CODING EXPERIENCE AL AGENT

Data Formatting: Convert and organize the data into a machine-readable format. Text formats like JSON, CSV, or plain text are generally preferable.
Chunking: Break down large documents into smaller, manageable pieces or "chunks" (e.g., paragraphs or sections).
Creating Embeddings: Use an embedding model to convert each chunk into a high-dimensional vector representation. Embeddings capture the semantic meaning of text, allowing for similarity searches.
Implementing Retrieval-Augmented Generation (RAG)
User Query Embedding
Vector Database: Store the embeddings and their corresponding text chunks in a vector database
Utilize AI frameworks that simplify the integration between language models and your data:
LangChain: A framework for developing applications powered by language models. It supports retrieval from vector databases and chaining multiple operations.
LlamaIndex (GPT Index): An interface that connects large language models with external data
Define the Agent's Role: Clearly specify what tasks the agent should perform and how it should use the data.
Integrate Tools: Equip the agent with tools like calculators, databases, or APIs if needed.
Set Up APIs: Use APIs to connect your application, vector database, and language model.