How do I integrate Fast.io with a vector database?

You do not need to integrate a separate vector database. Fast.io provides built-in vector search and indexing natively within the workspace through Intelligence Mode. Uploaded files are automatically embedded and made available for semantic retrieval via the API.

What is the best way to chunk documents in Fast.io?

Fast.io handles document chunking and embedding automatically during the ingestion phase. You do not need to manually configure chunk sizes or select embedding models, as the platform uses tuned defaults designed for high-accuracy retrieval.

Can OpenClaw use Fast.io's RAG capabilities automatically?

Yes, OpenClaw users can access workspace search by installing the official integration via `clawhub install dbalve/fast-io`. This provides zero-configuration tools for your agent to read files and query documents using natural language.

What is the storage limit for the Fast.io free agent tier?

The free agent tier includes multiple of persistent storage, a maximum file size of multiple, and multiple monthly credits. This plan does not require a credit card and provides full access to the MCP tools and intelligence features.

How to Build a RAG Pipeline with Fast.io API

What is a RAG Pipeline and Why Does it Matter?: how build rag pipeline with fast api

Retrieval-Augmented Generation transforms how language models interact with proprietary data. Instead of relying solely on baseline training knowledge, the system retrieves relevant documents and feeds them to the AI as context before generating an answer. This mechanism forces the model to ground its responses in verifiable facts rather than guessing. According to IBM, implementing RAG reduces hallucination rates by up to 80% in AI agents. For developers, building these pipelines has historically meant stitching together cloud storage, embedding models, and vector databases into a fragile sequence of operations. Every time a user updates a file, the entire chain must run again to keep the index synchronized. Maintaining this architecture drains engineering resources and introduces unnecessary points of failure.

Fast.io eliminates this complexity by making intelligence a native property of the workspace. When you store a document, the platform automatically handles the indexing and search requirements behind the scenes. Developers can focus on building capable agents instead of managing infrastructure.

The Mechanics of Retrieval-Augmented Generation

A standard pipeline operates in several distinct phases. First, the ingestion phase extracts text from files, splits the text into smaller chunks, and converts those chunks into mathematical vectors called embeddings. Second, the retrieval phase takes a user query, converts it into an embedding, and performs a similarity search against the database to find the most relevant chunks. Third, the generation phase passes the retrieved text to the language model along with the original question to produce an informed response. Each of these steps introduces latency and requires specific technical expertise to configure correctly. If chunk sizes are too large, the context window fills with irrelevant noise. If the sizes are too small, the model loses the broader meaning of the text. Balancing these parameters is a continuous challenge for AI engineering teams.

Why Most RAG Architectures Are Too Complex

Most guides assume developers will use Amazon Simple Storage Service for file storage alongside a separate vector database like Pinecone or Milvus. This approach creates immediate synchronization problems. If a user deletes a file in storage, the vector database must be updated immediately to prevent the AI from quoting deleted information. Building reliable webhooks and retry logic to maintain state between these two disconnected systems is notoriously difficult. Fast.io solves this gap by unifying storage and vector search into a single API. Because the file system and the index are the same entity, they are never out of sync. This architectural shift removes the need for middleware and reduces the total cost of running intelligent applications.

The Fast.io Approach to Built-In RAG

A RAG pipeline with Fast.io allows developers to ingest, embed, and retrieve document data directly from workspaces. This unified system removes the need to manage embedding models or provision search clusters. The intelligence is built directly into the file storage layer. When you upload a PDF, Word document, or text file, the platform automatically extracts the content and prepares it for semantic search. This native capability is available on the free agent tier, which includes multiple of storage and multiple monthly credits without requiring a credit card.

No Separate Vector Database Required

The main advantage of this approach is the elimination of external dependencies. Traditional setups require you to pay for storage and vector search separately. You also have to write integration code to move data between the two services. With Fast.io, the /search API endpoint queries the workspace directly using natural language. The platform handles the vectorization of your query and matches it against the document embeddings automatically. This unified approach ensures that your agent always has access to the most current version of a file without any synchronization delays.

Evidence and Benchmarks

The performance benefits of integrated RAG are measurable. By reducing the number of network hops required to retrieve context, response times decrease. More importantly, accuracy improves. According to IBM, RAG reduces hallucination rates by up to 80% in AI agents by grounding generation in retrieved texts. When the storage layer directly feeds the language model, the risk of serving stale or disconnected context drops to zero. Developers can trace every generated claim back to a specific document in the workspace, providing complete transparency for end users.

Prerequisites for Your Fast.io RAG Pipeline

Before you write any code, you need to configure your workspace for document intelligence. The platform uses a concept called Intelligence Mode to determine which folders should be processed for semantic search. By default, this feature is disabled to conserve credits and protect sensitive data. You must explicitly enable it for the workspaces where your agents will operate. This granular control allows you to mix standard file storage with intelligent retrieval within the same organization.

Interface showing intelligent document indexing

Enabling Intelligence Mode

To turn on built-in RAG, navigate to your workspace settings in the dashboard and toggle Intelligence Mode. You can also enable this programmatically via the API when creating a new workspace. Once activated, any compatible file uploaded to that directory will be queued for automatic text extraction and embedding. The system supports a wide range of formats including PDF, DOCX, TXT, and Markdown. It is important to note that large files may take a few moments to process. You can use webhooks to receive notifications when a document is indexed and ready for retrieval.

