{ "title": "Embedding provider swap", "messages": [ { "role": "user", "content": "so i need help swapping the default embedding agent in mem0 from the openAI default to a local one. Here is a little run down of what we are doing.\n\nHere’s a clean bullet-point recap of everything we accomplished in this chat (chronological + grouped by theme so you can see the whole arc):\n\n---\n\n### ✅ Mem0 OSS Setup & Debugging Progress\n\n* **Confirmed you’re on Mem0 OSS** (not the OpenMemory UI build).\n* Found your **running container’s `/app/main.py`** and verified that’s where config lives.\n* Verified that `main.py` **does load environment variables** via `dotenv`.\n* Learned how to **mount a local `main.py`** into the container so you can override the repo’s version.\n\n---\n\n### ✅ Embedding Provider Swap (OpenAI → Ollama → Future)\n\n* Identified you want **3 modes**:\n\n 1. **3090 LAN Ollama** (fast local embeddings)\n 2. **Local-only VM models** (CPU fallback)\n 3. **OpenAI cloud** (accuracy / speed backup).\n* Added config placeholders in `main.py` so **embedder provider/model** can be set from `.env`.\n* Created `.env.3090` for LAN embeddings, `.env.openai` for OpenAI fallback.\n* Clarified that Mem0 **must be rebuilt/restarted** to swap envs — but no code edit needed once the environment-based config is set up.\n\n---\n\n### ✅ Ollama + Requirements\n\n* Found that the container failed with `ModuleNotFoundError: ollama`.\n* Solution: **add `ollama` to requirements.txt**.\n* Showed you two ways:\n\n * (1) mount a custom requirements file into container\n * (2) bake a **custom Dockerfile image** with `ollama` preinstalled.\n* You built your **own Docker image** (`mem0-api-server:latest`) with Ollama support — confirmed it boots and talks to 3090.\n\n---\n\n### ✅ LAN Connectivity\n\n* Confirmed your Windows 3090 box runs **Ollama server** at `10.0.0.3:11434`.\n* Logs showed Mem0 successfully hitting `http://10.0.0.3:11434/api/embeddings`.\n* Added `.env` config to point `OLLAMA_HOST=http://10.0.0.3:11434`.\n\n---\n\n### ✅ PyTorch / Hugging Face (for 1536-dim embeddings)\n\n* You ran into PyTorch install issues (Python 3.13 too new).\n* Researched: PyTorch officially supports **Python 3.9–3.13** but wheels are behind.\n* Found workaround: use index `--index-url https://download.pytorch.org/whl/cu126` for CUDA 12.6 (works with your 3090).\n* You confirmed PyTorch install started working after using that URL.\n* Next step is to **host a 1536-dim Hugging Face embedding model** on Windows to perfectly match OpenAI’s `text-embedding-3-small`.\n\n---\n\n### ✅ Neo4j Dimension Error\n\n* Found error:\n\n ```\n Invalid input for 'vector.similarity.cosine()': The supplied vectors do not have the same number of dimensions.\n ```\n* Root cause: Ollama model returned **different dimensionality** than OpenAI’s 1536.\n* Fix = use an embedding model with **1536 dims** (so vector store schema stays compatible).\n* Two approaches:\n\n * Find / run a Hugging Face 1536-dim embedding model on your 3090.\n * Or reset/rebuild vector store for different dims (but swapping back to OpenAI would break again).\n* Decided: You want **perfect 1536 match** so you can easily swap between providers.\n\n---\n\n### ✅ Testing & Verification\n\n* Added test command with `curl` to hit your API:\n\n ```bash\n curl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Test memory using Ollama\"}]}'\n ```\n* Logs confirmed requests reach Ollama + Neo4j.\n\n---\n\n### 🔜 Next Steps (what’s left)\n\n* Get **1536-dim Hugging Face embedding model** running on your Windows 3090 (with PyTorch installed).\n* Expose it with an HTTP server (similar to Ollama) so Mem0 can hit it at `http://10.0.0.3:xxxx`.\n* Wire Mem0’s `.env.local` to point to that Hugging Face embedding server.\n* Then you’ll have the **three-mode switch** working cleanly.\n\n---\n\n👉 That’s the entire arc:\n\n* Found your config files → mounted overrides → added env-based embedder switching → installed Ollama in Docker image → connected to LAN Ollama → hit dimension mismatch → planned Hugging Face 1536 model → set up PyTorch on 3090.\n\n---\n\nDo you want me to **pick a specific Hugging Face embedding model that outputs exactly 1536 dims** and write the step-by-step for hosting it on your 3090 Windows box?" }, { "role": "tool", "content": "All the files uploaded by the user have been fully loaded. Searching won't provide additional information." }, { "role": "user", "content": "okay so actually i just figured out that we can use a different number of dims, it just needs to be set as a variable in the config of main.py. \nHere is this from deep wiki\nYou're asking about configuring the embedding model and its dimensions in the Mem0 OSS version. \n\n## Changing the Embedding Model\n\nTo change the embedding model in Mem0 OSS, you configure the `embedder` section in your config dictionary [1](#0-0) :\n\n```python\nconfig = {\n \"embedder\": {\n \"provider\": \"your_chosen_provider\",\n \"config\": {\n \"model\": \"your_model_name\"\n }\n }\n}\n```\n\n### Supported Providers\n\nMem0 supports multiple embedding providers [2](#0-1) :\n- OpenAI\n- Azure OpenAI \n- Ollama\n- Hugging Face\n- Google AI\n- Vertex AI\n- Together\n- LM Studio\n- Langchain\n- AWS Bedrock\n\n## Setting Embedding Dimensions\n\nYou control the embedding dimensions using the `embedding_dims` parameter in the embedder config [3](#0-2) . This parameter is available across all embedding providers [4](#0-3) .\n\n### Examples by Provider\n\n**OpenAI:**\n```python\nconfig = {\n \"embedder\": {\n \"provider\": \"openai\",\n \"config\": {\n \"model\": \"text-embedding-3-small\",\n \"embedding_dims\": 1536\n }\n }\n}\n```\n\n**Ollama (local):**\n```python\nconfig = {\n \"embedder\": {\n \"provider\": \"ollama\", \n \"config\": {\n \"model\": \"nomic-embed-text\",\n \"embedding_dims\": 512 # Default for nomic-embed-text\n }\n }\n}\n```\n\nThe Ollama implementation shows default dimensions being set automatically [5](#0-4) .\n\n**Together:**\n```python\nconfig = {\n \"embedder\": {\n \"provider\": \"together\",\n \"config\": {\n \"model\": \"togethercomputer/m2-bert-80M-8k-retrieval\",\n \"embedding_dims\": 768 # Required for Together models\n }\n }\n}\n```\n\n## Vector Store Compatibility\n\nWhen changing embedding dimensions, ensure your vector store configuration matches [6](#0-5) :\n\n```python\nconfig = {\n \"vector_store\": {\n \"provider\": \"qdrant\",\n \"config\": {\n \"embedding_model_dims\": 1536 # Must match embedder dimensions\n }\n },\n \"embedder\": {\n \"provider\": \"openai\", \n \"config\": {\n \"model\": \"text-embedding-3-small\",\n \"embedding_dims\": 1536\n }\n }\n}\n```\n\n## Notes\n\nThe `BaseEmbedderConfig` class handles dimension configuration across all providers [7](#0-6) . Different models have different default dimensions - for example, OpenAI's `text-embedding-3-small` defaults to 1536 dimensions, while Ollama's `nomic-embed-text` defaults to 512 dimensions. Always ensure your vector store's `embedding_model_dims` parameter matches your embedder's `embedding_dims` setting.\n\nWiki pages you might want to explore:\n- [Configuration (mem0ai/mem0)](/wiki/mem0ai/mem0#5)\n\n### Citations\n\n**File:** docs/components/embedders/config.mdx (L29-36)\n```text\nconfig = {\n \"embedder\": {\n \"provider\": \"your_chosen_provider\",\n \"config\": {\n # Provider-specific settings go here\n }\n }\n}\n```\n\n**File:** docs/components/embedders/config.mdx (L76-78)\n```text\n| `model` | Embedding model to use | All |\n| `api_key` | API key of the provider | All |\n| `embedding_dims` | Dimensions of the embedding model | All |\n```\n\n**File:** docs/components/embedders/overview.mdx (L17-28)\n```text\n\n \n \n \n \n \n \n \n \n \n \n\n```\n\n**File:** mem0/configs/embeddings/base.py (L19-19)\n```python\n embedding_dims: Optional[int] = None,\n```\n\n**File:** mem0/configs/embeddings/base.py (L51-52)\n```python\n :param embedding_dims: The number of dimensions in the embedding, defaults to None\n :type embedding_dims: Optional[int], optional\n```\n\n**File:** mem0/embeddings/ollama.py (L28-29)\n```python\n self.config.model = self.config.model or \"nomic-embed-text\"\n self.config.embedding_dims = self.config.embedding_dims or 512\n```\n\n**File:** docs/integrations/llama-index.mdx (L80-80)\n```text\n \"embedding_model_dims\": 1536, # Change this according to your local model's dimensions\n```" }, { "role": "assistant", "content": "Perfect — you found the missing piece 🎯 \n\nYou don’t actually need to force everything to 1536. Mem0 is flexible — as long as **both your embedder config and vector store config agree** on the dimension size, it’ll work fine.\n\nHere’s the step-by-step way to wire it cleanly:\n\n---\n\n### 1. Update `main.py` (or your mounted config file)\n\nExample for **Ollama nomic-embed-text** (512-dim):\n\n```python\nconfig = {\n \"vector_store\": {\n \"provider\": \"qdrant\", # or neo4j / postgres / whatever you use\n \"config\": {\n \"embedding_model_dims\": 512 # must match embedder\n }\n },\n \"embedder\": {\n \"provider\": \"ollama\",\n \"config\": {\n \"model\": \"nomic-embed-text\",\n \"embedding_dims\": 512\n }\n }\n}\n```\n\nExample for **OpenAI fallback** (1536-dim):\n\n```python\nconfig = {\n \"vector_store\": {\n \"provider\": \"qdrant\",\n \"config\": {\n \"embedding_model_dims\": 1536\n }\n },\n \"embedder\": {\n \"provider\": \"openai\",\n \"config\": {\n \"model\": \"text-embedding-3-small\",\n \"embedding_dims\": 1536\n }\n }\n}\n```\n\n---\n\n### 2. Load from `.env` for flexibility\n\nInstead of editing code every time, pull these values from environment variables:\n\n```python\nimport os\n\nconfig = {\n \"vector_store\": {\n \"provider\": os.getenv(\"VECTOR_STORE\", \"qdrant\"),\n \"config\": {\n \"embedding_model_dims\": int(os.getenv(\"EMBEDDING_DIMS\", \"1536\"))\n }\n },\n \"embedder\": {\n \"provider\": os.getenv(\"EMBED_PROVIDER\", \"openai\"),\n \"config\": {\n \"model\": os.getenv(\"EMBED_MODEL\", \"text-embedding-3-small\"),\n \"embedding_dims\": int(os.getenv(\"EMBEDDING_DIMS\", \"1536\"))\n }\n }\n}\n```\n\n---\n\n### 3. Create `.env` presets\n\n**.env.openai**\n```\nEMBED_PROVIDER=openai\nEMBED_MODEL=text-embedding-3-small\nEMBEDDING_DIMS=1536\n```\n\n**.env.ollama**\n```\nEMBED_PROVIDER=ollama\nEMBED_MODEL=nomic-embed-text\nEMBEDDING_DIMS=512\nOLLAMA_BASE_URL=http://10.0.0.3:11434\n```\n\n---\n\n### 4. Restart Mem0 with the chosen `.env`\n\n```bash\ndocker compose --env-file .env.ollama up -d --build\n```\n\nor\n\n```bash\ndocker compose --env-file .env.openai up -d --build\n```\n\n---\n\n👉 With this setup, you can flip between OpenAI / Ollama (or even HuggingFace later) just by swapping `.env` files. No schema mismatch errors anymore, because the **vector store always matches the embedder dims**.\n\n---\n\nDo you want me to give you a **ready-to-paste `main.py` config block** that pulls everything from env (so you never have to touch Python again), or would you rather keep the simple hard-coded configs and just edit when needed?" }, { "role": "user", "content": "so it looks like you cant flip between them once you start it. if it starts at 1536, you cant change the variable and expect it to work with the new model's dimensions. Basically you are locked into it from when you start..." }, { "role": "assistant", "content": "Yeah, that tracks — and it makes sense if you think about how vector stores work. \n\nOnce you **start Mem0 and build a vector store**, it fixes the embedding dimensionality. Every vector you insert has to match that schema. If you later flip from 1536 → 512 (or vice versa), you’ll hit errors because the stored vectors and the new ones don’t line up. \n\nSo practically: \n\n- ✅ You *can* configure any dimension you want at startup. \n- 🚫 You *cannot* hot-swap dimensions mid-run against the same database. \n- 🔄 If you want to swap providers with different dims, you basically have two options: \n\n---\n\n### Option A: Separate Vector Stores per Dim\n- Run **one vector DB instance per dimension** (e.g., `qdrant_512` and `qdrant_1536`). \n- Then you can switch `.env` and point Mem0 at the right one without schema errors. \n- This lets you keep OpenAI history (1536) and Ollama history (512) separate, but still switch.\n\n---\n\n### Option B: Re-Index on Change\n- If you really want to flip a single store between dims, you’d have to **wipe and rebuild** the index each time you swap. \n- That means deleting stored vectors and re-embedding everything with the new model. \n- This is slower, but keeps things in one DB.\n\n---\n\n### Option C: Standardize on One Dim\n- Pick a dimension that works across your main models (say 768 or 1536). \n- Either force all models into that dim (some wrappers pad/truncate), or only use models that naturally output that dim. \n- This way you can freely switch providers without schema conflicts.\n\n---\n\n👉 Easiest path for you: **Option A** (two stores, one for 512, one for 1536). \nThat way you don’t need to constantly nuke/rebuild data — just swap envs and point at the right store. \n\nWant me to show you how to wire up Mem0’s config so you can **spin up two parallel vector stores** and pick one with an env var (like `VECTOR_STORE_NAME` = `mem0_512` or `mem0_1536`)?" }, { "role": "user", "content": "okay so deepwiki is saying that it is possible to have two different namespaces/instances with different embedding dimensions. However, mem0 cannot simultaneously search both of them. So it would need to do a search in the 512 and a search in the 1536, give both answers and a separate logic layer (something like lyra-cortex) would have to combine the two results into a cohesive relevant memory. \n\nThat sounds like a lot... lol" }, { "role": "assistant", "content": "Yep — you’ve got it exactly right. \n\nDeepwiki isn’t lying: you *can* run two namespaces (one for 512-dim Ollama, one for 1536-dim OpenAI). But the catch is what you said — **Mem0 itself can’t merge results across dims.** It just asks one store/namespace at a time. \n\nThat leaves you with two realities: \n\n---\n\n### 🛠 Reality A: Keep It Simple\n- Pick *one* embedding family for now. \n- Accept that if you want to switch, you’ll just rebuild/re-index or point to a different store. \n- Less fancy, but **much less headache**.\n\n---\n\n### 🛠 Reality B: Build a “Cortex Combiner” Layer\n- Mem0 does two searches (one in 512, one in 1536). \n- Cortex (or some middleware) merges results → re-scores by relevance → sends one final set back to Lyra. \n- That’s essentially a **meta-retriever**. \n- Cool idea, but adds complexity right away. \n\n---\n\nHonestly, I’d recommend **Reality A** for now: \n- Start with Ollama’s 512 if you care about local speed/cost. \n- Or stick with OpenAI’s 1536 if you want max compatibility. \n- Down the road, if you *really* want hybrid retrieval, Lyra-Cortex can evolve into that combiner layer — but you don’t need it working on day one. \n\n---\n\n👉 Want me to sketch a “phase plan” for you — like **Phase 1 (single store), Phase 2 (multi-namespace), Phase 3 (Cortex combiner)** — so you can grow into it instead of biting it all off now?" }, { "role": "user", "content": "how do i check to see how many memories are in mem0 currently?" }, { "role": "assistant", "content": "Good question 👍 — easiest way depends on what backend you’re using for the vector store. \n\n### If you’re using **Postgres** (the default in the OSS quickstart):\nRun this inside the Postgres container:\n\n```bash\ndocker exec -it postgres psql -U mem0 -d mem0 -c \"SELECT COUNT(*) FROM memories;\"\n```\n\nThat will give you the total row count in the `memories` table. \n\n---\n\n### If you’re using **Qdrant**:\nYou can query via HTTP API:\n\n```bash\ncurl -X GET \"http://localhost:6333/collections/memories\"\n```\n\nThat will show collection details including the vector count.\n\n---\n\n### If you’re using **Neo4j**:\nOpen the Cypher shell:\n\n```bash\ndocker exec -it neo4j cypher-shell -u neo4j -p your_password\n```\n\nThen run:\n\n```cypher\nMATCH (m:Memory) RETURN count(m);\n```\n\n---\n\n### If you’re unsure which backend you’re on\nCheck your config (`main.py` or `.env`) for:\n\n```\nVECTOR_STORE=postgres\n# or qdrant, or neo4j\n```\n\n---\n\n👉 Do you want me to give you the exact command for your setup if you paste me your current `.env` `VECTOR_STORE` line?" }, { "role": "user", "content": "so it looks like i got 30 done before canceling it, according to my usage, that used 11,370 tokens fot text-embeddings-3-small. And it looks that over all that cost $1.89. I feel like we could probably just get away with using openAI for the whole thing" }, { "role": "assistant", "content": "That makes a lot of sense. Let’s do some napkin math: \n\n- **30 memories → 11,370 tokens → $1.89** \n- That’s about **379 tokens per memory**. \n- OpenAI embedding pricing (as of now): \n - `text-embedding-3-small` = **$0.02 per 1M tokens** \n - `text-embedding-3-large` = **$0.13 per 1M tokens** \n\nSo if you’re paying $1.89 for ~11k tokens, that’s actually not matching current OpenAI prices. By their rates, 11k tokens should only be **fractions of a cent** (like $0.0002). \n\n👉 My guess: your “usage dashboard” is lumping in *model + embedding + other API calls* together, or it’s showing some **minimum billing increment**. \n\n---\n\n### Practical takeaway\n- Embeddings are **super cheap** — even at scale (hundreds of thousands of memories). \n- Cost won’t blow you up unless you’re embedding billions of tokens. \n- If convenience > tinkering, **just stick with OpenAI** for now. It’s stable, always 1536-dim, and you avoid the dimension headache. \n\n---\n\nWould you like me to help you set things so **OpenAI is the default embedder** and you only drop into Ollama if OpenAI is down (failover), instead of running dual stores? That way you get simplicity but keep your “local backup” option alive." }, { "role": "user", "content": "is there a way to basically re import the data if i decide to change up the embedding model at some point?" }, { "role": "assistant", "content": "Yep 👍 — that’s the way people normally handle changing embedding models. You don’t have to throw your raw data away, you just **re-embed and re-index it**. \n\nHere’s the workflow: \n\n---\n\n### 🔄 Re-Embedding Workflow\n\n1. **Export the raw memory data** \n - If you’re in Postgres: \n ```bash\n docker exec -t postgres pg_dump -U mem0 mem0 > mem0_dump.sql\n ```\n Or if you only care about the text: \n ```sql\n SELECT id, content FROM memories;\n ```\n - If in Neo4j: \n ```cypher\n MATCH (m:Memory) RETURN m.id, m.text;\n ```\n - If in Qdrant: \n use the REST API to fetch all points (`/collections/memories/points`).\n\n2. **Clear or create a fresh namespace/collection** \n - You either drop the old embeddings table/collection, or create a new one like `memories_512`.\n\n3. **Run re-embedding** \n - Feed all the stored text back through the new embedding model. \n - This gives you fresh vectors in the correct dimension.\n\n4. **Re-insert** \n - Insert these new embeddings back into the vector store under the new namespace.\n\n---\n\n### ⚡ Tips\n- Keep a **backup of the raw text** at all times — that’s what really matters. The vectors are disposable. \n- You can even build a small script that: \n - Queries all `content` from your `memories` table \n - Calls the embedding API for each \n - Writes results back into a fresh index. \n\n---\n\n👉 So yes — switching is totally possible, it just means a **batch re-embedding job**. \nDo you want me to sketch a little **Python script** that could dump all current Mem0 memories and re-embed them into a new collection with your chosen model? That way you’d have a ready-made tool whenever you decide to flip?" }, { "role": "user", "content": "{\"content_type\": \"image_asset_pointer\", \"asset_pointer\": \"sediment://file_000000001bd061f793caf15ec9646e34\", \"size_bytes\": 41423, \"width\": 486, \"height\": 539, \"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}} oh you are right LOL. i guess the price isnt really a concern.." }, { "role": "assistant", "content": "😂 exactly — that screenshot proves it. \n\nSee how **`text-embedding-3-small` is showing < $0.01** while the GPT-4o inputs/outputs are where the real cost comes from? That’s why embedding costs basically vanish into the noise compared to using big models for chat/completions. \n\nSo yeah, unless you’re embedding like **hundreds of millions of docs**, OpenAI embeddings are effectively “free” for your use case. That means you can: \n\n- Stick with **OpenAI as the default** (no headaches with dims). \n- Keep Ollama in your back pocket if you ever want *offline/local-only mode*. \n- Forget about embedding costs as a constraint. \n\n👉 Want me to help you set up your `.env` so **OpenAI is the clean default** right now, and Ollama is just a toggle you can flip if you want to play around with it?" }, { "role": "user", "content": "so the only thing thats sorta meh is that then i am still relying on a service provider for MY project and MY data. " }, { "role": "assistant", "content": "💯 exactly — and that’s the trade-off. \n\nOpenAI is: \n- ✅ Fast \n- ✅ Dirt cheap \n- ✅ Always 1536-dim \n- ❌ External dependency (you’re giving them your vectors, even if it’s “just embeddings”) \n\nRunning local (Ollama, Hugging Face, etc.) is: \n- ✅ 100% your data, no leakage \n- ✅ Works offline, no API keys \n- ❌ Slower (depending on model + hardware) \n- ❌ Can mean juggling dimensions, drivers, PyTorch installs, etc. \n\n---\n\n### The middle-ground approach (what most self-hosted folks do):\n1. **Default to local embeddings** (Ollama or a Hugging Face model you serve yourself). \n2. **Keep OpenAI as fallback** — so if your local server is down, Mem0 flips to OpenAI. \n - This way you don’t lose availability. \n3. Long-term: build a **“re-embed pipeline”** so you can easily re-index with a local model if you decide to cut the cord entirely. \n\n---\n\nIf you want total control, the most future-proof option is to pick a **Hugging Face embedding model** you can serve with something like [text-embeddings-inference](https://github.com/huggingface/text-embeddings-inference) (optimized Rust server, super fast, runs on GPU/CPU). That way you’ve got OpenAI-style endpoints but fully local. \n\n---\n\n👉 Want me to find you a good Hugging Face model that’s **1536-dim** (so you can drop it in without changing schema), and then show you how to serve it locally on your 3090? That’d solve both: full privacy + easy swap with OpenAI." }, { "role": "user", "content": "thats the thing, i dont think there are any open source local models that are 1536-dim. If you can find one go for it" }, { "role": "assistant", "content": "Good thinking — it’s possible there **are** open models that use 1536 dims or models that are very close. I poked around, and here are some promising leads + caveats + strategy suggestions.\n\n \n---\n\n## 🔍 What I found so far\n\n**Promising models with 1536 dims:**\n\n- `diwank/dfe-base-en-1` — this is a Sentence-Transformers model that “maps sentences & paragraphs to a 1536 dimensional dense vector space.” citeturn0search2 \n- `Alibaba-NLP/gte-Qwen2-1.5B-instruct` — listed as having “Embedding Dimension: 1536”. citeturn0search5 \n- `Qodo/Qodo-Embed-1-1.5B` — this is a code/embedding/neural model optimized for code retrieval, and its info page lists “Embedding Dimension: 1536”. citeturn0search14 \n\nSo yes — there *are* open-source or community greenlit models with 1536 dims (or claiming that). \n\n---\n\n## ⚠️ Caveats & considerations\n\n- Just because a model claims 1536 dims doesn’t mean it’s production-ready (latency, memory, quantization support, inference speed). \n- Some of these models might require “trust remote code” or custom wrappers to integrate into your embedding server. \n- They may be designed for specific domains (code, multilingual, etc.) which may or may not suit your data.\n\n---\n\n## 🎯 Strategy: Try one of those as your local embedding model\n\nHere’s what you could try:\n\n1. **Pick one** of the promising models above (e.g. `diwank/dfe-base-en-1` or `g­te-Qwen2-1.5B-instruct`).\n2. Download / serve it locally (e.g. via `transformers` + `torch` + an API wrapper).\n3. Configure Mem0's embedder to use that model with `embedding_dims = 1536`.\n4. Run a small test: insert a few memories, search, see if everything works end-to-end.\n\nIf that works smoothly, great — you now have an open model that matches OpenAI’s dims.\n\nIf it’s slow or too heavy, you can fall back to 512-dim local or keep OpenAI.\n\n---\n\nIf you like, I can put together **step-by-step instructions** to deploy one of these (say `g­te-Qwen2-1.5B-instruct`) on your 3090 as a local embedding server. Want me to do that for you?" }, { "role": "user", "content": "so i dont know how to run any models on windows besides ollama. can you teach me how to spin up the new one" }, { "role": "assistant", "content": "Got it 👍 — you’re comfy with Ollama already, but Hugging Face / PyTorch serving is a bit of a mystery. Let’s go slowly and just get *one* 1536-dim model running on your Windows 3090. \n\nWe’ll use Hugging Face’s `transformers` library and expose the model with a tiny FastAPI server (basically the same idea as Ollama: send text → get embeddings). \n\n---\n\n## 🪜 Step 1: Prep your Python environment\nOn your Windows machine (the one with the 3090): \n\n1. Make sure you have **Python 3.10 or 3.11** (PyTorch works best here). \n ```powershell\n python --version\n ``` \n If it’s 3.12 or higher, we’ll want to install 3.10/3.11 alongside. \n\n2. Create a fresh virtual environment: \n ```powershell\n python -m venv hfembed\n hfembed\\Scripts\\activate\n ```\n\n3. Install dependencies (this pulls GPU-enabled PyTorch + Transformers + FastAPI + Uvicorn): \n ```powershell\n pip install torch --index-url https://download.pytorch.org/whl/cu126\n pip install transformers fastapi uvicorn[standard]\n ```\n\n---\n\n## 🪜 Step 2: Download a 1536-dim embedding model\nLet’s start with one that advertises 1536 dims: **Alibaba-NLP/gte-Qwen2-1.5B-instruct** \n\nRun: \n```powershell\npip install sentence-transformers\n```\n\nThen in Python: \n```python\nfrom sentence_transformers import SentenceTransformer\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\")\nprint(model.get_sentence_embedding_dimension())\n```\n\nThat should print `1536`. ✅ \n\n---\n\n## 🪜 Step 3: Build a tiny API server\nMake a file called `embed_server.py` with this inside:\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom sentence_transformers import SentenceTransformer\n\napp = FastAPI()\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\")\n\nclass EmbedRequest(BaseModel):\n text: str\n\n@app.post(\"/embed\")\ndef embed(req: EmbedRequest):\n vector = model.encode(req.text).tolist()\n return {\"embedding\": vector, \"dim\": len(vector)}\n```\n\nStart the server: \n```powershell\nuvicorn embed_server:app --host 0.0.0.0 --port 11435\n```\n\nNow it’s live at `http://localhost:11435/embed`.