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File: How to parse PDF docs for RAG > Data preparation > Embedding content

# Examples > How to parse PDF docs for RAG > Data preparation
## Embedding content

This diagram illustrates a framework for processing a return request using a language model (LLM) system. Here's a 
breakdown of the process:
. **User Input**: The user wants to return a T-shirt purchased on Amazon on March 3rd.
. **Router**: The initial input is processed by a router LLM, which determines the nature of the request. The 
expected and predicted outcomes are both "return," and the process passes this evaluation.
. **Return Assistant**: The request is then handled by a return assistant LLM. It interacts with a knowledge base 
to verify the return policy.
. **Knowledge Base**: The system checks the return policy, confirming that the item is eligible for return within 
14 days of purchase. The expected and predicted outcomes are "return_policy," and this step also passes.
. **Response to User**: The system responds to the user, confirming that the return can be processed because it is 
within the 14-day window.
. **Evaluation**: The response is evaluated for adherence to guidelines, scoring 5 for politeness, 4 for coherence,
and 4 for relevancy, resulting in a pass.
The framework uses both component evaluations (red dashed lines) and subjective evaluations (orange dashed lines) 
to ensure the process is accurate and user-friendly.



-------------------------------
# 
Example framework
I want to return aT-shirt I bought onAmazon on March 3rd.
User
Router
LLM
Expected: return
Predicted: return
PASS
Return
Assistant
LLM
Component evals
Subjective evals
Expected: return_policy
Predicted: return_policy
PASS
Knowledgebase
Question: Does this response adhere toour guidelines
Score:Politeness: 5, Coherence: 4, Relevancy: 4
PASS
Sure - because we’rewithin 14 days of thepurchase, I canprocess the return
Question: I want to return a T-shirt Ibought on Amazon on March 3rd.
Ground truth: Eligible for return
PASS

This diagram illustrates a framework for processing a return request using a language model (LLM) system. Here's a 
breakdown of the process:
. **User Input**: The user wants to return a T-shirt purchased on Amazon on March 3rd.
. **Router**: The initial input is processed by a router LLM, which determines the nature of the request. The 
expected and predicted outcomes are both "return," and the process passes this evaluation.
. **Return Assistant**: The request is then handled by a return assistant LLM. It interacts with a knowledge base 
to verify the return policy.
. **Knowledge Base**: The system checks the return policy, confirming that the item is eligible for return within 
14 days of purchase. The expected and predicted outcomes are "return_policy," and this step also passes.
. **Response to User**: The system responds to the user, confirming that the return can be processed because it is 
within the 14-day window.
. **Evaluation**: The response is evaluated for adherence to guidelines, scoring 5 for politeness, 4 for coherence,
and 4 for relevancy, resulting in a pass.
The framework uses both component evaluations (red dashed lines) and subjective evaluations (orange dashed lines) 
to ensure the process is accurate and user-friendly.



-------------------------------




---
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File: How to parse PDF docs for RAG > Data preparation > Embedding content (chunking)

# Examples > How to parse PDF docs for RAG > Data preparation
## Embedding content

Before embedding the content, we will chunk it logically by page.
For real-world scenarios, you could explore more advanced ways to chunk the content:

- Cutting it into smaller pieces
- Adding data - such as the slide title, deck title and/or the doc description - at the beginning of each piece of content. That way, each independent chunk can be in context

For the sake of brevity, we will use a very simple chunking strategy and rely on separators to split the text by page.

Chunking content by page and merging together slides text & description if applicable
content = []
for doc in docs:
# Removing first slide as well
text = doc['text'].split('\f')[1:]
description = doc['pages_description']
description_indexes = []
for i in range(len(text)):
slide_content = text[i] + '\n'
# Trying to find matching slide description
slide_title = text[i].split('\n')[0]
for j in range(len(description)):
description_title = description[j].split('\n')[0]
if slide_title.lower() == description_title.lower():
slide_content += description[j].replace(description_title, '')
# Keeping track of the descriptions added
description_indexes.append(j)
# Adding the slide content + matching slide description to the content pieces
content.append(slide_content)
# Adding the slides descriptions that weren't used
for j in range(len(description)):
if j not in description_indexes:
content.append(description[j])

# 
for c in content:
    print(c)
    print("\n\n-------------------------------\n\n")

Overview

Retrieval-Augmented Generation 
enhances the capabilities of language 
models by combining them with a 
retrieval system. This allows the model 
to leverage external knowledge sources 
to generate more accurate and 
contextually relevant responses.

