llm-bot

Async Python library and LLM-backed bot service.

The current implementation exposes stateless chat and grounded HRAG answering, embeddings, sentiment analysis, title generation, summary, named entity recognition, entity relationship extraction, graph query generation, translation, linking, clustering, and cybersecurity classification endpoints backed by OpenAI-compatible APIs.

Requirements

• uv
• Python 3.13

Python library

Install a published release into a Python 3.13 project with uv add taranis-llm-bot. The PyPI distribution is taranis-llm-bot; Python imports use llm_bot. Before publishing, install a locally built wheel with uv add /absolute/path/to/taranis_llm_bot-VERSION-py3-none-any.whl.

Set LLM_BASE_URL, LLM_API_KEY, and optionally LLM_MODEL and LLM_API_MODE in the environment or .env before importing the library. Call the async task functions directly; no bot HTTP server is needed:

import asyncio

from llm_bot.schemas import SummarizeRequest
from llm_bot.tasks.summarize import summarize


async def main():
    result = await summarize(
        SummarizeRequest(text="Text to summarize", language="en", max_words=80)
    )
    print(result.summary)
    # result.model_dump() returns a dictionary.


asyncio.run(main())

In an existing async application, use await summarize(...) directly. Other tasks follow the same pattern: request models in llm_bot.schemas, async functions in llm_bot.tasks (for example, ner.extract_entities and translate.translate_text). An optional client=LLMClient(...) argument overrides the LLM connection for a call; import it from llm_bot.client. Inputs and outputs are Pydantic models, and errors propagate to the caller. Linking also needs the LOOKUP_* configuration; embeddings use EMBEDDING_*.

To embed the HTTP service, import create_app from llm_bot.app and expose app = create_app() in your ASGI entry point.

Build and upload

From a clean X.Y.Z release tag and an empty dist/ directory:

./scripts/check.sh
uv build --no-sources
uvx twine check --strict dist/*
uv publish dist/*

Authenticate with UV_PUBLISH_TOKEN; use --publish-url <upload-endpoint> for another registry. The release workflow releases images and the PyPI package on tag pushes; see PyPI setup.

Setup

./scripts/check.sh
cp .env.example .env

Configure the following values in .env:

• LLM_BASE_URL
• LLM_API_KEY
• LLM_MODEL (optional if your OpenAI-compatible backend provides a default model)
• LLM_API_MODE: responses (default) or chat_completions

Optional:

• API_KEY: protects incoming requests to /chat, /hrag, /embed, /sentiment, /title, /translate, /summarize, /ner, /ner-link, /link, /cluster, /entity-relation-extraction, and /graph-query-generation
• LLM_TIMEOUT
• LLM_REASONING_PROFILE: use none, ministral, or gemma
• LLM_STRIP_REASONING_OUTPUT: strip [THINK]...[/THINK] blocks before parsing model output
• LLM_PARSE_REASONING_AS_OUTPUT: use structured reasoning text as fallback output when a provider emits no final message
• gemma reasoning is enabled by prefixing the system prompt with <|think|> and the service strips Gemma thought-channel output before parsing when output stripping is enabled
• EMBEDDING_BASE_URL: base URL for the OpenAI-compatible embedding service; required by /embed
• EMBEDDING_API_KEY
• EMBEDDING_MODEL
• EMBEDDING_TIMEOUT
• LOOKUP_BASE_URL
• LOOKUP_API_KEY
• LOOKUP_DEFAULT_LANGUAGE
• LOOKUP_CANDIDATE_LIMIT
• NER_LINKING_ENABLED
• NER_LINKING_MODE: use deterministic or llm
• SUMMARY_MAX_INPUT_CHARS

Run

uv run granian --interface asgi app:app --port 5500

API

Canonical paths are documented below. The service also accepts the same routes with a trailing slash.

Interactive Swagger docs are available at GET /docs. The raw OpenAPI 3.1 document is available at GET /openapi.yaml.

