diff --git a/.gitignore b/.gitignore index 96512b650..c2ffb8276 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,11 @@ lerna-debug.log* # Mac .DS_Store +# VSCode +.vscode +.chroma +.ruff_cache + # Diagnostic reports (https://nodejs.org/api/report.html) report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json @@ -233,5 +238,5 @@ venv.bak/ .dmypy.json dmypy.json -# Poetry -.testenv/* \ No newline at end of file +# Poetry +.testenv/* diff --git a/README.md b/README.md index 8de32742a..970496349 100644 --- a/README.md +++ b/README.md @@ -49,7 +49,7 @@ flow("Hey, have you heard of LangFlow?") ## 👋 Contributing -We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our contributing guidelines and help make LangFlow more accessible. +We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our [contributing guidelines](./CONTRIBUTING.md) and help make LangFlow more accessible. [![Star History Chart](https://api.star-history.com/svg?repos=logspace-ai/langflow&type=Timeline)](https://star-history.com/#logspace-ai/langflow&Date) diff --git a/dev.Dockerfile b/dev.Dockerfile index 7e439c69a..b38929db2 100644 --- a/dev.Dockerfile +++ b/dev.Dockerfile @@ -3,7 +3,7 @@ FROM python:3.10-slim WORKDIR /app # Install Poetry -RUN apt-get update && apt-get install gcc curl -y +RUN apt-get update && apt-get install gcc g++ curl build-essential postgresql-server-dev-all -y RUN curl -sSL https://install.python-poetry.org | python3 - # # Add Poetry to PATH ENV PATH="${PATH}:/root/.local/bin" diff --git a/poetry.lock b/poetry.lock index 353921ea0..5361880ee 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. 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= ["Logspace "] maintainers = [ @@ -34,8 +34,18 @@ openai = "^0.27.2" types-pyyaml = "^6.0.12.8" dill = "^0.3.6" pandas = "^1.5.3" +chromadb = "^0.3.21" huggingface-hub = "^0.13.3" rich = "^13.3.3" +llama-cpp-python = "0.1.23" +networkx = "^3.1" +unstructured = "^0.5.11" +pypdf = "^3.7.1" +lxml = "^4.9.2" +pysrt = "^1.1.2" +fake-useragent = "^1.1.3" +docstring-parser = "^0.15" +psycopg2 = "^2.9.6" [tool.poetry.group.dev.dependencies] black = "^23.1.0" diff --git a/src/backend/langflow/api/endpoints.py b/src/backend/langflow/api/endpoints.py index 22f548156..b8290e691 100644 --- a/src/backend/langflow/api/endpoints.py +++ b/src/backend/langflow/api/endpoints.py @@ -3,7 +3,7 @@ from typing import Any, Dict from fastapi import APIRouter, HTTPException -from langflow.interface.run import process_graph +from langflow.interface.run import process_graph_cached from langflow.interface.types import build_langchain_types_dict # build router @@ -19,7 +19,7 @@ def get_all(): @router.post("/predict") def get_load(data: Dict[str, Any]): try: - return process_graph(data) + return process_graph_cached(data) except Exception as e: # Log stack trace logger.exception(e) diff --git a/src/backend/langflow/cache/utils.py b/src/backend/langflow/cache/utils.py index 3c416f4d7..310f3be80 100644 --- a/src/backend/langflow/cache/utils.py +++ b/src/backend/langflow/cache/utils.py @@ -1,18 +1,62 @@ +import base64 import contextlib +import functools import hashlib import json import os import tempfile +from collections import OrderedDict from pathlib import Path import dill # type: ignore + +def create_cache_folder(func): + def wrapper(*args, **kwargs): + # Get the destination folder + cache_path = Path(tempfile.gettempdir()) / PREFIX + + # Create the destination folder if it doesn't exist + os.makedirs(cache_path, exist_ok=True) + + return func(*args, **kwargs) + + return wrapper + + +def memoize_dict(maxsize=128): + cache = OrderedDict() + + def decorator(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + hashed = compute_dict_hash(args[0]) + key = (func.__name__, hashed, frozenset(kwargs.items())) + if key not in cache: + result = func(*args, **kwargs) + cache[key] = result + if len(cache) > maxsize: + cache.popitem(last=False) + else: + result = cache[key] + return result + + def clear_cache(): + cache.clear() + + wrapper.clear_cache = clear_cache + return wrapper + + return decorator + + PREFIX = "langflow_cache" +@create_cache_folder def clear_old_cache_files(max_cache_size: int = 3): - cache_dir = Path(tempfile.gettempdir()) - cache_files = list(cache_dir.glob(f"{PREFIX}_*.dill")) + cache_dir = Path(tempfile.gettempdir()) / PREFIX + cache_files = list(cache_dir.glob("*.dill")) if len(cache_files) > max_cache_size: cache_files_sorted_by_mtime = sorted( @@ -24,6 +68,13 @@ def clear_old_cache_files(max_cache_size: int = 3): os.remove(cache_file) +def compute_dict_hash(graph_data): + graph_data = filter_json(graph_data) + + cleaned_graph_json = json.dumps(graph_data, sort_keys=True) + return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest() + + def filter_json(json_data): filtered_data = json_data.copy() @@ -48,15 +99,40 @@ def filter_json(json_data): return filtered_data -def compute_hash(graph_data): - graph_data = filter_json(graph_data) +@create_cache_folder +def save_binary_file(content: str, file_name: str, accepted_types: list[str]) -> str: + """ + Save a binary file to the specified folder. - cleaned_graph_json = json.dumps(graph_data, sort_keys=True) - return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest() + Args: + content: The content of the file as a bytes object. + file_name: The name of the file, including its extension. + + Returns: + The path to the saved file. + """ + if not any(file_name.endswith(suffix) for suffix in accepted_types): + raise ValueError(f"File {file_name} is not accepted") + + # Get the destination folder + cache_path = Path(tempfile.gettempdir()) / PREFIX + + data = content.split(",")[1] + decoded_bytes = base64.b64decode(data) + + # Create the full file path + file_path = os.path.join(cache_path, file_name) + + # Save the binary content to the file + with open(file_path, "wb") as file: + file.write(decoded_bytes) + + return file_path +@create_cache_folder def save_cache(hash_val: str, chat_data, clean_old_cache_files: bool): - cache_path = Path(tempfile.gettempdir()) / f"{PREFIX}_{hash_val}.dill" + cache_path = Path(tempfile.gettempdir()) / PREFIX / f"{hash_val}.dill" with cache_path.open("wb") as cache_file: dill.dump(chat_data, cache_file) @@ -64,8 +140,9 @@ def save_cache(hash_val: str, chat_data, clean_old_cache_files: bool): clear_old_cache_files() +@create_cache_folder def load_cache(hash_val): - cache_path = Path(tempfile.gettempdir()) / f"{PREFIX}_{hash_val}.dill" + cache_path = Path(tempfile.gettempdir()) / PREFIX / f"{hash_val}.dill" if cache_path.exists(): with cache_path.open("rb") as cache_file: return dill.load(cache_file) diff --git a/src/backend/langflow/config.yaml b/src/backend/langflow/config.yaml index a00d91bff..7f00167b4 100644 --- a/src/backend/langflow/config.yaml +++ b/src/backend/langflow/config.yaml @@ -12,6 +12,9 @@ agents: - JsonAgent - CSVAgent - initialize_agent + - VectorStoreAgent + - VectorStoreRouterAgent + - SQLAgent prompts: - PromptTemplate @@ -27,6 +30,7 @@ llms: # - AzureOpenAI - ChatOpenAI - HuggingFaceHub + - LlamaCpp tools: - Search @@ -43,16 +47,48 @@ wrappers: toolkits: - OpenAPIToolkit - JsonToolkit + - VectorStoreInfo + - VectorStoreRouterToolkit memories: - ConversationBufferMemory - ConversationSummaryMemory - ConversationKGMemory -embeddings: [] +embeddings: + - OpenAIEmbeddings -vectorstores: [] +vectorstores: + - Chroma -documentloaders: [] +documentloaders: + - AirbyteJSONLoader + - CoNLLULoader + - CSVLoader + - UnstructuredEmailLoader + - EverNoteLoader + - FacebookChatLoader + - GutenbergLoader + - BSHTMLLoader + - UnstructuredHTMLLoader + # - UnstructuredImageLoader # Issue with Python 3.11 (https://github.com/Unstructured-IO/unstructured-inference/issues/83) + - UnstructuredMarkdownLoader + - PyPDFLoader + - UnstructuredPowerPointLoader + - SRTLoader + - TelegramChatLoader + - TextLoader + - UnstructuredWordDocumentLoader + - WebBaseLoader + - AZLyricsLoader + - CollegeConfidentialLoader + - HNLoader + - IFixitLoader + - IMSDbLoader + - GitbookLoader + - ReadTheDocsLoader + +textsplitters: + - CharacterTextSplitter dev: false diff --git a/src/backend/langflow/custom/customs.py b/src/backend/langflow/custom/customs.py index 22e833362..e77b81ec6 100644 --- a/src/backend/langflow/custom/customs.py +++ b/src/backend/langflow/custom/customs.py @@ -8,6 +8,9 @@ CUSTOM_NODES = { "JsonAgent": nodes.JsonAgentNode(), "CSVAgent": nodes.CSVAgentNode(), "initialize_agent": nodes.InitializeAgentNode(), + "VectorStoreAgent": nodes.VectorStoreAgentNode(), + "VectorStoreRouterAgent": nodes.VectorStoreRouterAgentNode(), + "SQLAgent": nodes.SQLAgentNode(), }, } diff --git a/src/backend/langflow/graph/base.py b/src/backend/langflow/graph/base.py index 8eb78e06c..ff586c6da 100644 --- a/src/backend/langflow/graph/base.py +++ b/src/backend/langflow/graph/base.py @@ -3,13 +3,14 @@ # - Defer prompts building to the last moment or when they have all the tools # - Build each inner agent first, then build the outer agent +import contextlib import types import warnings from copy import deepcopy from typing import Any, Dict, List, Optional +from langflow.cache import utils as cache_utils from langflow.graph.constants