65 lines
12 KiB
JSON
65 lines
12 KiB
JSON
{
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"title": "Customize embedder setup",
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"messages": [
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{
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"role": "user",
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"content": "So how can set it up so i can customize what embedder i want to use? I want to experiment with running a very light weight one on the ryzen server"
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},
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{
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"role": "tool",
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"content": "All the files uploaded by the user have been fully loaded. Searching won't provide additional information."
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},
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{
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"role": "assistant",
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"content": "Right now your stack hard-codes **OpenAI embeddings** inside Mem0, but it’s set up in a way that we can make pluggable. Here’s the picture from the files:\n\n- Relay just forwards messages to Mem0 (`MEM0_URL` + `MEM0_API_KEY`)【12†.env†L25-L33】.\n- Mem0 is the one actually doing the embedding and recall (backed by Postgres + Neo4j)【10†README.md†L16-L24】.\n- The `.env` only exposes `OPENAI_API_KEY` for embeddings, no way to select another embedder right now【12†.env†L1-L8】.\n\n### How to make the embedder customizable\n\n1. **Add an `EMBEDDER` setting to `.env`:**\n ```ini\n # Which embedder to use: openai | ollama | local\n EMBEDDER=openai\n ```\n\n2. **Extend Mem0 to switch on this var.** \n In the Python FastAPI service (`mem0`), wherever embeddings are generated (probably a call to `openai.Embedding.create`), wrap it:\n ```python\n import os\n EMBEDDER = os.getenv(\"EMBEDDER\", \"openai\")\n\n def get_embedding(text: str):\n if EMBEDDER == \"openai\":\n return openai.Embedding.create(\n model=\"text-embedding-3-small\",\n input=text\n )[\"data\"][0][\"embedding\"]\n\n elif EMBEDDER == \"ollama\":\n r = requests.post(\n f\"{os.getenv('OLLAMA_URL')}/api/embeddings\",\n json={\"model\": \"nomic-embed-text\", \"prompt\": text}\n )\n return r.json()[\"embedding\"]\n\n elif EMBEDDER == \"local\":\n # plug in something like sentence-transformers\n from sentence_transformers import SentenceTransformer\n model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n return model.encode(text).tolist()\n ```\n\n3. **Expose the model name if you want.** \n Add another env:\n ```ini\n EMBEDDER_MODEL=all-MiniLM-L6-v2\n ```\n\n4. **Deploy on your Ryzen server.** \n - For **super lightweight**: `sentence-transformers/all-MiniLM-L6-v2` runs fine on CPU. \n - For **Ollama**: models like `nomic-embed-text` or `all-minilm` can run via `ollama pull`.\n\n5. **Docker tweaks.** \n Mount the right model or install sentence-transformers in the `mem0` container if using local CPU embeddings.\n\n---\n\n👉 That way you can flip between OpenAI, Ollama, or a CPU-friendly transformer just by editing `.env` and restarting. \n\nDo you want me to draft the actual `docker-compose.override.yml` and Python patch so you can hot-swap between these options on your Ryzen box?"
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},
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{
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"role": "user",
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"content": "i should probably experiment and see if any models will run well on the ryzen"
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},
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{
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"role": "assistant",
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"content": "Exactly — treat this like a bake-off. Your Ryzen has plenty of headroom for light-duty embedders, and the nice part is you don’t need a GPU if you pick wisely. \n\nHere’s a good ladder to try:\n\n### 🔦 Super-light CPU options\n- **`all-MiniLM-L6-v2`** (22M params, ~5ms per sentence on modern CPU) → sweet spot for speed.\n- **`paraphrase-MiniLM-L3-v2`** → even smaller, accuracy drops a bit.\n- **`intfloat/multilingual-MiniLM-L12`** → if you want multiple languages.\n\n### ⚖️ Medium (still CPU-friendly, better quality)\n- **`all-mpnet-base-v2`** (~110M params, ~60ms per sentence).\n- **`bge-small-en`** → tuned for retrieval; strong accuracy vs size.\n\n### 🚀 Heavier but high-quality\n- **`bge-base-en`** (~135M params) or **`bge-large-en`** (need GPU ideally).\n- **`nomic-embed-text`** (Ollama) → can run on CPU, but slower.\n\n### How to test quickly\n```bash\npip install sentence-transformers\npython -m sentence_transformers all-MiniLM-L6-v2\n```\nThen time an encode:\n```python\nfrom sentence_transformers import SentenceTransformer\nimport time\nmodel = SentenceTransformer(\"all-MiniLM-L6-v2\")\ntexts = [\"This is a test sentence.\"] * 100\nstart = time.time()\nembeds = model.encode(texts, convert_to_numpy=True)\nprint(len(embeds), embeds.shape, time.time()-start)\n```\n\nThat’ll tell you tokens/sec so you know whether it’s viable in production.\n\n---\n\n⚡ Suggestion: \nStart with `all-MiniLM-L6-v2` locally, then try `bge-small-en`. If both feel fast enough, wire them into the `get_embedding` switch. \n\nDo you want me to write a little benchmark script you can drop into your Ryzen box that tests 3–4 models back-to-back and spits out speed/quality numbers?"
