## Choosing Open-Source Embedding Models Choosing efficient, open-source embedding models is key for a local setup. Below are recommendations for text, image, and video (multimodal) embeddings that balance performance with resource limits: ### Text Embeddings (Local Models) - **SentenceTransformers MiniLM or MPNet:** Models like _all-MiniLM-L6-v2_ (384-dimensional) are lightweight and fast for semantic text embeddings ([sentence-transformers/all-MiniLM-L6-v2 · Hugging Face](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2#:~:text=all)). These can run on CPU or GPU and handle paragraphs of text efficiently. - **E5 or Instructor Models:** For higher accuracy, consider open models like _E5-base_ or _Instructor XL_. They often match or exceed proprietary models (like OpenAI Ada) in quality ([Choosing an Embedding Model | Pinecone](https://www.pinecone.io/learn/series/rag/embedding-models-rundown/#:~:text=Nowadays%2C%20many%20propriety%20embedding%20models,comparable%20performance%2C%20such%20as%20E5)). For example, E5 models are “tiny” but powerful open-source text encoders ([Choosing an Embedding Model | Pinecone](https://www.pinecone.io/learn/series/rag/embedding-models-rundown/#:~:text=Nowadays%2C%20many%20propriety%20embedding%20models,comparable%20performance%2C%20such%20as%20E5)). Ensure your GPU can handle larger models (Instructor-XL is ~5GB, which is within 2080 Super’s 8GB VRAM ([Choosing an Embedding Model | Pinecone](https://www.pinecone.io/learn/series/rag/embedding-models-rundown/#:~:text=than%20a%20paragraph%20of%20heft,can%20easily%20run%20on%20consumer))). - **Language-Specific or Multilingual Models:** If your text data is multi-language, use multilingual SBERT models (e.g. _paraphrase-multilingual-MiniLM_) which are open-source and locally runnable. These maintain decent speed while covering many languages. ### Image Embeddings (Local Models) - **OpenCLIP (CLIP by LAION/OpenAI):** CLIP models (e.g. ViT-B/32 or ViT-L/14) are excellent for image embeddings. OpenCLIP (open-source implementation of CLIP) can embed images _and_ text into a joint vector space ([Creating a semantic video search with OpenAI’s Clip | by Antti Havanko | Medium](https://anttihavanko.medium.com/creating-a-semantic-video-search-with-openais-clip-model-13ff14990fbd#:~:text=The%20Clip%20model%20is%20a,related%20tasks)) ([OpenClip - ️ LangChain](https://python.langchain.com/docs/integrations/text_embedding/open_clip/#:~:text=OpenClip%20,to%20embed%20images%20or%20text)). This means you can retrieve images by a text query and vice versa. For efficiency, ViT-B/32 (512-dimensional) is faster and uses less VRAM, while ViT-L/14 offers higher accuracy at the cost of more compute. - **Alternative Vision Encoders:** If CLIP is too heavy, consider smaller vision models or CNN-based embeddings (though these may be less semantically rich). For example, _MobileNet_ or _EfficientNet_ embeddings could be used for rough image similarity, but CLIP’s semantic power is preferred for RAG. - **Document Image Embedding (ColPali):** For documents with complex layouts (scanned pages, charts in PDFs), **ColPali** is a specialized open-source model. It generates an embedding from a page image that captures text and visuals without OCR ([Stop trying to parse your documents and use ColPali (Open Source) : r/Rag](https://www.reddit.com/r/Rag/comments/1fc184v/stop_trying_to_parse_your_documents_and_use/#:~:text=ColPali%20is%20an%20embedding%20model,more%20detail%20that%20vector%20embeddings)). This can greatly improve retrieval on visually rich documents. Use ColPali for PDF/DOCX pages where pure text embeddings might miss important diagrams or tables. ### Video Embeddings (Multimodal) - **Frame-Based (CLIP on Key Frames):** A practical strategy is to sample key frames from each video and use an image model (like CLIP) to embed those frames ([Creating a semantic video search with OpenAI’s Clip | by Antti Havanko | Medium](https://anttihavanko.medium.com/creating-a-semantic-video-search-with-openais-clip-model-13ff14990fbd#:~:text=Each%20frame%20can%20be%20considered,number%20to%20improve%20the%20results)). For example, extract 5–10 frames evenly or at scene changes per video. This avoids processing every frame (which is resource-intensive) and still captures the video’s content ([Creating a semantic video search with OpenAI’s Clip | by Antti Havanko | Medium](https://anttihavanko.medium.com/creating-a-semantic-video-search-with-openais-clip-model-13ff14990fbd#:~:text=Each%20frame%20can%20be%20considered,number%20to%20improve%20the%20results)). Each selected frame yields an image vector; store multiple vectors per video (with timestamps as metadata). - **Combined Video Embedding Models:** To treat an entire video as one vector, use an open model like _AskVideo-VideoCLIP_. This model samples (e.g. 16) frames and produces a single embedding representing the whole clip ([GitHub - AskYoutubeAI/AskVideos-VideoCLIP](https://github.com/AskYoutubeAI/AskVideos-VideoCLIP#:~:text=%2A%20AskVideos,the%20video%20embeddings%20to%20text)). Such models are trained to align video content with text, enabling text-to-video search. They run locally on GPU (with ffmpeg for frame extraction) and output one vector per video, which is efficient for indexing. - **Audio Transcripts (for Spoken Content):** If videos contain speech or subtitles, leverage an open-source speech-to-text model (like **OpenAI Whisper** running locally) to generate transcripts. You can then embed the transcript text using your text model. This complements visual embeddings – e.g. a video about a topic can be found via spoken words as well as imagery. Transcripts should be chunked (by sentence or time window) and embedded as text entries