Update block docs for: pinecone.md

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+# Pinecone Blocks Documentation
+
+## Pinecone Initialize
+
+### What it is
+A block that sets up and initializes a Pinecone vector database index.
+
+### What it does
+Creates a new Pinecone index if it doesn't exist, or connects to an existing one if it does. This index will be used to store and search through vector data.
+
+### How it works
+The block connects to Pinecone using your API credentials, checks if the requested index exists, and either creates a new one with your specified settings or connects to the existing one.
+
+### Inputs
+- Credentials: Your Pinecone API key for authentication
+- Index Name: The name you want to give to your Pinecone index
+- Dimension: The size of the vectors to be stored (default: 768)
+- Metric: The method used to measure similarity between vectors (default: cosine)
+- Cloud: The cloud provider for serverless deployment (default: aws)
+- Region: The geographical region for your index (default: us-east-1)
+
+### Outputs
+- Index: The name of the initialized Pinecone index
+- Message: A status message indicating whether the index was created or already existed
+
+### Possible use case
+Setting up a new vector database to store document embeddings for a semantic search application.
+
+## Pinecone Query
+
+### What it is
+A block that searches through vectors stored in a Pinecone index to find similar items.
+
+### What it does
+Takes a query vector and searches the Pinecone index to find the most similar vectors, returning the top matches along with their associated metadata.
+
+### How it works
+The block connects to your Pinecone index, performs a similarity search using your query vector, and returns the most relevant results based on your specified parameters.
+
+### Inputs
+- Credentials: Your Pinecone API key for authentication
+- Query Vector: The vector to search for in the index
+- Namespace: A specific section of the index to search in (optional)
+- Top K: Number of results to return (default: 3)
+- Include Values: Whether to include vector values in results (default: false)
+- Include Metadata: Whether to include metadata in results (default: true)
+- Host: The Pinecone host address
+- Index Name: The name of the Pinecone index to query
+
+### Outputs
+- Results: The raw query results from Pinecone
+- Combined Results: A consolidated version of the results
+
+### Possible use case
+Implementing a semantic search feature where users can find similar documents based on meaning rather than exact keyword matches.
+
+## Pinecone Insert
+
+### What it is
+A block that uploads new vector data to a Pinecone index.
+
+### What it does
+Takes text chunks and their corresponding vector embeddings and stores them in the Pinecone index along with any additional metadata.
+
+### How it works
+The block connects to your Pinecone index, processes the provided chunks and embeddings, assigns unique IDs to each vector, and uploads them to the specified namespace in the index.
+
+### Inputs
+- Credentials: Your Pinecone API key for authentication
+- Index: The name of the initialized Pinecone index
+- Chunks: List of text pieces to be stored
+- Embeddings: Vector representations of the text chunks
+- Namespace: Specific section of the index to store data in (optional)
+- Metadata: Additional information to store with the vectors
+
+### Outputs
+- Upsert Response: Confirmation message about the upload status
+
+### Possible use case
+Uploading a collection of documents to a vector database after converting them into embeddings, allowing them to be searched semantically later.