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Replicate
Contents
Setup
Calling a model
Chaining Calls
Replicate#
Replicate runs machine learning models in the cloud. We have a library of open-source models that you can run with a few lines of code. If you’re building your own machine learning models, Replicate makes it easy to deploy them at scale.
This example goes over how to use LangChain to interact with Replicate models
Setup#
To run this notebook, you’ll need to create a replicate account and install the replicate python client.
!pip install replicate
# get a token: https://replicate.com/account
from getpass import getpass
REPLICATE_API_TOKEN = getpass()
········
import os
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
from langchain.llms import Replicate
from langchain import PromptTemplate, LLMChain
Calling a model#
Find a model on the replicate explore page, and then paste in the model name and version in this format: model_name/version
For example, for this dolly model, click on the API tab. The model name/version would be: replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5
Only the model param is required, but we can add other model params when initializing.
For example, if we were running stable diffusion and wanted to change the image dimensions:
Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={'image_dimensions': '512x512'})
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/replicate.html
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Note that only the first output of a model will be returned.
llm = Replicate(model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5")
prompt = """
Answer the following yes/no question by reasoning step by step.
Can a dog drive a car?
"""
llm(prompt)
'The legal driving age of dogs is 2. Cars are designed for humans to drive. Therefore, the final answer is yes.'
We can call any replicate model using this syntax. For example, we can call stable diffusion.
text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
input={'image_dimensions': '512x512'})
image_output = text2image("A cat riding a motorcycle by Picasso")
image_output
'https://replicate.delivery/pbxt/Cf07B1zqzFQLOSBQcKG7m9beE74wf7kuip5W9VxHJFembefKE/out-0.png'
The model spits out a URL. Let’s render it.
from PIL import Image
import requests
from io import BytesIO
response = requests.get(image_output)
img = Image.open(BytesIO(response.content))
img
Chaining Calls#
The whole point of langchain is to… chain! Here’s an example of how do that.
from langchain.chains import SimpleSequentialChain
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/replicate.html
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from langchain.chains import SimpleSequentialChain
First, let’s define the LLM for this model as a flan-5, and text2image as a stable diffusion model.
dolly_llm = Replicate(model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5")
text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf")
First prompt in the chain
prompt = PromptTemplate(
input_variables=["product"],
template="What is a good name for a company that makes {product}?",
)
chain = LLMChain(llm=dolly_llm, prompt=prompt)
Second prompt to get the logo for company description
second_prompt = PromptTemplate(
input_variables=["company_name"],
template="Write a description of a logo for this company: {company_name}",
)
chain_two = LLMChain(llm=dolly_llm, prompt=second_prompt)
Third prompt, let’s create the image based on the description output from prompt 2
third_prompt = PromptTemplate(
input_variables=["company_logo_description"],
template="{company_logo_description}",
)
chain_three = LLMChain(llm=text2image, prompt=third_prompt)
Now let’s run it!
# Run the chain specifying only the input variable for the first chain.
overall_chain = SimpleSequentialChain(chains=[chain, chain_two, chain_three], verbose=True)
catchphrase = overall_chain.run("colorful socks")
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/replicate.html
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catchphrase = overall_chain.run("colorful socks")
print(catchphrase)
> Entering new SimpleSequentialChain chain...
novelty socks
todd & co.
https://replicate.delivery/pbxt/BedAP1PPBwXFfkmeD7xDygXO4BcvApp1uvWOwUdHM4tcQfvCB/out-0.png
> Finished chain.
https://replicate.delivery/pbxt/BedAP1PPBwXFfkmeD7xDygXO4BcvApp1uvWOwUdHM4tcQfvCB/out-0.png
response = requests.get("https://replicate.delivery/pbxt/eq6foRJngThCAEBqse3nL3Km2MBfLnWQNd0Hy2SQRo2LuprCB/out-0.png")
img = Image.open(BytesIO(response.content))
img
previous
Structured Decoding with RELLM
next
Runhouse
Contents
Setup
Calling a model
Chaining Calls
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/replicate.html
|
b2ec8626b0a7-0
|
.ipynb
.pdf
GooseAI
Contents
Install openai
Imports
Set the Environment API Key
Create the GooseAI instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
GooseAI#
GooseAI is a fully managed NLP-as-a-Service, delivered via API. GooseAI provides access to these models.
This notebook goes over how to use Langchain with GooseAI.
Install openai#
The openai package is required to use the GooseAI API. Install openai using pip3 install openai.
$ pip3 install openai
Imports#
import os
from langchain.llms import GooseAI
from langchain import PromptTemplate, LLMChain
Set the Environment API Key#
Make sure to get your API key from GooseAI. You are given $10 in free credits to test different models.
from getpass import getpass
GOOSEAI_API_KEY = getpass()
os.environ["GOOSEAI_API_KEY"] = GOOSEAI_API_KEY
Create the GooseAI instance#
You can specify different parameters such as the model name, max tokens generated, temperature, etc.
llm = GooseAI()
Create a Prompt Template#
We will create a prompt template for Question and Answer.
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
Initiate the LLMChain#
llm_chain = LLMChain(prompt=prompt, llm=llm)
Run the LLMChain#
Provide a question and run the LLMChain.
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.run(question)
previous
Google Cloud Platform Vertex AI PaLM
next
GPT4All
Contents
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/gooseai_example.html
|
b2ec8626b0a7-1
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Google Cloud Platform Vertex AI PaLM
next
GPT4All
Contents
Install openai
Imports
Set the Environment API Key
Create the GooseAI instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/gooseai_example.html
|
cea888ac1cdc-0
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.ipynb
.pdf
Amazon Bedrock
Contents
Using in a conversation chain
Amazon Bedrock#
Amazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case
%pip install boto3
from langchain.llms.bedrock import Bedrock
llm = Bedrock(credentials_profile_name="bedrock-admin", model_id="amazon.titan-tg1-large")
Using in a conversation chain#
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
conversation = ConversationChain(
llm=llm,
verbose=True,
memory=ConversationBufferMemory()
)
conversation.predict(input="Hi there!")
previous
Beam integration for langchain
next
CerebriumAI
Contents
Using in a conversation chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/bedrock.html
|
e564df3eb57f-0
|
.ipynb
.pdf
Google Cloud Platform Vertex AI PaLM
Google Cloud Platform Vertex AI PaLM#
Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.
PaLM API on Vertex AI is a Preview offering, subject to the Pre-GA Offerings Terms of the GCP Service Specific Terms.
Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions. Further, by using PaLM API on Vertex AI, you agree to the Generative AI Preview terms and conditions (Preview Terms).
For PaLM API on Vertex AI, you can process personal data as outlined in the Cloud Data Processing Addendum, subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).
To use Vertex AI PaLM you must have the google-cloud-aiplatform Python package installed and either:
Have credentials configured for your environment (gcloud, workload identity, etc…)
Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable
This codebase uses the google.auth library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.
For more information, see:
https://cloud.google.com/docs/authentication/application-default-credentials#GAC
https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth
#!pip install google-cloud-aiplatform
from langchain.llms import VertexAI
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm = VertexAI()
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/google_vertex_ai_palm.html
|
e564df3eb57f-1
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llm = VertexAI()
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.run(question)
'Justin Bieber was born on March 1, 1994. The Super Bowl in 1994 was won by the San Francisco 49ers.\nThe final answer: San Francisco 49ers.'
previous
ForefrontAI
next
GooseAI
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/google_vertex_ai_palm.html
|
d6ae6e20dace-0
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.ipynb
.pdf
Structured Decoding with RELLM
Contents
Hugging Face Baseline
RELLM LLM Wrapper
Structured Decoding with RELLM#
RELLM is a library that wraps local Hugging Face pipeline models for structured decoding.
It works by generating tokens one at a time. At each step, it masks tokens that don’t conform to the provided partial regular expression.
Warning - this module is still experimental
!pip install rellm > /dev/null
Hugging Face Baseline#
First, let’s establish a qualitative baseline by checking the output of the model without structured decoding.
import logging
logging.basicConfig(level=logging.ERROR)
prompt = """Human: "What's the capital of the United States?"
AI Assistant:{
"action": "Final Answer",
"action_input": "The capital of the United States is Washington D.C."
}
Human: "What's the capital of Pennsylvania?"
AI Assistant:{
"action": "Final Answer",
"action_input": "The capital of Pennsylvania is Harrisburg."
}
Human: "What 2 + 5?"
AI Assistant:{
"action": "Final Answer",
"action_input": "2 + 5 = 7."
}
Human: 'What's the capital of Maryland?'
AI Assistant:"""
from transformers import pipeline
from langchain.llms import HuggingFacePipeline
hf_model = pipeline("text-generation", model="cerebras/Cerebras-GPT-590M", max_new_tokens=200)
original_model = HuggingFacePipeline(pipeline=hf_model)
generated = original_model.generate([prompt], stop=["Human:"])
print(generated)
Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/rellm_experimental.html
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Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
generations=[[Generation(text=' "What\'s the capital of Maryland?"\n', generation_info=None)]] llm_output=None
That’s not so impressive, is it? It didn’t answer the question and it didn’t follow the JSON format at all! Let’s try with the structured decoder.
RELLM LLM Wrapper#
Let’s try that again, now providing a regex to match the JSON structured format.
import regex # Note this is the regex library NOT python's re stdlib module
# We'll choose a regex that matches to a structured json string that looks like:
# {
# "action": "Final Answer",
# "action_input": string or dict
# }
pattern = regex.compile(r'\{\s*"action":\s*"Final Answer",\s*"action_input":\s*(\{.*\}|"[^"]*")\s*\}\nHuman:')
from langchain.experimental.llms import RELLM
model = RELLM(pipeline=hf_model, regex=pattern, max_new_tokens=200)
generated = model.predict(prompt, stop=["Human:"])
print(generated)
{"action": "Final Answer",
"action_input": "The capital of Maryland is Baltimore."
}
Voila! Free of parsing errors.
previous
PromptLayer OpenAI
next
Replicate
Contents
Hugging Face Baseline
RELLM LLM Wrapper
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/rellm_experimental.html
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7cb53e667c4a-0
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.ipynb
.pdf
Manifest
Contents
Compare HF Models
Manifest#
This notebook goes over how to use Manifest and LangChain.
For more detailed information on manifest, and how to use it with local hugginface models like in this example, see https://github.com/HazyResearch/manifest
Another example of using Manifest with Langchain.
