Is this necessary? + nodes = payload.extract_input_variables(nodes) + # Nodes, edges and root node + edges = data_graph["edges"] + graph = Graph(nodes, edges) - extracted_json = loading.extract_json(data_graph) - + langchain_object = graph.build() message = data_graph["message"] - # Process json - result, thought = get_result_and_thought(extracted_json, message) + result, thought = get_result_and_thought_using_graph(langchain_object, message) return { "result": result, @@ -27,6 +34,26 @@ def process_data_graph(data_graph: Dict[str, Any]): } +def get_result_and_thought_using_graph(loaded_langchain, message: str): + """Get result and thought from extracted json""" + loaded_langchain.verbose = True + try: + with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer): + result = loaded_langchain(message) + + result = ( + result.get(loaded_langchain.output_keys[0]) + if isinstance(result, dict) + else result + ) + thought = output_buffer.getvalue() + + except Exception as e: + result = f"Error: {str(e)}" + thought = "" + return result, thought + + def get_result_and_thought(extracted_json: Dict[str, Any], message: str): """Get result and thought from extracted json""" try: @@ -41,6 +68,7 @@ def get_result_and_thought(extracted_json: Dict[str, Any], message: str): else result ) thought = output_buffer.getvalue() + except Exception as e: result = f"Error: {str(e)}" thought = "" diff --git a/src/backend/langflow/interface/signature.py b/src/backend/langflow/interface/signature.py index 69e83fad6..390f64671 100644 --- a/src/backend/langflow/interface/signature.py +++ b/src/backend/langflow/interface/signature.py @@ -6,7 +6,6 @@ from langchain.agents.load_tools import ( _EXTRA_LLM_TOOLS, _EXTRA_OPTIONAL_TOOLS, _LLM_TOOLS, - get_all_tool_names, ) from langflow.custom import customs @@ -14,6 +13,8 @@ from langflow.interface.custom_lists import ( llm_type_to_cls_dict, memory_type_to_cls_dict, ) +from langflow.interface.listing import CUSTOM_TOOLS, ALL_TOOLS_NAMES +from langflow.template.template import Field, Template from langflow.utils import util @@ -24,7 +25,7 @@ def get_signature(name: str, object_type: str): "agents": get_agent_signature, "prompts": get_prompt_signature, "llms": get_llm_signature, - "memories": get_memory_signature, + # "memories": get_memory_signature, "tools": get_tool_signature, }.get(object_type, lambda name: f"Invalid type: {name}")(name) @@ -33,8 +34,9 @@ def get_chain_signature(name: str): """Get the chain type by signature.""" try: return util.build_template_from_function( - name, chains.loading.type_to_loader_dict + name, chains.loading.type_to_loader_dict, add_function=True ) + except ValueError as exc: raise ValueError("Chain not found") from exc @@ -42,7 +44,9 @@ def get_chain_signature(name: str): def get_agent_signature(name: str): """Get the signature of an agent.""" try: - return util.build_template_from_class(name, agents.loading.AGENT_TO_CLASS) + return util.build_template_from_class( + name, agents.loading.AGENT_TO_CLASS, add_function=True + ) except ValueError as exc: raise ValueError("Agent not found") from exc @@ -50,8 +54,8 @@ def get_agent_signature(name: str): def get_prompt_signature(name: str): """Get the signature of a prompt.""" try: - if name in customs.get_custom_prompts().keys(): - return customs.get_custom_prompts()[name] + if name in customs.get_custom_nodes("prompts").keys(): + return customs.get_custom_nodes("prompts")[name] return util.build_template_from_function( name, prompts.loading.type_to_loader_dict ) @@ -78,28 +82,46 @@ def get_memory_signature(name: str): def get_tool_signature(name: str): """Get the signature of a tool.""" + NODE_INPUTS = ["llm", "func"] + base_classes = ["Tool"] all_tools = {} - for tool in get_all_tool_names(): - if tool_params := util.get_tool_params(util.get_tools_dict(tool)): - all_tools[tool_params["name"]] = tool + for tool in ALL_TOOLS_NAMES: + if tool_params := util.get_tool_params(util.get_tool_by_name(tool)): + tool_name = tool_params.get("name") or str(tool) + all_tools[tool_name] = {"type": tool, "params": tool_params} # Raise error if name is not in tools if name not in all_tools.keys(): raise ValueError("Tool not found") type_dict = { - "str": { - "type": "str", - "required": True, - "list": False, - "show": True, - "placeholder": "", - "value": "", - }, - "llm": {"type": "BaseLLM", "required": True, "list": False, "show": True}, + "str": Field( + field_type="str", + required=True, + is_list=False, + show=True, + placeholder="", + value="", + ), + "llm": Field(field_type="BaseLLM", required=True, is_list=False, show=True), + "func": Field( + field_type="function", + required=True, + is_list=False, + show=True, + multiline=True, + ), + "code": Field( + field_type="str", + required=True, + is_list=False, + show=True, + value="", + multiline=True, + ), } - tool_type = all_tools[name] + tool_type: str = all_tools[name]["type"] # type: ignore if tool_type in _BASE_TOOLS: params = [] @@ -111,23 +133,38 @@ def get_tool_signature(name: str): elif tool_type in _EXTRA_OPTIONAL_TOOLS: _, extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type] params = extra_keys + elif tool_type == "Tool": + params = ["name", "description", "func"] + elif tool_type in CUSTOM_TOOLS: + # Get custom tool params + params = all_tools[name]["params"] # type: ignore + base_classes = ["function"] + if node := customs.get_custom_nodes("tools").get(tool_type): + return node + else: params = [] - template = { - param: (type_dict[param].copy() if param == "llm" else type_dict["str"].copy()) - for param in params - } + # Copy the field and add the name + fields = [] + for param in params: + if param in NODE_INPUTS: + field = type_dict[param].copy() + else: + field = type_dict["str"].copy() + field.name = param + if param == "aiosession": + field.show = False + field.required = False + fields.append(field) - # Remove required from aiosession - if "aiosession" in template.keys(): - template["aiosession"]["required"] = False - template["aiosession"]["show"] = False - - template["_type"] = tool_type # type: ignore + template = Template(fields=fields, type_name=tool_type) + tool_params = util.get_tool_params(util.get_tool_by_name(tool_type)) + if tool_params is None: + tool_params = {} return { - "template": template, - **util.get_tool_params(util.get_tools_dict(tool_type)), - "base_classes": ["Tool"], + "template": util.format_dict(template.to_dict()), + **tool_params, + "base_classes": base_classes, } diff --git a/src/backend/langflow/interface/types.py b/src/backend/langflow/interface/types.py index 57f3a2578..595addb50 100644 --- a/src/backend/langflow/interface/types.py +++ b/src/backend/langflow/interface/types.py @@ -6,7 +6,7 @@ def get_type_list(): """Get a list of all langchain types""" all_types = build_langchain_types_dict() - all_types.pop("tools") + # all_types.pop("tools") for key, value in all_types.items(): all_types[key] = [item["template"]["_type"] for item in value.values()] diff --git a/src/backend/langflow/template/__init__.py b/src/backend/langflow/template/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/template/nodes.py b/src/backend/langflow/template/nodes.py new file mode 100644 index 000000000..642d0c237 --- /dev/null +++ b/src/backend/langflow/template/nodes.py @@ -0,0 +1,114 @@ +from langflow.template.template import Field, FrontendNode, Template +from langchain.agents.mrkl import prompt +from langflow.utils.constants import DEFAULT_PYTHON_FUNCTION + + +class ZeroShotPromptNode(FrontendNode): + name: str = "ZeroShotPrompt" + template: Template = Template( + type_name="zero_shot", + fields=[ + Field( + field_type="str", + required=False, + placeholder="", + is_list=False, + show=True, + multiline=True, + value=prompt.PREFIX, + name="prefix", + ), + Field( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=True, + value=prompt.SUFFIX, + name="suffix", + ), + Field( + field_type="str", + required=False, + placeholder="", + is_list=False, + show=True, + multiline=True, + value=prompt.FORMAT_INSTRUCTIONS, + name="format_instructions", + ), + ], + ) + description: str = "Prompt template for Zero Shot Agent." + base_classes: list[str] = ["BasePromptTemplate"] + + def to_dict(self): + return super().to_dict() + + +class PythonFunctionNode(FrontendNode): + name: str = "PythonFunction" + template: Template = Template( + type_name="python_function", + fields=[ + Field( + field_type="code", + required=True, + placeholder="", + is_list=False, + show=True, + value=DEFAULT_PYTHON_FUNCTION, + name="code", + ) + ], + ) + description: str = "Python function to be executed." + base_classes: list[str] = ["function"] + + def to_dict(self): + return super().to_dict() + + +class ToolNode(FrontendNode): + name: str = "Tool" + template: Template = Template( + type_name="tool", + fields=[ + Field( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=True, + value="", + name="name", + ), + Field( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=True, + value="", + name="description", + ), + Field( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=True, + value="", + name="func", + ), + ], + ) + description: str = "Tool to be used in the flow." + base_classes: list[str] = ["BaseTool"] + + def to_dict(self): + return super().to_dict() diff --git a/src/backend/langflow/template/template.py b/src/backend/langflow/template/template.py new file mode 100644 index 000000000..21b23486a --- /dev/null +++ b/src/backend/langflow/template/template.py @@ -0,0 +1,51 @@ +from typing import Any +from pydantic import BaseModel + + +class Field(BaseModel): + field_type: str = "str" + required: bool = False + placeholder: str = "" + is_list: bool = False + show: bool = True + multiline: bool = False + value: Any = None + # _name will be used to store the name of the field + # in the template + name: str = "" + + def to_dict(self): + result = self.dict() + # Remove key if it is None + for key in list(result.keys()): + if result[key] is None: + del result[key] + result["type"] = result.pop("field_type") + result["list"] = result.pop("is_list") + return result + + +class Template(BaseModel): + type_name: str + fields: list[Field] + + def to_dict(self): + result = {field.name: field.to_dict() for field in self.fields} + result["_type"] = self.type_name # type: ignore + return result + + +class FrontendNode(BaseModel): + template: Template + description: str + base_classes: list + name: str = "" + + def to_dict(self): + return { + self.name: { + "template": self.template.to_dict(), + "description": self.description, + "base_classes": self.base_classes, + } + } diff --git a/src/backend/langflow/utils/constants.py b/src/backend/langflow/utils/constants.py index 73a50ce40..2d101ab98 100644 --- a/src/backend/langflow/utils/constants.py +++ b/src/backend/langflow/utils/constants.py @@ -6,3 +6,10 @@ OPENAI_MODELS = [ "text-ada-001", ] CHAT_OPENAI_MODELS = ["gpt-3.5-turbo", "gpt-4", "gpt-4-32k"] + + +DEFAULT_PYTHON_FUNCTION = """ +def python_function(text: str) -> str: + \"\"\"This is a default python function that returns the input text\"\"\" + return text +""" diff --git a/src/backend/langflow/utils/payload.py b/src/backend/langflow/utils/payload.py index e7d38139e..380ec800c 100644 --- a/src/backend/langflow/utils/payload.py +++ b/src/backend/langflow/utils/payload.py @@ -1,5 +1,6 @@ import contextlib import re +from typing import Dict def extract_input_variables(nodes): @@ -27,48 +28,59 @@ def extract_input_variables(nodes): return nodes -def get_root_node(nodes, edges): +def get_root_node(graph): """ Returns the root node of the template. """ - incoming_edges = {edge["source"] for edge in edges} - return next((node for node in nodes if node["id"] not in incoming_edges), None) + incoming_edges = {edge.source for edge in graph.edges} + return next((node for node in graph.nodes if node not in incoming_edges), None) -def build_json(root, nodes, edges): - """ - Builds a json from the nodes and edges - """ - edge_ids = [edge["source"] for edge in edges if edge["target"] == root["id"]] - local_nodes = [node for node in nodes if node["id"] in edge_ids] +def build_json(root, graph) -> Dict: + if "node" not in root.data: + # If the root node has no "node" key, then it has only one child, + # which is the target of the single outgoing edge + edge = root.edges[0] + local_nodes = [edge.target] + else: + # Otherwise, find all children whose type matches the type + # specified in the template + node_type = root.node_type + local_nodes = graph.get_nodes_with_target(root) - if "node" not in root["data"]: - return build_json(local_nodes[0], nodes, edges) - - final_dict = root["data"]["node"]["template"].copy() + if len(local_nodes) == 1: + return build_json(local_nodes[0], graph) + # Build a dictionary from the template + template = root.data["node"]["template"] + final_dict = template.copy() for key, value in final_dict.items(): if key == "_type": continue - module_type = value["type"] + node_type = value["type"] if "value" in value and value["value"] is not None: + # If the value is specified, use it value = value["value"] - elif "dict" in module_type: + elif "dict" in node_type: + # If the value is a dictionary, create an empty dictionary value = {} else: + # Otherwise, recursively build the child nodes children = [] - for c in local_nodes: - module_types = [c["data"]["type"]] - if "node" in c["data"]: - module_types += c["data"]["node"]["base_classes"] - if module_type in module_types: - children.append(c) + for local_node in local_nodes: + node_children = graph.get_children_by_node_type(local_node, node_type) + children.extend(node_children) if value["required"] and not children: - raise ValueError(f"No child with type {module_type} found") - values = [build_json(child, nodes, edges) for child in children] - value = list(values) if value["list"] else next(iter(values), None) + raise ValueError(f"No child with type {node_type} found") + values = [build_json(child, graph) for child in children] + value = ( + list(values) + if value["list"] + else next(iter(values), None) # type: ignore + ) final_dict[key] = value + return final_dict diff --git a/src/backend/langflow/utils/util.py b/src/backend/langflow/utils/util.py index 4710114ed..664630890 100644 --- a/src/backend/langflow/utils/util.py +++ b/src/backend/langflow/utils/util.py @@ -2,7 +2,7 @@ import ast import importlib import inspect import re -from typing import Dict, Optional +from typing import Dict, Optional, Union from langchain.agents.load_tools import ( _BASE_TOOLS, @@ -11,10 +11,16 @@ from langchain.agents.load_tools import ( _LLM_TOOLS, ) + +from langchain.agents.tools import Tool + + from langflow.utils import constants -def build_template_from_function(name: str, type_to_loader_dict: Dict): +def build_template_from_function( + name: str, type_to_loader_dict: Dict, add_function: bool = False +): classes = [ item.__annotations__["return"].__name__ for item in type_to_loader_dict.values() ] @@ -52,6 +58,11 @@ def build_template_from_function(name: str, type_to_loader_dict: Dict): if class_field_items in docs["Attributes"] else "" ) + # Adding function to base classes to allow + # the output to be a function + base_classes = get_base_classes(_class) + if add_function: + base_classes.append("function") return { "template": format_dict(variables, name), @@ -60,7 +71,9 @@ def build_template_from_function(name: str, type_to_loader_dict: Dict): } -def build_template_from_class(name: str, type_to_cls_dict: Dict): +def build_template_from_class( + name: str, type_to_cls_dict: Dict, add_function: bool = False +): classes = [item.__name__ for item in type_to_cls_dict.values()] # Raise error if name is not in chains @@ -97,15 +110,22 @@ def build_template_from_class(name: str, type_to_cls_dict: Dict): if class_field_items in docs["Attributes"] else "" ) - + base_classes = get_base_classes(_class) + # Adding function to base classes to allow + # the output to be a function + if add_function: + base_classes.append("function") return { "template": format_dict(variables, name), "description": docs["Description"], - "base_classes": get_base_classes(_class), + "base_classes": base_classes, } def get_base_classes(cls): + """Get the base classes of a class. + These are used to determine the output of the nodes. + """ bases = cls.__bases__ if not bases: return [] @@ -128,19 +148,42 @@ def get_default_factory(module: str, function: str): return None -def get_tools_dict(name: Optional[str] = None): +def get_tools_dict(): """Get the tools dictionary.""" + from langflow.interface.listing import CUSTOM_TOOLS + tools = { **_BASE_TOOLS, - **_LLM_TOOLS, # type: ignore - **{k: v[0] for k, v in _EXTRA_LLM_TOOLS.items()}, # type: ignore + **_LLM_TOOLS, + **{k: v[0] for k, v in _EXTRA_LLM_TOOLS.items()}, **{k: v[0] for k, v in _EXTRA_OPTIONAL_TOOLS.items()}, + **CUSTOM_TOOLS, } - return tools[name] if name else tools + return tools -def get_tool_params(func, **kwargs): +def get_tool_by_name(name: str): + """Get a tool from the tools dictionary.""" + tools = get_tools_dict() + if name not in tools: + raise ValueError(f"{name} not found.") + return tools[name] + + +def get_tool_params(tool, **kwargs) -> Union[Dict, None]: # Parse the function code into an abstract syntax tree + # Define if it is a function or a class + if inspect.isfunction(tool): + return get_func_tool_params(tool, **kwargs) + elif inspect.isclass(tool): + # Get the parameters necessary to + # instantiate the class + return get_class_tool_params(tool, **kwargs) + else: + raise ValueError("Tool must be a function or class.") + + +def get_func_tool_params(func, **kwargs) -> Union[Dict, None]: tree = ast.parse(inspect.getsource(func)) # Iterate over the statements in the abstract syntax tree @@ -149,7 +192,7 @@ def get_tool_params(func, **kwargs): if isinstance(node, ast.Return): tool = node.value if isinstance(tool, ast.Call): - if tool.func.id == "Tool": + if isinstance(tool.func, ast.Name) and tool.func.id == "Tool": if tool.keywords: tool_params = {} for keyword in tool.keywords: @@ -159,11 +202,13 @@ def get_tool_params(func, **kwargs): tool_params["description"] = ast.literal_eval( keyword.value ) + return tool_params return { "name": ast.literal_eval(tool.args[0]), "description": ast.literal_eval(tool.args[2]), } + # else: # get the class object from the return statement try: @@ -177,11 +222,44 @@ def get_tool_params(func, **kwargs): "name": getattr(class_obj, "name"), "description": getattr(class_obj, "description"), } - - # Return None if no return statement was found + # Return None if no return statement was found return None +def get_class_tool_params(cls, **kwargs) -> Union[Dict, None]: + tree = ast.parse(inspect.getsource(cls)) + + tool_params = {} + + # Iterate over the statements in the abstract syntax tree + for node in ast.walk(tree): + if isinstance(node, ast.ClassDef): + # Find the class definition and look for methods + for stmt in node.body: + if isinstance(stmt, ast.FunctionDef) and stmt.name == "__init__": + # There is no assignment statements in the __init__ method + # So we need to get the params from the function definition + for arg in stmt.args.args: + if arg.arg == "name": + # It should be the name of the class + tool_params[arg.arg] = cls.