Enterprise documents and RAG inside Ask Titan
A business question can require a procedure, specification or policy rather than a database calculation. The right document passage must reach the agent first.
An implementation-grounded look at document processing, Qdrant retrieval and the dedicated LangGraph document specialist.
From documents to evidence
Enterprise documents
Procedures · specifications · policies
Searchable evidence
Chunks and embeddings in Qdrant
Document-specialist answer
Grounded in retrieved passages
Retrieve the passage. Then generate the answer.
The engineering problem
Not every business answer lives in a table
“How many batches were rejected?” is a structured-data question. “What should we do when a batch fails intake?” may require a quality procedure. An AI assistant needs different execution paths for these two questions.
A document is not a trustworthy answer simply because it has been uploaded. The system must extract usable text, make it retrievable, find relevant passages for this question and let the specialist reason from those passages.
Same factory · different evidence
“How many batches failed intake today?”
Query governed operational data through a structured specialist.
“What does the intake procedure require next?”
Retrieve the relevant instruction from enterprise documents.
Same orchestrator. Different specialist capability.
One Qdrant collection supports both paths: indexing prepares the search surface, retrieval uses it when a question arrives.
Architecture · two connected paths
Index documents. Retrieve when asked.
Ask Titan separates document preparation from question-time reasoning. Documents are processed ahead of time, chunked, enriched and embedded into a Qdrant collection so they can be searched efficiently later.
When a user asks a question, the document specialist does not search raw files directly. It reformulates the request, retrieves the most relevant passages from Qdrant and uses those passages as grounded evidence for the answer.
Step 1 · prepare the evidence
A file becomes searchable passages
Ask Titan sends uploaded files to a separate processing service. It supports PDFs, Word documents, presentations, spreadsheets, CSV, JSON and plain text. The service extracts their content and, where supported, preserves useful structure such as headings, column names and rows.
The extracted content is split into smaller passages and enriched with source metadata and an AI-generated document topic. Each passage is embedded and indexed in Qdrant for later retrieval. Content-derived chunk IDs help avoid duplicate entries during ingestion, but do not automatically manage document versions.
Indexing pipeline
Extract
Read text and available document structure from the source format.
Split + enrich
Create passages and attach source metadata, content identity and a document topic.
Embed + index
Store passage embeddings and associated text in the configured Qdrant collection.
Inside the retrieval boundary
Configured document collection
Qdrant
Passage text · embeddings · metadata
Retrieval result
Relevant passages
Selected document evidence returned to the specialist.
Step 2 · retrieve
The document collection is configured, not guessed by the model
Ask Titan exposes document retrieval through an MCP specialist associated with a configured Qdrant collection. When a user asks a document question, the specialist prepares a search query and retrieves relevant passages from that collection.
The inspected implementation uses vector similarity search. The retrieved passages become evidence for the specialist’s answer; they are not the answer itself. Using Qdrant does not, on its own, imply additional techniques such as hybrid search or reranking.
Step 3 · the document specialist
Three explicit steps. One specialist capability
The top-level orchestrator invokes the document specialist through a permitted MCP tool. Its dedicated LangGraph workflow prepares a standalone search question, retrieves passages from the configured Qdrant collection and passes that evidence to the answer-generating model.
For example, a user asks, “What are the next steps when the intake check fails?” The specialist retrieves relevant procedure passages and uses them to generate an answer. Those passages provide evidence, but they do not automatically verify a generated citation. Part 7 explores how answers can be traced back to their sources and tool execution.
Three separate access boundaries
Capability access
Can this user invoke the MCP tool?
Application policy
Collection scope
Which collection does the specialist search?
Specialist configuration
Document permissions
May this user receive the retrieved passage?
Requires separate enforcement
Tool access ≠ document access
Governance · scope and permissions
Finding a document is not the same as being authorized to read it
Document retrieval crosses different access boundaries. Application policy determines whether a user can invoke the document specialist. The specialist then searches its configured Qdrant collection.
Neither decision automatically establishes permission to read every document in that collection. Document-level access requires separate enforcement before restricted passages can be disclosed.
Engineering takeaway
Document retrieval belongs behind a specialist boundary
The top-level agent should not ingest PDFs or search the entire document corpus itself. Give it a permitted document capability, let that capability select and retrieve passages, then return the evidence-backed response to the conversation.
Patterns we keep
Next in the series
Part 7 · Answer traceability
Next we connect retrieved evidence, tool execution and user-facing answers. What can an operator or business user actually trace back to its source, and where are additional controls required?
Read Part 7FAQ
Enterprise document retrieval and RAG questions
Practical answers about documents, chunking, Qdrant retrieval, LangGraph specialists and source boundaries.
What does RAG mean in Ask Titan?
Retrieval-augmented generation means finding relevant passages in enterprise documents and supplying them to the document specialist before it generates an answer. It is separate from querying structured tables.
Which document formats are handled by the inspected upload path?
The repository contains loaders for PDF, TXT, DOCX, PPTX, XLSX/XLS, CSV and JSON. Extraction fidelity depends on the file and loader; scanned-page OCR is not established by this path.
Which vector database does Ask Titan use?
The inspected document path uses Qdrant through a LangChain vector-store integration. Weaviate is a public architecture reference for the illustrations, not the backend used in the inspected implementation.
Does Ask Titan use hybrid keyword and vector search?
The inspected retrieval path uses a standard vector-store retriever. It does not establish a configured BM25 hybrid, reranking or multi-stage search pipeline.
How does the document specialist answer a question?
Its LangGraph workflow prepares a standalone search question, retrieves relevant passages from a configured collection and generates an answer using the retrieved text.
Can the document specialist automatically see every previous chat message?
The inspected specialist tool receives a query and starts its internal graph with that query. Conversation continuity is managed at the top-level orchestrator, not proof that all prior messages reach the specialist.
Are the citations guaranteed to point to the correct source?
The retrieval API returns passage content and metadata, and the specialist instructs the model to cite sources. The inspected answer path does not establish a separate verified citation-mapping mechanism.
Does a permitted document tool automatically enforce per-document access rights?
No. Capability permission, collection choice and individual document access are separate controls. The inspected code is not proof of end-user document-ACL passthrough.
Ask Titan · enterprise knowledge
Put operational knowledge within reach of the people who need it
Procedures and specifications are useful when teams can find the right passage at the right time. Ask Titan connects document retrieval to the same question-driven assistant used for enterprise data.
From source to answer
Technical references
Ask Titan implementation details are simplified from a private repository snapshot. Weaviate's public RAG architecture informs the original diagrams, but Ask Titan's inspected vector store is Qdrant, not Weaviate. The links below describe the general mechanisms.