!pip install manifest-ml
from manifest import Manifest
from langchain.llms.manifest import ManifestWrapper
manifest = Manifest(
client_name = "huggingface",
client_connection = "http://127.0.0.1:5000"
)
print(manifest.client.get_model_params())
llm = ManifestWrapper(client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 256})
# Map reduce example
from langchain import PromptTemplate
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.mapreduce import MapReduceChain
_prompt = """Write a concise summary of the following:
{text}
CONCISE SUMMARY:"""
prompt = PromptTemplate(template=_prompt, input_variables=["text"])
text_splitter = CharacterTextSplitter()
mp_chain = MapReduceChain.from_params(llm, prompt, text_splitter)
with open('../../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
mp_chain.run(state_of_the_union)
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/manifest.html
|
7cb53e667c4a-1
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state_of_the_union = f.read()
mp_chain.run(state_of_the_union)
'President Obama delivered his annual State of the Union address on Tuesday night, laying out his priorities for the coming year. Obama said the government will provide free flu vaccines to all Americans, ending the government shutdown and allowing businesses to reopen. The president also said that the government will continue to send vaccines to 112 countries, more than any other nation. "We have lost so much to COVID-19," Trump said. "Time with one another. And worst of all, so much loss of life." He said the CDC is working on a vaccine for kids under 5, and that the government will be ready with plenty of vaccines when they are available. Obama says the new guidelines are a "great step forward" and that the virus is no longer a threat. He says the government is launching a "Test to Treat" initiative that will allow people to get tested at a pharmacy and get antiviral pills on the spot at no cost. Obama says the new guidelines are a "great step forward" and that the virus is no longer a threat. He says the government will continue to send vaccines to 112 countries, more than any other nation. "We are coming for your'
Compare HF Models#
from langchain.model_laboratory import ModelLaboratory
manifest1 = ManifestWrapper(
client=Manifest(
client_name="huggingface",
client_connection="http://127.0.0.1:5000"
),
llm_kwargs={"temperature": 0.01}
)
manifest2 = ManifestWrapper(
client=Manifest(
client_name="huggingface",
client_connection="http://127.0.0.1:5001"
),
llm_kwargs={"temperature": 0.01}
)
manifest3 = ManifestWrapper(
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/manifest.html
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7cb53e667c4a-2
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)
manifest3 = ManifestWrapper(
client=Manifest(
client_name="huggingface",
client_connection="http://127.0.0.1:5002"
),
llm_kwargs={"temperature": 0.01}
)
llms = [manifest1, manifest2, manifest3]
model_lab = ModelLaboratory(llms)
model_lab.compare("What color is a flamingo?")
Input:
What color is a flamingo?
ManifestWrapper
Params: {'model_name': 'bigscience/T0_3B', 'model_path': 'bigscience/T0_3B', 'temperature': 0.01}
pink
ManifestWrapper
Params: {'model_name': 'EleutherAI/gpt-neo-125M', 'model_path': 'EleutherAI/gpt-neo-125M', 'temperature': 0.01}
A flamingo is a small, round
ManifestWrapper
Params: {'model_name': 'google/flan-t5-xl', 'model_path': 'google/flan-t5-xl', 'temperature': 0.01}
pink
previous
Llama-cpp
next
Modal
Contents
Compare HF Models
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/manifest.html
|
56926ff53e01-0
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.ipynb
.pdf
StochasticAI
StochasticAI#
Stochastic Acceleration Platform aims to simplify the life cycle of a Deep Learning model. From uploading and versioning the model, through training, compression and acceleration to putting it into production.
This example goes over how to use LangChain to interact with StochasticAI models.
You have to get the API_KEY and the API_URL here.
from getpass import getpass
STOCHASTICAI_API_KEY = getpass()
import os
os.environ["STOCHASTICAI_API_KEY"] = STOCHASTICAI_API_KEY
YOUR_API_URL = getpass()
from langchain.llms import StochasticAI
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm = StochasticAI(api_url=YOUR_API_URL)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.run(question)
"\n\nStep 1: In 1999, the St. Louis Rams won the Super Bowl.\n\nStep 2: In 1999, Beiber was born.\n\nStep 3: The Rams were in Los Angeles at the time.\n\nStep 4: So they didn't play in the Super Bowl that year.\n"
previous
SageMakerEndpoint
next
Writer
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/stochasticai.html
|
fd97a1987941-0
|
.ipynb
.pdf
DeepInfra
Contents
Imports
Set the Environment API Key
Create the DeepInfra instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
DeepInfra#
DeepInfra provides several LLMs.
This notebook goes over how to use Langchain with DeepInfra.
Imports#
import os
from langchain.llms import DeepInfra
from langchain import PromptTemplate, LLMChain
Set the Environment API Key#
Make sure to get your API key from DeepInfra. You have to Login and get a new token.
You are given a 1 hour free of serverless GPU compute to test different models. (see here)
You can print your token with deepctl auth token
# get a new token: https://deepinfra.com/login?from=%2Fdash
from getpass import getpass
DEEPINFRA_API_TOKEN = getpass()
os.environ["DEEPINFRA_API_TOKEN"] = DEEPINFRA_API_TOKEN
Create the DeepInfra instance#
You can also use our open source deepctl tool to manage your model deployments. You can view a list of available parameters here.
llm = DeepInfra(model_id="databricks/dolly-v2-12b")
llm.model_kwargs = {'temperature': 0.7, 'repetition_penalty': 1.2, 'max_new_tokens': 250, 'top_p': 0.9}
Create a Prompt Template#
We will create a prompt template for Question and Answer.
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
Initiate the LLMChain#
llm_chain = LLMChain(prompt=prompt, llm=llm)
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/deepinfra_example.html
|
fd97a1987941-1
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llm_chain = LLMChain(prompt=prompt, llm=llm)
Run the LLMChain#
Provide a question and run the LLMChain.
question = "Can penguins reach the North pole?"
llm_chain.run(question)
"Penguins live in the Southern hemisphere.\nThe North pole is located in the Northern hemisphere.\nSo, first you need to turn the penguin South.\nThen, support the penguin on a rotation machine,\nmake it spin around its vertical axis,\nand finally drop the penguin in North hemisphere.\nNow, you have a penguin in the north pole!\n\nStill didn't understand?\nWell, you're a failure as a teacher."
previous
Databricks
next
ForefrontAI
Contents
Imports
Set the Environment API Key
Create the DeepInfra instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/deepinfra_example.html
|
445313b89a54-0
|
.ipynb
.pdf
MosaicML
MosaicML#
MosaicML offers a managed inference service. You can either use a variety of open source models, or deploy your own.
This example goes over how to use LangChain to interact with MosaicML Inference for text completion.
# sign up for an account: https://forms.mosaicml.com/demo?utm_source=langchain
from getpass import getpass
MOSAICML_API_TOKEN = getpass()
import os
os.environ["MOSAICML_API_TOKEN"] = MOSAICML_API_TOKEN
from langchain.llms import MosaicML
from langchain import PromptTemplate, LLMChain
template = """Question: {question}"""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm = MosaicML(inject_instruction_format=True, model_kwargs={'do_sample': False})
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What is one good reason why you should train a large language model on domain specific data?"
llm_chain.run(question)
previous
Modal
next
NLP Cloud
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/mosaicml.html
|
8ce661b71d9a-0
|
.ipynb
.pdf
PromptLayer OpenAI
Contents
Install PromptLayer
Imports
Set the Environment API Key
Use the PromptLayerOpenAI LLM like normal
Using PromptLayer Track
PromptLayer OpenAI#
PromptLayer is the first platform that allows you to track, manage, and share your GPT prompt engineering. PromptLayer acts a middleware between your code and OpenAI’s python library.
PromptLayer records all your OpenAI API requests, allowing you to search and explore request history in the PromptLayer dashboard.
This example showcases how to connect to PromptLayer to start recording your OpenAI requests.
Another example is here.
Install PromptLayer#
The promptlayer package is required to use PromptLayer with OpenAI. Install promptlayer using pip.
!pip install promptlayer
Imports#
import os
from langchain.llms import PromptLayerOpenAI
import promptlayer
Set the Environment API Key#
You can create a PromptLayer API Key at www.promptlayer.com by clicking the settings cog in the navbar.
Set it as an environment variable called PROMPTLAYER_API_KEY.
You also need an OpenAI Key, called OPENAI_API_KEY.
from getpass import getpass
PROMPTLAYER_API_KEY = getpass()
os.environ["PROMPTLAYER_API_KEY"] = PROMPTLAYER_API_KEY
from getpass import getpass
OPENAI_API_KEY = getpass()
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
Use the PromptLayerOpenAI LLM like normal#
You can optionally pass in pl_tags to track your requests with PromptLayer’s tagging feature.
llm = PromptLayerOpenAI(pl_tags=["langchain"])
llm("I am a cat and I want")
The above request should now appear on your PromptLayer dashboard.
Using PromptLayer Track#
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/promptlayer_openai.html
|
8ce661b71d9a-1
|
The above request should now appear on your PromptLayer dashboard.
Using PromptLayer Track#
If you would like to use any of the PromptLayer tracking features, you need to pass the argument return_pl_id when instantializing the PromptLayer LLM to get the request id.
llm = PromptLayerOpenAI(return_pl_id=True)
llm_results = llm.generate(["Tell me a joke"])
for res in llm_results.generations:
pl_request_id = res[0].generation_info["pl_request_id"]
promptlayer.track.score(request_id=pl_request_id, score=100)
Using this allows you to track the performance of your model in the PromptLayer dashboard. If you are using a prompt template, you can attach a template to a request as well.
Overall, this gives you the opportunity to track the performance of different templates and models in the PromptLayer dashboard.
previous
Basic LLM usage
next
Structured Decoding with RELLM
Contents
Install PromptLayer
Imports
Set the Environment API Key
Use the PromptLayerOpenAI LLM like normal
Using PromptLayer Track
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
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https://python.langchain.com/en/latest/modules/models/llms/integrations/promptlayer_openai.html
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0c0717be3f89-0
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.ipynb
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Writer
Writer#
Writer is a platform to generate different language content.
This example goes over how to use LangChain to interact with Writer models.
You have to get the WRITER_API_KEY here.
from getpass import getpass
WRITER_API_KEY = getpass()
import os
os.environ["WRITER_API_KEY"] = WRITER_API_KEY
from langchain.llms import Writer
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
# If you get an error, probably, you need to set up the "base_url" parameter that can be taken from the error log.
llm = Writer()
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.run(question)
previous
StochasticAI
next
LLMs
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/writer.html
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a06029df9d4e-0
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.ipynb
.pdf
GPT4All
Contents
Specify Model
GPT4All#
GitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.
This example goes over how to use LangChain to interact with GPT4All models.
%pip install gpt4all > /dev/null
Note: you may need to restart the kernel to use updated packages.
from langchain import PromptTemplate, LLMChain
from langchain.llms import GPT4All
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
Specify Model#
To run locally, download a compatible ggml-formatted model. For more info, visit https://github.com/nomic-ai/gpt4all
For full installation instructions go here.
The GPT4All Chat installer needs to decompress a 3GB LLM model during the installation process!
Note that new models are uploaded regularly - check the link above for the most recent .bin URL
local_path = './models/ggml-gpt4all-l13b-snoozy.bin' # replace with your desired local file path
Uncomment the below block to download a model. You may want to update url to a new version.
# import requests
# from pathlib import Path
# from tqdm import tqdm
# Path(local_path).parent.mkdir(parents=True, exist_ok=True)
# # Example model. Check https://github.com/nomic-ai/gpt4all for the latest models.
# url = 'http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin'
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# # send a GET request to the URL to download the file. Stream since it's large
# response = requests.get(url, stream=True)
# # open the file in binary mode and write the contents of the response to it in chunks
# # This is a large file, so be prepared to wait.
# with open(local_path, 'wb') as f:
# for chunk in tqdm(response.iter_content(chunk_size=8192)):
# if chunk:
# f.write(chunk)
# Callbacks support token-wise streaming
callbacks = [StreamingStdOutCallbackHandler()]
# Verbose is required to pass to the callback manager
llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)
# If you want to use a custom model add the backend parameter
# Check https://docs.gpt4all.io/gpt4all_python.html for supported backends
llm = GPT4All(model=local_path, backend='gptj', callbacks=callbacks, verbose=True)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.run(question)
previous
GooseAI
next
Hugging Face Hub
Contents
Specify Model
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/gpt4all.html
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ef8511b0914c-0
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.ipynb
.pdf
PipelineAI
Contents
Install pipeline-ai
Imports
Set the Environment API Key
Create the PipelineAI instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
PipelineAI#
PipelineAI allows you to run your ML models at scale in the cloud. It also provides API access to several LLM models.
This notebook goes over how to use Langchain with PipelineAI.
