Crafting and Enhancing a Custom Intelligent Agent with OpenAI

In this era of information, processing and understanding large data volumes have become a key competitive advantage. Artificial intelligence, particularly OpenAI's Large Language Models (LLMs) , provides powerful tools for navigating this extensive data landscape. This article deeply explores constructing and optimizing an intelligent agent using OpenAI. It meticulously guides you through managing tokens and context to advanced indexing of personalized content, ensuring the creation of an agent that not only grasps but also precisely and efficiently meets your specific needs. Get ready to unlock the full potential of customized AI, altering the way your agent interacts with and learns from your unique information universe.

Introduction to LLM and Token Management

At the heart of modern natural language processing are OpenAI's Large Language Models (LLMs) , utilizing tokens as the primary communication means between an agent and the API. These tokens, representing everything from words to punctuation, are critical in formulating coherent responses and comprehending the provided context.

Efficient token management is key due to the processing limit of tokens per request in LLMs. The challenge lies in utilizing tokens judiciously—ensuring the agent deeply understands and responds accurately to queries. While extensive context might be available, pinpointing and providing only the most relevant portion is essential for crafting precise and contextually rich responses, avoiding the wastage of model capacity on unnecessary information.

Basic Agent Creation in Python

Diving into intelligent agent creation with OpenAI is both thrilling and straightforward, particularly when using Python, known for its user-friendliness and robustness. This section walks you through crafting a basic agent, providing the essential code to communicate with the OpenAI API. Here's a snippet to get your agent set up and running:

from openai import OpenAI

client = OpenAI(api_key='your-api-key-here')

# Set your OpenAI API key

def ask_question(question, max_tokens=250):
    try:
        # Call the OpenAI API and save the response
        response = client.completions.create(
            model="gpt-4",
            prompt=question,
            max_tokens=max_tokens
        )
        return response.choices[0].text.strip()
    except Exception as e:
        print(f"Error while asking the question: {str(e)}")
        return ""

# Example usage
question = "What is the capital of France?"
response = ask_question(question)
print(f"Response: {response}")        

In this script:

1. API Configuration: Initially, set your API key to authenticate your requests.
2. Function Definition: Create a hacer_pregunta function that takes a question as input and sends a request to the OpenAI API.
3. Response Handling: The API processes your question and returns a response, which you can then manipulate or display as needed.

Context Optimization through Indexing

After setting up a basic agent, the next step is enhancing its context handling to be more intelligent and efficient. This involves indexing your custom content, which allows the agent to swiftly search and retrieve the most relevant information for each query. By utilizing embeddings generated by the OpenAI API and storing them in a vector database like Pinecone, you can significantly optimize your agent's context processing. Here's how you can start indexing your documents to refine your agent's context understanding:

import openai
from pinecone import Pinecone, PodSpec
from sentence_transformers import SentenceTransformer

# Configure your OpenAI and Pinecone API keys
pc = Pinecone(api_key='your-pinecone-api-key-here')

# Create or connect to an index in Pinecone
index_name = 'my-vector-index'
metadata_config = {
}
if index_name not in pc.list_indexes():
    pc.create_index(
        index_name, 
        dimension=384,
        spec=PodSpec(
            environment='us-west-2', 
            pod_type='p1.x1', 
            metadata_config=metadata_config
        )
    )
index = pc.Index(index_name)

# Load the model for generating embeddings
model = SentenceTransformer('all-MiniLM-L6-v2')

def index_document(title, content):
    # Generate embeddings for the document
    embeddings = model.encode([content])[0]
    
    # Index the document in Pinecone
    index.upsert(vectors=[(title, embeddings)])

# Example of document indexing
document_title = "History of Artificial Intelligence"
document_content = "Artificial Intelligence (AI) is an area of computer science..."
index_document(document_title, document_content)

print("Document successfully indexed in Pinecone.")        

In this code snippet:

1. Configuration and Connection: You set up and connect to your Pinecone index.
2. Embedding Generation: You use a SentenceTransformer model to convert your content into embeddings, which are vector representations capturing the text's meaning.
3. Indexing in Pinecone: You index these embeddings along with their titles in Pinecone, allowing for fast and precise semantic searches.

Indexing your documents in this manner equips your agent to not only answer questions based on a limited set of predefined answers but also to extract and utilize relevant information from a broad document corpus, enhancing both the precision of the responses and the versatility of your agent across various contexts.

