infra: update mypy 1.10, ruff 0.5 (#23721)

```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()

```

libs/experimental/tests/integration_tests/chains/test_cpal.py

@@ -81,7 +81,7 @@ class TestUnitCPALChain_MathWordProblems(unittest.TestCase):
                 return prompt
 
         narrative = LLMMockData(
-            **{
+            **{  # type: ignore[arg-type, arg-type]
                 "question": (
                     "jan has three times the number of pets as marcia. "
                     "marcia has two more pets than cindy."
@@ -100,7 +100,7 @@ class TestUnitCPALChain_MathWordProblems(unittest.TestCase):
         )
 
         causal_model = LLMMockData(
-            **{
+            **{  # type: ignore[arg-type, arg-type]
                 "question": (
                     "jan has three times the number of pets as marcia. "
                     "marcia has two more pets than cindy."
@@ -137,7 +137,7 @@ class TestUnitCPALChain_MathWordProblems(unittest.TestCase):
         )
 
         intervention = LLMMockData(
-            **{
+            **{  # type: ignore[arg-type, arg-type]
                 "question": ("if cindy has ten pets"),
                 "completion": (
                     "{\n"
@@ -152,7 +152,7 @@ class TestUnitCPALChain_MathWordProblems(unittest.TestCase):
         )
 
         query = LLMMockData(
-            **{
+            **{  # type: ignore[arg-type, arg-type]
                 "question": ("how many pets does jan have? "),
                 "completion": (
                     "{\n"

libs/experimental/tests/integration_tests/chains/test_sql_database.py

@@ -1,4 +1,5 @@
 """Test SQL Database Chain."""
+
 from langchain_community.llms.openai import OpenAI
 from langchain_community.utilities.sql_database import SQLDatabase
 from sqlalchemy import Column, Integer, MetaData, String, Table, create_engine, insert

libs/experimental/tests/integration_tests/llms/test_anthropic_functions.py

@@ -14,7 +14,7 @@ class TestAnthropicFunctions(unittest.TestCase):
     """
 
     def test_default_chat_anthropic(self) -> None:
-        base_model = AnthropicFunctions(model="claude-2")
+        base_model = AnthropicFunctions(model="claude-2")  # type: ignore[call-arg]
         self.assertIsInstance(base_model.model, ChatAnthropic)
 
         # bind functions
@@ -64,7 +64,7 @@ class TestAnthropicFunctions(unittest.TestCase):
             accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
           },
         });"""
-        llm = BedrockChat(
+        llm = BedrockChat(  # type: ignore[call-arg]
             model_id="anthropic.claude-v2",
             model_kwargs={"temperature": 0.1},
             region_name="us-east-1",

libs/experimental/tests/integration_tests/llms/test_ollama_functions.py

@@ -65,7 +65,7 @@ class TestOllamaFunctions(unittest.TestCase):
     def test_ollama_functions_tools(self) -> None:
         base_model = OllamaFunctions(model="phi3", format="json")
         model = base_model.bind_tools(
-            tools=[PubmedQueryRun(), DuckDuckGoSearchResults(max_results=2)]
+            tools=[PubmedQueryRun(), DuckDuckGoSearchResults(max_results=2)]  # type: ignore[call-arg]
         )
         res = model.invoke("What causes lung cancer?")
         self.assertIsInstance(res, AIMessage)

libs/experimental/tests/integration_tests/test_video_captioning.py

@@ -1,4 +1,5 @@
 """Integration test for video captioning."""
+
 from langchain_openai import ChatOpenAI
 
 from langchain_experimental.video_captioning.base import VideoCaptioningChain
@@ -11,7 +12,7 @@ def test_video_captioning_hard() -> None:
     -FXX%20USA%20%C2%ABPromo%20Noon%20-%204A%20Every%20Day%EF%BF%BD%EF
     %BF%BD%C2%BB%20November%202021%EF%BF%BD%EF%BF%BD-%281080p60%29.mp4
     """
-    chain = VideoCaptioningChain(
+    chain = VideoCaptioningChain(  # type: ignore[call-arg]
         llm=ChatOpenAI(
             model="gpt-4",
             max_tokens=4000,