Example use cases

- Provide answers with up-to-date 

information

- Generate contextual responses

What we’ll cover

● Technical patterns

● Best practices

● Common pitfalls

● Resources

3






-------------------------------




What is RAG

Retrieve information to Augment the model’s knowledge and Generate the output

“What is your 
return policy?”

ask

result

search

LLM

return information

Total refunds: 0-14 days
50% of value vouchers: 14-30 days
$5 discount on next order: > 30 days

“You can get a full refund up 
to 14 days after the 
purchase, then up to 30 days 
you would get a voucher for 
half the value of your order”

Knowledge 
Base / External 
sources

4




RAG stands for "Retrieve information to Augment the model’s knowledge and Generate the output." This process 
involves using a language model (LLM) to enhance its responses by accessing external information sources.

Here's how it works:

1. **User Query**: A user asks a question, such as "What is your return policy?"

2. **LLM Processing**: The language model receives the question and initiates a search for relevant information.

3. **Information Retrieval**: The LLM accesses a knowledge base or external sources to find the necessary details. 
In this example, the information retrieved includes:
   - Total refunds available from 0 to 14 days.
   - 50% value vouchers for returns between 14 to 30 days.
   - A $5 discount on the next order for returns after 30 days.
#
  1. Response Generation: The LLM uses the retrieved information to generate a coherent response for the user.
    For instance, it might say, "You can get a full refund up to 14 days after the purchase, then up to 30 days you
    would get a voucher for half the value of your order."

This method allows the model to provide accurate and up-to-date answers by leveraging external data sources.

When to use RAG

Good for ✅

Not good for ❌

●

●

Introducing new information to the model

●

---

## File: [Vector Databases > Pinecone > Step 2: Parsing PDFs and Extracting Visual Information](<https://cookbook.openai.com/examples/vector_databases/pinecone/using_vision_modality_for_rag_with_pinecone#step-2-parsing-pdfs-and-extracting-visual-information>)
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| 2 | 3 | images/page_3.png | **TRANSCRIPTION OF THE TEXT:**\\n\\nABOUT US\\n\\nThe World Bank Group is one of the world’s largest sources of funding and knowledge for developing countries. Our five institutions share a commitment to reducing poverty, increasing shared prosperity, and promoting sustainable development.\\n\\nOUR VISION \\nOur vision is to create a world free of poverty on a livable planet.\\n\\nOUR MISSION \\nOur mission is to end extreme poverty and boost shared prosperity on a livable planet. This is threatened by multiple, intertwined crises. Time is of the essence. We are building a better Bank to drive impactful development that is: \\n• Inclusive of everyone, including women and young people; \\n• Resilient to shocks, including against climate and biodiversity crises, pandemics and fragility; \\n• Sustainable, through growth and job creation, human development, fiscal and debt management, food security and access to clean air, water, and affordable energy.\\n\\nTo achieve this, we will work with all clients as one World Bank Group, in close partnership with other multilateral institutions, the private sector, and civil society.\\n\\nOUR CORE VALUES \\nOur work is guided by our core values: impact, integrity, respect, teamwork, and innovation. These inform everything we do, everywhere we work. |
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| 3 | 4 | images/page_4.png | TRANSCRIPTION OF THE TEXT:\n\nDRIVING ACTION, MEASURING RESULTS\n\nThe World Bank Group contributes to impactful, meaningful development results around the world. In the first half of fiscal 2024*, we:\n\n- Helped feed 156 million people\n- Improved schooling for 280 million students\n- Reached 287 million people living in poverty with effective social protection support†\n- Provided healthy water, sanitation, and/or hygiene to 59 million people\n- Enabled access to sustainable transportation for 77 million people\n- Provided 17 gigawatts of renewable energy capacity\n- Committed to devote 45 percent of annual financing to climate action by 2025, deployed equally between mitigation and adaptation\n\n*The development of the new Scorecard is ongoing at the time of printing; therefore, this report can only account for results up to December 31, 2023.\nAs of the 2024 IMF-World Bank Group Annual Meetings, the full fiscal 2024 Scorecard data will be available at: https://scorecard.worldbankgroup.org\\n\\n† IBRD and IDA only indicator.\n\nIn fiscal 2024, the Bank Group announced the development of a new Scorecard that will track results across 22 indicators—a fraction of the previous 150—to provide a streamlined, clear picture of progress on all aspects of the Bank Group’s mission, from improving access to healthcare to making food systems sustainable to boosting private investment.\n\nFor the first time, the work of all Bank Group financing institutions will be tracked through the same set of indicators. The new Scorecard will track the Bank Group’s overarching vision of ending poverty on a livable planet.\n\nTHE WORLD BANK ANNUAL REPORT 2024\n\nDESCRIPTION OF THE IMAGE OR CHART:\n\nThe image displays a series of circular photographs connected with text highlights depicting World Bank Group achievements.