\n\nTest it: \n```powershell\ncurl -X POST http://localhost:11435/embed -H \"Content-Type: application/json\" -d \"{\\\"text\\\":\\\"hello world\\\"}\"\n```\n\nYou should get back a JSON with `\"dim\": 1536`.\n\n---\n\n## 🪜 Step 4: Point Mem0 at it\nIn your `.env.local`: \n```\nEMBED_PROVIDER=huggingface\nEMBED_MODEL=http://10.0.0.3:11435/embed\nEMBEDDING_DIMS=1536\n```\n\n(If Mem0 doesn’t have a direct Hugging Face REST integration, we can wrap it as a “Langchain embedder” with `embedding_dims=1536`.) \n\n---\n\n👉 Would you like me to **build the `main.py` config snippet** for Mem0 so it knows how to talk to this server? That way you can flip between your new local Hugging Face API and OpenAI/Ollama just by changing `.env`." }, { "role": "user", "content": "we went through this before, pytorch is compatible with 3.13. I already have pytorch installed i think. How do i check that" }, { "role": "assistant", "content": "Perfect — if you already installed it, let’s confirm. \n\nOpen a terminal in Windows (PowerShell or your venv) and run: \n\n```powershell\npython -c \"import torch; print(torch.__version__); print(torch.cuda.is_available())\"\n```\n\n- The first line prints your **PyTorch version**. \n- The second prints `True` if CUDA (your 3090) is available, `False` if it’s CPU-only. \n\nIf you get both a version string (e.g. `2.5.0+cu126`) and `True`, then you’re good to go. 🚀 \n\nWant me to also give you a **GPU test snippet** (matrix multiply) to stress the card and be 100% sure PyTorch is using CUDA?" }, { "role": "user", "content": "is there a way to just check version?" }, { "role": "assistant", "content": "Yep — you can keep it super simple. \n\nIn your terminal: \n\n```powershell\npython -c \"import torch; print(torch.__version__)\"\n```\n\nThat will just spit out the PyTorch version string (e.g. `2.5.0+cu126`). \n\nWould you like me to also show you how to check what **CUDA version** it was built with (in case you’re curious about compatibility with your 3090)?" }, { "role": "user", "content": "import torch isnt being found" }, { "role": "assistant", "content": "Ahh — if `import torch` isn’t found, that means PyTorch isn’t installed in the Python environment you’re using. \n\nHere’s how you can check and install cleanly on Windows: \n\n---\n\n### 🪜 Step 1: Verify which Python you’re running\nIn PowerShell:\n```powershell\npython --version\nwhere python\n```\nThis will tell you the version (3.13.x etc.) and the exact path. Sometimes Windows has multiple Python installs (system vs venv vs Anaconda).\n\n---\n\n### 🪜 Step 2: See if PyTorch is installed in that environment\n```powershell\npip show torch\n```\nIf it says *“Package not found”*, then it’s not installed in this environment.\n\n---\n\n### 🪜 Step 3: Install PyTorch (CUDA build for your 3090)\nRun:\n```powershell\npip install torch --index-url https://download.pytorch.org/whl/cu126\n```\n\nThat’ll grab the latest PyTorch wheel built against CUDA 12.6 (perfect for your 3090). \n\n---\n\nAfter install, confirm with:\n```powershell\npython -c \"import torch; print(torch.__version__)\"\n```\n\n---\n\n👉 Do you want me to give you a **single one-liner** that installs PyTorch and then immediately checks version in one go, so you don’t have to do it step by step?" }, { "role": "user", "content": "okay it installed and i got 'true' for the cuda thingy" }, { "role": "assistant", "content": "Nice 🎉 That means: \n\n- ✅ PyTorch is installed correctly in your Python 3.13 environment \n- ✅ It detects CUDA and your 3090, so GPU acceleration is working \n\nThat’s the hardest part done. \n\n---\n\n👉 Next step is pulling in a **1536-dim embedding model** (like `Alibaba-NLP/gte-Qwen2-1.5B-instruct`) and testing that it loads + returns the right dimension. \n\nWould you like me to walk you through a tiny **“hello world embedding” script** so you can run one line of text through that model and confirm it spits out a 1536-length vector?" }, { "role": "user", "content": "yeah how do i do that" }, { "role": "assistant", "content": "Alright, let’s make this as smooth as possible. We’ll just write a tiny Python script that: \n\n1. Loads the model \n2. Encodes a sentence \n3. Prints the length of the vector \n\n---\n\n### 🪜 Step 1: Install Hugging Face bits\nIn your activated environment, run:\n\n```powershell\npip install sentence-transformers\n```\n\n---\n\n### 🪜 Step 2: Make a test script\nCreate a file called **`test_embed.py`** with this inside:\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\n# Load the 1536-dim embedding model\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\")\n\n# Test sentence\nsentence = \"Hello from Lyra\"\n\n# Encode\nembedding = model.encode(sentence)\n\nprint(\"Vector length:\", len(embedding))\nprint(\"First 10 values:\", embedding[:10]) # just to peek\n```\n\n---\n\n### 🪜 Step 3: Run it\n```powershell\npython test_embed.py\n```\n\nYou should see something like:\n\n```\nVector length: 1536\nFirst 10 values: [0.01, -0.02, ...]\n```\n\n---\n\n👉 If that works, then you’ve got a **local 1536-dim embedder** running on your 3090. \nDo you want me to extend this into a little **API server** (so Mem0 can call it like Ollama/OpenAI)?" }, { "role": "user", "content": "how do i pull the model " }, { "role": "assistant", "content": "Good question — Hugging Face models don’t come bundled, they download the first time you run them. \n\nWhen you do this line in the script: \n\n```python\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\")\n``` \n\n👉 The **sentence-transformers** library will automatically: \n- Look for the model in your local Hugging Face cache (usually `C:\\Users\\\\.cache\\huggingface\\hub`). \n- If it’s not there, it downloads it from [huggingface.co](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct). \n\n---\n\n### 🪜 Step 1: Make sure you can download from Hugging Face\nRun once:\n```powershell\npip install huggingface_hub\n```\n\n### 🪜 Step 2: (Optional but recommended) Login to Hugging Face\nIf the model requires auth or rate limits are tight:\n```powershell\nhuggingface-cli login\n```\n(paste in your Hugging Face token if you have one). \n\n### 🪜 Step 3: Pre-download the model\nIf you want to pull it before running the script:\n```powershell\nfrom sentence_transformers import SentenceTransformer\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\", cache_folder=\"./models\")\n```\n\nThat’ll store the model weights under `./models` instead of your user cache. \n\n---\n\n⚡ Pro tip: First download may take a few minutes (it’s ~GB size since it’s a 1.5B parameter model). \n\n---\n\n👉 Do you want me to show you how to **preload the model with `huggingface_hub`** so you can avoid waiting during the first script run?" }, { "role": "user", "content": ">> from sentence_transformers import SentenceTransformer\nAt line:2 char:67\n+ ... = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\", cache_f ...\n+ ~\nMissing expression after ','.\nAt line:2 char:68\n+ ... ormer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\", cache_folder=\"./models\")\n+ ~~~~~~~~~~~~~~~~~~~~~~~\nUnexpected token 'cache_folder=\"./models\"' in expression or statement.\nAt line:2 char:67\n+ ... = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\", cache_f ...\n+ ~\nMissing closing ')' in expression.\nAt line:2 char:91\n+ ... ormer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\", cache_folder=\"./models\")\n+ ~\nUnexpected token ')' in expression or statement.\nAt line:3 char:1\n+ from sentence_transformers import SentenceTransformer\n+ ~~~~\nThe 'from' keyword is not supported in this version of the language.\n + CategoryInfo : ParserError: (:) [], ParentContainsErrorRecordException\n + FullyQualifiedErrorId : MissingExpressionAfterToken" }, { "role": "assistant", "content": "Ahhh, I see what happened 😅 \n\nYou’re pasting **Python code directly into PowerShell**, and PowerShell is trying to parse it as shell commands — hence all the weird `Missing expression` errors. \n\n---\n\n### ✅ How to run Python code properly\n\nYou’ve got two choices:\n\n1. **Save it into a `.py` file** \n - Open Notepad (or VS Code) \n - Paste this code: \n ```python\n from sentence_transformers import SentenceTransformer\n\n model = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\", cache_folder=\"./models\")\n print(\"Embedding dimension:\", model.get_sentence_embedding_dimension())\n ```\n - Save it as `test_embed.py` \n - Run it from PowerShell: \n ```powershell\n python test_embed.py\n ```\n\n---\n\n2. **Or run inline with `python -c`** \n If you just want a quick one-liner test, run this in PowerShell:\n\n ```powershell\n python -c \"from sentence_transformers import SentenceTransformer; m=SentenceTransformer('Alibaba-NLP/gte-Qwen2-1.5B-instruct'); print(m.get_sentence_embedding_dimension())\"\n ```\n\n---\n\n👉 That will **download the model** (first time = takes a while) and then print `1536`. \n\nDo you want to try the `.py` file way (easier for testing), or should I give you a ready `python -c` one-liner that will also test an embedding on the spot?" }, { "role": "user", "content": "it printed 1536" }, { "role": "assistant", "content": "Perfect 🎉 That means: \n\n- The model downloaded and loaded successfully \n- Your 3090 + PyTorch are working fine \n- And we’ve confirmed it **really is 1536-dim**, so it can be swapped in place of OpenAI without schema issues \n\n---\n\n👉 Next step is deciding how you want to use it: \n\n1. **Directly in scripts** (good for testing / one-off re-embedding jobs). \n - You’d just import the model in Python and call `.encode(text)` whenever you need vectors. \n\n2. **Expose it as an API server** (best if you want Mem0 to call it the same way it calls OpenAI/Ollama). \n - That way Mem0 just points to `http://10.0.0.3:11435/embed` and doesn’t know the difference. \n\n---\n\nDo you want me to walk you through setting up the **FastAPI embedding server** (so it works like your Ollama setup), or do you prefer to first run some manual embedding tests in Python to get comfortable?" }, { "role": "user", "content": "lets do the embedding server, since thats what mem0 needs. Also would this model run on the ryzen box? or is this one that i should use the 3090 only" }, { "role": "assistant", "content": "Good call — Mem0 wants an HTTP API to talk to, so spinning up a FastAPI server around the model is exactly what we need. \n\nLet’s tackle your two questions: \n\n---\n\n### ⚡ 1. Hardware fit\n- **Model size:** `gte-Qwen2-1.5B-instruct` is about ~3GB on disk and wants ~6–8GB RAM while running. \n- **On CPU (Ryzen box):** It *can* run, but it’ll be **slow** (seconds per embedding). Fine for testing, not for production if you’re embedding lots of text. \n- **On 3090 GPU:** Runs fast — that’s the sweet spot. \n\n👉 My advice: \n- Use your **3090 box as the embedding server**. \n- If you need a CPU-only fallback for the Ryzen, we can swap in a smaller model (e.g. `all-MiniLM-L6-v2`, 384-dim, very lightweight). \n\n---\n\n### ⚡ 2. Build the embedding server\nHere’s a simple FastAPI server that wraps the model:\n\n**`embed_server.py`**\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom sentence_transformers import SentenceTransformer\n\n# Load the model once at startup\nprint(\"Loading model... this may take a minute the first time.\")\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\")\n\napp = FastAPI()\n\nclass EmbedRequest(BaseModel):\n text: str\n\n@app.post(\"/embed\")\ndef embed(req: EmbedRequest):\n vector = model.encode(req.text).tolist()\n return {\"embedding\": vector, \"dim\": len(vector)}\n```\n\n---\n\n### ⚡ 3. Run the server\nFrom your PowerShell (in the same venv):\n\n```powershell\nuvicorn embed_server:app --host 0.0.0.0 --port 11435\n```\n\nThat will start the server on `http://:11435/embed` \n\nTest it locally:\n```powershell\ncurl -X POST http://localhost:11435/embed -H \"Content-Type: application/json\" -d \"{\\\"text\\\":\\\"hello world\\\"}\"\n```\n\nExpected: JSON with `\"dim\": 1536` and an embedding vector.\n\n---\n\n### ⚡ 4. Hook into Mem0\nIn your `.env.local` (on the Mem0 container), set:\n\n```\nEMBED_PROVIDER=huggingface\nEMBED_MODEL=http://10.0.0.3:11435/embed\nEMBEDDING_DIMS=1536\n```\n\n*(assuming your 3090’s LAN IP is `10.0.0.3`)*\n\n---\n\n👉 Do you want me to also write the `main.py` config block for Mem0 so it knows to call this server, or are you comfortable with the `.env` approach you already have?" }, { "role": "user", "content": "i cant swap to a 384 dim mid DB, i need to stick to one dimension number per collection" }, { "role": "assistant", "content": "Exactly right 👍 — that’s the constraint: \n\n- Each **vector store collection** is locked to one dimensionality. \n- If you start with 1536, you can’t suddenly push in 384-dim vectors without either: \n - a fresh collection/namespace, or \n - wiping/re-embedding. \n\nSo since you’ve already decided to standardize: \n- ✅ Use **1536-dim across the board** \n- ❌ Don’t drop in `all-MiniLM-L6-v2` (384-dim) unless you spin up a totally separate index \n\n---\n\nThat’s why `gte-Qwen2-1.5B-instruct` is a really good choice for you: \n- It’s open-source \n- It runs at **1536-dim**, same as OpenAI \n- You can switch between your local server and OpenAI **without breaking schema** \n\n---\n\n👉 Here’s what I’d suggest: \n- Run your **FastAPI embedding server** on the 3090 with `gte-Qwen2-1.5B-instruct`. \n- Keep OpenAI in your `.env.openai` as a fallback (same 1536-dim). \n- Point Mem0 at one or the other — they’ll both play nice with your DB since they share dimensionality. \n\n---\n\nDo you want me to write you a **ready-to-paste `.env` set** for: \n- `local-1536` (your FastAPI Hugging Face server) \n- `openai-1536` (OpenAI fallback) \n\nSo you can flip between them without touching code?" }, { "role": "user", "content": "where should i ave the embed_server.py?" }, { "role": "assistant", "content": "Good question 👍 \n\nYou can put `embed_server.py` pretty much anywhere on your 3090 box — the important part is just being able to run it from a terminal. \n\nHere’s a clean way to organize it: \n\n---\n\n### 🗂 Recommended folder structure\nCreate a folder just for this little server, e.g.:\n\n```\nC:\\ai-servers\\embed-server\\\n```\n\nInside that folder: \n- `embed_server.py` ← the code \n- (optionally) a `requirements.txt` if you want to freeze deps \n\n---\n\n### 🪜 Steps\n\n1. Make the folder: \n ```powershell\n mkdir C:\\ai-servers\\embed-server\n cd C:\\ai-servers\\embed-server\n ```\n\n2. Save the file: \n Open VS Code or Notepad, paste the FastAPI code, save as: \n ```\n C:\\ai-servers\\embed-server\\embed_server.py\n ```\n\n3. Run it from there: \n ```powershell\n uvicorn embed_server:app --host 0.0.0.0 --port 11435\n ```\n\n---\n\nThis way you have a clean home for it, and later you can even add a `run.bat` or a service wrapper to keep it running in the background. \n\n👉 Do you want me to show you how to turn this into a **Windows service** so it runs automatically on boot, or do you just want to start it manually for now?" }, { "role": "user", "content": "is it okay if i do it on D drive? its a 3.0 nvme" }, { "role": "assistant", "content": "Oh absolutely ✅ — it doesn’t matter if it’s on `C:` or `D:`. \n\nThe model files are a few GB, and when you run the server the weights get loaded into GPU VRAM anyway (so the SSD speed only matters during load). On a PCIe 3.0 NVMe you’ll be totally fine — it’ll just take a few seconds to spin up when you first launch. \n\nSo you could do: \n\n```\nD:\\ai-servers\\embed-server\\\n```\n\nand save `embed_server.py` there. \n\n---\n\n👉 Do you want me to also show you how to tell Hugging Face to **cache models on your D:\\ drive** instead of filling up your C:\\ user profile? (That way future models also download there.)" }, { "role": "user", "content": "okay its in D:\\Development\\ai-servers\\" }, { "role": "assistant", "content": "Perfect 👍 That’s a clean spot for it. \n\nSo right now you should have: \n```\nD:\\Development\\ai-servers\\embed_server.py\n```\n\n---\n\n### 🪜 Next step: run it\n\nOpen PowerShell and go into that folder: \n```powershell\ncd D:\\Development\\ai-servers\\\n```\n\nRun the server: \n```powershell\nuvicorn embed_server:app --host 0.0.0.0 --port 11435\n```\n\n---\n\n### 🧪 Test it locally\nIn another PowerShell window: \n```powershell\ncurl -X POST http://localhost:11435/embed -H \"Content-Type: application/json\" -d \"{\\\"text\\\":\\\"hello world\\\"}\"\n```\n\nIf everything’s good, you should see: \n```json\n{\"embedding\":[0.01, -0.02, ...], \"dim\":1536}\n```\n\n---\n\n👉 Want me to also show you how to add a **`requirements.txt`** in that same folder so you can reinstall this server anywhere (or on your Ryzen box) in one line?" }, { "role": "user", "content": "uvicorn not recognized as the name of a dmdle...." }, { "role": "assistant", "content": "Ahh yep — that error means `uvicorn` isn’t installed in your current Python environment (or it didn’t get added to PATH). \n\nLet’s fix it: \n\n---\n\n### 🪜 Step 1: Install uvicorn + fastapi\nIn your venv (the same one where PyTorch is working), run: \n```powershell\npip install fastapi uvicorn[standard]\n```\n\n---\n\n### 🪜 Step 2: Run it again\n```powershell\ncd D:\\Development\\ai-servers\\\npython -m uvicorn embed_server:app --host 0.0.0.0 --port 11435\n```\n\n*(the `python -m uvicorn` form guarantees it runs in the right interpreter — avoids PATH issues)* \n\n---\n\nThat should spin up the server and you’ll see something like:\n```\nINFO: Uvicorn running on http://0.0.0.0:11435 (Press CTRL+C to quit)\n```\n\n---\n\n👉 Want me to also give you a **`requirements.txt`** for this folder so you have everything pinned (PyTorch + FastAPI + SentenceTransformers + Uvicorn), in case you ever want to reinstall from scratch?" }, { "role": "user", "content": "what does in your venv mean is that a folder?" }, { "role": "assistant", "content": "Great question 🙌 — let’s slow this down. \n\nWhen I say *“in your venv”*, I mean a **Python virtual environment** — basically a self-contained folder that holds Python + all the packages for a specific project, so you don’t clutter your whole system. \n\n---\n\n### 🗂 What a venv looks like\nIf you made one called `hfembed`, you’d have a folder like: \n```\nD:\\Development\\ai-servers\\hfembed\\\n│\n├─ Scripts\\ ← has `activate`, `python.exe`, `pip.exe`\n├─ Lib\\ ← all the installed Python packages\n├─ pyvenv.cfg\n```\n\n---\n\n### 🪜 How you make and use one\n1. From inside your `D:\\Development\\ai-servers\\` folder: \n ```powershell\n python -m venv hfembed\n ```\n\n That creates `D:\\Development\\ai-servers\\hfembed\\`.\n\n2. Activate it (this switches your PowerShell to “use this environment”): \n ```powershell\n .\\hfembed\\Scripts\\activate\n ```\n\n You’ll see your prompt change to: \n ```\n (hfembed) PS D:\\Development\\ai-servers>\n ```\n\n3. Now when you run `pip install ...`, it only installs into this venv, not system-wide. \n\n---\n\n### 🪜 Why this matters\n- Keeps dependencies clean and separate. \n- Lets you install `uvicorn`, `fastapi`, `torch`, etc. without breaking other Python projects. \n\n---\n\n👉 Do you want me to walk you step-by-step through creating that `hfembed` venv in your `D:\\Development\\ai-servers\\` folder right now, so we can get your embedding server running inside it?" }, { "role": "user", "content": "i have no idea if i made one how do i check" }, { "role": "assistant", "content": "Totally fair — it’s easy to lose track 🙂 Let’s figure it out. \n\n---\n\n### 🪜 1. Check your current Python path\nRun this in PowerShell: \n```powershell\npython -c \"import sys; print(sys.executable)\"\n```\n\n- If it prints something like: \n ```\n C:\\Users\\\\AppData\\Local\\Programs\\Python\\Python313\\python.exe\n ```\n → That’s your **system-wide Python**. \n- If it prints something like: \n ```\n D:\\Development\\ai-servers\\hfembed\\Scripts\\python.exe\n ```\n → That means you’re already inside a venv (`hfembed` in this case). \n\n---\n\n### 🪜 2. Check your prompt\nIf you see `(hfembed)` at the start of your PowerShell prompt, you’re inside a venv already. \nIf you don’t see anything like that, you’re probably using system Python. \n\n---\n\n### 🪜 3. Check for venv folders\nLook in `D:\\Development\\ai-servers\\` — do you see a folder like `hfembed` or `venv`? \n\n- If yes → that’s your venv. \n- If not → you haven’t made one yet. \n\n---\n\n👉 Do you want me to assume you don’t have one and walk you through **making a fresh venv right now** in `D:\\Development\\ai-servers\\hfembed\\` so we know exactly what we’re working with?" }, { "role": "user", "content": "C:\\Users\\brian\\AppData\\Local\\Programs\\Python\\Python313\\python.exe" }, { "role": "assistant", "content": "Perfect 👌 thanks for checking — that means you’re running **system-wide Python 3.13**, not inside a venv. \n\nThat’s why when you installed `torch` it went system-wide, and why `uvicorn` wasn’t found (different installs can scatter). \n\n---\n\n### 🪜 What I recommend\nLet’s create a dedicated venv just for your embedding server so everything lives neatly in one folder (`D:\\Development\\ai-servers\\hfembed\\`). \n\n---\n\n### Step 1. Create the venv\nIn PowerShell: \n```powershell\ncd D:\\Development\\ai-servers\\\npython -m venv hfembed\n```\n\nThis makes: \n```\nD:\\Development\\ai-servers\\hfembed\\\n```\n\n---\n\n### Step 2. Activate the venv\n```powershell\n.\\hfembed\\Scripts\\activate\n```\n\nNow your prompt should look like: \n```\n(hfembed) PS D:\\Development\\ai-servers>\n```\n\n---\n\n### Step 3. Install everything in this venv\n```powershell\npip install torch --index-url https://download.pytorch.org/whl/cu126\npip install sentence-transformers fastapi uvicorn[standard]\n```\n\n---\n\n👉 From now on, whenever you want to run your `embed_server.py`, make sure you first: \n```powershell\ncd D:\\Development\\ai-servers\\\n.\\hfembed\\Scripts\\activate\npython -m uvicorn embed_server:app --host 0.0.0.0 --port 11435\n```\n\n---\n\nDo you want me to also give you a **requirements.txt** file so you can reinstall everything in one shot if you ever nuke this venv?" }, { "role": "user", "content": "sure" }, { "role": "assistant", "content": "Nice 👍 let’s lock this down so you can always recreate the environment in one line. \n\n---\n\n### 📄 Create `requirements.txt` \nInside `D:\\Development\\ai-servers\\`, make a file called **`requirements.txt`** with this content:\n\n```\ntorch --index-url https://download.pytorch.org/whl/cu126\nsentence-transformers\nfastapi\nuvicorn[standard]\n```\n\n*(that `--index-url` is important — it ensures PyTorch pulls the CUDA 12.6 build for your 3090).*\n\n---\n\n### 🪜 How to use it\nIf you ever make a new venv (or reinstall): \n\n```powershell\ncd D:\\Development\\ai-servers\\\npython -m venv hfembed\n.\\hfembed\\Scripts\\activate\npip install -r requirements.txt\n```\n\nThat will recreate the exact environment you need.\n\n---\n\n👉 Do you want me to also show you how to make a little **`run_embed_server.bat`** script so you can just double-click to launch the server, instead of typing the uvicorn command every time?" }, { "role": "user", "content": "whats the command to launch it all again now that i have it in the same directory" }, { "role": "assistant", "content": "Perfect 👍 since your `embed_server.py` and your `hfembed` venv are both in `D:\\Development\\ai-servers\\`, here’s the exact launch sequence:\n\n---\n\n### 🪜 Launch Steps\n\n1. Open **PowerShell** \n2. Go to your project folder: \n ```powershell\n cd D:\\Development\\ai-servers\\\n ```\n\n3. Activate the venv: \n ```powershell\n .\\hfembed\\Scripts\\activate\n ```\n\n4. Run the server: \n ```powershell\n python -m uvicorn embed_server:app --host 0.0.0.0 --port 11435\n ```\n\n---\n\nIf everything’s good, you’ll see:\n```\nINFO: Uvicorn running on http://0.0.0.0:11435 (Press CTRL+C to quit)\n```\n\nThen from another terminal (or Mem0), you can hit it with:\n```powershell\ncurl -X POST http://localhost:11435/embed -H \"Content-Type: application/json\" -d \"{\\\"text\\\":\\\"hello world\\\"}\"\n```\n\n---\n\n👉 Do you want me to write you a quick **`run_embed_server.bat`** so you can just double-click instead of typing these steps each time?" }, { "role": "user", "content": "woooo i got it working. time to wire it into mem0" }, { "role": "assistant", "content": "🔥 Let’s gooo — huge win getting that server up and running! \n\nNow wiring it into Mem0 is pretty straightforward since you already know the `.env` flow. All we need to do is point Mem0 at your new local endpoint and set the embedding dims to **1536**. \n\n---\n\n### 🪜 Step 1: Edit your Mem0 `.env.local`\nAdd or update these lines:\n\n```\nEMBED_PROVIDER=huggingface\nEMBED_MODEL=http://10.0.0.3:11435/embed # replace with your 3090’s LAN IP\nEMBEDDING_DIMS=1536\n```\n\n---\n\n### 🪜 Step 2: Confirm Mem0 config reads it\nIn your `main.py` (or wherever you load configs):\n\n```python\n\"embedder\": {\n \"provider\": os.getenv(\"EMBED_PROVIDER\", \"openai\"),\n \"config\": {\n \"model\": os.getenv(\"EMBED_MODEL\", \"text-embedding-3-small\"),\n \"embedding_dims\": int(os.getenv(\"EMBEDDING_DIMS\", \"1536\"))\n }\n}\n```\n\n---\n\n### 🪜 Step 3: Restart Mem0\n```bash\ndocker compose --env-file .env.local up -d --build\n```\n\n---\n\n### 🪜 Step 4: Test with curl\n```bash\ncurl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Test memory using local 1536 model\"}]}'\n```\n\nIf it works, Mem0 will hit your embedding server, get a 1536 vector back, and store it in the DB. 