Upstream LLM transport:

• LLM_API_MODE=responses sends requests to /responses
• LLM_API_MODE=chat_completions sends requests to /chat/completions
• structured outputs are requested via text.format in responses mode and response_format in chat_completions mode
• LLM-backed request payloads may include an optional reasoning_effort field. The service forwards it upstream as reasoning.effort in responses mode and reasoning_effort in chat_completions mode.
• LLM-backed request payloads may include an optional thinking_budget_tokens field, which the service forwards upstream unchanged as a provider-specific extension. This is intended for servers such as llama.cpp; other OpenAI-compatible servers may reject it.

When using the Python clients directly, omitting api_key (or passing None) uses the configured key. Passing api_key="" explicitly disables the authorization header for that client.

POST /chat

Generates a general chat response. The optional messages array contains prior conversation turns for this request only; the service does not persist conversation state. Prior messages may use the user and assistant roles.

Request body:

{
  "message": "What should I do next?",
  "messages": [
    {
      "role": "user",
      "content": "Help me plan a release."
    },
    {
      "role": "assistant",
      "content": "Start by running the validation suite."
    }
  ]
}

Response body:

{
  "answer": "Review the validation results, then tag the release.",
  "model": "configured-model"
}

model is null when LLM_MODEL is unset and the upstream provider selects its own default.

POST /hrag

Answers a question using only evidence supplied in the request. The endpoint does not retrieve documents, query a graph, create embeddings, or persist data. IDs must be unique across passages and graph_facts; every item also requires a caller-supplied source reference.

Request body:

{
  "question": "Who operates the service?",
  "passages": [
    {
      "id": "passage-1",
      "source": "report.pdf#page=2",
      "text": "Example Corp operates the service."
    }
  ],
  "graph_facts": [
    {
      "id": "fact-1",
      "source": "graph://service/42",
      "fact": "Example Corp -[OPERATES]-> Service 42"
    }
  ]
}

Response body:

{
  "answer": "Example Corp operates the service.",
  "citations": ["passage-1", "fact-1"],
  "insufficient_evidence": false
}

The model is instructed to use no outside knowledge and to report insufficient evidence explicitly. The service validates that every returned citation is one of the evidence IDs supplied in the request.

POST /embed

Creates an embedding for one text using the separately configured OpenAI-compatible embedding service. The service sends the text to its /embeddings path and returns the first embedding vector.

Request body:

{
  "text": "Text to embed"
}

Response body:

{
  "embedding": [0.012, -0.034, 0.056]
}

If API_KEY is configured, send it as:

Authorization: Bearer <API_KEY>

POST /sentiment

Sentiment analysis endpoint.

Request body:

{
  "text": "The launch was a success.",
  "include_emotions": true,
  "thinking_budget_tokens": 256
}

Response body without emotions:

{
  "sentiment": {
    "label": "positive",
    "score": 0.88
  }
}

Response body with emotions:

{
  "sentiment": {
    "label": "negative",
    "score": 0.91,
    "emotions": ["anger", "fear"]
  }
}

When include_emotions is false or omitted, the response must not contain an emotions field. When it is true, emotions must be an array; an empty array is valid, null is not.

If API_KEY is configured, send it as:

Authorization: Bearer <API_KEY>

POST /cybersec-classification

Request body:

{
  "text": "The newest development in malware automation is concerning.",
  "reasoning_effort": "high",
  "thinking_budget_tokens": 256
}

Response body:

{
  "cybersecurity": 0.9999,
  "non-cybersecurity": 0.0001
}

This endpoint is LLM-backed and supports the same optional reasoning_effort and thinking_budget_tokens fields as the other LLM routes.

