Merge branch 'dev' into mainPage
This commit is contained in:
Lucas Oliveira
commit
56d0898861
33 changed files with 437 additions and 251 deletions
164
poetry.lock
generated
164
poetry.lock
generated
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@ -1,4 +1,4 @@
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@ -895,23 +916,23 @@ cffi = ">=1.12"
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|
||||
{file = "faiss_cpu-1.7.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c521cb8462f3b00c0c7dfb11caff492bb67816528b947be28a3b76373952c41d"},
|
||||
{file = "faiss_cpu-1.7.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:afdd9fe1141117fed85961fd36ee627c83fc3b9fd47bafb52d3c849cc2f088b7"},
|
||||
{file = "faiss_cpu-1.7.4-cp311-cp311-win_amd64.whl", hash = "sha256:2ff7f57889ea31d945e3b87275be3cad5d55b6261a4e3f51c7aba304d76b81fb"},
|
||||
{file = "faiss_cpu-1.7.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:eeaf92f27d76249fb53c1adafe617b0f217ab65837acf7b4ec818511caf6e3d8"},
|
||||
{file = "faiss_cpu-1.7.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:102b1bd763e9b0c281ac312590af3eaf1c8b663ccbc1145821fe6a9f92b8eaaf"},
|
||||
{file = "faiss_cpu-1.7.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5512da6707c967310c46ff712b00418b7ae28e93cb609726136e826e9f2f14fa"},
|
||||
{file = "faiss_cpu-1.7.4-cp37-cp37m-win_amd64.whl", hash = "sha256:0c2e5b9d8c28c99f990e87379d5bbcc6c914da91ebb4250166864fd12db5755b"},
|
||||
{file = "faiss_cpu-1.7.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:43f67f325393145d360171cd98786fcea6120ce50397319afd3bb78be409fb8a"},
|
||||
{file = "faiss_cpu-1.7.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:6a4e4af194b8fce74c4b770cad67ad1dd1b4673677fc169723e4c50ba5bd97a8"},
|
||||
{file = "faiss_cpu-1.7.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:31bfb7b9cffc36897ae02a983e04c09fe3b8c053110a287134751a115334a1df"},
|
||||
{file = "faiss_cpu-1.7.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:52d7de96abef2340c0d373c1f5cbc78026a3cebb0f8f3a5920920a00210ead1f"},
|
||||
{file = "faiss_cpu-1.7.4-cp38-cp38-win_amd64.whl", hash = "sha256:699feef85b23c2c729d794e26ca69bebc0bee920d676028c06fd0e0becc15c7e"},
|
||||
{file = "faiss_cpu-1.7.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:559a0133f5ed44422acb09ee1ac0acffd90c6666d1bc0d671c18f6e93ad603e2"},
|
||||
{file = "faiss_cpu-1.7.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ea1d71539fe3dc0f1bed41ef954ca701678776f231046bf0ca22ccea5cf5bef6"},
|
||||
{file = "faiss_cpu-1.7.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:12d45e0157024eb3249842163162983a1ac8b458f1a8b17bbf86f01be4585a99"},
|
||||
{file = "faiss_cpu-1.7.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2f0eab359e066d32c874f51a7d4bf6440edeec068b7fe47e6d803c73605a8b4c"},
|
||||
{file = "faiss_cpu-1.7.4-cp39-cp39-win_amd64.whl", hash = "sha256:98459ceeeb735b9df1a5b94572106ffe0a6ce740eb7e4626715dd218657bb4dc"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "fake-useragent"
|
||||
version = "1.1.3"
|
||||
|
|
@ -1211,14 +1265,14 @@ importlib-resources = {version = ">=5.0", markers = "python_version < \"3.10\""}
|
|||
|
||||
[[package]]
|
||||
name = "fastapi"
|
||||
version = "0.95.2"
|
||||
version = "0.96.0"
|
||||
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "fastapi-0.95.2-py3-none-any.whl", hash = "sha256:d374dbc4ef2ad9b803899bd3360d34c534adc574546e25314ab72c0c4411749f"},
|
||||
{file = "fastapi-0.95.2.tar.gz", hash = "sha256:4d9d3e8c71c73f11874bcf5e33626258d143252e329a01002f767306c64fb982"},
|
||||
{file = "fastapi-0.96.0-py3-none-any.whl", hash = "sha256:b8e11fe81e81eab4e1504209917338e0b80f783878a42c2b99467e5e1019a1e9"},
|
||||
{file = "fastapi-0.96.0.tar.gz", hash = "sha256:71232d47c2787446991c81c41c249f8a16238d52d779c0e6b43927d3773dbe3c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1387,14 +1441,14 @@ uritemplate = ">=3.0.1,<5"
|
|||
|
||||
[[package]]
|
||||
name = "google-auth"
|
||||
version = "2.19.0"
|
||||
version = "2.19.1"
|
||||
description = "Google Authentication Library"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "google-auth-2.19.0.tar.gz", hash = "sha256:f39d528077ac540793dd3c22a8706178f157642a67d874db25c640b7fead277e"},
|
||||
{file = "google_auth-2.19.0-py2.py3-none-any.whl", hash = "sha256:be617bfaf77774008e9d177573f782e109188c8a64ae6e744285df5cea3e7df6"},
|
||||
{file = "google-auth-2.19.1.tar.gz", hash = "sha256:a9cfa88b3e16196845e64a3658eb953992129d13ac7337b064c6546f77c17183"},
|
||||
{file = "google_auth-2.19.1-py2.py3-none-any.whl", hash = "sha256:ea165e014c7cbd496558796b627c271aa8c18b4cba79dc1cc962b24c5efdfb85"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2037,14 +2091,14 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
|
|||
|
||||
[[package]]
|
||||
name = "ipython"
|
||||
version = "8.13.2"
|
||||
version = "8.14.0"
|
||||
description = "IPython: Productive Interactive Computing"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "ipython-8.13.2-py3-none-any.whl", hash = "sha256:ffca270240fbd21b06b2974e14a86494d6d29290184e788275f55e0b55914926"},
|
||||
{file = "ipython-8.13.2.tar.gz", hash = "sha256:7dff3fad32b97f6488e02f87b970f309d082f758d7b7fc252e3b19ee0e432dbb"},
|
||||
{file = "ipython-8.14.0-py3-none-any.whl", hash = "sha256:248aca623f5c99a6635bc3857677b7320b9b8039f99f070ee0d20a5ca5a8e6bf"},
|
||||
{file = "ipython-8.14.0.tar.gz", hash = "sha256:1d197b907b6ba441b692c48cf2a3a2de280dc0ac91a3405b39349a50272ca0a1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2376,13 +2430,13 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "langchain-serve"
|
||||
version = "0.0.40"
|
||||
version = "0.0.41"
|
||||
description = "Langchain Serve - serve your langchain apps on Jina AI Cloud."
