Excerpt

In recent years, natural language processing (NLP) has rapidly advanced, thanks to the latest generation of large language models. However, not all developers are able to use these models due to the…

In recent years, natural language processing (NLP) has rapidly advanced, thanks to the latest generation of large language models. However, not all developers are able to use these models due to the high compute barriers and the lack of technical expertise required to do so. This is where Cohere comes in.

Cohere provide access to large language models that can be used to generate or analyse text to do things like write copy, moderate content, classify data and extract information, all at a massive scale. Cohere offer a generous free developer tier, meaning you can build and test your ideas quickly and with no cost.

Join the Discord channel here https://discord.gg/co-mmunity to join a supportive and innovative community of AI developers and be the first to know about exciting product developments

In this article, I will introduce Cofinder, a semantic search application built using Cohere’s technology. This was an entry for my first hackathon with Lablab.ai, I would recommend hackathons to anyone that wants to learn and push themselves in the space of AI.

Cofinder is designed to help the Cohere Community find relevant content in one place based on their personal goals. With Cofinder, users can ask natural language questions, and the tool will provide the most relevant content, aim to answer their questions, and provide context.

Its inspiration comes from Cohere’s mission to give technology language and to put large language models into more hands. However, there are two main barriers to achieving this goal: a compute barrier and a lack of knowledge among developers.

Cofinder is the result of my desire to make it easier for the Cohere community to access the wealth of knowledge and resources available on the platform. While Cohere provides an incredible variety of resources, such as product explanations, tutorials, open repositories, and a Discord channel, I recognised that finding specific information can still be time-consuming and inefficient. By creating a semantic search tool that brings together information from multiple sources, Cofinder aims to streamline the process and save users valuable time. The goal is to enhance the Cohere community experience by making it easy for developers, entrepreneurs, corporations, and data scientists to find what they need in one place.

Let’s build our own semantic search application to enhance search!

Cofinder | Streamlit demo

In the following sections, I will go through the 5 stages of building your application: pre-processing your data sources, creating a search index, building your Streamlit front end, adding your search and answer functions.

I will also provide a repository and explanations of the code snippets to help you follow along and build your own semantic search application.

But before we dive into the technical details, let’s take a closer look at the Cohere products we used in building Cofinder. We will use Cofinder to learn more about each product.

Cofinder uses two of Cohere’s products, co.embed and co.generate, to power its semantic search functionality. co.embed is a language model that generates vector representations of text, which can be used for various natural language processing (NLP) tasks such as clustering and classification. In Cofinder, co.embed is used to generate vector embeddings for the articles that users can search.

co.generate is a language model that generates text based on the given prompt. In Cofinder, co.generate is used to formulate an answer to the user’s question using the context from the search and the question itself. By leveraging these powerful NLP models, Cofinder is able to provide accurate and relevant search results to users, making it easier and more efficient to find information on Cohere

Let’s check out Cofinder to help find relevant Cohere resources.

Question: What are embeddings?

Cofinder | What are embeddings?

Results: As Cofinder quickly provides the user with content from across multiple platforms. Each relevant document contains:

• The category (video, blog, user and product documentation)
• The content title
• A answer generated using the search query and content as context
• Link to the content

We now have a variety of content to watch and read to help understand what embeddings are and formulate a good solid understanding.

Cofinder | Search response

Once you have a better understanding of what embeddings are, you may now want to explore what you can do with embeddings, let’s ask Cofinder again.

Question: What can embeddings be used for?

This time, our content is more focused on possible use cases, each one gives us the opportunity to explore multiple areas in more depth.

Cofinder | Search results

We can breakdown the process into 5 steps of development:

1. Data Sources: pre-processing the article text into chunks.
2. Embeddings & Search Index: use co.embed to obtain a vector representation of our data. Store your embeddings in a vector database which we will later use to search for relevant content.
3. Front End: Streamlit for our user to interact with our search engine.
4. Search: we use co.embed to get the vector representation of the user query, using nearest neighbours to return relevant content.
5. Answer: use co.generate to answer the query given the context from the search results and the question.

Cofinder | Development Stages

Here is a GitHub repository to follow along with the process:xxxx

Repository Contents

• requirements.txt: contains the libraries needed for our application
• cohere_text.csv: text prior to pre-processing
• main.py: this contains our code for the Streamlit app
• preprocessing.ipynb: this contains our code for generating embeddings and preparing our search index using the cohere_text.csv file

These files will be produced when you run preprocessing.ipynb

• cohere_text_final.csv: text after pre-processing
• search_index.ann: a search index containing our embeddings

You will need your Cohere API key to continue with the development.