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},
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{
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"role": "user",
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"content": "Command 'python' not found, did you mean:\n command 'python3' from deb python3\n command 'python' from deb python-is-python3"
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{
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"role": "assistant",
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"content": "Yep — that’s just your server telling you there’s no `python` shim installed. On most modern Debian/Ubuntu, the default is `python3`. \n\nSo you’ve got two options:\n\n### Quickest fix (just use python3)\nInstead of:\n```bash\npython -m sentence_transformers all-MiniLM-L6-v2\n```\ndo:\n```bash\npython3 -m sentence_transformers all-MiniLM-L6-v2\n```\n\nAnd in scripts:\n```bash\n#!/usr/bin/env python3\n```\n\n---\n\n### Optional: add a `python` alias\nIf you like typing `python`:\n```bash\nsudo apt install python-is-python3\n```\nThat drops in a symlink so `python` → `python3`.\n\n---\n\n👉 Want me to draft a simple benchmark script (Python3-ready) you can drop onto the Ryzen box to compare MiniLM vs BGE vs Ollama embeddings side by side? That way you’ll see which actually runs fast enough before wiring it into Mem0."
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"role": "user",
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"content": "oh should i run this on the lyra-core VM or a different environment?"
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"role": "assistant",
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"content": "Good question. Think of it in two phases:\n\n---\n\n### 🔎 Phase 1 — Experiment\nRun the benchmarks **outside** Lyra-Core first:\n- On the Ryzen host, or a small test VM/container. \n- Just `python3` + `sentence-transformers` installed. \n- This way you can quickly pip-install, test, and throw it away without messing with your production containers.\n\n---\n\n### 🧩 Phase 2 — Integrate\nOnce you find a model that feels fast enough:\n- Add it into **Mem0’s environment** (that’s the service inside Lyra-Core that actually generates embeddings). \n- That means either:\n - Baking `sentence-transformers` + the model into the `mem0` Docker image, or \n - Mounting a local model cache (`~/.cache/huggingface`) into the container so it doesn’t re-download every time. \n\n---\n\n### ✅ Recommendation\nDo the bake-off in a clean playground (fresh venv or scratch container). \nWhen you’ve picked a winner, then patch Mem0’s `get_embedding()` switcher and rebuild that container.\n\n---\n\nDo you want me to put together a **ready-to-run benchmark script** (Python3, minimal deps) you can run on the Ryzen to compare 3–4 candidates? That’ll give you concrete timing numbers."
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},
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"role": "user",
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"content": "crap well i ran the first command you sent, do i just leave that? then we can move on to a deifferent instance?"
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},
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{
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"role": "assistant",
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"content": "No harm done 👍 \n\nThat first command you ran (`python3 -m sentence_transformers all-MiniLM-L6-v2`) just tries to resolve the model entry point. If it pulled something down into `~/.cache/huggingface/` then you’ve already got it cached — which is actually useful later when we wire it into Mem0. Nothing is “stuck running” in the background, it just exits once it’s done. \n\nSo yes, you can leave it. For clean benchmarking: \n\n- Spin up a new venv or test VM/container (`python3 -m venv bench-env && source bench-env/bin/activate`). \n- Install the packages there (`pip install sentence-transformers`). \n- Run the benchmark script. \n\nThat way you keep Lyra-Core’s containers clean until you know which model you want. \n\n👉 Do you want me to hand you a drop-in **benchmark.py** that tests a few models (MiniLM, BGE-small, Nomic) so you can just run `python3 benchmark.py` in that test environment?"
