Lex LLM

The orchestrator for the Lex LLM project, enabling intelligent workflow execution and AI-driven responses.

Repositories

This project is part of the broader Lex ecosystem, which includes several interconnected repositories:

| Name | Description | |---------|-----------------------------| | lex-llm | Core Python orchestration logic and API | | lex-ui | Frontend interface for Lex users | | lex-db | Database backend with document storage and search |

API Structure

The main API is defined in src/lex_llm/api/routes.py. The available endpoints are:

• POST /workflows/{workflow_id}/run
  Executes a specific workflow by ID. Accepts a JSON payload containing user input, conversation history, and conversation ID. Returns a streaming response in NDJSON format.

• GET /workflows/metadata
  Retrieves metadata for all available workflows. Useful for discovering what workflows are supported by the system.

• GET /workflows/{workflow_id}/metadata
  Retrieves metadata for a specific workflow by ID. Returns 404 if the workflow does not exist, along with a list of available workflows.

• GET /health
  Simple health check endpoint. Returns {"status": "healthy"} when the service is running.

• Lifespan Events
  On startup and shutdown, logs are printed to indicate the state of the AI Orchestration Service.

Example: Calling the Workflow API

Use curl to stream results from a running workflow:

bash curl -N -X POST "http://0.0.0.0:10000/workflows/test_workflow/run" \ -H "Content-Type: application/json" \ -d '{ "user_input": "Tell me about artificial intelligence.", "conversation_history": [ {"role": "user", "content": "Hi!"}, {"role": "assistant", "content": "Hello, how can I help you?"} ], "conversation_id": "123e4567-e89b-12d3-a456-426614174000" }'

💡 Note: The -N flag disables buffering to ensure streaming works properly.

Running the API

This project uses a Makefile to simplify common development and deployment tasks.

Key Commands

• Run the production API:
  bash make run Generates the OpenAPI schema and starts the application server.

• Run in development mode (with hot reload):
  bash make run-dev Installs dev dependencies, generates the schema, and starts Uvicorn with auto-reload enabled on port 10000.

• Other Useful Commands: | Command | Description | |--------|-------------| | make install | Install project and API client dependencies | | make install-dev | Install development dependencies | | make lint | Format and fix code using ruff | | make lint-check | Check formatting and linting without fixing | | make static-type-check | Run mypy for type checking | | make test | Run unit tests with pytest | | make pr | Run all checks required for a pull request | | make generate-api | Generate OpenAPI client from lex-db.yaml | | make generate-openapi-schema | Generate OpenAPI schema (openapi/openapi.yaml) |

📌 Tip: Always run make pr before pushing changes to ensure everything is consistent.

Communication with LexDB

This project integrates with LexDB via an auto-generated OpenAPI client, enabling robust interaction with the database.

For Developers

• The OpenAPI client is generated using make generate-api, which uses Docker and OpenAPI Generator.
• Generated client is located at build/lex_db_api.
• Supported operations include:
  • Listing available tables
  • Full-text search across articles
  • Vector-based semantic search using embeddings
• Example usage can be found in src/examples/lex_db_search_example.py.

Example Usage

```python from lexdbapi.configuration import Configuration from lexdbapi.api.lexdbapi import LexDbApi from lexdbapi.models.vectorsearchrequest import VectorSearchRequest from lexdbapi.api_client import ApiClient import os

apihost = os.getenv("DBHOST", "http://0.0.0.0:8000") apiclient = ApiClient(configuration=Configuration(host=apihost)) api = LexDbApi(apiclient=apiclient)

Get available tables

tables = api.get_tables() print("Tables:", tables)

Full-text search

resultsfts = api.getarticles(query="Rundetårn", limit=2) print("Full-text results:", results_fts)

Vector search

reqvector = VectorSearchRequest( querytext="Hvad er Rundetårn?", topk=3, ) resultsvector = api.vectorsearch("openailarge3sections", reqvector) print("Vector search results:", resultsvector)

Fetch full articles from result IDs

if resultsvector.results: articleids = {int(result["sourcearticleid"]) for result in resultsvector.results} fullarticles = api.getarticles(ids=str(list(articleids))) for article in full_articles: print(article) ```

For Users

The LexDB integration enables: - Fast full-text search across all documents - Semantic search using vector embeddings - Access to structured metadata and article content

All database interactions are handled automatically by the application, so no manual setup is required for end users.

🚀 Tip for Contributors: Run make pr before submitting changes to ensure linting, typing, and tests pass.