__name__ + elif arg.arg == "self": + continue + # If there is not default value, set it to an empty string + else: + try: + annotation = ast.literal_eval(arg.annotation) # type: ignore + tool_params[arg.arg] = annotation + except ValueError: + tool_params[arg.arg] = "" + # Get the attribute name and the annotation + elif cls != Tool and isinstance(stmt, ast.AnnAssign): + # Get the attribute name and the annotation + tool_params[stmt.target.id] = "" # type: ignore + + return tool_params + + def get_class_doc(class_name): """ Extracts information from the docstring of a given class. @@ -272,7 +350,7 @@ def format_dict(d, name: Optional[str] = None): _type = _type.replace("Mapping", "dict") # Change type from str to Tool - value["type"] = "Tool" if key == "allowed_tools" else _type + value["type"] = "Tool" if key in ["allowed_tools"] else _type # Show or not field value["show"] = bool( @@ -291,11 +369,11 @@ def format_dict(d, name: Optional[str] = None): # Add password field value["password"] = any( - text in key for text in ["password", "token", "api", "key"] + text in key.lower() for text in ["password", "token", "api", "key"] ) # Add multline - value["multiline"] = key in ["suffix", "prefix", "template", "examples"] + value["multiline"] = key in ["suffix", "prefix", "template", "examples", "code"] # Replace default value with actual value if "default" in value: diff --git a/src/backend/langflow/utils/validate.py b/src/backend/langflow/utils/validate.py new file mode 100644 index 000000000..af5ddea3c --- /dev/null +++ b/src/backend/langflow/utils/validate.py @@ -0,0 +1,168 @@ +import ast +import importlib +import types +from typing import Dict + + +def add_type_ignores(): + if not hasattr(ast, "TypeIgnore"): + + class TypeIgnore(ast.AST): + _fields = () + + ast.TypeIgnore = TypeIgnore + + +def validate_code(code): + # Initialize the errors dictionary + errors = {"imports": {"errors": []}, "function": {"errors": []}} + + # Parse the code string into an abstract syntax tree (AST) + try: + tree = ast.parse(code) + except Exception as e: + errors["function"]["errors"].append(str(e)) + return errors + + # Add a dummy type_ignores field to the AST + add_type_ignores() + tree.type_ignores = [] + + # Evaluate the import statements + for node in tree.body: + if isinstance(node, ast.Import): + for alias in node.names: + try: + importlib.import_module(alias.name) + except ModuleNotFoundError as e: + errors["imports"]["errors"].append(str(e)) + + # Evaluate the function definition + for node in tree.body: + if isinstance(node, ast.FunctionDef): + code_obj = compile( + ast.Module(body=[node], type_ignores=[]), "", "exec" + ) + try: + exec(code_obj) + except Exception as e: + errors["function"]["errors"].append(str(e)) + + # Return the errors dictionary + return errors + + +def eval_function(function_string: str): + # Create an empty dictionary to serve as a separate namespace + namespace: Dict = {} + + # Execute the code string in the new namespace + exec(function_string, namespace) + function_object = next( + (obj for name, obj in namespace.items() if isinstance(obj, types.FunctionType)), + None, + ) + if function_object is None: + raise ValueError("Function string does not contain a function") + return function_object + + +def execute_function(code, function_name, *args, **kwargs): + add_type_ignores() + + module = ast.parse(code) + exec_globals = globals().copy() + + for node in module.body: + if isinstance(node, ast.Import): + for alias in node.names: + try: + exec( + f"{alias.asname or alias.name} = importlib.import_module('{alias.name}')", + exec_globals, + locals(), + ) + exec_globals[alias.asname or alias.name] = importlib.import_module( + alias.name + ) + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + f"Module {alias.name} not found. Please install it and try again." + ) from e + + function_code = next( + node + for node in module.body + if isinstance(node, ast.FunctionDef) and node.name == function_name + ) + function_code.parent = None + code_obj = compile( + ast.Module(body=[function_code], type_ignores=[]), "", "exec" + ) + try: + exec(code_obj, exec_globals, locals()) + except Exception as exc: + raise ValueError("Function string does not contain a function") from exc + + # Add the function to the exec_globals dictionary + exec_globals[function_name] = locals()[function_name] + + return exec_globals[function_name](*args, **kwargs) + + +def create_function(code, function_name): + if not hasattr(ast, "TypeIgnore"): + + class TypeIgnore(ast.AST): + _fields = () + + ast.TypeIgnore = TypeIgnore + + module = ast.parse(code) + exec_globals = globals().copy() + + for node in module.body: + if isinstance(node, ast.Import): + for alias in node.names: + try: + exec_globals[alias.asname or alias.name] = importlib.import_module( + alias.name + ) + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + f"Module {alias.name} not found. Please install it and try again." + ) from e + + function_code = next( + node + for node in module.body + if isinstance(node, ast.FunctionDef) and node.name == function_name + ) + function_code.parent = None + code_obj = compile( + ast.Module(body=[function_code], type_ignores=[]), "", "exec" + ) + try: + exec(code_obj, exec_globals, locals()) + except Exception: + pass + + exec_globals[function_name] = locals()[function_name] + + # Return a function that imports necessary modules and calls the target function + def wrapped_function(*args, **kwargs): + for module_name, module in exec_globals.items(): + if isinstance(module, type(importlib)): + globals()[module_name] = module + + return exec_globals[function_name](*args, **kwargs) + + return wrapped_function + + +def extract_function_name(code): + module = ast.parse(code) + for node in module.body: + if isinstance(node, ast.FunctionDef): + return node.name + raise ValueError("No function definition found in the code string") diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index f0a40a452..94c58fc36 100644 --- a/src/frontend/package-lock.json +++ b/src/frontend/package-lock.json @@ -21,9 +21,11 @@ "@types/node": "^16.18.12", "@types/react": "^18.0.27", "@types/react-dom": "^18.0.10", + "ace-builds": "^1.16.0", "axios": "^1.3.2", "lodash": "^4.17.21", "react": "^18.2.0", + "react-ace": "^10.1.0", "react-cookie": "^4.1.1", "react-dom": "^18.2.0", "react-error-boundary": "^4.0.2", @@ -5098,6 +5100,11 @@ "node": ">= 0.6" } }, + "node_modules/ace-builds": { + "version": "1.16.0", + "resolved": "https://registry.npmjs.org/ace-builds/-/ace-builds-1.16.0.tgz", + "integrity": "sha512-EriMhoxdfhh0zKm7icSt8EXekODAOVsYh9fpnlru9ALwf0Iw7J7bpuqLjhi3QRxvVKR7P0teQdJwTvjVMcYHuw==" + }, "node_modules/acorn": { "version": "8.8.2", "resolved": "https://registry.npmjs.org/acorn/-/acorn-8.8.2.tgz", @@ -7205,6 +7212,11 @@ "resolved": "https://registry.npmjs.org/didyoumean/-/didyoumean-1.2.2.tgz", "integrity": "sha512-gxtyfqMg7GKyhQmb056K7M3xszy/myH8w+B4RT+QXBQsvAOdc3XymqDDPHx1BgPgsdAA5SIifona89YtRATDzw==" }, + "node_modules/diff-match-patch": { + "version": "1.0.5", + "resolved": "https://registry.npmjs.org/diff-match-patch/-/diff-match-patch-1.0.5.tgz", + "integrity": "sha512-IayShXAgj/QMXgB0IWmKx+rOPuGMhqm5w6jvFxmVenXKIzRqTAAsbBPT3kWQeGANj3jGgvcvv4yK6SxqYmikgw==" + }, "node_modules/diff-sequences": { "version": "27.5.1", "resolved": "https://registry.npmjs.org/diff-sequences/-/diff-sequences-27.5.1.tgz", @@ -12409,6 +12421,16 @@ "resolved": "https://registry.npmjs.org/lodash.debounce/-/lodash.debounce-4.0.8.tgz", "integrity": "sha512-FT1yDzDYEoYWhnSGnpE/4Kj1fLZkDFyqRb7fNt6FdYOSxlUWAtp42Eh6Wb0rGIv/m9Bgo7x4GhQbm5Ys4SG5ow==" }, + "node_modules/lodash.get": { + "version": "4.4.2", + "resolved": "https://registry.npmjs.org/lodash.get/-/lodash.get-4.4.2.tgz", + "integrity": "sha512-z+Uw/vLuy6gQe8cfaFWD7p0wVv8fJl3mbzXh33RS+0oW2wvUqiRXiQ69gLWSLpgB5/6sU+r6BlQR0MBILadqTQ==" + }, + "node_modules/lodash.isequal": { + "version": "4.5.0", + "resolved": "https://registry.npmjs.org/lodash.isequal/-/lodash.isequal-4.5.0.tgz", + "integrity": "sha512-pDo3lu8Jhfjqls6GkMgpahsF9kCyayhgykjyLMNFTKWrpVdAQtYyB4muAMWozBB4ig/dtWAmsMxLEI8wuz+DYQ==" + }, "node_modules/lodash.memoize": { "version": "4.1.2", "resolved": "https://registry.npmjs.org/lodash.memoize/-/lodash.memoize-4.1.2.tgz", @@ -14803,6 +14825,22 @@ "node": ">=0.10.0" } }, + "node_modules/react-ace": { + "version": "10.1.0", + "resolved": "https://registry.npmjs.org/react-ace/-/react-ace-10.1.0.tgz", + "integrity": "sha512-VkvUjZNhdYTuKOKQpMIZi7uzZZVgzCjM7cLYu6F64V0mejY8a2XTyPUIMszC6A4trbeMIHbK5fYFcT/wkP/8VA==", + "dependencies": { + "ace-builds": "^1.4.14", + "diff-match-patch": "^1.0.5", + "lodash.get": "^4.4.2", + "lodash.isequal": "^4.5.0", + "prop-types": "^15.7.2" + }, + "peerDependencies": { + "react": "^0.13.0 || ^0.14.0 || ^15.0.1 || ^16.0.0 || ^17.0.0 || ^18.0.0", + "react-dom": "^0.13.0 || ^0.14.0 || ^15.0.1 || ^16.0.0 || ^17.0.0 || ^18.0.0" + } + }, "node_modules/react-app-polyfill": { "version": "3.0.0", "resolved": "https://registry.npmjs.org/react-app-polyfill/-/react-app-polyfill-3.0.0.tgz", diff --git a/src/frontend/package.json b/src/frontend/package.json index 312e52051..1af847684 100644 --- a/src/frontend/package.json +++ b/src/frontend/package.json @@ -16,9 +16,11 @@ "@types/node": "^16.18.12", "@types/react": "^18.0.27", "@types/react-dom": "^18.0.10", + "ace-builds": "^1.16.0", "axios": "^1.3.2", "lodash": "^4.17.21", "react": "^18.2.0", + "react-ace": "^10.1.0", "react-cookie": "^4.1.1", "react-dom": "^18.2.0", "react-error-boundary": "^4.0.2", @@ -57,4 +59,4 @@ ] }, "proxy": "http://backend:7860" -} +} \ No newline at end of file diff --git a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx index c1a6d5f5f..ff6099835 100644 --- a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx @@ -10,6 +10,7 @@ import { typesContext } from "../../../../contexts/typesContext"; import { ParameterComponentType } from "../../../../types/components"; import FloatComponent from "../../../../components/floatComponent"; import Dropdown from "../../../../components/dropdownComponent"; +import CodeAreaComponent from "../../../../components/codeAreaComponent"; export default function ParameterComponent({ left, @@ -136,9 +137,17 @@ export default function ParameterComponent({ data.node.template[name].options ? ( data.node.template[name].value=newValue} - value={data.node.template[name].value??"chose an option"} + onSelect={(newValue) => (data.node.template[name].value = newValue)} + value={data.node.template[name].value ?? "chose an option"} > + ) : left === true && type === "code" ? ( + { + data.node.template[name].value = t; + }} + /> ) : ( <> )} diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx index ddb9524ee..6e3aa6e67 100644 --- a/src/frontend/src/CustomNodes/GenericNode/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx @@ -23,7 +23,6 @@ export default function GenericNode({ const { types, deleteNode } = useContext(typesContext); const Icon = nodeIcons[types[data.type]]; if (!Icon) { - console.log(data); if (showError.current) { setErrorData({ title: data.type @@ -34,7 +33,6 @@ export default function GenericNode({ } return; } - return (
Chat
- + + + ); +} diff --git a/src/frontend/src/components/textAreaComponent/index.tsx b/src/frontend/src/components/textAreaComponent/index.tsx index 144c0c66a..df61e1ae1 100644 --- a/src/frontend/src/components/textAreaComponent/index.tsx +++ b/src/frontend/src/components/textAreaComponent/index.tsx @@ -1,6 +1,7 @@ import { ArrowTopRightOnSquareIcon } from "@heroicons/react/24/outline"; import { useContext, useEffect, useState } from "react"; import { PopUpContext } from "../../contexts/popUpContext"; +import CodeAreaModal from "../../modals/codeAreaModal"; import TextAreaModal from "../../modals/textAreaModal"; import { TextAreaComponentType } from "../../types/components"; @@ -9,8 +10,8 @@ export default function TextAreaComponent({ value, onChange, disabled }:TextArea const { openPopUp } = useContext(PopUpContext); useEffect(() => { if (disabled) { - setMyValue([""]); - onChange([""]); + setMyValue(""); + onChange(""); } }, [disabled, onChange]); return ( diff --git a/src/frontend/src/controllers/NodesServices/index.ts b/src/frontend/src/controllers/API/index.ts similarity index 62% rename from src/frontend/src/controllers/NodesServices/index.ts rename to src/frontend/src/controllers/API/index.ts index f0257ea00..c6315d1b3 100644 --- a/src/frontend/src/controllers/NodesServices/index.ts +++ b/src/frontend/src/controllers/API/index.ts @@ -1,3 +1,4 @@ +import { errorsTypeAPI } from './../../types/api/index'; import { APIObjectType, sendAllProps } from '../../types/api/index'; import axios, { AxiosResponse } from "axios"; @@ -7,4 +8,9 @@ export async function getAll():Promise> { export async function sendAll(data:sendAllProps) { return await axios.post(`/predict`, data); +} + +export async function checkCode(code:string):Promise>{ + + return await axios.post('/validate',{code}) } \ No newline at end of file diff --git a/src/frontend/src/modals/codeAreaModal/index.tsx b/src/frontend/src/modals/codeAreaModal/index.tsx new file mode 100644 index 000000000..72b40ed40 --- /dev/null +++ b/src/frontend/src/modals/codeAreaModal/index.tsx @@ -0,0 +1,178 @@ +import { Dialog, Transition } from "@headlessui/react"; +import { XMarkIcon, CommandLineIcon } from "@heroicons/react/24/outline"; +import { Fragment, useContext, useRef, useState } from "react"; +import { PopUpContext } from "../../contexts/popUpContext"; +import AceEditor from "react-ace"; +import "ace-builds/src-noconflict/mode-python"; +import "ace-builds/src-noconflict/theme-github"; +import "ace-builds/src-noconflict/theme-twilight"; +import "ace-builds/src-noconflict/ext-language_tools"; +import "ace-builds/webpack-resolver"; +import { darkContext } from "../../contexts/darkContext"; +import { checkCode } from "../../controllers/API"; +import { alertContext } from "../../contexts/alertContext"; +export default function CodeAreaModal({ + value, + setValue, +}: { + setValue: (value: string) => void; + value: string; +}) { + const [open, setOpen] = useState(true); + const [code, setCode] = useState(value); + const { dark } = useContext(darkContext); + const { setErrorData, setSuccessData } = useContext(alertContext); + const { closePopUp } = useContext(PopUpContext); + const ref = useRef(); + function setModalOpen(x: boolean) { + setOpen(x); + if (x === false) { + setTimeout(() => { + closePopUp(); + }, 300); + } + } + return ( + + + +
+ + +
+
+ + +
+ +
+
+
+
+
+
+ + Edit Code + +
+
+
+
+
+ {/* need to insert code editor */} + { + setCode(value); + }} + className="h-full w-full rounded-lg" + /> +
+
+
+
+ +
+
+ + +
+
+
+ + ); +} diff --git a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx index e489432de..2e9d2f23d 100644 --- a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx +++ b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx @@ -6,7 +6,7 @@ import { nodeNames, } from "../../../../utils"; import { useContext, useEffect, useState } from "react"; -import { getAll } from "../../../../controllers/NodesServices"; +import { getAll } from "../../../../controllers/API"; import { typesContext } from "../../../../contexts/typesContext"; import { APIClassType, APIKindType, APIObjectType } from "../../../../types/api"; diff --git a/src/frontend/src/types/api/index.ts b/src/frontend/src/types/api/index.ts index f85605ce4..e7ab4e0da 100644 --- a/src/frontend/src/types/api/index.ts +++ b/src/frontend/src/types/api/index.ts @@ -15,4 +15,5 @@ export type sendAllProps={ message:string; chatHistory:{message:string,isSend:boolean}[], -}; \ No newline at end of file +}; +export type errorsTypeAPI={function:{errors:Array},imports:{errors:Array}} \ No newline at end of file diff --git a/src/frontend/src/types/components/index.ts b/src/frontend/src/types/components/index.ts index 99a5dabe9..3f7fbf551 100644 --- a/src/frontend/src/types/components/index.ts +++ b/src/frontend/src/types/components/index.ts @@ -36,7 +36,7 @@ export type InputListComponentType = { export type TextAreaComponentType = { disabled: boolean; onChange: (value: string[] | string) => void; - value: string[] | string; + value: string; }; export type DisclosureComponentType = { diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 000000000..5fe1de280 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,28 @@ +from pathlib import Path +import pytest +from fastapi.testclient import TestClient + + +def pytest_configure(): + pytest.BASIC_EXAMPLE_PATH = ( + Path(__file__).parent.absolute() / "data" / "basic_example.json" + ) + pytest.COMPLEX_EXAMPLE_PATH = ( + Path(__file__).parent.absolute() / "data" / "complex_example.json" + ) + + pytest.CODE_WITH_SYNTAX_ERROR = """ +def get_text(): + retun "Hello World" + """ + + +# Create client fixture for FastAPI +@pytest.fixture(scope="module") +def client(): + from langflow.main import create_app + + app = create_app() + + with TestClient(app) as client: + yield client diff --git a/tests/data/example_flow.json b/tests/data/basic_example.json similarity index 71% rename from tests/data/example_flow.json rename to tests/data/basic_example.json index de3893b00..369272a6f 100644 --- a/tests/data/example_flow.json +++ b/tests/data/basic_example.json @@ -1,15 +1,16 @@ { - "name": "New Flow 9", - "id": "1", + "name": "New Flow ", + "id": "0", "data": { - "nodes": [{ + "nodes": [ + { "width": 384, "height": 391, - "id": "dndnode_61", + "id": "dndnode_1", "type": "genericNode", "position": { - "x": 