> For the complete documentation index, see [llms.txt](https://docs.tallygo.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tallygo.ai/context-graph-overview/query-powering-tallys-agentic-applications.md).

# Query: Powering Tally’s Agentic Applications

The final layer of the Tally architecture enables applications to query and reason over the connected dataset. Tally’s LCG serves as the semantic intelligence layer for all downstream workflows.

To query this network, we utilize Graph RAG, which bypasses the limitations of standard vector embeddings or BM25 keyword search. Rather than retrieving isolated documents based on text similarity, our querying agent performs deterministic graph traversal. It follows explicit edges, hopping from a Party node to a Shipment node, assessing Charge nodes, and validating against Document nodes. This multi-hop traversal allows the system to synthesize precise answers to complex supply chain queries that would break traditional search architectures.

#### Graph RAG: Natural Language Queries

Graph RAG, is a retrieval architecture where an AI agent dynamically generates and executes Cypher queries against the LCG in response to natural language inputs. Rather than retrieving documents by text similarity, the agent traverses explicit graph edges — following relationships from a Shipment node through its Charge nodes, validating against source Document nodes — to synthesize precise, provenance-backed answers. Because the agent introspects the schema at query time, it adapts to new entity types and relationships without retraining.<br>

<figure><img src="/files/SUJSphFx0qjc7EiEPVC4" alt=""><figcaption></figcaption></figure>

#### The agent is equipped with tools to:

| Tool              | Purpose                                                                                                             |
| ----------------- | ------------------------------------------------------------------------------------------------------------------- |
| Schema discovery  | Understand the graph's current shape — entity types, relationships, and properties — so queries are always accurate |
| Graph query       | Execute structured queries with automatic tenant scoping and safety validation                                      |
| Identifier search | Fuzzy search across all reference numbers (tracking numbers, BOLs, POs)                                             |
| Party search      | Fuzzy search across company names to resolve informal references                                                    |

The schema is introspected dynamically, so as your data evolves, the agent's understanding evolves with it.

#### REST API

The system exposes a structured API for direct access, some examples:

| Endpoint                                  | What You Get                                                                                                                         |
| ----------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| GET /graph/shipments                      | Paginated shipment list with filters like status, mode, date range, origin, destination, carrier, and customer                       |
| GET /graph/shipments/:shipmentId          | Complete shipment detail: identifiers, parties, route legs, cargo, charges, milestones, related documents, and linked communications |
| GET /graph/shipments/:shipmentId/timeline | Shipment timeline combining estimated, planned, and actual milestone events                                                          |
| GET /graph/shipments/:shipmentId/evidence | Provenance graph showing where each fact came from: email, attachment, document, or upstream system record                           |

Each endpoint exposes the following search parameters:

| Search Parameter     | Purpose                                                                                                                  |
| -------------------- | ------------------------------------------------------------------------------------------------------------------------ |
| q                    | Free-text query such as a bill of lading number, company name, container number, invoice number, or phrase from an email |
| types                | Restrict search to entity classes like shipment, identifier, party, document, or email                                   |
| filters              | Narrow results by tenant-safe dimensions such as date range, shipment mode, customer, carrier, status, or source system  |
| limit / cursor       | Paginate results for UI workflows and agent tooling                                                                      |
| includeEvidence=true | Return snippets, matched fields, and provenance so users can see why a result matched                                    |

<br>


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.tallygo.ai/context-graph-overview/query-powering-tallys-agentic-applications.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.