import DIRECT_TYPES -from langflow.graph.utils import load_file from langflow.interface import loading from langflow.interface.listing import ALL_TYPES_DICT from langflow.utils.logger import logger @@ -88,11 +89,12 @@ class Node: file_name = value.get("value") content = value.get("content") type_to_load = value.get("suffixes") - loaded_dict = load_file(file_name, content, type_to_load) - params[key] = loaded_dict + file_path = cache_utils.save_binary_file( + content=content, file_name=file_name, accepted_types=type_to_load + ) + + params[key] = file_path - # We should check if the type is in something not - # the opposite elif value.get("type") not in DIRECT_TYPES: # Get the edge that connects to this node edges = [ @@ -126,6 +128,9 @@ class Node: new_value = value.get("value") if new_value is None: warnings.warn(f"Value for {key} in {self.node_type} is None. ") + if value.get("type") == "int": + with contextlib.suppress(TypeError, ValueError): + new_value = int(new_value) # type: ignore params[key] = new_value # Add _type to params @@ -160,6 +165,7 @@ class Node: 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 @@ -189,6 +195,15 @@ class Node: def build(self, force: bool = False) -> Any: if not self._built or force: self._build() + + #! Deepcopy is breaking for vectorstores + if self.base_type in [ + "vectorstores", + "VectorStoreRouterAgent", + "VectorStoreAgent", + "VectorStoreInfo", + ] or self.node_type in ["VectorStoreInfo", "VectorStoreRouterToolkit"]: + return self._built_object return deepcopy(self._built_object) def add_edge(self, edge: "Edge") -> None: diff --git a/src/backend/langflow/graph/graph.py b/src/backend/langflow/graph/graph.py index 84b94a6a5..390cb9454 100644 --- a/src/backend/langflow/graph/graph.py +++ b/src/backend/langflow/graph/graph.py @@ -4,22 +4,30 @@ from langflow.graph.base import Edge, Node from langflow.graph.nodes import ( AgentNode, ChainNode, + DocumentLoaderNode, + EmbeddingNode, FileToolNode, LLMNode, MemoryNode, PromptNode, + TextSplitterNode, ToolkitNode, ToolNode, + VectorStoreNode, WrapperNode, ) from langflow.interface.agents.base import agent_creator from langflow.interface.chains.base import chain_creator +from langflow.interface.document_loaders.base import documentloader_creator +from langflow.interface.embeddings.base import embedding_creator from langflow.interface.llms.base import llm_creator from langflow.interface.memories.base import memory_creator from langflow.interface.prompts.base import prompt_creator +from langflow.interface.text_splitters.base import textsplitter_creator from langflow.interface.toolkits.base import toolkits_creator from langflow.interface.tools.base import tool_creator from langflow.interface.tools.constants import FILE_TOOLS +from langflow.interface.vector_store.base import vectorstore_creator from langflow.interface.wrappers.base import wrapper_creator from langflow.utils import payload @@ -117,6 +125,10 @@ class Graph: **{t: WrapperNode for t in wrapper_creator.to_list()}, **{t: LLMNode for t in llm_creator.to_list()}, **{t: MemoryNode for t in memory_creator.to_list()}, + **{t: EmbeddingNode for t in embedding_creator.to_list()}, + **{t: VectorStoreNode for t in vectorstore_creator.to_list()}, + **{t: DocumentLoaderNode for t in documentloader_creator.to_list()}, + **{t: TextSplitterNode for t in textsplitter_creator.to_list()}, } if node_type in FILE_TOOLS: diff --git a/src/backend/langflow/graph/nodes.py b/src/backend/langflow/graph/nodes.py index 5320adc61..7296a0c0d 100644 --- a/src/backend/langflow/graph/nodes.py +++ b/src/backend/langflow/graph/nodes.py @@ -32,6 +32,10 @@ class AgentNode(Node): chain_node.build(tools=self.tools) self._build() + + #! Cannot deepcopy VectorStore, VectorStoreRouter, or SQL agents + if self.node_type in ["VectorStoreAgent", "VectorStoreRouterAgent", "SQLAgent"]: + return self._built_object return deepcopy(self._built_object) @@ -39,11 +43,6 @@ class ToolNode(Node): def __init__(self, data: Dict): super().__init__(data, base_type="tools") - 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): @@ -109,32 +108,16 @@ class LLMNode(Node): def __init__(self, data: Dict): super().__init__(data, base_type="llms") - def build(self, force: bool = False) -> Any: - if not self._built or force: - self._build() - return deepcopy(self._built_object) - class ToolkitNode(Node): def __init__(self, data: Dict): super().__init__(data, base_type="toolkits") - def build(self, force: bool = False) -> Any: - if not self._built or force: - self._build() - - return deepcopy(self._built_object) - class FileToolNode(ToolNode): 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 WrapperNode(Node): def __init__(self, data: Dict): @@ -148,11 +131,26 @@ class WrapperNode(Node): return deepcopy(self._built_object) +class DocumentLoaderNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="documentloaders") + + +class EmbeddingNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="embeddings") + + +class VectorStoreNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="vectorstores") + + class MemoryNode(Node): def __init__(self, data: Dict): super().__init__(data, base_type="memory") - def build(self, force: bool = False) -> Any: - if not self._built or force: - self._build() - return deepcopy(self._built_object) + +class TextSplitterNode(Node): + def __init__(self, data: Dict): + super().__init__(data, base_type="textsplitters") diff --git a/src/backend/langflow/graph/utils.py b/src/backend/langflow/graph/utils.py index 3028ceeb0..6d56e933e 100644 --- a/src/backend/langflow/graph/utils.py +++ b/src/backend/langflow/graph/utils.py @@ -1,45 +1,4 @@ -import base64 -import csv -import io -import json import re -from typing import Any - -import yaml - - -def load_file(file_name, file_content, accepted_types) -> Any: - """Load a file from a string.""" - # Check if the file is accepted - if not any(file_name.endswith(suffix) for suffix in accepted_types): - raise ValueError(f"File {file_name} is not accepted") - # Get the suffix - suffix = file_name.split(".")[-1] - # file_content == 'data:application/x-yaml;base64,b3BlbmFwaTogIjMuMC4wIg...' - data = file_content.split(",")[1] - decoded_bytes = base64.b64decode(data) - - # Convert the bytes object to a string - decoded_string = decoded_bytes.decode("utf-8") - if suffix == "json": - # Return the json content - return json.loads(decoded_string) - elif suffix in ["yaml", "yml"]: - # Return the yaml content - loaded_yaml = yaml.load(decoded_string, Loader=yaml.FullLoader) - try: - from langchain.agents.agent_toolkits.openapi.spec import reduce_openapi_spec # type: ignore - - return reduce_openapi_spec(loaded_yaml) - except ImportError: - return loaded_yaml - - elif suffix == "csv": - # Load the csv content - csv_reader = csv.DictReader(io.StringIO(decoded_string)) - return list(csv_reader) - else: - raise ValueError(f"File {file_name} is not accepted") def validate_prompt(prompt: str): diff --git a/src/backend/langflow/interface/agents/base.py b/src/backend/langflow/interface/agents/base.py index 3774c3011..1d23b481e 100644 --- a/src/backend/langflow/interface/agents/base.py +++ b/src/backend/langflow/interface/agents/base.py @@ -6,6 +6,7 @@ from langflow.custom.customs import get_custom_nodes from langflow.interface.agents.custom import CUSTOM_AGENTS from langflow.interface.base import LangChainTypeCreator from langflow.settings import settings +from langflow.utils.logger import logger from langflow.utils.util import build_template_from_class @@ -31,6 +32,9 @@ class AgentCreator(LangChainTypeCreator): ) except ValueError as exc: raise ValueError("Agent not found") from exc + except AttributeError as exc: + logger.error(f"Agent {name} not loaded: {exc}") + return None # Now this is a generator def to_list(self) -> List[str]: diff --git a/src/backend/langflow/interface/agents/custom.py b/src/backend/langflow/interface/agents/custom.py index 4032eab19..851bd2af8 100644 --- a/src/backend/langflow/interface/agents/custom.py +++ b/src/backend/langflow/interface/agents/custom.py @@ -1,16 +1,37 @@ from typing import Any, List, Optional from langchain import LLMChain -from langchain.agents import AgentExecutor, Tool, ZeroShotAgent, initialize_agent +from langchain.agents import ( + AgentExecutor, + Tool, + ZeroShotAgent, + initialize_agent, +) +from langchain.agents.agent_toolkits import ( + SQLDatabaseToolkit, + VectorStoreInfo, + VectorStoreRouterToolkit, + VectorStoreToolkit, +) from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit from langchain.