764, - "y": 382 + "x": 644, + "y": 348 }, "data": { "type": "LLMChain", @@ -17,7 +18,7 @@ "template": { "_type": "llm_chain", "memory": { - "type": "Memory", + "type": "BaseMemory", "required": false, "placeholder": "", "list": false, @@ -34,7 +35,7 @@ "show": false, "password": false, "multiline": false, - "value": true + "value": false }, "prompt": { "type": "BasePromptTemplate", @@ -46,7 +47,7 @@ "multiline": false }, "llm": { - "type": "BaseLLM", + "type": "BaseLanguageModel", "required": true, "placeholder": "", "list": false, @@ -70,24 +71,22 @@ "Chain" ] }, - "id": "dndnode_61", + "id": "dndnode_1", "value": null }, - "selected": false, "positionAbsolute": { - "x": 764, - "y": 382 - }, - "dragging": false + "x": 644, + "y": 348 + } }, { "width": 384, "height": 351, - "id": "dndnode_62", + "id": "dndnode_4", "type": "genericNode", "position": { - "x": 1488, - "y": 550 + "x": 1236, + "y": 39.59999999999957 }, "data": { "type": "ZeroShotAgent", @@ -131,22 +130,22 @@ "Agent" ] }, - "id": "dndnode_62", + "id": "dndnode_4", "value": null }, "positionAbsolute": { - "x": 1488, - "y": 550 + "x": 1236, + "y": 39.59999999999957 } }, { "width": 384, "height": 529, - "id": "dndnode_63", + "id": "dndnode_5", "type": "genericNode", "position": { - "x": 206, - "y": 210 + "x": 96, + "y": -299.2000000000003 }, "data": { "type": "ZeroShotPrompt", @@ -186,24 +185,24 @@ "BasePromptTemplate" ] }, - "id": "dndnode_63", + "id": "dndnode_5", "value": null }, "selected": false, "positionAbsolute": { - "x": 206, - "y": 210 + "x": 96, + "y": -299.2000000000003 }, "dragging": false }, { "width": 384, - "height": 501, - "id": "dndnode_64", + "height": 477, + "id": "dndnode_8", "type": "genericNode", "position": { - "x": 210, - "y": 818 + "x": 75, + "y": 328 }, "data": { "type": "OpenAI", @@ -228,7 +227,7 @@ "show": false, "password": false, "multiline": false, - "value": true + "value": null }, "client": { "type": "Any", @@ -250,7 +249,6 @@ "multiline": false, "value": "text-davinci-003", "options": [ - "gpt-3.5-turbo", "text-davinci-003", "text-davinci-002" ] @@ -343,7 +341,7 @@ "show": true, "password": true, "multiline": false, - "value": "---" + "value": "sk-" }, "batch_size": { "type": "int", @@ -399,27 +397,28 @@ "description": "Generic OpenAI class that uses model name.", "base_classes": [ "BaseOpenAI", - "BaseLLM" + "BaseLLM", + "BaseLanguageModel" ] }, - "id": "dndnode_64", + "id": "dndnode_8", "value": null }, "selected": false, "positionAbsolute": { - "x": 210, - "y": 818 + "x": 75, + "y": 328 }, "dragging": false }, { "width": 384, "height": 397, - "id": "dndnode_65", + "id": "dndnode_9", "type": "genericNode", "position": { - "x": 776, - "y": 922 + "x": 643, + "y": 824 }, "data": { "type": "Serper Search", @@ -427,7 +426,7 @@ "template": { "serper_api_key": { "type": "str", - "required": false, + "required": true, "list": false, "show": true, "placeholder": "", @@ -441,101 +440,66 @@ "Tool" ] }, - "id": "dndnode_65", + "id": "dndnode_9", "value": null }, - "selected": false, + "selected": true, "positionAbsolute": { - "x": 776, - "y": 922 + "x": 643, + "y": 824 }, "dragging": false } ], - "edges": [{ - "source": "dndnode_63", - "sourceHandle": "ZeroShotPrompt|dndnode_63|BasePromptTemplate", - "target": "dndnode_61", - "targetHandle": "BasePromptTemplate|prompt|dndnode_61", + "edges": [ + { + "source": "dndnode_5", + "sourceHandle": "ZeroShotPrompt|dndnode_5|BasePromptTemplate", + "target": "dndnode_1", + "targetHandle": "BasePromptTemplate|prompt|dndnode_1", "className": "animate-pulse", - "id": "reactflow__edge-dndnode_63ZeroShotPrompt|dndnode_63|BasePromptTemplate-dndnode_61BasePromptTemplate|prompt|dndnode_61" + "id": "reactflow__edge-dndnode_5ZeroShotPrompt|dndnode_5|BasePromptTemplate-dndnode_1BasePromptTemplate|prompt|dndnode_1" }, { - "source": "dndnode_64", - "sourceHandle": "OpenAI|dndnode_64|BaseOpenAI,|BaseLLM", - "target": "dndnode_61", - "targetHandle": "BaseLLM|llm|dndnode_61", + "source": "dndnode_1", + "sourceHandle": "LLMChain|dndnode_1|Chain", + "target": "dndnode_4", + "targetHandle": "LLMChain|llm_chain|dndnode_4", "className": "animate-pulse", - "id": "reactflow__edge-dndnode_64OpenAI|dndnode_64|BaseOpenAI,|BaseLLM-dndnode_61BaseLLM|llm|dndnode_61" + "id": "reactflow__edge-dndnode_1LLMChain|dndnode_1|Chain-dndnode_4LLMChain|llm_chain|dndnode_4" }, { - "source": "dndnode_65", - "sourceHandle": "Serper Search|dndnode_65|Tool", - "target": "dndnode_62", - "targetHandle": "Tool|allowed_tools|dndnode_62", + "source": "dndnode_8", + "sourceHandle": "OpenAI|dndnode_8|BaseOpenAI|BaseLLM|BaseLanguageModel", + "target": "dndnode_1", + "targetHandle": "BaseLanguageModel|llm|dndnode_1", "className": "animate-pulse", - "id": "reactflow__edge-dndnode_65Serper Search|dndnode_65|Tool-dndnode_62Tool|allowed_tools|dndnode_62" + "id": "reactflow__edge-dndnode_8OpenAI|dndnode_8|BaseOpenAI|BaseLLM|BaseLanguageModel-dndnode_1BaseLanguageModel|llm|dndnode_1" }, { - "source": "dndnode_61", - "sourceHandle": "LLMChain|dndnode_61|Chain", - "target": "dndnode_62", - "targetHandle": "LLMChain|llm_chain|dndnode_62", + "source": "dndnode_9", + "sourceHandle": "Serper Search|dndnode_9|Tool", + "target": "dndnode_4", + "targetHandle": "Tool|allowed_tools|dndnode_4", "className": "animate-pulse", - "id": "reactflow__edge-dndnode_61LLMChain|dndnode_61|Chain-dndnode_62LLMChain|llm_chain|dndnode_62" + "id": "reactflow__edge-dndnode_9Serper Search|dndnode_9|Tool-dndnode_4Tool|allowed_tools|dndnode_4" } ], "viewport": { - "x": -103, - "y": -135, - "zoom": 1 + "x": 22.5, + "y": 42, + "zoom": 0.5 } }, - "chat": [{ - "message": "What is the new llm Prismer?", + "chat": [ + { + "message": "Langflow. What is it?", "isSend": true }, { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "Prismer LLM is a vision-language model with an ensemble of experts.", + "message": "Langflow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chrome extension powered by GPT-3.", "isSend": false, - "thought": "Thought: I should research what this is\nAction: Serper Search\nAction Input: \"prismer llm\"\nObservation: Announcing Prismer, my team's latest vision-language AI, empowered by domain-expert models in depth, surface normal, segmentation, etc. My team's work, Prismer, is a representative example. We use a textual LM as the backbone, and plug in many visual domain experts through a neural adapter ... “Prismer is able to achieve superior data efficiency compared to ... New OpenAI post about future of ChatGPT's (and its other LLM) alignment. Prismer: A Vision-Language Model with An Ensemble of Experts ... UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers. Emory Law's LLM program offers graduates of US and foreign law schools the opportunity to pursue the master of laws degree from one of the top nationally ranked ... Missing: prismer | Must include:prismer. Prismer: A Vision-Language Model with An Ensemble of Experts ... github: github.com/NVlabs/prismer ... into LLM embedding space viously to their being sworn, the prisoner, Michael. Solomon, standing at the bar, challenged two ... a« wa« brought before- them.\" The prisoner ?llM'eow*. RWKV is a RNN with transformer-level LLM performance. ... The implementation of \"Prismer: A Vision-Language Model with An Ensemble of Experts\". Announcing Prismer, our NVIDIA team's latest vision-language #ai empowered by domain-expert models in depth, surface normal, segmentation, etc. No paywall.\nThought:\n\n> Finished chain." - }, - { - "message": "What is the prismer llm?", - "isSend": true - }, - { - "message": "Prismer is an AI vision-language model with an ensemble of experts developed by a NVIDIA team. It is capable of achieving superior data efficiency compared to other models and can be directly trained like a GPT.", - "isSend": false, - "thought": "Thought: I need to search for the term\nAction: Serper Search\nAction Input: \"prismer llm\"\nObservation: Announcing Prismer, my team's latest vision-language AI, empowered by domain-expert models in depth, surface normal, segmentation, etc. My team's work, Prismer, is a representative example. We use a textual LM as the backbone, and plug in many visual domain experts through a neural adapter ... “Prismer is able to achieve superior data efficiency compared to ... New OpenAI post about future of ChatGPT's (and its other LLM) alignment. Prismer: A Vision-Language Model with An Ensemble of Experts ... UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers. A very insightful post about the potential issues with LLM chatbots for general ... Announcing Prismer, our NVIDIA team's latest vision-language #ai ... As technology continues to transform our economy and culture, businesses need a new breed of lawyers who understand the legal and commercial aspects of new ... Missing: prismer | Must include:prismer. RWKV is a RNN with transformer-level LLM performance. ... The implementation of \"Prismer: A Vision-Language Model with An Ensemble of Experts\". ... Bendix vorad collision warning system, Vegglampe med prismer, Ok magazine ... Alabama llm, Colt 25 caliber magazine, Poppie clinch, Mcds upper school, ... RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN ...\nThought:\n\n> Finished chain." + "thought": "> Entering new AgentExecutor chain...\n I need to research what Langflow is.\nAction: Serper Search\nAction Input: \"What is Langflow?\"\nObservation: Researchers introduce LangFlow, a graphical user interface (GUI) for LangChain that simplifies testing and creation of smart applications. The drag-and-drop feature provides a quick and effortless way to experiment and prototype, and the built-in chat interface enables real-time ... LangFlow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop ... LangFlow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop components and a ... an open-source chrome extension powered by GPT-3 to get the explanation of a piece of code on any site (Stackoverflow, Github, etc). ChatGPT correctly identified Mona Lisa but failed to guess the cartoon character Michael was thinking of, suggesting Bugs Bunny holding a ... Introducing LangFlow! An open-source UI for. @LangChainAI. that enables seamless combination of multiple deep learning models to perform ... See student reviews, school photos, Chinese Mandarin courses, and housing options available at Langflow Education Centre (Macau, Macau) - Reviews - Language ... Longfellow Elementary students play on the playground during recess. Longfellow Elementary students pause for a photo during class.\nThought: Langflow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chrome extension powered by GPT-3.\nFinal Answer: Langflow is a GUI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows with drag-and-drop components and a chrome extension powered by GPT-3.\n\n> Finished chain." } ] } \ No newline at end of file diff --git a/tests/data/complex_example.json b/tests/data/complex_example.json new file mode 100644 index 000000000..8d46a97d6 --- /dev/null +++ b/tests/data/complex_example.json @@ -0,0 +1,723 @@ +{ + "name": "New Flow", + "id": "0", + "data": { + "nodes": [ + { + "width": 384, + "height": 351, + "id": "dndnode_3", + "type": "genericNode", + "position": { + "x": 612.9299322834961, + "y": 194.75070242078417 + }, + "data": { + "type": "ZeroShotAgent", + "node": { + "template": { + "_type": "zero-shot-react-description", + "llm_chain": { + "type": "LLMChain", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "password": false, + "multiline": false + }, + "allowed_tools": { + "type": "Tool", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "password": false, + "multiline": false, + "value": null + }, + "return_values": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": false, + "password": false, + "multiline": false, + "value": [ + "output" + ] + } + }, + "description": "Agent for the MRKL chain.", + "base_classes": [ + "Agent", + "function" + ] + }, + "id": "dndnode_3", + "value": null + }, + "selected": false, + "positionAbsolute": { + "x": 612.9299322834961, + "y": 194.75070242078417 + }, + "dragging": false + }, + { + "width": 384, + "height": 463, + "id": "dndnode_27", + "type": "genericNode", + "position": { + "x": 86.29922452047686, + "y": 39.132143332238115 + }, + "data": { + "type": "Tool", + "node": { + "template": { + "name": { + "type": "str", + "required": true, + "list": false, + "show": true, + "placeholder": "", + "value": "Uppercase", + "password": false, + "multiline": false + }, + "description": { + "type": "str", + "required": true, + "list": false, + "show": true, + "placeholder": "", + "value": "Returns an uppercase version of the text passed.", + "password": false, + "multiline": false + }, + "func": { + "type": "function", + "required": true, + "list": false, + "show": true, + "value": "", + "multiline": false, + "password": false + }, + "_type": "Tool" + }, + "name": "Tool", + "func": "", + "description": "", + "base_classes": [ + "Tool" + ] + }, + "id": "dndnode_27", + "value": null + }, + "selected": false, + "positionAbsolute": { + "x": 86.29922452047686, + "y": 39.132143332238115 + }, + "dragging": false + }, + { + "width": 384, + "height": 463, + "id": "dndnode_28", + "type": "genericNode", + "position": { + "x": 1134.4549802672202, + "y": 287.9885910233929 + }, + "data": { + "type": "Tool", + "node": { + "template": { + "name": { + "type": "str", + "required": true, + "list": false, + "show": true, + "placeholder": "", + "value": "", + "password": false, + "multiline": false + }, + "description": { + "type": "str", + "required": true, + "list": false, + "show": true, + "placeholder": "", + "value": "", + "password": false, + "multiline": false + }, + "func": { + "type": "function", + "required": true, + "list": false, + "show": true, + "value": "", + "multiline": false, + "password": false + }, + "_type": "Tool" + }, + "name": "Tool", + "func": "", + "description": "", + "base_classes": [ + "Tool" + ] + }, + "id": "dndnode_28", + "value": null + }, + "positionAbsolute": { + "x": 1134.4549802672202, + "y": 287.9885910233929 + }, + "selected": false, + "dragging": false + }, + { + "width": 384, + "height": 357, + "id": "dndnode_40", + "type": "genericNode", + "position": { + "x": -366.4341715850213, + "y": 136.29836646158452 + }, + "data": { + "type": "PythonFunction", + "node": { + "template": { + "code": { + "required": true, + "placeholder": "", + "show": true, + "multiline": true, + "value": "\ndef upper_case(text: str) -> str:\n return text.upper()\n", + "name": "code", + "type": "str", + "list": false + }, + "_type": "python_function" + }, + "description": "Python function to be executed.", + "base_classes": [ + "function" + ] + }, + "id": "dndnode_40", + "value": null + }, + "selected": false, + "positionAbsolute": { + "x": -366.4341715850213, + "y": 136.29836646158452 + }, + "dragging": false + }, + { + "width": 384, + "height": 351, + "id": "dndnode_41", + "type": "genericNode", + "position": { + "x": 1642.7653281427417, + "y": 69.01105573790835 + }, + "data": { + "type": "ZeroShotAgent", + "node": { + "template": { + "_type": "zero-shot-react-description", + "llm_chain": { + "type": "LLMChain", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "password": false, + "multiline": false + }, + "allowed_tools": { + "type": "Tool", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "password": false, + "multiline": false, + "value": null + }, + "return_values": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": false, + "password": false, + "multiline": false, + "value": [ + "output" + ] + } + }, + "description": "Agent for the MRKL chain.", + "base_classes": [ + "Agent", + "function" + ] + }, + "id": "dndnode_41", + "value": null + }, + "selected": false, + "positionAbsolute": { + "x": 1642.7653281427417, + "y": 69.01105573790835 + }, + "dragging": false + }, + { + "width": 384, + "height": 529, + "id": "dndnode_42", + "type": "genericNode", + "position": { + "x": -379.23467185725826, + "y": -551.3889442620921 + }, + "data": { + "type": "ZeroShotPrompt", + "node": { + "template": { + "prefix": { + "required": false, + "placeholder": "", + "show": true, + "multiline": true, + "value": "Answer the following questions as best you can. You have access to the following tools:", + "name": "prefix", + "type": "str", + "list": false + }, + "suffix": { + "required": true, + "placeholder": "", + "show": true, + "multiline": true, + "value": "Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}", + "name": "suffix", + "type": "str", + "list": false + }, + "format_instructions": { + "required": false, + "placeholder": "", + "show": true, + "multiline": true, + "value": "Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question", + "name": "format_instructions", + "type": "str", + "list": false + }, + "_type": "zero_shot" + }, + "description": "Prompt template for Zero Shot Agent.", + "base_classes": [ + "BasePromptTemplate" + ] + }, + "id": "dndnode_42", + "value": null + }, + "selected": false, + "positionAbsolute": { + "x": -379.23467185725826, + "y": -551.3889442620921 + }, + "dragging": false + }, + { + "width": 384, + "height": 391, + "id": "dndnode_43", + "type": "genericNode", + "position": { + "x": 100.76532814274174, + "y": -437.78894426209195 + }, + "data": { + "type": "LLMChain", + "node": { + "template": { + "_type": "llm_chain", + "memory": { + "type": "BaseMemory", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "password": false, + "multiline": false, + "value": null + }, + "verbose": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": false, + "password": false, + "multiline": false, + "value": false + }, + "prompt": { + "type": "BasePromptTemplate", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "password": false, + "multiline": false + }, + "llm": { + "type": "BaseLanguageModel", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "password": false, + "multiline": false + }, + "output_key": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": false, + "password": true, + "multiline": false, + "value": "text" + } + }, + "description": "Chain to run queries against LLMs.", + "base_classes": [ + "Chain" + ] + }, + "id": "dndnode_43", + "value": null + }, + "selected": false, + "positionAbsolute": { + "x": 100.76532814274174, + "y": -437.78894426209195 + }, + "dragging": false + }, + { + "width": 