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56 lines
1.7 KiB
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56 lines
1.7 KiB
Markdown
# Prediction Guard
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This page covers how to use the Prediction Guard ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific Prediction Guard wrappers.
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## Installation and Setup
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- Install the Python SDK with `pip install predictionguard`
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- Get an Prediction Guard access token (as described [here](https://docs.predictionguard.com/)) and set it as an environment variable (`PREDICTIONGUARD_TOKEN`)
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## LLM Wrapper
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There exists a Prediction Guard LLM wrapper, which you can access with
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```python
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from langchain.llms import PredictionGuard
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```
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You can provide the name of your Prediction Guard "proxy" as an argument when initializing the LLM:
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```python
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pgllm = PredictionGuard(name="your-text-gen-proxy")
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```
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Alternatively, you can use Prediction Guard's default proxy for SOTA LLMs:
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```python
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pgllm = PredictionGuard(name="default-text-gen")
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```
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You can also provide your access token directly as an argument:
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```python
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pgllm = PredictionGuard(name="default-text-gen", token="<your access token>")
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```
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## Example usage
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Basic usage of the LLM wrapper:
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```python
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from langchain.llms import PredictionGuard
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pgllm = PredictionGuard(name="default-text-gen")
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pgllm("Tell me a joke")
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```
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Basic LLM Chaining with the Prediction Guard wrapper:
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```python
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from langchain import PromptTemplate, LLMChain
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from langchain.llms import PredictionGuard
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template = """Question: {question}
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Answer: Let's think step by step."""
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prompt = PromptTemplate(template=template, input_variables=["question"])
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llm_chain = LLMChain(prompt=prompt, llm=PredictionGuard(name="default-text-gen"), verbose=True)
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question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
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llm_chain.predict(question=question)
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``` |