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFIX from langchain.agents.agent_toolkits.pandas.prompt import SUFFIX as PANDAS_SUFFIX +from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX +from langchain.agents.agent_toolkits.vectorstore.prompt import ( + PREFIX as VECTORSTORE_PREFIX, +) +from langchain.agents.agent_toolkits.vectorstore.prompt import ( + ROUTER_PREFIX as VECTORSTORE_ROUTER_PREFIX, +) from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS +from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS as SQL_FORMAT_INSTRUCTIONS from langchain.llms.base import BaseLLM from langchain.memory.chat_memory import BaseChatMemory from langchain.schema import BaseLanguageModel +from langchain.sql_database import SQLDatabase from langchain.tools.python.tool import PythonAstREPLTool +from langchain.tools.sql_database.prompt import QUERY_CHECKER class JsonAgent(AgentExecutor): @@ -66,7 +87,7 @@ class CSVAgent(AgentExecutor): @classmethod def from_toolkit_and_llm( cls, - path: dict, + path: str, llm: BaseLanguageModel, pandas_kwargs: Optional[dict] = None, **kwargs: Any @@ -74,7 +95,7 @@ class CSVAgent(AgentExecutor): import pandas as pd # type: ignore _kwargs = pandas_kwargs or {} - df = pd.DataFrame.from_dict(path, **_kwargs) + df = pd.read_csv(path, **_kwargs) tools = [PythonAstREPLTool(locals={"df": df})] # type: ignore prompt = ZeroShotAgent.create_prompt( @@ -97,6 +118,153 @@ class CSVAgent(AgentExecutor): return super().run(*args, **kwargs) +class VectorStoreAgent(AgentExecutor): + """Vector Store agent""" + + @staticmethod + def function_name(): + return "VectorStoreAgent" + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm( + cls, llm: BaseLLM, vectorstoreinfo: VectorStoreInfo, **kwargs: Any + ): + """Construct a vectorstore agent from an LLM and tools.""" + + toolkit = VectorStoreToolkit(vectorstore_info=vectorstoreinfo, llm=llm) + + tools = toolkit.get_tools() + prompt = ZeroShotAgent.create_prompt(tools, prefix=VECTORSTORE_PREFIX) + llm_chain = LLMChain( + llm=llm, + prompt=prompt, + ) + tool_names = [tool.name for tool in tools] + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) + return AgentExecutor.from_agent_and_tools( + agent=agent, tools=tools, verbose=True + ) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +class SQLAgent(AgentExecutor): + """SQL agent""" + + @staticmethod + def function_name(): + return "SQLAgent" + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm(cls, llm: BaseLLM, database_uri: str, **kwargs: Any): + """Construct a sql agent from an LLM and tools.""" + db = SQLDatabase.from_uri(database_uri) + toolkit = SQLDatabaseToolkit(db=db) + + # The right code should be this, but there is a problem with tools = toolkit.get_tools() + # related to `OPENAI_API_KEY` + # return create_sql_agent(llm=llm, toolkit=toolkit, verbose=True) + from langchain.prompts import PromptTemplate + from langchain.tools.sql_database.tool import ( + InfoSQLDatabaseTool, + ListSQLDatabaseTool, + QueryCheckerTool, + QuerySQLDataBaseTool, + ) + + llmchain = LLMChain( + llm=llm, + prompt=PromptTemplate( + template=QUERY_CHECKER, input_variables=["query", "dialect"] + ), + ) + + tools = [ + QuerySQLDataBaseTool(db=db), # type: ignore + InfoSQLDatabaseTool(db=db), # type: ignore + ListSQLDatabaseTool(db=db), # type: ignore + QueryCheckerTool(db=db, llm_chain=llmchain), # type: ignore + ] + + prefix = SQL_PREFIX.format(dialect=toolkit.dialect, top_k=10) + prompt = ZeroShotAgent.create_prompt( + tools=tools, # type: ignore + prefix=prefix, + suffix=SQL_SUFFIX, + format_instructions=SQL_FORMAT_INSTRUCTIONS, + ) + llm_chain = LLMChain( + llm=llm, + prompt=prompt, + ) + tool_names = [tool.name for tool in tools] # type: ignore + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) + return AgentExecutor.from_agent_and_tools( + agent=agent, + tools=tools, # type: ignore + verbose=True, + max_iterations=15, + early_stopping_method="force", + ) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + +class VectorStoreRouterAgent(AgentExecutor): + """Vector Store Router Agent""" + + @staticmethod + def function_name(): + return "VectorStoreRouterAgent" + + @classmethod + def initialize(cls, *args, **kwargs): + return cls.from_toolkit_and_llm(*args, **kwargs) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_toolkit_and_llm( + cls, + llm: BaseLanguageModel, + vectorstoreroutertoolkit: VectorStoreRouterToolkit, + **kwargs: Any + ): + """Construct a vector store router agent from an LLM and tools.""" + + tools = vectorstoreroutertoolkit.get_tools() + prompt = ZeroShotAgent.create_prompt(tools, prefix=VECTORSTORE_ROUTER_PREFIX) + llm_chain = LLMChain( + llm=llm, + prompt=prompt, + ) + tool_names = [tool.name for tool in tools] + agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs) + return AgentExecutor.from_agent_and_tools( + agent=agent, tools=tools, verbose=True + ) + + def run(self, *args, **kwargs): + return super().run(*args, **kwargs) + + class InitializeAgent(AgentExecutor): """Implementation of initialize_agent function""" @@ -128,4 +296,7 @@ CUSTOM_AGENTS = { "JsonAgent": JsonAgent, "CSVAgent": CSVAgent, "initialize_agent": InitializeAgent, + "VectorStoreAgent": VectorStoreAgent, + "VectorStoreRouterAgent": VectorStoreRouterAgent, + "SQLAgent": SQLAgent, } diff --git a/src/backend/langflow/interface/base.py b/src/backend/langflow/interface/base.py index 2694fd7a5..663700ffd 100644 --- a/src/backend/langflow/interface/base.py +++ b/src/backend/langflow/interface/base.py @@ -4,6 +4,7 @@ from typing import Any, Dict, List, Optional, Type, Union from pydantic import BaseModel from langflow.template.base import FrontendNode, Template, TemplateField +from langflow.utils.logger import logger # Assuming necessary imports for Field, Template, and FrontendNode classes @@ -39,14 +40,18 @@ class LangChainTypeCreator(BaseModel, ABC): # frontend_node.to_dict() returns a dict with the following structure: # {name: {template: {fields}, description: str}} # so we should update the result dict - result[self.type_name].update(self.frontend_node(name).to_dict()) + node = self.frontend_node(name) + if node is not None: + node = node.to_dict() + result[self.type_name].update(node) return result - def frontend_node(self, name) -> FrontendNode: + def frontend_node(self, name) -> Union[FrontendNode, None]: signature = self.get_signature(name) if signature is None: - raise ValueError(f"{name} not found") + logger.error(f"Node {name} not loaded") + return None if isinstance(signature, FrontendNode): return signature fields = [ diff --git a/src/backend/langflow/interface/chains/base.py b/src/backend/langflow/interface/chains/base.py index 6f90b0e62..9dc8ded3f 100644 --- a/src/backend/langflow/interface/chains/base.py +++ b/src/backend/langflow/interface/chains/base.py @@ -5,6 +5,7 @@ from langflow.interface.base import LangChainTypeCreator from langflow.interface.custom_lists import chain_type_to_cls_dict from langflow.settings import settings from langflow.template.nodes import ChainFrontendNode +from langflow.utils.logger import logger from langflow.utils.util import build_template_from_class # Assuming necessary imports for Field, Template, and FrontendNode classes @@ -39,6 +40,9 @@ class ChainCreator(LangChainTypeCreator): return build_template_from_class(name, self.type_to_loader_dict) except ValueError as exc: raise ValueError("Chain not found") from exc + except AttributeError as exc: + logger.error(f"Chain {name} not loaded: {exc}") + return None def to_list(self) -> List[str]: custom_chains = list(get_custom_nodes("chains").keys()) diff --git a/src/backend/langflow/interface/custom_lists.py b/src/backend/langflow/interface/custom_lists.py index 746c58325..2ffc18b14 100644 --- a/src/backend/langflow/interface/custom_lists.py +++ b/src/backend/langflow/interface/custom_lists.py @@ -1,6 +1,6 @@ +import inspect from typing import Any -## LLM from langchain import ( chains, document_loaders, @@ -8,6 +8,7 @@ from langchain import ( llms, memory, requests, + text_splitter, vectorstores, ) from langchain.agents import agent_toolkits @@ -15,16 +16,17 @@ from langchain.chat_models import ChatOpenAI from langflow.interface.importing.utils import import_class -## LLM +## LLMs llm_type_to_cls_dict = llms.type_to_cls_dict llm_type_to_cls_dict["openai-chat"] = ChatOpenAI # type: ignore -## Chain +## Chains chain_type_to_cls_dict: dict[str, Any] = { chain_name: import_class(f"langchain.chains.{chain_name}") for chain_name in chains.__all__ } +## Toolkits toolkit_type_to_loader_dict: dict[str, Any] = { toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}") # if toolkit_name is lower case it is a loader @@ -69,3 +71,8 @@ documentloaders_type_to_cls_dict: dict[str, Any] = { ) for documentloader_name in document_loaders.__all__ } + +## Text Splitters +textsplitter_type_to_cls_dict: dict[str, Any] = dict( + inspect.getmembers(text_splitter, inspect.isclass) +) diff --git a/src/backend/langflow/interface/documentLoaders/base.py b/src/backend/langflow/interface/documentLoaders/base.py deleted file mode 100644 index 844820d7b..000000000 --- a/src/backend/langflow/interface/documentLoaders/base.py +++ /dev/null @@ -1,31 +0,0 @@ -from typing import Dict, List, Optional - -from langflow.interface.base import LangChainTypeCreator -from langflow.interface.custom_lists import documentloaders_type_to_cls_dict -from langflow.settings import settings -from langflow.utils.util import build_template_from_class - - -class DocumentLoaderCreator(LangChainTypeCreator): - type_name: str = "documentloader" - - @property - def type_to_loader_dict(self) -> Dict: - return documentloaders_type_to_cls_dict - - def get_signature(self, name: str) -> Optional[Dict]: - """Get the signature of a document loader.""" - try: - return build_template_from_class(name, documentloaders_type_to_cls_dict) - except ValueError as exc: - raise ValueError(f"Documment Loader {name} not found") from exc - - def to_list(self) -> List[str]: - return [ - documentloader.