|
||||
category = "main"
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "langchain-serve-0.0.40.tar.gz", hash = "sha256:c60b173fcf0b682fbb70d34e8f485ce168e2229f55cb5c4ffbc26a5206af1c06"},
|
||||
{file = "langchain-serve-0.0.41.tar.gz", hash = "sha256:fcf0d3ac9e48b5b24825ae2e1d8a383795c5744b18ef61cb74a99020a9e5d46a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4679,14 +4733,14 @@ idna2008 = ["idna"]
|
|||
|
||||
[[package]]
|
||||
name = "rich"
|
||||
version = "13.3.5"
|
||||
version = "13.4.1"
|
||||
description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.7.0"
|
||||
files = [
|
||||
{file = "rich-13.3.5-py3-none-any.whl", hash = "sha256:69cdf53799e63f38b95b9bf9c875f8c90e78dd62b2f00c13a911c7a3b9fa4704"},
|
||||
{file = "rich-13.3.5.tar.gz", hash = "sha256:2d11b9b8dd03868f09b4fffadc84a6a8cda574e40dc90821bd845720ebb8e89c"},
|
||||
{file = "rich-13.4.1-py3-none-any.whl", hash = "sha256:d204aadb50b936bf6b1a695385429d192bc1fdaf3e8b907e8e26f4c4e4b5bf75"},
|
||||
{file = "rich-13.4.1.tar.gz", hash = "sha256:76f6b65ea7e5c5d924ba80e322231d7cb5b5981aa60bfc1e694f1bc097fe6fe1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -5100,14 +5154,14 @@ doc = ["reno", "sphinx", "tornado (>=4.5)"]
|
|||
|
||||
[[package]]
|
||||
name = "textual"
|
||||
version = "0.26.0"
|
||||
version = "0.27.0"
|
||||
description = "Modern Text User Interface framework"
|
||||
category = "main"
|
||||
optional = true
|
||||
python-versions = ">=3.7,<4.0"
|
||||
files = [
|
||||
{file = "textual-0.26.0-py3-none-any.whl", hash = "sha256:1efd04e9f61b3e95fd1c65436d3262f99e3f86cdeb524d13045bb551eb615c02"},
|
||||
{file = "textual-0.26.0.tar.gz", hash = "sha256:78094c83017d2836b726513abdf434cc034a0e68cc45e63b3b056c9b8b7fa673"},
|
||||
{file = "textual-0.27.0-py3-none-any.whl", hash = "sha256:dc45eaf7da330686c56d6f76f59d05fd216ce6aad90fa44ee269881efc622151"},
|
||||
{file = "textual-0.27.0.tar.gz", hash = "sha256:8bdcb09dc35a706ef939b1276ccfdec10eaaee6147b41cb7587cf33298a8dd33"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -6180,4 +6234,4 @@ deploy = ["langchain-serve"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.9,<3.12"
|
||||
content-hash = "2bf357ad30f79c68751b34c991b4a73767ceb628657f4133228d4eb487d8a6fb"
|
||||
content-hash = "4cac7dea0c1222711ba7eed82d5716d5e361d454edb6e0299b387fbb115d2c3d"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow"
|
||||
version = "0.0.79"
|
||||
version = "0.0.80"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Logspace <contact@logspace.ai>"]
|
||||
maintainers = [
|
||||
|
|
@ -22,7 +22,7 @@ langflow = "langflow.__main__:main"
|
|||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.9,<3.12"
|
||||
fastapi = "^0.95.0"
|
||||
fastapi = "^0.96.0"
|
||||
uvicorn = "^0.20.0"
|
||||
beautifulsoup4 = "^4.11.2"
|
||||
google-search-results = "^2.4.1"
|
||||
|
|
@ -57,6 +57,8 @@ jina = "3.15.2"
|
|||
sentence-transformers = "^2.2.2"
|
||||
ctransformers = "^0.2.2"
|
||||
cohere = "^4.6.0"
|
||||
faiss-cpu = "^1.7.4"
|
||||
anthropic = "^0.2.9"
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from langflow.api.base import (
|
|||
PromptValidationResponse,
|
||||
validate_prompt,
|
||||
)
|
||||
from langflow.graph.node.types import VectorStoreNode
|
||||
from langflow.graph.vertex.types import VectorStoreVertex
|
||||
from langflow.interface.run import build_graph
|
||||
from langflow.utils.logger import logger
|
||||
from langflow.utils.validate import validate_code
|
||||
|
|
@ -49,7 +49,7 @@ def post_validate_node(node_id: str, data: dict):
|
|||
node = graph.get_node(node_id)
|
||||
if node is None:
|
||||
raise ValueError(f"Node {node_id} not found")
|
||||
if not isinstance(node, VectorStoreNode):
|
||||
if not isinstance(node, VectorStoreVertex):
|
||||
node.build()
|
||||
return json.dumps({"valid": True, "params": str(node._built_object_repr())})
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -55,6 +55,8 @@ llms:
|
|||
- LlamaCpp
|
||||
- CTransformers
|
||||
- Cohere
|
||||
- Anthropic
|
||||
- ChatAnthropic
|
||||
memories:
|
||||
- ConversationBufferMemory
|
||||
- ConversationSummaryMemory
