--- title: Upload Files as Text icon: Upload description: Drop any file into your chat and let LibreChat read it — no setup needed. --- # Upload Files as Text Ever wanted to hand a PDF, a code file, or a spreadsheet to the AI and just say _"read this"_? That's exactly what **Upload as Text** does. You attach a file, LibreChat extracts the text from it, and the full content gets pasted straight into your conversation. The AI can then read every word of it — no plugins, no vector databases, no extra services to configure. It works out of the box. Upload as Text works immediately on any LibreChat instance. It uses built-in text parsing — you don't need OCR, a RAG pipeline, or any external service to get started. --- ## How to use it ### Click the attachment icon In the chat input bar, click the **paperclip** (📎) icon. ### Pick "Upload as Text" From the dropdown menu, select **Upload as Text**. This tells LibreChat to read the file contents rather than pass it as a raw attachment. ### Choose your file Select the file from your device. LibreChat will extract the text and embed it directly into your message. ### Ask your question Type your prompt as usual. The AI now has the full text of your file in context and can reference any part of it. If "Upload as Text" doesn't appear, the `context` capability may have been disabled by your admin. It's on by default — but if the capabilities list was customized, `context` needs to be explicitly included. See the [configuration section](#the-context-capability) below. --- ## What happens under the hood When you upload a file this way, LibreChat doesn't just dump raw bytes into the prompt. It runs through a processing pipeline to extract clean, readable text: 1. **MIME type detection** — LibreChat checks what kind of file you uploaded (PDF, image, audio, source code, etc.) by inspecting its MIME type. 2. **Method selection** — Based on the file type and what services are available, it picks the best extraction method using this priority: | Priority | Method | When it's used | |----------|--------|---------------| | 1st | **OCR** | File is an image or scanned document, _and_ OCR is configured | | 2nd | **STT** (Speech-to-Text) | File is audio, _and_ STT is configured | | 3rd | **Text parsing** | File matches a known text MIME type | | 4th | **Fallback** | None of the above matched — tries text parsing anyway | **A `.pdf` on an instance with OCR configured:** → OCR kicks in. Great for scanned docs and complex layouts. **A `.pdf` on a default instance (no OCR):** → Text parsing handles it. Works well for digitally-created PDFs. **A `.py` Python file:** → Straight to text parsing. Source code is already text — no conversion needed. **An `.mp3` on an instance with STT configured:** → Speech-to-Text transcribes it into text for the conversation. **A `.png` screenshot with no OCR configured:** → Falls back to text parsing (limited results — consider setting up OCR for images). 3. **Token truncation** — The extracted text is trimmed to the `fileTokenLimit` (default: 100,000 tokens) so it doesn't blow past the model's context window. 4. **Prompt injection** — The text gets included in the conversation context, right alongside your message. --- ## Which files are supported These are parsed directly — they're already text, so no conversion is needed. - Plain text (`.txt`), Markdown (`.md`), CSV, JSON, XML, HTML, CSS - Programming languages — Python, JavaScript, TypeScript, Java, C#, PHP, Ruby, Go, Rust, Kotlin, Swift, Scala, Perl, Lua - Config files — YAML, TOML, INI - Shell scripts, SQL files Text parsing handles these out of the box. If OCR is configured, it takes over for better accuracy on complex layouts. - **PDF** — digital and scanned (scanned PDFs benefit from OCR) - **Word** — `.docx`, `.doc` - **PowerPoint** — `.pptx`, `.ppt` - **Excel** — `.xlsx`, `.xls` - **EPUB** books Images **require OCR** to produce useful text. Without it, results will be poor. - JPEG, PNG, GIF, WebP - HEIC, HEIF (Apple formats) - Screenshots, photos of documents, scanned pages Audio files **require STT** to be configured. There's no fallback — audio can't be "text parsed." - MP3, WAV, OGG, FLAC - M4A, WebM - Voice recordings, podcast clips --- ## Upload as Text vs. other upload options LibreChat has three ways to upload files. Each one works differently and suits different situations: Extracts the full file content and drops it into the conversation. Best for smaller files where you want the AI to read everything — contracts, code files, articles. Works with all models, no extra services needed. Indexes the file in a vector database and retrieves only the relevant chunks when you ask a question. Better for large files or collections of files where dumping everything into context would waste tokens. Requires the RAG API. Passes the file directly to the model — used for vision models analyzing images, or code interpreter running scripts. No text extraction happens. **Quick decision guide:** | Situation | Best option | |-----------|-------------| | _"Read this 5-page contract and summarize it"_ | **Upload as Text** | | _"I have 50 PDFs, find what mentions pricing"_ | **File Search (RAG)** | | _"What's in this screenshot?"