associated with the video. ## Embedding Strategy by Data Type Different file types in your 5TB dataset should be handled in an optimized way for embedding. Here’s a structured strategy for each type: - **Text Documents (Docs, Markdown, etc.):** Extract raw text from DOCX, PDF, or Markdown files. Split large texts into chunks (e.g. 200-500 words or by headings) for better retrieval granularity. Each chunk gets its own embedding vector so that specific sections of a document can be retrieved by relevant queries. This avoids losing context by embedding an entire long document as one vector. Ensure to preserve references (file name, section headers) as metadata with each vector. _Example:_ A 50-page manual could be split into paragraphs or sections, each embedded separately for precise matching in Qdrant. - **Spreadsheets (XLSX):** Convert spreadsheets to a text form before embedding. For instance, you can read tables and represent them as text (CSV format or bullet lists of key rows/columns). You might treat each sheet or table as a separate “document” and embed a summary of its content. If numbers are important, consider storing some numeric metadata (e.g. min/max or specific key values) but for semantic search, a text summary of each sheet or row works. _Example:_ an Excel file with sales data per region could produce text like “Sheet: Q1 Sales – Region: X, Revenue: Y…” which the text encoder can embed and allow queries like “Q1 revenue in region X” to hit the relevant vector. - **Images (PNG, JPG, images in documents):** For standalone images, use the image embedding model to get a vector. It’s often useful to also store a text description if available (for example, the file name or alt-text, or a brief manual caption) as metadata to aid in filtering or hybrid search. If images contain text (screenshots, scanned documents), run an OCR (Tesseract or similar) to extract that text and _also_ embed it with the text model – this way, a query that matches text in an image (e.g. words on a slide) can retrieve the image via the OCR’d text. Store the OCR-derived text in metadata or in the same Qdrant record for that image. For images embedded inside documents (like a figure in a DOCX), consider indexing them separately: e.g. save the figure image, embed it, and link it to the document (so the system can retrieve the figure if needed). - **Videos:** As mentioned, treat videos in a multi-faceted way. **Visual content:** extract a handful of key frames (using a tool like ffmpeg) rather than every frame to minimize data ([Creating a semantic video search with OpenAI’s Clip | by Antti Havanko | Medium](https://anttihavanko.medium.com/creating-a-semantic-video-search-with-openais-clip-model-13ff14990fbd#:~:text=Each%20frame%20can%20be%20considered,number%20to%20improve%20the%20results)). Each key frame image can be embedded like a regular image. Use a consistent strategy (e.g. 1 frame every 10 seconds or one per scene) to uniformly cover the video. **Audio content:** if relevant, transcribe audio to text and treat each segment of transcript as a text document chunk. This way, a question that refers to something said in the video can find the answer in the transcript text vectors, while a question about something seen can match one of the image frame vectors. You can also assign each frame or transcript chunk a metadata link to the video (filename and timestamp), so the system knows they come from the same source. If using a dedicated video embedding model (like VideoCLIP), then you will get one vector per video clip which simplifies retrieval (but you might lose some granularity; it works best if each video is fairly short or topically focused). For long videos, you could break them into smaller segments (e.g. per chapter or scene) and embed each segment separately so that retrieval can pinpoint the relevant segment. - **Multimodal Documents (PDFs, PPTs with text+images):** Some files (PDF, DOCX, PPT) contain both text and images. For such cases, a **hybrid embedding approach** is beneficial. Extract the text and embed the textual parts as usual. Simultaneously, for each page or slide, you can generate an image (render the page or slide to image) and embed it using a vision model (or ColPali for a specialized approach). This means each page has one or more text vectors (for different sections of text) and possibly an image-based vector capturing the whole page layout. A query can then find a page either by matching its text or the visual content. This is especially useful for diagrams or charts – e.g. a query “architecture diagram” might not appear verbatim in text, but the image embedding of the diagram page will be retrieved because the image model understands the diagram’s content. Ensure the metadata links these multiple embeddings (text chunks and page image) back to the same page/document, so the system can present the user with the original document page. **Chunk sizing and overlap:** For textual data, use a reasonable chunk size to balance context and relevance. 200-500 words per chunk is common – large enough to contain a complete thought, but small enough to stay specific. Optionally use a slight overlap between chunks (e.g. repeating a sentence between neighboring chunks) so that important info at boundaries isn’t lost. This improves the chance that a query finds the right chunk. Each chunk’s embedding should be stored with metadata like `{source: "Document X", chunk: 5, title: "Introduction"}` for traceability. By tailoring the embedding method to each data type, you ensure that the vectors capture the meaningful information in the data without waste. This structured approach yields more accurate retrieval across text, images, and videos. ---