|
||||
|
|
@ -79,7 +81,7 @@ tools:
|
|||
- Calculator
|
||||
- Serper Search
|
||||
- Tool
|
||||
- PythonFunction
|
||||
- PythonFunctionTool
|
||||
- JsonSpec
|
||||
- News API
|
||||
- TMDB API
|
||||
|
|
@ -118,6 +120,7 @@ vectorstores:
|
|||
- Chroma
|
||||
- Qdrant
|
||||
- Weaviate
|
||||
- FAISS
|
||||
wrappers:
|
||||
- RequestsWrapper
|
||||
# - ChatPromptTemplate
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from langflow.template import frontend_node
|
|||
CUSTOM_NODES = {
|
||||
"prompts": {"ZeroShotPrompt": frontend_node.prompts.ZeroShotPromptNode()},
|
||||
"tools": {
|
||||
"PythonFunction": frontend_node.tools.PythonFunctionNode(),
|
||||
"PythonFunctionTool": frontend_node.tools.PythonFunctionToolNode(),
|
||||
"Tool": frontend_node.tools.ToolNode(),
|
||||
},
|
||||
"agents": {
|
||||
|
|
|
|||
|
|
@ -1,35 +1,35 @@
|
|||
from langflow.graph.edge.base import Edge
|
||||
from langflow.graph.graph.base import Graph
|
||||
from langflow.graph.node.base import Node
|
||||
from langflow.graph.node.types import (
|
||||
AgentNode,
|
||||
ChainNode,
|
||||
DocumentLoaderNode,
|
||||
EmbeddingNode,
|
||||
LLMNode,
|
||||
MemoryNode,
|
||||
PromptNode,
|
||||
TextSplitterNode,
|
||||
ToolNode,
|
||||
ToolkitNode,
|
||||
VectorStoreNode,
|
||||
WrapperNode,
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.graph.vertex.types import (
|
||||
AgentVertex,
|
||||
ChainVertex,
|
||||
DocumentLoaderVertex,
|
||||
EmbeddingVertex,
|
||||
LLMVertex,
|
||||
MemoryVertex,
|
||||
PromptVertex,
|
||||
TextSplitterVertex,
|
||||
ToolVertex,
|
||||
ToolkitVertex,
|
||||
VectorStoreVertex,
|
||||
WrapperVertex,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"Graph",
|
||||
"Node",
|
||||
"Vertex",
|
||||
"Edge",
|
||||
"AgentNode",
|
||||
"ChainNode",
|
||||
"DocumentLoaderNode",
|
||||
"EmbeddingNode",
|
||||
"LLMNode",
|
||||
"MemoryNode",
|
||||
"PromptNode",
|
||||
"TextSplitterNode",
|
||||
"ToolNode",
|
||||
"ToolkitNode",
|
||||
"VectorStoreNode",
|
||||
"WrapperNode",
|
||||
"AgentVertex",
|
||||
"ChainVertex",
|
||||
"DocumentLoaderVertex",
|
||||
"EmbeddingVertex",
|
||||
"LLMVertex",
|
||||
"MemoryVertex",
|
||||
"PromptVertex",
|
||||
"TextSplitterVertex",
|
||||
"ToolVertex",
|
||||
"ToolkitVertex",
|
||||
"VectorStoreVertex",
|
||||
"WrapperVertex",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -2,13 +2,13 @@ from langflow.utils.logger import logger
|
|||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.graph.node.base import Node
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
|
||||
|
||||
class Edge:
|
||||
def __init__(self, source: "Node", target: "Node"):
|
||||
self.source: "Node" = source
|
||||
self.target: "Node" = target
|
||||
def __init__(self, source: "Vertex", target: "Vertex"):
|
||||
self.source: "Vertex" = source
|
||||
self.target: "Vertex" = target
|
||||
self.validate_edge()
|
||||
|
||||
def validate_edge(self) -> None:
|
||||
|
|
@ -41,7 +41,7 @@ class Edge:
|
|||
logger.debug(self.target_reqs)
|
||||
if no_matched_type:
|
||||
raise ValueError(
|
||||
f"Edge between {self.source.node_type} and {self.target.node_type} "
|
||||
f"Edge between {self.source.vertex_type} and {self.target.vertex_type} "
|
||||
f"has no matched type"
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,12 @@
|
|||
from typing import Dict, List, Type, Union
|
||||
|
||||
from langflow.graph.edge.base import Edge
|
||||
from langflow.graph.graph.constants import NODE_TYPE_MAP
|
||||
from langflow.graph.node.base import Node
|
||||
from langflow.graph.node.types import (
|
||||
FileToolNode,
|
||||
LLMNode,
|
||||
ToolkitNode,
|
||||
from langflow.graph.graph.constants import VERTEX_TYPE_MAP
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.graph.vertex.types import (
|
||||
FileToolVertex,
|
||||
LLMVertex,
|
||||
ToolkitVertex,
|
||||
)
|
||||
from langflow.interface.tools.constants import FILE_TOOLS
|
||||
from langflow.utils import payload
|
||||
|
|
@ -26,7 +26,7 @@ class Graph:
|
|||
|
||||
def _build_graph(self) -> None:
|
||||
"""Builds the graph from the nodes and edges."""