384, + "height": 477, + "id": "dndnode_44", + "type": "genericNode", + "position": { + "x": -841.2346718572583, + "y": 368.6110557379079 + }, + "data": { + "type": "OpenAI", + "node": { + "template": { + "_type": "openai", + "cache": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": false, + "password": false, + "multiline": false, + "value": null + }, + "verbose": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": false, + "password": false, + "multiline": false, + "value": null + }, + "client": { + "type": "Any", + "required": false, + "placeholder": "", + "list": false, + "show": false, + "password": false, + "multiline": false, + "value": null + }, + "model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "password": false, + "multiline": false, + "value": "text-davinci-003", + "options": [ + "text-davinci-003", + "text-davinci-002", + "text-curie-001", + "text-babbage-001", + "text-ada-001" + ] + }, + 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func.get_function()("text") == "text" + # the tool decorator should raise an error if + # the function is not str -> str + + # This raises ValidationError + with pytest.raises(SyntaxError): + func = PythonFunction(code=pytest.CODE_WITH_SYNTAX_ERROR) diff --git a/tests/test_endpoints.py b/tests/test_endpoints.py new file mode 100644 index 000000000..36daa926e --- /dev/null +++ b/tests/test_endpoints.py @@ -0,0 +1,76 @@ +from langflow.interface.listing import CUSTOM_TOOLS +from fastapi.testclient import TestClient + + +def test_get_all(client: TestClient): + response = client.get("/all") + assert response.status_code == 200 + json_response = response.json() + # We need to test the custom nodes + assert "ZeroShotPrompt" in json_response["prompts"] + # All CUSTOM_TOOLS(dict) should be in the response + assert all(tool in json_response["tools"] for tool in CUSTOM_TOOLS.keys()) + + +def test_post_validate_code(client: TestClient): + # Test case with a valid import and function + code1 = """ +import math + +def square(x): + return x ** 2 +""" + response1 = client.post("/validate", json={"code": code1}) + assert response1.status_code == 200 + assert response1.json() == {"imports": {"errors": []}, "function": {"errors": []}} + + # Test case with an invalid import and valid function + code2 = """ +import non_existent_module + +def square(x): + return x ** 2 +""" + response2 = client.post("/validate", json={"code": code2}) + assert response2.status_code == 200 + assert response2.json() == { + "imports": {"errors": ["No module named 'non_existent_module'"]}, + "function": {"errors": []}, + } + + # Test case with a valid import and invalid function syntax + code3 = """ +import math + +def square(x) + return x ** 2 +""" + response3 = client.post("/validate", json={"code": code3}) + assert response3.status_code == 200 + assert response3.json() == { + "imports": {"errors": []}, + "function": {"errors": ["expected ':' (, line 4)"]}, + } + + # Test case with invalid JSON payload + response4 = client.post("/validate", json={"invalid_key": code1}) + assert response4.status_code == 422 + + # Test case with an empty code string + response5 = client.post("/validate", json={"code": ""}) + assert response5.status_code == 200 + assert response5.json() == {"imports": {"errors": []}, "function": {"errors": []}} + + # Test case with a syntax error in the code + code6 = """ +import math + +def square(x) + return x ** 2 +""" + response6 = client.post("/validate", json={"code": code6}) + assert response6.status_code == 200 + assert response6.json() == { + "imports": {"errors": []}, + "function": {"errors": ["expected ':' (, line 4)"]}, + } diff --git a/tests/test_graph.py b/tests/test_graph.py new file mode 100644 index 000000000..dfb0e2323 --- /dev/null +++ b/tests/test_graph.py @@ -0,0 +1,300 @@ +import json +from langflow.graph.graph import Edge, Graph, Node +import pytest +from langflow.utils.payload import build_json, get_root_node +from langchain.agents import AgentExecutor + +# Test cases for the graph module + + +def get_graph(basic=True): + """Get a graph from a json file""" + path = pytest.BASIC_EXAMPLE_PATH if basic else pytest.COMPLEX_EXAMPLE_PATH + with open(path, "r") as f: + flow_graph = json.load(f) + data_graph = flow_graph["data"] + nodes = data_graph["nodes"] + edges = data_graph["edges"] + return Graph(nodes, edges) + + +def test_get_nodes_with_target(): + """Test getting connected nodes""" + graph = get_graph() + assert isinstance(graph, Graph) + # Get root node + root = get_root_node(graph) + assert root is not None + connected_nodes = graph.get_nodes_with_target(root) + assert connected_nodes is not None + + +def test_get_node_neighbors_basic(): + """Test getting node neighbors""" + + graph = get_graph(basic=True) + assert isinstance(graph, Graph) + # Get root node + root = get_root_node(graph) + assert root is not None + neighbors = graph.get_node_neighbors(root) + assert neighbors is not None + assert isinstance(neighbors, dict) + # Root Node is an Agent, it requires an LLMChain and tools + # We need to check if there is a Chain in the one of the neighbors' + # data attribute in the type key + assert any( + "Chain" in neighbor.data["type"] for neighbor, val in neighbors.items() if val + ) + # assert Serper Search is in the neighbors + assert any( + "Serper" in neighbor.data["type"] for neighbor, val in neighbors.items() if val + ) + # Now on to the Chain's neighbors + chain = next( + neighbor + for neighbor, val in neighbors.items() + if "Chain" in neighbor.data["type"] and val + ) + chain_neighbors = graph.get_node_neighbors(chain) + assert chain_neighbors is not None + assert isinstance(chain_neighbors, dict) + # Check if there is a LLM in the chain's neighbors + assert any( + "OpenAI" in neighbor.data["type"] + for neighbor, val in chain_neighbors.items() + if val + ) + # Chain should have a Prompt as a neighbor + assert any( + "Prompt" in neighbor.data["type"] + for neighbor, val in chain_neighbors.items() + if val + ) + + +def test_get_node_neighbors_complex(): + """Test getting node neighbors""" + + graph = get_graph(basic=False) + assert isinstance(graph, Graph) + # Get root node + root = get_root_node(graph) + assert root is not None + neighbors = graph.get_nodes_with_target(root) + assert neighbors is not None + # Neighbors should be a list of nodes + assert isinstance(neighbors, list) + # Root Node is an Agent, it requires an LLMChain and tools + # We need to check if there is a Chain in the one of the neighbors' + assert any("Chain" in neighbor.data["type"] for neighbor in neighbors) + # assert Tool is in the neighbors + assert any("Tool" in neighbor.data["type"] for neighbor in neighbors) + # Now on to the Chain's neighbors + chain = next(neighbor for neighbor in neighbors if "Chain" in neighbor.data["type"]) + chain_neighbors = graph.get_nodes_with_target(chain) + assert chain_neighbors is not None + # Check if there is a LLM in the chain's neighbors + assert any("OpenAI" in neighbor.data["type"] for neighbor in chain_neighbors) + # Chain should have a Prompt as a neighbor + assert any("Prompt" in neighbor.data["type"] for neighbor in chain_neighbors) + # Now on to the Tool's neighbors + tool = next(neighbor for neighbor in neighbors if "Tool" in neighbor.data["type"]) + tool_neighbors = graph.get_nodes_with_target(tool) + assert tool_neighbors is not None + # Check if there is an Agent in the tool's neighbors + assert any("Agent" in neighbor.data["type"] for neighbor in tool_neighbors) + # This Agent has a Tool that has a PythonFunction as func + agent = next( + neighbor for neighbor in tool_neighbors if "Agent" in neighbor.data["type"] + ) + agent_neighbors = graph.get_nodes_with_target(agent) + assert agent_neighbors is not None + # Check if there is a Tool in the agent's neighbors + assert any("Tool" in neighbor.data["type"] for neighbor in agent_neighbors) + # This Tool has a PythonFunction as func + tool = next( + neighbor for neighbor in agent_neighbors if "Tool" in neighbor.data["type"] + ) + tool_neighbors = graph.get_nodes_with_target(tool) + assert tool_neighbors is not None + # Check if there is a PythonFunction in the tool's neighbors + assert any("PythonFunction" in neighbor.data["type"] for neighbor in tool_neighbors) + + +def test_get_node(): + """Test getting a single node""" + graph = get_graph() + node_id = graph.nodes[0].id + node = graph.get_node(node_id) + assert isinstance(node, Node) + assert node.id == node_id + + +def test_build_nodes(): + """Test building nodes""" + graph = get_graph() + assert len(graph.nodes) == len(graph._nodes) + for node in graph.nodes: + assert isinstance(node, Node) + + +def test_build_edges(): + """Test building edges""" + graph = get_graph() + assert len(graph.edges) == len(graph._edges) + for edge in graph.edges: + assert isinstance(edge, Edge) + assert isinstance(edge.source, Node) + assert isinstance(edge.target, Node) + + +def test_get_root_node(): + """Test getting root node""" + graph = get_graph(basic=True) + assert isinstance(graph, Graph) + root = get_root_node(graph) + assert root is not None + assert isinstance(root, Node) + assert root.data["type"] == "ZeroShotAgent" + # For