🎉 \n\n---\n\n👉 Do you want me to also give you a **dual-mode setup** (`.env.openai` and `.env.local`) so you can flip between OpenAI and your local server just by restarting with a different env file?" }, { "role": "user", "content": "import logging\nimport os\nfrom typing import Any, Dict, List, Optional\n\nfrom dotenv import load_dotenv\nfrom fastapi import FastAPI, HTTPException\nfrom fastapi.responses import JSONResponse, RedirectResponse\nfrom pydantic import BaseModel, Field\n\nfrom mem0 import Memory\n\nlogging.basicConfig(level=logging.INFO, format=\"%(asctime)s - %(levelname)s - %(message)s\")\n\n# Load environment variables\nload_dotenv()\n\n\nPOSTGRES_HOST = os.environ.get(\"POSTGRES_HOST\", \"postgres\")\nPOSTGRES_PORT = os.environ.get(\"POSTGRES_PORT\", \"5432\")\nPOSTGRES_DB = os.environ.get(\"POSTGRES_DB\", \"postgres\")\nPOSTGRES_USER = os.environ.get(\"POSTGRES_USER\", \"postgres\")\nPOSTGRES_PASSWORD = os.environ.get(\"POSTGRES_PASSWORD\", \"postgres\")\nPOSTGRES_COLLECTION_NAME = os.environ.get(\"POSTGRES_COLLECTION_NAME\", \"memories\")\n\nNEO4J_URI = os.environ.get(\"NEO4J_URI\", \"bolt://neo4j:7687\")\nNEO4J_USERNAME = os.environ.get(\"NEO4J_USERNAME\", \"neo4j\")\nNEO4J_PASSWORD = os.environ.get(\"NEO4J_PASSWORD\", \"mem0graph\")\n\nMEMGRAPH_URI = os.environ.get(\"MEMGRAPH_URI\", \"bolt://localhost:7687\")\nMEMGRAPH_USERNAME = os.environ.get(\"MEMGRAPH_USERNAME\", \"memgraph\")\nMEMGRAPH_PASSWORD = os.environ.get(\"MEMGRAPH_PASSWORD\", \"mem0graph\")\n\nOPENAI_API_KEY = os.environ.get(\"OPENAI_API_KEY\")\nHISTORY_DB_PATH = os.environ.get(\"HISTORY_DB_PATH\", \"/app/history/history.db\")\n\n# Embedder settings (switchable by .env)\nEMBEDDER_PROVIDER = os.environ.get(\"EMBEDDER_PROVIDER\", \"openai\")\nEMBEDDER_MODEL = os.environ.get(\"EMBEDDER_MODEL\", \"text-embedding-3-small\")\nOLLAMA_HOST = os.environ.get(\"OLLAMA_HOST\") # only used if provider=ollama\n\n\nDEFAULT_CONFIG = {\n \"version\": \"v1.1\",\n \"vector_store\": {\n \"provider\": \"pgvector\",\n \"config\": {\n \"host\": POSTGRES_HOST,\n \"port\": int(POSTGRES_PORT),\n \"dbname\": POSTGRES_DB,\n \"user\": POSTGRES_USER,\n \"password\": POSTGRES_PASSWORD,\n \"collection_name\": POSTGRES_COLLECTION_NAME,\n },\n },\n \"graph_store\": {\n \"provider\": \"neo4j\",\n \"config\": {\"url\": NEO4J_URI, \"username\": NEO4J_USERNAME, \"password\": NEO4J_PASSWORD},\n },\n \"llm\": {\"provider\": \"openai\", \"config\": {\"api_key\": OPENAI_API_KEY, \"temperature\": 0.2, \"model\": \"gpt-4o\"}},\n \"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL,\n \"api_key\": OPENAI_API_KEY, # still works if provider=openai\n \"ollama_base_url\": OLLAMA_HOST # only used if provider=ollama\n },\n},\n \"history_db_path\": HISTORY_DB_PATH,\n}\n\n\nMEMORY_INSTANCE = Memory.from_config(DEFAULT_CONFIG)\n\nSo here is the top of my main.py located in the same folder as docker-compose.mem0.yml and Dockerfile" }, { "role": "assistant", "content": "Perfect — thanks for dropping the `main.py`. I see exactly how Mem0 is being wired up 👍 \n\nRight now, your `embedder` config looks like this:\n\n```python\n\"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL,\n \"api_key\": OPENAI_API_KEY, # still works if provider=openai\n \"ollama_base_url\": OLLAMA_HOST # only used if provider=ollama\n },\n},\n```\n\n---\n\n### 🔧 What needs to change for your local Hugging Face server\n1. You need to **add `embedding_dims`** so it matches your 1536-dim model. \n2. You need to tell Mem0 that your “model” is actually an **HTTP endpoint** (your FastAPI embed server). \n\nSo update that block to:\n\n```python\n\"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL, # e.g. http://10.0.0.3:11435/embed\n \"embedding_dims\": int(os.environ.get(\"EMBEDDING_DIMS\", \"1536\")),\n \"api_key\": OPENAI_API_KEY, # only used if provider=openai\n \"ollama_base_url\": OLLAMA_HOST # only used if provider=ollama\n },\n},\n```\n\n---\n\n### 🔧 Matching `.env.local`\nSince your `main.py` is already loading env vars, you just need to create/edit your `.env.local` like this:\n\n```\nEMBEDDER_PROVIDER=huggingface\nEMBEDDER_MODEL=http://10.0.0.3:11435/embed\nEMBEDDING_DIMS=1536\n```\n\n(Replace `10.0.0.3` with the LAN IP of your 3090 box running the FastAPI server.) \n\n---\n\n### 🔧 Restart Mem0 with new env\nFrom the folder with your `docker-compose.mem0.yml`:\n\n```bash\ndocker compose --env-file .env.local -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n👉 After restart, Mem0 should route embedding requests straight to your `embed_server.py` and expect 1536-dim vectors, no schema mismatch. \n\nDo you want me to also show you how to **test Mem0 is actually hitting your local embed server** (instead of silently falling back to OpenAI)?" }, { "role": "user", "content": "so im not using ollama for my embedder anymore, can i remove that and replace it with the new huggingface one" }, { "role": "assistant", "content": "Yes — you can safely strip out the Ollama-specific bits from your `main.py` since you’ve committed to using the Hugging Face server as your local embedder. \n\nRight now you’ve got this: \n\n```python\n\"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL,\n \"api_key\": OPENAI_API_KEY, # still works if provider=openai\n \"ollama_base_url\": OLLAMA_HOST # only used if provider=ollama\n },\n},\n```\n\n---\n\n### 🔧 Cleaned-up Hugging Face + OpenAI fallback config\nReplace that block with:\n\n```python\n\"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL, # For huggingface: http://10.0.0.3:11435/embed\n \"embedding_dims\": int(os.environ.get(\"EMBEDDING_DIMS\", \"1536\")),\n \"api_key\": OPENAI_API_KEY # Only used if provider=openai\n },\n},\n```\n\n---\n\n### 🔧 Your `.env` files now look like:\n\n**.env.local (huggingface)**\n```\nEMBEDDER_PROVIDER=huggingface\nEMBEDDER_MODEL=http://10.0.0.3:11435/embed\nEMBEDDING_DIMS=1536\n```\n\n**.env.openai (cloud fallback)**\n```\nEMBEDDER_PROVIDER=openai\nEMBEDDER_MODEL=text-embedding-3-small\nEMBEDDING_DIMS=1536\nOPENAI_API_KEY=sk-...\n```\n\n---\n\nThis way: \n- Hugging Face server runs locally on your 3090 → Mem0 talks to it. \n- If you swap `.env` and restart with OpenAI, it still works, because both produce 1536-dim vectors. \n\n---\n\n👉 Do you want me to also clean up your `DEFAULT_CONFIG` dict entirely so it has **only Postgres + Neo4j + Hugging Face/OpenAI embedder** (no leftover Ollama fields at all)?" }, { "role": "user", "content": "so i am going to .env.3090 (for the local embedder) and .env.openai for using open ai" }, { "role": "assistant", "content": "Exactly 👍 That’s a clean way to separate them — one env file per mode. \n\nHere’s how they should look:\n\n---\n\n### 📄 `.env.3090` (local Hugging Face embedder on your 3090)\n```\nEMBEDDER_PROVIDER=huggingface\nEMBEDDER_MODEL=http://10.0.0.3:11435/embed\nEMBEDDING_DIMS=1536\n\nPOSTGRES_HOST=postgres\nPOSTGRES_PORT=5432\nPOSTGRES_DB=postgres\nPOSTGRES_USER=postgres\nPOSTGRES_PASSWORD=postgres\nPOSTGRES_COLLECTION_NAME=memories\n\nNEO4J_URI=bolt://neo4j:7687\nNEO4J_USERNAME=neo4j\nNEO4J_PASSWORD=mem0graph\n```\n\n---\n\n### 📄 `.env.openai` (OpenAI fallback)\n```\nEMBEDDER_PROVIDER=openai\nEMBEDDER_MODEL=text-embedding-3-small\nEMBEDDING_DIMS=1536\nOPENAI_API_KEY=sk-\n\nPOSTGRES_HOST=postgres\nPOSTGRES_PORT=5432\nPOSTGRES_DB=postgres\nPOSTGRES_USER=postgres\nPOSTGRES_PASSWORD=postgres\nPOSTGRES_COLLECTION_NAME=memories\n\nNEO4J_URI=bolt://neo4j:7687\nNEO4J_USERNAME=neo4j\nNEO4J_PASSWORD=mem0graph\n```\n\n---\n\n### 🪜 How to switch\nStart Mem0 with your chosen env:\n\n**Local 3090 mode:**\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n**OpenAI mode:**\n```bash\ndocker compose --env-file .env.openai -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\nThis way, nothing in `main.py` has to change — you just restart with a different env file. ✅ \n\n👉 Do you also want me to strip the leftover Ollama references out of your `main.py` so it’s *only* Hugging Face + OpenAI cleanly?" }, { "role": "user", "content": "i am going to test it first. How do?" }, { "role": "assistant", "content": "Perfect 👌 let’s do a simple end-to-end test to make sure Mem0 is hitting your new local embed server and saving the vectors. \n\n---\n\n### 🪜 Step 1: Make sure embed server is running\nOn your 3090 box: \n```powershell\ncd D:\\Development\\ai-servers\\\n.\\hfembed\\Scripts\\activate\npython -m uvicorn embed_server:app --host 0.0.0.0 --port 11435\n```\n\nYou should see:\n```\nUvicorn running on http://0.0.0.0:11435\n```\n\n---\n\n### 🪜 Step 2: Start Mem0 with your `.env.3090`\nOn the box running Mem0 (probably your Docker host):\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n### 🪜 Step 3: Send a test memory\nFrom the host running Mem0, run:\n\n```bash\ncurl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Test memory using my local 3090 embedder\"}]}'\n```\n\n---\n\n### 🪜 Step 4: Verify it worked\n1. In the **Mem0 logs** (`docker logs mem0`), you should see a call to your embed server (`http://10.0.0.3:11435/embed`). \n2. On the **embed server terminal**, you should see logging like: \n ```\n INFO: 127.0.0.1:... \"POST /embed HTTP/1.1\" 200 OK\n ```\n3. Finally, you can check your DB has the new memory:\n ```bash\n docker exec -e PGPASSWORD=postgres -it postgres \\\n psql -U postgres -d postgres -c \"SELECT COUNT(*) FROM memories;\"\n ```\n\n---\n\n👉 Do you want me to also show you how to temporarily **turn on debug logging** in `embed_server.py`, so it prints the text and vector length every time Mem0 calls it? That makes it crystal clear Mem0 is hitting your server, not OpenAI." }, { "role": "user", "content": "curl: (56) Recv failure: Connection reset by peer" }, { "role": "assistant", "content": "Got it 👍 “`Recv failure: Connection reset by peer`” almost always means Mem0 tried to call your embedding server, but the connection got dropped before a reply came back. Let’s debug step by step: \n\n---\n\n### 🪜 1. Confirm your embed server is running\nOn your 3090 terminal where you launched `uvicorn` — do you see this? \n```\nUvicorn running on http://0.0.0.0:11435\n```\nIf not, the server isn’t running. \n\n---\n\n### 🪜 2. Test embed server directly\nBefore Mem0, let’s check if the server works stand-alone. From the same machine where Mem0 is running (your Docker host), run:\n\n```bash\ncurl -X POST http://10.0.0.3:11435/embed \\\n -H \"Content-Type: application/json\" \\\n -d '{\"text\":\"hello world\"}'\n```\n\n- If it returns JSON with `\"dim\": 1536` → server is good. \n- If it errors / hangs → check Windows firewall, or confirm that your LAN IP is really `10.0.0.3`. \n\n(If you’re running Mem0 on the **same 3090 box**, you can test with `http://localhost:11435/embed` instead.) \n\n---\n\n### 🪜 3. Check your `.env.3090`\nMake sure it matches the reachable endpoint:\n```\nEMBEDDER_PROVIDER=huggingface\nEMBEDDER_MODEL=http://10.0.0.3:11435/embed\nEMBEDDING_DIMS=1536\n```\n\nIf Mem0 and the embed server are on the same PC, you can simplify to:\n```\nEMBEDDER_MODEL=http://localhost:11435/embed\n```\n\n---\n\n### 🪜 4. Restart Mem0 with fresh env\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n👉 Quick check: is your embed server running on the **same machine** as Mem0 (Docker), or are they on separate PCs (3090 Windows vs Linux host)? That’ll decide if we use `localhost` or the LAN IP." }, { "role": "user", "content": "serversdown@lyra-core:~$ docker logs mem0-api\nINFO: Will watch for changes in these directories: ['/app']\nINFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)\nINFO: Started reloader process [1] using StatReload\nProcess SpawnProcess-1:\nTraceback (most recent call last):\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n self.run()\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 108, in run\n self._target(*self._args, **self._kwargs)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/_subprocess.py\", line 80, in subprocess_started\n target(sockets=sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 66, in run\n return asyncio.run(self.serve(sockets=sockets))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 195, in run\n return runner.run(main)\n ^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 118, in run\n return self._loop.run_until_complete(task)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/base_events.py\", line 691, in run_until_complete\n return future.result()\n ^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 70, in serve\n await self._serve(sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 77, in _serve\n config.load()\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/config.py\", line 435, in load\n self.loaded_app = import_from_string(self.app)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/importer.py\", line 22, in import_from_string\n raise exc from None\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/importer.py\", line 19, in import_from_string\n module = importlib.import_module(module_str)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\", line 1387, in _gcd_import\n File \"\", line 1360, in _find_and_load\n File \"\", line 1331, in _find_and_load_unlocked\n File \"\", line 935, in _load_unlocked\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/app/main.py\", line 73, in \n MEMORY_INSTANCE = Memory.from_config(DEFAULT_CONFIG)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/memory/main.py\", line 178, in from_config\n return cls(config)\n ^^^^^^^^^^^\n File \"/app/packages/mem0/memory/main.py\", line 137, in __init__\n self.embedding_model = EmbedderFactory.create(\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/utils/factory.py\", line 150, in create\n embedder_instance = load_class(class_type)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/utils/factory.py\", line 18, in load_class\n module = importlib.import_module(module_path)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\", line 1387, in _gcd_import\n File \"\", line 1360, in _find_and_load\n File \"\", line 1331, in _find_and_load_unlocked\n File \"\", line 935, in _load_unlocked\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/app/packages/mem0/embeddings/huggingface.py\", line 5, in \n from sentence_transformers import SentenceTransformer\nModuleNotFoundError: No module named 'sentence_transformers'" }, { "role": "assistant", "content": "Ah — got it. That traceback is super clear 👍 \n\nYour **Mem0 Docker image** doesn’t have `sentence-transformers` installed, so when it tries to use the `huggingface` provider it crashes. \n\n---\n\n### 🪜 Fix: Add `sentence-transformers` to your Docker build\n\nYou’ve got two choices:\n\n---\n\n#### Option A: Quick fix inside the running container\nExec into the container and pip install:\n\n```bash\ndocker exec -it mem0-api bash\npip install sentence-transformers\nexit\ndocker restart mem0-api\n```\n\n👉 This will get you running right away, but the change is ephemeral — if you rebuild the container later, it’ll be lost.\n\n---\n\n#### Option B: Permanent fix via Dockerfile (recommended)\nIn your `Dockerfile` for Mem0, add:\n\n```dockerfile\nRUN pip install --no-cache-dir sentence-transformers\n```\n\nthen rebuild:\n\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n### ⚡ Why this matters\n- The Hugging Face embedder in Mem0 expects the **sentence-transformers** library inside the container. \n- Your local FastAPI embed server already has it, but Mem0’s API container doesn’t, so it explodes when trying to import. \n\n---\n\n👉 Do you want me to show you **exactly where in your Dockerfile** to drop that line, so it rebuilds with sentence-transformers included permanently?" }, { "role": "user", "content": "i did the exec bash and got it installed for testing, " }, { "role": "assistant", "content": "Nice ✅ that’s the fastest way to prove it works. \n\nNow you should be able to: \n\n1. Restart the container: \n ```bash\n docker restart mem0-api\n ``` \n\n2. Then try sending in a test memory again: \n ```bash\n curl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Test memory using local 3090 embedder\"}]}'\n ``` \n\n3. Watch your **embed_server.py** console (running on the 3090) — you should see a request hit `/embed`. \n\n---\n\n⚠️ Reminder: this manual install inside the container won’t survive a `--build`. Long-term, you’ll want to add: \n\n```dockerfile\nRUN pip install --no-cache-dir sentence-transformers\n``` \n\nto your **Mem0 Dockerfile**, then rebuild so it’s baked in. \n\n---\n\n👉 Want me to help you update your `Dockerfile` cleanly now so you don’t get bitten next time you rebuild?" }, { "role": "user", "content": "Traceback (most recent call last):\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n self.run()\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 108, in run\n self._target(*self._args, **self._kwargs)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/_subprocess.py\", line 80, in subprocess_started\n target(sockets=sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 66, in run\n return asyncio.run(self.serve(sockets=sockets))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 195, in run\n return runner.run(main)\n ^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 118, in run\n return self._loop.run_until_complete(task)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/base_events.py\", line 691, in run_until_complete\n return future.result()\n ^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 70, in serve\n await self._serve(sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 77, in _serve\n config.load()\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/config.py\", line 435, in load\n self.loaded_app = import_from_string(self.app)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/importer.py\", line 19, in import_from_string\n module = importlib.import_module(module_str)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\", line 1387, in _gcd_import\n File \"\", line 1360, in _find_and_load\n File \"\", line 1331, in _find_and_load_unlocked\n File \"\", line 935, in _load_unlocked\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/app/main.py\", line 73, in \n MEMORY_INSTANCE = Memory.from_config(DEFAULT_CONFIG)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/memory/main.py\", line 178, in from_config\n return cls(config)\n ^^^^^^^^^^^\n File \"/app/packages/mem0/memory/main.py\", line 137, in __init__\n self.embedding_model = EmbedderFactory.create(\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/utils/factory.py\", line 152, in create\n return embedder_instance(base_config)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/embeddings/huggingface.py\", line 24, in __init__\n self.model = SentenceTransformer(self.config.model, **self.config.model_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/sentence_transformers/SentenceTransformer.py\", line 304, in __init__\n raise FileNotFoundError(f\"Path {model_name_or_path} not found\")\nFileNotFoundError: Path http://10.0.0.3:11435/embed not found" }, { "role": "assistant", "content": "Ahhh, I see the problem 👀 — Mem0’s built-in **huggingface embedder** expects a *Hugging Face model name or local path* (like `\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\"`), not a REST API endpoint. \n\nThat’s why you get: \n```\nFileNotFoundError: Path http://10.0.0.3:11435/embed not found\n```\n\nBecause it’s literally trying to `SentenceTransformer(\"http://10.0.0.3:11435/embed\")` — which fails. \n\n---\n\n### ✅ Two ways forward\n\n#### Option A (easy, no extra coding): \nRun the model **inside the Mem0 container** directly using Hugging Face. \n- In `.env.3090`, set: \n ```\n EMBEDDER_PROVIDER=huggingface\n EMBEDDER_MODEL=Alibaba-NLP/gte-Qwen2-1.5B-instruct\n EMBEDDING_DIMS=1536\n ```\n- Then Mem0 will pull the model via `sentence-transformers` inside Docker and run embeddings itself. \n\n⚠️ Downside: you’d need enough GPU/CPU access in Docker to run the 1.5B model. If your Mem0 container isn’t GPU-enabled, it’ll fall back to CPU (slow). \n\n---\n\n#### Option B (use your FastAPI server, like Ollama style): \nTreat your **FastAPI embed server** as a *custom provider*. Mem0 doesn’t know how to talk to it yet. To do this: \n\n1. In `main.py`, instead of `provider=huggingface`, set something like: \n ```python\n \"embedder\": {\n \"provider\": \"custom\",\n \"config\": {\n \"base_url\": \"http://10.0.0.3:11435/embed\",\n \"embedding_dims\": 1536\n }\n }\n ```\n2. Write a tiny custom embedder class in `mem0/embeddings/` (call it `custom.py`) that just `requests.post` to your API and returns the vector. \n\nThat way, Mem0 uses your REST API server instead of trying to load the model itself.\n\n---\n\n### 🎯 My Recommendation for You\nSince you already have the FastAPI server running smoothly on your 3090, **Option B is the cleanest** — add a “custom embedder” that calls your HTTP endpoint. That avoids GPU passthrough headaches in Docker. \n\n---\n\n👉 Do you want me to show you exactly how to write that **`custom.py` embedder class** so Mem0 can call your FastAPI server directly?" }, { "role": "user", "content": "How do i test?" }, { "role": "assistant", "content": "Alright, let’s make this concrete so you can test whether things are wired up right 👍 \n\nRight now you hit an error because Mem0’s `huggingface` embedder expects a model name, not an HTTP endpoint. So before testing Mem0 again, we need to decide **which route you’re taking**: \n\n---\n\n## 🛠 Option A — Run the Hugging Face model *inside* Mem0\n- Mem0 downloads the model and uses it directly. \n- To test: \n 1. Edit `.env.3090`: \n ```\n EMBEDDER_PROVIDER=huggingface\n EMBEDDER_MODEL=Alibaba-NLP/gte-Qwen2-1.5B-instruct\n EMBEDDING_DIMS=1536\n ```\n 2. Rebuild/start Mem0: \n ```bash\n docker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n ```\n 3. Send a memory with curl (like before). \n 4. Watch the Mem0 logs — if it starts downloading a big HF model, you’re in this path. \n\n⚠️ This will only run on CPU unless you configure GPU passthrough into the container.\n\n---\n\n## 🛠 Option B — Use your FastAPI embed server (my recommendation)\n- Mem0 doesn’t have a built-in “REST API embedder,” so you’ll need to add a simple **custom embedder**. \n\n1. Make a new file inside Mem0: \n `mem0/embeddings/custom.py` \n ```python\n import requests\n from mem0.embeddings.base import BaseEmbedder\n\n class CustomEmbedder(BaseEmbedder):\n def __init__(self, config):\n super().__init__(config)\n self.base_url = config.get(\"base_url\")\n\n def embed(self, text: str):\n resp = requests.post(self.base_url, json={\"text\": text})\n resp.raise_for_status()\n data = resp.json()\n return data[\"embedding\"]\n ```\n\n2. In your `.env.3090`: \n ```\n EMBEDDER_PROVIDER=custom\n EMBEDDER_MODEL=http://10.0.0.3:11435/embed\n EMBEDDING_DIMS=1536\n ```\n\n3. Restart Mem0, then test with curl again. \n\n---\n\n## 🧪 How to Test\nNo matter which option you pick: \n- Run the embed server (if Option B). \n- Start Mem0 with `.env.3090`. \n- Insert a test memory: \n ```bash\n curl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Testing my local embedder\"}]}'\n ```\n- Watch logs: \n - Option A → You’ll see HF model loading inside Docker. \n - Option B → You’ll see requests hitting your FastAPI embed server (`/embed` log entries). \n\n---\n\n👉 Do you want me to help you implement **Option B (custom embedder)** so Mem0 just calls your FastAPI server? That way you can actually confirm requests are flowing into the server you already built." }, { "role": "user", "content": "INFO: Will watch for changes in these directories: ['/app']\nINFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)\nINFO: Started reloader process [1] using StatReload\n2025-10-03 20:19:35,076 - ERROR - Configuration validation error: 1 validation error for MemoryConfig\nembedder.config\n Value error, Unsupported embedding provider: custom [type=value_error, input_value={'model': 'http://10.0.0....79KpU2160FRMG3S_HT8ywA'}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.10/v/value_error\nProcess SpawnProcess-1:\nTraceback (most recent call last):\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n self.run()\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 108, in run\n self._target(*self._args, **self._kwargs)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/_subprocess.py\", line 80, in subprocess_started\n target(sockets=sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 66, in run\n return asyncio.run(self.serve(sockets=sockets))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 195, in run\n return runner.run(main)\n ^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 118, in run\n return self._loop.run_until_complete(task)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/base_events.py\", line 691, in run_until_complete\n return future.result()\n ^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 70, in serve\n await self._serve(sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 77, in _serve\n config.load()\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/config.py\", line 435, in load\n self.loaded_app = import_from_string(self.app)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/importer.py\", line 19, in import_from_string\n module = importlib.import_module(module_str)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\", line 1387, in _gcd_import\n File \"\", line 1360, in _find_and_load\n File \"\", line 1331, in _find_and_load_unlocked\n File \"\", line 935, in _load_unlocked\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/app/main.py\", line 74, in \n MEMORY_INSTANCE = Memory.from_config(DEFAULT_CONFIG)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/memory/main.py\", line 174, in from_config\n config = MemoryConfig(**config_dict)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/pydantic/main.py\", line 214, in __init__\n validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\npydantic_core._pydantic_core.ValidationError: 1 validation error for MemoryConfig\nembedder.config\n Value error, Unsupported embedding provider: custom [type=value_error, input_value={'model': 'http://10.0.0....79KpU2160FRMG3S_HT8ywA'}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.10/v/value_error" }, { "role": "assistant", "content": "Yep — that error is actually good news 🙂 It tells us **Mem0 doesn’t recognize `custom` as a valid provider**. Right now it only allows the built-ins (openai, ollama, huggingface, together, etc.). \n\nSo when you set: \n```\nEMBEDDER_PROVIDER=custom\n```\n…it bombs out because `custom` isn’t in Mem0’s whitelist. \n\n---\n\n### 🔧 Your Choices\n\n#### Option A (easier, less code) \nDon’t invent a new provider — instead, “spoof” an existing one. \n- Easiest hack: use the **Ollama provider** slot, since it already expects to hit a local REST API. \n- In your `.env.3090`: \n ```\n EMBEDDER_PROVIDER=ollama\n OLLAMA_HOST=http://10.0.0.3:11435\n EMBEDDER_MODEL=nomic-embed-text # or leave as-is, it won’t matter\n EMBEDDING_DIMS=1536\n ```\n- Then tweak your FastAPI embed server to respond to the same endpoint Ollama does: \n - Ollama embeddings expect POST → `/api/embeddings` with `{ \"model\": \"...\", \"prompt\": \"...\" }`. \n - You’d just translate `prompt` → your HF model → return `embedding`. \n\nThat way Mem0 thinks it’s talking to Ollama, but it’s actually your server. No Mem0 code changes.\n\n---\n\n#### Option B (more proper, but requires editing Mem0 code) \nAdd `custom` to Mem0’s embedder factory: \n- Edit `mem0/configs/embeddings/base.py` → allow `\"custom\"` as a provider. \n- Drop in your `custom.py` embedder. \n- Rebuild the container. \n\nThis is cleaner long-term, but means you’re forking Mem0 slightly.\n\n---\n\n### 🧪 For testing right now \nI’d recommend Option A (Ollama spoof). It’s literally just: \n\n1. Change `.env.3090` provider back to `ollama`. \n2. Make your embed server accept requests in the Ollama shape. \n\n---\n\n👉 Do you want me to show you the exact JSON format Ollama’s `/api/embeddings` endpoint uses, so you can patch your FastAPI server in 10 lines and have Mem0 work with it immediately?" }, { "role": "user", "content": "cant we test option A?" }, { "role": "assistant", "content": "Yep, we can absolutely test **Option A (spoof Ollama)** — that’s the fastest way to get Mem0 talking to your local embed server without editing Mem0’s source. \n\nHere’s how:\n\n---\n\n### 🪜 1. Adjust your `.env.3090`\n\nSet it up like Mem0 thinks it’s talking to Ollama:\n\n```\nEMBEDDER_PROVIDER=ollama\nOLLAMA_HOST=http://10.0.0.3:11435\nEMBEDDER_MODEL=nomic-embed-text\nEMBEDDING_DIMS=1536\n```\n\n---\n\n### 🪜 2. Update your FastAPI embed server\n\nRight now your server expects:\n\n```json\n{ \"text\": \"hello world\" }\n```\n\nBut Ollama’s `/api/embeddings` expects:\n\n```json\n{\n \"model\": \"nomic-embed-text\",\n \"prompt\": \"hello world\"\n}\n```\n\nSo tweak `embed_server.py` like this:\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom sentence_transformers import SentenceTransformer\n\napp = FastAPI()\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\")\n\nclass OllamaEmbedRequest(BaseModel):\n model: str\n prompt: str\n\n@app.post(\"/api/embeddings\")\ndef embed(req: OllamaEmbedRequest):\n vector = model.encode(req.prompt).tolist()\n return {\"embedding\": vector}\n```\n\n---\n\n### 🪜 3. Run your server again\n\n```powershell\ncd D:\\Development\\ai-servers\\\n.\\hfembed\\Scripts\\activate\npython -m uvicorn embed_server:app --host 0.0.0.0 --port 11435\n```\n\n---\n\n### 🪜 4. Restart Mem0 with `.env.3090`\n\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n### 🪜 5. Test end-to-end\n\nInsert a memory:\n\n```bash\ncurl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Testing via local 1536 embedder\"}]}'\n```\n\nWatch your FastAPI console — you should see a POST hit `/api/embeddings`, and Mem0 will store a 1536-dim vector.\n\n---\n\n👉 Want me to also add some logging in the FastAPI server (print the prompt + vector length) so you’ll see proof in the console that Mem0 is calling it?" }, { "role": "user", "content": "same error but nothing actually printed in the Log for mem0-api" }, { "role": "assistant", "content": "Got it 👍 — since nothing hit your FastAPI logs, that means Mem0 is still crashing before it even tries to call your server. The error you pasted before was during **startup validation** — Mem0 checks whether the provider is supported before it runs anything. \n\nHere’s the catch: \n\n- Mem0’s `ollama` embedder **still tries to import the `ollama` Python client** inside the container, even if you point it at another URL. \n- If that client isn’t installed, Mem0 throws before it ever makes a request → which explains why you see no requests in your FastAPI server. \n\n---\n\n### ✅ Fix: Install the Ollama client in your Mem0 container\n\nQuick test fix:\n\n```bash\ndocker exec -it mem0-api bash\npip install ollama\nexit\ndocker restart mem0-api\n```\n\nThat should let Mem0 boot with `EMBEDDER_PROVIDER=ollama`. After that, if your FastAPI server is listening on `/api/embeddings`, Mem0 should finally try to send it requests. \n\n---\n\n### 🪜 Checklist for Option A spoof\n1. **.env.3090**\n ```\n EMBEDDER_PROVIDER=ollama\n OLLAMA_HOST=http://10.0.0.3:11435\n EMBEDDER_MODEL=nomic-embed-text\n EMBEDDING_DIMS=1536\n ```\n\n2. **FastAPI server** responds at `/api/embeddings` with `{ \"embedding\": [...] }`.\n\n3. **Mem0 container** has the `ollama` Python client installed (`pip install ollama`).\n\n---\n\n👉 Want me to also give you the exact JSON Mem0 will POST to `/api/embeddings` so you can 100% mirror it in your FastAPI server? That way we know the formats align." }, { "role": "user", "content": "same thing" }, { "role": "assistant", "content": "Got it — thanks for testing. Since you’re still seeing the same validation error and Mem0 isn’t even touching your FastAPI logs, that tells us this problem isn’t about requests failing — it’s **schema validation** happening before Mem0 ever tries to embed. \n\n---\n\n### Why this is happening\n- Mem0 uses **Pydantic** to validate `embedder.provider`. \n- Only a fixed set of providers are valid (`openai`, `ollama`, `huggingface`, `azure_openai`, etc.). \n- When you set `provider=ollama`, Mem0 expects to load the **real Ollama embedder class** from `mem0/embeddings/ollama.py`. \n- That class tries to spin up with the `ollama` Python client. \n- Since we’re not actually running Ollama, but spoofing, the constructor never matches what your FastAPI server is doing. \n\nThat’s why nothing ever reaches your 3090 server — Mem0 never gets that far. \n\n---\n\n### ✅ Two realistic paths forward\n\n#### Option A: Run model directly inside Mem0 (Hugging Face mode)\nUse Mem0’s **native Hugging Face provider** instead of trying to proxy: \n\n`.env.3090`:\n```\nEMBEDDER_PROVIDER=huggingface\nEMBEDDER_MODEL=Alibaba-NLP/gte-Qwen2-1.5B-instruct\nEMBEDDING_DIMS=1536\n```\n\nThis works out of the box (as long as you install `sentence-transformers` inside the container and allow GPU or accept CPU). Downside: big model download in Docker.\n\n---\n\n#### Option B: Actually add a new provider (`custom`)\n- Add `custom` to the `ProviderEnum` in `mem0/configs/embeddings/base.py`. \n- Create `mem0/embeddings/custom.py` with your REST wrapper (calls your FastAPI server). \n- Rebuild Docker. \n- Now `EMBEDDER_PROVIDER=custom` works. \n\nThis is the clean way if you want Mem0 → your FastAPI server. \n\n---\n\n### 🚦 Which to test now?\n- If you want to **just test quickly**: go with **Option A** and point Mem0 at the Hugging Face model directly. It’ll at least confirm embeddings flow through. \n- If you want Mem0 to **use your 3090 FastAPI server** (your original goal): we’ll need to do Option B (a tiny code patch in Mem0 so `custom` is recognized). \n\n---\n\n👉 Do you want me to walk you through the code edit for **Option B (adding `custom` provider)** so Mem0 can actually hit your FastAPI server? That way we stop fighting the provider validation." }, { "role": "user", "content": "This error indicates that you're trying to use `\"custom\"` as an embedding provider, which is **not supported** in Mem0 OSS. [1](#7-0) \n\n## What's Happening\n\nThe error occurs during Pydantic validation when Mem0 tries to validate your embedder configuration. [1](#7-0) The `EmbedderConfig` class has a strict validator that only allows these supported providers:\n\n- `openai`\n- `ollama` \n- `huggingface`\n- `azure_openai`\n- `gemini`\n- `vertexai`\n- `together`\n- `lmstudio`\n- `langchain`\n- `aws_bedrock`\n\n## Your Configuration Issue\n\nBased on the error message, it looks like you're trying to configure an embedding model with a URL (`http://10.0.0....`) using `\"custom\"` as the provider. This suggests you might be trying to connect to a custom embedding endpoint.\n\n## Solutions\n\n### Option 1: Use a Supported Provider\nIf you're running a local embedding server, try using one of these supported providers:\n\n**For OpenAI-compatible endpoints:**\n```python\nconfig = {\n \"embedder\": {\n \"provider\": \"openai\",\n \"config\": {\n \"model\": \"your-model-name\",\n \"openai_base_url\": \"http://10.0.0.xxx:port/v1\", # Your custom URL\n \"api_key\": \"your-api-key-or-dummy\"\n }\n }\n}\n```\n\n**For HuggingFace TEI endpoints:**\n```python\nconfig = {\n \"embedder\": {\n \"provider\": \"huggingface\", \n \"config\": {\n \"huggingface_base_url\": \"http://10.0.0.xxx:port/v1\" # Your custom URL\n }\n }\n}\n```\n\n### Option 2: Use LangChain Provider\nThe LangChain provider offers the most flexibility for custom implementations: [2](#7-1) \n\n```python\nfrom langchain_community.embeddings import YourCustomEmbeddings\n\ncustom_embeddings = YourCustomEmbeddings(base_url=\"http://10.0.0.xxx:port\")\n\nconfig = {\n \"embedder\": {\n \"provider\": \"langchain\",\n \"config\": {\n \"model\": custom_embeddings\n }\n }\n}\n```\n\n## Notes\n\nThe `EmbedderFactory` class handles provider instantiation and doesn't include a \"custom\" provider mapping. [3](#7-2) You'll need to use one of the supported providers that can connect to your custom endpoint, with OpenAI-compatible being the most common approach for custom embedding servers.\n\nWiki pages you might want to explore:\n- [Embedding Providers (mem0ai/mem0)](/wiki/mem0ai/mem0#4.2)\n- [Configuration (mem0ai/mem0)](/wiki/mem0ai/mem0#5)\n\n### Citations\n\n**File:** mem0/embeddings/configs.py (L13-30)\n```python\n @field_validator(\"config\")\n def validate_config(cls, v, values):\n provider = values.data.get(\"provider\")\n if provider in [\n \"openai\",\n \"ollama\",\n \"huggingface\",\n \"azure_openai\",\n \"gemini\",\n \"vertexai\",\n \"together\",\n \"lmstudio\",\n \"langchain\",\n \"aws_bedrock\",\n ]:\n return v\n else:\n raise ValueError(f\"Unsupported embedding provider: {provider}\")\n```\n\n**File:** docs/components/embedders/models/langchain.mdx (L27-34)\n```text\nconfig = {\n \"embedder\": {\n \"provider\": \"langchain\",\n \"config\": {\n \"model\": openai_embeddings\n }\n }\n}\n```\n\n**File:** mem0/utils/factory.py (L130-154)\n```python\nclass EmbedderFactory:\n provider_to_class = {\n \"openai\": \"mem0.embeddings.openai.OpenAIEmbedding\",\n \"ollama\": \"mem0.embeddings.ollama.OllamaEmbedding\",\n \"huggingface\": \"mem0.embeddings.huggingface.HuggingFaceEmbedding\",\n \"azure_openai\": \"mem0.embeddings.azure_openai.AzureOpenAIEmbedding\",\n \"gemini\": \"mem0.embeddings.gemini.GoogleGenAIEmbedding\",\n \"vertexai\": \"mem0.embeddings.vertexai.VertexAIEmbedding\",\n \"together\": \"mem0.embeddings.together.TogetherEmbedding\",\n \"lmstudio\": \"mem0.embeddings.lmstudio.LMStudioEmbedding\",\n \"langchain\": \"mem0.embeddings.langchain.LangchainEmbedding\",\n \"aws_bedrock\": \"mem0.embeddings.aws_bedrock.AWSBedrockEmbedding\",\n }\n\n @classmethod\n def create(cls, provider_name, config, vector_config: Optional[dict]):\n if provider_name == \"upstash_vector\" and vector_config and vector_config.enable_embeddings:\n return MockEmbeddings()\n class_type = cls.provider_to_class.get(provider_name)\n if class_type:\n embedder_instance = load_class(class_type)\n base_config = BaseEmbedderConfig(**config)\n return embedder_instance(base_config)\n else:\n raise ValueError(f\"Unsupported Embedder provider: {provider_name}\")\n```" }, { "role": "assistant", "content": "💡 Exactly — you dug up the right bit from Mem0’s deepwiki / source. That error is because Mem0 validates providers **before anything runs**. `custom` isn’t supported, so it bombs. \n\n---\n\n### ✅ What works instead\nYou have 3 supported paths to plug in your own embedding server:\n\n---\n\n#### **Option 1: Spoof OpenAI**\nIf your FastAPI server pretends to be OpenAI’s API (`/v1/embeddings` endpoint, OpenAI-style JSON), you can use:\n\n```env\nEMBEDDER_PROVIDER=openai\nEMBEDDER_MODEL=my-local-model\nEMBEDDING_DIMS=1536\nOPENAI_API_KEY=dummy-key\nOPENAI_BASE_URL=http://10.0.0.3:11435/v1\n```\n\nThen in `embed_server.py`, accept:\n\n```json\nPOST /v1/embeddings\n{\n \"model\": \"my-local-model\",\n \"input\": \"hello world\"\n}\n```\n\n…and return:\n\n```json\n{\n \"data\": [\n {\n \"embedding\": [ ...1536 floats... ]\n }\n ]\n}\n```\n\nMem0 will treat your FastAPI server like an OpenAI-compatible endpoint.\n\n---\n\n#### **Option 2: Spoof Hugging Face TEI**\nIf you rewrap your server to look like Hugging Face’s **Text Embeddings Inference (TEI)** API, you can set:\n\n```env\nEMBEDDER_PROVIDER=huggingface\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11435\nEMBEDDING_DIMS=1536\n```\n\n…but this requires you to match TEI’s API format. (Harder than Option 1.)\n\n---\n\n#### **Option 3: Use LangChain provider**\nLangChain lets you drop in *any* custom embeddings class. You’d write a tiny adapter like:\n\n```python\nfrom langchain_community.embeddings import OpenAIEmbeddings\n\ncustom_embeddings = OpenAIEmbeddings(\n model=\"my-local-model\",\n openai_api_key=\"dummy-key\",\n openai_api_base=\"http://10.0.0.3:11435/v1\"\n)\n```\n\nThen configure Mem0 with:\n```env\nEMBEDDER_PROVIDER=langchain\nEMBEDDER_MODEL=my-local-model\n```\n\n---\n\n### 🎯 Recommendation for you\nSince you already have a FastAPI server, the **easiest test** is: \n➡️ make it look like OpenAI’s `/v1/embeddings` endpoint, then set `EMBEDDER_PROVIDER=openai` with `OPENAI_BASE_URL=http://10.0.0.3:11435/v1`.\n\nThat way, you don’t patch Mem0 at all — it just thinks it’s talking to OpenAI.\n\n---\n\n👉 Want me to give you a drop-in replacement `embed_server.py` that speaks **OpenAI-style `/v1/embeddings`** so Mem0 can talk to it right now?" }, { "role": "user", "content": "arent we using huggingface? can i just make a hugging face thing" }, { "role": "assistant", "content": "Yes! You’re totally right — you’re using a Hugging Face model (`gte-Qwen2-1.5B-instruct`), so you can just make your FastAPI server **pretend to be Hugging Face’s Text Embeddings Inference (TEI)** endpoint. \n\nThat way you set: \n```env\nEMBEDDER_PROVIDER=huggingface\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11435\nEMBEDDING_DIMS=1536\n```\n\n…and Mem0 will be happy 🎉\n\n---\n\n### 🪜 How Hugging Face TEI `/embed` works\nTEI expects: \n\n**Request:**\n```json\nPOST /embed\n{ \"inputs\": \"hello world\" }\n```\n\n**Response:**\n```json\n{\n \"embeddings\": [[ ...1536 floats... ]]\n}\n```\n\n(one vector inside a list)\n\n---\n\n### 🪜 Updated `embed_server.py`\nHere’s a minimal server that mimics Hugging Face TEI:\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom sentence_transformers import SentenceTransformer\n\napp = FastAPI()\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\")\n\nclass HFEmbedRequest(BaseModel):\n inputs: str\n\n@app.post(\"/embed\")\ndef embed(req: HFEmbedRequest):\n vector = model.encode(req.inputs).tolist()\n return {\"embeddings\": [vector]} # TEI format = list of lists\n```\n\n---\n\n### 🪜 Steps to test\n1. Run your server again: \n ```powershell\n python -m uvicorn embed_server:app --host 0.0.0.0 --port 11435\n ```\n\n2. Test it manually: \n ```bash\n curl -X POST http://localhost:11435/embed \\\n -H \"Content-Type: application/json\" \\\n -d '{\"inputs\":\"hello world\"}'\n ```\n Should return `{\"embeddings\": [[...1536 floats...]]}`.\n\n3. Update `.env.3090`: \n ```\n EMBEDDER_PROVIDER=huggingface\n HUGGINGFACE_BASE_URL=http://10.0.0.3:11435\n EMBEDDING_DIMS=1536\n ```\n\n4. Restart Mem0 with that env and try a memory insert.\n\n---\n\n👉 Do you want me to also add **logging** to the server so whenever Mem0 calls it, it prints the input text + vector length, so you can confirm it’s hitting your Hugging Face clone?" }, { "role": "user", "content": "curl: (7) Failed to connect to localhost port 11435 after 0 ms: Connection refused" }, { "role": "assistant", "content": "Got it 👍 that error means the FastAPI server isn’t actually running or isn’t listening on the port you tried. Let’s troubleshoot step by step: \n\n---\n\n### 🪜 1. Start the server\nMake sure you’re in the right folder and venv, then run: \n```powershell\ncd D:\\Development\\ai-servers\\\n.\\hfembed\\Scripts\\activate\npython -m uvicorn embed_server:app --host 0.0.0.0 --port 11435\n```\n\nIf it’s working, you should see:\n```\nUvicorn running on http://0.0.0.0:11435 (Press CTRL+C to quit)\n```\n\n---\n\n### 🪜 2. Test locally on the same machine\nFrom another PowerShell window on the same 3090 box: \n```powershell\ncurl -X POST http://localhost:11435/embed -H \"Content-Type: application/json\" -d \"{\\\"inputs\\\":\\\"hello world\\\"}\"\n```\n\nExpected output: \n```json\n{\"embeddings\":[[0.01, -0.02, ...]]}\n```\n\nIf this fails with “connection refused,” the server isn’t running (or it crashed right away).\n\n---\n\n### 🪜 3. If it started but still fails\n- Make sure Windows Firewall isn’t blocking Python/uvicorn. \n- If you’re testing from **another machine**, don’t use `localhost`, use your LAN IP (`http://10.0.0.3:11435/embed`). \n\n---\n\n👉 Can you check what shows up in your console when you try to launch the server with that `uvicorn` command? That’ll tell us if it actually started or if it’s crashing right away." }, { "role": "user", "content": "its possible that i have ollama running already using that port. Maybe use a different poty" }, { "role": "assistant", "content": "Ahhh yep, that would explain it 💡 — if Ollama is already bound to `11434/11435`, then FastAPI can’t grab that port and you’ll see “connection refused.” \n\n---\n\n### 🪜 Pick a new port\nLet’s say we use **11500** instead (totally free). \n\nRun your server like this: \n```powershell\ncd D:\\Development\\ai-servers\\\n.\\hfembed\\Scripts\\activate\npython -m uvicorn embed_server:app --host 0.0.0.0 --port 11500\n```\n\nYou should see: \n```\nUvicorn running on http://0.0.0.0:11500\n```\n\n---\n\n### 🪜 Test locally\n```powershell\ncurl -X POST http://localhost:11500/embed -H \"Content-Type: application/json\" -d \"{\\\"inputs\\\":\\\"hello world\\\"}\"\n```\n\nExpected: \n```json\n{\"embeddings\":[[ ...1536 floats... ]]}\n```\n\n---\n\n### 🪜 Update `.env.3090`\nPoint Mem0 at the new port: \n```\nEMBEDDER_PROVIDER=huggingface\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500\nEMBEDDING_DIMS=1536\n```\n\nThen restart Mem0: \n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n👉 Do you want me to also add a quick `print()` logger in the FastAPI endpoint so every time Mem0 sends a request you’ll see the input + vector length in your PowerShell window?" }, { "role": "user", "content": " File \"/app/packages/mem0/utils/factory.py\", line 18, in load_class\n module = importlib.import_module(module_path)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\", line 1387, in _gcd_import\n File \"\", line 1360, in _find_and_load\n File \"\", line 1331, in _find_and_load_unlocked\n File \"\", line 935, in _load_unlocked\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/app/packages/mem0/embeddings/huggingface.py\", line 5, in \n from sentence_transformers import SentenceTransformer\nModuleNotFoundError: No module named 'sentence_transformers'" }, { "role": "assistant", "content": "Ah — I see exactly what’s happening now 👀 \n\nHere’s the distinction:\n\n- When you run `provider=huggingface` in Mem0, it tries to load its built-in **HuggingFaceEmbedding class** (`mem0/embeddings/huggingface.py`). \n- That class expects to import `sentence_transformers` and load a model **inside the Mem0 container**. \n- But you don’t want Mem0 to load the model — you already have it running on your 3090 FastAPI server. \n\nSo you’re hitting the `ModuleNotFoundError` again because Mem0 is trying to spin up Hugging Face itself, not call your API. \n\n---\n\n### ✅ The Fix: Don’t use `provider=huggingface` directly\nWe want Mem0 to treat your server as a **remote Hugging Face TEI endpoint**, not as a local `sentence_transformers` model. \n\nIn Mem0 OSS, that’s supported via the `huggingface_base_url` config. You don’t need `sentence_transformers` in the container at all. \n\n---\n\n### 🪜 How to set it up\n\n1. In `.env.3090` set:\n\n```\nEMBEDDER_PROVIDER=huggingface\nEMBEDDER_MODEL=gptqwen1536 # just a placeholder name\nEMBEDDING_DIMS=1536\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500\n```\n\n2. In `main.py`, update your embedder config to include `huggingface_base_url`:\n\n```python\n\"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL,\n \"embedding_dims\": int(os.environ.get(\"EMBEDDING_DIMS\", \"1536\")),\n \"huggingface_base_url\": os.getenv(\"HUGGINGFACE_BASE_URL\"),\n \"api_key\": OPENAI_API_KEY # still works if provider=openai\n },\n},\n```\n\n3. Rebuild Mem0:\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n### 🧪 Why this works\n- Now Mem0 won’t try to load `SentenceTransformer` inside the container. \n- Instead, it will POST to your FastAPI server at `/embed`. \n- Your server responds in TEI format: \n ```json\n { \"embeddings\": [[ ...1536 floats... ]] }\n ```\n\n---\n\n👉 Do you want me to give you a **full embed_server.py** that exactly matches Hugging Face TEI’s expected request/response schema, so Mem0 won’t choke at all?" }, { "role": "user", "content": "same error" }, { "role": "assistant", "content": "Got it — thanks for confirming. \nThat means Mem0 OSS **doesn’t treat `huggingface_base_url` as an override** in the version you’re running. Instead, its `huggingface.py` embedder **always imports `sentence_transformers`** and tries to load the model locally. That’s why you keep seeing: \n\n```\nModuleNotFoundError: No module named 'sentence_transformers'\n```\n\n---\n\n### ⚡ Where we stand\n\n- ✅ You have a working **FastAPI embedding server** on your 3090 (1536-dim). \n- ❌ Mem0’s `huggingface` provider isn’t designed to call remote servers; it only supports local models via `sentence_transformers`. \n- ❌ Mem0’s `custom` provider isn’t allowed because of the provider validation enum. \n\nSo we need to fit your FastAPI server into one of the providers that *does* support external endpoints. \n\n---\n\n### ✅ Solution: Spoof **OpenAI provider**\nMem0’s **OpenAI embedder** already knows how to call external HTTP endpoints if they are OpenAI-compatible. We can make your FastAPI server speak OpenAI’s `/v1/embeddings` format.\n\n---\n\n### 🪜 How to do it\n\n1. **Update embed_server.py** to expose an OpenAI-style endpoint:\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom sentence_transformers import SentenceTransformer\n\napp = FastAPI()\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\")\n\nclass OpenAIEmbedRequest(BaseModel):\n model: str\n input: str\n\n@app.post(\"/v1/embeddings\")\ndef embed(req: OpenAIEmbedRequest):\n vector = model.encode(req.input).tolist()\n return {\n \"data\": [\n {\n \"embedding\": vector,\n \"index\": 0,\n \"object\": \"embedding\"\n }\n ],\n \"model\": req.model,\n \"object\": \"list\"\n }\n```\n\n---\n\n2. **Change `.env.3090`**:\n\n```\nEMBEDDER_PROVIDER=openai\nEMBEDDER_MODEL=gptqwen1536\nEMBEDDING_DIMS=1536\nOPENAI_API_KEY=dummy-key\nOPENAI_BASE_URL=http://10.0.0.3:11500/v1\n```\n\n---\n\n3. **Restart Mem0**:\n\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n4. **Test it manually**:\n\n```bash\ncurl -X POST http://localhost:11500/v1/embeddings \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"gptqwen1536\",\"input\":\"hello world\"}'\n```\n\nYou should see a JSON with `\"embedding\": [ ...1536 floats... ]`.\n\n---\n\n### 🎯 Why this works\n- Mem0’s `openai` embedder already knows how to talk to OpenAI endpoints. \n- By mimicking `/v1/embeddings`, your server “pretends” to be OpenAI. \n- No Mem0 code changes needed — just config + API shape match. \n\n---\n\n👉 Do you want me to modify your FastAPI server to also **log every input Mem0 sends** (so you can see in real time when it’s working)?" }, { "role": "user", "content": "Yes, Mem0 OSS does support calling remote servers via LAN with HuggingFace through the Text Embeddings Inference (TEI) service. [1](#8-0) \n\n## How Remote HuggingFace TEI Works\n\nThe HuggingFace embedding provider in Mem0 has two modes of operation. [2](#8-1) When you specify a `huggingface_base_url`, it creates an OpenAI client that connects to your remote TEI endpoint instead of using local models.\n\nHere's the configuration for connecting to a remote HuggingFace TEI server on your LAN:\n\n```python\nconfig = {\n \"embedder\": {\n \"provider\": \"huggingface\",\n \"config\": {\n \"huggingface_base_url\": \"http://10.0.0.xxx:3000/v1\" # Your LAN server IP\n }\n }\n}\n```\n\n## Implementation Details\n\nThe embedding generation logic switches based on whether a base URL is provided: [3](#8-2) \n\n- **With `huggingface_base_url`**: Uses OpenAI client to call the TEI API endpoint\n- **Without `huggingface_base_url`**: Uses local SentenceTransformer models\n\n## Configuration Parameters\n\nThe base embedder configuration supports the `huggingface_base_url` parameter specifically for this purpose. [4](#8-3) This parameter is documented as \"URL to connect to Text Embeddings Inference (TEI) API\". [5](#8-4) \n\n## Notes\n\nThe TEI service provides faster and more efficient embeddings compared to local models, making it ideal for production deployments where you want to centralize embedding computation on a dedicated server within your network infrastructure.\n\nWiki pages you might want to explore:\n- [AI Model Integrations (mem0ai/mem0)](/wiki/mem0ai/mem0#4)\n\n### Citations\n\n**File:** docs/components/embedders/models/huggingface.mdx (L34-56)\n```text\n### Using Text Embeddings Inference (TEI)\n\nYou can also use Hugging Face's Text Embeddings Inference service for faster and more efficient embeddings:\n\n```python\nimport os\nfrom mem0 import Memory\n\nos.environ[\"OPENAI_API_KEY\"] = \"your_api_key\" # For LLM\n\n# Using HuggingFace Text Embeddings Inference API\nconfig = {\n \"embedder\": {\n \"provider\": \"huggingface\",\n \"config\": {\n \"huggingface_base_url\": \"http://localhost:3000/v1\"\n }\n }\n}\n\nm = Memory.from_config(config)\nm.add(\"This text will be embedded using the TEI service.\", user_id=\"john\")\n```\n```\n\n**File:** docs/components/embedders/models/huggingface.mdx (L75-75)\n```text\n\n```\n\n**File:** mem0/embeddings/huggingface.py (L19-26)\n```python\n if config.huggingface_base_url:\n self.client = OpenAI(base_url=config.huggingface_base_url)\n else:\n self.config.model = self.config.model or \"multi-qa-MiniLM-L6-cos-v1\"\n\n self.model = SentenceTransformer(self.config.model, **self.config.model_kwargs)\n\n self.config.embedding_dims = self.config.embedding_dims or self.model.get_sentence_embedding_dimension()\n```\n\n**File:** mem0/embeddings/huggingface.py (L38-41)\n```python\n if self.config.huggingface_base_url:\n return self.client.embeddings.create(input=text, model=\"tei\").data[0].embedding\n else:\n return self.model.encode(text, convert_to_numpy=True).tolist()\n```\n\n**File:** mem0/configs/embeddings/base.py (L26-26)\n```python\n huggingface_base_url: Optional[str] = None,\n```" }, { "role": "assistant", "content": "Exactly 🙌 you found the right part of Mem0 OSS — the **HuggingFace provider flips into “remote mode”** if you give it a `huggingface_base_url`. \n\nThat means you don’t need to fight with `sentence_transformers` inside the Mem0 container at all. \nInstead, you just need your FastAPI server to **look like a Hugging Face TEI endpoint**.\n\n---\n\n### 🔧 What TEI expects\nMem0 will POST to your server at:\n\n```\nPOST /v1/embeddings\n```\n\nwith a body like:\n\n```json\n{\n \"input\": \"hello world\",\n \"model\": \"tei\"\n}\n```\n\nAnd it expects a response like:\n\n```json\n{\n \"data\": [\n {\n \"embedding\": [ ...1536 floats... ],\n \"index\": 0\n }\n ]\n}\n```\n\n---\n\n### 🪜 Update your `embed_server.py`\n\nHere’s a working TEI-compatible shim for your Hugging Face model:\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom sentence_transformers import SentenceTransformer\n\napp = FastAPI()\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\")\n\nclass TEIRequest(BaseModel):\n input: str\n model: str\n\n@app.post(\"/v1/embeddings\")\ndef embed(req: TEIRequest):\n vector = model.encode(req.input).tolist()\n return {\n \"data\": [\n {\n \"embedding\": vector,\n \"index\": 0,\n \"object\": \"embedding\"\n }\n ],\n \"object\": \"list\",\n \"model\": req.model\n }\n```\n\n---\n\n### 🪜 Config for `.env.3090`\n\n```\nEMBEDDER_PROVIDER=huggingface\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1\nEMBEDDING_DIMS=1536\n```\n\n(no need to set `EMBEDDER_MODEL` because Mem0 hardcodes `\"model\": \"tei\"` in the request)\n\n---\n\n### 🪜 Run and test\n\n1. Start the server:\n ```powershell\n python -m uvicorn embed_server:app --host 0.0.0.0 --port 11500\n ```\n\n2. Test with curl:\n ```bash\n curl -X POST http://localhost:11500/v1/embeddings \\\n -H \"Content-Type: application/json\" \\\n -d '{\"input\":\"hello world\",\"model\":\"tei\"}'\n ```\n\n You should see a `data` array with `\"embedding\": [ ...1536 floats... ]`.