Aspen theme for 1.x (#3473)

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+---
+title: Configurations
+icon: "gear"
+iconType: "solid"
+---
+
+
+Config in mem0 is a dictionary that specifies the settings for your embedding models. It allows you to customize the behavior and connection details of your chosen embedder.
+
+## How to define configurations?
+
+The config is defined as an object (or dictionary) with two main keys:
+- `embedder`: Specifies the embedder provider and its configuration
+  - `provider`: The name of the embedder (e.g., "openai", "ollama")
+  - `config`: A nested object or dictionary containing provider-specific settings
+
+
+## How to use configurations?
+
+Here's a general example of how to use the config with mem0:
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "embedder": {
+        "provider": "your_chosen_provider",
+        "config": {
+            # Provider-specific settings go here
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Your text here", user_id="user", metadata={"category": "example"})
+```
+
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  embedder: {
+    provider: 'openai',
+    config: {
+      apiKey: process.env.OPENAI_API_KEY || '',
+      model: 'text-embedding-3-small',
+      // Provider-specific settings go here
+    },
+  },
+};
+
+const memory = new Memory(config);
+await memory.add("Your text here", { userId: "user", metadata: { category: "example" } });
+```
+</CodeGroup>
+
+## Why is Config Needed?
+
+Config is essential for:
+1. Specifying which embedding model to use.
+2. Providing necessary connection details (e.g., model, api_key, embedding_dims).
+3. Ensuring proper initialization and connection to your chosen embedder.
+
+## Master List of All Params in Config
+
+Here's a comprehensive list of all parameters that can be used across different embedders:
+
+<Tabs>
+<Tab title="Python">
+| Parameter | Description | Provider |
+|-----------|-------------|----------|
+| `model` | Embedding model to use | All |
+| `api_key` | API key of the provider | All |
+| `embedding_dims` | Dimensions of the embedding model | All |
+| `http_client_proxies` | Allow proxy server settings | All |
+| `ollama_base_url` | Base URL for the Ollama embedding model | Ollama |
+| `model_kwargs` | Key-Value arguments for the Huggingface embedding model | Huggingface |
+| `azure_kwargs` | Key-Value arguments for the AzureOpenAI embedding model | Azure OpenAI |
+| `openai_base_url`    | Base URL for OpenAI API                       | OpenAI            |
+| `vertex_credentials_json` | Path to the Google Cloud credentials JSON file for VertexAI                       | VertexAI            |
+| `memory_add_embedding_type` | The type of embedding to use for the add memory action                       | VertexAI            |
+| `memory_update_embedding_type` | The type of embedding to use for the update memory action                       | VertexAI            |
+| `memory_search_embedding_type` | The type of embedding to use for the search memory action                       | VertexAI            |
+| `lmstudio_base_url` | Base URL for LM Studio API                    | LM Studio         |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Provider |
+|-----------|-------------|----------|
+| `model` | Embedding model to use | All |
+| `apiKey` | API key of the provider | All |
+| `embeddingDims` | Dimensions of the embedding model | All |
+</Tab>
+</Tabs>
+
+## Supported Embedding Models
+
+For detailed information on configuring specific embedders, please visit the [Embedding Models](./models) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.
@@ -0,0 +1,62 @@
+---
+title: AWS Bedrock
+---
+
+To use AWS Bedrock embedding models, you need to have the appropriate AWS credentials and permissions. The embeddings implementation relies on the `boto3` library.
+
+### Setup
+- Ensure you have model access from the [AWS Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess)
+- Authenticate the boto3 client using a method described in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html)
+- Set up environment variables for authentication:
+  ```bash
+  export AWS_REGION=us-east-1
+  export AWS_ACCESS_KEY_ID=your-access-key
+  export AWS_SECRET_ACCESS_KEY=your-secret-key
+  ```
+
+### Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+# For LLM if needed
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+
+# AWS credentials
+os.environ["AWS_REGION"] = "us-west-2"
+os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-key"
+
+config = {
+    "embedder": {
+        "provider": "aws_bedrock",
+        "config": {
+            "model": "amazon.titan-embed-text-v2:0"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice")
+```
+</CodeGroup>
+
+### Config
+
+Here are the parameters available for configuring AWS Bedrock embedder:
+
+<Tabs>
+<Tab title="Python">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `amazon.titan-embed-text-v1` |
+</Tab>
+</Tabs>
@@ -0,0 +1,125 @@
+---
+title: Azure OpenAI
+---
+
+To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`, `EMBEDDING_AZURE_DEPLOYMENT`, `EMBEDDING_AZURE_ENDPOINT` and `EMBEDDING_AZURE_API_VERSION` environment variables. You can obtain the Azure OpenAI API key from the Azure.
