Fix typescript docs (#3357)

docs/changelog.mdx

@@ -7,9 +7,9 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
-<Update label="2025-07-24" description="v0.1.116">
+<Update label="2025-08-14" description="v0.1.116">
 
-**New Features:**
+**New Features & Updates:**
 - **Pinecone:** Added namespace support and improved type safety
 - **Milvus:** Added db_name field to MilvusDBConfig
 - **Vector Stores:** Added multi-id filters support
@@ -19,17 +19,36 @@ mode: "wide"
 - **LLM Monitoring:** Added monitoring callback support
 
 **Improvements:**
-- **Performance:** Improved async handling in AsyncMemory class
+- **Performance:**
-- **Documentation:** Added async add announcement, personalized search docs, Neptune examples, V5 migration docs
+  - Improved async handling in AsyncMemory class
-- **Configuration:** Refactored base class config for LLMs, added sslmode for pgvector
+- **Documentation:**
-- **Dependencies:** Updated psycopg to version 3, updated Docker compose
+  - Added async add announcement
+  - Added personalized search docs
+  - Added Neptune examples
+  - Added V5 migration docs
+- **Configuration:**
+  - Refactored base class config for LLMs
+  - Added sslmode for pgvector
+- **Dependencies:**
+  - Updated psycopg to version 3
+  - Updated Docker compose
 
 **Bug Fixes:**
-- **Tests:** Fixed failing tests and restricted package versions
+- **Tests:**
-- **Memgraph:** Fixed async attribute errors, n_embeddings usage, and indexing issues
+  - Fixed failing tests
-- **Vector Stores:** Fixed Qdrant cloud indexing, Neo4j Cypher syntax, and LLM parameters
+  - Restricted package versions
-- **Graph Store:** Fixed LM config prioritization
+- **Memgraph:**
-- **Dependencies:** Fixed JSON import for psycopg
+  - Fixed async attribute errors
+  - Fixed n_embeddings usage
+  - Fixed indexing issues
+- **Vector Stores:**
+  - Fixed Qdrant cloud indexing
+  - Fixed Neo4j Cypher syntax
+  - Fixed LLM parameters
+- **Graph Store:**
+  - Fixed LM config prioritization
+- **Dependencies:**
+  - Fixed JSON import for psycopg
 
 **Refactoring:**
 - **Google AI:** Refactored from Gemini to Google AI

docs/components/embedders/models/google_AI.mdx

@@ -6,7 +6,8 @@ To use Google AI embedding models, set the `GOOGLE_API_KEY` environment variable
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -26,12 +27,37 @@ 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": "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:

docs/components/embedders/models/langchain.mdx

@@ -44,29 +44,33 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
 ```typescript TypeScript
-import { Memory } from "mem0ai";
+import { Memory } from 'mem0ai/oss';
 import { OpenAIEmbeddings } from "@langchain/openai";
 
-const embeddings = new OpenAIEmbeddings();
+// 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": {
+  embedder: {
-        "provider": "langchain",
+    provider: 'langchain',
-        "config": {
+    config: {
-            "model": embeddings
+      model: openaiEmbeddings,
-        }
+    },
-    }
+  },
-}
+};
 
 const memory = new Memory(config);
-
 const messages = [
-    { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
+    {"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": "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": "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." }
+    {"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" } });
-memory.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 </CodeGroup>
 
@@ -96,9 +100,10 @@ When using LangChain as an embedder provider, you'll need to:
 
 ### Examples with Different Providers
 
+<CodeGroup>
 #### HuggingFace Embeddings
 
-```python
+```python Python
 from langchain_huggingface import HuggingFaceEmbeddings
 
 # Initialize a HuggingFace embeddings model
@@ -117,9 +122,33 @@ config = {
 }
 ```
 
+```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 Python
 from langchain_ollama import OllamaEmbeddings
 
 # Initialize an Ollama embeddings model
@@ -137,6 +166,27 @@ config = {
 }
 ```
 
+```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>

docs/components/embedders/models/ollama.mdx

@@ -2,7 +2,8 @@ You can use embedding models from Ollama to run Mem0 locally.
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -21,18 +22,52 @@ 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": "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 OpenAI model to use | `nomic-embed-text` |
+| `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` |
+| `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>

docs/components/llms/models/langchain.mdx

@@ -46,34 +46,34 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
 ```typescript TypeScript
-import { Memory } from "mem0ai";
+import { Memory } from 'mem0ai/oss';
 import { ChatOpenAI } from "@langchain/openai";
 
-const openai_model = new ChatOpenAI({
+// Initialize a LangChain model directly
-    model: "gpt-4o",
+const openaiModel = new ChatOpenAI({
+    modelName: "gpt-4",
     temperature: 0.2,
-    max_tokens: 2000
+    maxTokens: 2000,
-})
+    apiKey: process.env.OPENAI_API_KEY,
+});
 
 const config = {
-    "llm": {
+  llm: {
-        "provider": "langchain",
+    provider: 'langchain',
-        "config": {
+    config: {
-            "model": openai_model
+      model: openaiModel,
-        }
+    },
-    }
+  },
-}
+};
 
 const memory = new Memory(config);
-
 const messages = [
-    { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
+    {"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": "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": "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." }
+    {"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" } });
-memory.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 </CodeGroup>
 

docs/components/llms/models/ollama.mdx

@@ -2,7 +2,8 @@ You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -23,12 +24,37 @@ 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": "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';
+
+const config = {
+  llm: {
+    provider: 'ollama',
+    config: {
+      model: 'llama3.1:8b', // or any other Ollama model
+      url: 'http://localhost:11434', // Ollama server URL
+      temperature: 0.1,
+    },
+  },
+};
+
+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>
+
 ## Config
 
 All available parameters for the `ollama` config are present in [Master List of All Params in Config](../config).

docs/components/vectordbs/dbs/pgvector.mdx

@@ -2,7 +2,8 @@
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -24,12 +25,43 @@ 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": "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';
+
+const config = {
+  vectorStore: {
+    provider: 'pgvector',
+    config: {
+      collectionName: 'memories',
+      embeddingModelDims: 1536,
+      user: 'test',
+      password: '123',
+      host: '127.0.0.1',
+      port: 5432,
+      dbname: 'vector_store', // Optional, defaults to 'postgres'
+      diskann: false, // Optional, requires pgvectorscale extension
+      hnsw: false, // Optional, for HNSW indexing
+    },
+  },
+};
+
+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>
+
 ### Config
 
 Here's the parameters available for configuring pgvector: