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1. High-Level Goal
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• Introduce a “retrieval” phase into the conversation flow, so that agents can fetch relevant context from an external knowledge base (e.g., documents, code snippets, text data) before generating a final response.
• Ensure the feature is simple for end users to configure and use, matching the existing high-level, builder-style patterns in rswarm.
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2. RAG Architectural Overview
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• A typical RAG pipeline has three stages:
1. Query Construction: The user’s query (or Agent prompt) is transformed into a form suitable for retrieval (e.g., embeddings or text search).
2. Retrieval: The query is used to find relevant chunks from a knowledge repository (e.g., embedded text in a database or vector store).
3. Generation: The retrieved chunks are passed into the LLM to augment its context, forming an enriched prompt that leads to a more accurate or domain-specific answer.
• In rswarm, these steps will map to:
– A new “Retriever” component or trait (responsible for chunked-document retrieval).
– Integration with the “MemoryStore” or a new vector store module, so that vector embeddings can be stored/retrieved efficiently.
– A “RAG” function or agent function that orchestrates the retrieval step, passing relevant text back into the agent’s conversation flow.
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3. Storing and Indexing Content (Chunking & Embeddings)
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3.1 Chunking Strategy
• Decide on a chunk size strategy for documents (e.g., 512 tokens or 1,000 characters).
• Provide a small library or utility that breaks large texts into smaller chunks, each tagged with metadata (e.g., doc_id, source, etc.).
• For the user’s convenience, allow chunking as a separate “pre-processing” step. Keep the chunking logic simple to configure (e.g., a builder pattern or a single function call).
3.2 Storing Embeddings
• Add an embeddings-based indexing option inside (or alongside) the existing MemoryStore.
• If SurrealDB remains the user’s data store, store embeddings in a new table, such as vector embeddings. Alternatively, allow pluggable vector stores if bridging to one of the popular vector DBs is desired (e.g., Qdrant or Pinecone).
• Each chunk is stored as: (embedding_vector, chunk_text, metadata).
• Provide a trait-based interface for computing embeddings so users can plug in their embedding provider (e.g., OpenAI Embeddings, Hugging Face, or a local model).
• Expose a method (e.g., “index_documents”) that processes an entire set of documents: chunk -> embed -> store.
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4. Retrieval Phase: Designing a “Retriever” Trait
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• Define a “Retriever” trait that standardizes how retrieval is performed. It might have methods like:
– “retrieve(query: &str, top_k: u32) -> Vec<RetrievedChunk>”
– “retrieve_with_embedding(embedding: &[f32], top_k: u32) -> Vec<RetrievedChunk>”
– Or a single method that decides whether to do text or vector-based retrieval.
• Implement a default “VectorRetriever” that queries the existing “MemoryStore” or SurrealDB. This would handle approximate nearest neighbor or semantic search logic.
• The user can easily create a custom retriever if they want a different embedding flow or a separate DB.
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5. Integration with the Agent Conversation Flow
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5.1 New “RAG Retrieval” AgentFunction
• Create a function that can be registered with an Agent, something like “rag_retrieve”. This function would:
– Take user’s message or last user prompt from context variables (e.g., “query”) or from a conversation snippet.
– Convert it to an embedding or use a text-based retrieval approach.
– Call the retriever to fetch top-k chunks from the memory/vector store.
– Return these retrieved chunks in a structured format (e.g., concatenated into a single string, or a JSON list).
– Optionally store the results in context variables for subsequent usage.
5.2 Agent’s Execution
• Because rswarm Agents can already call functions in the conversation flow, the user configures the RAG function call either “automatically” or “manually.” For example, the agent might see the user’s question, decide it needs retrieval, and call “rag_retrieve”.
• The function’s output (the retrieved text chunks) is appended to the conversation’s messages or context variables before the final generation.
• Provide a recommended prompt template to show how to incorporate retrieved text in the expanded context (“Here are relevant documents: …”).
5.3 Example Integration Flow
1. User message arrives: “Explain concept X from my knowledge base.”
2. The agent uses “rag_retrieve(query=‘Explain concept X…’, top_k=3).”
3. The retrieval function fetches 3 relevant chunks from the memory store.
4. The function returns that text to the conversation or to context variables.
5. The agent re-renders a final prompt, including the retrieved text as context, and generates a final answer.
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6. Developer Experience (DX) Considerations
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• Provide a single, simplified API for installing the RAG feature. For instance:
– “swarm.install_rag(RagConfig { … })” that sets up a default retriever, chunk size, etc.
– Or a “rag::initialize_default_retriever()” function that handles behind-the-scenes steps for users who follow default conventions.
• Offer a minimal set of tuning parameters (e.g., “top_k”, “embedding_model_name”, “max_chunk_length”) with sensible defaults.
• Keep any advanced vector store or embedding logic behind optional feature flags if it introduces heavy dependencies (e.g., separate “rag” feature).
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7. Workflow for End Users
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1. Install the “rag” feature (if separate from the core).
2. Write a small script that:
– Chunks their domain documents.
– Creates embeddings for each chunk (using the user’s chosen embedding model).
– Stores them in the library’s memory or vector store (“index_documents”).
3. Add the “rag_retrieve” function to the Agent:
– Either with a builder method or by pushing it into the “functions” vector.
– Possibly configure something like: “agent.function_call = Some(“auto”)” to allow the agent to call retrieval automatically.
4. In the conversation, user or agent triggers “rag_retrieve”.
5. The agent receives retrieved text, merges it into context, and finalizes its answer.
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8. Memory Module Refinement
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• Update the existing memory module (currently SurrealDB-based) to support vector-based queries. If direct vector similarity search is not yet supported, use a library or implement approximate nearest neighbor search.
• Provide fallback to a naive textual matching approach if vector retrieval is not configured.
• Expose a new method (e.g., “search_similar_chunks(query_embedding: &[f32], top_k: u32) -> Vec<Chunk>”).
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9. Testing and Documentation
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9.1 Testing
• Write integration tests verifying the entire flow:
– Document indexing → retrieval → final agent answer.
– Edge cases like no results found or incomplete doc.
• Include performance checks with multiple chunk sizes.
• Keep unit tests for chunking, embedding, vector search, function-call integration, etc.
9.2 Documentation
• Provide a “RAG Quick Start” guide in rswarm’s README or docs folder.
• Show the minimal user steps:
– “Here’s how to embed your documents.”
– “Here’s how to add the retriever to an Agent’s functions.”
– “Here’s how the conversation triggers RAG.”
• Offer advanced usage tips: e.g., how to integrate with custom embedding providers or vector DBs.
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10. Future Enhancements
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• Add a default OpenAI-based embeddings pipeline if the user has an OpenAI API key.
• Explore advanced re-ranking approaches or multi-vector retrieval.
• Support structured data retrieval (metadata filtering, advanced queries).
• Provide additional RAG function calls (e.g., “rag_search” vs. “rag_summarize”) to handle domain-specific tasks.
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Summary
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This plan lays out how to fold Retrieval-Augmented Generation into rswarm, focusing on maximum developer convenience. By introducing a retrieving function, indexing/chunking utilities, and a trait-based retrieval approach, developers can seamlessly add domain data lookups to their AI agent workflows. Careful architectural choices (like a “Retriever” trait and a “rag_retrieve” function) will ensure the feature stays modular, easy to configure, and consistent with the existing agent-and-function design of rswarm.