If API_KEY is configured, send it as:

Authorization: Bearer <API_KEY>

POST /title

Request body:

{
  "text": "Text to title",
  "language": "de",
  "max_chars": 100
}

Response body:

{
  "title": "Concise story title"
}

language is optional. When provided, the title is generated in that language. When omitted and news_items are used, the service uses the majority news_items[*].language value when available. Otherwise it falls back to the input text language. The model is instructed to keep the title within max_chars characters. If omitted, max_chars defaults to 100. The service does not truncate longer model outputs.

If API_KEY is configured, send it as:

Authorization: Bearer <API_KEY>

POST /translate

Request body:

{
  "text": "Guten Morgen",
  "target_language": "en",
  "source_language": "de"
}

Response body:

{
  "translation": "Good morning"
}

source_language is optional. When omitted, the model is instructed to detect the source language from the input. target_language is required.

If API_KEY is configured, send it as:

Authorization: Bearer <API_KEY>

POST /summarize

Request body:

{
  "news_items": [
    {
      "title": "Story title",
      "content": "Text to summarize",
      "language": "en"
    }
  ],
  "language": "de",
  "max_words": 80
}

Response body:

{
  "summary": "Short summary"
}

language is optional. When provided, the summary is generated in that language. When omitted and news_items are used, the service uses the majority news_items[*].language value when available. Otherwise it falls back to the input text language.

If API_KEY is configured, send it as:

Authorization: Bearer <API_KEY>

POST /cluster

Each story requires its original id and a name-keyed tags dictionary (which may be empty). summary is optional and may be null or empty. Clustering uses only summaries and tags; extra fields such as news_items and title are ignored. CLUSTER_MAX_CONTENT_CHARS_PER_STORY limits each summary sent to the model (default: 800 characters). Returned clusters contain the original story IDs.

Request body:

{
  "stories": [
    {
      "id": "s1",
      "tags": {
        "APT29": { "tag_type": "APT" }
      },
      "summary": "APT29 targeted Microsoft users in Vienna."
    },
    {
      "id": "s2",
      "tags": {
        "Microsoft": { "tag_type": "Organization" }
      },
      "summary": "Users in Vienna were targeted in an APT29 campaign."
    }
  ]
}

Response body:

{
  "cluster_ids": {
    "event_clusters": [["s1", "s2"]]
  },
  "message": "Clustering completed"
}

If API_KEY is configured, send it as:

Authorization: Bearer <API_KEY>

POST /ner

Request body:

{
  "text": "APT29 used Mimikatz and PowerShell to dump credentials.",
  "cybersecurity": true
}

Response body:

{
  "APT29": "GROUP",
  "Mimikatz": "TOOL",
  "PowerShell": "PRODUCT"
}

This endpoint performs NER only. It does not run entity linking. If both the initial response and its repair are truncated, the service returns only complete, schema-valid entity/type pairs from the repaired response and discards its incomplete suffix.

If API_KEY is configured, send it as:

Authorization: Bearer <API_KEY>

POST /entity-relation-extraction

Extracts schema-constrained entities and directed relationships using only information explicitly stated in the supplied text. Entity and relation type names are caller-defined. Relation source and target types must reference declared entity types.

Request body:

{
  "text": "APT28 exploited CVE-2025-1234.",
  "schema": {
    "entity_types": [
      {"name": "ThreatActor", "description": "A named threat actor"},
      {"name": "Vulnerability", "description": "A named vulnerability"}
    ],
    "relation_types": [
      {
        "name": "EXPLOITS",
        "source_types": ["ThreatActor"],
        "target_types": ["Vulnerability"]
      }
    ]
  }
}

Response body:

{
  "entities": [
    {
      "id": "e1",
      "type": "ThreatActor",
      "name": "APT28"
    },
    {
      "id": "e2",
      "type": "Vulnerability",
      "name": "CVE-2025-1234"
    }
  ],
  "relations": [
    {
      "type": "EXPLOITS",
      "source_id": "e1",
      "target_id": "e2",
      "confidence": 0.9
    }
  ]
}

If no schema-valid explicit extraction exists, both response lists are empty.

