How to Analyze Local Documents Using LangChain and OpenAI API
Extracting insights from documents and data is crucial for making informed decisions, but privacy concerns often arise when dealing with sensitive information. LangChain, in combination with the OpenAI API, provides a solution that allows you to analyze local documents without the need to upload them online. By keeping your data locally and using embeddings and vectorization for analysis, LangChain ensures the privacy and security of your sensitive information. In this article, we will guide you through the steps of setting up your environment and using LangChain and the OpenAI API to analyze your local documents.
Setting Up Your Environment
To begin, you need to create a new Python virtual environment to avoid any library version conflicts. Once you have created the virtual environment, install the required libraries by running the following terminal command:
“`html
pip install langchain openai tiktoken faiss-cpu pypdf
“`
Here is a breakdown of how each library will be used:
– LangChain: This library provides modules for document loading, text splitting, embeddings, and vector storage. It allows you to create and manage linguistic chains for text processing and analysis.
– OpenAI: You will use the OpenAI library for running queries and obtaining results from a language model.
– tiktoken: This library helps count the number of tokens (units of text) in a given text. It is used to keep track of the token count when interacting with the OpenAI API, which charges based on the number of tokens used.
– FAISS: This library allows you to create and manage a vector store, enabling fast retrieval of similar vectors based on their embeddings.
– PyPDF: This library is used to extract text from PDFs. It helps load PDF files and extract their text for further processing.
Once you have installed all the required libraries, your environment is ready for use.
Getting an OpenAI API Key
To make requests to the OpenAI API, you need to include an API key as part of the request. This key helps verify that the requests are coming from a legitimate source and that you have the necessary permissions to access the API’s features. Here’s how you can obtain an OpenAI API key:
1. Proceed to the OpenAI platform.
2. Under your account’s profile in the top-right corner, click on “View API keys”.
3. On the API keys page, click on the “Create new secret key” button.
4. Name your key and click on “Create new secret key”.
OpenAI will generate your API key, which you should copy and keep in a safe place. Note that for security reasons, you won’t be able to view the key again through your OpenAI account. If you lose the secret key, you’ll need to generate a new one.
Importing the Required Libraries
To use the libraries installed in your virtual environment, you need to import them into your project. Here’s an example of how to import the necessary libraries from LangChain:
“`html
from langchain.document_loaders import PyPDFLoader, TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
“`
These import statements allow you to use specific features of the LangChain framework.
Loading the Document for Analysis
To analyze a document, start by creating a variable that holds your API key. This variable will be used later in the code for authentication. Here’s an example of how to create and use the variable:
“`html
openai_api_key = “Your API key”
“`
Note that it is not recommended to hard code your API key if you plan to share your code with third parties. For production code intended for distribution, it’s better to use an environment variable instead.
Next, create a function that can load a document. This function should be able to handle both PDF and text files. If the document is neither, the function should raise a ValueError. Here’s an example of how to load a document:
“`html
def load_document(filename):
if filename.endswith(“.pdf”):
loader = PyPDFLoader(filename)
documents = loader.load()
elif filename.endswith(“.txt”):
loader = TextLoader(filename)
documents = loader.load()
else:
raise ValueError(“Invalid file type”)
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=30, separator=”\n”)
return text_splitter.split_documents(documents=documents)
“`
Splitting the document into smaller chunks based on characters ensures that the chunks are of a manageable size and maintains some overlapping context. This is useful for tasks like text analysis and information retrieval.
Querying the Document
To derive insights from the uploaded document, you need a way to query it. Create a function that takes a query string and a retriever as input, and uses them to create a RetrievalQA instance. Here’s an example of how to create this function:
“`html
def query_pdf(query, retriever):
qa = RetrievalQA.from_chain_type(llm=OpenAI(openai_api_key=openai_api_key), chain_type=”stuff”, retriever=retriever)
result = qa.run(query)
print(result)
“`
This function utilizes the RetrievalQA instance to run the query and print the result.
Creating the Main Function
The main function controls the overall program flow. It prompts the user for a document filename, loads the document, creates an OpenAIEmbeddings instance for embeddings, and constructs a vector store based on the loaded documents and embeddings. This vector store is then saved to a local file. The main function also loads the persisted vector store from the local file and enters a loop where the user can input queries. Here’s an example of the main function:
“`html
def main():
filename = input(“Enter the name of the document (.pdf or .txt):\n”)
docs = load_document(filename)
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
vectorstore = FAISS.from_documents(docs, embeddings)
vectorstore.save_local(“faiss_index_constitution”)
persisted_vectorstore = FAISS.load_local(“faiss_index_constitution”, embeddings)
query = input(“Type in your query (type ‘exit’ to quit):\n”)
while query != “exit”:
query_pdf(query, persisted_vectorstore.as_retriever())
query = input(“Type in your query (type ‘exit’ to quit):\n”)
“`
Embeddings capture semantic relationships between words, and vectors represent pieces of text. In this code, the text data from the document is converted into vectors using the embeddings generated by OpenAIEmbeddings. These vectors are then indexed using FAISS, which allows for efficient retrieval and comparison of similar vectors. This enables the analysis of the uploaded document.
Finally, use the `__name__ == “__main__”` construct to call the main function if the program is run standalone:
“`html
if __name__ == “__main__”:
main()
“`
This allows the program to be executed directly from the command line. As an extension, you can use Streamlit to add a web interface to the application.
Performing Document Analysis
To perform document analysis, store the document you want to analyze in the same folder as your project and run the program. The program will prompt you for the name of the document, and then you can enter queries for the program to analyze. Ensure that the documents you want to analyze are in either PDF or text format. If your documents are in other formats, you can convert them to PDF using online tools.
Understanding the Technology Behind Large Language Models
LangChain simplifies the creation of applications using large language models, abstracting away the complex technologies behind them. However, to fully understand how your application works, it is beneficial to familiarize yourself with the technology behind large language models.
Editor Notes
LangChain, in combination with the OpenAI API, provides a powerful tool for analyzing local documents without compromising privacy. By keeping the data locally and utilizing embeddings and vectorization, LangChain ensures the security and confidentiality of sensitive information. This technology opens up a new world of possibilities for making informed decisions based on insights extracted from documents and data.
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