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Important

Create your secrets.toml file in the repository as described here and enter your cohere API Key as API_KEY = ‘…’

Our first three stages are to pre-process the text by splitting it into small chunks, we will then generate embeddings using co.embed and finally create a search index using Annoy. This section of code can be found in preprocessing.ipynb

Semantic Search (cohere.ai)

The repository contains a csv file called cohere_text_preprocessing.csv with a row for each url, the title and the text taken from the webpage. Each item contains a category and type which is return in the search results.

CSV File | cohere_text_preprocessing

Note: Some of the information may be outdated as this text was extracted in October 2022 and is for demonstration purposes only.

After importing the csv file, we need to pre-process the text into chunks, the chunks will be used in a later stage as context where we want to generate an answer. As we will use co.generate for this task, we need to make sure our text chunks are no larger than 1500 words.

To maintain context, we will split the text chunks into 1500 and overlap the chunks using 500 tokens from the previous chunk relevant to the article.

import textwrapdef chunk_text(df, width=1500, overlap=500):        
new_df = pd.DataFrame(columns=['id', 'text_chunk'])        
for index, row in df.iterrows():                
    chunks = []        for i in range(0, len(row['text']), width - overlap):            
    chunk = row['text'][i:i+width]            
    chunks.append(chunk)                
    for i, chunk in enumerate(chunks):                        
        start_index = i * (width - overlap)                                    
        new_row = {              'id': row['id'],               
        'text_chunk': chunk,               
        'start_index': start_index}            
        new_df = new_df.append(new_row, ignore_index=True)    
        return new_dfnew_df = chunk_text(df)

We use the co.embed to obtain a vector representation of our data.

import cohere
import numpy as np
co = cohere.Client('<API_KEY>')
embeds = co.embed(texts=list(df['text_chunk']),                  
    model="large",                  
    truncate="RIGHT").embeddings
embeds = np.array(embeds)embeds.shape

Great! We now have our pre-processed text and embeddings ready for the next stage where we create a search index to store the data.

• Step 3 — create a search index using Annoy

from annoy import AnnoyIndex
search_index = AnnoyIndex(embeds.shape[1], 'angular')
for i in range(len(embeds)):    
    search_index.add_item(i, embeds[i])search_index.build(10) 
    search_index.save('search_index.ann')

The final three stages are to create our Streamlit application, we load the relevant libraries, data and search index from the previous stages. We then add functions to generate embeddings to search the Annoy index for the user’s query, and generate an answer from the context**.** This is all tied together in the Streamlit app with widgets for user input and markdown to display the results. This section of code can be found in main.py

In the main.py file, we build our code for the Streamlit application.

Here we import the libraries we need, the API key, initiate our cohere client and load the search index and csv file

import streamlit as st
import cohere  
import numpy as np
import pandas as pd
from annoy import AnnoyIndex
from concurrent.futures import ThreadPoolExecutor
import toml
with open('secret.toml') as f:    
    secrets = toml.load(f)
api_key = secrets['API_KEY']

co = cohere.Client('api_key')

search_index = AnnoyIndex(f=4096, metric='angular')
search_index.load('search_index.ann')
df = pd.read_csv('cohere_text_final.csv')

st.title("Cofinder")
st.subheader("A semantic search tool built for the Cohere community")

The search function takes in a query, the number of search results to return n_results, a dataframe df, an index search_index, and an embedding model co.ebbed(). Here's a step-by-step breakdown of what the function does:

1. The function uses the co.embed function to get the embedding of the query using the specified model ("large").
2. The function uses the search_index.get_nns_by_vector function to get the n_results nearest neighbors to the query embedding in the specified index search_index. The function returns the indices of the nearest neighbors as well as the similarity scores.
3. The function filters the original dataframe (df) to include only the rows that correspond to the nearest neighbors returned in step 2.
4. The function adds two new columns to the filtered dataframe: similarity, which contains the similarity scores between the query embedding and the document embeddings; and nearest_neighbors, which contains the indices of the nearest neighbors.
5. Finally, the function sorts the filtered dataframe by similarity in descending order and returns it.