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},
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{
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"role": "user",
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"content": "{\"content_type\": \"image_asset_pointer\", \"asset_pointer\": \"file-service://file-SbcfnuBJoxiKH2PmSaS5ch\", \"size_bytes\": 213873, \"width\": 1173, \"height\": 841, \"fovea\": null, \"metadata\": {\"dalle\": null, \"gizmo\": null, \"generation\": null, \"container_pixel_height\": null, \"container_pixel_width\": null, \"emu_omit_glimpse_image\": null, \"emu_patches_override\": null, \"lpe_keep_patch_ijhw\": null, \"sanitized\": true, \"asset_pointer_link\": null, \"watermarked_asset_pointer\": null}} it did all this and more, do i need to delete that stuff?"
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{
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"role": "assistant",
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"content": "Nah, you don’t need to delete it. What happened is:\n\n- When you installed `sentence-transformers`, pip automatically pulled in **PyTorch**. \n- By default, PyTorch wheels ship with CUDA libraries embedded (those ~600 MB+ downloads like `nvidia_cublas_cu12`, `cudnn`, etc.). Even if you don’t have an NVIDIA GPU, they still download so the package works everywhere.\n\nSo now you’ve got ~2–3 GB of extra wheels in your Python environment. They’re harmless, just take up disk space. On your Ryzen (no GPU), PyTorch will automatically run in **CPU mode** — so those CUDA libs won’t even load.\n\n### Options\n1. **Leave it as-is** ✅ \n Everything works. No need to clean unless you’re tight on disk.\n\n2. **Reinstall CPU-only PyTorch** (saves space, smaller wheels): \n ```bash\n pip uninstall torch torchvision torchaudio\n pip install torch==2.3.0+cpu torchvision==0.18.0+cpu torchaudio==2.3.0+cpu \\\n -f https://download.pytorch.org/whl/cpu/torch_stable.html\n ```\n That replaces the GPU builds with CPU-only ones (hundreds of MB smaller).\n\n3. **Nuke the env and start clean** (common if you want to keep Lyra-Core neat): \n ```bash\n rm -rf venv_name/\n python3 -m venv venv_name\n source venv_name/bin/activate\n pip install sentence-transformers --extra-index-url https://download.pytorch.org/whl/cpu\n ```\n\n---\n\n👉 Since this is just your *test environment*, I’d say leave it unless the disk space bothers you. Later, when you integrate into `mem0`, we’ll want the **CPU-only torch** build so the Docker image doesn’t balloon. \n\nDo you want me to prep a **benchmark.py** script now, tuned to use CPU-only torch if possible, so you can immediately test throughput on MiniLM/BGE/etc?"
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},
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{
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"role": "user",
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"content": "i dont want it in the lyra-core VM, that isnt my \"test environment\" its tthe actual home of local lyra"
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{
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"role": "assistant",
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"content": "Got it — then you’ll definitely want to **keep Lyra-Core’s VM clean**. Right now you just dropped all those torch + CUDA wheels *into the real Lyra-Core VM*, which isn’t ideal since that VM is supposed to be production. \n\nHere’s the safe play: \n\n### 1. Clean up the Lyra-Core VM\nIf you want it pristine again:\n```bash\n# if you created a venv inside the VM\nrm -rf venv_name/\n\n# or if you installed system-wide\npip3 uninstall -y torch torchvision torchaudio sentence-transformers\n```\nThat’ll clear out the big PyTorch + CUDA junk.\n\n---\n\n### 2. Create a true “playground” environment\nRun this on the **Ryzen host** (or another VM/container):\n```bash\npython3 -m venv ~/embed-bench\nsource ~/embed-bench/bin/activate\npip install sentence-transformers --extra-index-url https://download.pytorch.org/whl/cpu\n```\nThis pulls the **CPU-only** build of torch and keeps everything isolated. \n\n---\n\n### 3. Run benchmarks there\nThat playground can be nuked and recreated at will. Once you find a model you like, then we’ll bake it properly into `mem0`’s Dockerfile.\n\n---\n\n👉 Do you want me to hand you the `benchmark.py` script now, so once you set up that clean venv you can just drop it in and test MiniLM vs BGE vs Nomic right away?"
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}
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]
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} |