__name__ - for documentloader in self.type_to_loader_dict.values() - if documentloader.__name__ in settings.documentloaders or settings.dev - ] - - -documentloader_creator = DocumentLoaderCreator() diff --git a/src/backend/langflow/interface/custom/__init__.py b/src/backend/langflow/interface/document_loaders/__init__.py similarity index 100% rename from src/backend/langflow/interface/custom/__init__.py rename to src/backend/langflow/interface/document_loaders/__init__.py diff --git a/src/backend/langflow/interface/document_loaders/base.py b/src/backend/langflow/interface/document_loaders/base.py new file mode 100644 index 000000000..aab017c0f --- /dev/null +++ b/src/backend/langflow/interface/document_loaders/base.py @@ -0,0 +1,146 @@ +from typing import Dict, List, Optional + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import documentloaders_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +def build_file_path_template( + suffixes: list, fileTypes: list, name: str = "file_path" +) -> Dict: + """Build a file path template for a document loader.""" + return { + "type": "file", + "required": True, + "show": True, + "name": name, + "value": "", + "suffixes": suffixes, + "fileTypes": fileTypes, + } + + +class DocumentLoaderCreator(LangChainTypeCreator): + type_name: str = "documentloaders" + + @property + def type_to_loader_dict(self) -> Dict: + return documentloaders_type_to_cls_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of a document loader.""" + try: + signature = build_template_from_class( + name, documentloaders_type_to_cls_dict + ) + + file_path_templates = { + "AirbyteJSONLoader": build_file_path_template( + suffixes=[".json"], fileTypes=["json"] + ), + "CoNLLULoader": build_file_path_template( + suffixes=[".csv"], fileTypes=["csv"] + ), + "CSVLoader": build_file_path_template( + suffixes=[".csv"], fileTypes=["csv"] + ), + "UnstructuredEmailLoader": build_file_path_template( + suffixes=[".eml"], fileTypes=["eml"] + ), + "EverNoteLoader": build_file_path_template( + suffixes=[".xml"], fileTypes=["xml"] + ), + "FacebookChatLoader": build_file_path_template( + suffixes=[".json"], fileTypes=["json"] + ), + "GutenbergLoader": build_file_path_template( + suffixes=[".txt"], fileTypes=["txt"] + ), + "BSHTMLLoader": build_file_path_template( + suffixes=[".html"], fileTypes=["html"] + ), + "UnstructuredHTMLLoader": build_file_path_template( + suffixes=[".html"], fileTypes=["html"] + ), + "UnstructuredImageLoader": build_file_path_template( + suffixes=[".jpg", ".jpeg", ".png", ".gif", ".bmp"], + fileTypes=["jpg", "jpeg", "png", "gif", "bmp"], + ), + "UnstructuredMarkdownLoader": build_file_path_template( + suffixes=[".md"], fileTypes=["md"] + ), + "PyPDFLoader": build_file_path_template( + suffixes=[".pdf"], fileTypes=["pdf"] + ), + "UnstructuredPowerPointLoader": build_file_path_template( + suffixes=[".pptx", ".ppt"], fileTypes=["pptx", "ppt"] + ), + "SRTLoader": build_file_path_template( + suffixes=[".srt"], fileTypes=["srt"] + ), + "TelegramChatLoader": build_file_path_template( + suffixes=[".json"], fileTypes=["json"] + ), + "TextLoader": build_file_path_template( + suffixes=[".txt"], fileTypes=["txt"] + ), + "UnstructuredWordDocumentLoader": build_file_path_template( + suffixes=[".docx", ".doc"], fileTypes=["docx", "doc"] + ), + } + + if name in file_path_templates: + signature["template"]["file_path"] = file_path_templates[name] + elif name in { + "WebBaseLoader", + "AZLyricsLoader", + "CollegeConfidentialLoader", + "HNLoader", + "IFixitLoader", + "IMSDbLoader", + }: + signature["template"]["web_path"] = { + "type": "str", + "required": True, + "show": True, + "name": "web_path", + "value": "", + "display_name": "Web Page", + } + elif name in {"GitbookLoader"}: + signature["template"]["web_page"] = { + "type": "str", + "required": True, + "show": True, + "name": "web_page", + "value": "", + "display_name": "Web Page", + } + elif name in {"ReadTheDocsLoader"}: + signature["template"]["path"] = { + "type": "str", + "required": True, + "show": True, + "name": "path", + "value": "", + "display_name": "Web Page", + } + + return signature + except ValueError as exc: + raise ValueError(f"Documment Loader {name} not found") from exc + except AttributeError as exc: + logger.error(f"Documment Loader {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return [ + documentloader.__name__ + for documentloader in self.type_to_loader_dict.values() + if documentloader.__name__ in settings.documentloaders or settings.dev + ] + + +documentloader_creator = DocumentLoaderCreator() diff --git a/src/backend/langflow/interface/embeddings/__init__.py b/src/backend/langflow/interface/embeddings/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/embeddings/base.py b/src/backend/langflow/interface/embeddings/base.py index 25038a8a5..061b1d3b5 100644 --- a/src/backend/langflow/interface/embeddings/base.py +++ b/src/backend/langflow/interface/embeddings/base.py @@ -3,6 +3,7 @@ from typing import Dict, List, Optional from langflow.interface.base import LangChainTypeCreator from langflow.interface.custom_lists import embedding_type_to_cls_dict from langflow.settings import settings +from langflow.utils.logger import logger from langflow.utils.util import build_template_from_class @@ -20,6 +21,10 @@ class EmbeddingCreator(LangChainTypeCreator): except ValueError as exc: raise ValueError(f"Embedding {name} not found") from exc + except AttributeError as exc: + logger.error(f"Embedding {name} not loaded: {exc}") + return None + def to_list(self) -> List[str]: return [ embedding.__name__ diff --git a/src/backend/langflow/interface/importing/utils.py b/src/backend/langflow/interface/importing/utils.py index 45ad2ba06..a3480928e 100644 --- a/src/backend/langflow/interface/importing/utils.py +++ b/src/backend/langflow/interface/importing/utils.py @@ -40,6 +40,10 @@ def import_by_type(_type: str, name: str) -> Any: "toolkits": import_toolkit, "wrappers": import_wrapper, "memory": import_memory, + "embeddings": import_embedding, + "vectorstores": import_vectorstore, + "documentloaders": import_documentloader, + "textsplitters": import_textsplitter, } if _type == "llms": key = "chat" if "chat" in name.lower() else "llm" @@ -113,3 +117,24 @@ def import_chain(chain: str) -> Type[Chain]: if chain in CUSTOM_CHAINS: return CUSTOM_CHAINS[chain] return import_class(f"langchain.chains.{chain}") + + +def import_embedding(embedding: str) -> Any: + """Import embedding from embedding name""" + return import_class(f"langchain.embeddings.{embedding}") + + +def import_vectorstore(vectorstore: str) -> Any: + """Import vectorstore from vectorstore name""" + return import_class(f"langchain.vectorstores.{vectorstore}") + + +def import_documentloader(documentloader: str) -> Any: + """Import documentloader from documentloader name""" + + return import_class(f"langchain.document_loaders.{documentloader}") + + +def import_textsplitter(textsplitter: str) -> Any: + """Import textsplitter from textsplitter name""" + return import_class(f"langchain.text_splitter.{textsplitter}") diff --git a/src/backend/langflow/interface/listing.py b/src/backend/langflow/interface/listing.py index b11b3cef9..cf45fd9c5 100644 --- a/src/backend/langflow/interface/listing.py +++ b/src/backend/langflow/interface/listing.py @@ -1,10 +1,14 @@ from langflow.interface.agents.base import agent_creator from langflow.interface.chains.base import chain_creator +from langflow.interface.document_loaders.base import documentloader_creator +from langflow.interface.embeddings.base import embedding_creator from langflow.interface.llms.base import llm_creator from langflow.interface.memories.base import memory_creator from langflow.interface.prompts.base import prompt_creator +from langflow.interface.text_splitters.base import textsplitter_creator from langflow.interface.toolkits.base import toolkits_creator from langflow.interface.tools.base import tool_creator +from langflow.interface.vector_store.base import vectorstore_creator from langflow.interface.wrappers.base import wrapper_creator @@ -18,6 +22,10 @@ def get_type_dict(): "memory": memory_creator.to_list(), "toolkits": toolkits_creator.to_list(), "wrappers": wrapper_creator.to_list(), + "documentLoaders": documentloader_creator.to_list(), + "vectorStore": vectorstore_creator.to_list(), + "embeddings": embedding_creator.to_list(), + "textSplitters": textsplitter_creator.to_list(), } diff --git a/src/backend/langflow/interface/llms/base.py b/src/backend/langflow/interface/llms/base.py index 85f9035db..04a36eb2d 100644 --- a/src/backend/langflow/interface/llms/base.py +++ b/src/backend/langflow/interface/llms/base.py @@ -1,14 +1,20 @@ -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Type from langflow.interface.base import LangChainTypeCreator from langflow.interface.custom_lists import llm_type_to_cls_dict from langflow.settings import settings +from langflow.template.nodes import LLMFrontendNode +from langflow.utils.logger import logger from langflow.utils.util import build_template_from_class class LLMCreator(LangChainTypeCreator): type_name: str = "llms" + @property + def frontend_node_class(self) -> Type[LLMFrontendNode]: + return LLMFrontendNode + @property def type_to_loader_dict(self) -> Dict: if self.type_dict is None: @@ -22,6 +28,10 @@ class LLMCreator(LangChainTypeCreator): except ValueError as exc: raise ValueError("LLM not found") from exc + except AttributeError as exc: + logger.error(f"LLM {name} not loaded: {exc}") + return None + def to_list(self) -> List[str]: return [ llm.