|
||||
self.nodes = self._build_nodes()
|
||||
self.nodes = self._build_vertices()
|
||||
self.edges = self._build_edges()
|
||||
for edge in self.edges:
|
||||
edge.source.add_edge(edge)
|
||||
|
|
@ -43,12 +43,12 @@ class Graph:
|
|||
llm_node = None
|
||||
for node in self.nodes:
|
||||
node._build_params()
|
||||
if isinstance(node, LLMNode):
|
||||
if isinstance(node, LLMVertex):
|
||||
llm_node = node
|
||||
|
||||
if llm_node:
|
||||
for node in self.nodes:
|
||||
if isinstance(node, ToolkitNode):
|
||||
if isinstance(node, ToolkitVertex):
|
||||
node.params["llm"] = llm_node
|
||||
|
||||
def _remove_invalid_nodes(self) -> None:
|
||||
|
|
@ -60,23 +60,23 @@ class Graph:
|
|||
or (len(self.nodes) == 1 and len(self.edges) == 0)
|
||||
]
|
||||
|
||||
def _validate_node(self, node: Node) -> bool:
|
||||
def _validate_node(self, node: Vertex) -> bool:
|
||||
"""Validates a node."""
|
||||
# All nodes that do not have edges are invalid
|
||||
return len(node.edges) > 0
|
||||
|
||||
def get_node(self, node_id: str) -> Union[None, Node]:
|
||||
def get_node(self, node_id: str) -> Union[None, Vertex]:
|
||||
"""Returns a node by id."""
|
||||
return next((node for node in self.nodes if node.id == node_id), None)
|
||||
|
||||
def get_nodes_with_target(self, node: Node) -> List[Node]:
|
||||
def get_nodes_with_target(self, node: Vertex) -> List[Vertex]:
|
||||
"""Returns the nodes connected to a node."""
|
||||
connected_nodes: List[Node] = [
|
||||
connected_nodes: List[Vertex] = [
|
||||
edge.source for edge in self.edges if edge.target == node
|
||||
]
|
||||
return connected_nodes
|
||||
|
||||
def build(self) -> List[Node]:
|
||||
def build(self) -> List[Vertex]:
|
||||
"""Builds the graph."""
|
||||
# Get root node
|
||||
root_node = payload.get_root_node(self)
|
||||
|
|
@ -84,9 +84,9 @@ class Graph:
|
|||
raise ValueError("No root node found")
|
||||
return root_node.build()
|
||||
|
||||
def get_node_neighbors(self, node: Node) -> Dict[Node, int]:
|
||||
def get_node_neighbors(self, node: Vertex) -> Dict[Vertex, int]:
|
||||
"""Returns the neighbors of a node."""
|
||||
neighbors: Dict[Node, int] = {}
|
||||
neighbors: Dict[Vertex, int] = {}
|
||||
for edge in self.edges:
|
||||
if edge.source == node:
|
||||
neighbor = edge.target
|
||||
|
|
@ -117,28 +117,30 @@ class Graph:
|
|||
edges.append(Edge(source, target))
|
||||
return edges
|
||||
|
||||
def _get_node_class(self, node_type: str, node_lc_type: str) -> Type[Node]:
|
||||
def _get_vertex_class(self, node_type: str, node_lc_type: str) -> Type[Vertex]:
|
||||
"""Returns the node class based on the node type."""
|
||||
if node_type in FILE_TOOLS:
|
||||
return FileToolNode
|
||||
if node_type in NODE_TYPE_MAP:
|
||||
return NODE_TYPE_MAP[node_type]
|
||||
return NODE_TYPE_MAP[node_lc_type] if node_lc_type in NODE_TYPE_MAP else Node
|
||||
return FileToolVertex
|
||||
if node_type in VERTEX_TYPE_MAP:
|
||||
return VERTEX_TYPE_MAP[node_type]
|
||||
return (
|
||||
VERTEX_TYPE_MAP[node_lc_type] if node_lc_type in VERTEX_TYPE_MAP else Vertex
|
||||
)
|
||||
|
||||
def _build_nodes(self) -> List[Node]:
|
||||
"""Builds the nodes of the graph."""
|
||||
nodes: List[Node] = []
|
||||
def _build_vertices(self) -> List[Vertex]:
|
||||
"""Builds the vertices of the graph."""