Agent Enhancement: Searching for Relevant Documents

After indexing your documents, the next critical step is to empower your agent to efficiently search and retrieve the most relevant documents from your vector index in response to specific queries. This enhancement not only boosts the relevance of your agent's responses but also streamlines the use of context, making it significantly more efficient. The following example will illustrate how to implement this feature in your agent:

import openai
from pinecone import Pinecone
from sentence_transformers import SentenceTransformer

# Configure your OpenAI and Pinecone API keys
pc = Pinecone(api_key='your-pinecone-api-key-here')

# Connect to your index in Pinecone
index = pc.Index('your-vector-index-name')

# Load the model for generating embeddings
model = SentenceTransformer('all-MiniLM-L6-v2')

def find_relevant_documents(query):
    # Generate embeddings for the query
    query_embedding = model.encode([query])[0]
    
    # Perform a search in the vector index
    results = index.query(queries=[query_embedding], top_k=3)
    
    # Extract the titles of the most relevant documents
    relevant_documents = [match['id'] for match in results['matches'][0]]
    
    return relevant_documents

# Example of searching for relevant documents
query = "History and evolution of artificial intelligence"
documents = find_relevant_documents(query)
print(f"Relevant documents found: {documents}")        

In this code segment:

1. Embedding Generation for Query: Transform the query into an embedding, similar to how documents are processed, allowing for comparison in the vector space.
2. Index Search: Employ Pinecone's search function to pinpoint the documents closest or most relevant to the query's embedding.
3. Document Retrieval: Extract titles or identifiers of the most pertinent documents as per the search outcomes.

This approach equips your agent to swiftly locate the most relevant documents from your dataset, ensuring informed and accurate responses based on your indexed custom content, significantly advancing towards a truly tailored and efficient intelligent agent.

To weave in the search for pertinent documents and leverage this data as extra context in your queries, consider adapting the hacer_pregunta function in the following manner:

# Example of searching for relevant documents
query = "History and evolution of artificial intelligence"
documents = find_relevant_documents(query)
print(f"Relevant documents found: {documents}")

def ask_question_with_context(question, max_tokens=250):
    try:
        # Search for relevant documents for the question
        relevant_document_titles = find_relevant_documents(question)
        
        # Retrieve the full document content for each title
        relevant_documents = [get_document_by_title(title) for title in relevant_document_titles]
        
        # Combine the relevant documents with the question to form the prompt
        prompt_with_context = f"{question}\n\nRelevant Documents:\n"
        for document in relevant_documents:
            prompt_with_context += f"{document}\n"

        # Call the OpenAI API and save the response
        response = openai.Completion.create(
          model="gpt-4",
          prompt=prompt_with_context,
          max_tokens=max_tokens
        )
        return response.choices[0].text.strip()
    except Exception as e:
        print(f"Error while asking the question with context: {str(e)}")
        return ""
        

The get_document_by_title function plays a crucial role in bridging the gap between vector search in Pinecone and retrieving the actual content. As Pinecone focuses on storing and searching through vector representations of your data, it doesn't keep the original documents. Hence, this function is designed to fetch the full content of a document from your primary database using a unique identifier (like a title) obtained from Pinecone's search results, ensuring you have access to the complete, original information for further processing or display.

In this revised function:

1. Incorporation of Relevant Documents: Retrieve titles of pertinent documents via buscar_documentos_relevantes before querying the OpenAI API.
2. Prompt Assembly: Merge the original question with the relevant document titles to form a comprehensive prompt, enhancing the context for the API.
3. Response Generation: Send the enriched prompt to the API and handle the response as usual.

This modification primes your agent to deliver responses informed by both the language model and the relevant content from your document collection, thus providing more comprehensive and contextually accurate answers.

Enhancing Efficiency with Contextual Summaries

To further refine your agent's efficiency and precision, consider focusing on how it utilizes context. Even with relevant documents, they may contain excess information unrelated to the specific query. By requesting contextual summaries of these documents from the OpenAI API, you provide your agent with a more focused understanding of the pertinent content. Here's how to implement the generation of contextual summaries:

def generate_summary(document, question, max_tokens=100):
    try:
        # Create a prompt to generate a contextual summary of the document
        summary_prompt = f"Question: {question}\nDocument: {document}\n\nSummary:"
        
        # Call the OpenAI API and save the response
        response = client.Completion.create(
          model="gpt-4",
          prompt=summary_prompt,
          max_tokens=max_tokens
        )
        return response.choices[0].text.strip()
    except Exception as e:
        print(f"Error generating summary: {str(e)}")
        return ""

def ask_question_with_summaries(question, max_tokens=250):
    try:
        # Search for relevant documents for the question
        relevant_documents = find_relevant_documents(question)
        
        # Generate contextual summaries for each relevant document
        summaries = [generate_summary(doc, question) for doc in relevant_documents]
        
        # Combine the summaries with the question to form the prompt
        prompt_with_summaries = f"{question}\n\nContextual Summaries:\n"
        for summary in summaries:
            prompt_with_summaries += f"{summary}\n"
        
        # Call the OpenAI API and save the response
        response = client.Completion.create(
          model="gpt-4",
          prompt=prompt_with_summaries,
          max_tokens=max_tokens
        )
        return response.choices[0].text.strip()
    except Exception as e:
        print(f"Error while asking the question with summaries: {str(e)}")
        return ""        

In this code section:

1. Summary Generation: Create contextual summaries for each relevant document using the OpenAI API. The prompt includes the query and the document content, prompting the model to produce a summary relevant to the specific question.
2. Enhanced Prompt Formation: Merge the original question with these contextual summaries to create a new, enriched prompt, providing the OpenAI API with more focused context.
3. Response Generation: Send this enhanced prompt to the OpenAI API and process the response as previously done.