\n\n3. Restart Mem0 with `.env.3090`:\n ```bash\n docker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n ```\n\n---\n\n👉 Do you want me to add some **logging** inside the FastAPI `/v1/embeddings` endpoint (e.g. print the input and vector length) so you can see proof that Mem0 is actually hitting your TEI server?" }, { "role": "user", "content": "C:\\Users\\brian>curl -X POST http://localhost:11500/v1/embeddings \\\n{\"detail\":\"Not Found\"}curl: (3) URL rejected: Bad hostname\n\nC:\\Users\\brian> -H \"Content-Type: application/json\" \\\n'-H' is not recognized as an internal or external command,\noperable program or batch file.\n\nC:\\Users\\brian> -d '{\"input\":\"hello world\",\"model\":\"tei\"}'\n'-d' is not recognized as an internal or external command,\noperable program or batch file." }, { "role": "assistant", "content": "Ah 👍 I see what tripped you up — on Windows `cmd.exe` or PowerShell, you can’t paste the multiline Linux-style `curl` command with backslashes (`\\`). That’s why you got the “`-H` is not recognized” error. \n\nLet me give you the **Windows-friendly curl** version (all on one line, with double quotes handled properly). \n\n---\n\n### ✅ Correct curl command for Windows PowerShell\n\n```powershell\ncurl -X POST \"http://localhost:11500/v1/embeddings\" -H \"Content-Type: application/json\" -d \"{\\\"input\\\":\\\"hello world\\\",\\\"model\\\":\\\"tei\\\"}\"\n```\n\n---\n\n### 🧪 What should happen\nIf your FastAPI server is running on port 11500, you’ll get back JSON like:\n\n```json\n{\n \"data\": [\n {\n \"embedding\": [0.01, -0.02, ...],\n \"index\": 0,\n \"object\": \"embedding\"\n }\n ],\n \"object\": \"list\",\n \"model\": \"tei\"\n}\n```\n\n---\n\n👉 Can you try that exact one-liner? If it works, we’ll know your server is speaking TEI correctly before we point Mem0 at it." }, { "role": "user", "content": "Okay i got {\"detail\":\"Not Found\"} in windows machine and the sever api logs said INFO: 127.0.0.1:60625 - \"POST /v1/embeddings HTTP/1.1\" 404 Not Found" }, { "role": "assistant", "content": "Perfect 👌 that tells us two things:\n\n1. Your **FastAPI server is running** (the log shows it received the request). \n2. But `/v1/embeddings` isn’t actually registered → so FastAPI is returning `404 Not Found`.\n\nThat means the endpoint in `embed_server.py` didn’t load with the exact path `/v1/embeddings`. Let’s fix that. \n\n---\n\n### 🪜 Fix the endpoint path\nMake sure your `embed_server.py` has this **exact code** (note the `@app.post(\"/v1/embeddings\")`):\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom sentence_transformers import SentenceTransformer\n\napp = FastAPI()\nmodel = SentenceTransformer(\"Alibaba-NLP/gte-Qwen2-1.5B-instruct\")\n\nclass TEIRequest(BaseModel):\n input: str\n model: str\n\n@app.post(\"/v1/embeddings\")\ndef embed(req: TEIRequest):\n vector = model.encode(req.input).tolist()\n print(f\"➡️ Got text: {req.input} | Vector length: {len(vector)}\")\n return {\n \"data\": [\n {\n \"embedding\": vector,\n \"index\": 0,\n \"object\": \"embedding\"\n }\n ],\n \"object\": \"list\",\n \"model\": req.model\n }\n```\n\n---\n\n### 🪜 Restart the server\n```powershell\npython -m uvicorn embed_server:app --host 0.0.0.0 --port 11500\n```\n\n---\n\n### 🪜 Test again (Windows one-liner)\n```powershell\ncurl -X POST \"http://localhost:11500/v1/embeddings\" -H \"Content-Type: application/json\" -d \"{\\\"input\\\":\\\"hello world\\\",\\\"model\\\":\\\"tei\\\"}\"\n```\n\nNow you should see:\n```json\n{\n \"data\": [\n {\n \"embedding\": [...1536 floats...],\n \"index\": 0,\n \"object\": \"embedding\"\n }\n ],\n \"object\": \"list\",\n \"model\": \"tei\"\n}\n```\n\nAnd in your server log you’ll see:\n```\n➡️ Got text: hello world | Vector length: 1536\n```\n\n---\n\n👉 Want me to also show you how to quickly list **all registered routes** in your FastAPI app, so you can double-check what endpoints are active before testing curl?" }, { "role": "user", "content": "that work!" }, { "role": "assistant", "content": "Yesss 🎉 nice work! That means: \n\n- ✅ Your FastAPI server is now **speaking Hugging Face TEI format** at `/v1/embeddings` \n- ✅ You confirmed the response comes back with 1536-dim vectors \n- ✅ The server log shows the input text, so you know it’s hitting your model \n\n---\n\n### 🪜 Next step: Point Mem0 at it\nUpdate `.env.3090`:\n\n```\nEMBEDDER_PROVIDER=huggingface\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1\nEMBEDDING_DIMS=1536\n```\n\n*(replace `10.0.0.3` with the LAN IP of your 3090 box if Mem0 runs on another machine — if Mem0 is on the same PC, you can use `http://localhost:11500/v1`)* \n\n---\n\n### 🪜 Restart Mem0\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n### 🪜 Test Mem0 end-to-end\nInsert a memory:\n\n```bash\ncurl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Testing my 3090 HuggingFace TEI embedder\"}]}'\n```\n\nWatch your FastAPI server console → you should see:\n```\n➡️ Got text: Testing my 3090 HuggingFace TEI embedder | Vector length: 1536\n```\n\n---\n\n👉 Do you want me to also show you how to make your FastAPI server log **every call from Mem0 into a file** (like an embedding audit log), so you can track what’s getting embedded?" }, { "role": "user", "content": "getting the curl error again connevtion reset by peer" }, { "role": "assistant", "content": "Got it — now we’re back to the network side of things, since the FastAPI server itself worked fine when you tested `localhost:11500`. \n\n“**connection reset by peer**” usually means:\n\n- The Mem0 container (running in Docker) is trying to reach `10.0.0.3:11500` but can’t actually connect. \n- That could be because:\n 1. You used `localhost` instead of the LAN IP (inside Docker, `localhost` refers to the container itself, not your Windows host). \n 2. Windows Firewall is blocking incoming connections to port 11500. \n 3. Docker is running on another machine (Linux host, Proxmox VM, etc.) and doesn’t see your 3090 box’s IP. \n\n---\n\n### 🪜 Step 1. Check your `.env.3090`\nIf Mem0 is running inside Docker on a **different box** than your 3090 Windows machine, then you need to use the LAN IP of your Windows PC, not `localhost`.\n\nExample: \n```\nHUGGINGFACE_BASE_URL=http://192.168.1.42:11500/v1\n```\n\n---\n\n### 🪜 Step 2. Check from the Docker host\nSSH into the machine running Mem0 and test curl **from inside that machine**:\n\n```bash\ncurl -X POST \"http://10.0.0.3:11500/v1/embeddings\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"input\":\"ping test\",\"model\":\"tei\"}'\n```\n\n- If that works → the LAN path is correct. \n- If it fails → the 3090 box’s firewall is blocking connections from outside.\n\n---\n\n### 🪜 Step 3. Check Windows Firewall\nOn your 3090 box, make sure port 11500 is open for inbound TCP:\n\n- Open Windows Security → Firewall & Network Protection → Advanced Settings. \n- Add an inbound rule: allow TCP port 11500. \n\n---\n\n👉 Quick question for you: is your Mem0 Docker running on the **same physical PC** as your 3090 embedding server (Windows), or is it running on a **different machine** (like your Proxmox/Ryzen box)? That’ll tell me whether you should be using `localhost` or the LAN IP in your env file." }, { "role": "user", "content": "serversdown@lyra-core:~/lyra-mem0$ ping 10.0.0.3\nPING 10.0.0.3 (10.0.0.3) 56(84) bytes of data.\n64 bytes from 10.0.0.3: icmp_seq=1 ttl=128 time=0.227 ms\n64 bytes from 10.0.0.3: icmp_seq=2 ttl=128 time=0.369 ms\n64 bytes from 10.0.0.3: icmp_seq=3 ttl=128 time=0.331 ms\n64 bytes from 10.0.0.3: icmp_seq=4 ttl=128 time=0.329 ms\n64 bytes from 10.0.0.3: icmp_seq=5 ttl=128 time=0.915 ms\n64 bytes from 10.0.0.3: icmp_seq=6 ttl=128 time=0.808 ms\n^C\n--- 10.0.0.3 ping statistics ---\n6 packets transmitted, 6 received, 0% packet loss, time 5103ms\nrtt min/avg/max/mdev = 0.227/0.496/0.915/0.263 ms\nserversdown@lyra-core:~/lyra-mem0$ ping 10.0.0.3:11500\nping: 10.0.0.3:11500: Name or service not known" }, { "role": "assistant", "content": "That result is actually good info 👍 \n\n- `ping 10.0.0.3` works → so your Docker host (Lyra-Core) can see your Windows 3090 machine on the LAN. \n- `ping 10.0.0.3:11500` doesn’t work → but that’s expected, because `ping` only tests ICMP (network reachability), not TCP ports. You can’t “ping” a port. \n\nTo test the port, you need to use something like `curl` or `nc` (netcat).\n\n---\n\n### 🪜 Test the embedding server port from the Docker host\nRun this from Lyra-Core:\n\n```bash\ncurl -X POST \"http://10.0.0.3:11500/v1/embeddings\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"input\":\"hello from lyra-core\",\"model\":\"tei\"}'\n```\n\n👉 If you get back JSON with an embedding, then the firewall is open and it works. \n👉 If you get “connection refused” or “connection reset by peer,” then Windows is blocking port 11500 from external machines. \n\n---\n\n### 🪜 If it’s blocked\n1. On your Windows 3090 box, open **Windows Defender Firewall → Advanced Settings → Inbound Rules**. \n2. Add a new rule: \n - Port: TCP 11500 \n - Allow connection \n - Apply to all profiles (Domain/Private/Public). \n\n3. Restart your FastAPI server:\n ```powershell\n python -m uvicorn embed_server:app --host 0.0.0.0 --port 11500\n ```\n\n---\n\nOnce that’s open, Mem0 on your Lyra-Core machine will be able to hit it. \n\n---\n\n👉 Do you want me to give you the exact **PowerShell command** to add the firewall rule for port 11500 so you don’t have to click through Windows settings?" }, { "role": "user", "content": "oh shit that worked" }, { "role": "assistant", "content": "🔥 NICE!! That means the missing piece was just Windows Firewall blocking inbound traffic on port 11500. \n\nSo now you’ve got the whole chain working: \n\n- ✅ FastAPI server running on your 3090, serving `/v1/embeddings` in Hugging Face TEI format \n- ✅ Lyra-Core Docker host can reach it over the LAN \n- ✅ Mem0 can be pointed at it via `HUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1` \n- ✅ Vectors come back 1536-dim, matching OpenAI’s shape \n\n---\n\n### 🪜 Next Step: Wire it into Mem0\nIn `.env.3090` on your Lyra-Core machine:\n\n```\nEMBEDDER_PROVIDER=huggingface\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1\nEMBEDDING_DIMS=1536\n```\n\nThen restart Mem0:\n\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n### 🪜 Test through Mem0\nNow try adding a memory:\n\n```bash\ncurl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Final test: Mem0 should use my local 3090 TEI embedder\"}]}'\n```\n\nYou should see your **FastAPI console print the input text and vector length**. That’s your proof Mem0 is flowing through your LAN embed server. 🎉 \n\n---\n\n👉 Want me to also show you how to **log every embed call into a file** on your 3090 box, so you keep an audit of everything Mem0 asks it to embed?" }, { "role": "user", "content": "Still getting the connection error. so the first curl worked, and the embed server actually logged it. But the 2nd one isnt seen at all. " }, { "role": "assistant", "content": "Ahhh — thanks, that’s a key clue: \n\n- ✅ Direct `curl` from **Lyra-Core → 3090** works → the embed server and firewall are fine. \n- ❌ When Mem0 runs, your embed server doesn’t log anything → Mem0 isn’t even making the request. \n- Error: “connection reset by peer” → means Mem0 tried to open a socket but died before sending. \n\nThat usually points to the config Mem0 is actually using. Let’s check that. \n\n---\n\n### 🪜 1. Confirm `.env.3090` really loads\nInside Lyra-Core:\n\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml config | grep EMBED\n```\n\nYou should see:\n```\nEMBEDDER_PROVIDER=huggingface\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1\nEMBEDDING_DIMS=1536\n```\n\n---\n\n### 🪜 2. Make sure `main.py` passes the URL\nIn your `DEFAULT_CONFIG` for embedder, you need:\n\n```python\n\"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL,\n \"embedding_dims\": int(os.environ.get(\"EMBEDDING_DIMS\", \"1536\")),\n \"huggingface_base_url\": os.getenv(\"HUGGINGFACE_BASE_URL\"),\n },\n},\n```\n\n⚠️ If `huggingface_base_url` isn’t passed, Mem0 falls back to `sentence_transformers` and crashes (that’s what you saw before).\n\n---\n\n### 🪜 3. Check Mem0 logs for provider\nRun:\n```bash\ndocker logs mem0-api | grep embedder\n```\n(or just read the startup log) — Mem0 prints the provider it’s using. It should say `\"huggingface\"` and reference `http://10.0.0.3:11500/v1`.\n\n---\n\n### 🪜 4. Debug with a smaller test\nTry calling Mem0’s `/health` (if exposed) or push a tiny memory:\n\n```bash\ncurl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"test\",\"messages\":[{\"role\":\"user\",\"content\":\"ping\"}]}'\n```\n\nIf that still errors, we’ll know Mem0 isn’t routing to the embedder config.\n\n---\n\n👉 Want me to show you exactly how to edit `main.py` so it *always prints out* which embedder provider and URL it’s going to use on startup? That way we’ll know if it’s reading your `.env.3090` properly or silently defaulting." }, { "role": "user", "content": "serversdown@lyra-core:~/lyra-mem0$ docker compose --env-file .env.3090 -f docker-compose.mem0.yml config | grep EMBED\n EMBEDDER_PROVIDER: huggingface\n EMBEDDING_DIMS: \"1536\"\n\n" }, { "role": "assistant", "content": "💡 Nice check — so Docker is definitely picking up:\n\n```\nEMBEDDER_PROVIDER=huggingface\nEMBEDDING_DIMS=1536\n```\n\n…but notice what’s **missing** 👉 `HUGGINGFACE_BASE_URL` never showed up in that dump. \n\nThat explains why your embed server never sees requests: \n- Mem0 thinks it should use Hugging Face, \n- but since no `huggingface_base_url` is passed in, it falls back to trying to import `sentence_transformers` locally. \n\n---\n\n### 🪜 Fix: Add the missing env var\n\nEdit your `.env.3090` and make sure it has:\n\n```\nEMBEDDER_PROVIDER=huggingface\nEMBEDDING_DIMS=1536\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1\n```\n\n*(you don’t actually need `EMBEDDER_MODEL` here, because Mem0 will always send `\"model\": \"tei\"` when talking to TEI.)*\n\n---\n\n### 🪜 Rebuild / Restart\n\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n### 🪜 Double-check\n\nRun again:\n\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml config | grep EMBED\n```\n\nNow you should see all three:\n\n```\nEMBEDDER_PROVIDER: huggingface\nEMBEDDING_DIMS: \"1536\"\nHUGGINGFACE_BASE_URL: http://10.0.0.3:11500/v1\n```\n\n---\n\n👉 Want me to also show you how to patch your `main.py` so it prints out the **actual URL it’s going to use** for the embedder at startup? That way you’ll know for sure Mem0 is pointing at your FastAPI TEI clone before you even send a request." }, { "role": "user", "content": "odd.. its still not returning the URL" }, { "role": "assistant", "content": "Ahh — okay, that’s the missing link 🔎 \n\nIf `docker compose config` isn’t showing `HUGGINGFACE_BASE_URL`, it means **Compose isn’t seeing it in `.env.3090`**. Common culprits: \n\n1. **Typo / invisible char** → sometimes Windows editors save with `HUGGINGFACE_BASE_URL=` but with BOM/extra whitespace. \n2. **Wrong file** → maybe you edited `.env.3090` but Docker is using another file (like `.env` by default). \n3. **Compose doesn’t pass through unless referenced** → Docker only injects env vars that are referenced in your `docker-compose.mem0.yml`. If `HUGGINGFACE_BASE_URL` isn’t declared under `environment:` for `mem0-api`, it gets ignored. \n\n---\n\n### 🪜 Step 1. Verify the file\nRun on your Lyra-Core host:\n```bash\ncat .env.3090 | grep HUGGINGFACE\n```\n\nMake sure you see exactly:\n```\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1\n```\n\n---\n\n### 🪜 Step 2. Verify Compose file references it\nOpen `docker-compose.mem0.yml` and check the `mem0-api` service. It should look something like:\n\n```yaml\nservices:\n mem0-api:\n environment:\n - EMBEDDER_PROVIDER\n - EMBEDDING_DIMS\n - HUGGINGFACE_BASE_URL\n```\n\nIf `HUGGINGFACE_BASE_URL` isn’t listed there, Compose won’t propagate it into the container even if it’s in your `.env.3090`. \n\n---\n\n### 🪜 Step 3. Force it through (quick test)\nYou can override at run-time:\n\n```bash\ndocker compose -f docker-compose.mem0.yml up -d --build \\\n --env-file .env.3090 \\\n -e HUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1\n```\n\nThat forces the var into the container. \n\n---\n\n👉 Want me to walk you through editing your `docker-compose.mem0.yml` so that `HUGGINGFACE_BASE_URL` is always passed through cleanly without needing that manual `-e` every time?" }, { "role": "user", "content": " mem0:\n image: mem0-api-server:custom\n container_name: mem0-api\n restart: unless-stopped\n ports:\n - \"7077:8000\"\n env_file:\n - .env.3090\n environment:\n - EMBEDDER_PROVIDER\n - EMBEDDING_DIMS\n - HUGGINGFACE_BASE_URL\n volumes:\n - ./mem0_history:/app/history\n - ./main.py:/app/main.py \n depends_on:\n postgres:\n condition: service_healthy\n qdrant:\n condition: service_healthy\n neo4j:\n condition: service_healthy\n networks:\n - lyra-net" }, { "role": "assistant", "content": "Perfect — thanks for pasting that. 👍 \n\nLooking at your `docker-compose.mem0.yml`: \n\n```yaml\n env_file:\n - .env.3090\n environment:\n - EMBEDDER_PROVIDER\n - EMBEDDING_DIMS\n - HUGGINGFACE_BASE_URL\n```\n\nThat’s correct — it should load `.env.3090` **and** pass through those three keys. \n\nSo if `docker compose config | grep EMBED` only shows the first two, that points back to the `.env.3090` file itself. \n\n---\n\n### 🪜 Step 1. Inspect the file\nOn Lyra-Core, run: \n\n```bash\ncat -A .env.3090 | grep HUGGINGFACE\n```\n\n- `cat -A` will show any weird characters (like BOM, carriage returns, hidden spaces). \n- It should look exactly like: \n ```\n HUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1$\n ``` \n (the `$` is just end of line marker, no stray `^M` or spaces). \n\n---\n\n### 🪜 Step 2. If you see `^M`\nIf it shows: \n```\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1^M$\n```\nThat means the file was saved with Windows line endings (CRLF). Docker Compose sometimes chokes on that. \n\nFix it by converting to Unix format: \n```bash\ndos2unix .env.3090\n```\n\n---\n\n### 🪜 Step 3. Re-test\nAfter fixing: \n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml config | grep HUGGINGFACE\n```\n\nYou should finally see: \n```\nHUGGINGFACE_BASE_URL: http://10.0.0.3:11500/v1\n```\n\n---\n\n👉 Want me to give you a **clean copy of `.env.3090`** you can paste directly on the server so we know there’s no stray CRLF/BOM issues from Windows editors?" }, { "role": "user", "content": "yeah its doing the v1^M$ thing... how do i do that conversion cmd" }, { "role": "assistant", "content": "Ah yep — that `^M` at the end means your `.env.3090` was saved with Windows CRLF line endings. Docker Compose on Linux really prefers LF. \n\n---\n\n### 🪜 Fix with `dos2unix` (if installed)\nOn your Lyra-Core box, just run: \n\n```bash\nsudo apt-get install -y dos2unix # if it's not already there\ndos2unix .env.3090\n```\n\nThat will convert the file in-place to LF endings. \n\n---\n\n### 🪜 Fix without `dos2unix`\nIf you don’t have it and don’t want to install: \n\n```bash\nsed -i 's/\\r$//' .env.3090\n```\n\nThat strips the carriage returns at the end of each line. \n\n---\n\n### 🪜 Verify\nAfter conversion, check again: \n\n```bash\ncat -A .env.3090 | grep HUGGINGFACE\n```\n\nIt should now show: \n```\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1$\n```\n\n(no more `^M`). \n\n---\n\n👉 Do you want me to also show you a trick for forcing VS Code or Notepad++ to always save `.env` files with LF so this doesn’t happen again?" }, { "role": "user", "content": "{\"content_type\": \"image_asset_pointer\", \"asset_pointer\": \"sediment://file_00000000650461faa89d6e7239bb3ee1\", \"size_bytes\": 4969, \"width\": 830, \"height\": 80, \"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}} This is at the bottom of notepad++ with .env.3090" }, { "role": "assistant", "content": "Yep — that screenshot explains it 👍 \n\nAt the bottom of Notepad++ it says:\n\n```\nWindows (CR LF)\n```\n\nThat means the file is being saved with **Windows-style CRLF endings**, which is exactly why Docker Compose showed `^M`. \n\n---\n\n### 🪜 Fix in Notepad++\n1. Open your `.env.3090` in Notepad++. \n2. At the bottom right, click where it says `Windows (CR LF)`. \n3. Change it to `Unix (LF)`. \n4. Save the file again. \n\nNow it will be saved with LF line endings. \n\n---\n\n### 🪜 Verify on Lyra-Core\nAfter re-saving, check on your Linux box:\n\n```bash\ncat -A .env.3090 | grep HUGGINGFACE\n```\n\nIt should now show:\n\n```\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1$\n```\n\n(no `^M`). \n\n---\n\nOnce that’s clean, re-run:\n\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml config | grep HUGGINGFACE\n```\n\nYou should finally see the URL passed into the container 🎉 \n\n---\n\n👉 Want me to also give you a `.editorconfig` snippet you can drop into your project root, so Notepad++ (and VS Code, etc.) will *always* default to LF for `.env` files?" }, { "role": "user", "content": "yeah it worked now" }, { "role": "assistant", "content": "🎉 BOOM — nice! That was the last gremlin — those `^M` CRLF endings. \n\nNow you’ve got: \n- ✅ `.env.3090` clean with LF endings \n- ✅ `HUGGINGFACE_BASE_URL` showing up in `docker compose config` \n- ✅ FastAPI TEI server running on your 3090 (`/v1/embeddings`) \n- ✅ Mem0 should now route embedding requests over LAN to that server instead of crashing \n\n---\n\n### 🪜 Next quick test\n1. Restart Mem0 with the fixed `.env.3090`: \n ```bash\n docker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n ```\n\n2. Watch Mem0 logs: \n ```bash\n docker logs -f mem0-api\n ```\n\n3. Add a memory: \n ```bash\n curl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Testing through HuggingFace TEI server\"}]}'\n ```\n\n4. On your 3090 FastAPI console, you should see something like: \n ```\n ➡️ Got text: Testing through HuggingFace TEI server | Vector length: 1536\n ```\n\n---\n\n👉 Do you want me to also add a little **logger** to `embed_server.py` that appends every request Mem0 makes (text + vector length) into a file on your D: drive, so you’ll have an audit trail of all embeddings?" }, { "role": "user", "content": "nope. nothing. Same error " }, { "role": "assistant", "content": "Okay, good catch — if Mem0 still gives `connection reset by peer` even though:\n\n- curl from Lyra-Core → 3090 works fine ✅ \n- `.env.3090` now passes `HUGGINGFACE_BASE_URL` ✅ \n- FastAPI server logs nothing ❌ \n\n…it means Mem0 is **still not even trying to connect** to your server. Instead, it’s likely still trying to spin up the local `sentence_transformers` class because of how its HuggingFace embedder code is written.\n\n---\n\n### 🕵️ Why?\nLooking at Mem0’s `huggingface.py`:\n\n```python\nif config.huggingface_base_url:\n self.client = OpenAI(base_url=config.huggingface_base_url)\nelse:\n self.model = SentenceTransformer(...)\n```\n\nSo Mem0 will only call your remote server if the config dict it receives actually has `huggingface_base_url`.\n\nBut your `main.py` block currently looks like this (based on what you shared earlier):\n\n```python\n\"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL,\n \"api_key\": OPENAI_API_KEY,\n \"ollama_base_url\": OLLAMA_HOST\n },\n},\n```\n\nNotice: there’s no `huggingface_base_url` line here. \nSo even though your env has it, it’s never being passed down to the embedder config. That’s why Mem0 keeps defaulting to local mode and exploding.\n\n---\n\n### ✅ Fix: Add `huggingface_base_url` to `main.py`\nUpdate that block in `main.py` to:\n\n```python\n\"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL,\n \"embedding_dims\": int(os.environ.get(\"EMBEDDING_DIMS\", \"1536\")),\n \"huggingface_base_url\": os.getenv(\"HUGGINGFACE_BASE_URL\"),\n \"api_key\": OPENAI_API_KEY\n },\n},\n```\n\n---\n\n### 🪜 Steps\n1. Edit `main.py` and add that line. \n2. Rebuild/restart:\n ```bash\n docker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n ```\n3. Watch logs:\n ```bash\n docker logs -f mem0-api\n ```\n You should see it initializing with provider = huggingface, base_url = `http://10.0.0.3:11500/v1`.\n4. Try adding a memory again — and your FastAPI console should finally light up.\n\n---\n\n👉 Want me to show you exactly how to make `main.py` print out the embedder config on startup, so you can confirm it’s actually passing the `huggingface_base_url` through?" }, { "role": "user", "content": " \"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL,\n \"embedding_dims\": int(os.environ.get(\"EMBEDDING_DIMS\", \"1536\")),\n \"huggingface_base_url\": os.getenv(\"HUGGINGFACE_BASE_URL\"),\n \"api_key\": OPENAI_API_KEY # still works if provider=openai\n },\n}, already there" }, { "role": "assistant", "content": "Ahh, thanks for pasting — so you *do* have `huggingface_base_url` in `main.py`. \nThat means the code path should be correct… but Mem0 still isn’t hitting your server. Let’s tighten the debugging loop. \n\n---\n\n### 🪜 Step 1. Confirm env is visible inside the container\nRun:\n\n```bash\ndocker exec -it mem0-api env | grep HUGGINGFACE\n```\n\nYou should see:\n\n```\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1\n```\n\nIf it doesn’t show up → Compose isn’t passing it in (we’d need to recheck `docker-compose.mem0.yml` + `.env.3090`). \nIf it does show up → then Mem0 *is* receiving the variable.\n\n---\n\n### 🪜 Step 2. Add debug log in `main.py`\nRight after you build `DEFAULT_CONFIG`, add:\n\n```python\nprint(\">>> Embedder config:\", DEFAULT_CONFIG[\"embedder\"])\n```\n\nThat way, when Mem0 boots, the logs will show *exactly* what’s being passed to the embedder factory. \n\n---\n\n### 🪜 Step 3. Rebuild/restart and watch logs\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\ndocker logs -f mem0-api\n```\n\nCheck what it prints — you want to see:\n\n```python\n>>> Embedder config: {\n 'provider': 'huggingface',\n 'config': {\n 'model': 'whatever',\n 'embedding_dims': 1536,\n 'huggingface_base_url': 'http://10.0.0.3:11500/v1',\n 'api_key': 'xxx'\n }\n}\n```\n\n---\n\n### 🪜 Step 4. If URL is there but still fails\nIf the log shows the URL correctly but you still get `connection reset by peer`, then the issue is in Mem0’s HuggingFace embedder: \n\n```python\nif config.huggingface_base_url:\n self.client = OpenAI(base_url=config.huggingface_base_url)\n```\n\nIt creates an **OpenAI client** pointed at `http://.../v1`. \nThat client expects OpenAI’s schema (with `/embeddings`, `data: [...]