refac: bring functions from langchain to langflow

This commit is contained in:
Ibis Prevedello 2023-03-16 13:26:32 -03:00
commit aec3179a93
5 changed files with 110 additions and 711 deletions

View file

@ -1,10 +1,22 @@
import json
from typing import Any, Dict
from typing import Any, Dict, Optional
from langflow_backend.interface.types import get_type_list
from langchain.agents.loading import load_agent_executor_from_config
from langchain.agents.loading import load_agent_from_config
from langchain.chains.loading import load_chain_from_config
from langchain.llms.loading import load_llm_from_config
from langflow_backend.utils import payload
from langflow_backend.utils import util
from langchain.llms.base import BaseLLM
from langchain.agents.agent import AgentExecutor
from langchain.callbacks.base import BaseCallbackManager
from langchain.agents.tools import Tool
from langchain.agents.load_tools import (
_BASE_TOOLS,
_LLM_TOOLS,
_EXTRA_LLM_TOOLS,
_EXTRA_OPTIONAL_TOOLS,
)
def load_flow_from_json(path: str):
@ -49,15 +61,77 @@ def load_langchain_type_from_config(config: Dict[str, Any]):
# Get type list
type_list = get_type_list()
if config["_type"] in type_list["agents"]:
return load_agent_executor_from_config(config)
config = util.update_verbose(config, new_value=False)
return load_agent_executor_from_config(config, verbose=True)
elif config["_type"] in type_list["chains"]:
return load_chain_from_config(config)
config = util.update_verbose(config, new_value=False)
return load_chain_from_config(config, verbose=True)
elif config["_type"] in type_list["llms"]:
config = util.update_verbose(config, new_value=True)
return load_llm_from_config(config)
else:
raise ValueError("Type should be either agent, chain or llm")
def load_agent_executor_from_config(
config: dict,
llm: Optional[BaseLLM] = None,
tools: Optional[list[Tool]] = None,
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
):
tools = load_tools_from_config(config["allowed_tools"])
config["allowed_tools"] = [tool.name for tool in tools] if tools else []
agent_obj = load_agent_from_config(config, llm, tools, **kwargs)
return AgentExecutor.from_agent_and_tools(
agent=agent_obj,
tools=tools,
callback_manager=callback_manager,
**kwargs,
)
def load_tools_from_config(tool_list: list[dict]) -> list[Tool]:
"""Load tools based on a config list.
Args:
config: config list.
Returns:
List of tools.
"""
tools = []
for tool in tool_list:
tool_type = tool.pop("_type")
llm_config = tool.pop("llm", None)
llm = load_llm_from_config(llm_config) if llm_config else None
kwargs = tool
if tool_type in _BASE_TOOLS:
tools.append(_BASE_TOOLS[tool_type]())
elif tool_type in _LLM_TOOLS:
if llm is None:
raise ValueError(f"Tool {tool_type} requires an LLM to be provided")
tools.append(_LLM_TOOLS[tool_type](llm))
elif tool_type in _EXTRA_LLM_TOOLS:
if llm is None:
raise ValueError(f"Tool {tool_type} requires an LLM to be provided")
_get_llm_tool_func, extra_keys = _EXTRA_LLM_TOOLS[tool_type]
if missing_keys := set(extra_keys).difference(kwargs):
raise ValueError(
f"Tool {tool_type} requires some parameters that were not "
f"provided: {missing_keys}"
)
tools.append(_get_llm_tool_func(llm=llm, **kwargs))
elif tool_type in _EXTRA_OPTIONAL_TOOLS:
_get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[tool_type]
kwargs = {k: value for k, value in kwargs.items() if value}
tools.append(_get_tool_func(**kwargs))
else:
raise ValueError(f"Got unknown tool {tool_type}")
return tools
def build_prompt_template(prompt, tools):
"""Build PromptTemplate from ZeroShotPrompt"""
prefix = prompt["node"]["template"]["prefix"]["value"]

View file

@ -14,20 +14,15 @@ def process_data_graph(data_graph: Dict[str, Any]):
extracted_json = loading.extract_json(data_graph)
message = data_graph["message"]
# flow_id = data_graph["id"]
# Process json
result, thought = get_result_and_thought(extracted_json, message)
# Remove unnecessary data from response
begin = thought.rfind(message)
thought = thought[(begin + len(message)) :]
return {
"result": result,
"thought": re.sub(
r"\x1b\[([0-9,A-Z]{1,2}(;[0-9,A-Z]{1,2})?)?[m|K]", "", thought
).strip(),
# "id": flow_id,
}

View file

@ -303,3 +303,23 @@ def format_dict(d, name: Optional[str] = None):
value["options"] = ["text-davinci-003", "text-davinci-002"]
return d
def update_verbose(d: dict, new_value: bool) -> dict:
"""
Recursively updates the value of the 'verbose' key in a dictionary.
Args:
d: the dictionary to update
new_value: the new value to set
Returns:
The updated dictionary.
"""
for k, v in d.items():
if isinstance(v, dict):
update_verbose(v, new_value)
elif k == "verbose":
d[k] = new_value
return d

710
poetry.lock generated
View file

@ -1,62 +1,5 @@
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@ -166,36 +109,6 @@ yarl = ">=1.0,<2.0"
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@ -400,46 +287,6 @@ d = ["aiohttp (>=3.7.4)"]
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@ -1297,18 +1061,6 @@ docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alab
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@ -1355,22 +1107,20 @@ docs = ["myst-parser", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "
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[[package]]
name = "yarl"
version = "1.8.2"
@ -3096,4 +2406,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
[metadata]
lock-version = "2.0"
python-versions = "^3.9"
content-hash = "8694011daccb5d49e12e2fa79f1ea3df0262d737cd24f475c235cea8682603da"
content-hash = "de85751ffee58e67b57ed3fedd5ec3115a65c8fdb0fcd1e904d96ecb3ce2b803"

View file

@ -21,9 +21,9 @@ uvicorn = "^0.20.0"
beautifulsoup4 = "^4.11.2"
google-search-results = "^2.4.1"
google-api-python-client = "^2.79.0"
langchain_ibis = "0.0.100"
typer = "^0.7.0"
gunicorn = "^20.1.0"
langchain = "^0.0.113"
[tool.poetry.group.dev.dependencies]
black = "^23.1.0"