Make api_version=v1.1 default and version bump -> 0.1.59 (#2278)

Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>

docs/components/embedders/config.mdx

@@ -8,16 +8,18 @@ Config in mem0 is a dictionary that specifies the settings for your embedding mo
 
 ## How to define configurations?
 
-The config is defined as a Python dictionary with two main keys:
+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 dictionary containing provider-specific settings
+  - `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:
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -36,6 +38,25 @@ 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:
@@ -47,21 +68,31 @@ Config is essential for:
 
 Here's a comprehensive list of all parameters that can be used across different embedders:
 
-| Parameter | Description |
+<Tabs>
-|-----------|-------------|
+<Tab title="Python">
-| `model` | Embedding model to use |
+| Parameter | Description | Provider |
-| `api_key` | API key of the provider |
+|-----------|-------------|----------|
-| `embedding_dims` | Dimensions of the embedding model |
+| `model` | Embedding model to use | All |
-| `http_client_proxies` | Allow proxy server settings |
+| `api_key` | API key of the provider | All |
-| `ollama_base_url` | Base URL for the Ollama embedding model |
+| `embedding_dims` | Dimensions of the embedding model | All |
-| `model_kwargs` | Key-Value arguments for the Huggingface embedding model |
+| `http_client_proxies` | Allow proxy server settings | All |
-| `azure_kwargs` | Key-Value arguments for the AzureOpenAI embedding model |
+| `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            |
-
+</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
 

docs/components/embedders/models/openai.mdx

@@ -6,7 +6,8 @@ To use OpenAI embedding models, set the `OPENAI_API_KEY` environment variable. Y
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -25,12 +26,41 @@ m = Memory.from_config(config)
 m.add("I'm visiting Paris", 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>

docs/components/embedders/overview.mdx

@@ -10,6 +10,10 @@ Mem0 offers support for various embedding models, allowing users to choose the o
 
 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>

docs/components/llms/config.mdx

@@ -6,26 +6,40 @@ iconType: "solid"
 
 ## How to define configurations?
 
-The `config` is defined as a Python dictionary with two main keys:
+<Tabs>
-- `llm`: Specifies the llm provider and its configuration
+  <Tab title="Python">
-  - `provider`: The name of the llm (e.g., "openai", "groq")
+    The `config` is defined as a Python dictionary with two main keys:
-  - `config`: A nested dictionary containing provider-specific settings
+    - `llm`: Specifies the llm provider and its configuration
+      - `provider`: The name of the llm (e.g., "openai", "groq")
+      - `config`: A nested dictionary containing provider-specific settings
+  </Tab>
+  <Tab title="TypeScript">
+    The `config` is defined as a TypeScript object with these keys:
+    - `llm`: Specifies the LLM provider and its configuration (required)
+      - `provider`: The name of the LLM (e.g., "openai", "groq")
+      - `config`: A nested object containing provider-specific settings
+    - `embedder`: Specifies the embedder provider and its configuration (optional)
+    - `vectorStore`: Specifies the vector store provider and its configuration (optional)
+    - `historyDbPath`: Path to the history database file (optional)
+  </Tab>
+</Tabs>
 
 ### Config Values Precedence
 
 Config values are applied in the following order of precedence (from highest to lowest):
 
-1. Values explicitly set in the `config` dictionary
+1. Values explicitly set in the `config` object/dictionary
 2. Environment variables (e.g., `OPENAI_API_KEY`, `OPENAI_API_BASE`)
 3. Default values defined in the LLM implementation
 
-This means that values specified in the `config` dictionary will override corresponding environment variables, which in turn override default values.
+This means that values specified in the `config` will override corresponding environment variables, which in turn override default values.
 
 ## How to Use Config
 
-Here's a general example of how to use the config with mem0:
+Here's a general example of how to use the config with Mem0:
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -44,39 +58,70 @@ m = Memory.from_config(config)
 m.add("Your text here", user_id="user", metadata={"category": "example"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+// Minimal configuration with just the LLM settings
+const config = {
+  llm: {
+    provider: 'your_chosen_provider',
+    config: {
+      // Provider-specific settings go here
+    }
+  }
+};
+
+const memory = new Memory(config);
+await memory.add("Your text here", { userId: "user123", metadata: { category: "example" } });
+```
+</CodeGroup>
+
 ## Why is Config Needed?
 
 Config is essential for:
-1. Specifying which llm to use.
+1. Specifying which LLM to use.
 2. Providing necessary connection details (e.g., model, api_key, temperature).
-3. Ensuring proper initialization and connection to your chosen llm.
+3. Ensuring proper initialization and connection to your chosen LLM.
 
 ## Master List of All Params in Config
 
-Here's a comprehensive list of all parameters that can be used across different llms:
+Here's a comprehensive list of all parameters that can be used across different LLMs:
 
-Here's the table based on the provided parameters:
+<Tabs>
-
+  <Tab title="Python">
-| Parameter            | Description                                   | Provider          |
+    | Parameter            | Description                                   | Provider          |
-|----------------------|-----------------------------------------------|-------------------|
+    |----------------------|-----------------------------------------------|-------------------|
-| `model`              | Embedding model to use                        | All               |
+    | `model`              | Embedding model to use                        | All               |
-| `temperature`        | Temperature of the model                      | All               |
+    | `temperature`        | Temperature of the model                      | All               |
-| `api_key`            | API key to use                                | All               |
+    | `api_key`            | API key to use                                | All               |
-| `max_tokens`         | Tokens to generate                            | All               |
+    | `max_tokens`         | Tokens to generate                            | All               |
-| `top_p`              | Probability threshold for nucleus sampling    | All               |
+    | `top_p`              | Probability threshold for nucleus sampling    | All               |
-| `top_k`              | Number of highest probability tokens to keep  | All               |
+    | `top_k`              | Number of highest probability tokens to keep  | All               |
-| `http_client_proxies`| Allow proxy server settings                   | AzureOpenAI       |
+    | `http_client_proxies`| Allow proxy server settings                   | AzureOpenAI       |
-| `models`             | List of models                                | Openrouter        |
+    | `models`             | List of models                                | Openrouter        |
-| `route`              | Routing strategy                              | Openrouter        |
+    | `route`              | Routing strategy                              | Openrouter        |
-| `openrouter_base_url`| Base URL for Openrouter API                   | Openrouter        |
+    | `openrouter_base_url`| Base URL for Openrouter API                   | Openrouter        |
-| `site_url`           | Site URL                                      | Openrouter        |
+    | `site_url`           | Site URL                                      | Openrouter        |
-| `app_name`           | Application name                              | Openrouter        |
+    | `app_name`           | Application name                              | Openrouter        |
-| `ollama_base_url`    | Base URL for Ollama API                       | Ollama            |
+    | `ollama_base_url`    | Base URL for Ollama API                       | Ollama            |
-| `openai_base_url`    | Base URL for OpenAI API                       | OpenAI            |
+    | `openai_base_url`    | Base URL for OpenAI API                       | OpenAI            |
-| `azure_kwargs`       | Azure LLM args for initialization             | AzureOpenAI       |
+    | `azure_kwargs`       | Azure LLM args for initialization             | AzureOpenAI       |
-| `deepseek_base_url`  | Base URL for DeepSeek API                     | DeepSeek          |
+    | `deepseek_base_url`  | Base URL for DeepSeek API                     | DeepSeek          |
-| `xai_base_url`       | Base URL for XAI API                          | XAI               |
+    | `xai_base_url`       | Base URL for XAI API                          | XAI               |
+  </Tab>
+  <Tab title="TypeScript">
+    | Parameter            | Description                                   | Provider          |
+    |----------------------|-----------------------------------------------|-------------------|
+    | `model`              | Embedding model to use                        | All               |
+    | `temperature`        | Temperature of the model                      | All               |
+    | `apiKey`             | API key to use                                | All               |
+    | `maxTokens`          | Tokens to generate                            | All               |
+    | `topP`               | Probability threshold for nucleus sampling    | All               |
+    | `topK`               | Number of highest probability tokens to keep  | All               |
+    | `openaiBaseUrl`      | Base URL for OpenAI API                       | OpenAI            |
+  </Tab>
+</Tabs>
 
 ## Supported LLMs
 
-For detailed information on configuring specific llms, please visit the [LLMs](./models) section. There you'll find information for each supported llm with provider-specific usage examples and configuration details.
+For detailed information on configuring specific LLMs, please visit the [LLMs](./models) section. There you'll find information for each supported LLM with provider-specific usage examples and configuration details.

docs/components/llms/models/anthropic.mdx

@@ -1,8 +1,13 @@
+---
+title: Anthropic
+---
+
 To use anthropic's models, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -24,6 +29,26 @@ m = Memory.from_config(config)
 m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'anthropic',
+    config: {
+      apiKey: process.env.ANTHROPIC_API_KEY || '',
+      model: 'claude-3-7-sonnet-latest',
+      temperature: 0.1,
+      maxTokens: 2000,
+    },
+  },
+};
+
+const memory = new Memory(config);
+await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
+```
+</CodeGroup>
+
 ## Config
 
 All available parameters for the `anthropic` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/groq.mdx

@@ -1,10 +1,15 @@
+---
+title: Groq
+---
+
 [Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.
 
