mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +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
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
"""Test GradientAI API wrapper.
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In order to run this test, you need to have an GradientAI api key.
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You can get it by registering for free at https://gradient.ai/.
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You'll then need to set:
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- `GRADIENT_ACCESS_TOKEN` environment variable to your api key.
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- `GRADIENT_WORKSPACE_ID` environment variable to your workspace id.
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- `GRADIENT_MODEL` environment variable to your workspace id.
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"""
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import os
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from langchain_community.llms import GradientLLM
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def test_gradient_acall() -> None:
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"""Test simple call to gradient.ai."""
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model = os.environ["GRADIENT_MODEL"]
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gradient_access_token = os.environ["GRADIENT_ACCESS_TOKEN"]
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gradient_workspace_id = os.environ["GRADIENT_WORKSPACE_ID"]
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llm = GradientLLM(
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model=model,
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gradient_access_token=gradient_access_token,
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gradient_workspace_id=gradient_workspace_id,
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)
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output = llm("Say hello:", temperature=0.2, max_tokens=250)
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assert llm._llm_type == "gradient"
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assert isinstance(output, str)
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assert len(output)
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async def test_gradientai_acall() -> None:
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"""Test async call to gradient.ai."""
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model = os.environ["GRADIENT_MODEL"]
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gradient_access_token = os.environ["GRADIENT_ACCESS_TOKEN"]
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gradient_workspace_id = os.environ["GRADIENT_WORKSPACE_ID"]
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llm = GradientLLM(
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model=model,
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gradient_access_token=gradient_access_token,
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gradient_workspace_id=gradient_workspace_id,
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)
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output = await llm.agenerate(["Say hello:"], temperature=0.2, max_tokens=250)
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assert llm._llm_type == "gradient"
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assert isinstance(output, str)
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assert len(output)
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