Install pipeline-ai#
The pipeline-ai library is required to use the PipelineAI API, AKA Pipeline Cloud. Install pipeline-ai using pip install pipeline-ai.
# Install the package
!pip install pipeline-ai
Imports#
import os
from langchain.llms import PipelineAI
from langchain import PromptTemplate, LLMChain
Set the Environment API Key#
Make sure to get your API key from PipelineAI. Check out the cloud quickstart guide. You’ll be given a 30 day free trial with 10 hours of serverless GPU compute to test different models.
os.environ["PIPELINE_API_KEY"] = "YOUR_API_KEY_HERE"
Create the PipelineAI instance#
When instantiating PipelineAI, you need to specify the id or tag of the pipeline you want to use, e.g. pipeline_key = "public/gpt-j:base". You then have the option of passing additional pipeline-specific keyword arguments:
llm = PipelineAI(pipeline_key="YOUR_PIPELINE_KEY", pipeline_kwargs={...})
Create a Prompt Template#
We will create a prompt template for Question and Answer.
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
Initiate the LLMChain#
llm_chain = LLMChain(prompt=prompt, llm=llm)
Run the LLMChain#
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https://python.langchain.com/en/latest/modules/models/llms/integrations/pipelineai_example.html
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Run the LLMChain#
Provide a question and run the LLMChain.
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.run(question)
previous
Petals
next
Basic LLM usage
Contents
Install pipeline-ai
Imports
Set the Environment API Key
Create the PipelineAI instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/pipelineai_example.html
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64a63b65948b-0
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.ipynb
.pdf
Anyscale
Anyscale#
Anyscale is a fully-managed Ray platform, on which you can build, deploy, and manage scalable AI and Python applications
This example goes over how to use LangChain to interact with Anyscale service
import os
os.environ["ANYSCALE_SERVICE_URL"] = ANYSCALE_SERVICE_URL
os.environ["ANYSCALE_SERVICE_ROUTE"] = ANYSCALE_SERVICE_ROUTE
os.environ["ANYSCALE_SERVICE_TOKEN"] = ANYSCALE_SERVICE_TOKEN
from langchain.llms import Anyscale
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm = Anyscale()
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "When was George Washington president?"
llm_chain.run(question)
With Ray, we can distribute the queries without asyncrhonized implementation. This not only applies to Anyscale LLM model, but to any other Langchain LLM models which do not have _acall or _agenerate implemented
prompt_list = [
"When was George Washington president?",
"Explain to me the difference between nuclear fission and fusion.",
"Give me a list of 5 science fiction books I should read next.",
"Explain the difference between Spark and Ray.",
"Suggest some fun holiday ideas.",
"Tell a joke.",
"What is 2+2?",
"Explain what is machine learning like I am five years old.",
"Explain what is artifical intelligence.",
]
import ray
@ray.remote
def send_query(llm, prompt):
resp = llm(prompt)
return resp
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https://python.langchain.com/en/latest/modules/models/llms/integrations/anyscale.html
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resp = llm(prompt)
return resp
futures = [send_query.remote(llm, prompt) for prompt in prompt_list]
results = ray.get(futures)
previous
Aleph Alpha
next
Azure OpenAI
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/anyscale.html
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c874924f2a60-0
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.ipynb
.pdf
Banana
Banana#
Banana is focused on building the machine learning infrastructure.
This example goes over how to use LangChain to interact with Banana models
# Install the package https://docs.banana.dev/banana-docs/core-concepts/sdks/python
!pip install banana-dev
# get new tokens: https://app.banana.dev/
# We need two tokens, not just an `api_key`: `BANANA_API_KEY` and `YOUR_MODEL_KEY`
import os
from getpass import getpass
os.environ["BANANA_API_KEY"] = "YOUR_API_KEY"
# OR
# BANANA_API_KEY = getpass()
from langchain.llms import Banana
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm = Banana(model_key="YOUR_MODEL_KEY")
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.run(question)
previous
Azure OpenAI
next
Beam integration for langchain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/banana.html
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28722f954f09-0
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.ipynb
.pdf
Runhouse
Runhouse#
The Runhouse allows remote compute and data across environments and users. See the Runhouse docs.
This example goes over how to use LangChain and Runhouse to interact with models hosted on your own GPU, or on-demand GPUs on AWS, GCP, AWS, or Lambda.
Note: Code uses SelfHosted name instead of the Runhouse.
!pip install runhouse
from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
from langchain import PromptTemplate, LLMChain
import runhouse as rh
INFO | 2023-04-17 16:47:36,173 | No auth token provided, so not using RNS API to save and load configs
# For an on-demand A100 with GCP, Azure, or Lambda
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)
# For an on-demand A10G with AWS (no single A100s on AWS)
# gpu = rh.cluster(name='rh-a10x', instance_type='g5.2xlarge', provider='aws')
# For an existing cluster
# gpu = rh.cluster(ips=['<ip of the cluster>'],
# ssh_creds={'ssh_user': '...', 'ssh_private_key':'<path_to_key>'},
# name='rh-a10x')
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm = SelfHostedHuggingFaceLLM(model_id="gpt2", hardware=gpu, model_reqs=["pip:./", "transformers", "torch"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
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llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.run(question)
INFO | 2023-02-17 05:42:23,537 | Running _generate_text via gRPC
INFO | 2023-02-17 05:42:24,016 | Time to send message: 0.48 seconds
"\n\nLet's say we're talking sports teams who won the Super Bowl in the year Justin Beiber"
You can also load more custom models through the SelfHostedHuggingFaceLLM interface:
llm = SelfHostedHuggingFaceLLM(
model_id="google/flan-t5-small",
task="text2text-generation",
hardware=gpu,
)
llm("What is the capital of Germany?")
INFO | 2023-02-17 05:54:21,681 | Running _generate_text via gRPC
INFO | 2023-02-17 05:54:21,937 | Time to send message: 0.25 seconds
'berlin'
Using a custom load function, we can load a custom pipeline directly on the remote hardware:
def load_pipeline():
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline # Need to be inside the fn in notebooks
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
return pipe
def inference_fn(pipeline, prompt, stop = None):
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/runhouse.html
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)
return pipe
def inference_fn(pipeline, prompt, stop = None):
return pipeline(prompt)[0]["generated_text"][len(prompt):]
llm = SelfHostedHuggingFaceLLM(model_load_fn=load_pipeline, hardware=gpu, inference_fn=inference_fn)
llm("Who is the current US president?")
INFO | 2023-02-17 05:42:59,219 | Running _generate_text via gRPC
INFO | 2023-02-17 05:42:59,522 | Time to send message: 0.3 seconds
'john w. bush'
You can send your pipeline directly over the wire to your model, but this will only work for small models (<2 Gb), and will be pretty slow:
pipeline = load_pipeline()
llm = SelfHostedPipeline.from_pipeline(
pipeline=pipeline, hardware=gpu, model_reqs=model_reqs
)
Instead, we can also send it to the hardware’s filesystem, which will be much faster.
rh.blob(pickle.dumps(pipeline), path="models/pipeline.pkl").save().to(gpu, path="models")
llm = SelfHostedPipeline.from_pipeline(pipeline="models/pipeline.pkl", hardware=gpu)
previous
Replicate
next
SageMakerEndpoint
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/runhouse.html
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289af6aa352c-0
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.ipynb
.pdf
CerebriumAI
Contents
Install cerebrium
Imports
Set the Environment API Key
Create the CerebriumAI instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
CerebriumAI#
Cerebrium is an AWS Sagemaker alternative. It also provides API access to several LLM models.
This notebook goes over how to use Langchain with CerebriumAI.
Install cerebrium#
The cerebrium package is required to use the CerebriumAI API. Install cerebrium using pip3 install cerebrium.
# Install the package
!pip3 install cerebrium
Imports#
import os
from langchain.llms import CerebriumAI
from langchain import PromptTemplate, LLMChain
Set the Environment API Key#
Make sure to get your API key from CerebriumAI. See here. You are given a 1 hour free of serverless GPU compute to test different models.
os.environ["CEREBRIUMAI_API_KEY"] = "YOUR_KEY_HERE"
Create the CerebriumAI instance#
You can specify different parameters such as the model endpoint url, max length, temperature, etc. You must provide an endpoint url.
llm = CerebriumAI(endpoint_url="YOUR ENDPOINT URL HERE")
Create a Prompt Template#
We will create a prompt template for Question and Answer.
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
Initiate the LLMChain#
llm_chain = LLMChain(prompt=prompt, llm=llm)
Run the LLMChain#
Provide a question and run the LLMChain.
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https://python.langchain.com/en/latest/modules/models/llms/integrations/cerebriumai_example.html
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Run the LLMChain#
Provide a question and run the LLMChain.
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.run(question)
previous
Amazon Bedrock
next
Cohere
Contents
Install cerebrium
Imports
Set the Environment API Key
Create the CerebriumAI instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/cerebriumai_example.html
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51ac4084a46c-0
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.ipynb
.pdf
Hugging Face Local Pipelines
Contents
Load the model
Integrate the model in an LLMChain
Hugging Face Local Pipelines#
Hugging Face models can be run locally through the HuggingFacePipeline class.
The Hugging Face Model Hub hosts over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.
These can be called from LangChain either through this local pipeline wrapper or by calling their hosted inference endpoints through the HuggingFaceHub class. For more information on the hosted pipelines, see the HuggingFaceHub notebook.
To use, you should have the transformers python package installed.
!pip install transformers > /dev/null
Load the model#
from langchain import HuggingFacePipeline
llm = HuggingFacePipeline.from_model_id(model_id="bigscience/bloom-1b7", task="text-generation", model_kwargs={"temperature":0, "max_length":64})
WARNING:root:Failed to default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x1117f9790>: Failed to establish a new connection: [Errno 61] Connection refused'))
Integrate the model in an LLMChain#
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What is electroencephalography?"
print(llm_chain.run(question))
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https://python.langchain.com/en/latest/modules/models/llms/integrations/huggingface_pipelines.html
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question = "What is electroencephalography?"
print(llm_chain.run(question))
/Users/wfh/code/lc/lckg/.venv/lib/python3.11/site-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (64) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.
warnings.warn(
WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x144d06910>: Failed to establish a new connection: [Errno 61] Connection refused'))
First, we need to understand what is an electroencephalogram. An electroencephalogram is a recording of brain activity. It is a recording of brain activity that is made by placing electrodes on the scalp. The electrodes are placed
previous
Hugging Face Hub
next
Huggingface TextGen Inference
Contents
Load the model
Integrate the model in an LLMChain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/huggingface_pipelines.html
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20dbfeda26e6-0
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.ipynb
.pdf
OpenLM
Contents
Setup
Using LangChain with OpenLM
OpenLM#
OpenLM is a zero-dependency OpenAI-compatible LLM provider that can call different inference endpoints directly via HTTP.
It implements the OpenAI Completion class so that it can be used as a drop-in replacement for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added code.
This examples goes over how to use LangChain to interact with both OpenAI and HuggingFace. You’ll need API keys from both.
Setup#
Install dependencies and set API keys.
# Uncomment to install openlm and openai if you haven't already
# !pip install openlm
# !pip install openai
from getpass import getpass
import os
import subprocess
# Check if OPENAI_API_KEY environment variable is set
if "OPENAI_API_KEY" not in os.environ:
print("Enter your OpenAI API key:")
os.environ["OPENAI_API_KEY"] = getpass()
# Check if HF_API_TOKEN environment variable is set
if "HF_API_TOKEN" not in os.environ:
print("Enter your HuggingFace Hub API key:")
os.environ["HF_API_TOKEN"] = getpass()
Using LangChain with OpenLM#
Here we’re going to call two models in an LLMChain, text-davinci-003 from OpenAI and gpt2 on HuggingFace.
from langchain.llms import OpenLM
from langchain import PromptTemplate, LLMChain
question = "What is the capital of France?"