 In order to use LLMs from Groq, go to their [platform](https://console.groq.com/keys) and get the API key. Set the API key as `GROQ_API_KEY` environment variable to use the model as given below in the example.
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -26,6 +31,26 @@ m = Memory.from_config(config)
 m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'groq',
+    config: {
+      apiKey: process.env.GROQ_API_KEY || '',
+      model: 'mixtral-8x7b-32768',
+      temperature: 0.1,
+      maxTokens: 1000,
+    },
+  },
+};
+
+const memory = new Memory(config);
+await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
+```
+</CodeGroup>
+
 ## Config
 
 All available parameters for the `groq` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/openai.mdx

@@ -6,7 +6,8 @@ To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment varia
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -38,6 +39,26 @@ m = Memory.from_config(config)
 m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'openai',
+    config: {
+      apiKey: process.env.OPENAI_API_KEY || '',
+      model: 'gpt-4-turbo-preview',
+      temperature: 0.2,
+      maxTokens: 1500,
+    },
+  },
+};
+
+const memory = new Memory(config);
+await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
+```
+</CodeGroup>
+
 We also support the new [OpenAI structured-outputs](https://platform.openai.com/docs/guides/structured-outputs/introduction) model.
 
 ```python
@@ -59,7 +80,9 @@ config = {
 m = Memory.from_config(config)
 ```
 
-
+<Note>
+  OpenAI structured-outputs is currently only available in the Python implementation.
+</Note>
 
 ## Config
 

docs/components/llms/overview.mdx

@@ -14,20 +14,24 @@ For a comprehensive list of available parameters for llm configuration, please r
 
 To view all supported llms, visit the [Supported LLMs](./models).
 
+<Note>
+  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, and **Groq**.
+</Note>
+
 <CardGroup cols={4}>
-  <Card title="OpenAI" href="/components/llms/models/openai"></Card>
+  <Card title="OpenAI" href="/components/llms/models/openai" />
-    <Card title="Ollama" href="/components/llms/models/ollama"></Card>
+  <Card title="Ollama" href="/components/llms/models/ollama" />
-    <Card title="Azure OpenAI" href="/components/llms/models/azure_openai"></Card>
+  <Card title="Azure OpenAI" href="/components/llms/models/azure_openai" />
-    <Card title="Anthropic" href="/components/llms/models/anthropic"></Card>
+  <Card title="Anthropic" href="/components/llms/models/anthropic" />
-    <Card title="Together" href="/components/llms/models/together"></Card>
+  <Card title="Together" href="/components/llms/models/together" />
-    <Card title="Groq" href="/components/llms/models/groq"></Card>
+  <Card title="Groq" href="/components/llms/models/groq" />
-    <Card title="Litellm" href="/components/llms/models/litellm"></Card>
+  <Card title="Litellm" href="/components/llms/models/litellm" />
-    <Card title="Mistral AI" href="/components/llms/models/mistral_ai"></Card>
+  <Card title="Mistral AI" href="/components/llms/models/mistral_ai" />
-    <Card title="Google AI" href="/components/llms/models/google_ai"></Card>
+  <Card title="Google AI" href="/components/llms/models/google_ai" />
-    <Card title="AWS bedrock" href="/components/llms/models/aws_bedrock"></Card>
+  <Card title="AWS bedrock" href="/components/llms/models/aws_bedrock" />
-    <Card title="Gemini" href="/components/llms/models/gemini"></Card>
+  <Card title="Gemini" href="/components/llms/models/gemini" />
-    <Card title="DeepSeek" href="/components/llms/models/deepseek"></Card>
+  <Card title="DeepSeek" href="/components/llms/models/deepseek" />
-    <Card title="xAI" href="/components/llms/models/xAI"></Card>
+  <Card title="xAI" href="/components/llms/models/xAI" />
 </CardGroup>
 
 ## Structured vs Unstructured Outputs

docs/components/vectordbs/config.mdx

@@ -6,16 +6,17 @@ iconType: "solid"
 
 ## How to define configurations?
 
-The `config` is defined as a Python dictionary with two main keys:
+The `config` is defined as an object with two main keys:
 - `vector_store`: Specifies the vector database provider and its configuration
-  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus","azure_ai_search")
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "azure_ai_search")
-  - `config`: A nested dictionary containing provider-specific settings
+  - `config`: A nested object containing provider-specific settings
 