|
||||
nodes: List[Vertex] = []
|
||||
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
|
||||
|
||||
NodeClass = self._get_node_class(node_type, node_lc_type)
|
||||
nodes.append(NodeClass(node))
|
||||
VertexClass = self._get_vertex_class(node_type, node_lc_type)
|
||||
nodes.append(VertexClass(node))
|
||||
|
||||
return nodes
|
||||
|
||||
def get_children_by_node_type(self, node: Node, node_type: str) -> List[Node]:
|
||||
def get_children_by_node_type(self, node: Vertex, node_type: str) -> List[Vertex]:
|
||||
"""Returns the children of a node based on the node type."""
|
||||
children = []
|
||||
node_types = [node.data["type"]]
|
||||
|
|
|
|||
|
|
@ -1,17 +1,17 @@
|
|||
from langflow.graph.node.base import Node
|
||||
from langflow.graph.node.types import (
|
||||
AgentNode,
|
||||
ChainNode,
|
||||
DocumentLoaderNode,
|
||||
EmbeddingNode,
|
||||
LLMNode,
|
||||
MemoryNode,
|
||||
PromptNode,
|
||||
TextSplitterNode,
|
||||
ToolNode,
|
||||
ToolkitNode,
|
||||
VectorStoreNode,
|
||||
WrapperNode,
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.graph.vertex.types import (
|
||||
AgentVertex,
|
||||
ChainVertex,
|
||||
DocumentLoaderVertex,
|
||||
EmbeddingVertex,
|
||||
LLMVertex,
|
||||
MemoryVertex,
|
||||
PromptVertex,
|
||||
TextSplitterVertex,
|
||||
ToolVertex,
|
||||
ToolkitVertex,
|
||||
VectorStoreVertex,
|
||||
WrapperVertex,
|
||||
)
|
||||
from langflow.interface.agents.base import agent_creator
|
||||
from langflow.interface.chains.base import chain_creator
|
||||
|
|
@ -33,17 +33,17 @@ from typing import Dict, Type
|
|||
DIRECT_TYPES = ["str", "bool", "code", "int", "float", "Any", "prompt"]
|
||||
|
||||
|
||||
NODE_TYPE_MAP: Dict[str, Type[Node]] = {
|
||||
**{t: PromptNode for t in prompt_creator.to_list()},
|
||||
**{t: AgentNode for t in agent_creator.to_list()},
|
||||
**{t: ChainNode for t in chain_creator.to_list()},
|
||||
**{t: ToolNode for t in tool_creator.to_list()},
|
||||
**{t: ToolkitNode for t in toolkits_creator.to_list()},
|
||||
**{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()},
|
||||
VERTEX_TYPE_MAP: Dict[str, Type[Vertex]] = {
|
||||
**{t: PromptVertex for t in prompt_creator.to_list()},
|
||||
**{t: AgentVertex for t in agent_creator.to_list()},
|
||||
**{t: ChainVertex for t in chain_creator.to_list()},
|
||||
**{t: ToolVertex for t in tool_creator.to_list()},
|
||||
**{t: ToolkitVertex for t in toolkits_creator.to_list()},
|
||||
**{t: WrapperVertex for t in wrapper_creator.to_list()},
|
||||
**{t: LLMVertex for t in llm_creator.to_list()},
|
||||
**{t: MemoryVertex for t in memory_creator.to_list()},
|
||||
**{t: EmbeddingVertex for t in embedding_creator.to_list()},
|
||||
**{t: VectorStoreVertex for t in vectorstore_creator.to_list()},
|
||||
**{t: DocumentLoaderVertex for t in documentloader_creator.to_list()},
|
||||
**{t: TextSplitterVertex for t in textsplitter_creator.to_list()},
|
||||
}
|
||||
|
|
|
|||
0
src/backend/langflow/graph/vertex/__init__.py
Normal file
0
src/backend/langflow/graph/vertex/__init__.py
Normal file
229
src/backend/langflow/graph/vertex/base.py
Normal file
229
src/backend/langflow/graph/vertex/base.py
Normal file
|
|
@ -0,0 +1,229 @@
|
|||
from langflow.cache import base as cache_utils
|
||||
from langflow.graph.vertex.constants import DIRECT_TYPES
|
||||
from langflow.interface import loading
|
||||
from langflow.interface.listing import ALL_TYPES_DICT
|
||||
from langflow.utils.logger import logger
|
||||
from langflow.utils.util import sync_to_async
|
||||
|
||||
|
||||
import contextlib
|
||||
import inspect
|
||||
import types
|
||||
import warnings
|
||||
from typing import Any, Dict, List, Optional
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.graph.edge.base import Edge
|
||||
|
||||
|
||||
class Vertex:
|
||||
def __init__(self, data: Dict, base_type: Optional[str] = None) -> None:
|
||||
self.id: str = data["id"]
|
||||
self._data = data
|
||||
self.edges: List["Edge"] = []
|
||||
self.base_type: Optional[str] = base_type
|
||||
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.vertex_type = (
|
||||
self.data["type"] if "Tool" not in self.output else template_dict["_type"]
|
||||
)
|
||||
if self.base_type is None:
|
||||
for base_type, value in ALL_TYPES_DICT.items():
|
||||
if self.vertex_type in value:
|
||||
self.base_type = base_type
|
||||
break
|
||||
|
||||
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.get("type") == "file":
|
||||
# Load the type in value.get('suffixes') using
|
||||
# what is inside value.get('content')
|
||||
# value.get('value') is the file name
|
||||
file_name = value.get("value")
|
||||
content = value.get("content")
|
||||
