forked from Archives/langchain
985496f4be
Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
20 lines
603 B
Markdown
20 lines
603 B
Markdown
# 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.llms.manifest import ManifestWrapper
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```
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