[README]

README.md

@@ -344,9 +344,48 @@ img = "playground/demos/multi_modal_chain_of_thought/eyetest.jpg"
 # Run the workflow on a task
 out = llm.run(task=task, img=img)
 print(out)
+```
+
+
+### `Anthropic`
+```python
+# Import necessary modules and classes
+from swarms.models import Anthropic
+
+# Initialize an instance of the Anthropic class
+model = Anthropic(
+    anthropic_api_key=""
+)
 
+# Using the run method
+completion_1 = model.run("What is the capital of France?")
+print(completion_1)
 
+# Using the __call__ method
+completion_2 = model("How far is the moon from the earth?", stop=["miles", "km"])
+print(completion_2)
+
+```
+
+
+### `HuggingFaceLLM`
+```python
+from swarms.models import HuggingfaceLLM
+
+# Initialize with custom configuration
+custom_config = {
+    "quantize": True,
+    "quantization_config": {"load_in_4bit": True},
+    "verbose": True
+}
+inference = HuggingfaceLLM(model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config)
+
+# Generate text based on a prompt
+prompt_text = "Create a list of known biggest risks of structural collapse with references"
+generated_text = inference(prompt_text)
+print(generated_text)
 ```
+
 ---
 
 # Features 🤖 

docs/swarms/memory/pinecone.md

@@ -1,4 +1,4 @@
-# `PineconDB` Documentation
+# `PineconeDB` Documentation
 
 ## Table of Contents
 

mkdocs.yml

@@ -106,7 +106,7 @@ nav:
     - Conversation: "swarms/structs/conversation.md"
   - swarms.memory:
     - Weaviate: "swarms/memory/weaviate.md"
-    - PineconDB: "swarms/memory/pinecone.md"
+    - PineconeDB: "swarms/memory/pinecone.md"
     - PGVectorStore: "swarms/memory/pg.md"
     - ShortTermMemory: "swarms/memory/short_term_memory.md"
   - swarms.utils:

swarms/memory/pinecone.py

@@ -6,9 +6,9 @@ from swarms.utils.hash import str_to_hash
 
 
 @define
-class PineconDB(VectorDatabase):
+class PineconeDB(VectorDatabase):
     """
-    PineconDB is a vector storage driver that uses Pinecone as the underlying storage engine.
+    PineconeDB is a vector storage driver that uses Pinecone as the underlying storage engine.
 
     Pinecone is a vector database that allows you to store, search, and retrieve high-dimensional vectors with
     blazing speed and low latency. It is a managed service that is easy to use and scales effortlessly, so you can
@@ -34,14 +34,14 @@ class PineconDB(VectorDatabase):
             Creates a new index.
 
     Usage:
-    >>> from swarms.memory.vector_stores.pinecone import PineconDB
+    >>> from swarms.memory.vector_stores.pinecone import PineconeDB
     >>> from swarms.utils.embeddings import USEEmbedding
     >>> from swarms.utils.hash import str_to_hash
     >>> from swarms.utils.dataframe import dataframe_to_hash
     >>> import pandas as pd
     >>>
-    >>> # Create a new PineconDB instance:
-    >>> pv = PineconDB(
+    >>> # Create a new PineconeDB instance:
+    >>> pv = PineconeDB(
     >>>     api_key="your-api-key",
     >>>     index_name="your-index-name",
     >>>     environment="us-west1-gcp",
@@ -166,7 +166,7 @@ class PineconDB(VectorDatabase):
         count: Optional[int] = None,
         namespace: Optional[str] = None,
         include_vectors: bool = False,
-        # PineconDBStorageDriver-specific params:
+        # PineconeDBStorageDriver-specific params:
         include_metadata=True,
         **kwargs,
     ):

tests/memory/test_pinecone.py

@@ -1,6 +1,6 @@
 import os
 from unittest.mock import patch
-from swarms.memory.pinecone import PineconDB
+from swarms.memory.pinecone import PineconeDB
 
 api_key = os.getenv("PINECONE_API_KEY") or ""
 
@@ -9,7 +9,7 @@ def test_init():
     with patch("pinecone.init") as MockInit, patch(
         "pinecone.Index"
     ) as MockIndex:
-        store = PineconDB(
+        store = PineconeDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",
@@ -21,7 +21,7 @@ def test_init():
 
 def test_upsert_vector():
     with patch("pinecone.init"), patch("pinecone.Index") as MockIndex:
-        store = PineconDB(
+        store = PineconeDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",
@@ -37,7 +37,7 @@ def test_upsert_vector():
 
 def test_load_entry():
     with patch("pinecone.init"), patch("pinecone.Index") as MockIndex:
-        store = PineconDB(
+        store = PineconeDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",
@@ -48,7 +48,7 @@ def test_load_entry():
 
 def test_load_entries():
     with patch("pinecone.init"), patch("pinecone.Index") as MockIndex:
-        store = PineconDB(
+        store = PineconeDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",
@@ -59,7 +59,7 @@ def test_load_entries():
 
 def test_query():
     with patch("pinecone.init"), patch("pinecone.Index") as MockIndex:
-        store = PineconDB(
+        store = PineconeDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",
@@ -72,7 +72,7 @@ def test_create_index():
     with patch("pinecone.init"), patch("pinecone.Index"), patch(
         "pinecone.create_index"
     ) as MockCreateIndex:
-        store = PineconDB(
+        store = PineconeDB(
             api_key=api_key,
             index_name="test_index",
             environment="test_env",