POST /graph-query-generation

Generates one AGE-compatible, read-only Cypher query from a natural-language question and a caller-supplied graph schema. The service returns parameters separately, validates the query against the allowed labels, relationship directions, and queryable properties, and requires a bounded LIMIT. It does not execute Cypher, connect to PostgreSQL, or persist anything.

graph_name is part of the caller contract but is never embedded in the generated Cypher. The caller must bind that fixed name separately when it executes the returned query.

Request body:

{
  "question": "Which organization employs Alice?",
  "graph_name": "knowledge_graph",
  "schema": {
    "node_labels": [
      {
        "label": "Person",
        "properties": [
          {"name": "name", "type": "string"}
        ]
      },
      {
        "label": "Organization",
        "properties": [
          {"name": "name", "type": "string"}
        ]
      }
    ],
    "relationship_types": [
      {
        "type": "WORKS_AT",
        "source_labels": ["Person"],
        "target_labels": ["Organization"],
        "properties": []
      }
    ],
    "default_limit": 25,
    "maximum_limit": 100
  }
}

Response body:

{
  "cypher": "MATCH (p:Person)-[:WORKS_AT]->(o:Organization) WHERE p.name = $person_name RETURN o.name AS result LIMIT 25",
  "parameters": {
    "person_name": "Alice"
  },
  "explanation": "Returns organizations that employ the named person."
}

Only standard unquoted identifiers are accepted in the supplied graph schema. Generated values must use named $parameter placeholders. Mutations, procedures, administration, external data loading, dynamic schema access, comments, multiple statements, and unbounded results are rejected. Every relationship must specify an allowed type, and RETURN must contain one expression aliased as result (which may be a map or a function call). Invalid model output receives the service's standard single repair attempt.

POST /ner-link

Request body:

{
  "text": "Apple announced new Mac hardware during its developer event in Cupertino.",
  "language": "en",
  "linking_mode": "llm",
  "cybersecurity": false
}

Response body:

{
  "entities": [
    {
      "mention": "Apple",
      "type": "ORG",
      "wikidata_qid": "Q312",
      "wikidata_label": "Apple Inc.",
      "wikidata_description": "American technology company",
      "matched_alias": "Apple",
      "match_type": "alias",
      "score": 0.98,
      "candidate_count": 5
    }
  ]
}

This endpoint performs NER first and then links the extracted entities. When NER finds no entities, it returns {"entities": []} without making lookup or disambiguation requests.

Deterministic example:

{
  "text": "Apple released a new device.",
  "language": "en",
  "linking_mode": "deterministic"
}

If API_KEY is configured, send it as:

Authorization: Bearer <API_KEY>

POST /link

Request body:

{
  "text": "Apple announced new Mac hardware during its developer event in Cupertino.",
  "language": "en",
  "linking_mode": "llm",
  "entities": [
    { "mention": "Apple", "type": "ORG" },
    { "mention": "Cupertino", "type": "GPE" },
    { "mention": "Mac", "type": "PRODUCT" }
  ]
}

Response body:

{
  "entities": [
    {
      "mention": "Apple",
      "type": "ORG",
      "wikidata_qid": "Q312",
      "wikidata_label": "Apple Inc.",
      "wikidata_description": "American technology company",
      "matched_alias": "Apple",
      "match_type": "alias",
      "score": 0.98,
      "candidate_count": 5
    }
  ]
}

This endpoint performs linking only. It does not run NER first.

If API_KEY is configured, send it as:

Authorization: Bearer <API_KEY>

GET /health

Returns:

{"status": "ok"}

GET /info

Returns discoverable service information and current non-secret feature configuration, including:

• supported reasoning profiles
• supported linking modes
• canonical endpoint paths
• active non-secret config such as the current reasoning profile and whether lookup/linking is configured

Development checks

./scripts/check.sh

The script installs development dependencies, checks lint and formatting, and runs the full test suite.