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
for model in ["text-davinci-003", "huggingface.co/gpt2"]:
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/openlm.html
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llm = OpenLM(model=model)
llm_chain = LLMChain(prompt=prompt, llm=llm)
result = llm_chain.run(question)
print("""Model: {}
Result: {}""".format(model, result))
Model: text-davinci-003
Result: France is a country in Europe. The capital of France is Paris.
Model: huggingface.co/gpt2
Result: Question: What is the capital of France?
Answer: Let's think step by step. I am not going to lie, this is a complicated issue, and I don't see any solutions to all this, but it is still far more
previous
OpenAI
next
Petals
Contents
Setup
Using LangChain with OpenLM
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/openlm.html
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8480ed408398-0
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.ipynb
.pdf
C Transformers
C Transformers#
The C Transformers library provides Python bindings for GGML models.
This example goes over how to use LangChain to interact with C Transformers models.
Install
%pip install ctransformers
Load Model
from langchain.llms import CTransformers
llm = CTransformers(model='marella/gpt-2-ggml')
Generate Text
print(llm('AI is going to'))
Streaming
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = CTransformers(model='marella/gpt-2-ggml', callbacks=[StreamingStdOutCallbackHandler()])
response = llm('AI is going to')
LLMChain
from langchain import PromptTemplate, LLMChain
template = """Question: {question}
Answer:"""
prompt = PromptTemplate(template=template, input_variables=['question'])
llm_chain = LLMChain(prompt=prompt, llm=llm)
response = llm_chain.run('What is AI?')
previous
Cohere
next
Databricks
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/ctransformers.html
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c1f44a663278-0
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.ipynb
.pdf
SageMakerEndpoint
Contents
Set up
Example
SageMakerEndpoint#
Amazon SageMaker is a system that can build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
This notebooks goes over how to use an LLM hosted on a SageMaker endpoint.
!pip3 install langchain boto3
Set up#
You have to set up following required parameters of the SagemakerEndpoint call:
endpoint_name: The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region.
credentials_profile_name: The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
Example#
from langchain.docstore.document import Document
example_doc_1 = """
Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and was rushed to the hospital.
Since she was diagnosed with a brain injury, the doctor told Peter to stay besides her until she gets well.
Therefore, Peter stayed with her at the hospital for 3 days without leaving.
"""
docs = [
Document(
page_content=example_doc_1,
)
]
from typing import Dict
from langchain import PromptTemplate, SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import LLMContentHandler
from langchain.chains.question_answering import load_qa_chain
import json
query = """How long was Elizabeth hospitalized?
"""
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https://python.langchain.com/en/latest/modules/models/llms/integrations/sagemaker.html
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import json
query = """How long was Elizabeth hospitalized?
"""
prompt_template = """Use the following pieces of context to answer the question at the end.
{context}
Question: {question}
Answer:"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
class ContentHandler(LLMContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps({prompt: prompt, **model_kwargs})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generated_text"]
content_handler = ContentHandler()
chain = load_qa_chain(
llm=SagemakerEndpoint(
endpoint_name="endpoint-name",
credentials_profile_name="credentials-profile-name",
region_name="us-west-2",
model_kwargs={"temperature":1e-10},
content_handler=content_handler
),
prompt=PROMPT
)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
previous
Runhouse
next
StochasticAI
Contents
Set up
Example
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/integrations/sagemaker.html
|
ccec0a0857f5-0
|
.ipynb
.pdf
How to track token usage
How to track token usage#
This notebook goes over how to track your token usage for specific calls. It is currently only implemented for the OpenAI API.
Let’s first look at an extremely simple example of tracking token usage for a single LLM call.
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback
llm = OpenAI(model_name="text-davinci-002", n=2, best_of=2)
with get_openai_callback() as cb:
result = llm("Tell me a joke")
print(cb)
Tokens Used: 42
Prompt Tokens: 4
Completion Tokens: 38
Successful Requests: 1
Total Cost (USD): $0.00084
Anything inside the context manager will get tracked. Here’s an example of using it to track multiple calls in sequence.
with get_openai_callback() as cb:
result = llm("Tell me a joke")
result2 = llm("Tell me a joke")
print(cb.total_tokens)
91
If a chain or agent with multiple steps in it is used, it will track all those steps.
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
with get_openai_callback() as cb:
response = agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
|
https://python.langchain.com/en/latest/modules/models/llms/examples/token_usage_tracking.html
|
ccec0a0857f5-1
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print(f"Total Tokens: {cb.total_tokens}")
print(f"Prompt Tokens: {cb.prompt_tokens}")
print(f"Completion Tokens: {cb.completion_tokens}")
print(f"Total Cost (USD): ${cb.total_cost}")
> Entering new AgentExecutor chain...
I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.
Action: Search
Action Input: "Olivia Wilde boyfriend"
Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling.
Thought: I need to find out Harry Styles' age.
Action: Search
Action Input: "Harry Styles age"
Observation: 29 years
Thought: I need to calculate 29 raised to the 0.23 power.
Action: Calculator
Action Input: 29^0.23
Observation: Answer: 2.169459462491557
Thought: I now know the final answer.
Final Answer: Harry Styles, Olivia Wilde's boyfriend, is 29 years old and his age raised to the 0.23 power is 2.169459462491557.
> Finished chain.
Total Tokens: 1506
Prompt Tokens: 1350
Completion Tokens: 156
Total Cost (USD): $0.03012
previous
How to stream LLM and Chat Model responses
next
Integrations
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/examples/token_usage_tracking.html
|
2b8910665260-0
|
.ipynb
.pdf
How (and why) to use the fake LLM
How (and why) to use the fake LLM#
We expose a fake LLM class that can be used for testing. This allows you to mock out calls to the LLM and simulate what would happen if the LLM responded in a certain way.
In this notebook we go over how to use this.
We start this with using the FakeLLM in an agent.
from langchain.llms.fake import FakeListLLM
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
tools = load_tools(["python_repl"])
responses=[
"Action: Python REPL\nAction Input: print(2 + 2)",
"Final Answer: 4"
]
llm = FakeListLLM(responses=responses)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("whats 2 + 2")
> Entering new AgentExecutor chain...
Action: Python REPL
Action Input: print(2 + 2)
Observation: 4
Thought:Final Answer: 4
> Finished chain.
'4'
previous
How to write a custom LLM wrapper
next
How (and why) to use the human input LLM
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/examples/fake_llm.html
|
8db58dce53f9-0
|
.ipynb
.pdf
How to stream LLM and Chat Model responses
How to stream LLM and Chat Model responses#
LangChain provides streaming support for LLMs. Currently, we support streaming for the OpenAI, ChatOpenAI, and ChatAnthropic implementations, but streaming support for other LLM implementations is on the roadmap. To utilize streaming, use a CallbackHandler that implements on_llm_new_token. In this example, we are using StreamingStdOutCallbackHandler.
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI, ChatAnthropic
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema import HumanMessage
llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)
resp = llm("Write me a song about sparkling water.")
Verse 1
I'm sippin' on sparkling water,
It's so refreshing and light,
It's the perfect way to quench my thirst
On a hot summer night.
Chorus
Sparkling water, sparkling water,
It's the best way to stay hydrated,
It's so crisp and so clean,
It's the perfect way to stay refreshed.
Verse 2
I'm sippin' on sparkling water,
It's so bubbly and bright,
It's the perfect way to cool me down
On a hot summer night.
Chorus
Sparkling water, sparkling water,
It's the best way to stay hydrated,
It's so crisp and so clean,
It's the perfect way to stay refreshed.
Verse 3
I'm sippin' on sparkling water,
It's so light and so clear,
It's the perfect way to keep me cool
On a hot summer night.
Chorus
Sparkling water, sparkling water,
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https://python.langchain.com/en/latest/modules/models/llms/examples/streaming_llm.html
|
8db58dce53f9-1
|
On a hot summer night.
Chorus
Sparkling water, sparkling water,
It's the best way to stay hydrated,
It's so crisp and so clean,
It's the perfect way to stay refreshed.
We still have access to the end LLMResult if using generate. However, token_usage is not currently supported for streaming.
llm.generate(["Tell me a joke."])
Q: What did the fish say when it hit the wall?
A: Dam!
LLMResult(generations=[[Generation(text='\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {}, 'model_name': 'text-davinci-003'})
Here’s an example with the ChatOpenAI chat model implementation:
chat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)
resp = chat([HumanMessage(content="Write me a song about sparkling water.")])
Verse 1:
Bubbles rising to the top
A refreshing drink that never stops
Clear and crisp, it's oh so pure
Sparkling water, I can't ignore
Chorus:
Sparkling water, oh how you shine
A taste so clean, it's simply divine
You quench my thirst, you make me feel alive
Sparkling water, you're my favorite vibe
Verse 2:
No sugar, no calories, just H2O
A drink that's good for me, don't you know
With lemon or lime, you're even better
Sparkling water, you're my forever
Chorus:
Sparkling water, oh how you shine
A taste so clean, it's simply divine
You quench my thirst, you make me feel alive
|
https://python.langchain.com/en/latest/modules/models/llms/examples/streaming_llm.html
|
8db58dce53f9-2
|
You quench my thirst, you make me feel alive
Sparkling water, you're my favorite vibe
Bridge:
You're my go-to drink, day or night
You make me feel so light
I'll never give you up, you're my true love
Sparkling water, you're sent from above
Chorus:
Sparkling water, oh how you shine
A taste so clean, it's simply divine
You quench my thirst, you make me feel alive
Sparkling water, you're my favorite vibe
Outro:
Sparkling water, you're the one for me
I'll never let you go, can't you see
You're my drink of choice, forevermore
Sparkling water, I adore.
Here is an example with the ChatAnthropic chat model implementation, which uses their claude model.
chat = ChatAnthropic(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)
resp = chat([HumanMessage(content="Write me a song about sparkling water.")])
Here is my attempt at a song about sparkling water:
Sparkling water, bubbles so bright,
Dancing in the glass with delight.
Refreshing and crisp, a fizzy delight,
Quenching my thirst with each sip I take.
The carbonation tickles my tongue,
As the refreshing water song is sung.
Lime or lemon, a citrus twist,
Makes sparkling water such a bliss.
Healthy and hydrating, a drink so pure,
Sparkling water, always alluring.
Bubbles ascending in a stream,
Sparkling water, you're my dream!
previous
How to serialize LLM classes
next
How to track token usage
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/examples/streaming_llm.html
|
b80e41fed43a-0
|
.ipynb
.pdf
How to use the async API for LLMs
How to use the async API for LLMs#
LangChain provides async support for LLMs by leveraging the asyncio library.
Async support is particularly useful for calling multiple LLMs concurrently, as these calls are network-bound. Currently, OpenAI, PromptLayerOpenAI, ChatOpenAI and Anthropic are supported, but async support for other LLMs is on the roadmap.