 ## How to Use Config
 
 Here's a general example of how to use the config with mem0:
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -34,6 +35,29 @@ m = Memory.from_config(config)
 m.add("Your text here", user_id="user", metadata={"category": "example"})
 ```
 
+```typescript TypeScript
+// Example for in-memory vector database (Only supported in TypeScript)
+import { Memory } from 'mem0ai/oss';
+
+const configMemory = {
+  vector_store: {
+    provider: 'memory',
+    config: {
+      collectionName: 'memories',
+      dimension: 1536,
+    },
+  },
+};
+
+const memory = new Memory(configMemory);
+await memory.add("Your text here", { userId: "user", metadata: { category: "example" } });
+```
+</CodeGroup>
+
+<Note>
+  The in-memory vector database is only supported in the TypeScript implementation.
+</Note>
+
 ## Why is Config Needed?
 
 Config is essential for:
@@ -46,6 +70,8 @@ Config is essential for:
 
 Here's a comprehensive list of all parameters that can be used across different vector databases:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description |
 |-----------|-------------|
 | `collection_name` | Name of the collection |
@@ -60,6 +86,24 @@ Here's a comprehensive list of all parameters that can be used across different
 | `url` | Full URL for the server |
 | `api_key` | API key for the server |
 | `on_disk` | Enable persistent storage |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description |
+|-----------|-------------|
+| `collectionName` | Name of the collection |
+| `embeddingModelDims` | Dimensions of the embedding model |
+| `dimension` | Dimensions of the embedding model (for memory provider) |
+| `host` | Host where the server is running |
+| `port` | Port where the server is running |
+| `url` | URL for the server |
+| `apiKey` | API key for the server |
+| `path` | Path for the database |
+| `onDisk` | Enable persistent storage |
+| `redisUrl` | URL for the Redis server |
+| `username` | Username for database connection |
+| `password` | Password for database connection |
+</Tab>
+</Tabs>
 
 ## Customizing Config
 

docs/components/vectordbs/dbs/qdrant.mdx

@@ -2,7 +2,8 @@
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -23,10 +24,32 @@ m = Memory.from_config(config)
 m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  vectorStore: {
+    provider: 'qdrant',
+    config: {
+      collectionName: 'memories',
+      embeddingModelDims: 1536,
+      host: 'localhost',
+      port: 6333,
+    },
+  },
+};
+
+const memory = new Memory(config);
+await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
+```
+</CodeGroup>
+
 ### Config
 
 Let's see the available parameters for the `qdrant` config:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collection_name` | The name of the collection to store the vectors | `mem0` |
@@ -37,4 +60,18 @@ Let's see the available parameters for the `qdrant` config:
 | `path` | Path for the qdrant database | `/tmp/qdrant` |
 | `url` | Full URL for the qdrant server | `None` |
 | `api_key` | API key for the qdrant server | `None` |
-| `on_disk` | For enabling persistent storage | `False` |
+| `on_disk` | For enabling persistent storage | `False` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collectionName` | The name of the collection to store the vectors | `mem0` |
+| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
+| `host` | The host where the Qdrant server is running | `None` |
+| `port` | The port where the Qdrant server is running | `None` |
+| `path` | Path for the Qdrant database | `/tmp/qdrant` |
+| `url` | Full URL for the Qdrant server | `None` |
+| `apiKey` | API key for the Qdrant server | `None` |
+| `onDisk` | For enabling persistent storage | `False` |
+</Tab>
+</Tabs>

docs/components/vectordbs/dbs/redis.mdx

@@ -12,7 +12,8 @@ docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:lat
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -34,12 +35,46 @@ m = Memory.from_config(config)
 m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  vectorStore: {
+    provider: 'redis',
+    config: {
+      collectionName: 'memories',
+      embeddingModelDims: 1536,
+      redisUrl: 'redis://localhost:6379',
+      username: 'your-redis-username',
+      password: 'your-redis-password',
+    },
+  },
+};
+
+const memory = new Memory(config);
+await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
+```
+</CodeGroup>
+
 ### Config
 
 Let's see the available parameters for the `redis` config:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collection_name` | The name of the collection to store the vectors | `mem0` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
-| `redis_url` | The URL of the Redis server | `None` |
+| `redis_url` | The URL of the Redis server | `None` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collectionName` | The name of the collection to store the vectors | `mem0` |
+| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
+| `redisUrl` | The URL of the Redis server | `None` |
+| `username` | Username for Redis connection | `None` |
+| `password` | Password for Redis connection | `None` |
+</Tab>
+</Tabs>

docs/components/vectordbs/overview.mdx

@@ -10,6 +10,10 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
 
 See the list of supported vector databases below.
 
+<Note>
+  The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis and in-memory vector database.
+</Note>
+
 <CardGroup cols={3}>
   <Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card>
   <Card title="Chroma" href="/components/vectordbs/dbs/chroma"></Card>

docs/docs.json

@@ -65,6 +65,7 @@
                 "pages": [
                   "open-source/quickstart",
                   "open-source/python-quickstart",
+                  "open-source-typescript/quickstart",
                   {
                     "group": "Features",
                     "icon": "wrench",
@@ -151,67 +152,6 @@
                         ]
                       }
                     ]
-                  },
-                  {
-                    "group": "Node.js",
-                    "icon": "js",
-                    "pages": [
-                      "open-source-typescript/quickstart",
-                      {
-                        "group": "Features",
-                        "icon": "wrench",
-                        "pages": [
-                          "open-source-typescript/features/custom-prompts"
-                        ]
-                      },
-                      {
-                        "group": "LLMs",
-                        "icon": "brain",
-                        "pages": [
-                          "open-source-typescript/components/llms/overview",
-                          "open-source-typescript/components/llms/config",
-                          {
-                            "group": "Supported LLMs",
-                            "icon": "list",
-                            "pages": [
-                              "open-source-typescript/components/llms/models/openai",
-                              "open-source-typescript/components/llms/models/anthropic",
-                              "open-source-typescript/components/llms/models/groq"
-                            ]
-                          }
-                        ]
-                      },{
-                        "group": "Vector Databases",
-                        "icon": "database",
-                        "pages": [
-                          "open-source-typescript/components/vectordbs/overview",
-                          "open-source-typescript/components/vectordbs/config",
-                          {
-                            "group": "Supported Vector Databases",
-                            "icon": "server",
-                            "pages": [
-                              "open-source-typescript/components/vectordbs/dbs/qdrant",
-                              "open-source-typescript/components/vectordbs/dbs/redis"
-                            ]
-                          }
-                        ]
-                      },
-                      {
-                        "group": "Embedding Models",
-                        "icon": "layer-group",
-                        "pages": [
-                          "open-source-typescript/components/embedders/overview",
-                          "open-source-typescript/components/embedders/config",
-                          {
-                            "group": "Supported Embedding Models",
-                            "icon": "list",
-                            "pages": [
-                              "open-source-typescript/components/embedders/models/openai"
-                            ]
-                          }
-                        ]
-                      }
-                    ]
                   }
                 ]
               }

docs/examples/ai_companion_js.mdx

@@ -29,7 +29,7 @@ const openaiClient = new OpenAI();
 const memory = new Memory();
 
 async function chatWithMemories(message, userId = "default_user") {
-  const relevantMemories = await memory.search(message, userId);
+  const relevantMemories = await memory.search(message, { userId: userId });
   
   const memoriesStr = relevantMemories.results
     .map(entry => `- ${entry.memory}`)
@@ -52,7 +52,7 @@ ${memoriesStr}`;
   const assistantResponse = response.choices[0].message.content || "";
   
   messages.push({ role: "assistant", content: assistantResponse });
-  await memory.add(messages, userId);
+  await memory.add(messages, { userId: userId });
   
   return assistantResponse;
 }

docs/features/custom-prompts.mdx

@@ -17,7 +17,8 @@ To create an effective custom prompt:
 
 Example of a custom prompt:
 
-```python
+<CodeGroup>
+```python Python
 custom_prompt = """
 Please only extract entities containing customer support information, order details, and user information. 
 Here are some few shot examples:
@@ -39,12 +40,37 @@ Output: {{"facts" : ["Ordered red shirt, size medium", "Received blue shirt inst
 
 Return the facts and customer information in a json format as shown above.
 """
-
 ```
 
-Here we initialize the custom prompt in the config.
+```typescript TypeScript
+const customPrompt = `
+Please only extract entities containing customer support information, order details, and user information. 
+Here are some few shot examples:
 
-```python
+Input: Hi.
+Output: {"facts" : []}
+
+Input: The weather is nice today.
+Output: {"facts" : []}
+
+Input: My order #12345 hasn't arrived yet.
+Output: {"facts" : ["Order #12345 not received"]}
+
+Input: I am John Doe, and I would like to return the shoes I bought last week.
+Output: {"facts" : ["Customer name: John Doe", "Wants to return shoes", "Purchase made last week"]}
+
+Input: I ordered a red shirt, size medium, but received a blue one instead.
+Output: {"facts" : ["Ordered red shirt, size medium", "Received blue shirt instead"]}
+
+Return the facts and customer information in a json format as shown above.
+`;
+```
+</CodeGroup>
+
+Here we initialize the custom prompt in the config:
+
+<CodeGroup>
+```python Python
 from mem0 import Memory
 
 config = {
@@ -63,15 +89,40 @@ config = {
 m = Memory.from_config(config_dict=config, user_id="alice")
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  version: 'v1.1',
+  llm: {
+    provider: 'openai',
+    config: {
+      apiKey: process.env.OPENAI_API_KEY || '',
+      model: 'gpt-4-turbo-preview',
+      temperature: 0.2,
+      maxTokens: 1500,
+    },
+  },
+  customPrompt: customPrompt
+};
+
+const memory = new Memory(config);
+```
+</CodeGroup>
+
 ### Example 1
 
 In this example, we are adding a memory of a user ordering a laptop. As seen in the output, the custom prompt is used to extract the relevant information from the user's message.
 
 <CodeGroup>
-```python Code
+```python Python
 m.add("Yesterday, I ordered a laptop, the order id is 12345", user_id="alice")
 ```
 
+```typescript TypeScript
+await memory.add('Yesterday, I ordered a laptop, the order id is 12345', { userId: "user123" });
+```
+
 ```json Output
 {
   "results": [
@@ -97,11 +148,16 @@ m.add("Yesterday, I ordered a laptop, the order id is 12345", user_id="alice")
 
