mirror of
https://github.com/hwchase17/langchain
synced 2024-11-11 19:11:02 +00:00
a0c2281540
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
print(path)
with open(path, "rb") as f:
pyproject = tomllib.load(f)
try:
pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
"^1.10"
)
pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
"^0.5"
)
except KeyError:
continue
with open(path, "w") as f:
toml.dump(pyproject, f)
cwd = "/".join(path.split("/")[:-1])
completed = subprocess.run(
"poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
logs = completed.stdout.split("\n")
to_ignore = {}
for l in logs:
if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
path, line_no, error_type = re.match(
"^(.*)\:(\d+)\: error:.*\[(.*)\]", l
).groups()
if (path, line_no) in to_ignore:
to_ignore[(path, line_no)].append(error_type)
else:
to_ignore[(path, line_no)] = [error_type]
print(len(to_ignore))
for (error_path, line_no), error_types in to_ignore.items():
all_errors = ", ".join(error_types)
full_path = f"{cwd}/{error_path}"
try:
with open(full_path, "r") as f:
file_lines = f.readlines()
except FileNotFoundError:
continue
file_lines[int(line_no) - 1] = (
file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n"
)
with open(full_path, "w") as f:
f.write("".join(file_lines))
subprocess.run(
"poetry run ruff format .; poetry run ruff --select I --fix .",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
if __name__ == "__main__":
main()
```
105 lines
3.6 KiB
Python
105 lines
3.6 KiB
Python
"""Question answering over a graph."""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain_core.callbacks.manager import CallbackManagerForChainRun
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.prompts import BasePromptTemplate
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from langchain_core.pydantic_v1 import Field
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from langchain_community.chains.graph_qa.prompts import (
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ENTITY_EXTRACTION_PROMPT,
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GRAPH_QA_PROMPT,
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)
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from langchain_community.graphs.networkx_graph import NetworkxEntityGraph, get_entities
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class GraphQAChain(Chain):
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"""Chain for question-answering against a graph.
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*Security note*: Make sure that the database connection uses credentials
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that are narrowly-scoped to only include necessary permissions.
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Failure to do so may result in data corruption or loss, since the calling
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code may attempt commands that would result in deletion, mutation
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of data if appropriately prompted or reading sensitive data if such
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data is present in the database.
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The best way to guard against such negative outcomes is to (as appropriate)
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limit the permissions granted to the credentials used with this tool.
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See https://python.langchain.com/docs/security for more information.
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"""
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graph: NetworkxEntityGraph = Field(exclude=True)
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entity_extraction_chain: LLMChain
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qa_chain: LLMChain
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input_key: str = "query" #: :meta private:
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output_key: str = "result" #: :meta private:
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@property
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def input_keys(self) -> List[str]:
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"""Input keys.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Output keys.
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:meta private:
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"""
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_output_keys = [self.output_key]
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return _output_keys
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@classmethod
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def from_llm(
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cls,
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llm: BaseLanguageModel,
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qa_prompt: BasePromptTemplate = GRAPH_QA_PROMPT,
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entity_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT,
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**kwargs: Any,
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) -> GraphQAChain:
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"""Initialize from LLM."""
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qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
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entity_chain = LLMChain(llm=llm, prompt=entity_prompt)
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return cls(
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qa_chain=qa_chain,
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entity_extraction_chain=entity_chain,
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**kwargs,
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)
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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"""Extract entities, look up info and answer question."""
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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question = inputs[self.input_key]
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entity_string = self.entity_extraction_chain.run(question)
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_run_manager.on_text("Entities Extracted:", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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entity_string, color="green", end="\n", verbose=self.verbose
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)
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entities = get_entities(entity_string)
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context = ""
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all_triplets = []
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for entity in entities:
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all_triplets.extend(self.graph.get_entity_knowledge(entity))
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context = "\n".join(all_triplets)
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_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
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_run_manager.on_text(context, color="green", end="\n", verbose=self.verbose)
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result = self.qa_chain(
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{"question": question, "context": context},
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callbacks=_run_manager.get_child(),
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)
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return {self.output_key: result[self.qa_chain.output_key]}
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