_ (vision model) | **Standard Upload** | | _"Run this Python script"_ (code interpreter) | **Standard Upload** | | _"Review this code file for bugs"_ | **Upload as Text** | | _"Search through our company docs"_ | **File Search (RAG)** | --- ## The `context` capability Under the hood, Upload as Text is powered by the **`context` capability**. This is what controls whether the feature appears in your chat UI. The `context` capability is **enabled by default**. You only need to touch this if your admin has customized the capabilities list and accidentally left it out. ```yaml title="librechat.yaml" endpoints: agents: capabilities: - "context" # This is what enables "Upload as Text" ``` The same `context` capability also powers **Agent File Context** (uploading files through the Agent Builder to embed text into an agent's system instructions). The difference is _where_ the text ends up: | | Upload as Text | Agent File Context | |---|---|---| | **Where** | Chat input (any conversation) | Agent Builder panel | | **Scope** | Current conversation only | Persists in agent's instructions | | **Use case** | One-off document questions | Building specialized agents with baked-in knowledge | --- ## Token limits and truncation When a file is too long to fit in the model's context window, LibreChat truncates the extracted text to stay within bounds. This happens automatically — you don't need to worry about it, but it's good to know how it works. ```yaml title="librechat.yaml" fileConfig: fileTokenLimit: 100000 # Default: 100,000 tokens ``` If your file exceeds the limit, the text is cut off at the end. If you're getting incomplete answers, this might be why. You can increase `fileTokenLimit`, but keep in mind that larger values use more tokens per message — which increases cost and may hit the model's own context limit. **Rules of thumb:** - 100k tokens ≈ a 300-page book (plenty for most use cases) - If you're working with very large files, consider [File Search (RAG)](/docs/features/rag_api) instead — it only retrieves the relevant sections rather than stuffing everything into context --- ## Optional: boosting extraction with OCR Text parsing works fine for digitally-created documents (PDFs saved from Word, code files, plain text). But if you're uploading **scanned documents, photos of pages, or images with text**, the built-in parser won't get great results. That's where OCR comes in. When configured, LibreChat automatically uses OCR for file types that benefit from it — you don't need to do anything differently as a user. Point them to the [OCR configuration docs](/docs/features/ocr). The short version: ```yaml title="librechat.yaml" ocr: strategy: "mistral_ocr" apiKey: "${OCR_API_KEY}" baseURL: "https://api.mistral.ai/v1" mistralModel: "mistral-ocr-latest" ``` Once configured, OCR automatically handles images and scanned PDFs — no changes needed on the user side. For transcribing audio uploads, the admin needs to configure a Speech-to-Text service. See the [STT configuration reference](/docs/configuration/librechat_yaml/object_structure/file_config#stt). --- ## File handling configuration reference This section is for admins who want to control which file types get processed by which method. The defaults work well — you only need to touch this if you want to fine-tune behavior. ```yaml title="librechat.yaml" fileConfig: # Max tokens extracted from a single file before truncation fileTokenLimit: 100000 # Files matching these MIME patterns use OCR (if configured) ocr: supportedMimeTypes: - "^image/(jpeg|gif|png|webp|heic|heif)$" - "^application/pdf$" - "^application/vnd\\.openxmlformats-officedocument\\.(wordprocessingml\\.document|presentationml\\.presentation|spreadsheetml\\.sheet)$" - "^application/vnd\\.ms-(word|powerpoint|excel)$" - "^application/epub\\+zip$" # Files matching these MIME patterns use text parsing text: supportedMimeTypes: - "^text/(plain|markdown|csv|json|xml|html|css|javascript|typescript|x-python|x-java|x-csharp|x-php|x-ruby|x-go|x-rust|x-kotlin|x-swift|x-scala|x-perl|x-lua|x-shell|x-sql|x-yaml|x-toml)$" # Files matching these MIME patterns use STT (if configured) stt: supportedMimeTypes: - "^audio/(mp3|mpeg|mpeg3|wav|wave|x-wav|ogg|vorbis|mp4|x-m4a|flac|x-flac|webm)$" ``` **Priority reminder:** OCR > STT > text parsing > fallback. For the full reference, see [File Config Object Structure](/docs/configuration/librechat_yaml/object_structure/file_config). --- ## Troubleshooting The `context` capability was likely removed from your configuration. Ask your admin to add it back: ```yaml endpoints: agents: capabilities: - "context" ``` A few things to check: - **Scanned PDFs / images** — These need OCR to extract text properly. Without it, the parser might return garbage or nothing. Ask your admin to [configure OCR](/docs/features/ocr). - **Audio files** — These need STT. There's no text fallback for audio. - **Corrupted files** — Try opening the file locally to make sure it's not damaged. - **Unsupported format** — If the MIME type doesn't match any configured pattern, LibreChat attempts a text parse fallback, which may not work for binary formats. Your file probably exceeded the `fileTokenLimit`. The text was truncated. **Options:** - Ask your admin to increase `fileTokenLimit` in `librechat.yaml` - Use [File Search (RAG)](/docs/features/rag_api) instead, which retrieves relevant chunks rather than loading the entire file - Split the file into smaller parts and upload them separately Without OCR, images are processed through text parsing, which can't actually "see" text in an image. You need OCR configured for this to work. See [OCR for Documents](/docs/features/ocr). Alternatively, use a **vision model** with standard file upload — the model itself can read text in images. --- ## Related - [OCR for Documents](/docs/features/ocr) — Set up optical character recognition for images and scans - [RAG API (Chat with Files)](/docs/features/rag_api) — Semantic search over large document collections - [Agents — File Context](/docs/features/agents#file-context) — Embed file content into an agent's system instructions - [File Config reference](/docs/configuration/librechat_yaml/object_structure/file_config) — Full YAML schema for file handling