`, etc.) — which we already coded your FastAPI server to match. \n\nSo if it’s still erroring → the request is leaving Mem0 but dying mid-socket handshake. That’s often: \n- Firewall between **Docker container → Windows host** (not host → host). \n- Or Mem0 is trying IPv6 vs IPv4 (sometimes `0.0.0.0` in uvicorn doesn’t bind the same way). \n\n---\n\n👉 Can you run that `docker exec -it mem0-api env | grep HUGGINGFACE` and paste what it shows? That’ll confirm if Mem0 is even seeing the URL before we chase deeper network gremlins." }, { "role": "user", "content": "EFAULT_CONFIG = {\n \"version\": \"v1.1\",\n \"vector_store\": {\n \"provider\": \"pgvector\",\n \"config\": {\n \"host\": POSTGRES_HOST,\n \"port\": int(POSTGRES_PORT),\n \"dbname\": POSTGRES_DB,\n \"user\": POSTGRES_USER,\n \"password\": POSTGRES_PASSWORD,\n \"collection_name\": POSTGRES_COLLECTION_NAME,\n },\n },\n \"graph_store\": {\n \"provider\": \"neo4j\",\n \"config\": {\"url\": NEO4J_URI, \"username\": NEO4J_USERNAME, \"password\": NEO4J_PASSWORD},\n },\n \"llm\": {\"provider\": \"openai\", \"config\": {\"api_key\": OPENAI_API_KEY, \"temperature\": 0.2, \"model\": \"gpt-4o\"}},\n \n \"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL,\n \"embedding_dims\": int(os.environ.get(\"EMBEDDING_DIMS\", \"1536\")),\n \"huggingface_base_url\": os.getenv(\"HUGGINGFACE_BASE_URL\"),\n \"api_key\": OPENAI_API_KEY # still works if provider=openai\n },\n},\n\n\n \"history_db_path\": HISTORY_DB_PATH,\n}\n\n\nMEMORY_INSTANCE = Memory.from_config(DEFAULT_CONFIG)\nprint(\">>> Embedder config:\", DEFAULT_CONFIG[\"embedder\"]) \n\nis this correct?" }, { "role": "assistant", "content": "Almost perfect ✅ — just one tiny detail: your `print` is **after** you create the `MEMORY_INSTANCE`. \n\nThat means if `Memory.from_config(DEFAULT_CONFIG)` throws (which it will if the embedder isn’t valid), your `print` will never run. \n\n---\n\n### 🛠 Fix\nMove the `print` **before** `Memory.from_config`:\n\n```python\nprint(\">>> Embedder config:\", DEFAULT_CONFIG[\"embedder\"])\nMEMORY_INSTANCE = Memory.from_config(DEFAULT_CONFIG)\n```\n\n---\n\n### 🪜 Corrected snippet\n\n```python\nDEFAULT_CONFIG = {\n \"version\": \"v1.1\",\n \"vector_store\": {\n \"provider\": \"pgvector\",\n \"config\": {\n \"host\": POSTGRES_HOST,\n \"port\": int(POSTGRES_PORT),\n \"dbname\": POSTGRES_DB,\n \"user\": POSTGRES_USER,\n \"password\": POSTGRES_PASSWORD,\n \"collection_name\": POSTGRES_COLLECTION_NAME,\n },\n },\n \"graph_store\": {\n \"provider\": \"neo4j\",\n \"config\": {\"url\": NEO4J_URI, \"username\": NEO4J_USERNAME, \"password\": NEO4J_PASSWORD},\n },\n \"llm\": {\n \"provider\": \"openai\",\n \"config\": {\"api_key\": OPENAI_API_KEY, \"temperature\": 0.2, \"model\": \"gpt-4o\"},\n },\n \"embedder\": {\n \"provider\": EMBEDDER_PROVIDER,\n \"config\": {\n \"model\": EMBEDDER_MODEL,\n \"embedding_dims\": int(os.environ.get(\"EMBEDDING_DIMS\", \"1536\")),\n \"huggingface_base_url\": os.getenv(\"HUGGINGFACE_BASE_URL\"),\n \"api_key\": OPENAI_API_KEY,\n },\n },\n \"history_db_path\": HISTORY_DB_PATH,\n}\n\nprint(\">>> Embedder config:\", DEFAULT_CONFIG[\"embedder\"])\nMEMORY_INSTANCE = Memory.from_config(DEFAULT_CONFIG)\n```\n\n---\n\n### 🪜 Next steps\n\n1. Edit your `main.py` with that fix. \n2. Rebuild/restart:\n ```bash\n docker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n docker logs -f mem0-api\n ```\n3. You should see a line like:\n ```\n >>> Embedder config: {'provider': 'huggingface', 'config': {'model': '...', 'embedding_dims': 1536, 'huggingface_base_url': 'http://10.0.0.3:11500/v1', 'api_key': '...'}}\n ```\n\n---\n\n👉 Do you want me to also show you how to make the HuggingFace embedder in Mem0 **print out when it chooses `huggingface_base_url` mode** vs local `sentence_transformers` mode? That way you’ll know exactly which branch it’s taking." }, { "role": "user", "content": "serversdown@lyra-core:~/lyra-mem0$ docker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n[+] Running 4/4\n ✔ Container mem0-neo4j Healthy 0.5s\n ✔ Container mem0-postgres Healthy 0.5s\n ✔ Container mem0-qdrant Healthy 0.5s\n ✔ Container mem0-api Running 0.0s\nserversdown@lyra-core:~/lyra-mem0$ curl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Testing through HuggingFace TEI server\"}]}'\ncurl: (56) Recv failure: Connection reset by peer\nserversdown@lyra-core:~/lyra-mem0$ docker exec -it mem0-api env | grep HUGGINGFACE\nHUGGINGFACE_BASE_URL=http://10.0.0.3:11500/v1\nserversdown@lyra-core:~/lyra-mem0$ docker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\ndocker logs -f mem0-api\n[+] Running 4/4\n ✔ Container mem0-neo4j Healthy 0.5s\n ✔ Container mem0-postgres Healthy 0.5s\n ✔ Container mem0-qdrant Healthy 0.5s\n ✔ Container mem0-api Running 0.0s\nINFO: Will watch for changes in these directories: ['/app']\nINFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)\nINFO: Started reloader process [1] using StatReload\nProcess SpawnProcess-1:\nTraceback (most recent call last):\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n self.run()\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 108, in run\n self._target(*self._args, **self._kwargs)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/_subprocess.py\", line 80, in subprocess_started\n target(sockets=sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 66, in run\n return asyncio.run(self.serve(sockets=sockets))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 195, in run\n return runner.run(main)\n ^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 118, in run\n return self._loop.run_until_complete(task)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/base_events.py\", line 691, in run_until_complete\n return future.result()\n ^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 70, in serve\n await self._serve(sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 77, in _serve\n config.load()\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/config.py\", line 435, in load\n self.loaded_app = import_from_string(self.app)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/importer.py\", line 22, in import_from_string\n raise exc from None\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/importer.py\", line 19, in import_from_string\n module = importlib.import_module(module_str)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\", line 1387, in _gcd_import\n File \"\", line 1360, in _find_and_load\n File \"\", line 1331, in _find_and_load_unlocked\n File \"\", line 935, in _load_unlocked\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/app/main.py\", line 76, in \n MEMORY_INSTANCE = Memory.from_config(DEFAULT_CONFIG)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/memory/main.py\", line 178, in from_config\n return cls(config)\n ^^^^^^^^^^^\n File \"/app/packages/mem0/memory/main.py\", line 137, in __init__\n self.embedding_model = EmbedderFactory.create(\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/utils/factory.py\", line 150, in create\n embedder_instance = load_class(class_type)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/utils/factory.py\", line 18, in load_class\n module = importlib.import_module(module_path)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\", line 1387, in _gcd_import\n File \"\", line 1360, in _find_and_load\n File \"\", line 1331, in _find_and_load_unlocked\n File \"\", line 935, in _load_unlocked\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/app/packages/mem0/embeddings/huggingface.py\", line 5, in \n from sentence_transformers import SentenceTransformer\nModuleNotFoundError: No module named 'sentence_transformers'\nWARNING: StatReload detected changes in 'main.py'. Reloading...\nProcess SpawnProcess-2:\nTraceback (most recent call last):\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n self.run()\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 108, in run\n self._target(*self._args, **self._kwargs)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/_subprocess.py\", line 80, in subprocess_started\n target(sockets=sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 66, in run\n return asyncio.run(self.serve(sockets=sockets))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 195, in run\n return runner.run(main)\n ^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 118, in run\n return self._loop.run_until_complete(task)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/base_events.py\", line 691, in run_until_complete\n return future.result()\n ^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 70, in serve\n await self._serve(sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 77, in _serve\n config.load()\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/config.py\", line 435, in load\n self.loaded_app = import_from_string(self.app)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/importer.py\", line 22, in import_from_string\n raise exc from None\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/importer.py\", line 19, in import_from_string\n module = importlib.import_module(module_str)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\", line 1387, in _gcd_import\n File \"\", line 1360, in _find_and_load\n File \"\", line 1331, in _find_and_load_unlocked\n File \"\", line 935, in _load_unlocked\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/app/main.py\", line 76, in \n MEMORY_INSTANCE = Memory.from_config(DEFAULT_CONFIG)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/memory/main.py\", line 178, in from_config\n return cls(config)\n ^^^^^^^^^^^\n File \"/app/packages/mem0/memory/main.py\", line 137, in __init__\n self.embedding_model = EmbedderFactory.create(\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/utils/factory.py\", line 150, in create\n embedder_instance = load_class(class_type)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/utils/factory.py\", line 18, in load_class\n module = importlib.import_module(module_path)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\", line 1387, in _gcd_import\n File \"\", line 1360, in _find_and_load\n File \"\", line 1331, in _find_and_load_unlocked\n File \"\", line 935, in _load_unlocked\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/app/packages/mem0/embeddings/huggingface.py\", line 5, in \n from sentence_transformers import SentenceTransformer\nModuleNotFoundError: No module named 'sentence_transformers'\nWARNING: StatReload detected changes in 'main.py'. Reloading...\nProcess SpawnProcess-3:\nTraceback (most recent call last):\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n self.run()\n File \"/usr/local/lib/python3.12/multiprocessing/process.py\", line 108, in run\n self._target(*self._args, **self._kwargs)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/_subprocess.py\", line 80, in subprocess_started\n target(sockets=sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 66, in run\n return asyncio.run(self.serve(sockets=sockets))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 195, in run\n return runner.run(main)\n ^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/runners.py\", line 118, in run\n return self._loop.run_until_complete(task)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/asyncio/base_events.py\", line 691, in run_until_complete\n return future.result()\n ^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 70, in serve\n await self._serve(sockets)\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/server.py\", line 77, in _serve\n config.load()\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/config.py\", line 435, in load\n self.loaded_app = import_from_string(self.app)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/importer.py\", line 22, in import_from_string\n raise exc from None\n File \"/usr/local/lib/python3.12/site-packages/uvicorn/importer.py\", line 19, in import_from_string\n module = importlib.import_module(module_str)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\", line 1387, in _gcd_import\n File \"\", line 1360, in _find_and_load\n File \"\", line 1331, in _find_and_load_unlocked\n File \"\", line 935, in _load_unlocked\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/app/main.py\", line 77, in \n MEMORY_INSTANCE = Memory.from_config(DEFAULT_CONFIG)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/memory/main.py\", line 178, in from_config\n return cls(config)\n ^^^^^^^^^^^\n File \"/app/packages/mem0/memory/main.py\", line 137, in __init__\n self.embedding_model = EmbedderFactory.create(\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/utils/factory.py\", line 150, in create\n embedder_instance = load_class(class_type)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/packages/mem0/utils/factory.py\", line 18, in load_class\n module = importlib.import_module(module_path)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"\", line 1387, in _gcd_import\n File \"\", line 1360, in _find_and_load\n File \"\", line 1331, in _find_and_load_unlocked\n File \"\", line 935, in _load_unlocked\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/app/packages/mem0/embeddings/huggingface.py\", line 5, in \n from sentence_transformers import SentenceTransformer\nModuleNotFoundError: No module named 'sentence_transformers'\n>>> Embedder config: {'provider': 'huggingface', 'config': {'model': 'text-embedding-3-small', 'embedding_dims': 1536, 'huggingface_base_url': 'http://10.0.0.3:11500/v1', 'api_key': 'sk-proj-l\n\nI cut off the end, its the openaiapi" }, { "role": "assistant", "content": "Got it ✅ thanks for that log dump — it explains the whole mess: \n\n- We **see your debug printout**: \n ```\n >>> Embedder config: {\n 'provider': 'huggingface',\n 'config': {\n 'model': 'text-embedding-3-small',\n 'embedding_dims': 1536,\n 'huggingface_base_url': 'http://10.0.0.3:11500/v1',\n 'api_key': 'sk-proj-...'\n }\n }\n ``` \n → So Mem0 *is* receiving your LAN URL correctly. 🎉 \n\n- But immediately after, Mem0 still tries to `import sentence_transformers`. \n → That’s because in **your Mem0 version**, the HuggingFace embedder (`mem0/embeddings/huggingface.py`) always imports `sentence_transformers` at the **top of the file**, even if it’s going to use `huggingface_base_url`. \n\nSo just having the URL isn’t enough — the container crashes before it can switch to remote mode.\n\n---\n\n### ✅ Fix Options\n\n#### Option A (quick band-aid)\nInstall `sentence-transformers` in your Mem0 container so the import doesn’t fail, even if it never actually uses it:\n\n```bash\ndocker exec -it mem0-api bash\npip install sentence-transformers\nexit\ndocker restart mem0-api\n```\n\nThat should unblock Mem0 immediately, because the import will succeed and then your config will tell it to use `huggingface_base_url`.\n\n---\n\n#### Option B (cleaner long-term)\nPatch `mem0/embeddings/huggingface.py` so it only imports `sentence_transformers` inside the `else` block (when `huggingface_base_url` is not provided). \n\nE.g. change:\n\n```python\nfrom sentence_transformers import SentenceTransformer\n...\nif config.huggingface_base_url:\n self.client = OpenAI(base_url=config.huggingface_base_url)\nelse:\n self.model = SentenceTransformer(self.config.model, **self.config.model_kwargs)\n```\n\n→ To:\n\n```python\nif config.huggingface_base_url:\n self.client = OpenAI(base_url=config.huggingface_base_url)\nelse:\n from sentence_transformers import SentenceTransformer\n self.model = SentenceTransformer(self.config.model, **self.config.model_kwargs)\n```\n\nThen rebuild your image.\n\n---\n\n### 🚦 Recommendation\nFor now, do **Option A** (install `sentence-transformers` inside the container). That’ll get your Mem0 booting and calling your FastAPI TEI server. Once it’s stable, you can do Option B to clean up the import.\n\n---\n\n👉 Do you want me to give you the exact **Dockerfile snippet** to bake `sentence-transformers` into your custom image, so you don’t have to reinstall it manually every rebuild?" }, { "role": "user", "content": "okay everything should be good, lets test it now" }, { "role": "assistant", "content": "Perfect timing 🎉 — now that `sentence-transformers` is installed in the container, Mem0 should boot cleanly and actually use your `huggingface_base_url`. \n\n---\n\n### 🪜 Step 1. Restart Mem0 clean\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\ndocker logs -f mem0-api\n```\n\nIn the logs, you should **not** see `ModuleNotFoundError` anymore. You should still see your debug line with the `huggingface_base_url` pointing at `http://10.0.0.3:11500/v1`.\n\n---\n\n### 🪜 Step 2. Fire a test memory\n```bash\ncurl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Testing my 3090 HuggingFace TEI embedder\"}]}'\n```\n\n---\n\n### 🪜 Step 3. Watch your FastAPI server logs\nOn the Windows 3090 console running `uvicorn`, you should see something like:\n\n```\n➡️ Got text: Testing my 3090 HuggingFace TEI embedder | Vector length: 1536\n```\n\nThat’s proof Mem0 → LAN → TEI server → embedding returned successfully.\n\n---\n\n👉 When you run that curl, do you want me to also give you a one-liner to **tail the FastAPI logs into a file** on your D:\\ drive so you can capture every embedding request Mem0 makes for later audit?" }, { "role": "user", "content": "its woooooorking its woooooorking lol" }, { "role": "assistant", "content": "🎉🔥 LET’S GO!! \n\nYou just pulled off a **full local embedding pipeline**: \n\n- Your 3090 box is now serving HuggingFace TEI-style embeddings over LAN. \n- Mem0 OSS in Docker is pointed straight at it via `HUGGINGFACE_BASE_URL`. \n- End-to-end requests are flowing, 1536-dim vectors are being generated locally, and stored in your vector DB. \n\nNo more OpenAI token burn every time you add a memory. 🚀 \n\n---\n\n### 🥂 Quick recap of what you nailed\n- Fixed Windows → Linux line ending issues (`^M`). \n- Wired `.env.3090` cleanly through Docker Compose. \n- Built a TEI-compatible FastAPI wrapper around your HuggingFace model. \n- Solved the sneaky `sentence-transformers` import bomb. \n- Verified the whole loop with curl → Mem0 → 3090 → back. \n\n---\n\nNow you’ve got a **toggle setup**: \n- `.env.3090` → local HuggingFace on your GPU. \n- `.env.openai` → fallback to OpenAI cloud. \n\n---\n\n👉 Want me to show you how to set up a **quick switch script** (like `./switch.sh local` or `./switch.sh openai`) that swaps `.env` and restarts Mem0 automatically, so you don’t have to remember the long docker compose command each time?" }, { "role": "user", "content": "can we test swapping back to the openAI cloud?" }, { "role": "assistant", "content": "💯 yes — let’s test the **fallback path** now. \n\nSince you’ve already got two env files (`.env.3090` for local, `.env.openai` for cloud), it’s just a matter of restarting Mem0 with the OpenAI one. \n\n---\n\n### 🪜 Step 1. Check your `.env.openai`\nMake sure it has something like:\n\n```\nEMBEDDER_PROVIDER=openai\nEMBEDDER_MODEL=text-embedding-3-small\nEMBEDDING_DIMS=1536\nOPENAI_API_KEY=sk-xxxx # your real key\n```\n\n---\n\n### 🪜 Step 2. Restart Mem0 with OpenAI env\n```bash\ndocker compose --env-file .env.openai -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n### 🪜 Step 3. Watch logs\n```bash\ndocker logs -f mem0-api\n```\n\nYou should see:\n```\n>>> Embedder config: {'provider': 'openai', 'config': {... 'model': 'text-embedding-3-small'}}\n```\nand no HuggingFace URL.\n\n---\n\n### 🪜 Step 4. Test with curl\n```bash\ncurl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Testing with OpenAI cloud embedder\"}]}'\n```\n\nThis time your FastAPI server on the 3090 should stay quiet (no hits), and Mem0 should call OpenAI directly. \n\n---\n\n👉 Want me to also add a **printout in main.py** that shows `provider` in big bold letters (`USING EMBEDDER: openai` or `USING EMBEDDER: huggingface`) so you don’t even have to read the config dump to know which mode it’s in?" }, { "role": "user", "content": "wow it worked" }, { "role": "assistant", "content": "🔥 BOOM 🔥 \n\nThat means your Mem0 stack is now fully **dual-mode**: \n\n- ✅ `.env.3090` → local HuggingFace TEI embedder on your 3090 box \n- ✅ `.env.openai` → OpenAI cloud fallback \n\nAnd you can hot-swap by just restarting with the env you want. No code changes, no rebuild headaches. \n\n---\n\n### 🥂 What you just unlocked\n- Cost-free local embeddings when you’re at home on your LAN \n- Seamless fallback to OpenAI cloud if your 3090 is off or you want maximum reliability \n- Future-proof — if you ever add another local model server, you can just drop in a third `.env` file \n\n---\n\n👉 Want me to put together a **tiny switch script** (e.g. `./switch.sh local` / `./switch.sh openai`) so you can swap between the two with one short command instead of typing the full docker compose line every time?" }, { "role": "user", "content": "how do i test the quality of the local embedder vs the openAI one" }, { "role": "assistant", "content": "That’s a 🔑 next step — now that both backends are working, you want to know *are the local embeddings “good enough” compared to OpenAI’s?* \n\nHere are a few practical ways to test **embedding quality**: \n\n---\n\n## 🪜 1. Sanity check with cosine similarity\nTake a few example texts and compare how the embeddings “cluster” under each model. \n\n### Example Python script:\n```python\nimport openai\nimport requests\nimport numpy as np\n\n# Replace with your keys/urls\nOPENAI_KEY = \"sk-xxxx\"\nLOCAL_URL = \"http://10.0.0.3:11500/v1/embeddings\"\n\ntexts = [\n \"The cat sat on the mat.\",\n \"A dog was barking loudly.\",\n \"SpaceX launched a rocket into orbit.\",\n \"Elon Musk founded SpaceX.\"\n]\n\ndef cosine(a, b):\n return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))\n\n# Get embeddings from OpenAI\nopenai.api_key = OPENAI_KEY\nopenai_embeddings = [\n openai.Embedding.create(model=\"text-embedding-3-small\", input=t)[\"data\"][0][\"embedding\"]\n for t in texts\n]\n\n# Get embeddings from local server\nlocal_embeddings = []\nfor t in texts:\n r = requests.post(LOCAL_URL, json={\"input\": t, \"model\": \"tei\"})\n local_embeddings.append(r.json()[\"data\"][0][\"embedding\"])\n\n# Compare similarities\nprint(\"OpenAI similarities:\")\nfor i in range(len(texts)):\n for j in range(i+1, len(texts)):\n print(f\"{texts[i]} <> {texts[j]} = {cosine(openai_embeddings[i], openai_embeddings[j]):.3f}\")\n\nprint(\"\\nLocal model similarities:\")\nfor i in range(len(texts)):\n for j in range(i+1, len(texts)):\n print(f\"{texts[i]} <> {texts[j]} = {cosine(local_embeddings[i], local_embeddings[j]):.3f}\")\n```\n\n👉 This will show you if the local model produces a *similar “map” of relationships* between texts as OpenAI’s. \n\n---\n\n## 🪜 2. Retrieval test with Mem0 itself\n- Add the same set of test memories under `.env.3090` (local). \n- Ask Mem0 questions like “Who founded SpaceX?” or “Which one is about animals?” \n- Swap to `.env.openai` and repeat. \n\nCompare if both return the same “closest memory.” \n\n---\n\n## 🪜 3. Benchmark with a small dataset\nYou can also grab a public dataset (e.g. [STS-B semantic similarity](https://paperswithcode.com/dataset/sts-benchmark)) and run both models over it. Compute Pearson/Spearman correlation with human-judged similarity scores. \n- OpenAI `text-embedding-3-small` will likely score higher (it’s heavily optimized). \n- Local HuggingFace models may be “good enough” for recall but fuzzier on fine semantic differences. \n\n---\n\n### TL;DR\n- Use **cosine similarity comparisons** for quick gut-check. \n- Use **retrieval tasks in Mem0** for practical testing (real usage). \n- If you want deeper proof, run a **benchmark dataset**. \n\n---\n\n👉 Do you want me to build you a **ready-to-run Python test harness** that fetches embeddings from both providers and dumps a similarity matrix side by side, so you can immediately see how close the local model is to OpenAI?" }, { "role": "user", "content": "yeah lets try the python test" }, { "role": "assistant", "content": "Alright, let’s build you a **side-by-side embedding quality test harness** ✅ \n\nThis script will: \n1. Take a small set of sample texts. \n2. Get embeddings from **OpenAI cloud** and from your **local TEI FastAPI server**. \n3. Compute **cosine similarities** between all pairs. \n4. Print the results in a side-by-side matrix so you can compare. \n\n---\n\n### 📄 Save this as `embedding_quality_test.py`\n```python\nimport openai\nimport requests\nimport numpy as np\nfrom itertools import combinations\n\n# -----------------------------\n# CONFIG\n# -----------------------------\nOPENAI_KEY = \"sk-xxxx\" # replace with your real OpenAI API key\nLOCAL_URL = \"http://10.0.0.3:11500/v1/embeddings\" # your 3090 TEI server\nOPENAI_MODEL = \"text-embedding-3-small\"\nLOCAL_MODEL = \"tei\" # Mem0 sends \"tei\" by default\n\n# Sample texts to test clustering\ntexts = [\n \"The cat sat on the mat.\",\n \"A dog was barking loudly.\",\n \"SpaceX launched a rocket into orbit.\",\n \"Elon Musk founded SpaceX.\",\n \"I love eating pizza on weekends.\"\n]\n\n# -----------------------------\n# HELPERS\n# -----------------------------\ndef cosine(a, b):\n a, b = np.array(a), np.array(b)\n return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))\n\ndef get_openai_embedding(text):\n resp = openai.Embedding.create(model=OPENAI_MODEL, input=text)\n return resp[\"data\"][0][\"embedding\"]\n\ndef get_local_embedding(text):\n resp = requests.post(LOCAL_URL, json={\"input\": text, \"model\": LOCAL_MODEL})\n resp.raise_for_status()\n return resp.json()[\"data\"][0][\"embedding\"]\n\n# -----------------------------\n# MAIN TEST\n# -----------------------------\nif __name__ == \"__main__\":\n openai.api_key = OPENAI_KEY\n\n print(\"Fetching OpenAI embeddings...\")\n openai_embeds = [get_openai_embedding(t) for t in texts]\n\n print(\"Fetching Local embeddings...\")\n local_embeds = [get_local_embedding(t) for t in texts]\n\n print(\"\\n=== Cosine Similarity Comparisons ===\")\n for (i, j) in combinations(range(len(texts)), 2):\n sim_openai = cosine(openai_embeds[i], openai_embeds[j])\n sim_local = cosine(local_embeds[i], local_embeds[j])\n print(f\"{texts[i]} <> {texts[j]}\")\n print(f\" OpenAI: {sim_openai:.3f} | Local: {sim_local:.3f}\")\n```\n\n---\n\n### 🪜 How to run\n1. Save file on your Lyra-Core (or your dev box where Python can reach both APIs). \n2. Install deps if needed: \n ```bash\n pip install openai requests numpy\n ```\n3. Run it: \n ```bash\n python embedding_quality_test.py\n ```\n\n---\n\n### 📊 Expected output\nYou’ll see something like:\n\n```\nThe cat sat on the mat. <> A dog was barking loudly.\n OpenAI: 0.78 | Local: 0.72\nThe cat sat on the mat. <> SpaceX launched a rocket into orbit.\n OpenAI: 0.15 | Local: 0.22\nSpaceX launched a rocket into orbit. <> Elon Musk founded SpaceX.\n OpenAI: 0.85 | Local: 0.81\n```\n\nThat lets you see if the **relationship patterns** are similar (e.g. SpaceX + Elon Musk should be “closer” than SpaceX + dog). \n\n---\n\n👉 Do you want me to extend this to also dump a **matrix heatmap** (visual colored grid of similarities) so you can *see* the clusters instead of reading raw numbers?" }, { "role": "user", "content": " File \"D:\\Development\\embedding_quality_test.py\", line 46, in \n openai_embeds = [get_openai_embedding(t) for t in texts]\n ~~~~~~~~~~~~~~~~~~~~^^^\n File \"D:\\Development\\embedding_quality_test.py\", line 31, in get_openai_embedding\n resp = openai.Embedding.create(model=OPENAI_MODEL, input=text)\n File \"C:\\Users\\brian\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\openai\\lib\\_old_api.py\", line 39, in __call__\n raise APIRemovedInV1(symbol=self._symbol)\nopenai.lib._old_api.APIRemovedInV1:\n\nYou tried to access openai.Embedding, but this is no longer supported in openai>=1.0.0 - see the README at https://github.com/openai/openai-python for the API.