Recently, there has been a lot of buzz in the legal community about the potential of Artificial Intelligence (AI) and Natural Language Processing (NLP) to revolutionize the way legal professionals work. One area where these technologies can have a big impact is in the way legal documents are analyzed and searched. In this blog post, we are going to take a closer look at how vector databases can make calls more efficient and provide an example of how to create one using Python and the OpenAI API.

When we think about legal documents, we often think about large volumes of text that need to be analyzed and searched. The traditional way of doing this is by using a full-text search, which can be slow and inaccurate. With the advent of vector databases, it is now possible to convert text into numerical vectors that can be easily searched, compared, and processed.

A Hands-On Introduction

One potential project could be the creation of a vector database that combines free open-source Q&A large language models and feeds context to the main language model to provide more specific answers. This would be a valuable tool for legal professionals, researchers, and administrators, who could use it to quickly and accurately answer questions related to legal documents, laws, and regulations.

Vector databases make calls more efficient because they allow for fast and accurate retrieval of information.

In traditional databases, text is typically stored in a format that is not easily searchable or comparable. To retrieve information, the database must perform a full-text search, which can be slow and inaccurate. With vector databases, text is converted into numerical vectors, which can be easily searched and compared. This allows for faster and more accurate retrieval of information.

Vector databases make calls more efficient by converting text into numerical vectors that can be easily searched, compared and processed. It also allows to perform machine learning task to improve the accuracy and performance of the retrieval. This can make it a valuable tool for legal professionals, researchers, and administrators who need to quickly and accurately answer legal questions based on large document corpus.

Vectorization and Indexes

One of the most important steps when creating a vector database for legal documents is the process of vectorization. This is the process of converting text into numerical vectors, which can be easily searched, compared and processed. A common method for vectorizing text is called Term Frequency-Inverse Document Frequency (TF-IDF). This technique represents the importance of a word or phrase in a document relative to the entire corpus of documents, allowing to represent the text in numerical form.

TF-IDF method is a combination of two statistics: term frequency (TF) and inverse document frequency (IDF). The TF measures how often a term appears in a document and the IDF measures how often a term appears in the corpus of documents. Together, they can represent the relevance of a term to the document, and by extension, the relevance of the document to the corpus.

After vectorization, the next step is to use those vector representation to create an index, the most commonly used method for this is by using a library such as Faiss. Faiss (Facebook AI Similarity Search) is a library for efficient similarity search and clustering of dense vectors. Faiss provides a set of tools for indexing, searching, and clustering large amounts of high-dimensional data. This library is particularly useful for large data sets and real-time applications.

By using Faiss, it is possible to perform fast and accurate similarity search on the vectorized legal documents, and retrieve the most relevant documents based on a query. This can be a powerful tool for legal professionals, researchers, and administrators, who need to quickly and accurately answer legal questions based on large document corpus.

Furthermore, when using faiss, different indexing structures can be used depending on the number of dimensions and size of data, and this can affect the speed and memory usage. For instance, it provides indexing structures that scale well to millions or billions of vectors, and allow to search in less than a microsecond per vector.

Step-by-step guide for creating a vector database

Step 1 Collecting the Documents

The first step in creating a vector database for legal documents is to collect a large number of relevant documents. This can be done by scraping websites that contain legal information, such as government websites, legal databases, and online law libraries. It is important to ensure that the documents are in a format that can be easily processed, such as .txt or .pdf.

Step 2 Preprocessing the Documents

Once the documents have been collected, they must be preprocessed to prepare them for vectorization. This includes cleaning and standardizing the text, removing any irrelevant information, and tokenizing the text into individual words or phrases.

Step 3 Vectorizing the Documents

The next step is to vectorize the documents. Vectorization is the process of converting the text into numerical vectors, which can be easily processed by machine learning models. One commonly used method for vectorization is Term Frequency-Inverse Document Frequency (TF-IDF), which represents the importance of a word or phrase in a document relative to the entire corpus of documents.

Step 4 Combining and Sorting the Vectors

Once the documents have been vectorized, they can be combined into one large vector database. The vectors can then be sorted by relevance, using a similarity metric such as cosine similarity. This will ensure that the most relevant documents are returned first when a query is made.

Step 5 Adding Context

The final step is to add context to the vector database. This can be done by feeding additional information, such as legal precedents or annotations, to the main language model (such as the instructor). This will allow the model to provide context-specific answers to legal questions.

Sample Code

import openai_secret_manager
import openai
import faiss
import numpy as np

Authenticate using the OpenAI API key

secrets = openai_secret_manager.get_secret("openai")
openai.api_key = secrets["api_key"]

Define the prompt

prompt = (f"Vectorize the text of legal documents and sort the vectors by relevance")

Make the API call

response = openai.Completion.create(
engine="text-davinci-002",
prompt=prompt,
temperature=0.5,
max_tokens=2048,
top_p=1,
frequency_penalty=1,
presence_penalty=0.5
)

Extract the vectors

vectors = response.choices[0].text

Convert the vectors to a numpy array

vectors = np.array(vectors)

Use faiss to create a index

index = faiss.IndexFlatL2(vectors.shape[1])

Add the vectors to the index

index.add(vectors)

Define a function to search the index

def search_vectors(query):

Vectorize the query

query = faiss.vector_float_to_array(query)

Search the index

distances, indices = index.search(query, k=10)

Return the results

return distances, indices

def process_document(document):
chunks = document.split("\n")
for chunk in chunks:

Vectorize the chunks

chunk_vectors = openai.Completion.create(
engine="text-davinci-002",
prompt=chunk,
temperature=0.5,
max_tokens=2048,
top_p=1,
frequency_penalty=1,
presence_penalty=0.5)
search_vectors(chunk_vectors)

Example usage

document = "large legal document"
process_document(document)