Configure Summarization

Summarization provides a chatbot-like experience to your users by enhancing the efficiency of information delivery. Offering quick and concise summaries can lead to faster decision-making, improved user satisfaction, and higher engagement levels. The versatility of summarizations makes it a valuable tool across multiple industries, and any application requiring the summarization of complex data into more manageable insights.

Whether it's streamlining customer service interactions, accelerating research, or enhancing educational tools, the ability to summarize vast amounts of data into digestible, actionable insights can significantly boost productivity across various business operations and industries.

Mockingbird: RAG-specific LLM

For users requiring superior performance in Retrieval Augmented Generation (RAG) use cases, Vectara has designed the new Mockingbird LLM. Mockingbird provides a cutting-edge LLM designed specifically for RAG, such as enterprise applications that demand high-quality summaries and structured outputs. This advanced LLM was designed specifically for RAG:

• Enhanced RAG output quality
• Improved citation accuracy
• Excellent multilingual performance
• High-precision structured data generation

info

Excluding the generation field disables summarization. For more information about the generation object, see Query API Definition.

To enable summarization, send a generation request with your query. This example configuration instructs Vectara not only to search for relevant data but also also to generate a summary based on the most relevant five results, which provides a comprehensive response rooted in the queried data.

https://api.vectara.io/v2/query

POST https://api.vectara.io/v2/query
{
  "query": "What is the infinite improbability drive?",
  "search": {
    "corpora": [
      {
        "corpus_key": "hitchhikers-guide"
      }
    ],
    "offset": 0,
    "limit": 10
  },
  "generation": {
    "prompt_name": "mockingbird-1.0-2024-07-16",
    "max_used_search_results": 5
  }
}

This query tells Vectara to return a summary in English using the mockingbird-1.0-2024-07-16 summarizer and to consider the first 5 results when summarizing.

Example query response

When Vectara responds, it will contain the list of results as well as the generative summary. Here is an example response to the query What is the infinite improbability drive? when searching across the Hitchhiker's Guide to the Galaxy:

response.json

{
  "summary": "The Infinite Improbability Drive is a form of propulsion developed by the Galactic 
    Government on Damogran which allows for vast interstellar distances to be crossed in a 
    nothingth of a second without the use of hyperspace [3]. It is incredibly powerful and rare, 
    with only rumors circulating of its existence prior to its development [1]. It has been known 
    to cause problems with other forms of propulsion, like the photon drive [4]. It is a remarkable 
    breakthrough in Improbability Physics [2].",
  "summary_language": "eng",
  "search_results": [
    {
      "text": "Ford was wildly excited. \"Arthur!\" he said, \"this is fantastic! We've been picked 
        up by a ship powered by the Infinite Improbability Drive! This is incredible! I heard 
        rumors about it before! They were all officially denied, but they must have done it!",
      "score": 0.9203816652297974,
      "part_metadata": {
        "title": "Chapter 9",
        "lang": "eng",
        "breadcrumb": "[\"THE HITCHHIKER'S GUIDE TO THE GALAXY\"]",
        "section": "12",
        "offset": "8137",
        "len": "76"
      },
      "document_metadata": {},
      "document_id": "doc_123456789",
      "request_corpora_index": 0
    },
    {
      "text": "Look, I was right.\" Ford jabbed at one of the pages and showed it to Arthur. \"It 
        says: 'Sensational new breakthrough in Improbability Physics. As soon as the ship's drive 
        reaches Infinite Improbability it passes through every point in the Universe. Be the envy 
        of other major governments.' Wow, this is big league stuff.",
      "score": 0.8904105424880981,
      "part_metadata": {
        "title": "Chapter 11",
        "lang": "eng",
        "breadcrumb": "[\"THE HITCHHIKER'S GUIDE TO THE GALAXY\"]",
        "section": "14",
        "offset": "8211",
        "len": "107"
      },
      "document_metadata": {},
      "document_id": "doc_123456789",
      "request_corpora_index": 0
    },
    {
      "text": "Chapter 10 Chapter 10 The Infinite Improbability Drive is a wonderful new method of 
        crossing vast interstellar distances in a mere nothingth of a second, without all that 
        tedious mucking about in hyperspace. It was discovered by a lucky chance, and then 
        developed into a governable form of propulsion by the Galactic Government's research 
        team on Damogran. This, briefly, is the story of its discovery.",
      "score": 0.8826565742492676,
      "part_metadata": {
        "title": "Chapter 10",
        "lang": "eng",
        "breadcrumb": "[\"THE HITCHHIKER'S GUIDE TO THE GALAXY\"]",
        "section": "13",
        "offset": "8",
        "len": "187"
      },
      "document_metadata": {},
      "document_id": "doc_123456789",
      "request_corpora_index": 0
    },
    {
      "text": "The photon drive gave a sickly judder and cut out again. said Arthur. \"Hey, didja hear 
        that?\" muttered Zaphod as he leapt now for the manual controls of the Infinite Improbability 
        Drive, \"the monkey spoke!\" The Improbability Drive gave two small whines and then also cut 
        out. \"Pure history, man,\" said Zaphod, kicking the Improbability Drive, \"a talking monkey!\"",
      "score": 0.7908053398132324,
      "part_metadata": {
        "title": "Chapter 3",
        "lang": "eng",
        "breadcrumb": "[\"THE RESTAURANT AT THE END OF THE UNIVERSE\"]",
        "section": "42",
        "offset": "769",
        "len": "114"
      },
      "document_metadata": {},
      "document_id": "doc_987654321",
      "request_corpora_index": 0
    }
  ]
}