 In this example, we are adding a memory of a user liking to go on hikes. This add message is not specific to the use-case mentioned in the custom prompt. 
 Hence, the memory is not added.
+
 <CodeGroup>
-```python Code
+```python Python
 m.add("I like going to hikes", user_id="alice")
 ```
 
+```typescript TypeScript
+await memory.add('I like going to hikes', { userId: "user123" });
+```
+
 ```json Output
 {
   "results": [],
@@ -109,3 +165,5 @@ m.add("I like going to hikes", user_id="alice")
 }
 ```
 </CodeGroup>
+
+The custom prompt will process both the user and assistant messages to extract relevant information according to the defined format.

docs/open-source-typescript/components/embedders/config.mdx

@@ -1,56 +0,0 @@
----
-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 a TypeScript object 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 containing provider-specific settings
-
-## How to use configurations?
-
-Here's a general example of how to use the config with Mem0:
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  embedder: {
-    provider: 'openai',
-    config: {
-      apiKey: 'your-openai-api-key',
-      model: 'text-embedding-3-small',
-    },
-  },
-};
-
-const memory = new Memory(config);
-await memory.add("Your text here", { userId: "user", metadata: { category: "example" } });
-```
-
-## 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:
-
-| Parameter              | Description                                      |
-|------------------------|--------------------------------------------------|
-| `model`                | Embedding model to use                           |
-| `apiKey`              | API key of the provider                          |
-| `embeddingDims`       | Dimensions of the embedding model                 |
-
-## 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.

docs/open-source-typescript/components/embedders/models/openai.mdx

@@ -1,36 +0,0 @@
----
-title: OpenAI
----
-
-To use OpenAI embedding models, you need to provide the API key directly in your configuration. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
-
-### Usage
-
-Here's how to configure OpenAI embedding models in your application:
-
-```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" });
-```
-
-### Config
-
-Here are the parameters available for configuring the 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`        |
-| `api_key`              | The OpenAI API key                               | `None`        |

docs/open-source-typescript/components/embedders/overview.mdx

@@ -1,21 +0,0 @@
----
-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.
-
-<CardGroup cols={1}>
-  <Card title="OpenAI" href="/components/embedders/models/openai"></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).

docs/open-source-typescript/components/llms/config.mdx

@@ -1,85 +0,0 @@
----
-title: Configurations
-icon: "gear"
-iconType: "solid"
----
-
-## How to define configurations?
-
-The `config` is defined as a TypeScript object with two main keys:
-- `llm`: Specifies the LLM provider and its configuration
-  - `provider`: The name of the LLM (e.g., "openai", "groq")
-  - `config`: A nested object containing provider-specific settings
-
-### Config Values Precedence
-
-Config values are applied in the following order of precedence (from highest to lowest):
-
-1. Values explicitly set in the `config` object
-2. Environment variables (e.g., `OPENAI_API_KEY`, `OPENAI_API_BASE`)
-3. Default values defined in the LLM implementation
-
-This means that values specified in the `config` object will override corresponding environment variables, which in turn override default values.
-
-## How to Use Config
-
-Here's a general example of how to use the config with Mem0:
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  llm: {
-    provider: 'openai',
-    config: {
-      apiKey: process.env.OPENAI_API_KEY || '',
-      model: 'gpt-4-turbo-preview',
-      temperature: 0.2,
-      maxTokens: 1500,
-    },
-  },
-  embedder: {
-    provider: 'openai',
-    config: {
-      apiKey: process.env.OPENAI_API_KEY || '',
-      model: 'text-embedding-3-small',
-    },
-  },
-  vectorStore: {
-    provider: 'memory',
-    config: {
-      collectionName: 'memories',
-      dimension: 1536,
-    },
-  },
-  historyDbPath: 'memory.db',
-};
-
-const memory = new Memory(config);
-memory.add("Your text here", { userId: "user123", metadata: { category: "example" } });
-```
-
-## Why is Config Needed?
-
-Config is essential for:
-1. Specifying which LLM to use.
-2. Providing necessary connection details (e.g., model, api_key, temperature).
-3. Ensuring proper initialization and connection to your chosen LLM.
-
-## Master List of All Params in Config
-
-Here's a comprehensive list of all parameters that can be used across different LLMs:
-
-| Parameter            | Description                                   | Provider          |
-|----------------------|-----------------------------------------------|-------------------|
-| `model`              | Embedding model to use                        | All               |
-| `temperature`        | Temperature of the model                      | All               |
-| `apiKey`            | API key to use                                | All               |
-| `maxTokens`         | Tokens to generate                            | All               |
-| `topP`              | Probability threshold for nucleus sampling    | All               |
-| `topK`              | Number of highest probability tokens to keep  | All               |
-| `openaiBaseUrl`    | Base URL for OpenAI API                       | OpenAI            |
-
-## Supported LLMs
-
-For detailed information on configuring specific LLMs, please visit the [LLMs](./models) section. There you'll find information for each supported LLM with provider-specific usage examples and configuration details.

docs/open-source-typescript/components/llms/models/anthropic.mdx

@@ -1,30 +0,0 @@
----
-title: Anthropic
----
-
-To use Anthropic's models, please set the `ANTHROPIC_API_KEY`, which you can find on their [Account Settings Page](https://console.anthropic.com/account/keys).
-
-## Usage
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  llm: {
-    provider: 'anthropic',
-    config: {
-      apiKey: process.env.ANTHROPIC_API_KEY || '',
-      model: 'claude-3-7-sonnet-latest',
-      temperature: 0.1,
-      maxTokens: 2000,
-    },
-  },
-};
-
-const memory = new Memory(config);
-await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
-```
-
-## Config
-
-All available parameters for the `anthropic` config are present in the [Master List of All Params in Config](../config).

docs/open-source-typescript/components/llms/models/groq.mdx

@@ -1,32 +0,0 @@
----
-title: Groq
----
-
-[Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.
-
-In order to use LLMs from Groq, go to their [platform](https://console.groq.com/keys) and get the API key. Set the API key as `GROQ_API_KEY` environment variable to use the model as given below in the example.
-
-## Usage
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  llm: {
-    provider: 'groq',
-    config: {
-      apiKey: process.env.GROQ_API_KEY || '',
-      model: 'mixtral-8x7b-32768',
-      temperature: 0.1,
-      maxTokens: 1000,
-    },
-  },
-};
-
-const memory = new Memory(config);
-await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
-```
-
-## Config
-
-All available parameters for the `groq` config are present in the [Master List of All Params in Config](../config).

docs/open-source-typescript/components/llms/models/openai.mdx

@@ -1,30 +0,0 @@
----
-title: OpenAI
----
-
-To use OpenAI LLM models, you need to 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
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  llm: {
-    provider: 'openai',
-    config: {
-      apiKey: process.env.OPENAI_API_KEY || '',
-      model: 'gpt-4-turbo-preview',
-      temperature: 0.2,
-      maxTokens: 1500,
-    },
-  },
-};
-
-const memory = new Memory(config);
-await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
-```
-
-## Config
-
-All available parameters for the `openai` config are present in the [Master List of All Params in Config](../config).

docs/open-source-typescript/components/llms/overview.mdx

@@ -1,45 +0,0 @@
----
-title: Overview
-icon: "info"
-iconType: "solid"
----
-
-Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.
-
-## Usage
-
-To use a llm, 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 llm.
-
-For a comprehensive list of available parameters for llm configuration, please refer to [Config](./config).
-
-To view all supported llms, visit the [Supported LLMs](./models).
-
-<CardGroup cols={3}>
-  <Card title="OpenAI" href="/open-source-typescript/components/llms/models/openai"></Card>
-  <Card title="Anthropic" href="/open-source-typescript/components/llms/models/anthropic"></Card>
-  <Card title="Groq" href="/open-source-typescript/components/llms/models/groq"></Card>
-</CardGroup>
-
-## Structured vs Unstructured Outputs
-
-Mem0 supports two types of OpenAI LLM formats, each with its own strengths and use cases:
-
-### Structured Outputs
-
-Structured outputs are LLMs that align with OpenAI's structured outputs model:
-
-- **Optimized for:** Returning structured responses (e.g., JSON objects)
-- **Benefits:** Precise, easily parseable data
-- **Ideal for:** Data extraction, form filling, API responses
-- **Learn more:** [OpenAI Structured Outputs Guide](https://platform.openai.com/docs/guides/structured-outputs/introduction)
-
-### Unstructured Outputs
-
-Unstructured outputs correspond to OpenAI's standard, free-form text model:
-
-- **Flexibility:** Returns open-ended, natural language responses
-- **Customization:** Use the `response_format` parameter to guide output
-- **Trade-off:** Less efficient than structured outputs for specific data needs
-- **Best for:** Creative writing, explanations, general conversation
-
-Choose the format that best suits your application's requirements for optimal performance and usability.

docs/open-source-typescript/components/vectordbs/config.mdx

@@ -1,100 +0,0 @@
----
-title: Configurations
-icon: "gear"
-iconType: "solid"
----
-
-## How to define configurations?
-
-The `config` is defined as a TypeScript object with two main keys:
-- `vectorStore`: Specifies the vector database provider and its configuration
-  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "azure_ai_search", "redis", "memory")
-  - `config`: A nested object containing provider-specific settings
-
-## In-Memory Storage Option
-
-We also support an in-memory storage option for the vector store, which is useful for reduced overhead and faster access times. Here's how to configure it:
-
-### Example for In-Memory Storage
-
-```typescript
-const configMemory = {
-  vector_store: {
-    provider: 'memory',
-    config: {
-      collectionName: 'memories',
-      dimension: 1536,
-    },
-  },
-};
-
-const memory = new Memory(configMemory);
-await memory.add("Your text here", { userId: "user", metadata: { category: "example" } });
-```
-
-## How to Use Config
-
-Here's a general example of how to use the config with Mem0:
-
-### Example for qdrant
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  vector_store: {
-    provider: 'qdrant',
-    config: {
-      collectionName: 'memories',
-      embeddingModelDims: 1536,
-      host: 'localhost',
-      port: 6333,
-      url: 'https://your-qdrant-url.com',
-      apiKey: 'your-qdrant-api-key',
-      onDisk: true,
-    },
-  },
-};
-
-const memory = new Memory(config);
-await memory.add("Your text here", { userId: "user", metadata: { category: "example" } });
-```
-
-## Why is Config Needed?
-
-Config is essential for:
-1. Specifying which vector database to use.
-2. Providing necessary connection details (e.g., host, port, credentials).
-3. Customizing database-specific settings (e.g., collection name, path).
-4. Ensuring proper initialization and connection to your chosen vector store.
-
-## Master List of All Params in Config
-
-Here's a comprehensive list of all parameters that can be used across different vector databases:
-
-| Parameter              | Description                          |
-|------------------------|--------------------------------------|
-| `collectionName`       | Name of the collection               |
