[README]

.env.examples

@@ -0,0 +1 @@
+PINECONE_API_KEYS="your_pinecone_api_key"

README.md

@@ -4,6 +4,11 @@
 # Swarms Memory
 A collection of memory systems for `swarms` such as Pinecone, ChromaDB, Weaviate
 
+
+### Requirements:
+- `python 3.10` 
+- `.env` with your respective keys like `PINECONE_API_KEY` can be found in the `.env.examples`
+
 ## Install
 ```bash
 $ pip install swarms-memory
@@ -12,6 +17,98 @@ $ pip install swarms-memory
 
 ## Usage
 
+### Pinecone
+```python
+from typing import List, Dict, Any
+from swarms_memory import PineconeMemory
+
+
+# Example usage
+if __name__ == "__main__":
+    from transformers import AutoTokenizer, AutoModel
+    import torch
+
+    # Custom embedding function using a HuggingFace model
+    def custom_embedding_function(text: str) -> List[float]:
+        tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+        model = AutoModel.from_pretrained("bert-base-uncased")
+        inputs = tokenizer(
+            text,
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=512,
+        )
+        with torch.no_grad():
+            outputs = model(**inputs)
+        embeddings = (
+            outputs.last_hidden_state.mean(dim=1).squeeze().tolist()
+        )
+        return embeddings
+
+    # Custom preprocessing function
+    def custom_preprocess(text: str) -> str:
+        return text.lower().strip()
+
+    # Custom postprocessing function
+    def custom_postprocess(
+        results: List[Dict[str, Any]],
+    ) -> List[Dict[str, Any]]:
+        for result in results:
+            result["custom_score"] = (
+                result["score"] * 2
+            )  # Example modification
+        return results
+
+    # Initialize the wrapper with custom functions
+    wrapper = PineconeMemory(
+        api_key="your-api-key",
+        environment="your-environment",
+        index_name="your-index-name",
+        embedding_function=custom_embedding_function,
+        preprocess_function=custom_preprocess,
+        postprocess_function=custom_postprocess,
+        logger_config={
+            "handlers": [
+                {
+                    "sink": "custom_rag_wrapper.log",
+                    "rotation": "1 GB",
+                },
+                {
+                    "sink": lambda msg: print(
+                        f"Custom log: {msg}", end=""
+                    )
+                },
+            ],
+        },
+    )
+
+    # Adding documents
+    wrapper.add(
+        "This is a sample document about artificial intelligence.",
+        {"category": "AI"},
+    )
+    wrapper.add(
+        "Python is a popular programming language for data science.",
+        {"category": "Programming"},
+    )
+
+    # Querying
+    results = wrapper.query("What is AI?", filter={"category": "AI"})
+    for result in results:
+        print(
+            f"Score: {result['score']}, Custom Score: {result['custom_score']}, Text: {result['metadata']['text']}"
+        )
+
+
+
+```
+
+
+### ChromaDB
+```
+
+```
 
 
 # License