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…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
20 lines
613 B
Plaintext
20 lines
613 B
Plaintext
# Hazy Research
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This page covers how to use the Hazy Research ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific Hazy Research wrappers.
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## Installation and Setup
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- To use the `manifest`, install it with `pip install manifest-ml`
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## Wrappers
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### LLM
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There exists an LLM wrapper around Hazy Research's `manifest` library.
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`manifest` is a python library which is itself a wrapper around many model providers, and adds in caching, history, and more.
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To use this wrapper:
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```python
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from langchain_community.llms.manifest import ManifestWrapper
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```
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