This strategy enhances your agent's ability to efficiently handle context by zeroing in on the most crucial information, ensuring it's presented in a way that directly aids in producing detailed, precise responses. With these contextual summaries in place, your agent is better equipped to navigate complex queries and offer valuable, accurate information.

To implement contextual summary generation in your agent, you'll need to revise the code section where your agent constructs the prompt for the OpenAI API, incorporating summaries of relevant documents instead of full documents or their titles. Initially, the function hacer_pregunta_con_contexto was designed to send the question along with the document titles to the API:

def ask_question_with_context(question, max_tokens=250):
    try:
        # ... previous code ...

        # Retrieve the full document content for each title
        relevant_documents = [get_document_by_title(title) for title in relevant_documents]
        
        # Combine the relevant documents with the question to form the prompt
        prompt_with_context = f"{question}\n\nRelevant Documents:\n"
        for document in relevant_documents:
            prompt_with_context += f"{document}\n"

        # ... code to call the API ...
    except Exception as e:
        # ... exception handling ...
        

To integrate the contextual summaries, you would adjust the function to look like this:

def ask_question_with_context(question, max_tokens=250):
    try:
        # Search for relevant documents for the question
        relevant_document_titles = find_relevant_documents(question)
        
        # Retrieve the full document content for each title
        relevant_documents = [get_document_by_title(title) for title in relevant_document_titles]
        
        # Combine the relevant documents with the question to form the prompt
        prompt_with_context = f"{question}\n\nRelevant Documents:\n"
        for document in relevant_documents:
            prompt_with_context += f"{document}\n"

        # Call the OpenAI API and save the response
        response = client.Completion.create(
          model="gpt-4",
          prompt=prompt_with_context,
          max_tokens=max_tokens
        )
        return response.choices[0].text.strip()
    except Exception as e:
        # ... exception handling ...        

In this updated version of the function:

1. Contextual Summary Generation: Use the generar_resumen function to create summaries of relevant documents based on the query.
2. Enhanced Prompt Formation: Merge the original question with these contextual summaries to craft a new prompt.
3. Response Generation: Send the enriched prompt to the OpenAI API and process the response as previously.

With these changes, your agent is now equipped to provide answers informed by contextual summaries of pertinent documents, significantly enhancing the accuracy and relevance of the provided information.

The concept of using OpenAI to generate contextual summaries for documents aligns well with current practices in AI-driven summarization. The approach of breaking down complex tasks into simpler, manageable parts, such as summarizing individual sections of a document before integrating them, is echoed in OpenAI's strategies for handling extensive content like books. They emphasize the importance of aligning AI-generated summaries with human preferences, fine-tuning the models to enhance their comprehension and summarization capabilities.Moreover, metrics like ROUGE are commonly used to evaluate the overlap between the AI-generated summaries and reference texts, ensuring that the essential content is captured effectively. While such metrics provide quantitative measures, the nuanced understanding and alignment of the AI's output with human intention remain crucial. OpenAI's iterative approach to summarization, focusing on recursive task decomposition and reinforcement learning from human feedback, reflects a deep understanding of the complexities involved in generating concise, relevant summaries from large texts.For more detailed insights and methodologies, you can explore the resources provided by OpenAI on summarizing books with human feedback here and delve into the evaluation of summarization tasks in the OpenAI Cookbook here .

Turnkey Solutions: CodeGPT

For those preferring to sidestep intricate coding, CodeGPT emerges as a sleek, effective alternative. This innovative platform streamlines the process of creating and training tailored AI agents, effortlessly blending specialized knowledge and customized context. With CodeGPT, the focus shifts to the agent's creative construction and design, alleviating the need to navigate the complexities of fine-tuning large-scale language models. Dive deeper into this intuitive platform at CodeGPT .

Conclusion: Your First Customized Agent

This guide has led you through the essentials of Large Language Models and token management to the development and refinement of a smart agent with OpenAI. Integrating sophisticated techniques like document indexing, relevant content retrieval, and generating contextual summaries, your agent is now proficient in providing accurate, context-aware responses. Whether coding from scratch or using platforms like CodeGPT, you're well-prepared to delve into the potential of customized agents in natural language processing and AI.