You can use the agenerate method to call an OpenAI LLM asynchronously.
import time
import asyncio
from langchain.llms import OpenAI
def generate_serially():
llm = OpenAI(temperature=0.9)
for _ in range(10):
resp = llm.generate(["Hello, how are you?"])
print(resp.generations[0][0].text)
async def async_generate(llm):
resp = await llm.agenerate(["Hello, how are you?"])
print(resp.generations[0][0].text)
async def generate_concurrently():
llm = OpenAI(temperature=0.9)
tasks = [async_generate(llm) for _ in range(10)]
await asyncio.gather(*tasks)
s = time.perf_counter()
# If running this outside of Jupyter, use asyncio.run(generate_concurrently())
await generate_concurrently()
elapsed = time.perf_counter() - s
print('\033[1m' + f"Concurrent executed in {elapsed:0.2f} seconds." + '\033[0m')
s = time.perf_counter()
generate_serially()
elapsed = time.perf_counter() - s
print('\033[1m' + f"Serial executed in {elapsed:0.2f} seconds." + '\033[0m')
|
https://python.langchain.com/en/latest/modules/models/llms/examples/async_llm.html
|
b80e41fed43a-1
|
I'm doing well, thank you. How about you?
I'm doing well, thank you. How about you?
I'm doing well, how about you?
I'm doing well, thank you. How about you?
I'm doing well, thank you. How about you?
I'm doing well, thank you. How about yourself?
I'm doing well, thank you! How about you?
I'm doing well, thank you. How about you?
I'm doing well, thank you! How about you?
I'm doing well, thank you. How about you?
Concurrent executed in 1.39 seconds.
I'm doing well, thank you. How about you?
I'm doing well, thank you. How about you?
I'm doing well, thank you. How about you?
I'm doing well, thank you. How about you?
I'm doing well, thank you. How about yourself?
I'm doing well, thanks for asking. How about you?
I'm doing well, thanks! How about you?
I'm doing well, thank you. How about you?
I'm doing well, thank you. How about yourself?
I'm doing well, thanks for asking. How about you?
Serial executed in 5.77 seconds.
previous
Generic Functionality
next
How to write a custom LLM wrapper
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/examples/async_llm.html
|
cfc7b2cd6579-0
|
.ipynb
.pdf
How to write a custom LLM wrapper
How to write a custom LLM wrapper#
This notebook goes over how to create a custom LLM wrapper, in case you want to use your own LLM or a different wrapper than one that is supported in LangChain.
There is only one required thing that a custom LLM needs to implement:
A _call method that takes in a string, some optional stop words, and returns a string
There is a second optional thing it can implement:
An _identifying_params property that is used to help with printing of this class. Should return a dictionary.
Let’s implement a very simple custom LLM that just returns the first N characters of the input.
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
class CustomLLM(LLM):
n: int
@property
def _llm_type(self) -> str:
return "custom"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
if stop is not None:
raise ValueError("stop kwargs are not permitted.")
return prompt[:self.n]
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"n": self.n}
We can now use this as an any other LLM.
llm = CustomLLM(n=10)
llm("This is a foobar thing")
'This is a '
We can also print the LLM and see its custom print.
|
https://python.langchain.com/en/latest/modules/models/llms/examples/custom_llm.html
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cfc7b2cd6579-1
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'This is a '
We can also print the LLM and see its custom print.
print(llm)
CustomLLM
Params: {'n': 10}
previous
How to use the async API for LLMs
next
How (and why) to use the fake LLM
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/examples/custom_llm.html
|
08165be5c401-0
|
.ipynb
.pdf
How to cache LLM calls
Contents
In Memory Cache
SQLite Cache
Redis Cache
Standard Cache
Semantic Cache
GPTCache
Momento Cache
SQLAlchemy Cache
Custom SQLAlchemy Schemas
Optional Caching
Optional Caching in Chains
How to cache LLM calls#
This notebook covers how to cache results of individual LLM calls.
import langchain
from langchain.llms import OpenAI
# To make the caching really obvious, lets use a slower model.
llm = OpenAI(model_name="text-davinci-002", n=2, best_of=2)
In Memory Cache#
from langchain.cache import InMemoryCache
langchain.llm_cache = InMemoryCache()
%%time
# The first time, it is not yet in cache, so it should take longer
llm("Tell me a joke")
CPU times: user 35.9 ms, sys: 28.6 ms, total: 64.6 ms
Wall time: 4.83 s
"\n\nWhy couldn't the bicycle stand up by itself? It was...two tired!"
%%time
# The second time it is, so it goes faster
llm("Tell me a joke")
CPU times: user 238 µs, sys: 143 µs, total: 381 µs
Wall time: 1.76 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
SQLite Cache#
!rm .langchain.db
# We can do the same thing with a SQLite cache
from langchain.cache import SQLiteCache
langchain.llm_cache = SQLiteCache(database_path=".langchain.db")
%%time
# The first time, it is not yet in cache, so it should take longer
llm("Tell me a joke")
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https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html
|
08165be5c401-1
|
llm("Tell me a joke")
CPU times: user 17 ms, sys: 9.76 ms, total: 26.7 ms
Wall time: 825 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
%%time
# The second time it is, so it goes faster
llm("Tell me a joke")
CPU times: user 2.46 ms, sys: 1.23 ms, total: 3.7 ms
Wall time: 2.67 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
Redis Cache#
Standard Cache#
Use Redis to cache prompts and responses.
# We can do the same thing with a Redis cache
# (make sure your local Redis instance is running first before running this example)
from redis import Redis
from langchain.cache import RedisCache
langchain.llm_cache = RedisCache(redis_=Redis())
%%time
# The first time, it is not yet in cache, so it should take longer
llm("Tell me a joke")
CPU times: user 6.88 ms, sys: 8.75 ms, total: 15.6 ms
Wall time: 1.04 s
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
%%time
# The second time it is, so it goes faster
llm("Tell me a joke")
CPU times: user 1.59 ms, sys: 610 µs, total: 2.2 ms
Wall time: 5.58 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
Semantic Cache#
Use Redis to cache prompts and responses and evaluate hits based on semantic similarity.
|
https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html
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08165be5c401-2
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Semantic Cache#
Use Redis to cache prompts and responses and evaluate hits based on semantic similarity.
from langchain.embeddings import OpenAIEmbeddings
from langchain.cache import RedisSemanticCache
langchain.llm_cache = RedisSemanticCache(
redis_url="redis://localhost:6379",
embedding=OpenAIEmbeddings()
)
%%time
# The first time, it is not yet in cache, so it should take longer
llm("Tell me a joke")
CPU times: user 351 ms, sys: 156 ms, total: 507 ms
Wall time: 3.37 s
"\n\nWhy don't scientists trust atoms?\nBecause they make up everything."
%%time
# The second time, while not a direct hit, the question is semantically similar to the original question,
# so it uses the cached result!
llm("Tell me one joke")
CPU times: user 6.25 ms, sys: 2.72 ms, total: 8.97 ms
Wall time: 262 ms
"\n\nWhy don't scientists trust atoms?\nBecause they make up everything."
GPTCache#
We can use GPTCache for exact match caching OR to cache results based on semantic similarity
Let’s first start with an example of exact match
from gptcache import Cache
from gptcache.manager.factory import manager_factory
from gptcache.processor.pre import get_prompt
from langchain.cache import GPTCache
import hashlib
def get_hashed_name(name):
return hashlib.sha256(name.encode()).hexdigest()
def init_gptcache(cache_obj: Cache, llm: str):
hashed_llm = get_hashed_name(llm)
cache_obj.init(
pre_embedding_func=get_prompt,
|
https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html
|
08165be5c401-3
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cache_obj.init(
pre_embedding_func=get_prompt,
data_manager=manager_factory(manager="map", data_dir=f"map_cache_{hashed_llm}"),
)
langchain.llm_cache = GPTCache(init_gptcache)
%%time
# The first time, it is not yet in cache, so it should take longer
llm("Tell me a joke")
CPU times: user 21.5 ms, sys: 21.3 ms, total: 42.8 ms
Wall time: 6.2 s
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
%%time
# The second time it is, so it goes faster
llm("Tell me a joke")
CPU times: user 571 µs, sys: 43 µs, total: 614 µs
Wall time: 635 µs
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
Let’s now show an example of similarity caching
from gptcache import Cache
from gptcache.adapter.api import init_similar_cache
from langchain.cache import GPTCache
import hashlib
def get_hashed_name(name):
return hashlib.sha256(name.encode()).hexdigest()
def init_gptcache(cache_obj: Cache, llm: str):
hashed_llm = get_hashed_name(llm)
init_similar_cache(cache_obj=cache_obj, data_dir=f"similar_cache_{hashed_llm}")
langchain.llm_cache = GPTCache(init_gptcache)
%%time
# The first time, it is not yet in cache, so it should take longer
llm("Tell me a joke")
CPU times: user 1.42 s, sys: 279 ms, total: 1.7 s
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https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html
|
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Wall time: 8.44 s
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
%%time
# This is an exact match, so it finds it in the cache
llm("Tell me a joke")
CPU times: user 866 ms, sys: 20 ms, total: 886 ms
Wall time: 226 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
%%time
# This is not an exact match, but semantically within distance so it hits!
llm("Tell me joke")
CPU times: user 853 ms, sys: 14.8 ms, total: 868 ms
Wall time: 224 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
Momento Cache#
Use Momento to cache prompts and responses.
Requires momento to use, uncomment below to install:
# !pip install momento
You’ll need to get a Momemto auth token to use this class. This can either be passed in to a momento.CacheClient if you’d like to instantiate that directly, as a named parameter auth_token to MomentoChatMessageHistory.from_client_params, or can just be set as an environment variable MOMENTO_AUTH_TOKEN.
from datetime import timedelta
from langchain.cache import MomentoCache
cache_name = "langchain"
ttl = timedelta(days=1)
langchain.llm_cache = MomentoCache.from_client_params(cache_name, ttl)
%%time
# The first time, it is not yet in cache, so it should take longer
llm("Tell me a joke")
CPU times: user 40.7 ms, sys: 16.5 ms, total: 57.2 ms
Wall time: 1.73 s
|
https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html
|
08165be5c401-5
|
Wall time: 1.73 s
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
%%time
# The second time it is, so it goes faster
# When run in the same region as the cache, latencies are single digit ms
llm("Tell me a joke")
CPU times: user 3.16 ms, sys: 2.98 ms, total: 6.14 ms
Wall time: 57.9 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
SQLAlchemy Cache#
# You can use SQLAlchemyCache to cache with any SQL database supported by SQLAlchemy.
# from langchain.cache import SQLAlchemyCache
# from sqlalchemy import create_engine
# engine = create_engine("postgresql://postgres:postgres@localhost:5432/postgres")
# langchain.llm_cache = SQLAlchemyCache(engine)
Custom SQLAlchemy Schemas#
# You can define your own declarative SQLAlchemyCache child class to customize the schema used for caching. For example, to support high-speed fulltext prompt indexing with Postgres, use:
from sqlalchemy import Column, Integer, String, Computed, Index, Sequence
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy_utils import TSVectorType
from langchain.cache import SQLAlchemyCache
Base = declarative_base()
class FulltextLLMCache(Base): # type: ignore
"""Postgres table for fulltext-indexed LLM Cache"""
__tablename__ = "llm_cache_fulltext"
id = Column(Integer, Sequence('cache_id'), primary_key=True)
prompt = Column(String, nullable=False)
llm = Column(String, nullable=False)
idx = Column(Integer)
response = Column(String)
|
https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html
|
08165be5c401-6
|
idx = Column(Integer)
response = Column(String)
prompt_tsv = Column(TSVectorType(), Computed("to_tsvector('english', llm || ' ' || prompt)", persisted=True))
__table_args__ = (
Index("idx_fulltext_prompt_tsv", prompt_tsv, postgresql_using="gin"),
)
engine = create_engine("postgresql://postgres:postgres@localhost:5432/postgres")
langchain.llm_cache = SQLAlchemyCache(engine, FulltextLLMCache)
Optional Caching#
You can also turn off caching for specific LLMs should you choose. In the example below, even though global caching is enabled, we turn it off for a specific LLM
llm = OpenAI(model_name="text-davinci-002", n=2, best_of=2, cache=False)
%%time
llm("Tell me a joke")
CPU times: user 5.8 ms, sys: 2.71 ms, total: 8.51 ms
Wall time: 745 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
%%time
llm("Tell me a joke")
CPU times: user 4.91 ms, sys: 2.64 ms, total: 7.55 ms
Wall time: 623 ms
'\n\nTwo guys stole a calendar. They got six months each.'