-| `dimension`            | Dimensions of the embedding model     |
-| `host`                 | Host where the server is running     |
-| `port`                 | Port where the server is running     |
-| `embeddingModelDims`   | Dimensions of the embedding model     |
-| `url`                  | URL for the Qdrant server            |
-| `apiKey`               | API key for the Qdrant server        |
-| `path`                 | Path for the Qdrant server           |
-| `onDisk`               | Enable persistent storage (for Qdrant) |
-| `redisUrl`             | URL for the Redis server             |
-| `username`             | Username for Redis connection         |
-| `password`             | Password for Redis connection         |
-
-## Customizing Config
-
-Each vector database has its own specific configuration requirements. To customize the config for your chosen vector store:
-
-1. Identify the vector database you want to use from [supported vector databases](./dbs).
-2. Refer to the `Config` section in the respective vector database's documentation.
-3. Include only the relevant parameters for your chosen database in the `config` object.
-
-## Supported Vector Databases
-
-For detailed information on configuring specific vector databases, please visit the [Supported Vector Databases](./dbs) section. There you'll find individual pages for each supported vector store with provider-specific usage examples and configuration details.

docs/open-source-typescript/components/vectordbs/dbs/pgvector.mdx

@@ -1,44 +0,0 @@
-[pgvector](https://github.com/pgvector/pgvector) is open-source vector similarity search for Postgres. After connecting with Postgres, run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.
-
-### Usage
-
-Here's how to configure pgvector in your application:
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  vector_store: {
-    provider: 'pgvector',
-    config: {
-      collectionName: 'memories',
-      dimension: 1536,
-      dbname: 'vectordb',
-      user: 'postgres',
-      password: 'postgres',
-      host: 'localhost',
-      port: 5432,
-      embeddingModelDims: 1536,
-      hnsw: true,
-    },
-  },
-};
-
-const memory = new Memory(config);
-await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
-```
-
-### Config
-
-Here's the parameters available for configuring pgvector:
-
-| Parameter              | Description                                      | Default Value |
-|------------------------|--------------------------------------------------|---------------|
-| `dbname`               | The name of the database                         | `postgres`    |
-| `collectionName`       | The name of the collection                       | `mem0`        |
-| `embeddingModelDims`   | Dimensions of the embedding model                 | `1536`        |
-| `user`                 | Username to connect to the database              | `None`        |
-| `password`             | Password to connect to the database              | `None`        |
-| `host`                 | The host where the Postgres server is running    | `None`        |
-| `port`                 | The port where the Postgres server is running    | `None`        |
-| `hnsw`                 | Enable HNSW indexing                             | `False`       |

docs/open-source-typescript/components/vectordbs/dbs/qdrant.mdx

@@ -1,42 +0,0 @@
-[Qdrant](https://qdrant.tech/) is an open-source vector search engine. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data.
-
-### Usage
-
-Here's how to configure Qdrant in your application:
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  vector_store: {
-    provider: 'qdrant',
-    config: {
-      collectionName: 'memories',
-      embeddingModelDims: 1536,
-      host: 'localhost',
-      port: 6333,
-      url: 'https://your-qdrant-url.com',
-      apiKey: 'your-qdrant-api-key',
-      onDisk: true,
-    },
-  },
-};
-
-const memory = new Memory(config);
-await memory.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
-```
-
-### Config
-
-Let's see the available parameters for the `qdrant` config:
-
-| Parameter              | Description                                      | Default Value |
-|------------------------|--------------------------------------------------|---------------|
-| `collectionName`      | The name of the collection to store the vectors   | `mem0`        |
-| `embeddingModelDims` | Dimensions of the embedding model                 | `1536`        |
-| `host`                 | The host where the Qdrant server is running      | `None`        |
-| `port`                 | The port where the Qdrant server is running      | `None`        |
-| `path`                 | Path for the Qdrant database                      | `/tmp/qdrant` |
-| `url`                  | Full URL for the Qdrant server                   | `None`        |
-| `apiKey`              | API key for the Qdrant server                    | `None`        |
-| `onDisk`              | For enabling persistent storage                    | `False`       |

docs/open-source-typescript/components/vectordbs/dbs/redis.mdx

@@ -1,47 +0,0 @@
-[Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data.
-
-### Installation
-```bash
-pip install redis redisvl
-```
-
-Redis Stack using Docker:
-```bash
-docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
-```
-
-### Usage
-
-Here's how to configure Redis in your application:
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  vector_store: {
-    provider: 'redis',
-    config: {
-      collectionName: 'memories',
-      embeddingModelDims: 1536,
-      redisUrl: 'redis://localhost:6379',
-      username: 'your-redis-username',
-      password: 'your-redis-password',
-    },
-  },
-};
-
-const memoryRedis = new Memory(config);
-await memoryRedis.add("Likes to play cricket on weekends", { userId: "alice", metadata: { category: "hobbies" } });
-```
-
-### Config
-
-Let's see the available parameters for the `redis` config:
-
-| Parameter              | Description                                      | Default Value |
-|------------------------|--------------------------------------------------|---------------|
-| `collectionName`      | The name of the collection to store the vectors   | `mem0`        |
-| `embeddingModelDims` | Dimensions of the embedding model                 | `1536`        |
-| `redisUrl`            | The URL of the Redis server                       | `None`        |
-| `username`             | Username for Redis connection                     | `None`        |
-| `password`             | Password for Redis connection                     | `None`        |

docs/open-source-typescript/components/vectordbs/overview.mdx

@@ -1,36 +0,0 @@
----
-title: Overview
-icon: "info"
-iconType: "solid"
----
-
-Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
-
-## Supported Vector Databases
-
-See the list of supported vector databases below.
-
-<CardGroup cols={2}>
-  <Card title="Memory" href="/components/vectordbs/dbs/memory"></Card>
-  <Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card>
-  <Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
-  <Card title="Pgvector" href="/components/vectordbs/dbs/pgvector"></Card>
-</CardGroup>
-
-## Usage
-
-To utilize a vector database, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `Memory` will be used as the vector database.
-
-For a comprehensive list of available parameters for vector database configuration, please refer to [Config](./config).
-
-## Common issues
-
-### Using model with different dimensions
-
-If you are using customized model, which is having different dimensions other than 1536
-for example 768, you may encounter below error:
-
-`ValueError: shapes (0,1536) and (768,) not aligned: 1536 (dim 1) != 768 (dim 0)`
-
-you could add `"embedding_model_dims": 768,` to the config of the vector_store to overcome this issue.
-

docs/open-source-typescript/features/custom-prompts.mdx

@@ -1,141 +0,0 @@
----
-title: Custom Prompts
-description: 'Enhance your product experience by adding custom prompts tailored to your needs'
-icon: "pencil"
-iconType: "solid"
----
-
-## Introduction to Custom Prompts
-
-Custom prompts allow you to tailor the behavior of your Mem0 instance to specific use cases or domains. 
-By defining a custom prompt, you can control how information is extracted, processed, and stored in your memory system.
-
-To create an effective custom prompt:
-1. Be specific about the information to extract.
-2. Provide few-shot examples to guide the LLM.
-3. Ensure examples follow the format shown below.
-
-Example of a custom prompt:
-
-```typescript
-const customPrompt = `
-Please only extract entities containing customer support information, order details, and user information. 
-Here are some few shot examples:
-
-Input: Hi.
-Output: {"facts" : []}
-
-Input: The weather is nice today.
-Output: {"facts" : []}
-
-Input: My order #12345 hasn't arrived yet.
-Output: {"facts" : ["Order #12345 not received"]}
-
-Input: I'm John Doe, and I'd like to return the shoes I bought last week.
-Output: {"facts" : ["Customer name: John Doe", "Wants to return shoes", "Purchase made last week"]}
-
-Input: I ordered a red shirt, size medium, but received a blue one instead.
-Output: {"facts" : ["Ordered red shirt, size medium", "Received blue shirt instead"]}
-
-Return the facts and customer information in a json format as shown above.
-`;
-```
-
-Here we initialize the custom prompt in the config:
-
-```typescript
-import { Memory } from 'mem0ai/oss';
-
-const config = {
-  version: 'v1.1',
-  embedder: {
-    provider: 'openai',
-    config: {
-      apiKey: process.env.OPENAI_API_KEY || '',
-      model: 'text-embedding-3-small',
-    },
-  },
-  vectorStore: {
-    provider: 'memory',
-    config: {
-      collectionName: 'memories',
-      dimension: 1536,
-    },
-  },
-  llm: {
-    provider: 'openai',
-    config: {
-      apiKey: process.env.OPENAI_API_KEY || '',
-      model: 'gpt-4-turbo-preview',
-      temperature: 0.2,
-      maxTokens: 1500,
-    },
-  },
-  customPrompt: customPrompt,
-  historyDbPath: 'memory.db',
-};
-
-const memory = new Memory(config);
-```
-
-### Example 1
-
-In this example, we are adding a memory of a user ordering a laptop. As seen in the output, the custom prompt is used to extract the relevant information from the user's message.
-
-<CodeGroup>
-```typescript Code
-await memory.add('Yesterday, I ordered a laptop, the order id is 12345', 'user123');
-```
-
-```json Output
-{
-  "results": [
-    {
-      "id": "c03c9045-df76-4949-bbc5-d5dc1932aa5c",
-      "memory": "Ordered a laptop",
-      "metadata": {}
-    },
-    {
-      "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
-      "memory": "Order ID: 12345",
-      "metadata": {}
-    },
-    {
-      "id": "e5f2a012-3b45-4c67-9d8e-123456789abc",
-      "memory": "Order placed yesterday",
-      "metadata": {}
-    }
-  ]
-}
-```
-</CodeGroup>
-
-### Example 2
-
-In this example, we are adding a memory of a user liking to go on hikes. This add message is not specific to the use-case mentioned in the custom prompt. 
-Hence, the memory is not added.
-
-<CodeGroup>
-```typescript Code
-await memory.add('I like going to hikes', 'user123');
-```
-
-```json Output
-{
-  "results": []
-}
-```
-</CodeGroup>
-
-You can also use custom prompts with chat messages:
-
-```typescript
-const messages = [
-  { role: 'user', content: 'Hi, I ordered item #54321 last week but haven\'t received it yet.' },
-  { role: 'assistant', content: 'I understand you\'re concerned about your order #54321. Let me help track that for you.' }
-];
-
-await memory.add(messages, 'user123');
-```
-
-The custom prompt will process both the user and assistant messages to extract relevant information according to the defined format.

docs/open-source-typescript/quickstart.mdx

@@ -1,5 +1,5 @@
 ---
-title: Node.js Guide
+title: Node SDK
 description: 'Get started with Mem0 quickly!'
 icon: "node"
 iconType: "solid"
@@ -67,15 +67,14 @@ const memory = new Memory({
 
 <CodeGroup>
 ```typescript Code
-// For a user
+const messages = [
-const result = await memory.add('Hi, my name is John and I am a software', 'user123');
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-console.log(result);
+    {"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."}
+]
 
