Stream Query API Definition

The Stream Query API enables continuous streamed responses as data becomes available, improving responsiveness and reducing latency. Instead of receiving a complete response like with the Standard Query API, consumers
receive partial responses in this order:

1. Search results.
2. If summarization is enabled, chunks of the summary, like "This", "is", "a", "summary".
3. If the Factual Consistency Score (FCS) is enabled, then the FCS is the final response.

This streaming approach is beneficial when generating summaries using LLMs like GPT-4, which can have significant latencies of 5-10 seconds. The Standard Query API makes users wait for the full summarization process before receiving any results. Streaming processes the summary request with near-zero latency, significantly enhancing the user experience.

tip

Check out our interactive API Playground that lets you experiment with this REST endpoint to stream query responses.

Stream Query Request Body

The Stream Query API has the same request parameters as the Standard Query API. The stream-query endpoint enables streaming. Use this endpoint instead of the standard query endpoint.

The Stream Query request body specifies different parameters that ask questions about the data within corpora. The Stream Query request requires the following parameters:

• query - Contains your question and number of results to return.
• corpusKey - Specifies which corpora to run the query

The query response message encapsulates a single query result. It is a subdocument provided at indexing time. The text is the subdocument text, the score indicates how well the text answers the query (higher scores are better).

The metadata list holds any subdocument-level metadata that was stored with the item at indexing time. The corpus_key indicates which corpus the result came from: recall that a single query can execute against multiple corpora.

Finally, the document_index points at a specific document within the enclosing response set's document array. This is useful for retrieving the document id and document-level metadata.

Stream Query Response Types

Each streamed chunk contains a portion of the summary text, identified by a unique future_id. Once the full summary is streamed, you receive a final response with the done field set to true, allowing flexible handling and processing of results. If you enabled the Factual Consistency Score, this value appears after the summary shows done as true. The Stream Query API request has three different types of responses:

Preamble Response

This initial response serves as a preamble, like a "heads up." It contains the batchQueryResponse with placeholders for different parts of the response, such as search results or the summary. These placeholders help you correlate the subsequent streamed chunks with their respective parts.

Search Results Response

This second response type contains the search results as the batchQueryResponse populates with these results in real time.

Streamed Parts of the Summary Response

The third response type, which streams until you get the final done value, returns the subsequent streamed chunks of the summary. Each response has a batchQueryResponse that contains a portion of the summary text.

Combining the Streamed Summary Response

The consuming code must combine the stream's chunks as it receives them. The best method for doing so will depend on the language being used.

JavaScript

If the consuming code is JavaScript, we recommend using our Stream-Query-Client to mediate requests to the Stream Query API. It will handle the complexity of combining the streamed chunks for you.

To refer to how this is done, see the Stream-Query-Client source code.

Query Definition

A single query consists of a query, which is specified in plain text. For example, "Where can I buy the latest iPhone?". Optionally, the query context provides additional information that the system may use to refine the results. For example, "The Apple store near my house is closed due to Covid."

The start field controls the starting position within the list of results, while num_results dictates how many results are returned. Thus, setting start=5 and num_results=20 would return twenty results beginning at position five. These fields are mainly used to provide pagination.

The corpusKey specifies a list of corpora against which to run the query. While it's most often the case that a query is run against a single corpus, it's sometimes useful to run against several in parallel.

Finally, the reranking configuration enables reranking of results, to further increase relevance in certain scenarios. For details about our English cross-attentional (Scale only) and Maximal Marginal Relevance (MMR) rerankers, see Reranking.

Corpus Key Definition

The corpusKey specifies the ID of the corpus being searched. The metadata_filter allows specifying a predicate expression that restricts the search to a part of the corpus. The filter is written in a simplified SQL dialect and can reference metadata that was marked as filterable during corpus creation.

note

See the Filter Expressions Overview for a description of their syntax, and Corpus Administration to learn how referenceable metadata is specified during corpus creation.

By default, Vectara only uses its neural/semantic retrieval model, and does not attempt to use keyword matching. To enable hybrid search with a mix of both keyword and neural results, edit the lambda value.

If the corpus specifies custom dimensions (Scale only), weights can be assigned to each dimension as well.

Finally, it's possible to override the semantic interpretation of the query string. Usually, the default settings for the corpus are sufficient. In more advanced scenarios, it's desirable to force it to be treated as a query, or, more rarely, as a response.

Query Summarization Request - Retrieval Augmented Generation

To use Retrieval Augmented Generation (RAG), which Vectara also refers to as "Grounded Generation" -- our groundbreaking way of producing generative summaries on top of your own data -- you can submit a SummarizationRequest alongside your query. This produces a summary that attempts to answer the end-user's question, citing the results as references. For more information, read about Retrieval Augmented Generation.

The summary object enables you to tailor the results of the query summarization. Growth users can specify the maxSummarizedResults and responseLang.

Factual Consistency Score

The Factual Consistency Score, based on a more advanced version of Hughes Hallucination Evaluation Model (HHEM), enables you to evaluate the likelihood of an AI-generated summary being factually correct based on search results. This calibrated score can range from 0.0 to 1.0. A higher scores indicates a greater probability of being factually accurate, while a lower score indicates a greater probability of hallucinations.

In your summarization request, set the factual_consistency_score field to true. The Factual Consistency Score returns a calibrated value in the factual_consistency field of the summary message. The score field contains the value between 0.0 and 1.0.

For example, a score of 0.95 suggests a 95% likelihood that the summary is free of hallucinations and would align with the original content. A lower score of 0.40 indicates a 40% chance which would be probably much less factually accurate. We suggest starting with a setting of 0.5 as an initial guideline for cutoffs between good and bad.

Advanced Summarization Customization Options

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

The summarizerPromptName allows you to specify one of our available summarizers. Use promptText to 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.

The debug option lets you view detailed logs to help in troubleshooting and optimization. The responseChars lets you control the length of the summary, but note that it is not a hard limit like with the maxTokens parameter. The modelParams object provides even more fine-grained controls for the summarizer model:

• maxToken specifies a hard limit on the number of characters in a response. This value supercedes the responseChars parameter in the summary object.
• 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.
• frequencyPenalty provides even more granular control to help ensure that the summarization decreases the likelihood of repeating words. The values range from 0.0 to 1.0
• presencePenalty provides more control over whether you want the summary to include new topics. The values also range from 0.0 to 1.0.

By leveraging these advanced capabilities, application builders can fine-tune the behavior and output style of the summarizer to align with your unique application requirements.

Chat Conversation Located within the Summary

If you enabled chat on the corpus, the summary object contains a conversation from Vectara Chat which includes a conversationId. You enable Vectara Chat by setting the store value to true.

The Vectara Chat APIs have more details about conversations.

REST Example

Stream Query API Endpoint Address

Vectara exposes a REST endpoint at the following URL to search content from a corpus:

https://api.vectara.io/v1/stream-query

The API Playground shows the full Stream Query REST definition.

gRPC Example

You can find the full Stream Query gRPC definition at serving.proto.