Sunday, May 26, 2024

Generative AI with LangChain, RStudio, and simply sufficient Python


LangChain is among the hottest improvement platforms for creating functions that use generative AI—but it surely’s solely out there for Python and JavaScript. What to do should you’re an R programmer who desires to make use of LangChain?

Fortuitously, you are able to do numerous helpful issues in LangChain with fairly fundamental Python code. And, because of the reticulate R package deal, R and RStudio customers can write and run Python within the atmosphere they’re snug with—together with passing objects and knowledge forwards and backwards between Python and R.

On this LangChain tutorial, I will present you the right way to work with Python and R to entry LangChain and OpenAI APIs. This may allow you to use a massive language mannequin (LLM)—the expertise behind ChatGPT—to question ggplot2‘s 300-page PDF documentation. Our first pattern question: “How do you rotate textual content on the x-axis of a graph?”

Here is how the method breaks down, step-by-step:

  1. If you have not already, arrange your system to run Python and reticulate.
  2. Import the ggplot2 PDF documentation file as a LangChain object with plain textual content.
  3. Cut up the textual content into smaller items that may be learn by a big language mannequin, since these fashions have limits to how a lot they’ll learn directly. The 300 pages of textual content will exceed OpenAI’s limits.
  4. Use an LLM to create “embeddings” for every chunk of textual content and save all of them in a database. An embedding is a string of numbers that represents the semantic which means of textual content in multidimensional area.
  5. Create an embedding for the consumer’s query, then evaluate the query embedding to all the prevailing ones from the textual content. Discover and retrieve probably the most related textual content items.
  6. Feed solely these related parts of textual content to an LLM like GPT-3.5 and ask it to generate a solution.

If you are going to observe the examples and use the OpenAI APIs, you may want an API key. You possibly can enroll at platform.openai.com. If you happen to’d reasonably use one other mannequin, LangChain has elements to construct chains for quite a few LLMs, not solely OpenAI’s, so you are not locked in to at least one LLM supplier.

LangChain has the elements to deal with most of those steps simply, particularly should you’re glad with its defaults. That is why it is changing into so in style.

Let’s get began.

Step 1: Arrange your system to run Python in RStudio

If you happen to already run Python and reticulate, you’ll be able to skip to the following step. In any other case, let’s ensure you have a current model of Python in your system. There are various methods to put in Python, however merely downloading from python.org labored for me. Then, set up the reticulate R package deal the standard method with set up.packages("reticulate").

If you happen to’ve obtained Python intalled however reticulate cannot discover it, you should utilize the command use_python("/path/to/your/python")

It is good Python observe to make use of digital environments, which let you set up package deal variations that will not battle with the necessities of different tasks elsewhere in your system. Here is the right way to create a brand new Python digital atmosphere and use R code to put in the packages you may want:


library(reticulate)
virtualenv_create(envname = "langchain_env", packages = c( "langchain", "openai", "pypdf", "bs4", "python-dotenv", "chromadb", "tiktoken")) # Solely do that as soon as

Word which you can title your atmosphere no matter you want. If it’s good to set up packages after creating the atmosphere, use py_install(), like this:


py_install(packages = c( "langchain", "openai", "pypdf", "bs4", "python-dotenv", "chromadb", "tiktoken"), envname = "langchain_env")

As in R, it’s best to solely want to put in packages as soon as, not each time it’s good to use the atmosphere. Additionally, do not forget to activate your digital atmosphere, with


use_virtualenv("langchain_env")

You may do that every time you come again to the undertaking and earlier than you begin operating Python code.

You possibly can check whether or not your Python atmosphere is working with the compulsory


reticulate::py_run_string('
print("Whats up, world!") ')

You possibly can set your OpenAI API key with a Python variable should you like. Since I have already got it in an R atmosphere variable, I normally set the OpenAI API key utilizing R. It can save you any R variable to a Python-friendly format with reticulate’s r_to_py() operate, together with atmosphere variables:


api_key_for_py <- r_to_py(Sys.getenv("OPENAI_API_KEY"))

That takes the OPENAI_API_KEY atmosphere variable, ensures it is Python-friendly, and shops it in a brand new variable: api_key_for_py (once more, the title could be something).

Eventually, we’re able to code!