-// const messages = [
+await memory.add(messages, { userId: "user123", metadata: { category: "movie_recommendations" } });
-//    {"role": "user", "content": "Hi, I'm Alex. I like to play cricket on weekends."},
-//    {"role": "assistant", "content": "Hello Alex! It's great to know that you enjoy playing cricket on weekends. I'll remember that for future reference."}
-// ]
-// await memory.add(messages, 'user123');
 ```
 
 ```json Output
@@ -83,13 +82,24 @@ console.log(result);
   "results": [
     {
       "id": "c03c9045-df76-4949-bbc5-d5dc1932aa5c",
-      "memory": "Name is John",
+      "memory": "User is planning to watch a movie tonight.",
-      "metadata": [Object]
+      "metadata": {
+        "category": "movie_recommendations"
+      }
     },
     {
       "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
-      "memory": "Is a software",
+      "memory": "User is not a big fan of thriller movies.",
-      "metadata": [Object]
+      "metadata": {
+        "category": "movie_recommendations"
+      }
+    },
+    {
+      "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
+      "memory": "User loves sci-fi movies.",
+      "metadata": {
+        "category": "movie_recommendations"
+      }
     }
   ]
 }
@@ -101,7 +111,7 @@ console.log(result);
 <CodeGroup>
 ```typescript Code
 // Get all memories
-const allMemories = await memory.getAll('user123');
+const allMemories = await memory.getAll({ userId: "user123" });
 console.log(allMemories)
 ```
 
@@ -110,20 +120,35 @@ console.log(allMemories)
   "results": [
     {
       "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
-      "memory": "Name is Alex Jones",
+      "memory": "User is planning to watch a movie tonight.",
       "hash": "1a271c007316c94377175ee80e746a19",
       "createdAt": "2025-02-27T16:33:20.557Z",
       "updatedAt": "2025-02-27T16:33:27.051Z",
-      "metadata": {},
+      "metadata": {
+        "category": "movie_recommendations"
+      },
       "userId": "user123"
     },
     {
       "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
-      "memory": "Likes to play cricket on weekends",
+      "memory": "User loves sci-fi movies.",
       "hash": "285d07801ae42054732314853e9eadd7",
       "createdAt": "2025-02-27T16:33:20.560Z",
       "updatedAt": undefined,
-      "metadata": {},
+      "metadata": {
+        "category": "movie_recommendations"
+      },
+      "userId": "user123"
+    },
+    {
+      "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
+      "memory": "User is not a big fan of thriller movies.",
+      "hash": "285d07801ae42054732314853e9eadd7",
+      "createdAt": "2025-02-27T16:33:20.560Z",
+      "updatedAt": undefined,
+      "metadata": {
+        "category": "movie_recommendations"
+      },
       "userId": "user123"
     }
   ]
@@ -137,18 +162,20 @@ console.log(allMemories)
 <CodeGroup>
 ```typescript Code
 // Get a single memory by ID
-const singleMemory = await memory.get('6c1c11a2-4fbc-4a2b-8e8a-d60e67e57aaa');
+const singleMemory = await memory.get('892db2ae-06d9-49e5-8b3e-585ef9b85b8e');
 console.log(singleMemory);
 ```
 
 ```json Output
 {
-  "id": "6c1c11a2-4fbc-4a2b-8e8a-d60e67e57aaa",
+  "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
-  "memory": "Name is Alex",
+  "memory": "User is planning to watch a movie tonight.",
-  "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
+  "hash": "1a271c007316c94377175ee80e746a19",
-  "createdAt": "2025-02-27T16:37:04.378Z",
+  "createdAt": "2025-02-27T16:33:20.557Z",
   "updatedAt": undefined,
-  "metadata": {},
+  "metadata": {
+    "category": "movie_recommendations"
+  },
   "userId": "user123"
 }
 ```
@@ -158,7 +185,7 @@ console.log(singleMemory);
 
 <CodeGroup>
 ```typescript Code
-const result = await memory.search('What do you know about me?', 'user123');
+const result = await memory.search('What do you know about me?', { userId: "user123" });
 console.log(result);
 ```
 
@@ -166,23 +193,39 @@ console.log(result);
 {
   "results": [
     {
-      "id": "28c3eee7-186e-4644-8c5d-13b306233d4e",
+      "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
-      "memory": "Name is Alex",
+      "memory": "User is planning to watch a movie tonight.",
-      "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
+      "hash": "1a271c007316c94377175ee80e746a19",
-      "createdAt": "2025-02-27T16:43:56.310Z",
+      "createdAt": "2025-02-27T16:33:20.557Z",
       "updatedAt": undefined,
-      "score": 0.08920719231944799,
+      "score": 0.38920719231944799,
-      "metadata": {},
+      "metadata": {
+        "category": "movie_recommendations"
+      },
       "userId": "user123"
     },
     {
-      "id": "f3433da0-45f4-444f-a4bc-59a170890a1f",
+      "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
-      "memory": "Likes to play cricket on weekends",
+      "memory": "User loves sci-fi movies.",
       "hash": "285d07801ae42054732314853e9eadd7",
-      "createdAt": "2025-02-27T16:43:56.314Z",
+      "createdAt": "2025-02-27T16:33:20.560Z",
       "updatedAt": undefined,
-      "score": 0.06869761478135689,
+      "score": 0.36869761478135689,
-      "metadata": {},
+      "metadata": {
+        "category": "movie_recommendations"
+      },
+      "userId": "user123"
+    },
+    {
+      "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
+      "memory": "User is not a big fan of thriller movies.",
+      "hash": "285d07801ae42054732314853e9eadd7",
+      "createdAt": "2025-02-27T16:33:20.560Z",
+      "updatedAt": undefined,
+      "score": 0.33855272141248272,
+      "metadata": {
+        "category": "movie_recommendations"
+      },
       "userId": "user123"
     }
   ]
@@ -195,9 +238,8 @@ console.log(result);
 <CodeGroup>
 ```typescript Code
 const result = await memory.update(
-  '6c1c11a2-4fbc-4a2b-8e8a-d60e67e57aaa',
+  '892db2ae-06d9-49e5-8b3e-585ef9b85b8e',
-  'I love India, it is my favorite country.',
+  'I love India, it is my favorite country.'
-  'user123'
 );
 console.log(result);
 ```
@@ -213,7 +255,7 @@ console.log(result);
 
 <CodeGroup>
 ```typescript Code
-const history = await memory.history('d2cc4cef-e0c1-47dd-948a-677030482e9e');
+const history = await memory.history('892db2ae-06d9-49e5-8b3e-585ef9b85b8e');
 console.log(history);
 ```
 