+
+### Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["EMBEDDING_AZURE_OPENAI_API_KEY"] = "your-api-key"
+os.environ["EMBEDDING_AZURE_DEPLOYMENT"] = "your-deployment-name"
+os.environ["EMBEDDING_AZURE_ENDPOINT"] = "your-api-base-url"
+os.environ["EMBEDDING_AZURE_API_VERSION"] = "version-to-use"
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+
+config = {
+    "embedder": {
+        "provider": "azure_openai",
+        "config": {
+            "model": "text-embedding-3-large"
+            "azure_kwargs": {
+                  "api_version": "",
+                  "azure_deployment": "",
+                  "azure_endpoint": "",
+                  "api_key": "",
+                  "default_headers": {
+                    "CustomHeader": "your-custom-header",
+                  }
+              }
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+    embedder: {
+        provider: "azure_openai",
+        config: {
+            model: "text-embedding-3-large",
+            modelProperties: {
+                endpoint: "your-api-base-url",
+                deployment: "your-deployment-name",
+                apiVersion: "version-to-use",
+            }
+        }
+    }
+}
+
+const memory = new Memory(config);
+
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+
+await memory.add(messages, { userId: "john" });
+```
+</CodeGroup>
+
+As an alternative to using an API key, the Azure Identity credential chain can be used to authenticate with [Azure OpenAI role-based security](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control). 
+
+<Note> If an API key is provided, it will be used for authentication over an Azure Identity </Note>
+
+Below is a sample configuration for using Mem0 with Azure OpenAI and Azure Identity:
+
+```python
+import os
+from mem0 import Memory
+# You can set the values directly in the config dictionary or use environment variables
+
+os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
+os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
+os.environ["LLM_AZURE_API_VERSION"] = "version-to-use"
+
+config = {
+    "llm": {
+        "provider": "azure_openai_structured",
+        "config": {
+            "model": "your-deployment-name",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+            "azure_kwargs": {
+                  "azure_deployment": "<your-deployment-name>",
+                  "api_version": "<version-to-use>",
+                  "azure_endpoint": "<your-api-base-url>",
+                  "default_headers": {
+                    "CustomHeader": "your-custom-header",
+                  }
+              }
+        }
+    }
+}
+```
+
+Refer to [Azure Identity troubleshooting tips](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/identity/azure-identity/TROUBLESHOOTING.md#troubleshoot-environmentcredential-authentication-issues) for setting up an Azure Identity credential.
