Rename embedchain to mem0 and open sourcing code for long term memory (#1474)

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

embedchain/docs/components/vector-databases/pinecone.mdx

@@ -0,0 +1,109 @@
+---
+title: Pinecone
+---
+
+## Overview
+
+Install pinecone related dependencies using the following command:
+
+```bash
+pip install --upgrade 'pinecone-client pinecone-text'
+```
+
+In order to use Pinecone as vector database, set the environment variable `PINECONE_API_KEY` which you can find on [Pinecone dashboard](https://app.pinecone.io/).
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# Load pinecone configuration from yaml file
+app = App.from_config(config_path="pod_config.yaml")
+# Or
+app = App.from_config(config_path="serverless_config.yaml")
+```
+
+```yaml pod_config.yaml
+vectordb:
+  provider: pinecone
+  config:
+    metric: cosine
+    vector_dimension: 1536
+    index_name: my-pinecone-index
+    pod_config:
+      environment: gcp-starter
+      metadata_config:
+        indexed:
+          - "url"
+          - "hash"
+```
+
+```yaml serverless_config.yaml
+vectordb:
+  provider: pinecone
+  config:
+    metric: cosine
+    vector_dimension: 1536
+    index_name: my-pinecone-index
+    serverless_config:
+      cloud: aws
+      region: us-west-2
+```
+
+</CodeGroup>
+
+<br />
+<Note>
+You can find more information about Pinecone configuration [here](https://docs.pinecone.io/docs/manage-indexes#create-a-pod-based-index).
+You can also optionally provide `index_name` as a config param in yaml file to specify the index name. If not provided, the index name will be `{collection_name}-{vector_dimension}`.
+</Note>
+
+## Usage
+
+### Hybrid search
+
+Here is an example of how you can do hybrid search using Pinecone as a vector database through Embedchain.
+
+```python
+import os
+
+from embedchain import App
+
+config = {
+    'app': {
+        "config": {
+            "id": "ec-docs-hybrid-search"
+        }
+    },
+    'vectordb': {
+        'provider': 'pinecone',
+        'config': {
+            'metric': 'dotproduct',
+            'vector_dimension': 1536,
+            'index_name': 'my-index',
+            'serverless_config': {
+                'cloud': 'aws',
+                'region': 'us-west-2'
+            },
+            'hybrid_search': True, # Remember to set this for hybrid search
+        }
+    }
+}
+
+# Initialize app
+app = App.from_config(config=config)
+
+# Add documents
+app.add("/path/to/file.pdf", data_type="pdf_file", namespace="my-namespace")
+
+# Query
+app.query("<YOUR QUESTION HERE>", namespace="my-namespace")
+
+# Chat
+app.chat("<YOUR QUESTION HERE>", namespace="my-namespace")
+```
+
+Under the hood, Embedchain fetches the relevant chunks from the documents you added by doing hybrid search on the pinecone index.
+If you have questions on how pinecone hybrid search works, please refer to their [offical documentation here](https://docs.pinecone.io/docs/hybrid-search).
+
+<Snippet file="missing-vector-db-tip.mdx" />