@@ -221,21 +263,21 @@ console.log(history);
 [
   {
     "id": 39,
-    "memory_id": "d2cc4cef-e0c1-47dd-948a-677030482e9e",
+    "memory_id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
-    "previous_value": "Name is Alex",
+    "previous_value": "User is planning to watch a movie tonight.",
-    "new_value": "Name is Alex Jones",
+    "new_value": "I love India, it is my favorite country.",
     "action": "UPDATE",
-    "created_at": "2025-02-27T16:46:15.853Z",
+    "created_at": "2025-02-27T16:33:20.557Z",
-    "updated_at": "2025-02-27T16:46:20.909Z",
+    "updated_at": "2025-02-27T16:33:27.051Z",
     "is_deleted": 0
   },
   {
     "id": 37,
-    "memory_id": "d2cc4cef-e0c1-47dd-948a-677030482e9e",
+    "memory_id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
     "previous_value": null,
-    "new_value": "Name is Alex",
+    "new_value": "User is planning to watch a movie tonight.",
     "action": "ADD",
-    "created_at": "2025-02-27T16:46:15.853Z",
+    "created_at": "2025-02-27T16:33:20.557Z",
     "updated_at": null,
     "is_deleted": 0
   }
@@ -247,10 +289,10 @@ console.log(history);
 
 ```typescript
 // Delete a memory by id
-await memory.delete('bf4d4092-cf91-4181-bfeb-b6fa2ed3061b');
+await memory.delete('892db2ae-06d9-49e5-8b3e-585ef9b85b8e');
 
 // Delete all memories for a user
-await memory.deleteAll('alice');
+await memory.deleteAll({ userId: "user123" });
 ```
 
 ### Reset Memory

docs/open-source/graph_memory/features.mdx

@@ -28,8 +28,7 @@ config = {
             "password": "xxx"
         },
         "custom_prompt": "Please only extract entities containing sports related relationships and nothing else.",
-    },
+    }
-    "version": "v1.1"
 }
 
 m = Memory.from_config(config_dict=config)

docs/open-source/graph_memory/overview.mdx

@@ -38,8 +38,7 @@ allowfullscreen
 ## Initialize Graph Memory
 
 To initialize Graph Memory you'll need to set up your configuration with graph store providers.
-Currently, we support Neo4j as a graph store provider. You can setup [Neo4j](https://neo4j.com/) locally or use the hosted [Neo4j AuraDB](https://neo4j.com/product/auradb/). 
+Currently, we support Neo4j as a graph store provider. You can setup [Neo4j](https://neo4j.com/) locally or use the hosted [Neo4j AuraDB](https://neo4j.com/product/auradb/).
-Moreover, you also need to set the version to `v1.1` (*prior versions are not supported*).
 
 <Note>If you are using Neo4j locally, then you need to install [APOC plugins](https://neo4j.com/labs/apoc/4.1/installation/).</Note>
 
@@ -65,8 +64,7 @@ config = {
             "username": "neo4j",
             "password": "xxx"
         }
-    },
+    }
-    "version": "v1.1"
 }
 
 m = Memory.from_config(config_dict=config)
@@ -98,8 +96,7 @@ config = {
                 "temperature": 0.0,
             }
         }
-    },
+    }
-    "version": "v1.1"
 }
 
 m = Memory.from_config(config_dict=config)

docs/open-source/python-quickstart.mdx

@@ -1,5 +1,5 @@
 ---
-title: Python Guide
+title: Python SDK
 description: 'Get started with Mem0 quickly!'
 icon: "python"
 iconType: "solid"
@@ -71,8 +71,7 @@ config = {
             "username": "neo4j",
             "password": "---"
         }
-    },
+    }
-    "version": "v1.1"
 }
 