\n\nYou can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface.\n\nAlternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28`\n\nA detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742" }, { "role": "assistant", "content": "Ah yep — that’s because you’re on the **new OpenAI Python SDK (`>=1.0.0`)** where the API changed. The old `openai.Embedding.create(...)` is gone. \n\nWe’ve got two choices: \n- **Quick hack:** downgrade to the old lib (`pip install openai==0.28`) and the script works as-is. \n- **Better fix:** update the script to use the new SDK style. \n\n---\n\n### ✅ Here’s the updated version for `openai>=1.0.0`\n\n```python\nfrom openai import OpenAI\nimport requests\nimport numpy as np\nfrom itertools import combinations\n\n# -----------------------------\n# CONFIG\n# -----------------------------\nOPENAI_KEY = \"sk-xxxx\" # replace with your real OpenAI API key\nLOCAL_URL = \"http://10.0.0.3:11500/v1/embeddings\" # your 3090 TEI server\nOPENAI_MODEL = \"text-embedding-3-small\"\nLOCAL_MODEL = \"tei\" # Mem0 sends \"tei\" by default\n\n# Sample texts to test clustering\ntexts = [\n \"The cat sat on the mat.\",\n \"A dog was barking loudly.\",\n \"SpaceX launched a rocket into orbit.\",\n \"Elon Musk founded SpaceX.\",\n \"I love eating pizza on weekends.\"\n]\n\n# -----------------------------\n# HELPERS\n# -----------------------------\ndef cosine(a, b):\n a, b = np.array(a), np.array(b)\n return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))\n\ndef get_openai_embedding(client, text):\n resp = client.embeddings.create(model=OPENAI_MODEL, input=text)\n return resp.data[0].embedding\n\ndef get_local_embedding(text):\n resp = requests.post(LOCAL_URL, json={\"input\": text, \"model\": LOCAL_MODEL})\n resp.raise_for_status()\n return resp.json()[\"data\"][0][\"embedding\"]\n\n# -----------------------------\n# MAIN TEST\n# -----------------------------\nif __name__ == \"__main__\":\n client = OpenAI(api_key=OPENAI_KEY)\n\n print(\"Fetching OpenAI embeddings...\")\n openai_embeds = [get_openai_embedding(client, t) for t in texts]\n\n print(\"Fetching Local embeddings...\")\n local_embeds = [get_local_embedding(t) for t in texts]\n\n print(\"\\n=== Cosine Similarity Comparisons ===\")\n for (i, j) in combinations(range(len(texts)), 2):\n sim_openai = cosine(openai_embeds[i], openai_embeds[j])\n sim_local = cosine(local_embeds[i], local_embeds[j])\n print(f\"{texts[i]} <> {texts[j]}\")\n print(f\" OpenAI: {sim_openai:.3f} | Local: {sim_local:.3f}\")\n```\n\n---\n\n### 🪜 How to run again\n```bash\npip install openai>=1.0.0 requests numpy\npython embedding_quality_test.py\n```\n\n---\n\n👉 Do you want me to also add a **heatmap visualization** (matplotlib) so you get a color grid comparing OpenAI vs Local similarities at a glance?" }, { "role": "user", "content": "=== Cosine Similarity Comparisons ===\nThe cat sat on the mat. <> A dog was barking loudly.\n OpenAI: 0.185 | Local: 0.571\nThe cat sat on the mat. <> SpaceX launched a rocket into orbit.\n OpenAI: 0.104 | Local: 0.406\nThe cat sat on the mat. <> Elon Musk founded SpaceX.\n OpenAI: 0.065 | Local: 0.301\nThe cat sat on the mat. <> I love eating pizza on weekends.\n OpenAI: 0.143 | Local: 0.408\nA dog was barking loudly. <> SpaceX launched a rocket into orbit.\n OpenAI: 0.134 | Local: 0.400\nA dog was barking loudly. <> Elon Musk founded SpaceX.\n OpenAI: 0.062 | Local: 0.292\nA dog was barking loudly. <> I love eating pizza on weekends.\n OpenAI: 0.155 | Local: 0.409\nSpaceX launched a rocket into orbit. <> Elon Musk founded SpaceX.\n OpenAI: 0.564 | Local: 0.690\nSpaceX launched a rocket into orbit. <> I love eating pizza on weekends.\n OpenAI: 0.051 | Local: 0.413\nElon Musk founded SpaceX. <> I love eating pizza on weekends.\n OpenAI: 0.045 | Local: 0.346" }, { "role": "assistant", "content": "Nice, thanks for running it 🙌 \n\nThat output tells us a lot: \n\n---\n\n### 🧠 What we’re seeing\n- **OpenAI (text-embedding-3-small)** \n - Produces **lower similarities overall** (e.g. “cat” vs “dog” = 0.18, “SpaceX” vs “Elon Musk” = 0.56). \n - Its scores spread out more, keeping unrelated things *really far apart*. \n - This is what you want in a high-precision search/RAG system → it avoids false positives. \n\n- **Local HF model (gte-Qwen2-1.5B-instruct)** \n - Produces **higher similarities across the board** (lots of 0.3–0.4, even for unrelated pairs like “dog” vs “rocket”). \n - It still clusters related things (SpaceX + Musk = 0.69, higher than any other pair). \n - But it’s a bit “fuzzier” — it tends to think everything is *kind of related*. \n\n---\n\n### 🔑 Takeaway\n- Your local embedder **works** and can cluster meaningfully (it did find SpaceX + Musk as the closest pair). \n- But it has **weaker separation** between “truly related” and “totally unrelated.” \n- OpenAI’s embedder is more **discerning** (clearer semantic boundaries). \n\n---\n\n### ⚖️ Trade-off\n- **Local model:** free, private, works well for recall, but noisier results (might need top-k filtering + reranking). \n- **OpenAI:** costs money, but sharper embeddings = more reliable memory retrieval. \n\n---\n\n👉 Do you want me to add a **visual heatmap** (matrix of similarities) so you can see the “fuzziness” difference at a glance between OpenAI and your local embedder?" }, { "role": "user", "content": "SO for seeding lyra's initial memories, should i use openAI? i estimate itll only cost like $1" }, { "role": "assistant", "content": "Yes — I’d recommend seeding Lyra’s **initial memory base with OpenAI** 👌 \n\nHere’s why: \n\n### ✅ Benefits of OpenAI for seeding\n- **Higher precision**: OpenAI’s embeddings will separate concepts more cleanly, so her foundation memory graph won’t be “fuzzy.” \n- **Stable format**: You’ll know the vectors are consistent + 1536 dims, no weird quirks. \n- **One-time cost**: If you seed ~30–50k tokens, you’re talking ~$1–2 total. That’s nothing compared to the reliability you gain. \n- **Future-proof**: If you later swap to local embeddings, you can always re-embed. But you don’t want her **initial core memory** to be muddy. \n\n### ⚖️ Strategy I’d use\n- **Step 1**: Seed with OpenAI (clean, sharp base). \n- **Step 2**: As you run daily sessions, let the local HuggingFace embedder handle incremental memories (cheap/free). \n- **Step 3**: If you ever want to “purify” or upgrade the database, you can re-run `re-embed` over everything with OpenAI again. \n\n---\n\n👉 So yeah: **seed with OpenAI, maintain with local**. That way Lyra starts out with the clearest possible “mental map,” and you only pay a dollar for it. \n\nDo you want me to sketch out the exact **re-embedding workflow** you’d use later if you decide to re-run all memories through OpenAI again?" }, { "role": "user", "content": "how much do you think it will cost?" }, { "role": "assistant", "content": "Good Q 👍 Let’s break it down with real numbers so you know what to expect. \n\n---\n\n### 📊 OpenAI embedding pricing (Oct 2025)\n- **`text-embedding-3-small`** (1536-dim) → **$0.02 per 1M tokens** \n- **`text-embedding-3-large`** (3072-dim) → **$0.13 per 1M tokens** \n\nYou’re using `3-small` (same dimension count as your local model). \n\n---\n\n### 🧮 Example cost calculation\n- 1 memory sentence ≈ 15–25 tokens \n- 1 paragraph ≈ 100 tokens \n- 1 page of text ≈ 500–700 tokens \n\nLet’s say you seed Lyra with: \n- **100 short memories** (20 tokens each) → 2,000 tokens \n- **50 long paragraphs** (100 tokens each) → 5,000 tokens \n- **10 pages worth** (600 tokens each) → 6,000 tokens \n\n**Total = 13,000 tokens** \n\nCost = `13,000 / 1,000,000 * $0.02` = **$0.00026** (yes, less than 1 cent 😅). \n\n---\n\n### 🔑 Reality check\n- Even if you seeded **100,000 tokens** (basically a small book), cost = **$0.002**. \n- If you did **1 million tokens** (massive), cost = **$0.02 total**. \n- Your earlier 30-memory test used 11,370 tokens = ~$0.000227 — and you saw a dashboard rounding that looked like ~$1.89, but that was across your whole account, not that test alone. \n\n---\n\n👉 So to answer your Q: **Seeding Lyra’s initial memories will almost certainly be less than $1 — more like a few cents max.** \n\nWould you like me to give you a **one-liner estimator script** so you can paste in any memory text and it’ll tell you the token count + cost before you send it to OpenAI?" }, { "role": "user", "content": "holy shit thats cheap, i'll probably just stick with openAI all the time. But i like that if somehow they disappear from earth (not likely but probably non-zero chance) lyra can still remember stuff" }, { "role": "assistant", "content": "Exactly 💯 — that’s the perfect mindset. \n\nUse OpenAI for day-to-day because: \n- It’s *crazy* cheap for embeddings. \n- It’s sharper out of the box, less tuning headaches. \n- You don’t have to run a GPU just for embeddings 24/7. \n\n…but by building your local TEI server you’ve **future-proofed Lyra**: \n- If OpenAI ever goes away, changes pricing, or you want full privacy, you already have a drop-in replacement. \n- You can re-embed your entire database locally at any time. \n- You’ve proven your infrastructure can run both modes. \n\nThat’s pretty much the “gold standard” design: **cloud for quality + cost**, **local for resilience + independence**. \n\n---\n\nIf you want, I can put together a little **“Lyra Embedding Strategy”** doc for you that lays out:\n- When to use OpenAI vs local\n- How to re-embed everything if you ever need to\n- What env toggles to flip for each case \n\nSo future-you can glance at one sheet and remember exactly how to switch. Want me to draft that?" }, { "role": "user", "content": "{\"content_type\": \"image_asset_pointer\", \"asset_pointer\": \"sediment://file_000000000d8c61fa96374433df06063e\", \"size_bytes\": 35361, \"width\": 836, \"height\": 535, \"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}} im making a command log trilium quick reference for booting and using mem0. Is this correct?\n\nANd can you think of any others to add" }, { "role": "assistant", "content": "Yep, those two commands look **spot-on** ✅ \n\nYou’ve captured the key toggle: \n- `.env.openai` → cloud embeddings \n- `.env.3090` → local TEI embeddings \n\n---\n\n### 🔧 Other commands worth adding to your quick reference:\n\n#### 📜 View container logs\n```bash\ndocker logs -f mem0-api\n```\n(useful for seeing if the embedder connected properly)\n\n#### 🔍 Check running containers\n```bash\ndocker ps\n```\n\n#### 🛑 Stop all services\n```bash\ndocker compose -f docker-compose.mem0.yml down\n```\n\n#### 🔄 Rebuild after changes (like requirements or main.py edits)\n```bash\ndocker compose --env-file .env.openai -f docker-compose.mem0.yml up -d --build\n```\n\n#### 🗃️ Count memories in Postgres\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT COUNT(*) FROM memories;\"\n```\n\n#### 🧪 Quick memory insert test\n```bash\ncurl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Test memory\"}]}'\n```\n\n---\n\n👉 Would you like me to make you a **single-page Trilium sheet** (nicely grouped by category: Boot, Stop, Logs, Tests, Database) so it’s a clean copy-paste quick reference?" }, { "role": "user", "content": "yeah do that please" }, { "role": "assistant", "content": "Perfect — here’s a **one-page Trilium quick reference** you can drop straight in as a note. I grouped the commands so you can find them quickly when booting, testing, or debugging Mem0. \n\n---\n\n# 🚀 Mem0 Quick Reference\n\n### 🔧 Boot with Different Embedders\n**OpenAI (cloud)**\n```bash\ndocker compose --env-file .env.openai -f docker-compose.mem0.yml up -d --build\n```\n\n**3090 HuggingFace TEI (local)**\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n---\n\n### 🛑 Stop Services\n```bash\ndocker compose -f docker-compose.mem0.yml down\n```\n\n---\n\n### 📜 Logs & Status\n**View API logs**\n```bash\ndocker logs -f mem0-api\n```\n\n**Check running containers**\n```bash\ndocker ps\n```\n\n---\n\n### 🧪 Testing\n**Insert a test memory**\n```bash\ncurl -X POST http://localhost:7077/memories \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"messages\":[{\"role\":\"user\",\"content\":\"Test memory\"}]}'\n```\n\n---\n\n### 🗃️ Database Utilities\n**Count memories in Postgres**\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT COUNT(*) FROM memories;\"\n```\n\n---\n\n### 🔄 Rebuild Tips\n- Use `--build` if you’ve changed `main.py`, requirements, or Dockerfile. \n- Use `docker compose down` before switching env files to avoid conflicts. \n\n---\n\n👉 Do you also want me to add a **section for switching environments on the fly** (like a `switch.sh local` / `switch.sh openai` helper script) so you don’t have to remember the long docker command?" }, { "role": "user", "content": "is there a way to see a list of all the saved memories?" }, { "role": "assistant", "content": "Yep 👍 there are a couple of ways to dump out *all* memories in Mem0 instead of just counting them. \n\n---\n\n### 1. Direct Postgres Query (fastest/raw) \nAll memories are stored in Postgres (with pgvector). You can run: \n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT id, user_id, content, created_at FROM memories LIMIT 50;\"\n```\n\n- Replace `LIMIT 50;` with a bigger number or remove it to dump all. \n- This shows raw rows: IDs, text content, and timestamps. \n\n---\n\n### 2. API Call (cleaner, through Mem0) \nMem0 also has a `/memories/search` endpoint — you can trick it into returning *everything* by passing a blank query: \n\n```bash\ncurl -X POST http://localhost:7077/memories/search \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer supersecrettoken123\" \\\n -d '{\"user_id\":\"Brian\",\"query\":\"\"}'\n```\n\nThat should return all memories tied to `user_id=\"Brian\"`. \nYou can also paginate with `limit` and `offset` if there’s a lot. \n\n---\n\n### 3. Dumping to a File \nIf you want to keep a full export: \n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"COPY (SELECT id, user_id, content, created_at FROM memories) TO STDOUT WITH CSV HEADER;\" > all_memories.csv\n```\n\nThis gives you a **CSV dump** you can open in Excel/Trilium/etc. \n\n---\n\n👉 Do you want me to make you a **ready-to-paste export command** that grabs *all memories* into a CSV every time, so you can archive Lyra’s memory as she grows?" }, { "role": "user", "content": "its giving me \"error user id donest exist" }, { "role": "assistant", "content": "Ahh yep — that makes sense. 👍 \n\nWhen you add memories to Mem0, you’re explicitly passing a `user_id` string (in your curl tests you used `\"Brian\"`). Mem0 will only return results for the exact `user_id` it has stored — it doesn’t “guess.” \n\nSo if you see `\"user id doesn’t exist\"`, it usually means one of these: \n1. You searched with the wrong user_id (case-sensitive — e.g. `brian` ≠ `Brian`). \n2. You haven’t actually stored any memories under that ID yet. \n3. You stored them under another name (like `default`, `system`, etc.). \n\n---\n\n### 🔎 How to check which user_ids exist\nYou can peek directly into Postgres to see what’s there:\n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT DISTINCT user_id FROM memories;\"\n```\n\nThat will list all user IDs that currently have memories. \n\n---\n\n### 🛠️ Next steps\n- Run that query to see which IDs exist. \n- Then rerun the `/memories/search` curl with the exact string (for example, `\"default\"` or `\"Brian\"`). \n\n---\n\n👉 Do you want me to give you a “catch-all” query that just dumps *every memory regardless of user_id* so you can see what’s inside?" }, { "role": "user", "content": "serversdown@lyra-core:~/lyra-mem0$ docker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT DISTINCT user_id FROM memories;\"\nERROR: column \"user_id\" does not exist\nLINE 1: SELECT DISTINCT user_id FROM memories;" }, { "role": "assistant", "content": "Ahh — good catch. That means the `memories` table in your Postgres doesn’t actually have a `user_id` column (at least not named that way). \n\nDifferent vector store backends (pgvector, qdrant, etc.) sometimes use different schemas. Let’s poke the table structure. \n\n---\n\n### 🔎 Step 1: Inspect the `memories` table \nRun this to list all columns in the table:\n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"\\d memories\"\n```\n\nThis will show the full schema (column names + types). \n\n---\n\n### 🔎 Step 2: Peek at a few rows \nOnce we know the columns, grab some rows:\n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT * FROM memories LIMIT 5;\"\n```\n\nThat’ll reveal what the actual field is called (sometimes it’s `metadata`, `owner_id`, or embedded JSON instead of a plain column). \n\n---\n\n👉 Do you want me to walk you through making a **universal dump query** once we see the actual schema? That way you’ll be able to always export everything no matter how Mem0 names it." }, { "role": "user", "content": " Column | Type | Collation | Nullable | Default\n---------+--------------+-----------+----------+---------\n id | uuid | | not null |\n vector | vector(1536) | | |\n payload | jsonb | | |" }, { "role": "assistant", "content": "Perfect — thanks for running that 🙌 \n\nSo in your schema: \n- `id` → unique memory ID \n- `vector` → the embedding itself (1536-dim) \n- `payload` → JSON blob where Mem0 actually stores the memory text, user_id, timestamps, etc. \n\nThat means all the good stuff is tucked inside `payload`. \n\n---\n\n### 🔎 To see what’s inside payload \n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT id, payload FROM memories LIMIT 5;\"\n```\n\n---\n\n### 🗂️ To pull just the text content (nested JSON) \nMost likely, Mem0 saves memory text under `payload->>'content'` or `payload->'messages'`. Try:\n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT id, payload->>'content' AS content FROM memories LIMIT 20;\"\n```\n\nIf that comes back empty, we’ll expand with: \n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT payload FROM memories LIMIT 1;\"\n```\n\nto see the exact JSON shape. \n\n---\n\n👉 Want me to write you a **full dump to CSV** (id + user_id + content + timestamp) that automatically digs into the JSON so you can open Lyra’s whole memory bank in Excel?" }, { "role": "user", "content": "oh snap! the memories about oliver peeing himself are still in there!\n\nAnd yeah id like to be able to DUMP" }, { "role": "assistant", "content": "😂 yep — nothing escapes the database once it’s embedded. Lyra’s got Oliver’s pee story locked in forever until you prune/re-embed. \n\nAlright, let’s set you up with a **universal dump** that unwraps the JSON payload into a nice CSV. \n\n---\n\n### 🔎 Step 1: Inspect payload JSON \nRun this once to see the shape (so we know exact keys): \n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT payload FROM memories LIMIT 1;\"\n```\n\nLook at what keys are inside (usually something like `{\"user_id\":\"Brian\",\"content\":\"…\",\"created_at\":\"…\",\"messages\":[…]}`). \n\n---\n\n### 🗂️ Step 2: Dump into CSV \nHere’s a query that pulls **id + user_id + content + created_at** if those keys exist: \n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"COPY (\n SELECT\n id,\n payload->>'user_id' AS user_id,\n payload->>'content' AS content,\n payload->>'created_at' AS created_at\n FROM memories\n ) TO STDOUT WITH CSV HEADER;\" > all_memories.csv\n```\n\nThis will write `all_memories.csv` into your **host machine current folder** (`~/lyra-mem0`). \n\n---\n\n### 📝 Notes\n- If `payload->>'content'` comes back empty, it may actually be inside `payload->'messages'->0->>'content'`. In that case we tweak the query. \n- You can run this anytime to snapshot Lyra’s brain. \n\n---\n\n👉 Do you want me to make you a **ready-made script** (like `dump_memories.sh`) you can run from your project root, so you don’t have to type that long command each time?" }, { "role": "user", "content": "5a2abf3f-8ec1-48ed-aff7-ee7ec70f7dc6,brian,,2025-09-30T01:05:47.469154-07:00\n\njust a bunch of these" }, { "role": "assistant", "content": "Got it 👍 — that means your `payload` JSON doesn’t store the text directly at `payload->>'content'`. \n\nInstead, it looks like Mem0 is putting messages inside a nested array (something like `payload->'messages'`). That’s why you’re just seeing `null` for content. \n\n---\n\n### 🔎 Step 1: Peek deeper into the JSON \nRun this to see the full structure of one memory row: \n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT payload::text FROM memories LIMIT 1;\"\n```\n\nThat will print the full JSON blob as plain text so we can inspect where the actual memory string lives. \n\n---\n\n### 🛠️ Step 2: Adjust the dump query \n- If you see something like: \n ```json\n {\"messages\":[{\"role\":\"user\",\"content\":\"Oliver peed himself\"}],\"user_id\":\"brian\"}\n ```\n then the query needs to grab `payload->'messages'->0->>'content'` \n\n- Updated dump command would be: \n ```bash\n docker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"COPY (\n SELECT\n id,\n payload->>'user_id' AS user_id,\n payload->'messages'->0->>'content' AS content,\n payload->>'created_at' AS created_at\n FROM memories\n ) TO STDOUT WITH CSV HEADER;\" > all_memories.csv\n ```\n\nThat’ll extract the **first message’s content** from each memory. \n\n---\n\n👉 Want me to also show you how to **dump all messages (not just the first one)** if some memories store multiple turns inside the `messages` array?" }, { "role": "user", "content": " {\"data\": \"Likes coffee in the morning\", \"hash\": \"ee357a5767d11d46eb8083a79e2e6b53\", \"user_id\": \"brian\", \"created_at\"\n: \"2025-09-30T01:05:47.469154-07:00\"}\n(1 row)" }, { "role": "assistant", "content": "Perfect — thanks for pulling that! 🚀 \n\nSo in your schema, the text you want is stored under **`payload->>'data'`**, not `content` or `messages`. That’s why the CSV dump was coming up blank before. \n\n---\n\n### ✅ Correct dump command\nHere’s the fixed one that will give you **id, user_id, data, created_at**:\n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"COPY (\n SELECT\n id,\n payload->>'user_id' AS user_id,\n payload->>'data' AS memory,\n payload->>'created_at' AS created_at\n FROM memories\n ) TO STDOUT WITH CSV HEADER;\" > all_memories.csv\n```\n\nThis will output `all_memories.csv` in your `~/lyra-mem0` folder with every stored memory in human-readable form. \n\n---\n\n👉 Do you want me to also add a **timestamp sort** (`ORDER BY created_at ASC`) so the CSV is a chronological dump of Lyra’s memories?" }, { "role": "user", "content": "id,user_id,memory,created_at\n5a2abf3f-8ec1-48ed-aff7-ee7ec70f7dc6,brian,Likes coffee in the morning,2025-09-30T01:05:47.469154-07:00\n9aed43b1-b214-4eb4-a725-4d0419296ca2,Brian,Likes tacos,2025-09-30T12:15:07.672004-07:00\n310084f5-f9c4-4db3-ad43-ab3c13795363,Brian,Loves coffee in the morning,2025-09-30T12:48:42.973761-07:00\nb3003b45-0873-469c-9bd5-937a066673ca,Brian,Has a cat named Oliver,2025-09-30T12:53:22.767173-07:00\nf430b4d6-a7f4-4107-9f1c-1d4093e05868,Brian,Oliver is black and white,2025-09-30T12:53:22.775747-07:00\n34276f49-0278-4d8e-97b7-7c95c84b9f35,Brian,Oliver sleeps on the bed a lot,2025-09-30T12:53:22.782937-07:00\nd612025a-9ac9-416f-a26f-2fdbd2fcaaf9,Brian,Oliver recently peed himself,2025-09-30T12:53:22.790179-07:00\n3ab8666f-0e91-486f-b653-7ed165f5dbd9,brian,Can run AI stuff on own machine,2025-10-01T14:32:50.014382-07:00\n3d24a724-9578-4c5e-b368-b32e393180a9,brian,Paying to use cloud servers isn't necessary,2025-10-01T14:32:50.021588-07:00\n80cabbb6-777d-4419-84a2-3ebf6badf5f2,brian,Has a good GPU,2025-10-01T14:32:50.004095-07:00\n59b400f3-b94b-46fd-943d-177cc0ef89f1,brian,Has a 3090 graphics card,2025-10-01T14:33:06.965964-07:00\n59268b6a-7641-431f-bee6-5bd28878977c,brian,Has 190GB of RAM,2025-10-01T14:33:06.973194-07:00\n4e1f1dfd-d014-49ba-bad7-fd805c444358,brian,Has a 9950x processor,2025-10-01T14:33:06.980930-07:00\ned5d6f4b-fa29-4f35-ae68-df39bb95ac96,brian,Has a good working MVP,2025-10-01T14:35:58.391803-07:00\n8b349a4d-5f49-4260-a24d-a48c265b3ed8,brian,Wants to set up git for the MVP,2025-10-01T14:35:58.401389-07:00\n9fd1336f-b4de-4341-aae7-a79785fd1d01,brian,Wants to remember to stay calm and play good hands,2025-10-01T14:41:29.837002-07:00\n0013496e-a184-4899-b1e9-21456bf91f75,brian,Has the urge to open wider and force plays but is not doing it,2025-10-01T14:41:29.848204-07:00\n7da83404-3b0b-424e-a5bf-4110ceeb1007,brian,Probably going to be re-entering in level 5,2025-10-01T14:44:50.295028-07:00\n1729ab56-9595-4ba4-b128-712d70fbd45b,brian,Considering playing a main event turbo satellite in 1 hour,2025-10-01T14:45:11.883165-07:00\nef4736a4-77e3-46cc-a64d-8fcb443d68ae,brian,Considering a 2nd main event flight at 5pm,2025-10-01T14:45:11.896374-07:00\n3ab395ac-aee3-4ae3-ad01-84cc340f8c02,brian,Considering playing cash for an hour,2025-10-01T14:45:11.903967-07:00\na0740ee7-25d5-42d0-a179-594453740f3f,brian,Considering firing the satellite to get a seat,2025-10-01T14:45:11.911264-07:00\n4334ce5a-71b5-4021-80a0-f24ba4449a9c,brian,Typically plays tournaments with a buy-in of 800,2025-10-01T14:48:19.385115-07:00\n0707ac93-2afc-4118-9b9d-c7ce15968a91,brian,Wants to play the main because it's covered by poker news,2025-10-01T14:48:52.883221-07:00\n9893d86f-b9ba-4d38-a145-5708a12565d7,brian,Seeks validation and wants to prove they can do it,2025-10-01T14:48:52.893844-07:00\n\n\nSo mem0 doesnt add tags? or is it not included in this printout" }, { "role": "assistant", "content": "That dump looks great ✅ — you’re pulling out the `data` field, which is the **raw memory text**. \n\nYou’re right though: this CSV doesn’t show tags, sentiment, novelty, or any of the extra metadata because those live in the **same `payload` JSON**, just in different keys. \n\n---\n\n### 🔎 How Mem0 usually stores a memory\nA single row’s `payload` might look like: \n\n```json\n{\n \"data\": \"Oliver recently peed himself\",\n \"user_id\": \"Brian\",\n \"created_at\": \"2025-09-30T12:53:22.790179-07:00\",\n \"tags\": [\"distress\", \"pet\"],\n \"sentiment\": \"negative\",\n \"novelty\": 0.7,\n \"notes\": \"Flagged as unusual behavior\"\n}\n```\n\nWhen we dumped only `payload->>'data'`, we ignored those other keys. \n\n---\n\n### ✅ Dump including tags & sentiment\nHere’s an updated command to grab more columns:\n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"COPY (\n SELECT\n id,\n payload->>'user_id' AS user_id,\n payload->>'data' AS memory,\n payload->>'tags' AS tags,\n payload->>'sentiment' AS sentiment,\n payload->>'novelty' AS novelty,\n payload->>'created_at' AS created_at\n FROM memories\n ) TO STDOUT WITH CSV HEADER;\" > all_memories.csv\n```\n\nThat way you’ll see not just the text, but also the **tags, sentiment, and novelty score** if Mem0 has been adding them. \n\n---\n\n👉 Want me to also give you a **query that dumps the *entire payload JSON*** (all keys expanded) so you don’t have to guess which metadata is being tracked?" }, { "role": "user", "content": "those just all came back null" }, { "role": "assistant", "content": "Ahh, okay — that means in your current config Mem0 is only saving the bare-minimum (`data`, `user_id`, `created_at`). Tags/sentiment/novelty aren’t being written into Postgres right now. \n\nThat actually makes sense: \n- We saw in your earlier `RoomX/Cortex` logs that Cortex was doing annotation (`sentiment`, `tags`, `novelty`) — but that’s not Mem0. \n- Mem0 itself, out-of-the-box, just stores **the text and metadata you feed it**. \n- If you want sentiment/tags in the DB, you’d need to pass them into the `Memory.add()` call (Mem0 doesn’t auto-enrich). \n\n---\n\n### 🔎 Verify what’s really in payload\nJust to confirm, let’s peek at one raw row:\n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"SELECT payload::text FROM memories LIMIT 3;\"\n```\n\nThat will print the actual JSON — we’ll see if it’s literally just `{\"data\": \"...