Step 2: Obtain and import the PDF file

I will create a brand new docs subdirectory of my major undertaking listing and use R to obtain the file there.


# Create the listing if it does not exist
if(!(dir.exists("docs"))) {
  dir.create("docs")
}

# Obtain the file
obtain.file("https://cran.r-project.org/net/packages/ggplot2/ggplot2.pdf", destfile = "docs/ggplot2.pdf", mode = "wb")

Subsequent comes the Python code to import the file as a LangChain doc object that features content material and metadata. I will create a brand new Python script file referred to as prep_docs.py for this work. I may preserve operating Python code proper inside an R script through the use of the py_run_string() operate as I did above. Nevertheless, that is not ultimate should you’re engaged on a bigger job, since you lose out on issues like code completion.

Essential level for Python newbies: Do not use the identical title in your script file as a Python module you may be loading! In different phrases, whereas the file does not need to be referred to as prep_docs.py, do not title it, say, langchain.py if you may be importing the langchain package deal! They’re going to battle. This is not a problem in R.

Here is the primary a part of my new prep_docs.py file:


# If operating from RStudio, bear in mind to first run in R:
# library(reticulate)
# use_virtualenv("the_virtual_environment_you_set_up")
# api_key_py <- r_to_py(Sys.getenv("OPENAI_API_KEY"))

from langchain.document_loaders import PyPDFLoader
my_loader = PyPDFLoader('docs/ggplot2.pdf')
# print(sort (my_loader))
all_pages = my_loader.load()
# print(sort(all_pages)) 
print( len(all_pages) )

This code first imports the PDF doc loader PyPDFLoader. Subsequent, it creates an occasion of that PDF loader class. Then, it runs the loader and its load technique, storing the ends in a variable named all_pages. That object is a Python listing.

I’ve included some commented traces that can print the article sorts if you would like to see them. The ultimate line prints the size of the listing, which on this case is 304, one for every web page within the PDF.

You possibly can click on the supply button in RStudio to run a full Python script. Or, spotlight some traces of code and solely run these, simply as with an R script. The Python code appears to be like just a little completely different when operating than R code does, because it opens a Python interactive REPL session proper inside your R console. You may be instructed to sort exit or give up (with out parentheses) to exit and return to your common R console while you’re completed.

python in r screenshot Sharon Machlis for IDG

You possibly can look at the all_pages Python object in R through the use of reticulate‘s py object. The next R code shops that Python all_pages object into an R variable named all_pages_in_r (you’ll be able to name it something you would like). You possibly can then work with the article like another R object. On this case, it is a listing.


all_pages_in_r <- py$all_pages
# Examples:
all_pages_in_r[[1]]$metadata # See metadata within the first merchandise
nchar(all_pages_in_r[[100]]$page_content) # Depend variety of characters within the one hundredth merchandise

Step 3: Cut up the doc into items

LangChain has a number of transformers for breaking apart paperwork into chunks, together with splitting by characters, tokens, and markdown headers for markdown paperwork. One really useful default is the RecursiveCharacterTextSplitter, which “recursively tries to separate by completely different characters to seek out one which works.” One other in style possibility is the CharacterTextSplitter, which is designed to have the consumer set its parameters.

You possibly can set this splitter’s most text-chunk dimension, whether or not it ought to rely by characters or LLM tokens (tokens are sometimes one to 4 characters), and the way a lot the chunks ought to overlap. I hadn’t thought of the necessity to have textual content chunks overlap till I started utilizing LangChain, but it surely is sensible until you’ll be able to separate by logical items like chapters or sections separated by headers. In any other case, your textual content could get break up mid-sentence, and an vital piece of knowledge may find yourself divided between two chunks, with out the total which means being clear in both.

You too can choose what separators you need the splitter to prioritize when it divvies up your textual content. CharacterTextSplitter‘s default is to separate first on two new traces (nn), then one new line, an area, and at last no separator in any respect.

