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General anthropic functions, steps towards experimental integration tests (#11727)

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To match change in js here
https://github.com/langchain-ai/langchainjs/pull/2892

Some integration tests need a bit more work in experimental:
![Screenshot 2023-10-12 at 12 02 49
PM](https://github.com/langchain-ai/langchain/assets/9557659/262d7d22-c405-40e9-afef-669e8d585307)

Pretty sure the sqldatabase ones are an actual regression or change in
interface because it's returning a placeholder.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Erick Friis
2023-10-13 09:48:24 -07:00
committed by GitHub
parent 98c8516ef1
commit 1861cc7100
7 changed files with 128 additions and 7 deletions
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2
libs/experimental/tests/integration_tests/chains/test_cpal.py
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@@ -39,7 +39,7 @@ from langchain_experimental.cpal.templates.univariate.narrative import (
from langchain_experimental.cpal.templates.univariate.query import (
template as query_template,
)
from tests.unit_tests.llms.fake_llm import FakeLLM
from tests.unit_tests.fake_llm import FakeLLM
class TestUnitCPALChain_MathWordProblems(unittest.TestCase):

3
libs/experimental/tests/integration_tests/chains/test_pal.py
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@@ -1,8 +1,9 @@
"""Test PAL chain."""
from langchain.chains.pal.base import PALChain
from langchain.llms import OpenAI
from langchain_experimental.pal_chain.base import PALChain
def test_math_prompt() -> None:
"""Test math prompt."""

5
libs/experimental/tests/integration_tests/chains/test_sql_database.py
View File

@@ -1,11 +1,12 @@
"""Test SQL Database Chain."""
from langchain.llms.openai import OpenAI
from langchain.utilities.sql_database import SQLDatabase
from libs.experimental.langchain_experimental.sql.base import (
from sqlalchemy import Column, Integer, MetaData, String, Table, create_engine, insert
from langchain_experimental.sql.base import (
SQLDatabaseChain,
SQLDatabaseSequentialChain,
)
from sqlalchemy import Column, Integer, MetaData, String, Table, create_engine, insert
metadata_obj = MetaData()

109
libs/experimental/tests/integration_tests/llms/test_anthropic_functions.py Normal file
View File

@@ -0,0 +1,109 @@
"""Test AnthropicFunctions"""
import unittest
from langchain.chat_models.anthropic import ChatAnthropic
from langchain.chat_models.bedrock import BedrockChat
from langchain_experimental.llms.anthropic_functions import AnthropicFunctions
class TestAnthropicFunctions(unittest.TestCase):
"""
Test AnthropicFunctions with default llm (ChatAnthropic) as well as a passed-in llm
"""
def test_default_chat_anthropic(self) -> None:
base_model = AnthropicFunctions(model="claude-2")
self.assertIsInstance(base_model.model, ChatAnthropic)
# bind functions
model = base_model.bind(
functions=[
{
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, "
"e.g. San Francisco, CA",
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["location"],
},
}
],
function_call={"name": "get_current_weather"},
)
res = model.invoke("What's the weather in San Francisco?")
function_call = res.additional_kwargs.get("function_call")
assert function_call
self.assertEqual(function_call.get("name"), "get_current_weather")
self.assertEqual(
function_call.get("arguments"),
'{"location": "San Francisco, CA", "unit": "fahrenheit"}',
)
def test_bedrock_chat_anthropic(self) -> None:
"""
const chatBedrock = new ChatBedrock({
region: process.env.BEDROCK_AWS_REGION ?? "us-east-1",
model: "anthropic.claude-v2",
temperature: 0.1,
credentials: {
secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
},
});"""
llm = BedrockChat(
model_id="anthropic.claude-v2",
model_kwargs={"temperature": 0.1},
region_name="us-east-1",
)
base_model = AnthropicFunctions(llm=llm)
assert isinstance(base_model.model, BedrockChat)
# bind functions
model = base_model.bind(
functions=[
{
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, "
"e.g. San Francisco, CA",
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["location"],
},
}
],
function_call={"name": "get_current_weather"},
)
res = model.invoke("What's the weather in San Francisco?")
function_call = res.additional_kwargs.get("function_call")
assert function_call
self.assertEqual(function_call.get("name"), "get_current_weather")
self.assertEqual(
function_call.get("arguments"),
'{"location": "San Francisco, CA", "unit": "fahrenheit"}',
)