type_to_load = value.get("suffixes")
|
||||
file_path = cache_utils.save_binary_file(
|
||||
content=content, file_name=file_name, accepted_types=type_to_load
|
||||
)
|
||||
|
||||
params[key] = file_path
|
||||
|
||||
elif value.get("type") not in DIRECT_TYPES:
|
||||
# Get the edge that connects to this node
|
||||
edges = [
|
||||
edge
|
||||
for edge in self.edges
|
||||
if edge.target == self and edge.matched_type in value["type"]
|
||||
]
|
||||
|
||||
# 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 value["required"] and not edges:
|
||||
# If a required parameter is not found, raise an error
|
||||
raise ValueError(
|
||||
f"Required input {key} for module {self.vertex_type} not found"
|
||||
)
|
||||
elif value["list"]:
|
||||
# If this is a list parameter, append all sources to a list
|
||||
params[key] = [edge.source for edge in edges]
|
||||
elif edges:
|
||||
# If a single parameter is found, use its source
|
||||
params[key] = edges[0].source
|
||||
|
||||
elif value["required"] or value.get("value"):
|
||||
# If value does not have value this still passes
|
||||
# but then gives a keyError
|
||||
# so we need to check if value has value
|
||||
new_value = value.get("value")
|
||||
if new_value is None:
|
||||
warnings.warn(f"Value for {key} in {self.vertex_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
|
||||
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
|
||||
logger.debug(f"Building {self.vertex_type}")
|
||||
# Build each node in the params dict
|
||||
for key, value in self.params.copy().items():
|
||||
# Check if Node or list of Nodes and not self
|
||||
# to avoid recursion
|
||||
if isinstance(value, Vertex):
|
||||
if value == self:
|
||||
del self.params[key]
|
||||
continue
|
||||
result = value.build()
|
||||
# If the key is "func", then we need to use the run method
|
||||
if key == "func":
|
||||
if 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
|
||||
elif inspect.iscoroutinefunction(result):
|
||||
self.params["coroutine"] = result
|
||||
else:
|
||||
# turn result which is a function into a coroutine
|
||||
# so that it can be awaited
|
||||
self.params["coroutine"] = sync_to_async(result)
|
||||
|
||||
self.params[key] = result
|
||||
elif isinstance(value, list) and all(
|
||||
isinstance(node, Vertex) for node in value
|
||||
):
|
||||
self.params[key] = []
|
||||
for node in value:
|
||||
built = node.build()
|
||||
if isinstance(built, list):
|
||||
self.params[key].extend(built)
|
||||
else:
|
||||
self.params[key].append(built)
|
||||
|
||||
# Get the class from LANGCHAIN_TYPES_DICT
|
||||
# and instantiate it with the params
|
||||
# and return the instance
|
||||
|
||||
try:
|
||||
self._built_object = loading.instantiate_class(
|
||||
node_type=self.vertex_type,
|
||||
base_type=self.base_type,
|
||||
params=self.params,
|
||||
)
|
||||
except Exception as exc:
|
||||
raise ValueError(
|
||||
f"Error building node {self.vertex_type}: {str(exc)}"
|
||||
) from exc
|
||||
|
||||
if self._built_object is None:
|
||||
raise ValueError(f"Node type {self.vertex_type} not found")
|
||||
|
||||
self._built = True
|
||||
|
||||
def build(self, force: bool = False) -> Any:
|
||||
if not self._built or force:
|
||||
self._build()
|
||||
|
||||
return 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, Vertex) else False
|
||||
|
||||
def __hash__(self) -> int:
|
||||
return id(self)
|
||||
|
||||
def _built_object_repr(self):
|
||||
return repr(self._built_object)
|
||||
1
src/backend/langflow/graph/vertex/constants.py
Normal file
1
src/backend/langflow/graph/vertex/constants.py
Normal file
|
|
@ -0,0 +1 @@
|
|||
DIRECT_TYPES = ["str", "bool", "code", "int", "float", "Any", "prompt"]
|
||||
198
src/backend/langflow/graph/vertex/types.py
Normal file
198
src/backend/langflow/graph/vertex/types.py
Normal file
|
|
@ -0,0 +1,198 @@
|
|||
from typing import Any, Dict, List, Optional, Union
|
||||
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.graph.utils import extract_input_variables_from_prompt
|
||||
|
||||
|
||||
class AgentVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="agents")
|
||||
|
||||
self.tools: List[ToolVertex] = []
|
||||
self.chains: List[ChainVertex] = []
|
||||
|
||||
def _set_tools_and_chains(self) -> None:
|
||||
for edge in self.edges:
|
||||
source_node = edge.source
|
||||
if isinstance(source_node, ToolVertex):
|
||||
self.tools.append(source_node)
|
||||
elif isinstance(source_node, ChainVertex):
|
||||
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()
|
||||
|
||||
#! Cannot deepcopy VectorStore, VectorStoreRouter, or SQL agents
|
||||
if self.vertex_type in [
|
||||
"VectorStoreAgent",
|
||||
"VectorStoreRouterAgent",
