docs: navigation (#5900)
This commit is contained in:
Kartik
@@ -1,63 +1,47 @@
|
||||
---
|
||||
title: "Configure the OSS Stack"
|
||||
description: "Wire up Mem0 OSS with your preferred LLM, vector store, embedder, and reranker."
|
||||
description: "Configure Mem0 OSS in Python or TypeScript with your own LLM, embedder, and vector store."
|
||||
icon: "sliders"
|
||||
---
|
||||
|
||||
# Configure Mem0 OSS Components
|
||||
Mem0 OSS works out of the box with OpenAI defaults. Point it at your own LLM, embedder, and vector store by passing a config when you create `Memory`. The Python SDK also supports a reranker and graph memory.
|
||||
|
||||
<Info>
|
||||
**Prerequisites**
|
||||
- Python 3.10+ with `pip` available
|
||||
- Running vector database (e.g., Qdrant, Postgres + pgvector) or access credentials for a managed store
|
||||
- API keys for your chosen LLM, embedder, and reranker providers
|
||||
- Python 3.10+ (`pip`) or Node.js 18+ (`npm`)
|
||||
- A running vector store such as Qdrant or Postgres + pgvector (Python's default Qdrant and Node's in-memory store need nothing extra)
|
||||
- API keys for your chosen LLM and embedder providers
|
||||
</Info>
|
||||
|
||||
<Tip>
|
||||
Start from the <Link href="/open-source/python-quickstart">Python quickstart</Link> if you still need the base CLI and repository.
|
||||
New to Mem0 OSS? Run the <Link href="/open-source/python-quickstart">Python</Link> or <Link href="/open-source/node-quickstart">Node.js</Link> quickstart first, then come back to swap in your own providers.
|
||||
</Tip>
|
||||
|
||||
## Install dependencies
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Python">
|
||||
<Steps>
|
||||
<Step title="Install Mem0 OSS">
|
||||
```bash
|
||||
<CodeGroup>
|
||||
```bash pip
|
||||
pip install mem0ai
|
||||
```
|
||||
</Step>
|
||||
<Step title="Add provider SDKs (example: Qdrant + OpenAI)">
|
||||
```bash
|
||||
pip install qdrant-client openai
|
||||
|
||||
```bash npm
|
||||
npm install mem0ai
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
<Tab title="Docker Compose">
|
||||
<Steps>
|
||||
<Step title="Clone the repo and copy the compose file">
|
||||
</CodeGroup>
|
||||
|
||||
Using Qdrant as your vector store? Install its Python client (the Node SDK talks to Qdrant over REST) and run the server locally:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/mem0ai/mem0.git
|
||||
cd mem0/examples/docker-compose
|
||||
pip install qdrant-client # Python only
|
||||
docker run -p 6333:6333 qdrant/qdrant
|
||||
```
|
||||
</Step>
|
||||
<Step title="Install dependencies for local overrides">
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Define your configuration
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Python">
|
||||
<Steps>
|
||||
<Step title="Create a configuration dictionary">
|
||||
```python
|
||||
Each component takes a `provider` and a `config`. Keys are `snake_case` in Python and `camelCase` in TypeScript. Pass the config when you create `Memory`:
|
||||
|
||||
<CodeGroup>
|
||||
```python Python
|
||||
from mem0 import Memory
|
||||
|
||||
config = {
|
||||
@@ -67,11 +51,11 @@ config = {
|
||||
},
|
||||
"llm": {
|
||||
"provider": "openai",
|
||||
"config": {"model": "gpt-4.1-mini", "temperature": 0.1},
|
||||
"config": {"model": "gpt-5-mini", "temperature": 0.1},
|
||||
},
|
||||
"embedder": {
|
||||
"provider": "vertexai",
|
||||
"config": {"model": "textembedding-gecko@003"},
|
||||
"provider": "openai",
|
||||
"config": {"model": "text-embedding-3-small"},
|
||||
},
|
||||
"reranker": {
|
||||
"provider": "cohere",
|
||||
@@ -81,86 +65,95 @@ config = {
|
||||
|
||||
memory = Memory.from_config(config)
|
||||
```
|
||||
</Step>
|
||||
<Step title="Store secrets as environment variables">
|
||||
|
||||
```ts Node.js
|
||||
import { Memory } from "mem0ai/oss";
|
||||
|
||||
const memory = new Memory({
|
||||
llm: {
|
||||
provider: "openai",
|
||||
config: { apiKey: process.env.OPENAI_API_KEY || "", model: "gpt-5-mini", temperature: 0.1 },
|
||||
},
|
||||
embedder: {
|
||||
provider: "openai",
|
||||
config: { apiKey: process.env.OPENAI_API_KEY || "", model: "text-embedding-3-small" },
|
||||
},
|
||||
vectorStore: {
|
||||
provider: "qdrant",
|
||||
config: { host: "localhost", port: 6333, collectionName: "memories" },
|
||||
},
|
||||
});
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
Set your provider keys as environment variables:
|
||||
|
||||
```bash
|
||||
export QDRANT_API_KEY="..."
|
||||
export OPENAI_API_KEY="..."
|
||||
export COHERE_API_KEY="..."