complex example, the root node is a ZeroShotAgent too + graph = get_graph(basic=False) + assert isinstance(graph, Graph) + root = get_root_node(graph) + assert root is not None + assert isinstance(root, Node) + assert root.data["type"] == "ZeroShotAgent" + + +def test_build_json(): + """Test building JSON from graph""" + graph = get_graph() + assert isinstance(graph, Graph) + root = get_root_node(graph) + json_data = build_json(root, graph) + assert isinstance(json_data, dict) + assert json_data["_type"] == "zero-shot-react-description" + assert isinstance(json_data["llm_chain"], dict) + assert json_data["llm_chain"]["_type"] == "llm_chain" + assert json_data["llm_chain"]["memory"] is None + assert json_data["llm_chain"]["verbose"] is False + assert isinstance(json_data["llm_chain"]["prompt"], dict) + assert isinstance(json_data["llm_chain"]["llm"], dict) + assert json_data["llm_chain"]["output_key"] == "text" + assert isinstance(json_data["allowed_tools"], list) + assert all(isinstance(tool, dict) for tool in json_data["allowed_tools"]) + assert isinstance(json_data["return_values"], list) + assert all(isinstance(val, str) for val in json_data["return_values"]) + + +def test_validate_edges(): + """Test validating edges""" + graph = get_graph() + assert isinstance(graph, Graph) + # all edges should be valid + assert all(edge.valid for edge in graph.edges) + + +def test_matched_type(): + """Test matched type attribute in Edge""" + graph = get_graph() + assert isinstance(graph, Graph) + # all edges should be valid + assert all(edge.valid for edge in graph.edges) + # all edges should have a matched_type attribute + assert all(hasattr(edge, "matched_type") for edge in graph.edges) + # The matched_type attribute should be in the source_types attr + assert all(edge.matched_type in edge.source_types for edge in graph.edges) + + +def test_build_params(): + """Test building params""" + graph = get_graph() + assert isinstance(graph, Graph) + # all edges should be valid + assert all(edge.valid for edge in graph.edges) + # all edges should have a matched_type attribute + assert all(hasattr(edge, "matched_type") for edge in graph.edges) + # The matched_type attribute should be in the source_types attr + assert all(edge.matched_type in edge.source_types for edge in graph.edges) + # Get the root node + root = get_root_node(graph) + # Root node is a ZeroShotAgent + # which requires an llm_chain, allowed_tools and return_values + assert isinstance(root.params, dict) + assert "llm_chain" in root.params + assert "allowed_tools" in root.params + assert "return_values" in root.params + # The llm_chain should be a Node + assert isinstance(root.params["llm_chain"], Node) + # The allowed_tools should be a list of Nodes + assert isinstance(root.params["allowed_tools"], list) + assert all(isinstance(tool, Node) for tool in root.params["allowed_tools"]) + # The return_values is of type str so it should be a list of strings + assert isinstance(root.params["return_values"], list) + assert all(isinstance(val, str) for val in root.params["return_values"]) + # The llm_chain should have a prompt and llm + llm_chain_node = root.params["llm_chain"] + assert isinstance(llm_chain_node.params, dict) + assert "prompt" in llm_chain_node.params + assert "llm" in llm_chain_node.params + # The prompt should be a Node + assert isinstance(llm_chain_node.params["prompt"], Node) + # The llm should be a Node + assert isinstance(llm_chain_node.params["llm"], Node) + # The prompt should have format_insctructions, suffix, prefix + prompt_node = llm_chain_node.params["prompt"] + assert isinstance(prompt_node.params, dict) + assert "format_instructions" in prompt_node.params + assert "suffix" in prompt_node.params + assert "prefix" in prompt_node.params + # All of them should be of type str + assert isinstance(prompt_node.params["format_instructions"], str) + assert isinstance(prompt_node.params["suffix"], str) + assert isinstance(prompt_node.params["prefix"], str) + # The llm should have a model + llm_node = llm_chain_node.params["llm"] + assert isinstance(llm_node.params, dict) + assert "model_name" in llm_node.params + # The model should be a str + assert isinstance(llm_node.params["model_name"], str) + + +def test_build(): + """Test Node's build method""" + # def build(self): + # # The params dict is used to build the module + # # it contains values and keys that point to nodes which + # # have their own params dict + # # When build is called, we iterate through the params dict + # # and if the value is a node, we call build on that node + # # and use the output of that build as the value for the param + # # if the value is not a node, then we use the value as the param + # # and continue + # # Another aspect is that the node_type is the class that we need to import + # # and instantiate with these built params + + # # Build each node in the params dict + # for key, value in self.params.items(): + # if isinstance(value, Node): + # self.params[key] = value.build() + + # # Get the class from LANGCHAIN_TYPES_DICT + # # and instantiate it with the params + # # and return the instance + # return LANGCHAIN_TYPES_DICT[self.node_type](**self.params) + graph = get_graph() + assert isinstance(graph, Graph) + # Now we test the build method + # Build the Agent + agent = graph.build() + # The agent should be a AgentExecutor + assert isinstance(agent, AgentExecutor) + + # Now we test the complex example + graph = get_graph(basic=False) + assert isinstance(graph, Graph) + # Now we test the build method + agent = graph.build() + # The agent should be a AgentExecutor + assert isinstance(agent, AgentExecutor) diff --git a/tests/test_loading.py b/tests/test_loading.py index 9478e9be1..a824ec4e5 100644 --- a/tests/test_loading.py +++ b/tests/test_loading.py @@ -1,11 +1,27 @@ -from pathlib import Path +import json +from langflow.graph.graph import Graph +import pytest from langflow import load_flow_from_json +from langflow.utils.payload import get_root_node +from langchain.agents import AgentExecutor def test_load_flow_from_json(): """Test loading a flow from a json file""" - path = Path(__file__).parent.absolute() - path = f"{path}/data/example_flow.json" - loaded = load_flow_from_json(path) + loaded = load_flow_from_json(pytest.BASIC_EXAMPLE_PATH) assert loaded is not None + assert isinstance(loaded, AgentExecutor) + + +def test_get_root_node(): + with open(pytest.BASIC_EXAMPLE_PATH, "r") as f: + flow_graph = json.load(f) + data_graph = flow_graph["data"] + nodes = data_graph["nodes"] + edges = data_graph["edges"] + graph = Graph(nodes, edges) + root = get_root_node(graph) + assert root is not None + assert hasattr(root, "id") + assert hasattr(root, "data") diff --git a/tests/test_validate_code.py b/tests/test_validate_code.py new file mode 100644 index 000000000..9cb47f6e1 --- /dev/null +++ b/tests/test_validate_code.py @@ -0,0 +1,106 @@ +from langflow.utils.validate import ( + create_function, + extract_function_name, + validate_code, + execute_function, +) +import pytest +from requests.exceptions import MissingSchema +from unittest import mock + + +def test_validate_code(): + # Test case with a valid import and function + code1 = """ +import math + +def square(x): + return x ** 2 +""" + errors1 = validate_code(code1) + assert errors1 == {"imports": {"errors": []}, "function": {"errors": []}} + + # Test case with an invalid import and valid function + code2 = """ +import non_existent_module + +def square(x): + return x ** 2 +""" + errors2 = validate_code(code2) + assert errors2 == { + "imports": {"errors": ["No module named 'non_existent_module'"]}, + "function": {"errors": []}, + } + + # Test case with a valid import and invalid function syntax + code3 = """ +import math + +def square(x) + return x ** 2 +""" + errors3 = validate_code(code3) + assert errors3 == { + "imports": {"errors": []}, + "function": {"errors": ["expected ':' (, line 4)"]}, + } + + +def test_execute_function_success(): + code = """ +import math + +def my_function(x): + return math.sin(x) + 1 + """ + result = execute_function(code, "my_function", 0.5) + assert result == 1.479425538604203 + + +def test_execute_function_missing_module(): + code = """ +import some_missing_module + +def my_function(x): + return some_missing_module.some_function(x) + """ + with pytest.raises(ModuleNotFoundError): + execute_function(code, "my_function", 0.5) + + +def test_execute_function_missing_function(): + code = """ +import math + +def my_function(x): + return math.some_missing_function(x) + """ + with pytest.raises(AttributeError): + execute_function(code, "my_function", 0.5) + + +def test_execute_function_missing_schema(): + code = """ +import requests + +def my_function(x): + return requests.get(x).text + """ + with mock.patch("requests.get", side_effect=MissingSchema): + with pytest.raises(MissingSchema): + execute_function(code, "my_function", "invalid_url") + + +def test_create_function(): + code = """ +import math + +def my_function(x): + return math.sin(x) + 1 +""" + + function_name = extract_function_name(code) + function = create_function(code, function_name) + result = function(0.5) + assert result == 1.479425538604203