Optional Caching in Chains#
You can also turn off caching for particular nodes in chains. Note that because of certain interfaces, its often easier to construct the chain first, and then edit the LLM afterwards.
As an example, we will load a summarizer map-reduce chain. We will cache results for the map-step, but then not freeze it for the combine step.
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llm = OpenAI(model_name="text-davinci-002")
no_cache_llm = OpenAI(model_name="text-davinci-002", cache=False)
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.mapreduce import MapReduceChain
text_splitter = CharacterTextSplitter()
with open('../../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
texts = text_splitter.split_text(state_of_the_union)
from langchain.docstore.document import Document
docs = [Document(page_content=t) for t in texts[:3]]
from langchain.chains.summarize import load_summarize_chain
chain = load_summarize_chain(llm, chain_type="map_reduce", reduce_llm=no_cache_llm)
%%time
chain.run(docs)
CPU times: user 452 ms, sys: 60.3 ms, total: 512 ms
Wall time: 5.09 s
'\n\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infrastructure Law, which will create jobs and help Americans. He also talks about his vision for America, which includes investing in education and infrastructure. In response to Russian aggression in Ukraine, the United States is joining with European allies to impose sanctions and isolate Russia. American forces are being mobilized to protect NATO countries in the event that Putin decides to keep moving west. The Ukrainians are bravely fighting back, but the next few weeks will be hard for them. Putin will pay a high price for his actions in the long run. Americans should not be alarmed, as the United States is taking action to protect its interests and allies.'
When we run it again, we see that it runs substantially faster but the final answer is different. This is due to caching at the map steps, but not at the reduce step.
%%time
chain.run(docs)
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%%time
chain.run(docs)
CPU times: user 11.5 ms, sys: 4.33 ms, total: 15.8 ms
Wall time: 1.04 s
'\n\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infrastructure Law, which will create jobs and help Americans. He also talks about his vision for America, which includes investing in education and infrastructure.'
!rm .langchain.db sqlite.db
previous
How (and why) to use the human input LLM
next
How to serialize LLM classes
Contents
In Memory Cache
SQLite Cache
Redis Cache
Standard Cache
Semantic Cache
GPTCache
Momento Cache
SQLAlchemy Cache
Custom SQLAlchemy Schemas
Optional Caching
Optional Caching in Chains
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
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https://python.langchain.com/en/latest/modules/models/llms/examples/llm_caching.html
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7bd6fce3cb05-0
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.ipynb
.pdf
How to serialize LLM classes
Contents
Loading
Saving
How to serialize LLM classes#
This notebook walks through how to write and read an LLM Configuration to and from disk. This is useful if you want to save the configuration for a given LLM (e.g., the provider, the temperature, etc).
from langchain.llms import OpenAI
from langchain.llms.loading import load_llm
Loading#
First, lets go over loading an LLM from disk. LLMs can be saved on disk in two formats: json or yaml. No matter the extension, they are loaded in the same way.
!cat llm.json
{
"model_name": "text-davinci-003",
"temperature": 0.7,
"max_tokens": 256,
"top_p": 1.0,
"frequency_penalty": 0.0,
"presence_penalty": 0.0,
"n": 1,
"best_of": 1,
"request_timeout": null,
"_type": "openai"
}
llm = load_llm("llm.json")
!cat llm.yaml
_type: openai
best_of: 1
frequency_penalty: 0.0
max_tokens: 256
model_name: text-davinci-003
n: 1
presence_penalty: 0.0
request_timeout: null
temperature: 0.7
top_p: 1.0
llm = load_llm("llm.yaml")
Saving#
If you want to go from an LLM in memory to a serialized version of it, you can do so easily by calling the .save method. Again, this supports both json and yaml.
llm.save("llm.json")
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https://python.langchain.com/en/latest/modules/models/llms/examples/llm_serialization.html
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llm.save("llm.json")
llm.save("llm.yaml")
previous
How to cache LLM calls
next
How to stream LLM and Chat Model responses
Contents
Loading
Saving
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/llms/examples/llm_serialization.html
|
9b3fe37d86ee-0
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.ipynb
.pdf
How (and why) to use the human input LLM
How (and why) to use the human input LLM#
Similar to the fake LLM, LangChain provides a pseudo LLM class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the LLM and simulate how a human would respond if they received the prompts.
In this notebook, we go over how to use this.
We start this with using the HumanInputLLM in an agent.
from langchain.llms.human import HumanInputLLM
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
Since we will use the WikipediaQueryRun tool in this notebook, you might need to install the wikipedia package if you haven’t done so already.
%pip install wikipedia
tools = load_tools(["wikipedia"])
llm = HumanInputLLM(prompt_func=lambda prompt: print(f"\n===PROMPT====\n{prompt}\n=====END OF PROMPT======"))
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("What is 'Bocchi the Rock!'?")
> Entering new AgentExecutor chain...
===PROMPT====
Answer the following questions as best you can. You have access to the following tools:
Wikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [Wikipedia]
Action Input: the input to the action
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Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: What is 'Bocchi the Rock!'?
Thought:
=====END OF PROMPT======
I need to use a tool.
Action: Wikipedia
Action Input: Bocchi the Rock!, Japanese four-panel manga and anime series.
Observation: Page: Bocchi the Rock!
Summary: Bocchi the Rock! (ぼっち・ざ・ろっく!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tankōbon volumes as of November 2022.
An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.
Page: Manga Time Kirara
Summary: Manga Time Kirara (まんがタイムきらら, Manga Taimu Kirara) is a Japanese seinen manga magazine published by Houbunsha which mainly serializes four-panel manga. The magazine is sold on the ninth of each month and was first published as a special edition of Manga Time, another Houbunsha magazine, on May 17, 2002. Characters from this magazine have appeared in a crossover role-playing game called Kirara Fantasia.
Page: Manga Time Kirara Max
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Page: Manga Time Kirara Max
Summary: Manga Time Kirara Max (まんがタイムきららMAX) is a Japanese four-panel seinen manga magazine published by Houbunsha. It is the third magazine of the "Kirara" series, after "Manga Time Kirara" and "Manga Time Kirara Carat". The first issue was released on September 29, 2004. Currently the magazine is released on the 19th of each month.
Thought:
===PROMPT====
Answer the following questions as best you can. You have access to the following tools:
Wikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [Wikipedia]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: What is 'Bocchi the Rock!'?
Thought:I need to use a tool.
Action: Wikipedia
Action Input: Bocchi the Rock!, Japanese four-panel manga and anime series.
Observation: Page: Bocchi the Rock!
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Observation: Page: Bocchi the Rock!
Summary: Bocchi the Rock! (ぼっち・ざ・ろっく!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tankōbon volumes as of November 2022.
An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.
Page: Manga Time Kirara
Summary: Manga Time Kirara (まんがタイムきらら, Manga Taimu Kirara) is a Japanese seinen manga magazine published by Houbunsha which mainly serializes four-panel manga. The magazine is sold on the ninth of each month and was first published as a special edition of Manga Time, another Houbunsha magazine, on May 17, 2002. Characters from this magazine have appeared in a crossover role-playing game called Kirara Fantasia.
Page: Manga Time Kirara Max
Summary: Manga Time Kirara Max (まんがタイムきららMAX) is a Japanese four-panel seinen manga magazine published by Houbunsha. It is the third magazine of the "Kirara" series, after "Manga Time Kirara" and "Manga Time Kirara Carat". The first issue was released on September 29, 2004. Currently the magazine is released on the 19th of each month.
Thought:
=====END OF PROMPT======
These are not relevant articles.
Action: Wikipedia
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=====END OF PROMPT======
These are not relevant articles.
Action: Wikipedia
Action Input: Bocchi the Rock!, Japanese four-panel manga series written and illustrated by Aki Hamaji.
Observation: Page: Bocchi the Rock!
Summary: Bocchi the Rock! (ぼっち・ざ・ろっく!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tankōbon volumes as of November 2022.
An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.
Thought:
===PROMPT====
Answer the following questions as best you can. You have access to the following tools:
Wikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [Wikipedia]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: What is 'Bocchi the Rock!'?
Thought:I need to use a tool.
Action: Wikipedia
Action Input: Bocchi the Rock!, Japanese four-panel manga and anime series.
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Action Input: Bocchi the Rock!, Japanese four-panel manga and anime series.
Observation: Page: Bocchi the Rock!
Summary: Bocchi the Rock! (ぼっち・ざ・ろっく!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tankōbon volumes as of November 2022.
An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.
Page: Manga Time Kirara
Summary: Manga Time Kirara (まんがタイムきらら, Manga Taimu Kirara) is a Japanese seinen manga magazine published by Houbunsha which mainly serializes four-panel manga. The magazine is sold on the ninth of each month and was first published as a special edition of Manga Time, another Houbunsha magazine, on May 17, 2002. Characters from this magazine have appeared in a crossover role-playing game called Kirara Fantasia.
Page: Manga Time Kirara Max
Summary: Manga Time Kirara Max (まんがタイムきららMAX) is a Japanese four-panel seinen manga magazine published by Houbunsha. It is the third magazine of the "Kirara" series, after "Manga Time Kirara" and "Manga Time Kirara Carat". The first issue was released on September 29, 2004. Currently the magazine is released on the 19th of each month.
Thought:These are not relevant articles.
Action: Wikipedia
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9b3fe37d86ee-6
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Thought:These are not relevant articles.
Action: Wikipedia
Action Input: Bocchi the Rock!, Japanese four-panel manga series written and illustrated by Aki Hamaji.
Observation: Page: Bocchi the Rock!
Summary: Bocchi the Rock! (ぼっち・ざ・ろっく!, Bocchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tankōbon volumes as of November 2022.
An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.
Thought:
=====END OF PROMPT======
It worked.
Final Answer: Bocchi the Rock! is a four-panel manga series and anime television series. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.
> Finished chain.
"Bocchi the Rock! is a four-panel manga series and anime television series. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim."
previous
How (and why) to use the fake LLM
next
How to cache LLM calls
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
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https://python.langchain.com/en/latest/modules/models/llms/examples/human_input_llm.html
|
7d45020d986d-0
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.rst
.pdf
Integrations
Integrations#
The examples here all highlight how to integrate with different chat models.
Anthropic
Azure
Google Cloud Platform Vertex AI PaLM
OpenAI
PromptLayer ChatOpenAI
previous
How to stream responses
next
Anthropic
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
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https://python.langchain.com/en/latest/modules/models/chat/integrations.html
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69794a56aa6a-0
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.rst
.pdf
How-To Guides
How-To Guides#
The examples here all address certain “how-to” guides for working with chat models.
How to use few shot examples
How to stream responses
previous
Getting Started
next
How to use few shot examples
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
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https://python.langchain.com/en/latest/modules/models/chat/how_to_guides.html
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19a01905d5c6-0
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.ipynb
.pdf
Getting Started
Contents
PromptTemplates
LLMChain
Streaming
Getting Started#
This notebook covers how to get started with chat models. The interface is based around messages rather than raw text.