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The photos include people and infrastructure related to food, education, social protection, water, transportation, renewable energy, and environmental initiatives. Each photo correlates with a text entry describing a specific achievement or commitment. |

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| 4 | 5 | images/page_5.png | TRANSCRIPTION OF THE TEXT:\n\nMESSAGE FROM THE PRESIDENT\n\nDELIVERING ON OUR COMMITMENTS REQUIRES US TO DEVELOP NEW AND BETTER WAYS OF WORKING. IN FISCAL 2024, WE DID JUST THAT.\n\nAJAY BANGA\n\nIn fiscal 2024, the World Bank Group adopted a bold new vision of a world free of poverty on a livable planet. To achieve this, the Bank Group is enacting reforms to become a better partner to governments, the private sector, and, ultimately, the people we serve. Rarely in our 80-year history has our work been more urgent: We face declining progress in our fight against poverty, an existential climate crisis, mounting public debt, food insecurity, an unequal pandemic recovery, and the effects of geopolitical conflict.\n\nResponding to these intertwined challenges requires a faster, simpler, and more efficient World Bank Group. We are refocusing to confront these challenges not just through funding, but with knowledge. Our Knowledge Compact for Action, published in fiscal 2024, details how we will empower all Bank Group clients, public and private, by making our wealth of development knowledge more accessible. And we have reorganized the World Bank’s global practices into five Vice Presidency units—People, Prosperity, Planet, Infrastructure, and Digital—for more flexible and faster engagements with clients. Each of these units reached important milestones in fiscal 2024.\n\nWe are supporting countries in delivering quality, affordable health services to 1.5 billion people by 2030 so our children and grandchildren will lead healthier, better lives. This is part of our larger global effort to address a basic standard of care through every stage of a person’s life—infancy, childhood, adolescence, and adulthood. To help people withstand food-affected shocks and crises, we are strengthening social protection services to support half a

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billion people by the end of 2030—aiming for half of these beneficiaries to be women.\n\nWe are helping developing countries create jobs and employment, the surest enablers of prosperity. In the next 10 years, 1.2 billion young people across the Global South will become working-age adults. Yet, in the same period and the same countries, only 424 million jobs are expected to be created. The cost of hundreds of millions of young people with no hope for a decent job or future is unimaginable, and we are working urgently to create opportunity for all.\n\nIn response to climate change—arguably the greatest challenge of our generation—we’re channeling 45 percent of annual financing to climate action by 2025, deployed equally between mitigation and adaptation. Among other efforts, we intend to launch at least 15 country-led methane-reduction programs by fiscal 2026, and our Forest Carbon Partnership Facility has helped strengthen high-integrity carbon markets.\n\nAccess to electricity is a fundamental human right and foundational to any successful development effort. It will accelerate the digital development of developing countries, strengthen public infrastructure, and prepare people for the jobs of tomorrow. But half the population of Africa—600 million people—lacks access to electricity. In response, we have committed to provide electricity to 300 million people in Sub-Saharan Africa by 2030 in partnership with the African Development Bank.\n\nRecognizing that digitalization is the transformational opportunity of our time, we are collaborating with governments in more than 100 developing countries to enable digital economies. Our digital lending portfolio totaled $6.5 billion in commitments as of June 2024, and our new Digital Vice Presidency unit will guide our efforts to establish the foundations of a digital economy. Key measures include building and

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enhancing digital and data infrastructure, ensuring cybersecurity and data privacy for institutions, businesses, and citizens, and advancing digital government services.\n\nDelivering on our commitments

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