|
||||
"SQLAgent",
|
||||
]:
|
||||
return self._built_object
|
||||
return self._built_object
|
||||
|
||||
|
||||
class ToolVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="tools")
|
||||
|
||||
|
||||
class PromptVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="prompts")
|
||||
|
||||
def build(
|
||||
self,
|
||||
force: bool = False,
|
||||
tools: Optional[Union[List[Vertex], List[ToolVertex]]] = None,
|
||||
) -> Any:
|
||||
if not self._built or force:
|
||||
if (
|
||||
"input_variables" not in self.params
|
||||
or self.params["input_variables"] is None
|
||||
):
|
||||
self.params["input_variables"] = []
|
||||
# Check if it is a ZeroShotPrompt and needs a tool
|
||||
if "ShotPrompt" in self.vertex_type:
|
||||
tools = (
|
||||
[tool_node.build() for tool_node in tools]
|
||||
if tools is not None
|
||||
else []
|
||||
)
|
||||
self.params["tools"] = tools
|
||||
prompt_params = [
|
||||
key
|
||||
for key, value in self.params.items()
|
||||
if isinstance(value, str) and key != "format_instructions"
|
||||
]
|
||||
else:
|
||||
prompt_params = ["template"]
|
||||
for param in prompt_params:
|
||||
prompt_text = self.params[param]
|
||||
variables = extract_input_variables_from_prompt(prompt_text)
|
||||
self.params["input_variables"].extend(variables)
|
||||
self.params["input_variables"] = list(set(self.params["input_variables"]))
|
||||
|
||||
self._build()
|
||||
return self._built_object
|
||||
|
||||
|
||||
class ChainVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="chains")
|
||||
|
||||
def build(
|
||||
self,
|
||||
force: bool = False,
|
||||
tools: Optional[Union[List[Vertex], List[ToolVertex]]] = 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, PromptVertex):
|
||||
# Build the PromptNode, passing the tools if available
|
||||
self.params[key] = value.build(tools=tools, force=force)
|
||||
|
||||
self._build()
|
||||
|
||||
#! Cannot deepcopy SQLDatabaseChain
|
||||
if self.vertex_type in ["SQLDatabaseChain"]:
|
||||
return self._built_object
|
||||
return self._built_object
|
||||
|
||||
|
||||
class LLMVertex(Vertex):
|
||||
built_node_type = None
|
||||
class_built_object = None
|
||||
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="llms")
|
||||
|
||||
def build(self, force: bool = False) -> Any:
|
||||
# LLM is different because some models might take up too much memory
|
||||
# or time to load. So we only load them when we need them.ß
|
||||
if self.vertex_type == self.built_node_type:
|
||||
return self.class_built_object
|
||||
if not self._built or force:
|
||||
self._build()
|
||||
self.built_node_type = self.vertex_type
|
||||
self.class_built_object = self._built_object
|
||||
# Avoid deepcopying the LLM
|
||||
# that are loaded from a file
|
||||
return self._built_object
|
||||
|
||||
|
||||
class ToolkitVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="toolkits")
|
||||
|
||||
|
||||
class FileToolVertex(ToolVertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data)
|
||||
|
||||
|
||||
class WrapperVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="wrappers")
|
||||
|
||||
def build(self, force: bool = False) -> Any:
|
||||
if not self._built or force:
|
||||
if "headers" in self.params:
|
||||
self.params["headers"] = eval(self.params["headers"])
|
||||
self._build()
|
||||
return self._built_object
|
||||
|
||||
|
||||
class DocumentLoaderVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="documentloaders")
|
||||
|
||||
def _built_object_repr(self):
|
||||
# This built_object is a list of documents. Maybe we should
|
||||
# show how many documents are in the list?
|
||||
if self._built_object:
|
||||
return f"""{self.vertex_type}({len(self._built_object)} documents)
|
||||
Documents: {self._built_object[:3]}..."""
|
||||
return f"{self.vertex_type}()"
|
||||
|
||||
|
||||
class EmbeddingVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="embeddings")
|
||||
|
||||
|
||||
class VectorStoreVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="vectorstores")
|
||||
|
||||
def _built_object_repr(self):
|
||||
return "Vector stores can take time to build. It will build on the first query."
|
||||
|
||||
|
||||
class MemoryVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="memory")
|
||||
|
||||
|
||||
class TextSplitterVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="textsplitters")
|
||||
|
||||
def _built_object_repr(self):
|
||||
# This built_object is a list of documents. Maybe we should
|
||||
# show how many documents are in the list?
|
||||
if self._built_object:
|
||||
return f"""{self.vertex_type}({len(self._built_object)} documents)
|
||||
\nDocuments: {self._built_object[:3]}..."""