__name__ diff --git a/src/backend/langflow/interface/loading.py b/src/backend/langflow/interface/loading.py index 9a8d28d05..ca2017ff3 100644 --- a/src/backend/langflow/interface/loading.py +++ b/src/backend/langflow/interface/loading.py @@ -22,6 +22,7 @@ from langflow.interface.agents.custom import CUSTOM_AGENTS from langflow.interface.importing.utils import import_by_type from langflow.interface.toolkits.base import toolkits_creator from langflow.interface.types import get_type_list +from langflow.interface.utils import load_file_into_dict from langflow.utils import util, validate @@ -36,29 +37,44 @@ def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any: if base_type == "agents": # We need to initialize it differently return load_agent_executor(class_object, params) - elif node_type == "ZeroShotPrompt": - if "tools" not in params: - params["tools"] = [] - return ZeroShotAgent.create_prompt(**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") + elif base_type == "prompts": + if node_type == "ZeroShotPrompt": + if "tools" not in params: + params["tools"] = [] + return ZeroShotAgent.create_prompt(**params) + elif base_type == "tools": + if node_type == "JsonSpec": + params["dict_"] = load_file_into_dict(params.pop("path")) + 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") elif base_type == "toolkits": loaded_toolkit = class_object(**params) # Check if node_type has a loader if toolkits_creator.has_create_function(node_type): return load_toolkits_executor(node_type, loaded_toolkit, params) return loaded_toolkit - else: + elif base_type == "embeddings": + params.pop("model") return class_object(**params) + elif base_type == "vectorstores": + return class_object.from_documents(**params) + elif base_type == "documentloaders": + return class_object(**params).load() + elif base_type == "textsplitters": + documents = params.pop("documents") + text_splitter = class_object(**params) + return text_splitter.split_documents(documents) + + return class_object(**params) def load_flow_from_json(path: str): diff --git a/src/backend/langflow/interface/memories/base.py b/src/backend/langflow/interface/memories/base.py index 7db99c0a1..f26b09351 100644 --- a/src/backend/langflow/interface/memories/base.py +++ b/src/backend/langflow/interface/memories/base.py @@ -5,6 +5,7 @@ from langflow.interface.custom_lists import memory_type_to_cls_dict from langflow.settings import settings from langflow.template.base import FrontendNode from langflow.template.nodes import MemoryFrontendNode +from langflow.utils.logger import logger from langflow.utils.util import build_template_from_class @@ -28,6 +29,9 @@ class MemoryCreator(LangChainTypeCreator): return build_template_from_class(name, memory_type_to_cls_dict) except ValueError as exc: raise ValueError("Memory not found") from exc + except AttributeError as exc: + logger.error(f"Memory {name} not loaded: {exc}") + return None def to_list(self) -> List[str]: return [ diff --git a/src/backend/langflow/interface/prompts/base.py b/src/backend/langflow/interface/prompts/base.py index 73f842833..5f83a5412 100644 --- a/src/backend/langflow/interface/prompts/base.py +++ b/src/backend/langflow/interface/prompts/base.py @@ -7,6 +7,7 @@ from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class from langflow.settings import settings from langflow.template.nodes import PromptFrontendNode +from langflow.utils.logger import logger from langflow.utils.util import build_template_from_class @@ -43,7 +44,11 @@ class PromptCreator(LangChainTypeCreator): return get_custom_nodes(self.type_name)[name] return build_template_from_class(name, self.type_to_loader_dict) except ValueError as exc: - raise ValueError("Prompt not found") from exc + # raise ValueError("Prompt not found") from exc + logger.error(f"Prompt {name} not found: {exc}") + except AttributeError as exc: + logger.error(f"Prompt {name} not loaded: {exc}") + return None def to_list(self) -> List[str]: custom_prompts = get_custom_nodes("prompts") diff --git a/src/backend/langflow/interface/run.py b/src/backend/langflow/interface/run.py index c413dfc4f..74dd1aa8f 100644 --- a/src/backend/langflow/interface/run.py +++ b/src/backend/langflow/interface/run.py @@ -2,7 +2,7 @@ import contextlib import io from typing import Any, Dict -from langflow.cache.utils import compute_hash, load_cache, save_cache +from langflow.cache.utils import compute_dict_hash, load_cache, memoize_dict from langflow.graph.graph import Graph from langflow.interface import loading from langflow.utils.logger import logger @@ -12,7 +12,7 @@ def load_langchain_object(data_graph, is_first_message=False): """ Load langchain object from cache if it exists, otherwise build it. """ - computed_hash = compute_hash(data_graph) + computed_hash = compute_dict_hash(data_graph) if is_first_message: langchain_object = build_langchain_object(data_graph) else: @@ -22,6 +22,32 @@ def load_langchain_object(data_graph, is_first_message=False): return computed_hash, langchain_object +def load_or_build_langchain_object(data_graph, is_first_message=False): + """ + Load langchain object from cache if it exists, otherwise build it. + """ + if is_first_message: + build_langchain_object_with_caching.clear_cache() + return build_langchain_object_with_caching(data_graph) + + +@memoize_dict(maxsize=1) +def build_langchain_object_with_caching(data_graph): + """ + Build langchain object from data_graph. + """ + + logger.debug("Building langchain object") + nodes = data_graph["nodes"] + # Add input variables + # nodes = payload.extract_input_variables(nodes) + # Nodes, edges and root node + edges = data_graph["edges"] + graph = Graph(nodes, edges) + + return graph.build() + + def build_langchain_object(data_graph): """ Build langchain object from data_graph. @@ -67,11 +93,35 @@ def process_graph(data_graph: Dict[str, Any]): # We have to save it here because if the # memory is updated we need to keep the new values logger.debug("Saving langchain object to cache") - save_cache(computed_hash, langchain_object, is_first_message) + # save_cache(computed_hash, langchain_object, is_first_message) logger.debug("Saved langchain object to cache") return {"result": str(result), "thought": thought.strip()} +def process_graph_cached(data_graph: Dict[str, Any]): + """ + Process graph by extracting input variables and replacing ZeroShotPrompt + with PromptTemplate,then run the graph and return the result and thought. + """ + # Load langchain object + message = data_graph.pop("message", "") + is_first_message = len(data_graph.get("chatHistory", [])) == 0 + langchain_object = load_or_build_langchain_object(data_graph, is_first_message) + logger.debug("Loaded langchain object") + + if langchain_object is None: + # Raise user facing error + raise ValueError( + "There was an error loading the langchain_object. Please, check all the nodes and try again." + ) + + # Generate result and thought + logger.debug("Generating result and thought") + result, thought = get_result_and_thought_using_graph(langchain_object, message) + logger.debug("Generated result and thought") + return {"result": str(result), "thought": thought.strip()} + + def get_memory_key(langchain_object): """ Given a LangChain object, this function retrieves the current memory key from the object's memory attribute. diff --git a/src/backend/langflow/interface/text_splitters/__init__.py b/src/backend/langflow/interface/text_splitters/__init__.py new file mode 100644 index 000000000..4bb9dd1b0 --- /dev/null +++ b/src/backend/langflow/interface/text_splitters/__init__.py @@ -0,0 +1,3 @@ +from langflow.interface.text_splitters.base import TextSplitterCreator + +__all__ = ["TextSplitterCreator"] diff --git a/src/backend/langflow/interface/text_splitters/base.py b/src/backend/langflow/interface/text_splitters/base.py new file mode 100644 index 000000000..e58a5bfa6 --- /dev/null +++ b/src/backend/langflow/interface/text_splitters/base.py @@ -0,0 +1,71 @@ +from typing import Dict, List, Optional + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import textsplitter_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class TextSplitterCreator(LangChainTypeCreator): + type_name: str = "textsplitters" + + @property + def type_to_loader_dict(self) -> Dict: + return textsplitter_type_to_cls_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of a text splitter.""" + try: + signature = build_template_from_class(name, textsplitter_type_to_cls_dict) + + signature["template"]["documents"] = { + "type": "BaseLoader", + "required": True, + "show": True, + "name": "documents", + } + + signature["template"]["separator"] = { + "type": "str", + "required": True, + "show": True, + "value": ".", + "name": "separator", + "display_name": "Separator", + } + + signature["template"]["chunk_size"] = { + "type": "int", + "required": True, + "show": True, + "value": 4000, + "name": "chunk_size", + "display_name": "Chunk Size", + } + + signature["template"]["chunk_overlap"] = { + "type": "int", + "required": True, + "show": True, + "value": 200, + "name": "chunk_overlap", + "display_name": "Chunk Overlap", + } + + return signature + except ValueError as exc: + raise ValueError(f"Text Splitter {name} not found") from exc + except AttributeError as exc: + logger.error(f"Text Splitter {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return [ + textsplitter.