The results of the search will be used as context to answer the user’s question.

def search(query, n_results, df, search_index, co):        
    query_embed = co.embed(texts=[query],                    
    model="large",                    
    truncate="LEFT").embeddings                
    
    nearest_neighbors = search_index.get_nns_by_vector(        
        query_embed[0],         
        n_results,         
        include_distances=True)        
    
    df = df[df.index.isin(nearest_neighbors[0])]    
    df['similarity'] = nearest_neighbors[1]    
    index_similarity_df = pd.DataFrame({'similarity':nearest_neighbors[1]}, index=nearest_neighbors[0])
    df = df.join(index_similarity_df,) # Match similarities based on indexes
    df = df.sort_values(by='similarity', ascending=False)    
    return df

When we run this function, we get a JSON output containing the article title and the context relevant to the query

Question: How do I use Cohere to build a chatbot?

[0:[0:" Use Cohere's Classify endpoint for intent recognition and text classification."1:"Paragraph:Use Cohere’s Classify for intent recognition and text classification. Starting from the same Transformer architecture as Google’s Search and Translate, Cohere’s Classify endpoint can read, understand, and label the intent behind unstructured customer queries with exceptional speed and accuracy. Developers can further finetune Cohere’s large language models with their own dataset per their business or industry. Set the road rules, define your core customer intent categories, and train Cohere’s Classify model by providing it with example customer queries for each category. Start the engine, Classify then combines this custom understanding of your intent categories with a deep understanding of language and context provided by our large language models to create a finetuned model, ready to handle your customer data. Get driving, every time a user enters a prompt into your chatbot, Cohere will classify the request by intent, allowing your bot to provide the most relevant answer back. Answer the question using this paragraph. Question: How do I use Cohere to build a chatbot? Answer:"]
1:[0:" Cohere uses billions of examples to train their models, which allows them to understand and recognize the intent behind a specific phrase. This intent recognition is used to power sophisticated chatbots that move beyond basic keyword recognition."1:"Paragraph:Billions of examples, Cohere’s models are trained on billions of sentences, allowing them to understand and recognize the intent behind a specific phrase. Our models excel at intent recognition tasks and can power sophisticated chatbots that move beyond basic keyword recognition. Answer the question using this paragraph. Question: How do I use Cohere to build a chatbot? Answer:"]
2:[0:" Cohere is a chatbot platform that is powered by basic keyword recognition. This means that the bot's ability to 'understand' queries is dependent upon customers using very specific phrases."1:"Paragraph:Keywords aren’t enough. Many chatbot platforms are powered by basic keyword recognition. This means that the bot’s ability to “understand” queries is dependent upon customers using very specific phrases. Developers could augment this by painstakingly predicting the myriad ways that a customer could phrase a specific query, including slang, misspellings, and differences in context. However, that task would be time consuming, if not downright impossible Answer the question using this paragraph. Question: How do I use Cohere to build a chatbot? Answer:"]
3:[0:" "Cohere's API is created to help you build natural language understanding and generation into your production with a few lines of code. Our Quickstart Tutorials will show you how to implement our API from zero-to-one in under 5 minutes.""1:"Paragraph:["Cohere's API is created to help you build natural language understanding and generation into your production with a few lines of code. Our Quickstart Tutorials will show you how to implement our API from zero-to-one in under 5 minutes. ", 'Chatbots are designed to understand and respond to human language. They need to be able to understand the text they hear and understand the context of the conversation. They also need to be able to respond to people’s questions and comments in a meaningful way. To accomplish this, chatbots must be able to recognize specific intents that people express in conversation.Here is an example of classifying the intent of customer inquiries on an eCommerce website into three categories: Shipping and handling policy, Start return or exchange, or Track order.', 'Updated about 1 month ago '] Answer the question using this paragraph. Question: How do I use Cohere to build a chatbot? Answer:"]
4:[0:" Our product is a Web based Application which improves the efficiency of chat based support systems by automating repetitive parts of the workflow. This is done by utilising Cohere's API in order to provide smart shortcuts for the Chat Support Agents."1:"Paragraph:Our product is a Web based Application which improves the efficiency of chat based support systems by automating repetitive parts of the workflow. This is done by utilising Cohere’s API in order to provide smart shortcuts for the Chat Support Agents. We aim to maximise Customer and Customer Support Agent satisfaction by making the lookup of product and service related answers instantaneous, thereby allowing the Customer Support Agent to put more effort into the interaction with the customer rather than the mundane task of researching answers. Answer the question using this paragraph. Question: How do I use Cohere to build a chatbot? Answer:"]]

We now want to take the user question and generate an answer to the question given the search results from the previous function.