|
||||
return f"{self.vertex_type}()"
|
||||
|
|
@ -11,12 +11,14 @@ from langchain import (
|
|||
text_splitter,
|
||||
)
|
||||
from langchain.agents import agent_toolkits
|
||||
from langchain.chat_models import ChatAnthropic
|
||||
from langchain.chat_models import ChatOpenAI
|
||||
|
||||
from langflow.interface.importing.utils import import_class
|
||||
|
||||
## LLMs
|
||||
llm_type_to_cls_dict = llms.type_to_cls_dict
|
||||
llm_type_to_cls_dict["anthropic-chat"] = ChatAnthropic # type: ignore
|
||||
llm_type_to_cls_dict["openai-chat"] = ChatOpenAI # type: ignore
|
||||
|
||||
## Chains
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ from langchain.base_language import BaseLanguageModel
|
|||
from langchain.chains.base import Chain
|
||||
from langchain.chat_models.base import BaseChatModel
|
||||
from langchain.tools import BaseTool
|
||||
from langflow.utils import validate
|
||||
|
||||
|
||||
def import_module(module_path: str) -> Any:
|
||||
|
|
@ -147,3 +148,10 @@ def import_utility(utility: str) -> Any:
|
|||
if utility == "SQLDatabase":
|
||||
return import_class(f"langchain.sql_database.{utility}")
|
||||
return import_class(f"langchain.utilities.{utility}")
|
||||
|
||||
|
||||
def get_function(code):
|
||||
"""Get the function"""
|
||||
function_name = validate.extract_function_name(code)
|
||||
|
||||
return validate.create_function(code, function_name)
|
||||
|
|
|
|||
|
|
@ -21,12 +21,12 @@ from langchain.llms.loading import load_llm_from_config
|
|||
from pydantic import ValidationError
|
||||
|
||||
from langflow.interface.agents.custom import CUSTOM_AGENTS
|
||||
from langflow.interface.importing.utils import import_by_type
|
||||
from langflow.interface.importing.utils import get_function, import_by_type
|
||||
from langflow.interface.run import fix_memory_inputs
|
||||
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
|
||||
from langflow.utils import util
|
||||
|
||||
|
||||
def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
|
||||
|
|
@ -100,11 +100,9 @@ def instantiate_tool(node_type, class_object, params):
|
|||
if node_type == "JsonSpec":
|
||||
params["dict_"] = load_file_into_dict(params.pop("path"))
|
||||
return class_object(**params)
|
||||
elif node_type == "PythonFunction":
|
||||
function_string = params["code"]
|
||||
if isinstance(function_string, str):
|
||||
return validate.eval_function(function_string)
|
||||
raise ValueError("Function should be a string")
|
||||
elif node_type == "PythonFunctionTool":
|
||||
params["func"] = get_function(params.get("code"))
|
||||
return class_object(**params)
|
||||
elif node_type.lower() == "tool":
|
||||
return class_object(**params)
|
||||
return class_object(**params)
|
||||
|
|
|
|||
|
|
@ -71,7 +71,8 @@ class ToolCreator(LangChainTypeCreator):
|
|||
|
||||
for tool, tool_fcn in ALL_TOOLS_NAMES.items():
|
||||
tool_params = get_tool_params(tool_fcn)
|
||||
tool_name = tool_params.get("name", tool)
|
||||
|
||||
tool_name = tool_params.get("name") or tool
|
||||
|
||||
if tool_name in settings.tools or settings.dev:
|
||||
if tool_name == "JsonSpec":
|
||||
|
|
|
|||
|
|
@ -9,10 +9,10 @@ from langchain.agents.load_tools import (
|
|||
from langchain.tools.json.tool import JsonSpec
|
||||
|
||||
from langflow.interface.importing.utils import import_class
|
||||
from langflow.interface.tools.custom import PythonFunction
|
||||
from langflow.interface.tools.custom import PythonFunctionTool
|
||||
|
||||
FILE_TOOLS = {"JsonSpec": JsonSpec}
|
||||
CUSTOM_TOOLS = {"Tool": Tool, "PythonFunction": PythonFunction}
|
||||
CUSTOM_TOOLS = {"Tool": Tool, "PythonFunctionTool": PythonFunctionTool}
|
||||
|
||||
OTHER_TOOLS = {tool: import_class(f"langchain.tools.{tool}") for tool in tools.__all__}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,13 +1,14 @@
|
|||
from typing import Callable, Optional
|
||||
from typing import Optional
|
||||
from langflow.interface.importing.utils import get_function
|
||||
|
||||
from pydantic import BaseModel, validator
|
||||
|
||||
from langflow.utils import validate
|
||||
from langchain.agents.tools import Tool
|
||||
|
||||
|
||||
class Function(BaseModel):
|
||||
code: str
|
||||
function: Optional[Callable] = None
|
||||
imports: Optional[str] = None
|
||||
|
||||
# Eval code and store the function
|
||||
|
|
@ -24,14 +25,17 @@ class Function(BaseModel):
|
|||
|
||||
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):
|
||||
class PythonFunctionTool(Function, Tool):
|
||||
"""Python function"""
|
||||
|
||||
name: str = "Custom Tool"
|
||||
description: str
|
||||
code: str
|
||||
|
||||
def ___init__(self, name: str, description: str, code: str):
|
||||
self.name = name
|
||||
self.description = description
|
||||
self.code = code
|
||||
self.func = get_function(self.code)
|
||||
super().__init__(name=name, description=description, func=self.func)
|
||||
|
|
|
|||
|
|
@ -125,6 +125,9 @@ class FrontendNode(BaseModel):
|
|||
elif name == "ChatOpenAI" and key == "model_name":
|
||||
field.options = constants.CHAT_OPENAI_MODELS
|
||||
field.is_list = True
|
||||
elif (name == "Anthropic" or name == "ChatAnthropic") and key == "model_name":
|
||||
field.options = constants.ANTHROPIC_MODELS
|
||||
field.is_list = True
|
||||
if "api_key" in key and "OpenAI" in str(name):
|
||||
field.display_name = "OpenAI API Key"
|
||||
field.required = False
|
||||
|
|
|
|||
Some files were not shown because too many files have changed in this diff Show more
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Reference in a new issue