 m = Memory.from_config(config_dict=config)
@@ -100,10 +99,6 @@ result = m.add("Likes to play cricket on weekends", user_id="alice", metadata={"
 {
     "results": [
         {"id": "bf4d4092-cf91-4181-bfeb-b6fa2ed3061b", "memory": "Likes to play cricket on weekends", "event": "ADD"}
-    ],
-    "relations": [
-        {"source": "alice", "relationship": "likes_to_play", "target": "cricket"}, 
-        {"source": "alice", "relationship": "plays_on", "target": "weekends"}
     ]
 }
 ```
@@ -129,10 +124,6 @@ all_memories = m.get_all(user_id="alice")
             "updated_at": None, 
             "user_id": "alice"
         }
-    ], 
-    "relations": [
-        {"source": "alice", "relationship": "likes_to_play", "target": "cricket"}, 
-        {"source": "alice", "relationship": "plays_on", "target": "weekends"}
     ]
 }
 ```
@@ -180,10 +171,6 @@ related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
             "updated_at": None, 
             "user_id": "alice"
         }
-    ], 
-    "relations": [
-        {"source": "alice", "relationship": "plays_on", "target": "weekends"}, 
-        {"source": "alice", "relationship": "likes_to_play", "target": "cricket"}
     ]
 }
 ```
@@ -303,7 +290,7 @@ Mem0 offers extensive configuration options to customize its behavior according
 | Parameter         | Description                          | Default                    |
 |------------------|--------------------------------------|----------------------------|
 | `history_db_path` | Path to the history database         | "{mem0_dir}/history.db"    |
-| `version`         | API version                          | "v1.0"                     |
+| `version`         | API version                          | "v1.1"                     |
 | `custom_prompt`   | Custom prompt for memory processing  | None                       |
 </Accordion>
 

docs/quickstart.mdx

@@ -330,7 +330,7 @@ result = m.add("I like to drink coffee in the morning and go for a walk.", user_
 ```
 
 ```typescript TypeScript
-const result = memory.add("I like to drink coffee in the morning and go for a walk.", 'alice');
+const result = memory.add("I like to drink coffee in the morning and go for a walk.", { userId: "alice", metadata: { category: "preferences" } });
 ```
 
 ```json Output
@@ -361,7 +361,7 @@ related_memories = m.search("Should I drink coffee or tea?", user_id="alice")
 ```
 
 ```typescript TypeScript
-const relatedMemories = memory.search("Should I drink coffee or tea?", "alice");
+const relatedMemories = memory.search("Should I drink coffee or tea?", { userId: "alice" });
 ```
 
 ```json Output

mem0-ts/README.md

@@ -2,6 +2,9 @@
 
 Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. We offer both cloud and open-source solutions to cater to different needs.
 
+See the complete [OSS Docs](https://docs.mem0.ai/open-source-typescript/quickstart).
+See the complete [Platform API Reference](https://docs.mem0.ai/api-reference/overview).
+
 ## 1. Installation
 
 For the open-source version, you can install the Mem0 package using npm:

mem0-ts/package.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0ai",
-  "version": "2.0.0",
+  "version": "2.0.1",
   "description": "The Memory Layer For Your AI Apps",
   "main": "./dist/index.js",
   "module": "./dist/index.mjs",

mem0-ts/src/oss/src/index.ts

@@ -1,4 +1,5 @@
 export * from "./memory";
+export * from "./memory/memory.types";
 export * from "./types";
 export * from "./embeddings/base";
 export * from "./embeddings/openai";
@@ -9,4 +10,6 @@ export * from "./llms/anthropic";
 export * from "./llms/groq";
 export * from "./vector_stores/base";
 export * from "./vector_stores/memory";
+export * from "./vector_stores/qdrant";
+export * from "./vector_stores/redis";
 export * from "./utils/factory";

mem0-ts/src/oss/src/memory/index.ts

@@ -24,6 +24,12 @@ import { Embedder } from "../embeddings/base";
 import { LLM } from "../llms/base";
 import { VectorStore } from "../vector_stores/base";
 import { ConfigManager } from "../config/manager";
+import {
+  AddMemoryOptions,
+  SearchMemoryOptions,
+  DeleteAllMemoryOptions,
+  GetAllMemoryOptions,
+} from "./memory.types";
 
 export class Memory {
   private config: MemoryConfig;
@@ -69,13 +75,17 @@ export class Memory {
 
   async add(
     messages: string | Message[],
-    userId?: string,
+    config: AddMemoryOptions,
-    agentId?: string,
-    runId?: string,
-    metadata: Record<string, any> = {},
-    filters: SearchFilters = {},
-    prompt?: string,
   ): Promise<SearchResult> {
+    const {
+      userId,
+      agentId,
+      runId,
+      metadata = {},
+      filters = {},
+      prompt,
+    } = config;
+
     if (userId) filters.userId = metadata.userId = userId;
     if (agentId) filters.agentId = metadata.agentId = agentId;
     if (runId) filters.runId = metadata.runId = runId;
@@ -87,7 +97,7 @@ export class Memory {
     }
 
     const parsedMessages = Array.isArray(messages)
-      ? messages
+      ? (messages as Message[])
       : [{ role: "user", content: messages }];
 
     const vectorStoreResult = await this.addToVectorStore(
@@ -260,12 +270,10 @@ export class Memory {
 
   async search(
     query: string,
-    userId?: string,
+    config: SearchMemoryOptions,
-    agentId?: string,
-    runId?: string,
-    limit: number = 100,
-    filters: SearchFilters = {},
   ): Promise<SearchResult> {
+    const { userId, agentId, runId, limit = 100, filters = {} } = config;
+
     if (userId) filters.userId = userId;
     if (agentId) filters.agentId = agentId;
     if (runId) filters.runId = runId;
@@ -322,10 +330,10 @@ export class Memory {
   }
 
   async deleteAll(
-    userId?: string,
+    config: DeleteAllMemoryOptions,
-    agentId?: string,
-    runId?: string,
   ): Promise<{ message: string }> {
+    const { userId, agentId, runId } = config;
+
     const filters: SearchFilters = {};
     if (userId) filters.userId = userId;
     if (agentId) filters.agentId = agentId;
@@ -358,12 +366,9 @@ export class Memory {
     );
   }
 
-  async getAll(
+  async getAll(config: GetAllMemoryOptions): Promise<SearchResult> {
-    userId?: string,
+    const { userId, agentId, runId, limit = 100 } = config;
-    agentId?: string,
+
-    runId?: string,
-    limit: number = 100,
-  ): Promise<SearchResult> {
     const filters: SearchFilters = {};
     if (userId) filters.userId = userId;
     if (agentId) filters.agentId = agentId;

mem0-ts/src/oss/src/memory/memory.types.ts

@@ -0,0 +1,26 @@
+import { Message } from "../types";
+import { SearchFilters } from "../types";
+
+export interface Entity {
+  userId?: string;
+  agentId?: string;
+  runId?: string;
+}
+
+export interface AddMemoryOptions extends Entity {
+  metadata?: Record<string, any>;
+  filters?: SearchFilters;
+  prompt?: string;
+}
+
+export interface SearchMemoryOptions extends Entity {
+  query: string;
+  limit?: number;
+  filters?: SearchFilters;
+}
+
+export interface GetAllMemoryOptions extends Entity {
+  limit?: number;
+}
+
+export interface DeleteAllMemoryOptions extends Entity {}

mem0/configs/base.py

@@ -46,7 +46,7 @@ class MemoryConfig(BaseModel):
     )
     version: str = Field(
         description="The version of the API",
-        default="v1.0",
+        default="v1.1",
     )
     custom_prompt: Optional[str] = Field(
         description="Custom prompt for the memory",

mem0/memory/main.py

@@ -46,7 +46,7 @@ class Memory(MemoryBase):
 
         self.enable_graph = False
 
-        if self.api_version == "v1.1" and self.config.graph_store.config:
+        if self.config.graph_store.config:
             from mem0.memory.graph_memory import MemoryGraph
 
             self.graph = MemoryGraph(self.config)
@@ -126,12 +126,7 @@ class Memory(MemoryBase):
             vector_store_result = future1.result()
             graph_result = future2.result()
 
-        if self.api_version == "v1.1":
+        if self.api_version == "v1.0":
-            return {
-                "results": vector_store_result,
-                "relations": graph_result,
-            }
-        else:
             warnings.warn(
                 "The current add API output format is deprecated. "
                 "To use the latest format, set `api_version='v1.1'`. "
@@ -141,6 +136,14 @@ class Memory(MemoryBase):
             )
             return vector_store_result
 
+        if self.enable_graph:
+            return {
+                "results": vector_store_result,
+                "relations": graph_result,
+            }
+
+        return {"results": vector_store_result}
+
     def _add_to_vector_store(self, messages, metadata, filters):
         parsed_messages = parse_messages(messages)
 
@@ -252,7 +255,7 @@ class Memory(MemoryBase):
 
     def _add_to_graph(self, messages, filters):
         added_entities = []
-        if self.api_version == "v1.1" and self.enable_graph:
+        if self.enable_graph:
             if filters.get("user_id") is None:
                 filters["user_id"] = "user"
 
@@ -324,11 +327,7 @@ class Memory(MemoryBase):
 
         with concurrent.futures.ThreadPoolExecutor() as executor:
             future_memories = executor.submit(self._get_all_from_vector_store, filters, limit)
-            future_graph_entities = (
+            future_graph_entities = executor.submit(self.graph.get_all, filters, limit) if self.enable_graph else None
-                executor.submit(self.graph.get_all, filters, limit)
-                if self.api_version == "v1.1" and self.enable_graph
-                else None
-            )
 
             concurrent.futures.wait(
                 [future_memories, future_graph_entities] if future_graph_entities else [future_memories]
@@ -337,12 +336,10 @@ class Memory(MemoryBase):
             all_memories = future_memories.result()
             graph_entities = future_graph_entities.result() if future_graph_entities else None
 
-        if self.api_version == "v1.1":
+        if self.enable_graph:
-            if self.enable_graph:
+            return {"results": all_memories, "relations": graph_entities}
-                return {"results": all_memories, "relations": graph_entities}
+
-            else:
+        if self.api_version == "v1.0":
-                return {"results": all_memories}
-        else:
             warnings.warn(
                 "The current get_all API output format is deprecated. "
                 "To use the latest format, set `api_version='v1.1'`. "
@@ -351,6 +348,8 @@ class Memory(MemoryBase):
                 stacklevel=2,
             )
             return all_memories
+        else:
+            return {"results": all_memories}
 
     def _get_all_from_vector_store(self, filters, limit):
         memories = self.vector_store.list(filters=filters, limit=limit)
@@ -419,9 +418,7 @@ class Memory(MemoryBase):
         with concurrent.futures.ThreadPoolExecutor() as executor:
             future_memories = executor.submit(self._search_vector_store, query, filters, limit)
             future_graph_entities = (
-                executor.submit(self.graph.search, query, filters, limit)
+                executor.submit(self.graph.search, query, filters, limit) if self.enable_graph else None
-                if self.api_version == "v1.1" and self.enable_graph
-                else None
             )
 
             concurrent.futures.wait(
@@ -431,20 +428,20 @@ class Memory(MemoryBase):
             original_memories = future_memories.result()
             graph_entities = future_graph_entities.result() if future_graph_entities else None
 
-        if self.api_version == "v1.1":
+        if self.enable_graph:
-            if self.enable_graph:
+            return {"results": original_memories, "relations": graph_entities}
-                return {"results": original_memories, "relations": graph_entities}
+
-            else:
+        if self.api_version == "v1.0":
-                return {"results": original_memories}
-        else:
             warnings.warn(
-                "The current search API output format is deprecated. "
+                "The current get_all API output format is deprecated. "
                 "To use the latest format, set `api_version='v1.1'`. "
                 "The current format will be removed in mem0ai 1.1.0 and later versions.",
                 category=DeprecationWarning,
                 stacklevel=2,
             )
             return original_memories
+        else:
+            return {"results": original_memories}
 
     def _search_vector_store(self, query, filters, limit):
         embeddings = self.embedding_model.embed(query, "search")
@@ -540,7 +537,7 @@ class Memory(MemoryBase):
 
         logger.info(f"Deleted {len(memories)} memories")
 
-        if self.api_version == "v1.1" and self.enable_graph:
+        if self.enable_graph:
             self.graph.delete_all(filters)
 
         return {"message": "Memories deleted successfully!"}

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "mem0ai"
-version = "0.1.58"
+version = "0.1.59"
 description = "Long-term memory for AI Agents"
 authors = ["Mem0 <founders@mem0.ai>"]
 exclude = [

tests/test_main.py

@@ -41,10 +41,14 @@ def test_add(memory_instance, version, enable_graph):
 
     result = memory_instance.add(messages=[{"role": "user", "content": "Test message"}], user_id="test_user")
 
-    assert "results" in result
+    if enable_graph:
-    assert result["results"] == [{"memory": "Test memory", "event": "ADD"}]
+        assert "results" in result
-    assert "relations" in result
+        assert result["results"] == [{"memory": "Test memory", "event": "ADD"}]
-    assert result["relations"] == []
+        assert "relations" in result
+        assert result["relations"] == []
+    else:
+        assert "results" in result
+        assert result["results"] == [{"memory": "Test memory", "event": "ADD"}]
 
     memory_instance._add_to_vector_store.assert_called_once_with(
         [{"role": "user", "content": "Test message"}], {"user_id": "test_user"}, {"user_id": "test_user"}

tests/vector_stores/test_qdrant.py

@@ -55,9 +55,9 @@ class TestQdrant(unittest.TestCase):
 
         results = self.qdrant.search(query=query_vector, limit=1)
 
-        self.client_mock.search.assert_called_once_with(
+        self.client_mock.query_points.assert_called_once_with(
             collection_name="test_collection",
-            query_vector=query_vector,
+            query=query_vector,
             query_filter=None,
             limit=1,
         )