community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)

Moved the following modules to new package langchain-community in a backwards compatible fashion:

```
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
mv langchain/langchain/adapters community/langchain_community
mv langchain/langchain/callbacks community/langchain_community/callbacks
mv langchain/langchain/chat_loaders community/langchain_community
mv langchain/langchain/chat_models community/langchain_community
mv langchain/langchain/document_loaders community/langchain_community
mv langchain/langchain/docstore community/langchain_community
mv langchain/langchain/document_transformers community/langchain_community
mv langchain/langchain/embeddings community/langchain_community
mv langchain/langchain/graphs community/langchain_community
mv langchain/langchain/llms community/langchain_community
mv langchain/langchain/memory/chat_message_histories community/langchain_community
mv langchain/langchain/retrievers community/langchain_community
mv langchain/langchain/storage community/langchain_community
mv langchain/langchain/tools community/langchain_community
mv langchain/langchain/utilities community/langchain_community
mv langchain/langchain/vectorstores community/langchain_community
mv langchain/langchain/agents/agent_toolkits community/langchain_community
mv langchain/langchain/cache.py community/langchain_community
```

Moved the following to core
```
mv langchain/langchain/utils/json_schema.py core/langchain_core/utils
mv langchain/langchain/utils/html.py core/langchain_core/utils
mv langchain/langchain/utils/strings.py core/langchain_core/utils
cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py
rm langchain/langchain/utils/env.py
```

See .scripts/community_split/script_integrations.sh for all changes

libs/community/tests/integration_tests/llms/test_vertexai.py

@@ -0,0 +1,151 @@
+"""Test Vertex AI API wrapper.
+In order to run this test, you need to install VertexAI SDK:
+pip install google-cloud-aiplatform>=1.36.0
+
+Your end-user credentials would be used to make the calls (make sure you've run 
+`gcloud auth login` first).
+"""
+import os
+from typing import Optional
+
+import pytest
+from langchain_core.outputs import LLMResult
+
+from langchain_community.llms import VertexAI, VertexAIModelGarden
+
+
+def test_vertex_initialization() -> None:
+    llm = VertexAI()
+    assert llm._llm_type == "vertexai"
+    assert llm.model_name == llm.client._model_id
+
+
+def test_vertex_call() -> None:
+    llm = VertexAI(temperature=0)
+    output = llm("Say foo:")
+    assert isinstance(output, str)
+
+
+@pytest.mark.scheduled
+def test_vertex_generate() -> None:
+    llm = VertexAI(temperature=0.3, n=2, model_name="text-bison@001")
+    output = llm.generate(["Say foo:"])
+    assert isinstance(output, LLMResult)
+    assert len(output.generations) == 1
+    assert len(output.generations[0]) == 2
+
+
+@pytest.mark.scheduled
+def test_vertex_generate_code() -> None:
+    llm = VertexAI(temperature=0.3, n=2, model_name="code-bison@001")
+    output = llm.generate(["generate a python method that says foo:"])
+    assert isinstance(output, LLMResult)
+    assert len(output.generations) == 1
+    assert len(output.generations[0]) == 2
+
+
+@pytest.mark.scheduled
+async def test_vertex_agenerate() -> None:
+    llm = VertexAI(temperature=0)
+    output = await llm.agenerate(["Please say foo:"])
+    assert isinstance(output, LLMResult)
+
+
+@pytest.mark.scheduled
+def test_vertex_stream() -> None:
+    llm = VertexAI(temperature=0)
+    outputs = list(llm.stream("Please say foo:"))
+    assert isinstance(outputs[0], str)
+
+
+async def test_vertex_consistency() -> None:
+    llm = VertexAI(temperature=0)
+    output = llm.generate(["Please say foo:"])
+    streaming_output = llm.generate(["Please say foo:"], stream=True)
+    async_output = await llm.agenerate(["Please say foo:"])
+    assert output.generations[0][0].text == streaming_output.generations[0][0].text
+    assert output.generations[0][0].text == async_output.generations[0][0].text
+
+
+@pytest.mark.parametrize(
+    "endpoint_os_variable_name,result_arg",
+    [("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
+)
+def test_model_garden(
+    endpoint_os_variable_name: str, result_arg: Optional[str]
+) -> None:
+    """In order to run this test, you should provide endpoint names.
+
+    Example:
+    export FALCON_ENDPOINT_ID=...
+    export LLAMA_ENDPOINT_ID=...
+    export PROJECT=...
+    """
+    endpoint_id = os.environ[endpoint_os_variable_name]
+    project = os.environ["PROJECT"]
+    location = "europe-west4"
+    llm = VertexAIModelGarden(
+        endpoint_id=endpoint_id,
+        project=project,
+        result_arg=result_arg,
+        location=location,
+    )
+    output = llm("What is the meaning of life?")
+    assert isinstance(output, str)
+    assert llm._llm_type == "vertexai_model_garden"
+
+
+@pytest.mark.parametrize(
+    "endpoint_os_variable_name,result_arg",
+    [("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
+)
+def test_model_garden_generate(
+    endpoint_os_variable_name: str, result_arg: Optional[str]
+) -> None:
+    """In order to run this test, you should provide endpoint names.
+
+    Example:
+    export FALCON_ENDPOINT_ID=...
+    export LLAMA_ENDPOINT_ID=...
+    export PROJECT=...
+    """
+    endpoint_id = os.environ[endpoint_os_variable_name]
+    project = os.environ["PROJECT"]
+    location = "europe-west4"
+    llm = VertexAIModelGarden(
+        endpoint_id=endpoint_id,
+        project=project,
+        result_arg=result_arg,
+        location=location,
+    )
+    output = llm.generate(["What is the meaning of life?", "How much is 2+2"])
+    assert isinstance(output, LLMResult)
+    assert len(output.generations) == 2
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "endpoint_os_variable_name,result_arg",
+    [("FALCON_ENDPOINT_ID", "generated_text"), ("LLAMA_ENDPOINT_ID", None)],
+)
+async def test_model_garden_agenerate(
+    endpoint_os_variable_name: str, result_arg: Optional[str]
+) -> None:
+    endpoint_id = os.environ[endpoint_os_variable_name]
+    project = os.environ["PROJECT"]
+    location = "europe-west4"
+    llm = VertexAIModelGarden(
+        endpoint_id=endpoint_id,
+        project=project,
+        result_arg=result_arg,
+        location=location,
+    )
+    output = await llm.agenerate(["What is the meaning of life?", "How much is 2+2"])
+    assert isinstance(output, LLMResult)
+    assert len(output.generations) == 2
+
+
+def test_vertex_call_count_tokens() -> None:
+    llm = VertexAI()
+    output = llm.get_num_tokens("How are you?")
+    assert output == 4