The code under imports my OpenAI API key from the R api_key_for_py variable through the use of reticulate’s r object within Python. It additionally hundreds the openai Python package deal and LangChain’s Recursive Character Splitter, creates an occasion of the RecursiveCharacterTextSplitter class, and runs that occasion’s split_documents() strategies on the all_pages chunks.


import openai
openai.api_key = r.api_key_for_py  
from langchain.text_splitter import RecursiveCharacterTextSplitter
my_doc_splitter_recursive = RecursiveCharacterTextSplitter()
my_split_docs = my_doc_splitter_recursive.split_documents(all_pages)

Once more, you’ll be able to ship these end result to R with R code equivalent to:


my_split_docs <- py$my_split_docs

Are you questioning what’s the most variety of characters in a piece? I can verify this in R with just a little customized operate for the listing:


get_characters <- operate(the_chunk) {
x <- nchar(the_chunk$page_content)
return(x)
}

purrr::map_int(my_split_docs, get_characters) |>
max()

That generated 3,985, so it appears to be like just like the default chunk most is 4,000 characters.

If I needed smaller textual content chunks, I would first strive the CharacterTextSplitter and manually set chunk_size to lower than 4,000, equivalent to


chunk_size = 1000
chunk_overlap = 150
from langchain.text_splitter import CharacterTextSplitter
c_splitter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap, separator=" ")
c_split_docs = c_splitter.split_documents(all_pages)
print(len(c_split_docs)) # To see in Python what number of chunks there at the moment are

I can examine the ends in R in addition to Python:


c_split_docs <- py$c_split_docs
size(c_split_docs)

That code generated 695 chunks with a most dimension of 1,000.

What’s the associated fee?

Earlier than going additional, I would prefer to know if it may be insanely costly to generate embeddings for all these chunks. I will begin with the default recursive splitting’s 306 objects. I can calculate the overall variety of characters in these chunks on the R object with:


purrr::map_int(my_split_docs, get_characters) |>
  sum()

The reply is 513,506. Conservatively estimating two characters per token, that might work out to round 200,000.

If you wish to be extra precise, the TheOpenAIR R package deal has a count_tokens() operate (make sure that to put in each that and purrr to make use of the R code under):


purrr::map_int(my_split_docs, ~ TheOpenAIR::count_tokens(.x$page_content)) |> 
   sum()

That code exhibits 126,343 tokens.

How a lot would that price? OpenAI’s mannequin designed to generate embeddings is ada-2. As of this writing ada-2 prices $0.0001 for 1K tokens, or about 1.3 cents for 126,000. That is inside finances!

Step 4: Generate embeddings

LangChain has pre-made elements to each create embeddings from textual content chunks and retailer them. For storage, I will use one of many easiest choices out there in LangChain: Chroma, an open-source embedding database that you should utilize domestically.

First, I will create a subdirectory of my docs listing with R code only for the database, because it’s recommended to not have something however your database in a Chroma listing. That is R code:


if(!dir.exists("docs/chroma_db")) {
  dir.create("docs/chromaba_db")
}

Beneath is a few Python code to generate the embeddings utilizing LangChain’s OpenAIEmbeddings. That presently defaults to OpenAI’s ada-2 mannequin, so that you needn’t specify it. LangChain helps a lot of different LLMs with its Embeddings class, although, together with Hugging Face Hub, Cohere, Llama-cpp, and Spacy.

The Python code under is barely modified from DeepLearning.AI’s LangChain Chat with Your Knowledge on-line tutorial.


from langchain.embeddings.openai import OpenAIEmbeddings
embed_object = OpenAIEmbeddings()

from langchain.vectorstores import Chroma
chroma_store_directory = "docs/chroma_db"

vectordb = Chroma.from_documents(
    paperwork=my_split_docs,
    embedding=embed_object,
    persist_directory=chroma_store_directory
)

# Examine what number of embeddings had been created
print(vectordb._collection.rely())

Word the underscore in _collection.rely()!

I see 306 embeddings, the identical quantity as my ggplot2 textual content chunks.

One other notice for Python newbies: Indentation issues in Python. Make sure that non-indented traces don’t have any areas earlier than them and indented traces all use the identical variety of indent areas.

It seems on my system that this code saved the information to disk. Nevertheless, the tutorial says we should always run the next Python code to avoid wasting the embeddings for later use. I will do this, too, since I do not wish to need to re-generate embeddings until the doc adjustments.


vectordb.persist()

We’re now achieved with prepping the paperwork for querying. I will create a brand new file, qanda.py, to make use of the vector embeddings we have created.