Overall, the display function below is broken down into two tasks,

1. The gen_answer and gen_better_answer functions are used to generate answers for the query and each of the search results. The gen_answer function generates an initial answer based on a prompt that includes the paragraph and the question, while gen_better_answer generates a better answer by incorporating the initial answers and the question.
2. The results are displayed in a user-friendly format using the st module from the Streamlit library. The search query is displayed as a subheader, followed by the better answer generated by gen_better_answer. The relevant documents are then displayed, one by one. Each document is displayed with its type, category, title, and link, followed by the initial answer generated by gen_answer. The text of the document is then collapsed and can be expanded by clicking on "Read more".

def display(query, results):            
    with ThreadPoolExecutor(max_workers=1) as executor:        
        results['answer'] = list(executor.map(gen_answer,                                               
        [query]*len(results),                                              
        results['text']))    
        answers = results['answer'].tolist()        
        answ = gen_better_answer(query, answers)        
        st.subheader(query)    
        st.write(answ)        
        st.write('')    
        st.write('')    
        st.subheader("Relevant documents")        
        for i, row in results.iterrows():                
            st.markdown(f'**{row["Type"]}**')        
            st.markdown(f'**{row["Category"]}**')        
            st.markdown(f'{row["title"]}')                        
            st.markdown(f'[{row["link"]}]({row["link"]})')        
            st.write(row['answer'])                
            with st.expander('Read more'):            
                st.write(row['text'])        
                st.write('')

Let’s explore the the gen_answer and gen_better_answer functions to see what is happening.

Cofinder | search context and generate best answer

Here we create two co.generate() prompts, these functions use Cohere’s pre-trained models to generate text that answers a given question from the context provided from the search results.

• gen_answer(q, para): This function returns an answer to the question given the context returned from our search function. In the display function above, we ran this function by iterating over each of the search results from the JSON output to gather context from multiple sources.
• gen_better_answer(ques, ans): This function takes the question and all of the responses from thegen_answer function, it uses the combined answers to formulate a more rounded answer taking into account all the available resources.

The max_tokens and temperature parameters can be tuned to control the length and randomness of the generated text.

def gen_answer(q, para):     
    response = co.generate(         
        model='command-xlarge-20221108',         
        prompt=f'''Paragraph:{para}\n\n                
        Answer the question using this paragraph.\n\nQuestion: {q}\nAnswer:''',
        max_tokens=100,         
        temperature=0.4)    
        return response.generations[0].text
        
    def gen_better_answer(ques, ans):     
        response = co.generate(         
            model='command-xlarge-20221108',         
            prompt=f'''Answers:{ans}\n\nQuestion: {ques}\n\nGenerate a new answer that uses the best answers and makes reference to the question.''',         max_tokens=100,         
            temperature=0.4)            
            return response.generations[0].text

Finally, we add a streamlit search input along with some question examples for the user, and button that runs our functions.

query = st.text_input('Ask a question about Cohere')
st.markdown('''Try some of these examples: - What is the Cohere API?- What are embeddings?- What is the Cohere playground?- How can I build a chatbot?''')
if st.button('Search'):    
    results = search(query, 3, df, search_index, co)    
    display(query, results)

Well done! You are now you are ready to publish your Streamlit application to the cloud!

The vision for Cofinder was “Built for the community, by the community”, can you help make Cofinder a better search tool for our community? Please contact me on LinkedIn or Discord Elle Neal#0726 to discuss how we can make this happen.

As a starting point, here are some potential features I captured at the end of the hackathon, I am excited to see how we can take this application to the next level!

Cofinder | Feature Pipeline

So there you have it, a step-by-step guide to building an AI-powered semantic search application using Cohere’s API. But this is just the beginning! Cofinder was built for the community, by the community, and we are always looking for ways to make it better. As a community member, your input is crucial in shaping the future of Cofinder. Please reach out to me on LinkedIn or Discord to discuss potential features and how we can work together to take this application to the next level.

Let’s continue to innovate and make the Cohere community experience even better.