__name__ + for textsplitter in self.type_to_loader_dict.values() + if textsplitter.__name__ in settings.textsplitters or settings.dev + ] + + +textsplitter_creator = TextSplitterCreator() diff --git a/src/backend/langflow/interface/toolkits/base.py b/src/backend/langflow/interface/toolkits/base.py index 5346ae4b5..cbe625f0d 100644 --- a/src/backend/langflow/interface/toolkits/base.py +++ b/src/backend/langflow/interface/toolkits/base.py @@ -5,6 +5,7 @@ from langchain.agents import agent_toolkits from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class, import_module from langflow.settings import settings +from langflow.utils.logger import logger from langflow.utils.util import build_template_from_class @@ -44,6 +45,9 @@ class ToolkitCreator(LangChainTypeCreator): return build_template_from_class(name, self.type_to_loader_dict) except ValueError as exc: raise ValueError("Prompt not found") from exc + except AttributeError as exc: + logger.error(f"Prompt {name} not loaded: {exc}") + return None def to_list(self) -> List[str]: return list(self.type_to_loader_dict.keys()) diff --git a/src/backend/langflow/interface/toolkits/custom.py b/src/backend/langflow/interface/toolkits/custom.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/tools/base.py b/src/backend/langflow/interface/tools/base.py index 8a89c429d..f175a9902 100644 --- a/src/backend/langflow/interface/tools/base.py +++ b/src/backend/langflow/interface/tools/base.py @@ -47,12 +47,14 @@ TOOL_INPUTS = { value="", multiline=True, ), - "dict_": TemplateField( + "path": TemplateField( field_type="file", required=True, is_list=False, show=True, value="", + suffixes=[".json", ".yaml", ".yml"], + fileTypes=["json", "yaml", "yml"], ), } @@ -114,6 +116,8 @@ class ToolCreator(LangChainTypeCreator): return node elif tool_type in FILE_TOOLS: params = all_tools[name]["params"] # type: ignore + if tool_type == "JsonSpec": + params["path"] = params.pop("dict_") # type: ignore base_classes += [name] else: params = [] diff --git a/src/backend/langflow/interface/tools/constants.py b/src/backend/langflow/interface/tools/constants.py index 89a6bad01..2ec20cef9 100644 --- a/src/backend/langflow/interface/tools/constants.py +++ b/src/backend/langflow/interface/tools/constants.py @@ -7,7 +7,7 @@ from langchain.agents.load_tools import ( ) from langchain.tools.json.tool import JsonSpec -from langflow.interface.custom.types import PythonFunction +from langflow.interface.tools.custom import PythonFunction FILE_TOOLS = {"JsonSpec": JsonSpec} CUSTOM_TOOLS = {"Tool": Tool, "PythonFunction": PythonFunction} diff --git a/src/backend/langflow/interface/custom/types.py b/src/backend/langflow/interface/tools/custom.py similarity index 100% rename from src/backend/langflow/interface/custom/types.py rename to src/backend/langflow/interface/tools/custom.py diff --git a/src/backend/langflow/interface/types.py b/src/backend/langflow/interface/types.py index bf3cea372..fd5d9ec3e 100644 --- a/src/backend/langflow/interface/types.py +++ b/src/backend/langflow/interface/types.py @@ -1,13 +1,14 @@ from langflow.interface.agents.base import agent_creator from langflow.interface.chains.base import chain_creator -from langflow.interface.documentLoaders.base import documentloader_creator +from langflow.interface.document_loaders.base import documentloader_creator from langflow.interface.embeddings.base import embedding_creator from langflow.interface.llms.base import llm_creator from langflow.interface.memories.base import memory_creator from langflow.interface.prompts.base import prompt_creator +from langflow.interface.text_splitters.base import textsplitter_creator from langflow.interface.toolkits.base import toolkits_creator from langflow.interface.tools.base import tool_creator -from langflow.interface.vectorStore.base import vectorstore_creator +from langflow.interface.vector_store.base import vectorstore_creator from langflow.interface.wrappers.base import wrapper_creator @@ -40,6 +41,7 @@ def build_langchain_types_dict(): # sourcery skip: dict-assign-update-to-union embedding_creator, vectorstore_creator, documentloader_creator, + textsplitter_creator, ] all_types = {} diff --git a/src/backend/langflow/interface/utils.py b/src/backend/langflow/interface/utils.py new file mode 100644 index 000000000..b3b154790 --- /dev/null +++ b/src/backend/langflow/interface/utils.py @@ -0,0 +1,22 @@ +import json +import os + +import yaml + + +def load_file_into_dict(file_path: str) -> dict: + if not os.path.exists(file_path): + raise FileNotFoundError(f"File not found: {file_path}") + + file_extension = os.path.splitext(file_path)[1].lower() + + if file_extension == ".json": + with open(file_path, "r") as json_file: + data = json.load(json_file) + elif file_extension in [".yaml", ".yml"]: + with open(file_path, "r") as yaml_file: + data = yaml.safe_load(yaml_file) + else: + raise ValueError("Unsupported file type. Please provide a JSON or YAML file.") + + return data diff --git a/src/backend/langflow/interface/vectorStore/base.py b/src/backend/langflow/interface/vectorStore/base.py deleted file mode 100644 index 6c2baa86d..000000000 --- a/src/backend/langflow/interface/vectorStore/base.py +++ /dev/null @@ -1,31 +0,0 @@ -from typing import Dict, List, Optional - -from langflow.interface.base import LangChainTypeCreator -from langflow.interface.custom_lists import vectorstores_type_to_cls_dict -from langflow.settings import settings -from langflow.utils.util import build_template_from_class - - -class VectorstoreCreator(LangChainTypeCreator): - type_name: str = "vectorstore" - - @property - def type_to_loader_dict(self) -> Dict: - return vectorstores_type_to_cls_dict - - def get_signature(self, name: str) -> Optional[Dict]: - """Get the signature of an embedding.""" - try: - return build_template_from_class(name, vectorstores_type_to_cls_dict) - except ValueError as exc: - raise ValueError(f"Vector Store {name} not found") from exc - - def to_list(self) -> List[str]: - return [ - vectorstore - for vectorstore in self.type_to_loader_dict.keys() - if vectorstore in settings.vectorstores or settings.dev - ] - - -vectorstore_creator = VectorstoreCreator() diff --git a/src/backend/langflow/interface/vector_store/__init__.py b/src/backend/langflow/interface/vector_store/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/interface/vector_store/base.py b/src/backend/langflow/interface/vector_store/base.py new file mode 100644 index 000000000..7fca2ba0c --- /dev/null +++ b/src/backend/langflow/interface/vector_store/base.py @@ -0,0 +1,55 @@ +from typing import Dict, List, Optional + +from langflow.interface.base import LangChainTypeCreator +from langflow.interface.custom_lists import vectorstores_type_to_cls_dict +from langflow.settings import settings +from langflow.utils.logger import logger +from langflow.utils.util import build_template_from_class + + +class VectorstoreCreator(LangChainTypeCreator): + type_name: str = "vectorstores" + + @property + def type_to_loader_dict(self) -> Dict: + return vectorstores_type_to_cls_dict + + def get_signature(self, name: str) -> Optional[Dict]: + """Get the signature of an embedding.""" + try: + signature = build_template_from_class(name, vectorstores_type_to_cls_dict) + + # TODO: Use FrontendendNode class to build the signature + signature["template"] = { + "documents": { + "type": "TextSplitter", + "required": True, + "show": True, + "name": "documents", + "display_name": "Text Splitter", + }, + "embedding": { + "type": "Embeddings", + "required": True, + "show": True, + "name": "embedding", + "display_name": "Embedding", + }, + } + return signature + + except ValueError as exc: + raise ValueError(f"Vector Store {name} not found") from exc + except AttributeError as exc: + logger.error(f"Vector Store {name} not loaded: {exc}") + return None + + def to_list(self) -> List[str]: + return [ + vectorstore + for vectorstore in self.type_to_loader_dict.keys() + if vectorstore in settings.vectorstores or settings.dev + ] + + +vectorstore_creator = VectorstoreCreator() diff --git a/src/backend/langflow/interface/wrappers/base.py b/src/backend/langflow/interface/wrappers/base.py index 8d0df8141..f5773d07a 100644 --- a/src/backend/langflow/interface/wrappers/base.py +++ b/src/backend/langflow/interface/wrappers/base.py @@ -3,6 +3,7 @@ from typing import Dict, List, Optional from langchain import requests from langflow.interface.base import LangChainTypeCreator +from