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- added missed integration to `docs/ecosystem/integrations/` - updated notebooks to consistent format: changed titles, file names; added descriptions #### Who can review? @hwchase17 @dev2049
102 lines
3.6 KiB
Markdown
102 lines
3.6 KiB
Markdown
# Prediction Guard
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>[Prediction Guard](https://docs.predictionguard.com/) gives a quick and easy access to state-of-the-art open and closed access LLMs, without needing to spend days and weeks figuring out all of the implementation details, managing a bunch of different API specs, and setting up the infrastructure for model deployments.
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## Installation and Setup
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- Install the Python SDK:
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```bash
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pip install predictionguard
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```
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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
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```python
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from langchain.llms import PredictionGuard
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```
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### Example
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You can provide the name of the Prediction Guard model as an argument when initializing the LLM:
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```python
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pgllm = PredictionGuard(model="MPT-7B-Instruct")
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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(model="MPT-7B-Instruct", token="<your access token>")
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```
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Also, you can provide an "output" argument that is used to structure/ control the output of the LLM:
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```python
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pgllm = PredictionGuard(model="MPT-7B-Instruct", output={"type": "boolean"})
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```
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#### Basic usage of the controlled or guarded LLM:
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```python
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import os
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import predictionguard as pg
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from langchain.llms import PredictionGuard
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from langchain import PromptTemplate, LLMChain
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# Your Prediction Guard API key. Get one at predictionguard.com
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os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"
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# Define a prompt template
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template = """Respond to the following query based on the context.
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Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦
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Exclusive Candle Box - $80
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Monthly Candle Box - $45 (NEW!)
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Scent of The Month Box - $28 (NEW!)
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Head to stories to get ALLL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉
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Query: {query}
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Result: """
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prompt = PromptTemplate(template=template, input_variables=["query"])
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# With "guarding" or controlling the output of the LLM. See the
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# Prediction Guard docs (https://docs.predictionguard.com) to learn how to
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# control the output with integer, float, boolean, JSON, and other types and
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# structures.
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pgllm = PredictionGuard(model="MPT-7B-Instruct",
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output={
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"type": "categorical",
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"categories": [
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"product announcement",
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"apology",
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"relational"
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]
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})
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pgllm(prompt.format(query="What kind of post is this?"))
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```
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#### Basic LLM Chaining with the Prediction Guard:
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```python
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import os
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from langchain import PromptTemplate, LLMChain
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from langchain.llms import PredictionGuard
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# Optional, add your OpenAI API Key. This is optional, as Prediction Guard allows
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# you to access all the latest open access models (see https://docs.predictionguard.com)
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os.environ["OPENAI_API_KEY"] = "<your OpenAI api key>"
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# Your Prediction Guard API key. Get one at predictionguard.com
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os.environ["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"
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pgllm = PredictionGuard(model="OpenAI-text-davinci-003")
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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=pgllm, 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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``` |