Step 5: Embed the consumer question and discover doc chunks

Now it is time to ask a query, generate embeddings for that query, and retrieve the paperwork which can be most related to the query based mostly on the chunks’ embeddings.

LangChain offers us a number of methods to do all of this in a single line of code, because of the vectordb object’s built-in strategies. Its similarity_search() technique does a simple calculation of vector similarities and returns probably the most related textual content chunks.

There are a number of different methods to do that, although, together with max_marginal_relevance_search(). The thought behind that one is you do not essentially need three textual content chunks which can be nearly the identical. Possibly you’d find yourself with a richer response if there was just a little variety within the textual content to get further helpful data. So, max_marginal_relevance_search() retrieves a number of extra related texts than you really plan to cross to the LLM for a solution (you determine what number of extra). It then selects the ultimate textual content items, incorporating a point of variety.

You specify what number of related textual content chunks you would like similarity_search() to return with its okay argument. For max_marginal_relevance(), you specify what number of chunks ought to initially be retrieved with fetch_k, and what number of closing textual content items you need the LLM to look by way of for its reply with okay.

I do not wish to run the doc prep file if the paperwork have not modified, so I will first load the required packages and atmosphere variables (i.e., my OpenAI API key) within the new qanda.py file, as I did earlier than utilizing doc_prep.py. Then, I will load my chromadb vector database:


# If operating from RStudio, bear in mind to first run in R:
# library(reticulate)
# use_virtualenv("the_virtual_environment_you_set_up")
# api_key_py <- r_to_py(Sys.getenv("OPENAI_API_KEY"))

import openai
openai.api_key = r.api_key_for_py  
from langchain.embeddings.openai import OpenAIEmbeddings
embed_object = OpenAIEmbeddings()

from langchain.vectorstores import Chroma
chroma_store_directory = "docs/chroma_db"
vectordb = Chroma(persist_directory=chroma_store_directory, 
                  embedding_function=embed_object)

Subsequent, I will hard-code a query and retrieve the related paperwork. Word that I can retrieve the paperwork with a single line of code:


my_question = "How do you rotate textual content on the x-axis of a graph?"
# For simple similarity looking out
sim_docs = vectordb.similarity_search(my_question)

# For optimum marginal relevance search retrieving 5 attainable chunks and selecting 3 finalists:
mm_docs = vectordb.max_marginal_relevance_search(my_question, okay = 3, fetch_k = 5)

If you wish to view the retrieved doc items, you’ll be able to print them in Python with one thing like the next:


for doc in mm_docs:
    print(doc.page_content)
    
for doc in sim_docs:
    print(doc.page_content)

Word the indents as a part of the for loops.

You too can view their metadata with:


for doc in mm_docs:
    print(doc.metadata)
    
for docs in sim_docs:
    print(docs.metadata)

As with the opposite objects, it’s also possible to take a look at these in R:


mm_relevant <- py$mm_docs
sim_relevant <- py$sim_docs

I am undecided why fashions typically return 4 paperwork once I ask for 3, however that should not be an issue—until it is too many tokens for the LLM when it goes by way of the textual content to generate a response.

Step 6: Generate your reply

It is now time to ask an LLM like GPT-3.5 to generate a written response to the consumer’s query based mostly on the related paperwork. You are able to do that with LangChain’s RetrievalQA operate.

I recommend first making an attempt LangChain’s default template for this, which is simple and infrequently works properly for prototyping or your personal use:


# Arrange the LLM you wish to use, on this instance OpenAI's gpt-3.5-turbo
from langchain.chat_models import ChatOpenAI
the_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)

# Create a series utilizing the RetrievalQA element
from langchain.chains import RetrievalQA
qa_chain = RetrievalQA.from_chain_type(the_llm,retriever=vectordb.as_retriever())

# Run the chain on the query, and print the end result
print(qa_chain.run(my_question))

The AI despatched again the next response:


To rotate textual content on the x-axis of a graph, you should utilize the `theme()` operate in ggplot2. Particularly, you should utilize the `axis.textual content.x` argument to switch the looks of the x-axis textual content. Right here is an instance:

```R
library(ggplot2)

# Create a fundamental scatter plot
p <- ggplot(mtcars, aes(x = mpg, y = wt)) +
  geom_point()

# Rotate x-axis textual content by 45 levels
p + theme(axis.textual content.x = element_text(angle = 45, hjust = 1))
```

On this instance, the `angle` argument is about to 45, which rotates the x-axis textual content by 45 levels. The `hjust` argument is about to 1, which aligns the textual content to the correct. You possibly can regulate the angle and alignment values to realize the specified rotation and alignment of the x-axis textual content.

