Knowledge-based video intelligent QA solution

Background

Knowledge-based videos — online courses, academic lectures, technical training — pack dense content into recordings that often exceed 30 minutes. Finding a specific topic in a long video is hard:

• No fine-grained navigation: viewers scrub the timeline manually to find a topic.

• Shallow search: search covers only titles and descriptions — not content inside the video.

• No verification loop: QA results don't link back to the video clip, so viewers can't verify answers.

• Context-window limit: naive splitting breaks semantic continuity and degrades QA accuracy.

LangStudio video knowledge bases solve this with ASR transcription, semantic chunking, multimodal vector retrieval, and LLM-powered QA — returning timestamped answers linked to the exact video segment.

Use cases

Architecture

Key capabilities

• Accurate ASR: high-precision speech transcription in multiple languages

  • Powered by the Qwen3-ASR model with multi-language support

  • Stable, high-quality speech-to-text output

• Precise retrieval: semantic and keyword hybrid search

  • Multimodal embedding: DashScope multimodal embedding and PAI-EAS custom services produce high-quality semantic vectors

  • Hybrid search: Dense + BM25 Sparse retrieval with RRF (Reciprocal Rank Fusion) and weighted fusion ranking balances semantic similarity and keyword matching

  • Multiple storage engines: Elasticsearch and Milvus vector backends for flexible scale

• Better answers: reranking and query rewriting

  • Reranking: a second-pass reranker rescores recalled clips so top results match user intent. Supports models from DashScope, PAI-EAS, and Alibaba Cloud OpenSearch AI.

  • Query rewriting: expands vague or elliptical questions into complete search queries using multi-turn conversation context (for example, "How do I get there?" → "How do I get to the Sahara?")

  • Similarity threshold filtering: filters low-relevance clips by similarity score to reduce noise

• Timestamp accuracy: precise video segment location

  • Each chunk carries timestamp metadata (chunk_start, chunk_end, chunk_seq), so results map directly to a time range.

  • One-click jump from question to the exact video segment, cutting knowledge verification time.

Create a knowledge base and upload videos

1. Create a video-type knowledge base. Configure the key parameters as follows. For other parameters, see Manage knowledge bases.

   • Type: select Video.

   • Chunking Strategy: we recommend Speech Semantic Segmentation, which transcribes video audio via ASR and creates chunk-level text.

     Note

     Default Slicing chunks by video content only and ignores audio.

2. Import video files. Open the knowledge base and switch to the Video tab. Click Upload to upload videos from your local machine to the knowledge base OSS data source.

3. Switch between Gallery view and Table view to browse video sources in the OSS directory.

Update the index and view processing results

1. After upload, click Update Index to configure compute resources and VPC for the index update task.

   Note

   The selected VPC must reach the VPC where the embedding service and vector database are deployed. Using the same VPC is simplest.

2. Click Operation Records to view execution details for the index task.

   • Click View Task in the Actions column. The workflow task log contains the Job URL (link to the DLC index update task).

   • If the task fails, check the DLC task log for the specific error.

   • After a successful update, click View Results to view the updated document and chunk statistics.

3. After indexing, the video status changes from Not indexed to Indexed. Click a video to view its chunks and transcript. Click a chunk to enable or disable it for retrieval.

Metadata management and automatic index updates

Retrieval testing

On the knowledge base Recall Test page, enter a question to retrieve the most relevant text chunks.

In the retrieval settings panel, set Query Mode to Hybrid retrieval and enabling Rerank.

Build a video QA workflow

# System prompt template / citation format

You are a helpful assistant.
1. Answer the user's question based on the context provided. If the context is insufficient, tell the user directly. Do not use the model's internal knowledge.
2. Detect the language of the user's input and respond in the same language.

Citation and source rules (must follow)
1. When the answer references the provided context, add a citation number at the end of the sentence, for example [1] [2].
2. Number citations sequentially in order of first appearance. Multiple sources in one sentence: …[1][3].
3. A "Sources" section must appear at the end of the answer, listing each citation on its own line.
4. Source entry format:
[n] Video name — video source URL
5. Do not fabricate sources: only cite information that appears in the provided context.

# User prompt jinja2 template

## Context:
{% for item in context_docs %}
Index: {{ loop.index }}
Content: {{ item.content }}
Video name: {{ item.metadata.file_name}}
Source: {{ item.metadata.chunk_uri }}
{% endfor %}

## User question: {{ query }}