Setting Up Your MCP Server

If you are building an agent using the Model Context Protocol, you can connect to Fast.io using the official MCP integration. The platform provides multiple MCP tools via Streamable HTTP and Server-Sent Events. These tools map directly to the underlying API capabilities. For OpenClaw users, you can install the integration by running clawhub install dbalve/fast-io in your terminal. This zero-configuration setup gives your agent the ability to search workspaces, read file contents, and manage documents without needing custom API wrappers.

Ingesting and Structuring Documents

The first technical step in building your pipeline is getting documents into the system. Fast.io provides multiple ways to ingest data depending on your application architecture. You can upload files directly via multipart form data, or you can use the URL Import feature to pull files directly from external services like Google Drive or Dropbox. URL Import is useful for agents because it bypasses local input and output operations, streaming the file directly from the source to the workspace.

Automated Embedding on Upload

When a file lands in an Intelligence Mode workspace, the platform initiates a background job. The text extraction engine parses the document structure, preserving headings, paragraphs, and lists. It then splits the text into manageable chunks and generates embeddings for each piece. You do not need to specify chunk sizes or select an embedding model. The platform uses tuned defaults designed specifically for retrieval tasks. This automation saves hours of configuration and testing, allowing you to focus on the search experience.

Handling Different File Types

Document formats present unique parsing challenges. A PDF uses absolute positioning for text, making it difficult to extract reading order accurately. Word documents use complex XML structures that can hide relevant content in headers or footers. The native ingestion engine handles these edge cases automatically. It strips out unnecessary formatting and focuses on semantic text. For developers building client portals, this means you can accept uploads in any format and trust that the content will be available for agent retrieval without manual sanitization.

Retrieving Context via the Fast.io API

Once your documents are indexed, you can begin querying the workspace. The retrieval API is designed to accept natural language questions, not just keyword matches. When you send a query, the system converts your text into a vector, compares it against the workspace index, and returns the most semantically relevant text blocks. This search capability operates across all files in the directory, ensuring that answers pull from the corpus of available knowledge.

Making the Search API Call

To retrieve context, you will send a POST request to the /search endpoint. The request payload requires the user query string. In response, you receive a JSON array containing the matching text chunks, along with metadata identifying the source file and the relevance score. The response includes direct download links for the source files, which is essential for providing citations in your final user interface. Agents can use these links to download the full document if they need more context than the snippet provides.

Managing Chunk Size and Relevancy

While Fast.io handles chunking automatically, developers still control how many results to return. Requesting too many results can overwhelm the language model context window and increase token costs. Requesting too few might miss important information. A common best practice is to request the most relevant chunks. You can then use a lightweight reranking step in your application code if you need strict ordering, though the default API results are sufficient for standard generative tasks. The relevancy scores allow you to filter out low-confidence matches before passing the text to your AI.

Feeding Context to Your LLM

The final stage of the pipeline connects the retrieved data to your language model. Whether you are using Claude, GPT-multiple, or a local LLaMA instance, the process remains similar. You must construct a prompt that separates the user question from the retrieved context. This separation prevents the model from confusing external facts with its own instructions. Properly formatted prompts improve the accuracy of the generated response and ensure the AI attributes information to the provided sources.

Prompt Construction Best Practices

A successful RAG prompt uses clear boundary markers. Start by instructing the model to answer the question using only the provided context. Next, insert the text chunks returned from the Fast.io API, labeling each one with its source filename. Finally, append the user question at the end of the prompt. Placing the question last ensures it remains salient in the model attention mechanism. If the retrieved context does not contain the answer, instruct the model to state that it does not know, rather than trying to guess.

Generating Sourced Answers

To build trust with your users, your agent should cite its sources. When constructing your prompt, ask the language model to include inline citations referencing the filenames you provided in the context block. Because the Fast.io search API returns file metadata with every result, you can map these AI-generated citations back to clickable links in your user interface. This transparency proves to the user that the agent is relying on their documents, reinforcing the value of the intelligent workspace.

Advanced Use Cases for Fast.io RAG

Beyond simple question answering, native document intelligence enables complex automated workflows. Because agents and humans share the same workspaces, they can collaborate on data directly. An agent can monitor a folder, automatically summarize new uploads using retrieved context from older files, and generate a synthesis report without human intervention. This capability is enhanced by the platform webhook system, which can trigger agent actions the moment an upload completes.

Multi-Agent Collaboration

In sophisticated systems, different agents handle different tasks. A researcher agent might ingest documents and organize them into folders. A writer agent might then use the search API to query those documents and draft an article. To prevent conflicts, Fast.io provides file locks for concurrent multi-agent access. An agent can acquire a lock before updating a synthesis document, ensuring that no other process overwrites its changes. This coordination layer makes the platform ideal for complex, multi-step AI architectures.

Ownership Transfer for Agent Builds

A unique feature of the platform is the ability for agents to build workspaces and transfer ownership to human clients. An agent can provision a workspace, upload reference documents, configure Intelligence Mode, and then generate a secure invite link. The human recipient takes ownership of the space, while the agent retains administrative access to continue assisting with retrieval tasks. This pattern is powerful for AI-driven service businesses that deliver custom data rooms or curated research portals to their customers.