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate, LLMChain
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
chat = ChatOpenAI(temperature=0)
You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are AIMessage, HumanMessage, SystemMessage, and ChatMessage – ChatMessage takes in an arbitrary role parameter. Most of the time, you’ll just be dealing with HumanMessage, AIMessage, and SystemMessage
chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
AIMessage(content="J'aime programmer.", additional_kwargs={})
OpenAI’s chat model supports multiple messages as input. See here for more information. Here is an example of sending a system and user message to the chat model:
messages = [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="I love programming.")
]
chat(messages)
AIMessage(content="J'aime programmer.", additional_kwargs={})
You can go one step further and generate completions for multiple sets of messages using generate. This returns an LLMResult with an additional message parameter.
batch_messages = [
[
SystemMessage(content="You are a helpful assistant that translates English to French."),
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https://python.langchain.com/en/latest/modules/models/chat/getting_started.html
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[
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="I love programming.")
],
[
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="I love artificial intelligence.")
],
]
result = chat.generate(batch_messages)
result
LLMResult(generations=[[ChatGeneration(text="J'aime programmer.", generation_info=None, message=AIMessage(content="J'aime programmer.", additional_kwargs={}))], [ChatGeneration(text="J'aime l'intelligence artificielle.", generation_info=None, message=AIMessage(content="J'aime l'intelligence artificielle.", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}})
You can recover things like token usage from this LLMResult
result.llm_output
{'token_usage': {'prompt_tokens': 57,
'completion_tokens': 20,
'total_tokens': 77}}
PromptTemplates#
You can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate’s format_prompt – this returns a PromptValue, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.
For convenience, there is a from_template method exposed on the template. If you were to use this template, this is what it would look like:
template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
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system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
# get a chat completion from the formatted messages
chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
AIMessage(content="J'adore la programmation.", additional_kwargs={})
If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:
prompt=PromptTemplate(
template="You are a helpful assistant that translates {input_language} to {output_language}.",
input_variables=["input_language", "output_language"],
)
system_message_prompt = SystemMessagePromptTemplate(prompt=prompt)
LLMChain#
You can use the existing LLMChain in a very similar way to before - provide a prompt and a model.
chain = LLMChain(llm=chat, prompt=chat_prompt)
chain.run(input_language="English", output_language="French", text="I love programming.")
"J'adore la programmation."
Streaming#
Streaming is supported for ChatOpenAI through callback handling.
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
chat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)
resp = chat([HumanMessage(content="Write me a song about sparkling water.")])
Verse 1:
Bubbles rising to the top
A refreshing drink that never stops
Clear and crisp, it's pure delight
A taste that's sure to excite
Chorus:
Sparkling water, oh so fine
A drink that's always on my mind
With every sip, I feel alive
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A drink that's always on my mind
With every sip, I feel alive
Sparkling water, you're my vibe
Verse 2:
No sugar, no calories, just pure bliss
A drink that's hard to resist
It's the perfect way to quench my thirst
A drink that always comes first
Chorus:
Sparkling water, oh so fine
A drink that's always on my mind
With every sip, I feel alive
Sparkling water, you're my vibe
Bridge:
From the mountains to the sea
Sparkling water, you're the key
To a healthy life, a happy soul
A drink that makes me feel whole
Chorus:
Sparkling water, oh so fine
A drink that's always on my mind
With every sip, I feel alive
Sparkling water, you're my vibe
Outro:
Sparkling water, you're the one
A drink that's always so much fun
I'll never let you go, my friend
Sparkling
previous
Chat Models
next
How-To Guides
Contents
PromptTemplates
LLMChain
Streaming
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
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https://python.langchain.com/en/latest/modules/models/chat/getting_started.html
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3692518f8db4-0
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.ipynb
.pdf
OpenAI
OpenAI#
This notebook covers how to get started with OpenAI chat models.
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
chat = ChatOpenAI(temperature=0)
messages = [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love programming.")
]
chat(messages)
AIMessage(content="J'aime programmer.", additional_kwargs={}, example=False)
You can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate’s format_prompt – this returns a PromptValue, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.
For convenience, there is a from_template method exposed on the template. If you were to use this template, this is what it would look like:
template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
# get a chat completion from the formatted messages
chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
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https://python.langchain.com/en/latest/modules/models/chat/integrations/openai.html
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AIMessage(content="J'adore la programmation.", additional_kwargs={})
previous
Google Cloud Platform Vertex AI PaLM
next
PromptLayer ChatOpenAI
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/chat/integrations/openai.html
|
3b90e3843203-0
|
.ipynb
.pdf
PromptLayer ChatOpenAI
Contents
Install PromptLayer
Imports
Set the Environment API Key
Use the PromptLayerOpenAI LLM like normal
Using PromptLayer Track
PromptLayer ChatOpenAI#
This example showcases how to connect to PromptLayer to start recording your ChatOpenAI requests.
Install PromptLayer#
The promptlayer package is required to use PromptLayer with OpenAI. Install promptlayer using pip.
pip install promptlayer
Imports#
import os
from langchain.chat_models import PromptLayerChatOpenAI
from langchain.schema import HumanMessage
Set the Environment API Key#
You can create a PromptLayer API Key at www.promptlayer.com by clicking the settings cog in the navbar.
Set it as an environment variable called PROMPTLAYER_API_KEY.
os.environ["PROMPTLAYER_API_KEY"] = "**********"
Use the PromptLayerOpenAI LLM like normal#
You can optionally pass in pl_tags to track your requests with PromptLayer’s tagging feature.
chat = PromptLayerChatOpenAI(pl_tags=["langchain"])
chat([HumanMessage(content="I am a cat and I want")])
AIMessage(content='to take a nap in a cozy spot. I search around for a suitable place and finally settle on a soft cushion on the window sill. I curl up into a ball and close my eyes, relishing the warmth of the sun on my fur. As I drift off to sleep, I can hear the birds chirping outside and feel the gentle breeze blowing through the window. This is the life of a contented cat.', additional_kwargs={})
The above request should now appear on your PromptLayer dashboard.
Using PromptLayer Track#
If you would like to use any of the PromptLayer tracking features, you need to pass the argument return_pl_id when instantializing the PromptLayer LLM to get the request id.
|
https://python.langchain.com/en/latest/modules/models/chat/integrations/promptlayer_chatopenai.html
|
3b90e3843203-1
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chat = PromptLayerChatOpenAI(return_pl_id=True)
chat_results = chat.generate([[HumanMessage(content="I am a cat and I want")]])
for res in chat_results.generations:
pl_request_id = res[0].generation_info["pl_request_id"]
promptlayer.track.score(request_id=pl_request_id, score=100)
Using this allows you to track the performance of your model in the PromptLayer dashboard. If you are using a prompt template, you can attach a template to a request as well.
Overall, this gives you the opportunity to track the performance of different templates and models in the PromptLayer dashboard.
previous
OpenAI
next
Text Embedding Models
Contents
Install PromptLayer
Imports
Set the Environment API Key
Use the PromptLayerOpenAI LLM like normal
Using PromptLayer Track
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/chat/integrations/promptlayer_chatopenai.html
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84a16219f836-0
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.ipynb
.pdf
Azure
Azure#
This notebook goes over how to connect to an Azure hosted OpenAI endpoint
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage
BASE_URL = "https://${TODO}.openai.azure.com"
API_KEY = "..."
DEPLOYMENT_NAME = "chat"
model = AzureChatOpenAI(
openai_api_base=BASE_URL,
openai_api_version="2023-03-15-preview",
deployment_name=DEPLOYMENT_NAME,
openai_api_key=API_KEY,
openai_api_type = "azure",
)
model([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
AIMessage(content="\n\nJ'aime programmer.", additional_kwargs={})
previous
Anthropic
next
Google Cloud Platform Vertex AI PaLM
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/chat/integrations/azure_chat_openai.html
|
06351b9cdb24-0
|
.ipynb
.pdf
Google Cloud Platform Vertex AI PaLM
Google Cloud Platform Vertex AI PaLM#
Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.
PaLM API on Vertex AI is a Preview offering, subject to the Pre-GA Offerings Terms of the GCP Service Specific Terms.
Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions. Further, by using PaLM API on Vertex AI, you agree to the Generative AI Preview terms and conditions (Preview Terms).
For PaLM API on Vertex AI, you can process personal data as outlined in the Cloud Data Processing Addendum, subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).
To use Vertex AI PaLM you must have the google-cloud-aiplatform Python package installed and either:
Have credentials configured for your environment (gcloud, workload identity, etc…)
Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable
This codebase uses the google.auth library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.
For more information, see:
https://cloud.google.com/docs/authentication/application-default-credentials#GAC
https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth
#!pip install google-cloud-aiplatform
from langchain.chat_models import ChatVertexAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
HumanMessage,
SystemMessage
)
chat = ChatVertexAI()
|
https://python.langchain.com/en/latest/modules/models/chat/integrations/google_vertex_ai_palm.html
|
06351b9cdb24-1
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HumanMessage,
SystemMessage
)
chat = ChatVertexAI()
messages = [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love programming.")
]
chat(messages)
AIMessage(content='Sure, here is the translation of the sentence "I love programming" from English to French:\n\nJ\'aime programmer.', additional_kwargs={}, example=False)
You can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate’s format_prompt – this returns a PromptValue, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.
For convenience, there is a from_template method exposed on the template. If you were to use this template, this is what it would look like:
template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
# get a chat completion from the formatted messages
chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
AIMessage(content='Sure, here is the translation of "I love programming" in French:\n\nJ\'aime programmer.', additional_kwargs={}, example=False)
previous
Azure
next
OpenAI
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/chat/integrations/google_vertex_ai_palm.html
|
ce2dc56d44a1-0
|
.ipynb
.pdf
Anthropic
Contents
ChatAnthropic also supports async and streaming functionality:
Anthropic#
This notebook covers how to get started with Anthropic chat models.
from langchain.chat_models import ChatAnthropic
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
chat = ChatAnthropic()
messages = [
HumanMessage(content="Translate this sentence from English to French. I love programming.")
]
chat(messages)
AIMessage(content=" J'aime programmer. ", additional_kwargs={})
ChatAnthropic also supports async and streaming functionality:#
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
await chat.agenerate([messages])
LLMResult(generations=[[ChatGeneration(text=" J'aime la programmation.", generation_info=None, message=AIMessage(content=" J'aime la programmation.", additional_kwargs={}))]], llm_output={})
chat = ChatAnthropic(streaming=True, verbose=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
chat(messages)
J'adore programmer.
AIMessage(content=" J'adore programmer.", additional_kwargs={})
previous
Integrations
next
Azure
Contents
ChatAnthropic also supports async and streaming functionality:
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/chat/integrations/anthropic.html
|
98e71397c445-0
|
.ipynb
.pdf
How to use few shot examples
Contents
Alternating Human/AI messages
System Messages
How to use few shot examples#
This notebook covers how to use few shot examples in chat models.
There does not appear to be solid consensus on how best to do few shot prompting. As a result, we are not solidifying any abstractions around this yet but rather using existing abstractions.
Alternating Human/AI messages#
The first way of doing few shot prompting relies on using alternating human/ai messages. See an example of this below.
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate, LLMChain
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
chat = ChatOpenAI(temperature=0)
template="You are a helpful assistant that translates english to pirate."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
example_human = HumanMessagePromptTemplate.from_template("Hi")
example_ai = AIMessagePromptTemplate.from_template("Argh me mateys")
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, example_human, example_ai, human_message_prompt])
chain = LLMChain(llm=chat, prompt=chat_prompt)
# get a chat completion from the formatted messages
chain.run("I love programming.")
"I be lovin' programmin', me hearty!"