langflow.utils.logger import logger from langflow.utils.util import build_template_from_class @@ -22,6 +23,9 @@ class WrapperCreator(LangChainTypeCreator): return build_template_from_class(name, self.type_to_loader_dict) except ValueError as exc: raise ValueError("Wrapper not found") from exc + except AttributeError as exc: + logger.error(f"Wrapper {name} not loaded: {exc}") + return None def to_list(self) -> List[str]: return list(self.type_to_loader_dict.keys()) diff --git a/src/backend/langflow/settings.py b/src/backend/langflow/settings.py index c0fd66c58..c5377c85a 100644 --- a/src/backend/langflow/settings.py +++ b/src/backend/langflow/settings.py @@ -17,6 +17,7 @@ class Settings(BaseSettings): documentloaders: List[str] = [] wrappers: List[str] = [] toolkits: List[str] = [] + textsplitters: List[str] = [] dev: bool = False class Config: @@ -40,6 +41,7 @@ class Settings(BaseSettings): self.memories = new_settings.memories or [] self.wrappers = new_settings.wrappers or [] self.toolkits = new_settings.toolkits or [] + self.textsplitters = new_settings.textsplitters or [] self.dev = new_settings.dev or False diff --git a/src/backend/langflow/template/nodes.py b/src/backend/langflow/template/nodes.py index c5d9bf23d..c31f3c40f 100644 --- a/src/backend/langflow/template/nodes.py +++ b/src/backend/langflow/template/nodes.py @@ -248,6 +248,93 @@ class CSVAgentNode(FrontendNode): return super().to_dict() +class VectorStoreAgentNode(FrontendNode): + name: str = "VectorStoreAgent" + template: Template = Template( + type_name="vectorstore_agent", + fields=[ + TemplateField( + field_type="VectorStoreInfo", + required=True, + show=True, + name="vectorstoreinfo", + display_name="Vector Store Info", + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + display_name="LLM", + ), + ], + ) + description: str = """Construct an agent from a Vector Store.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + +class VectorStoreRouterAgentNode(FrontendNode): + name: str = "VectorStoreRouterAgent" + template: Template = Template( + type_name="vectorstorerouter_agent", + fields=[ + TemplateField( + field_type="VectorStoreRouterToolkit", + required=True, + show=True, + name="vectorstoreroutertoolkit", + display_name="Vector Store Router Toolkit", + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + display_name="LLM", + ), + ], + ) + description: str = """Construct an agent from a Vector Store Router.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + +class SQLAgentNode(FrontendNode): + name: str = "SQLAgent" + template: Template = Template( + type_name="sql_agent", + fields=[ + TemplateField( + field_type="str", + required=True, + placeholder="", + is_list=False, + show=True, + multiline=False, + value="", + name="database_uri", + ), + TemplateField( + field_type="BaseLanguageModel", + required=True, + show=True, + name="llm", + display_name="LLM", + ), + ], + ) + description: str = """Construct an agent from a Vector Store Router.""" + base_classes: list[str] = ["AgentExecutor"] + + def to_dict(self): + return super().to_dict() + + class PromptFrontendNode(FrontendNode): @staticmethod def format_field(field: TemplateField, name: Optional[str] = None) -> None: @@ -275,6 +362,9 @@ class PromptFrontendNode(FrontendNode): ): field.field_type = "BaseMessagePromptTemplate" + # All prompt fields should be password=False + field.password = False + class MemoryFrontendNode(FrontendNode): @staticmethod @@ -289,6 +379,7 @@ class MemoryFrontendNode(FrontendNode): field.field_type = "int" field.value = 10 field.display_name = "Memory Size" + field.password = False class ChainFrontendNode(FrontendNode): @@ -299,3 +390,39 @@ class ChainFrontendNode(FrontendNode): if "key" in field.name: field.password = False field.show = False + if field.name in ["input_key", "output_key"]: + field.required = True + field.show = True + # Separated for possible future changes + if field.name == "prompt": + # if no prompt is provided, use the default prompt + field.required = False + field.show = True + + +class LLMFrontendNode(FrontendNode): + @staticmethod + def format_field(field: TemplateField, name: Optional[str] = None) -> None: + display_names_dict = { + "huggingfacehub_api_token": "HuggingFace Hub API Token", + } + FrontendNode.format_field(field, name) + SHOW_FIELDS = ["repo_id", "task", "model_kwargs"] + if field.name in SHOW_FIELDS: + field.show = True + + if "api" in field.name and ("key" in field.name or "token" in field.name): + field.password = True + field.show = True + field.required = True + + if field.name == "task": + field.required = True + field.show = True + field.is_list = True + field.options = ["text-generation", "text2text-generation"] + + if display_name := display_names_dict.get(field.name): + field.display_name = display_name + if field.name == "model_kwargs": + field.field_type = "code" diff --git a/src/backend/langflow/utils/util.py b/src/backend/langflow/utils/util.py index 1ca73746b..b31a3bed1 100644 --- a/src/backend/langflow/utils/util.py +++ b/src/backend/langflow/utils/util.py @@ -3,6 +3,8 @@ import inspect import re from typing import Dict, Optional +from docstring_parser import parse # type: ignore + from langflow.template.constants import FORCE_SHOW_FIELDS from langflow.utils import constants @@ -65,7 +67,8 @@ def build_template_from_function( if v.__annotations__["return"].__name__ == name: _class = v.__annotations__["return"] - docs = get_class_doc(_class) + # Get the docstring + docs = parse(_class.__doc__) variables = {"_type": _type} for class_field_items, value in _class.__fields__.items(): @@ -86,8 +89,8 @@ def build_template_from_function( variables[class_field_items][name_] = value_ variables[class_field_items]["placeholder"] = ( - docs["Attributes"][class_field_items] - if class_field_items in docs["Attributes"] + docs.params[class_field_items] + if class_field_items in docs.params else "" ) # Adding function to base classes to allow @@ -98,7 +101,7 @@ def build_template_from_function( return { "template": format_dict(variables, name), - "description": docs["Description"], + "description": docs.short_description or "", "base_classes": base_classes, } @@ -117,7 +120,7 @@ def build_template_from_class( _class = v # Get the docstring - docs = get_class_doc(_class) + docs = parse(_class.__doc__) variables = {"_type": _type} @@ -140,8 +143,8 @@ def build_template_from_class( variables[class_field_items][name_] = value_ variables[class_field_items]["placeholder"] = ( - docs["Attributes"][class_field_items] - if class_field_items in docs["Attributes"] + docs.params[class_field_items] + if class_field_items in docs.params else "" ) base_classes = get_base_classes(_class) @@ -151,7 +154,7 @@ def build_template_from_class( base_classes.append("function") return { "template": format_dict(variables, name), - "description": docs["Description"], + "description": docs.short_description or "", "base_classes": base_classes, } @@ -188,60 +191,6 @@ def get_default_factory(module: str, function: str): return None -def get_class_doc(class_name): - """ - Extracts information from the docstring of a given class. - - Args: - class_name: the class to extract information from - - Returns: - A dictionary containing the extracted information, with keys - for 'Description', 'Parameters', 'Attributes', and 'Returns'. - """ - # Template - data = { - "Description": "", - "Parameters": {}, - "Attributes": {}, - "Example": [], - "Returns": {}, - } - - # Get the class docstring - docstring = class_name.__doc__ - - if not docstring: - return data - - # Parse the docstring to extract information - lines = docstring.split("\n") - - current_section = "Description" - - for line in lines: - line = line.strip() - - if not line: - continue - - if ( - line.startswith(tuple(data.keys())) - and len(line.split()) == 1 - and line.endswith(":") - ): - current_section = line[:-1] - continue - - if current_section in ["Description", "Example"]: - data[current_section] += line - else: - param, desc = line.split(":") - data[current_section][param.strip()] = desc.strip() - - return data - - def format_dict(d, name: Optional[str] = None): """ Formats a dictionary by removing certain keys and modifying the diff --git a/src/frontend/src/App.css b/src/frontend/src/App.css index 60ff72aba..319e2ba39 100644 --- a/src/frontend/src/App.css +++ b/src/frontend/src/App.css @@ -40,3 +40,8 @@ transform: rotate(360deg); } } + +@font-face{ + font-family: text-security-disc; + src: url("assets/text-security-disc.woff") format("woff"); +} \ No newline at end of file diff --git a/src/frontend/src/assets/text-security-disc.woff b/src/frontend/src/assets/text-security-disc.woff new file mode 100644 index 000000000..bc3cd0e63 Binary files /dev/null and b/src/frontend/src/assets/text-security-disc.woff differ diff --git a/src/frontend/src/components/inputComponent/index.tsx b/src/frontend/src/components/inputComponent/index.tsx index 66ac9b862..5858d4bf6 100644 --- a/src/frontend/src/components/inputComponent/index.tsx +++ b/src/frontend/src/components/inputComponent/index.tsx @@ -23,7 +23,7 @@ export default function InputComponent({ className={classNames( "block w-full form-input dark:bg-gray-900 dark:border-gray-600 rounded-md border-gray-300 shadow-sm focus:border-indigo-500 focus:ring-indigo-500 sm:text-sm", disabled ? " bg-gray-200 dark:bg-gray-700" : "", - password?"password":"" + password && myValue.length>0?"password":"" )} placeholder="Type a text" onChange={(e) => { diff --git a/src/frontend/src/pages/FlowPage/index.tsx b/src/frontend/src/pages/FlowPage/index.tsx index 602e0cd7f..4fc315926 100644 --- a/src/frontend/src/pages/FlowPage/index.tsx +++ b/src/frontend/src/pages/FlowPage/index.tsx @@ -6,9 +6,10 @@ import ReactFlow, { useEdgesState, useNodesState, useReactFlow, - ControlButton, + updateEdge, EdgeChange, Connection, + Edge, } from "reactflow"; import { locationContext } from "../../contexts/locationContext"; import ExtraSidebar from "./components/extraSidebarComponent"; @@ -20,6 +21,7 @@ import { typesContext } from "../../contexts/typesContext"; import ConnectionLineComponent from "./components/ConnectionLineComponent"; import { FlowType, NodeType } from "../../types/flow"; import { APIClassType } from "../../types/api"; +import { isValidConnection } from "../../utils"; const nodeTypes = { genericNode: GenericNode, @@ -28,7 +30,7 @@ const nodeTypes = { var _ = require("lodash"); export default function FlowPage({ flow }:{flow:FlowType}) { - let { updateFlow, incrementNodeId, downloadFlow, uploadFlow } = + let { updateFlow, incrementNodeId} = useContext(TabsContext); const { types, reactFlowInstance, setReactFlowInstance } = useContext(typesContext); @@ -43,6 +45,7 @@ export default function FlowPage({ flow }:{flow:FlowType}) { flow.data?.edges ?? [] ); const { setViewport } = useReactFlow(); + const edgeUpdateSuccessful = useRef(true) useEffect(() => { if (reactFlowInstance && flow) { @@ -155,6 +158,26 @@ export default function FlowPage({ flow }:{flow:FlowType}) { setEdges(edges.filter((ns) => !nodes.some((n) => ns.source === n.id || ns.target === n.id))); } + const onEdgeUpdateStart = useCallback(() => { + edgeUpdateSuccessful.current = false; + }, []); + + + const onEdgeUpdate = useCallback((oldEdge:Edge, newConnection:Connection) => { + if(isValidConnection(newConnection,reactFlowInstance)){ + edgeUpdateSuccessful.current = true; + setEdges((els) => updateEdge(oldEdge, newConnection, els)); + } + }, []); + + const onEdgeUpdateEnd = useCallback((_, edge) => { + if (!edgeUpdateSuccessful.current) { + setEdges((eds) => eds.filter((e) => e.id !== edge.id)); + } + + edgeUpdateSuccessful.current = true; + }, []); + return (
{Object.keys(types).length > 0 ? ( @@ -171,6 +194,9 @@ export default function FlowPage({ flow }:{flow:FlowType}) { onLoad={setReactFlowInstance} onInit={setReactFlowInstance} nodeTypes={nodeTypes} + onEdgeUpdate={onEdgeUpdate} + onEdgeUpdateStart={onEdgeUpdateStart} + onEdgeUpdateEnd={onEdgeUpdateEnd} connectionLineComponent={ConnectionLineComponent} onDragOver={onDragOver} onDrop={onDrop} diff --git a/src/frontend/src/utils.ts b/src/frontend/src/utils.ts index b5e804bd6..5ab47f31e 100644 --- a/src/frontend/src/utils.ts +++ b/src/frontend/src/utils.ts @@ -9,7 +9,11 @@ import { ComputerDesktopIcon, Bars3CenterLeftIcon, GiftIcon, + PaperClipIcon, QuestionMarkCircleIcon, + FingerPrintIcon, + ScissorsIcon, + CircleStackIcon } from "@heroicons/react/24/outline"; import { Connection, Edge, Node, ReactFlowInstance } from "reactflow"; import { FlowType } from "./types/flow"; @@ -71,11 +75,14 @@ export const nodeColors: {[char: string]: string} = { chains: "#FE7500", agents: "#903BBE", tools: "#FF3434", - memories: "#FF9135", + memories: "#F5B85A", advanced: "#000000", chat: "#454173", thought:"#272541", - docloaders:"#FF9135", + embeddings:"#42BAA7", + documentloaders:"#7AAE42", + vectorstores: "#AA8742", + textsplitters: "#B47CB5", toolkits:"#DB2C2C", wrappers:"#E6277A", unknown:"#9CA3AF" @@ -90,9 +97,12 @@ export const nodeNames:{[char: string]: string} = { memories: "Memories", advanced: "Advanced", chat: "Chat", - docloaders:"Document Loader", + embeddings: "Embeddings", + documentloaders: "Document Loaders", + vectorstores: "Vector Stores", toolkits:"Toolkits", wrappers:"Wrappers", + textsplitters: "Text Splitters", unknown:"Unknown" }; @@ -105,8 +115,11 @@ export const nodeIcons:{[char: string]: React.ForwardRefExoticComponent Union[Node, None]: """Get a node by type""" return next((node for node in graph.nodes if isinstance(node, node_type)), None) @@ -418,3 +421,28 @@ def test_wrapper_node_build(openapi_graph): built_object = wrapper_node.build() assert built_object is not None # Add any further assertions specific to the WrapperNode's build() method + + +def test_get_result_and_thought(basic_graph): + """Test the get_result_and_thought method""" + responses = [ + "Final Answer: I am a response", + ] + message = "Hello" + # Find the node that is an LLMNode and change the + # _built_object to a FakeListLLM + llm_node = get_node_by_type(basic_graph, LLMNode) + assert llm_node is not None + llm_node._built_object = FakeListLLM(responses=responses) + llm_node._built = True + langchain_object = basic_graph.build() + # assert all nodes are built + assert all(node._built for node in basic_graph.nodes) + # now build again and check if FakeListLLM was used + + # Get the result and thought + result, thought = get_result_and_thought_using_graph(langchain_object, message) + # The result should be a str + assert isinstance(result, str) + # The thought should be a Thought + assert isinstance(thought, str) diff --git a/tests/test_llms_template.py b/tests/test_llms_template.py index e5bfe7e6e..2e6ee606b 100644 --- a/tests/test_llms_template.py +++ b/tests/test_llms_template.py @@ -53,7 +53,7 @@ def test_hugging_face_hub(client: TestClient): assert template["repo_id"] == { "required": False, "placeholder": "", - "show": False, + "show": True, "multiline": False, "value": "gpt2", "password": False, @@ -62,19 +62,20 @@ def test_hugging_face_hub(client: TestClient): "list": False, } assert template["task"] == { - "required": False, + "required": True, "placeholder": "", - "show": False, + "show": True, "multiline": False, "password": False, + "options": ["text-generation", "text2text-generation"], "name": "task", "type": "str", - "list": False, + "list": True, } assert template["model_kwargs"] == { "required": False, "placeholder": "", - "show": False, + "show": True, "multiline": False, "password": False, "name": "model_kwargs", @@ -82,12 +83,13 @@ def test_hugging_face_hub(client: TestClient): "list": False, } assert template["huggingfacehub_api_token"] == { - "required": False, + "required": True, "placeholder": "", "show": True, "multiline": False, "password": True, "name": "huggingfacehub_api_token", + "display_name": "HuggingFace Hub API Token", "type": "str", "list": False, } @@ -231,7 +233,7 @@ def test_openai(client: TestClient): assert template["model_kwargs"] == { "required": False, "placeholder": "", - "show": False, + "show": True, "multiline": False, "password": False, "name": "model_kwargs", @@ -361,7 +363,7 @@ def test_chat_open_ai(client: TestClient): assert template["model_kwargs"] == { "required": False, "placeholder": "", - "show": False, + "show": True, "multiline": False, "password": False, "name": "model_kwargs", @@ -438,7 +440,7 @@ def test_chat_open_ai(client: TestClient): assert template["_type"] == "ChatOpenAI" assert ( model["description"] - == "Wrapper around OpenAI Chat large language models.To use, you should have the ``openai`` python package installed, and theenvironment variable ``OPENAI_API_KEY`` set with your API key.Any parameters that are valid to be passed to the openai.create call can be passedin, even if not explicitly saved on this class." # noqa E501 + == "Wrapper around OpenAI Chat large language models." # noqa E501 ) assert set(model["base_classes"]) == { "BaseChatModel", diff --git a/tests/test_template.py b/tests/test_template.py index b5a424dab..a9b5a71ed 100644 --- a/tests/test_template.py +++ b/tests/test_template.py @@ -8,7 +8,6 @@ from langflow.utils.util import ( build_template_from_function, format_dict, get_base_classes, - get_class_doc, get_default_factory, ) from pydantic import BaseModel @@ -280,12 +279,3 @@ def test_get_default_factory(): default_value = get_default_factory(module_name, function_repr) assert default_value == "default_value" - - -# Test get_class_doc -def test_get_class_doc(): - class_doc_parent = get_class_doc(Parent) - class_doc_child = get_class_doc(Child) - - assert class_doc_parent["Description"] == "Parent Class" - assert class_doc_child["Description"] == "Child Class" diff --git a/tests/test_vectorstore_template.py b/tests/test_vectorstore_template.py new file mode 100644 index 000000000..5b1d7e5bc --- /dev/null +++ b/tests/test_vectorstore_template.py @@ -0,0 +1,12 @@ +from fastapi.testclient import TestClient +from langflow.settings import settings + + +# check that all agents are in settings.agents +# are in json_response["agents"] +def test_vectorstores_settings(client: TestClient): + response = client.get("/all") + assert response.status_code == 200 + json_response = response.json() + vectorstores = json_response["vectorstores"] + assert set(vectorstores.keys()) == set(settings.vectorstores)