+
+### Config
+
+Here are the parameters available for configuring Azure OpenAI embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `text-embedding-3-small` |
+| `embedding_dims` | Dimensions of the embedding model | `1536` |
+| `azure_kwargs` | The Azure OpenAI configs | `config_keys` |
@@ -0,0 +1,69 @@
+---
+title: Google AI
+---
+
+To use Google AI embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
+
+### Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["GOOGLE_API_KEY"] = "key"
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "gemini",
+        "config": {
+            "model": "models/text-embedding-004",
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  embedder: {
+    provider: 'google',
+    config: {
+      apiKey: process.env.GOOGLE_API_KEY || '',
+      model: 'text-embedding-004',
+      // The output dimensionality is fixed at 768 for Google AI embeddings
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "john" });
+```
+</CodeGroup>
+
+### Config
+
+Here are the parameters available for configuring Gemini embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `models/text-embedding-004` |
+| `embedding_dims` | Dimensions of the embedding model (output_dimensionality will be considered as embedding_dims, so please set embedding_dims accordingly) | `768` |
+| `api_key` | The Google API key | `None` |
@@ -0,0 +1,75 @@
+---
+title: Hugging Face
+---
+
+You can use embedding models from Huggingface to run Mem0 locally.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "huggingface",
+        "config": {
+            "model": "multi-qa-MiniLM-L6-cos-v1"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+
+### Using Text Embeddings Inference (TEI)
+
+You can also use Hugging Face's Text Embeddings Inference service for faster and more efficient embeddings:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+# Using HuggingFace Text Embeddings Inference API
+config = {
+    "embedder": {
+        "provider": "huggingface",
+        "config": {
+            "huggingface_base_url": "http://localhost:3000/v1"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("This text will be embedded using the TEI service.", user_id="john")
+```
+
+To run the TEI service, you can use Docker:
+
+```bash
+docker run -d -p 3000:80 -v huggingfacetei:/data --platform linux/amd64 \
+    ghcr.io/huggingface/text-embeddings-inference:cpu-1.6 \
+    --model-id BAAI/bge-small-en-v1.5
+```
+
+### Config
+
+Here are the parameters available for configuring Huggingface embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the model to use | `multi-qa-MiniLM-L6-cos-v1` |
+| `embedding_dims` | Dimensions of the embedding model | `selected_model_dimensions` |
+| `model_kwargs` | Additional arguments for the model | `None` |
+| `huggingface_base_url` | URL to connect to Text Embeddings Inference (TEI) API | `None` |
@@ -0,0 +1,196 @@
+---
+title: LangChain
+---
+
+Mem0 supports LangChain as a provider to access a wide range of embedding models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various embedding providers through a consistent interface.
+
+For a complete list of available embedding models supported by LangChain, refer to the [LangChain Text Embedding documentation](https://python.langchain.com/docs/integrations/text_embedding/).
+
+## Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+from langchain_openai import OpenAIEmbeddings
+
+# Set necessary environment variables for your chosen LangChain provider
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+# Initialize a LangChain embeddings model directly
+openai_embeddings = OpenAIEmbeddings(
+    model="text-embedding-3-small",
+    dimensions=1536
+)
+
+# Pass the initialized model to the config
+config = {
+    "embedder": {
+        "provider": "langchain",
+        "config": {
+            "model": openai_embeddings
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+import { OpenAIEmbeddings } from "@langchain/openai";
+
+// Initialize a LangChain embeddings model directly
+const openaiEmbeddings = new OpenAIEmbeddings({
+    modelName: "text-embedding-3-small",
+    dimensions: 1536,
+    apiKey: process.env.OPENAI_API_KEY,
+});
+
+const config = {
+  embedder: {
+    provider: 'langchain',
+    config: {
+      model: openaiEmbeddings,
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
+
+## Supported LangChain Embedding Providers
+
+LangChain supports a wide range of embedding providers, including:
+
+- OpenAI (`OpenAIEmbeddings`)
+- Cohere (`CohereEmbeddings`)
+- Google (`VertexAIEmbeddings`)
+- Hugging Face (`HuggingFaceEmbeddings`)
+- Sentence Transformers (`HuggingFaceEmbeddings`)
+- Azure OpenAI (`AzureOpenAIEmbeddings`)
+- Ollama (`OllamaEmbeddings`)
+- Together (`TogetherEmbeddings`)
+- And many more
+
+You can use any of these model instances directly in your configuration. For a complete and up-to-date list of available embedding providers, refer to the [LangChain Text Embedding documentation](https://python.langchain.com/docs/integrations/text_embedding/).