You can see in the results that the specific sources are referenced in the summary. Vectara issues these citations by putting them in [number] format in the summary text, where number starts from 1 and increases by 1 in each result in the search_results array.

Citation Formatting

Vectara also enables users to format citations in summaries so that they appear as numeric (default), HTML, Markdown, or none. For details, see the documentation on formatting citations in summaries.

Advanced summarization parameters

Users also have access to more powerful summarization capabilities, which present a powerful toolkit for tailoring summarizations to specific application and user needs.

Select the summarizer model

Selecting the summarizer gives you more options to try official and experimental versions of our available summarizers,
like our GPT 4.0 and GPT 4.0-Turbo summarizers.

Customize the prompt

Override the default prompt text with a custom prompt. Your use case might require a chatbot to be more human like, so you decide to create a custom response format that behaves more playfully in a conversation or summary.

Control the summary length

The max_response_characters parameter enables users to suggest a preferred length for the summary. While it is not a strict limit like the fine-grained max_tokens parameter, it provides a guideline, helping to maintain a balance between verbosity and conciseness.

Fine-grained control over summarization output

The generation object provides even more fine-grained controls for the summarizer model:

• max_tokens specifies a hard limit on the number of characters in a response.
• temperature indicates whether you want the summarization to not be creative at all 0.0, or for the summarization to take more creative liberties as you approach the maximium value of 1.0.
• frequency_penalty provides even more granular control to help ensure that the summarization decreases the likelihood of repeating words. These values range from 0.0 to 1.0
• presence_penalty provides more control over whether you want the summary to include new topics. These values also range from 0.0 to 1.0.

Example summarization use cases

Application builders can leverage these advanced capabilties to fine-tune the behavior and output style of the summarizer to align with unique application requirements. Experiment with different values in these controls.

Concise summary without creative deviations

Some use cases require more concise and with minimal creative interpretations:

• max_tokens = 200 – Brief summary but comprehensive enough to cover critical information.
• temperature = 0.0 – Factual summary with no creative deviations, and accuracy is crucial.
• frequency_penalty = 0.1 – Minimally reduces repetition to keep essential recurring terminology.
• presence_penalty = 0.0 – Focuses on existing topics in the content history without introducing new topics.

Brief abstract summary

Researchers often need to distill complex research papers into abstracts that convey the essence of the work creatively to capture the attention of other scholars:

• max_tokens = 150 – Limits the summary to an abstract length, suitable for quick scanning.
• temperature = 0.7 – Allows for a moderate level of creativity to articulate the research's novelty and implications effectively.
• frequency_penalty = 0.5 – Reduces repetition to ensure the summary is more succinct and to the point.
• presence_penalty = 0.3 – Encourages the inclusion of new topics or findings from the research, adding depth to the summary.

Product description summarization

E-commerce platforms require concise summarizations of product descriptions to provide engaging and informative summaries that can influence purchase decisions.

• max_tokens = 100 – Short length for quick browsing without overwhelming the customer with information.
• temperature = 0.5 – Balances creativity and factus, making the description more lively and appealing.
• frequency_penalty = 0.4 – Helps avoid redundancy in describing product features, keeping the summary fresh and engaging.
• presence_penalty = 0.2 – Slightly encourages the introduction of new but relevant topics, such as possible use-cases or product comparisons.