|
||||
export COHERE_API_KEY="..." # Python reranker only
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
<Tab title="config.yaml">
|
||||
<Steps>
|
||||
<Step title="Create a `config.yaml` file">
|
||||
```yaml
|
||||
vector_store:
|
||||
provider: qdrant
|
||||
config:
|
||||
host: localhost
|
||||
port: 6333
|
||||
|
||||
llm:
|
||||
provider: azure_openai
|
||||
config:
|
||||
api_key: ${AZURE_OPENAI_KEY}
|
||||
deployment_name: gpt-4.1-mini
|
||||
<Note>
|
||||
The TypeScript OSS SDK configures the LLM, embedder, vector store, and history store. Reranker and graph memory are Python-only today.
|
||||
</Note>
|
||||
|
||||
embedder:
|
||||
provider: ollama
|
||||
config:
|
||||
model: nomic-embed-text
|
||||
Prefer a config file? Load YAML into Python's `from_config`:
|
||||
|
||||
reranker:
|
||||
provider: zero_entropy
|
||||
config:
|
||||
api_key: ${ZERO_ENTROPY_KEY}
|
||||
```
|
||||
</Step>
|
||||
<Step title="Load the config file at runtime">
|
||||
```python
|
||||
import yaml
|
||||
from mem0 import Memory
|
||||
|
||||
memory = Memory.from_config_file("config.yaml")
|
||||
with open("config.yaml") as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
memory = Memory.from_config(config)
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Info icon="check">
|
||||
Run `memory.add("Remember my favorite cafe in Tokyo.", user_id="alex")` and then `memory.search("favorite cafe", filters={"user_id": "alex"})`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
|
||||
Verify it works: add a memory and search it back. `memory.add(...)` followed by `memory.search(...)` should populate your vector store and return the memory as a top hit.
|
||||
</Info>
|
||||
|
||||
## Available providers
|
||||
|
||||
Change the `provider` string to switch backends. The most common options:
|
||||
|
||||
| Component | Python | TypeScript |
|
||||
| --- | --- | --- |
|
||||
| LLM | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `aws_bedrock`, `azure_openai`, `litellm` | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `azure_openai`, `mistral`, `deepseek` |
|
||||
| Embedder | `openai`, `gemini`, `azure_openai`, `ollama`, `huggingface`, `vertexai`, `aws_bedrock` | `openai`, `gemini`, `azure_openai`, `ollama` |
|
||||
| Vector store | `qdrant`, `pgvector`, `chroma`, `pinecone`, `redis`, `weaviate`, `milvus`, `elasticsearch` | `memory`, `qdrant`, `pgvector`, `redis`, `supabase`, `azure-ai-search`, `vectorize` |
|
||||
|
||||
See the full catalog in <Link href="/components/llms/overview">Components</Link>.
|
||||
|
||||
## Tune component settings
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Vector store collections">
|
||||
Name collections explicitly in production (`collection_name`) to isolate tenants and enable per-tenant retention policies.
|
||||
Name collections explicitly in production (`collection_name` / `collectionName`) to isolate tenants and enable per-tenant retention policies.
|
||||
</Accordion>
|
||||
<Accordion title="LLM extraction temperature">
|
||||
Keep extraction temperatures ≤0.2 so advanced memories stay deterministic. Raise it only when you see missing facts.
|
||||
Keep extraction temperature at or below 0.2 so memories stay deterministic. Raise it only when you see facts being missed.
|
||||
</Accordion>
|
||||
<Accordion title="Reranker depth">
|
||||
Limit `top_k` to 10–20 results; sending more adds latency without meaningful gains.
|
||||
<Accordion title="Reranker depth (Python)">
|
||||
Limit `top_k` to 10 to 20 results. Sending more adds latency without meaningful gains.
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
<Warning>
|
||||
Mixing managed and self-hosted components? Make sure every outbound provider call happens through a secure network path. Managed rerankers often require outbound internet even if your vector store is on-prem.
|
||||
Mixing managed and self-hosted components? Make sure every outbound provider call has a secure network path. Managed rerankers and embedders often require outbound internet even if your vector store is on-prem.
|
||||
</Warning>
|
||||
|
||||
## Quick recovery
|
||||
|
||||
- Qdrant connection errors → confirm port `6333` is exposed and API key (if set) matches.
|
||||
- Empty search results → verify the embedder model name; a mismatch causes dimension errors.
|
||||
- `Unknown reranker` → update the SDK (`pip install --upgrade mem0ai`) to load the latest provider registry.
|
||||
- Qdrant connection errors: confirm port `6333` is exposed and the API key (if set) matches.
|
||||
- Empty search results: verify the embedder model name. A mismatch causes dimension errors.
|
||||
- `Unknown reranker` (Python): upgrade the SDK with `pip install --upgrade mem0ai` to load the latest provider registry.
|
||||
- `Cannot find module` (Node): import from the OSS entry point, `import { Memory } from "mem0ai/oss"`, not `"mem0ai"`.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card
|
||||
title="Pick Providers"
|
||||
description="Review the LLM, vector store, embedder, and reranker catalogs."
|
||||
description="Browse the LLM, vector store, embedder, and reranker catalogs."
|
||||
icon="sitemap"
|
||||
href="/components/llms/overview"
|
||||
/>
|
||||
|
||||
Reference in New Issue
Block a user