System Messages#
OpenAI provides an optional name parameter that they also recommend using in conjunction with system messages to do few shot prompting. Here is an example of how to do that below.
|
https://python.langchain.com/en/latest/modules/models/chat/examples/few_shot_examples.html
|
98e71397c445-1
|
template="You are a helpful assistant that translates english to pirate."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
example_human = SystemMessagePromptTemplate.from_template("Hi", additional_kwargs={"name": "example_user"})
example_ai = SystemMessagePromptTemplate.from_template("Argh me mateys", additional_kwargs={"name": "example_assistant"})
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, example_human, example_ai, human_message_prompt])
chain = LLMChain(llm=chat, prompt=chat_prompt)
# get a chat completion from the formatted messages
chain.run("I love programming.")
"I be lovin' programmin', me hearty."
previous
How-To Guides
next
How to stream responses
Contents
Alternating Human/AI messages
System Messages
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/chat/examples/few_shot_examples.html
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84684baeedf7-0
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.ipynb
.pdf
How to stream responses
How to stream responses#
This notebook goes over how to use streaming with a chat model.
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
HumanMessage,
)
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
chat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)
resp = chat([HumanMessage(content="Write me a song about sparkling water.")])
Verse 1:
Bubbles rising to the top
A refreshing drink that never stops
Clear and crisp, it's pure delight
A taste that's sure to excite
Chorus:
Sparkling water, oh so fine
A drink that's always on my mind
With every sip, I feel alive
Sparkling water, you're my vibe
Verse 2:
No sugar, no calories, just pure bliss
A drink that's hard to resist
It's the perfect way to quench my thirst
A drink that always comes first
Chorus:
Sparkling water, oh so fine
A drink that's always on my mind
With every sip, I feel alive
Sparkling water, you're my vibe
Bridge:
From the mountains to the sea
Sparkling water, you're the key
To a healthy life, a happy soul
A drink that makes me feel whole
Chorus:
Sparkling water, oh so fine
A drink that's always on my mind
With every sip, I feel alive
Sparkling water, you're my vibe
Outro:
Sparkling water, you're the one
A drink that's always so much fun
I'll never let you go, my friend
Sparkling
previous
How to use few shot examples
next
Integrations
By Harrison Chase
|
https://python.langchain.com/en/latest/modules/models/chat/examples/streaming.html
|
84684baeedf7-1
|
How to use few shot examples
next
Integrations
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/chat/examples/streaming.html
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882fb64ddf7f-0
|
.ipynb
.pdf
OpenAI
OpenAI#
Let’s load the OpenAI Embedding class.
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
Let’s load the OpenAI Embedding class with first generation models (e.g. text-search-ada-doc-001/text-search-ada-query-001). Note: These are not recommended models - see here
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
# if you are behind an explicit proxy, you can use the OPENAI_PROXY environment variable to pass through
os.environ["OPENAI_PROXY"] = "http://proxy.yourcompany.com:8080"
previous
MosaicML embeddings
next
SageMaker Endpoint Embeddings
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/openai.html
|
80e6c6d11483-0
|
.ipynb
.pdf
TensorflowHub
TensorflowHub#
Let’s load the TensorflowHub Embedding class.
from langchain.embeddings import TensorflowHubEmbeddings
embeddings = TensorflowHubEmbeddings()
2023-01-30 23:53:01.652176: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-01-30 23:53:34.362802: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_results = embeddings.embed_documents(["foo"])
doc_results
previous
Sentence Transformers Embeddings
next
Prompts
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/tensorflowhub.html
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99841c93fdb6-0
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.ipynb
.pdf
AzureOpenAI
AzureOpenAI#
Let’s load the OpenAI Embedding class with environment variables set to indicate to use Azure endpoints.
# set the environment variables needed for openai package to know to reach out to azure
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(deployment="your-embeddings-deployment-name")
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
previous
Aleph Alpha
next
Bedrock Embeddings
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/azureopenai.html
|
bed9c7184688-0
|
.ipynb
.pdf
Self Hosted Embeddings
Self Hosted Embeddings#
Let’s load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes.
from langchain.embeddings import (
SelfHostedEmbeddings,
SelfHostedHuggingFaceEmbeddings,
SelfHostedHuggingFaceInstructEmbeddings,
)
import runhouse as rh
# For an on-demand A100 with GCP, Azure, or Lambda
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)
# For an on-demand A10G with AWS (no single A100s on AWS)
# gpu = rh.cluster(name='rh-a10x', instance_type='g5.2xlarge', provider='aws')
# For an existing cluster
# gpu = rh.cluster(ips=['<ip of the cluster>'],
# ssh_creds={'ssh_user': '...', 'ssh_private_key':'<path_to_key>'},
# name='my-cluster')
embeddings = SelfHostedHuggingFaceEmbeddings(hardware=gpu)
text = "This is a test document."
query_result = embeddings.embed_query(text)
And similarly for SelfHostedHuggingFaceInstructEmbeddings:
embeddings = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)
Now let’s load an embedding model with a custom load function:
def get_pipeline():
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
pipeline,
) # Must be inside the function in notebooks
model_id = "facebook/bart-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/self-hosted.html
|
bed9c7184688-1
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
def inference_fn(pipeline, prompt):
# Return last hidden state of the model
if isinstance(prompt, list):
return [emb[0][-1] for emb in pipeline(prompt)]
return pipeline(prompt)[0][-1]
embeddings = SelfHostedEmbeddings(
model_load_fn=get_pipeline,
hardware=gpu,
model_reqs=["./", "torch", "transformers"],
inference_fn=inference_fn,
)
query_result = embeddings.embed_query(text)
previous
SageMaker Endpoint Embeddings
next
Sentence Transformers Embeddings
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/self-hosted.html
|
b7812bf301cc-0
|
.ipynb
.pdf
Cohere
Cohere#
Let’s load the Cohere Embedding class.
from langchain.embeddings import CohereEmbeddings
embeddings = CohereEmbeddings(cohere_api_key=cohere_api_key)
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
previous
Bedrock Embeddings
next
Elasticsearch
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/cohere.html
|
2d5f80c687a6-0
|
.ipynb
.pdf
Llama-cpp
Llama-cpp#
This notebook goes over how to use Llama-cpp embeddings within LangChain
!pip install llama-cpp-python
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model/ggml-model-q4_0.bin")
text = "This is a test document."
query_result = llama.embed_query(text)
doc_result = llama.embed_documents([text])
previous
Jina
next
MiniMax
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/llamacpp.html
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f4617ae85499-0
|
.ipynb
.pdf
ModelScope
ModelScope#
Let’s load the ModelScope Embedding class.
from langchain.embeddings import ModelScopeEmbeddings
model_id = "damo/nlp_corom_sentence-embedding_english-base"
embeddings = ModelScopeEmbeddings(model_id=model_id)
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_results = embeddings.embed_documents(["foo"])
previous
MiniMax
next
MosaicML embeddings
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/modelscope_hub.html
|
5c35fb9b1955-0
|
.ipynb
.pdf
Aleph Alpha
Contents
Asymmetric
Symmetric
Aleph Alpha#
There are two possible ways to use Aleph Alpha’s semantic embeddings. If you have texts with a dissimilar structure (e.g. a Document and a Query) you would want to use asymmetric embeddings. Conversely, for texts with comparable structures, symmetric embeddings are the suggested approach.
Asymmetric#
from langchain.embeddings import AlephAlphaAsymmetricSemanticEmbedding
document = "This is a content of the document"
query = "What is the contnt of the document?"
embeddings = AlephAlphaAsymmetricSemanticEmbedding()
doc_result = embeddings.embed_documents([document])
query_result = embeddings.embed_query(query)
Symmetric#
from langchain.embeddings import AlephAlphaSymmetricSemanticEmbedding
text = "This is a test text"
embeddings = AlephAlphaSymmetricSemanticEmbedding()
doc_result = embeddings.embed_documents([text])
query_result = embeddings.embed_query(text)
previous
Text Embedding Models
next
AzureOpenAI
Contents
Asymmetric
Symmetric
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/aleph_alpha.html
|
7f61c52ad78e-0
|
.ipynb
.pdf
MiniMax
MiniMax#
MiniMax offers an embeddings service.
This example goes over how to use LangChain to interact with MiniMax Inference for text embedding.
import os
os.environ["MINIMAX_GROUP_ID"] = "MINIMAX_GROUP_ID"
os.environ["MINIMAX_API_KEY"] = "MINIMAX_API_KEY"
from langchain.embeddings import MiniMaxEmbeddings
embeddings = MiniMaxEmbeddings()
query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)
document_text = "This is a test document."
document_result = embeddings.embed_documents([document_text])
import numpy as np
query_numpy = np.array(query_result)
document_numpy = np.array(document_result[0])
similarity = np.dot(query_numpy, document_numpy) / (np.linalg.norm(query_numpy)*np.linalg.norm(document_numpy))
print(f"Cosine similarity between document and query: {similarity}")
Cosine similarity between document and query: 0.1573236279277012
previous
Llama-cpp
next
ModelScope
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/minimax.html
|
b8f28e5f950e-0
|
.ipynb
.pdf
Bedrock Embeddings
Bedrock Embeddings#
%pip install boto3
from langchain.embeddings import BedrockEmbeddings
embeddings = BedrockEmbeddings(credentials_profile_name="bedrock-admin")
embeddings.embed_query("This is a content of the document")
embeddings.embed_documents(["This is a content of the document"])
previous
AzureOpenAI
next
Cohere
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/bedrock.html
|
e09f0fc0e148-0
|
.ipynb
.pdf
Sentence Transformers Embeddings
Sentence Transformers Embeddings#
SentenceTransformers embeddings are called using the HuggingFaceEmbeddings integration. We have also added an alias for SentenceTransformerEmbeddings for users who are more familiar with directly using that package.
SentenceTransformers is a python package that can generate text and image embeddings, originating from Sentence-BERT
!pip install sentence_transformers > /dev/null
[notice] A new release of pip is available: 23.0.1 -> 23.1.1
[notice] To update, run: pip install --upgrade pip
from langchain.embeddings import HuggingFaceEmbeddings, SentenceTransformerEmbeddings
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
# Equivalent to SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text, "This is not a test document."])
previous
Self Hosted Embeddings
next
TensorflowHub
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/sentence_transformers.html
|
e82efbe145af-0
|
.ipynb
.pdf
Google Cloud Platform Vertex AI PaLM
Google Cloud Platform Vertex AI PaLM#
Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.
PaLM API on Vertex AI is a Preview offering, subject to the Pre-GA Offerings Terms of the GCP Service Specific Terms.
Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions. Further, by using PaLM API on Vertex AI, you agree to the Generative AI Preview terms and conditions (Preview Terms).
For PaLM API on Vertex AI, you can process personal data as outlined in the Cloud Data Processing Addendum, subject to applicable restrictions and obligations in the Agreement (as defined in the Preview Terms).
To use Vertex AI PaLM you must have the google-cloud-aiplatform Python package installed and either:
Have credentials configured for your environment (gcloud, workload identity, etc…)
Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable
This codebase uses the google.auth library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.
For more information, see:
https://cloud.google.com/docs/authentication/application-default-credentials#GAC
https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth
#!pip install google-cloud-aiplatform
from langchain.embeddings import VertexAIEmbeddings
embeddings = VertexAIEmbeddings()
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
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Fake Embeddings
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Hugging Face Hub
By Harrison Chase
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https://python.langchain.com/en/latest/modules/models/text_embedding/examples/google_vertex_ai_palm.html
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e82efbe145af-1
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previous
Fake Embeddings
next
Hugging Face Hub
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 02, 2023.
|
https://python.langchain.com/en/latest/modules/models/text_embedding/examples/google_vertex_ai_palm.html
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