+
+## Provider-Specific Configuration
+
+When using LangChain as an embedder provider, you'll need to:
+
+1. Set the appropriate environment variables for your chosen embedding provider
+2. Import and initialize the specific model class you want to use
+3. Pass the initialized model instance to the config
+
+### Examples with Different Providers
+
+<CodeGroup>
+#### HuggingFace Embeddings
+
+```python Python
+from langchain_huggingface import HuggingFaceEmbeddings
+
+# Initialize a HuggingFace embeddings model
+hf_embeddings = HuggingFaceEmbeddings(
+    model_name="BAAI/bge-small-en-v1.5",
+    encode_kwargs={"normalize_embeddings": True}
+)
+
+config = {
+    "embedder": {
+        "provider": "langchain",
+        "config": {
+            "model": hf_embeddings
+        }
+    }
+}
+```
+
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+import { HuggingFaceEmbeddings } from "@langchain/community/embeddings/hf";
+
+// Initialize a HuggingFace embeddings model
+const hfEmbeddings = new HuggingFaceEmbeddings({
+    modelName: "BAAI/bge-small-en-v1.5",
+    encode: {
+        normalize_embeddings: true,
+    },
+});
+
+const config = {
+  embedder: {
+    provider: 'langchain',
+    config: {
+      model: hfEmbeddings,
+    },
+  },
+};
+```
+</CodeGroup>
+
+<CodeGroup>
+#### Ollama Embeddings
+
+```python Python
+from langchain_ollama import OllamaEmbeddings
+
+# Initialize an Ollama embeddings model
+ollama_embeddings = OllamaEmbeddings(
+    model="nomic-embed-text"
+)
+
+config = {
+    "embedder": {
+        "provider": "langchain",
+        "config": {
+            "model": ollama_embeddings
+        }
+    }
+}
+```
+
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama";
+
+// Initialize an Ollama embeddings model
+const ollamaEmbeddings = new OllamaEmbeddings({
+    model: "nomic-embed-text",
+    baseUrl: "http://localhost:11434", // Ollama server URL
+});
+
+const config = {
+  embedder: {
+    provider: 'langchain',
+    config: {
+      model: ollamaEmbeddings,
+    },
+  },
+};
+```
+</CodeGroup>
+
+<Note>
+  Make sure to install the necessary LangChain packages and any provider-specific dependencies.
+</Note>
+
+## Config
+
+All available parameters for the `langchain` embedder config are present in [Master List of All Params in Config](../config).
@@ -0,0 +1,38 @@
+You can use embedding models from LM Studio to run Mem0 locally.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "lmstudio",
+        "config": {
+            "model": "nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring Ollama embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the OpenAI model to use | `nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf` |
+| `embedding_dims` | Dimensions of the embedding model | `1536` |
+| `lmstudio_base_url` | Base URL for LM Studio connection | `http://localhost:1234/v1` |
@@ -0,0 +1,73 @@
+You can use embedding models from Ollama to run Mem0 locally.
+
+### Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "ollama",
+        "config": {
+            "model": "mxbai-embed-large"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  embedder: {
+    provider: 'ollama',
+    config: {
+      model: 'nomic-embed-text:latest', // or any other Ollama embedding model
+      url: 'http://localhost:11434', // Ollama server URL
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "john" });
+```
+</CodeGroup>
+
+### Config
+
+Here are the parameters available for configuring Ollama embedder:
+
+<Tabs>
+<Tab title="Python">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the Ollama model to use | `nomic-embed-text` |
+| `embedding_dims` | Dimensions of the embedding model | `512` |
+| `ollama_base_url` | Base URL for ollama connection | `None` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the Ollama model to use | `nomic-embed-text:latest` |
+| `url` | Base URL for Ollama server | `http://localhost:11434` |
+</Tab>
+</Tabs>
@@ -0,0 +1,72 @@
+---
+title: OpenAI
+---
+
+To use OpenAI embedding models, set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
+
+### Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key"
+
+config = {
+    "embedder": {
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-large"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  embedder: {
+    provider: 'openai',
+    config: {
+      apiKey: 'your-openai-api-key',
+      model: 'text-embedding-3-large',
+    },
+  },
+};
+
+const memory = new Memory(config);
+await memory.add("I'm visiting Paris", { userId: "john" });
+```
+</CodeGroup>
+
+### Config
+
+Here are the parameters available for configuring OpenAI embedder:
+
+<Tabs>
+<Tab title="Python">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `text-embedding-3-small` |
+| `embedding_dims` | Dimensions of the embedding model | `1536` |
+| `api_key` | The OpenAI API key | `None` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `text-embedding-3-small` |
+| `embeddingDims` | Dimensions of the embedding model | `1536` |
+| `apiKey` | The OpenAI API key | `None` |
+</Tab>
+</Tabs>
@@ -0,0 +1,45 @@
+---
+title: Together
+---
+
+To use Together embedding models, set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from the [Together Platform](https://api.together.xyz/settings/api-keys).