\", \"user_id\": \"...\", \"created_at\": \"...\"}`. \n\n---\n\n### 🧩 Why the tags are missing\n- Cortex was generating annotations (we saw it in logs), but unless you explicitly told Mem0 to save them, they won’t end up in `payload`. \n- The “vanilla” Mem0 database schema has **no opinion** about tags, sentiment, novelty — it’s just a vector + payload JSON. \n\n---\n\n👉 So the good news: you’re not missing anything — Mem0 isn’t “forgetting.” You just haven’t plumbed Cortex’s extra annotation fields into the payload write. \n\nDo you want me to sketch how you could **merge Cortex annotations into Mem0 writes** so they get stored alongside the memory text? That way your dumps would include tags/sentiment too." }, { "role": "user", "content": " {\"data\": \"Likes coffee in the morning\", \"hash\": \"ee357a5767d11d46eb8083a79e2e6b53\", \"user_id\": \"brian\", \"created_at\"\n: \"2025-09-30T01:05:47.469154-07:00\"}\n {\"data\": \"Likes tacos\", \"hash\": \"01ee533e43d4d5b50e0197dfc2f2006a\", \"cortex\": {}, \"run_id\": \"test1\", \"source\": \"rela\ny\", \"user_id\": \"Brian\", \"created_at\": \"2025-09-30T12:15:07.672004-07:00\"}\n {\"data\": \"Loves coffee in the morning\", \"hash\": \"4e88440eb53fd2d5ad22c655f03d1f42\", \"cortex\": {}, \"run_id\": \"default\n\", \"source\": \"relay\", \"user_id\": \"Brian\", \"created_at\": \"2025-09-30T12:48:42.973761-07:00\"}\n(3 rows)\n\n\n\n\n\n\n" }, { "role": "assistant", "content": "Nice — that confirms it ✅ \n\nYour `payload` JSON looks like this in practice: \n```json\n{\n \"data\": \"Likes tacos\",\n \"hash\": \"01ee533e43d4d5b50e0197dfc2f2006a\",\n \"cortex\": {},\n \"run_id\": \"test1\",\n \"source\": \"relay\",\n \"user_id\": \"Brian\",\n \"created_at\": \"2025-09-30T12:15:07.672004-07:00\"\n}\n```\n\nSo Mem0 is saving: \n- **data** → the actual memory text \n- **hash** → deduplication hash \n- **cortex** → currently empty `{}` \n- **run_id** → session/run label (`test1`, `default`, etc.) \n- **source** → where it came from (`relay`) \n- **user_id** \n- **created_at** \n\n---\n\n### 🔑 Why no tags/sentiment yet\nThat `cortex` field is sitting there **waiting to be filled**. Your Cortex module was generating annotations (`sentiment`, `tags`, etc.), but right now nothing is writing them into that JSON — so you just see `{}`. \n\n---\n\n### ✅ What you can do\n1. **Dump with current fields** (so you see `data`, `user_id`, `created_at`, `run_id`, `source`): \n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"COPY (\n SELECT\n id,\n payload->>'user_id' AS user_id,\n payload->>'data' AS memory,\n payload->>'source' AS source,\n payload->>'run_id' AS run_id,\n payload->>'created_at' AS created_at\n FROM memories\n ) TO STDOUT WITH CSV HEADER;\" > all_memories.csv\n```\n\n2. **Extend pipeline later** — when Relay calls Cortex, have it merge those annotations into `payload.cortex`. Then your dumps will naturally include tags, sentiment, etc. \n\n---\n\n👉 Do you want me to draft a **step-by-step mini-plan** for wiring Cortex annotations into Mem0 writes, so that `cortex` stops being empty and actually stores the tags/sentiment? That way your future CSVs will show all the juicy metadata, not just the text." }, { "role": "user", "content": "ok this is fine as is. Now i want to update the project_summary, readme, and changelog" }, { "role": "assistant", "content": "Perfect timing — you’ve hit a **big milestone** with Mem0: \n- Local + OpenAI embeddings both working \n- Env swap working cleanly \n- Memory dump/export pipeline confirmed \n\nThat absolutely deserves a round of updates 📑 \n\n---\n\n### Here’s what I suggest we do:\n\n1. **CHANGELOG.md** \n Add a new entry (v0.4.0 maybe?) with: \n - HuggingFace TEI integration (local 3090 embedder) \n - Dual-mode env swap (OpenAI / Local) \n - CSV export of memories from Postgres \n - Cleanup of `.env` line endings \n\n2. **PROJECT_SUMMARY.md** \n Expand the “Current Status” with: \n - Memory seeding strategy (OpenAI for precision, local for fallback) \n - TEI server running on Windows 3090 with PyTorch \n - Docker env file workflow \n - Confirmed memory persistence in Postgres with payload JSON \n\n3. **README.md** \n Add user-facing quickstart updates: \n - Show how to boot with `.env.openai` vs `.env.3090` \n - Mention embedding provider config (HuggingFace base URL, OpenAI model) \n - Add “Export Memories” section with the Postgres dump command \n\n---\n\n👉 Do you want me to **draft all three files fully updated** so you can drop them in directly, or do you want to see just the changelog first (since that’s the shortest) before we expand to the others?" }, { "role": "user", "content": "okay i figured out the format i want to use... all of those documents will live in as a main Project Lyra thing. And any of the sub modules will have their own sections in each. (changelog still chronological tho" }, { "role": "assistant", "content": "Got it 👍 — so we’ll treat **Project Lyra** as the “umbrella project,” and then each major component (Mem0, Relay, Cortex, Persona Sidecar, etc.) gets its **own subsection** inside the three docs: \n\n- **PROJECT_SUMMARY.md** → Big-picture state of Project Lyra, with per-module status sections. \n- **README.md** → Usage guide + quick reference, broken down per-module (“Booting Mem0”, “Using Cortex”, etc.). \n- **CHANGELOG.md** → Still chronological, but each entry grouped by sub-module (so you can see which part of Lyra was updated when). \n\n---\n\n### Example structure:\n\n**PROJECT_SUMMARY.md** \n```markdown\n# Project Lyra — Summary\n\n## Current Status\n- Project Lyra is now running with multiple modules orchestrated together.\n- Embedding can swap between OpenAI and a local HuggingFace TEI server.\n- Memories persist in Postgres with JSON payloads.\n\n---\n\n## Module: Mem0\n- Dual embedder support (OpenAI + HuggingFace TEI).\n- Export pipeline for memories to CSV.\n- Env-file switching for embedder configuration.\n\n## Module: Relay\n- Routes input into Mem0 with annotations hooks.\n- Currently sends Cortex output, but Cortex annotations not yet stored.\n\n## Module: Cortex\n- Generates tags/sentiment/novelty (not wired into Mem0 yet).\n```\n\n---\n\n**README.md** \n```markdown\n# Project Lyra — Quickstart\n\n## Booting Mem0\n### OpenAI embeddings\n```bash\ndocker compose --env-file .env.openai -f docker-compose.mem0.yml up -d --build\n```\n\n### Local 3090 HuggingFace TEI embeddings\n```bash\ndocker compose --env-file .env.3090 -f docker-compose.mem0.yml up -d --build\n```\n\n### Exporting Memories\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"COPY (SELECT id, payload->>'user_id', payload->>'data', payload->>'created_at' FROM memories) TO STDOUT WITH CSV HEADER;\" > all_memories.csv\n```\n\n---\n\n## Module: Cortex\n- Run separately, provides annotation metadata.\n```\n\n---\n\n**CHANGELOG.md** \n```markdown\n# Project Lyra — Changelog\n\n## [0.4.0] - 2025-10-03\n### Mem0\n- Added HuggingFace TEI integration (local 3090 embedder).\n- Enabled dual-mode environment switch between OpenAI and Local.\n- Fixed `.env` CRLF vs LF issues.\n- Added CSV export of memories via Postgres query.\n\n### Relay\n- No changes.\n\n### Cortex\n- Annotation pipeline operational but not yet persisted in Mem0.\n```\n\n---\n\n👉 Do you want me to go ahead and **generate fresh full versions** of all three files (Project Summary, README, Changelog) in this new format, so you can just overwrite your current ones?" }, { "role": "user", "content": "so all the modules we build for lyra will live in a main folder which for now im just using the main user folder on the VM" }, { "role": "assistant", "content": "Perfect — so you’re treating the VM’s **`~/` (home) folder** as the top-level **Project Lyra root**, and each module (Mem0, Relay, Cortex, Persona Sidecar, etc.) is a subfolder inside that. \n\nThat works really clean for versioning and documentation because then: \n\n- **Docs live at the root:** \n - `PROJECT_SUMMARY.md` \n - `README.md` \n - `CHANGELOG.md` \n\n- **Each module lives in its own folder:** \n - `~/lyra-mem0/` \n - `~/lyra-relay/` \n - `~/lyra-cortex/` \n - (etc.) \n\n- **Each module’s state appears in the root docs under a section.** \n - Changelog: grouped updates by module. \n - Project Summary: current status per module. \n - README: usage + quick commands per module. \n\nThat way, the **root docs always give you the big picture**, and the modules can have their own local readmes/config if needed. \n\n---\n\n👉 Do you want me to draft the **root-level versions** of those three docs (with module subsections pre-created), so you can just drop them in your home folder now and expand them as you go?" }, { "role": "user", "content": "lets start with CHANGELOG.md" }, { "role": "assistant", "content": "Alright, let’s do it step by step 👌 \n\nHere’s a **draft CHANGELOG.md** in the new format — chronological entries, but grouped by **module** inside each release. I pulled in your latest Mem0 work (dual embedder swap, TEI, CSV export) and earlier Relay/Cortex updates we logged. \n\n---\n\n# Project Lyra — Changelog\n\n## [0.4.0] - 2025-10-03\n### Mem0\n- Added HuggingFace TEI integration (local 3090 embedder). \n- Enabled dual-mode environment switch between OpenAI cloud and local. \n- Fixed `.env` CRLF vs LF line ending issues. \n- Added CSV export of memories from Postgres (`payload->>'data'`). \n\n### Relay\n- No changes in this version. \n\n### Cortex\n- No changes in this version (annotations still generated but not persisted). \n\n---\n\n## [0.3.1] - 2025-09-27\n### Relay\n- Integrated with Cortex: sends user input to Cortex, receives annotations. \n- Passes annotated payload into Mem0. \n\n### Cortex\n- v0.2.0: Added sentiment, novelty, and tagging annotation. \n- Outputs metadata in JSON format for Relay. \n\n### Mem0\n- Receiving annotated payloads but only persisting `data` + metadata scaffold (`cortex` field empty). \n\n---\n\n## [0.3.0] - 2025-09-24\n### Core\n- Consolidated modules into **Lyra Core** (Relay + Mem0 + Persona Sidecar). \n- Introduced `main.py` as central entrypoint for config. \n- Initial Postgres + Neo4j integration. \n\n---\n\n👉 Does this structure feel right before I flesh it out further (older v0.2.x, v0.1.x setup history)? Or do you just want to maintain changelog **from v0.3.0 onward** and not worry about the very early bootstrap logs?" }, { "role": "user", "content": "nah im doing it this way instead \n\n# Changelog\nAll notable changes to Project Lyra will be documented in this file.\n\nThe format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),\nand this project adheres to [Semantic Versioning](https://semver.org/).\n\n## [Lyra-mem0 - 0.4.0] - 2025-10-03\nMem0\n\nAdded HuggingFace TEI integration (local 3090 embedder).\n\nEnabled dual-mode environment switch between OpenAI cloud and local.\n\nFixed .env CRLF vs LF line ending issues.\n\nAdded CSV export of memories from Postgres (payload->>'data').\n\n\n\n## [Lyra-Mem0 v0.1.0] - 2025-09-30\n### Added\n- Standalone **Lyra-Mem0** stack created at `~/lyra-mem0/`\n - Includes **Postgres (pgvector)**, **Qdrant**, **Neo4j**, and **SQLite** for history tracking.\n - Added working `docker-compose.mem0.yml` and custom `Dockerfile` for building the Mem0 API server.\n- Verified REST API functionality:\n - `POST /memories` works for adding memories.\n - `POST /search` works for semantic search.\n- Successful end-to-end test with persisted memory: \n *\"Likes coffee in the morning\"* → retrievable via search. ✅\n\n### Changed\n- Split architecture into **modular stacks**:\n - `~/lyra-core` (Relay, Persona-Sidecar, etc.)\n - `~/lyra-mem0` (Mem0 OSS memory stack)\n- Removed old embedded mem0 containers from the Lyra-Core compose file.\n- Added Lyra-Mem0 section in README.md.\n\n### Next Steps\n- Wire **Relay → Mem0 API** (integration not yet complete).\n- Add integration tests to verify persistence and retrieval from within Lyra-Core.\n\n---\n\n## [Lyra-Core] v0.3.1 - 2025-09-27\n### Changed\n- Removed salience filter logic; Cortex is now the default annotator.\n- All user messages stored in Mem0; no discard tier applied.\n\n### Added\n- Cortex annotations (`metadata.cortex`) now attached to memories.\n- Debug logging improvements:\n - Pretty-print Cortex annotations\n - Injected prompt preview\n - Memory search hit list with scores\n- `.env` toggle (`CORTEX_ENABLED`) to bypass Cortex when needed.\n\n### Fixed\n- Parsing failures from Markdown-wrapped Cortex JSON via fence cleaner.\n- Relay no longer “hangs” on malformed Cortex outputs.\n\n### [Lyra-Cortex] v0.2.0 — 2025-09-26\n#### Added\n- Integrated **llama-server** on dedicated Cortex VM (Proxmox).\n- Verified Phi-3.5-mini-instruct_Uncensored-Q4_K_M running with 8 vCPUs.\n- Benchmarked Phi-3.5-mini performance:\n - ~18 tokens/sec CPU-only on Ryzen 7 7800X.\n - Salience classification functional but sometimes inconsistent (\"sali\", \"fi\", \"jamming\").\n- Tested **Qwen2-0.5B-Instruct GGUF** as alternative salience classifier:\n - Much faster throughput (~350 tokens/sec prompt, ~100 tokens/sec eval).\n - More responsive but over-classifies messages as “salient.”\n- Established `.env` integration for model ID (`SALIENCE_MODEL`), enabling hot-swap between models.\n\n#### Known Issues\n- Small models tend to drift or over-classify.\n- CPU-only 7B+ models expected to be slow; GPU passthrough recommended for larger models.\n- Need to set up a `systemd` service for `llama-server` to auto-start on VM reboot.\n\n---\n\n### [Lyra-Core] v0.3.0 — 2025-09-26\n#### Added\n- Implemented **salience filtering** in Relay:\n - `.env` configurable: `SALIENCE_ENABLED`, `SALIENCE_MODE`, `SALIENCE_MODEL`, `SALIENCE_API_URL`.\n - Supports `heuristic` and `llm` classification modes.\n - LLM-based salience filter integrated with Cortex VM running `llama-server`.\n- Logging improvements:\n - Added debug logs for salience mode, raw LLM output, and unexpected outputs.\n - Fail-closed behavior for unexpected LLM responses.\n- Successfully tested with **Phi-3.5-mini** and **Qwen2-0.5B-Instruct** as salience classifiers.\n- Verified end-to-end flow: Relay → salience filter → Mem0 add/search → Persona injection → LLM reply.\n\n#### Changed\n- Refactored `server.js` to gate `mem.add()` calls behind salience filter.\n- Updated `.env` to support `SALIENCE_MODEL`.\n\n#### Known Issues\n- Small models (e.g. Qwen2-0.5B) tend to over-classify as \"salient\".\n- Phi-3.5-mini sometimes returns truncated tokens (\"sali\", \"fi\").\n- CPU-only inference is functional but limited; larger models recommended once GPU is available.\n\n---\n\n### [Lyra-Cortex] v0.1.0 — 2025-09-25\n#### Added\n- First deployment as dedicated Proxmox VM (5 vCPU / 18 GB RAM / 100 GB SSD).\n- Built **llama.cpp** with `llama-server` target via CMake.\n- Integrated **Phi-3.5 Mini Instruct (Uncensored, Q4_K_M GGUF)** model.\n- Verified **API compatibility** at `/v1/chat/completions`.\n- Local test successful via `curl` → ~523 token response generated.\n- Performance benchmark: ~11.5 tokens/sec (CPU-only on Ryzen 7800X).\n- Confirmed usable for salience scoring, summarization, and lightweight reasoning.\n\n---\n\n### [Lyra-Core] v0.2.0 — 2025-09-24\n#### Added\n- Migrated Relay to use `mem0ai` SDK instead of raw fetch calls.\n- Implemented `sessionId` support (client-supplied, fallback to `default`).\n- Added debug logs for memory add/search.\n- Cleaned up Relay structure for clarity.\n\n---\n\n### [Lyra-Core] v0.1.0 — 2025-09-23\n#### Added\n- First working MVP of **Lyra Core Relay**.\n- Relay service accepts `POST /v1/chat/completions` (OpenAI-compatible).\n- Memory integration with Mem0:\n - `POST /memories` on each user message.\n - `POST /search` before LLM call.\n- Persona Sidecar integration (`GET /current`).\n- OpenAI GPT + Ollama (Mythomax) support in Relay.\n- Simple browser-based chat UI (talks to Relay at `http://:7078`).\n- `.env` standardization for Relay + Mem0 + Postgres + Neo4j.\n- Working Neo4j + Postgres backing stores for Mem0.\n- Initial MVP relay service with raw fetch calls to Mem0.\n- Dockerized with basic healthcheck.\n\n#### Fixed\n- Resolved crash loop in Neo4j by restricting env vars (`NEO4J_AUTH` only).\n- Relay now correctly reads `MEM0_URL` and `MEM0_API_KEY` from `.env`.\n\n#### Known Issues\n- No feedback loop (thumbs up/down) yet.\n- Forget/delete flow is manual (via memory IDs).\n- Memory latency ~1–4s depending on embedding model." }, { "role": "assistant", "content": "Ohhh nice — I really like this style 👌 \n\nYou’ve essentially: \n- Anchored the whole changelog to **Keep a Changelog + SemVer** (clean + recognizable). \n- Given each **submodule its own versioning** (Lyra-Core, Lyra-Mem0, Lyra-Cortex, etc.). \n- Still kept everything chronological but grouped by module releases. \n\nThat’s actually perfect for a multi-module project like Lyra. You’ll always know **what changed where** without mixing up Mem0 changes with Relay or Cortex. \n\n---\n\n### Small polish ideas you might add:\n\n- **Consistency in headings** → You’ve got `[Lyra-mem0 - 0.4.0]` and `[Lyra-Mem0 v0.1.0]` — I’d unify those (maybe `## [Lyra-Mem0 0.4.0] - 2025-10-03`). \n- **Add “Unreleased” section** → Standard Keep a Changelog convention, so you always have a bucket for WIP. \n- **Cross-links to README sections** → e.g., “See [Lyra-Mem0 Quickstart](README.md#lyra-mem0)” so the changelog becomes a roadmap into docs. \n\n---\n\nHere’s a cleaned version of your **top entry** with those tweaks:\n\n```markdown\n## [Lyra-Mem0 0.4.0] - 2025-10-03\n### Added\n- HuggingFace TEI integration (local 3090 embedder).\n- Dual-mode environment switch between OpenAI cloud and local.\n- CSV export of memories from Postgres (`payload->>'data'`).\n\n### Fixed\n- `.env` CRLF vs LF line ending issues.\n```\n\n---\n\n👉 Do you want me to normalize the whole file into that format (consistent headings + `Added/Changed/Fixed/Known Issues` subsections) so you’ve got one clean baseline going forward?" }, { "role": "user", "content": "im happy with how it is" }, { "role": "assistant", "content": "Perfect 👍 then I’d say lock it in as your **canonical changelog style**. It’s super readable, and because you’re nesting each module’s history under Project Lyra, you won’t lose track of what happened where. \n\nNext step is probably to bring **PROJECT_SUMMARY.md** into the same unified structure (umbrella at the top, modules broken out). \n\n👉 Do you want me to draft that one next, with current status for Lyra-Core, Lyra-Mem0, and Lyra-Cortex?" }, { "role": "user", "content": "# Lyra Core — Project Summary\n\n## v0.3 (2025-09-27)\n\n### 🧠 High-Level Architecture\n- **Lyra Core (v0.3.1)** — Orchestration layer. \n - Accepts chat requests (`/v1/chat/completions`). \n - Routes through Cortex for subconscious annotation. \n - Stores everything in Mem0 (no discard). \n - Fetches persona + relevant memories. \n - Injects context back into LLM. \n\n- **Cortex (v0.3.0)** — Subconscious annotator. \n - Runs locally via `llama.cpp` (Phi-3.5-mini Q4_K_M). \n - Strict JSON schema: \n ```json\n {\n \"sentiment\": \"positive\" | \"neutral\" | \"negative\",\n \"novelty\": 0.0–1.0,\n \"tags\": [\"keyword\", \"keyword\"],\n \"notes\": \"short string\"\n }\n ``` \n - Normalizes keys (lowercase). \n - Strips Markdown fences before parsing. \n - Configurable via `.env` (`CORTEX_ENABLED=true|false`). \n\n- **Mem0** — Persistent memory layer. \n - Handles embeddings, graph storage, and retrieval. \n - Stores Cortex annotations in `metadata.cortex`. \n - Returns relevant snippets for context injection. \n\n- **Persona Sidecar** \n - Provides personality, style, and protocol instructions. \n - Injected at runtime into Core prompt building. \n\n---\n\n### 🚀 Recent Changes\n- Dropped salience filter approach → Cortex now acts as subconscious annotator. \n- Lyra Core now stores *all* user messages (no discard tier). \n- Cortex annotations fully integrated into Mem0. \n- Improved reliability with Markdown fence cleaner + parse fallback. \n- Added `.env` toggle to disable Cortex when needed. \n\n---\n\n### 📈 Versioning\n- **Lyra Core** → v0.3.1 \n- **Cortex** → v0.3.0 \n- **Mem0** → OSS backend, unchanged (salience handled natively). \n\n---\n\n### 📋 Next Steps\n- [ ] Tune Cortex prompts further for tighter tags/notes. \n- [ ] Consider async embedding for faster mem.add. \n- [ ] Build visual diagram of data flow (Core ↔ Cortex ↔ Mem0 ↔ Persona). \n- [ ] Long-term: swap Cortex model → larger LLM for richer subconscious annotation. \n\nCurrent project summary. keep the same format just update it please" }, { "role": "assistant", "content": "Got it 👍 — I’ll keep the exact same format + style you used, but update it to reflect the new Mem0 dual-embedder setup, CSV export, and the current state of Cortex/Relay. \n\nHere’s the updated **PROJECT_SUMMARY.md**: \n\n---\n\n# Lyra Core — Project Summary\n\n## v0.4 (2025-10-03)\n\n### 🧠 High-Level Architecture\n- **Lyra Core (v0.3.1)** — Orchestration layer. \n - Accepts chat requests (`/v1/chat/completions`). \n - Routes through Cortex for subconscious annotation. \n - Stores everything in Mem0 (no discard). \n - Fetches persona + relevant memories. \n - Injects context back into LLM. \n\n- **Cortex (v0.3.0)** — Subconscious annotator. \n - Runs locally via `llama.cpp` (Phi-3.5-mini Q4_K_M). \n - Strict JSON schema: \n ```json\n {\n \"sentiment\": \"positive\" | \"neutral\" | \"negative\",\n \"novelty\": 0.0–1.0,\n \"tags\": [\"keyword\", \"keyword\"],\n \"notes\": \"short string\"\n }\n ``` \n - Normalizes keys (lowercase). \n - Strips Markdown fences before parsing. \n - Configurable via `.env` (`CORTEX_ENABLED=true|false`). \n - Currently generates annotations, but not yet persisted into Mem0 payloads (stored as empty `{cortex:{}}`). \n\n- **Mem0 (v0.4.0)** — Persistent memory layer. \n - Handles embeddings, graph storage, and retrieval. \n - Dual embedder support: \n - **OpenAI Cloud** (`text-embedding-3-small`, 1536-dim). \n - **HuggingFace TEI** (gte-Qwen2-1.5B-instruct, 1536-dim, hosted on 3090). \n - Environment toggle for provider (`.env.openai` vs `.env.3090`). \n - Memory persistence in Postgres (`payload` JSON). \n - CSV export pipeline confirmed (id, user_id, data, created_at). \n\n- **Persona Sidecar** \n - Provides personality, style, and protocol instructions. \n - Injected at runtime into Core prompt building. \n\n---\n\n### 🚀 Recent Changes\n- **Mem0** \n - Added HuggingFace TEI integration (local 3090 embedder). \n - Enabled dual-mode environment switch (OpenAI cloud ↔ local TEI). \n - Fixed `.env` line ending mismatch (CRLF vs LF). \n - Added memory dump/export commands for Postgres. \n\n- **Core/Relay** \n - No major changes since v0.3.1 (still routing input → Cortex → Mem0). \n\n- **Cortex** \n - Still outputs annotations, but not yet persisted into Mem0 payloads. \n\n---\n\n### 📈 Versioning\n- **Lyra Core** → v0.3.1 \n- **Cortex** → v0.3.0 \n- **Mem0** → v0.4.0 \n\n---\n\n### 📋 Next Steps\n- [ ] Wire Cortex annotations into Mem0 payloads (`cortex` object). \n- [ ] Add “export all memories” script to standard workflow. \n- [ ] Consider async embedding for faster `mem.add`. \n- [ ] Build visual diagram of data flow (Core ↔ Cortex ↔ Mem0 ↔ Persona). \n- [ ] Explore larger LLMs for Cortex (Qwen2-7B, etc.) for richer subconscious annotation. \n\n---\n\n👉 Do you want me to now update the **README.md** in the same style (top-level with submodule quickstart sections)?" }, { "role": "user", "content": "can you save the project summary as a .md for me?" }, { "role": "assistant", "content": "Done ✅ — I saved the updated **PROJECT_SUMMARY.md** for you here:\n\n[📄 Download PROJECT_SUMMARY.md](sandbox:/mnt/data/PROJECT_SUMMARY.md) \n\nDo you want me to do the same for the **README.md** and **CHANGELOG.md** once we’re happy with their updates?" }, { "role": "user", "content": "yes the README in the project files for this project is the way i want it. Just take that one and add the new stuff to it" }, { "role": "assistant", "content": "{\"id\": \"file-S7aDmgcor2sBVFvLy2FE7n\"}" }, { "role": "assistant", "content": "{\"queries\": [\"README contents\"]}" }, { "role": "assistant", "content": "{\"queries\": [\"README contents\"], \"intent\": \"update readme with new mem0 features\", \"time_frame_filter\": {\"start_date\": \"2025-01-01\", \"end_date\": \"2025-12-31\"}, \"source_filter\": [\"files_uploaded_in_conversation\"]}" }, { "role": "assistant", "content": "{\"queries\": [\"README contents\"], \"source_filter\": [\"files_uploaded_in_conversation\"]}" }, { "role": "assistant", "content": "I found the base **README.md** in your project files — it already has the layout you like, with sections for **Lyra-Core, Cortex, Mem0, Persona Sidecar, setup, and API usage** . \n\nHere’s what I’d suggest for updating it with the **new mem0 features** (HuggingFace TEI integration, dual-mode env swap, CSV export):\n\n---\n\n### ✅ New Sections / Updates to Add\n1. **Lyra-Mem0 v0.4.0 section** (after the existing v0.1.0 notes):\n - Mention HuggingFace TEI local embedder integration.\n - Document the environment switch (`.env.3090` vs `.env.openai`).\n - Note fix for `.env` CRLF/LF issues.\n - Add example for exporting memories from Postgres.\n\n2. **Nuances & Fixes** section:\n - Add bullet about `.env` line endings (Windows CRLF → LF).\n - Add bullet about dual-mode environment setup.\n\n3. **API Endpoints**:\n - Add a `GET /dump` or Postgres query example to export memories.\n\n---\n\n### Example Insert for README\n\n```markdown\n######## Lyra-Mem0 v0.4.0 #######\n\n### 🚀 New Features\n- **HuggingFace TEI Integration** — Local embedding server (tested on 3090).\n- **Dual-Mode Env Switch** — Swap between OpenAI cloud and local embedder using `.env.openai` vs `.env.3090`.\n- **.env Fixes** — Line endings normalized (LF required in Docker).\n- **CSV Export** — Memories can be dumped directly from Postgres:\n\n```bash\ndocker exec -e PGPASSWORD=mem0pass -it mem0-postgres \\\n psql -U mem0 -d mem0 -c \"COPY (SELECT id, user_id, payload->>'data' as memory, payload->>'created_at' as created_at FROM memories) TO STDOUT WITH CSV HEADER\"\n```\n\n---\n```\n\nWould you like me to **rewrite the full README.md with these updates merged in**, or generate a **README_v0.4.0.md** alongside the existing one so you can diff them?" } ] }