Seems right!

Now that the chain is about up, you’ll be able to run it on different questions with only one command utilizing an R script: 


py_run_string('
print(qa_chain.run("How can I make a bar chart the place the bars are metal blue?"))
')

Here is the response:


```R
library(ggplot2)

# Create a bar chart with metal blue bars
p <- ggplot(mtcars, aes(issue(cyl)))
p + geom_bar(fill = "steelblue")
```

On this instance, we use the `fill` aesthetic to specify the colour of the bars as "steelblue". You possibly can regulate the colour to your desire by altering the colour title or utilizing a hexadecimal colour code.

It is a higher reply than I typically obtain when asking ChatGPT 3.5 the identical query. (Generally the code it sends again does not really work.)

You may additionally wish to affirm that the solutions aren’t being pulled from the final ChatGPT data base, however are actually coming from the doc you uploaded. To seek out out, you would ask one thing fully unrelated to ggplot2 that would not be within the documentation:


py_run_string('
print(qa_chain.run("What's the capital of Australia?"))
')

You must get again:


I do not know.

“I do not know” could also be just a little terse should you’re creating an utility for wider use. You possibly can try the LangChain documentation if you would like to customise the default template. Personalizing the response is sensible if you’re creating an utility for greater than your self or a small group .

Template tweaks is one space the place LangChain could really feel overly complicated—it may possibly take a number of traces of code to implement small adjustments to a template. Nevertheless, that is a danger in utilizing any opinionated framework, and it is as much as every developer to determine if the undertaking’s general advantages are price such prices. Whereas it’s enormously in style, not everyone seems to be a fan of LangChain

What else are you able to do with LangChain?

The simplest addition to the applying to this point could be to incorporate extra paperwork. LangChain has a DirectoryLoader to make this simple. If you happen to’re looking out throughout a number of paperwork, you would possibly wish to know which docs had been used to generate the response. You possibly can add the return_source_documents=True argument to RetrievalQA, like this:


qa_chain = RetrievalQA.from_chain_type(the_llm,retriever=vectordb.as_retriever(), return_source_documents=True) 
my_result = qa_chain({"question": my_question})
print(my_result['result'])

The code is initially solely helpful to run domestically for a single consumer, but it surely may very well be the logic foundation for an interactive net utility utilizing a framework like Streamlit or Shiny for Python. Or, you mix Python and R, ship the LLM’s closing reply again to R, and create an utility utilizing the Shiny R net framework (though I’ve found that deploying a Shiny app with each Python and R is considerably sophisticated). 

Word, too, that this utility is not technically a “chatbot” because it will not bear in mind your earlier questions. So, you could not have a “dialog” equivalent to “How can I modify the scale of a graph’s headline textual content?” adopted by “What concerning the legend?” You’d need to spell out every new query fully, equivalent to “How do you modify the scale of a legend’s headline?”

Nevertheless, you would add reminiscence to the applying to show it right into a chatbot with LangChain’s ConversationBufferMemory.

Additional sources

To be taught extra about LangChain, along with the LangChain documentation, there’s a LangChain Discord server that options an AI chatbot, kapa.ai, that may question the docs.

I’ve labored by way of about one-third of Udemy’s Develop LLM powered functions with LangChain by Eden Marco, and to this point it’s been useful. Whereas that course says try to be “proficient in Python,” I believe understanding different programming languages together with willingness to do loads of looking out/ChatGPT-ing ought to be sufficient.

For information on Streamlit and LangChain, there are a number of tutorials on the Streamlit website, together with LangChain tutorial #1: Construct an LLM-powered app in 18 traces of code.

Lastly, Posit’s Winston Chang is engaged on an experimental chatstream package deal for Shiny Python that can embody a “streaming” interface so the LLM response seems steadily by character or phrase, the way in which ChatGPT’s does, as a substitute of abruptly as in these examples. 

Copyright © 2023 IDG Communications, Inc.

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