+
+### Usage
+
+<Note> The `embedding_model_dims` parameter for `vector_store` should be set to `768` for Together embedder. </Note>
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["TOGETHER_API_KEY"] = "your_api_key"
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "together",
+        "config": {
+            "model": "togethercomputer/m2-bert-80M-8k-retrieval"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring Together embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `togethercomputer/m2-bert-80M-8k-retrieval` |
+| `embedding_dims` | Dimensions of the embedding model | `768` |
+| `api_key` | The Together API key | `None` |
@@ -0,0 +1,55 @@
+### Vertex AI
+
+To use Google Cloud's Vertex AI for text embedding models, set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to the path of your service account's credentials JSON file. These credentials can be created in the [Google Cloud Console](https://console.cloud.google.com/).
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+# Set the path to your Google Cloud credentials JSON file
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/your/credentials.json"
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "vertexai",
+        "config": {
+            "model": "text-embedding-004",
+            "memory_add_embedding_type": "RETRIEVAL_DOCUMENT",
+            "memory_update_embedding_type": "RETRIEVAL_DOCUMENT",
+            "memory_search_embedding_type": "RETRIEVAL_QUERY"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+The embedding types can be one of the following:
+- SEMANTIC_SIMILARITY
+- CLASSIFICATION
+- CLUSTERING
+- RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION
+- CODE_RETRIEVAL_QUERY  
+Check out the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/task-types#supported_task_types) for more information.  
+  
+### Config
+
+Here are the parameters available for configuring the Vertex AI embedder:
+
+| Parameter                 | Description                                      | Default Value        |
+| ------------------------- | ------------------------------------------------ | -------------------- |
+| `model`                   | The name of the Vertex AI embedding model to use | `text-embedding-004` |
+| `vertex_credentials_json` | Path to the Google Cloud credentials JSON file   | `None`               |
+| `embedding_dims`          | Dimensions of the embedding model                | `256`                |
+| `memory_add_embedding_type` | The type of embedding to use for the add memory action | `RETRIEVAL_DOCUMENT` |
+| `memory_update_embedding_type` | The type of embedding to use for the update memory action | `RETRIEVAL_DOCUMENT` |
+| `memory_search_embedding_type` | The type of embedding to use for the search memory action | `RETRIEVAL_QUERY` |
@@ -0,0 +1,34 @@
+---
+title: Overview
+icon: "info"
+iconType: "solid"
+---
+
+Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
+
+## Supported Embedders
+
+See the list of supported embedders below.
+
+<Note>
+  The following embedders are supported in the Python implementation. The TypeScript implementation currently only supports OpenAI.
+</Note>
+
+<CardGroup cols={4}>
+  <Card title="OpenAI" href="/components/embedders/models/openai"></Card>
+  <Card title="Azure OpenAI" href="/components/embedders/models/azure_openai"></Card>
+  <Card title="Ollama" href="/components/embedders/models/ollama"></Card>
+  <Card title="Hugging Face" href="/components/embedders/models/huggingface"></Card>
+  <Card title="Google AI" href="/components/embedders/models/google_AI"></Card>
+  <Card title="Vertex AI" href="/components/embedders/models/vertexai"></Card>
+  <Card title="Together" href="/components/embedders/models/together"></Card>
+  <Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
+  <Card title="Langchain" href="/components/embedders/models/langchain"></Card>
+  <Card title="AWS Bedrock" href="/components/embedders/models/aws_bedrock"></Card>
+</CardGroup>
+
+## Usage
+
+To utilize a embedder, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `OpenAI` will be used as the embedder.
+
+For a comprehensive list of available parameters for embedder configuration, please refer to [Config](./config).