mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
ed58eeb9c5
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
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
"""Test Anthropic Chat API wrapper."""
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from typing import List
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from unittest.mock import MagicMock
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import pytest
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain_community.chat_models import BedrockChat
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from langchain_community.chat_models.meta import convert_messages_to_prompt_llama
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@pytest.mark.parametrize(
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("messages", "expected"),
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[
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([HumanMessage(content="Hello")], "[INST] Hello [/INST]"),
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(
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[HumanMessage(content="Hello"), AIMessage(content="Answer:")],
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"[INST] Hello [/INST]\nAnswer:",
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),
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(
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[
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SystemMessage(content="You're an assistant"),
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HumanMessage(content="Hello"),
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AIMessage(content="Answer:"),
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],
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"<<SYS>> You're an assistant <</SYS>>\n[INST] Hello [/INST]\nAnswer:",
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),
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],
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)
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def test_formatting(messages: List[BaseMessage], expected: str) -> None:
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result = convert_messages_to_prompt_llama(messages)
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assert result == expected
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def test_anthropic_bedrock() -> None:
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client = MagicMock()
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respbody = MagicMock(
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read=MagicMock(
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return_value=MagicMock(
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decode=MagicMock(return_value=b'{"completion":"Hi back"}')
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)
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)
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)
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client.invoke_model.return_value = {"body": respbody}
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model = BedrockChat(model_id="anthropic.claude-v2", client=client)
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# should not throw an error
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model.invoke("hello there")
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