diff --git a/.env_example b/.env_example
new file mode 100644
index 0000000..90602d8
--- /dev/null
+++ b/.env_example
@@ -0,0 +1,10 @@
+# Base URL for the Named Entity Recognition (NER) service
+NER_SERVICE_BASE_URL=http://localhost:8010
+
+# Base URL for the Named Entity Normalization (NEN) and Description service (ragu-lm)
+NEN_SERVICE_BASE_URL=http://localhost:8002
+
+# Base URL for the Relation Extraction (RE) service
+RE_SERVICE_BASE_URL=http://localhost:8003
+
+LLM_API_KEY = ""
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000..b2b4710
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,31 @@
+version: '3.8'
+
+services:
+  ner_service:
+    image: mrpzzios/runne_contrastive_ner_tf:fixed
+    ports:
+      - "8010:8010"
+#    runtime: nvidia
+#    environment:
+#      - NVIDIA_VISIBLE_DEVICES=all
+    command: -c "python3 server.py"
+
+  re_service:
+    image: mrpzzios/bertre:1.3
+    ports:
+      - "8003:8000"
+    runtime: nvidia
+    environment:
+      - NVIDIA_VISIBLE_DEVICES=all
+
+  custom_service:
+    build:
+      context: ./services
+    ports:
+      - "8002:8000"
+    runtime: nvidia
+    environment:
+      - NVIDIA_VISIBLE_DEVICES=all
+    env_file:
+      - .env
+    shm_size: '32g'
diff --git a/docs/en/pipeline_guide.md b/docs/en/pipeline_guide.md
new file mode 100644
index 0000000..49bbb16
--- /dev/null
+++ b/docs/en/pipeline_guide.md
@@ -0,0 +1,152 @@
+# Pipeline-based Triplet Extraction Guide
+
+This guide explains how to use the OOP-based pipeline for triplet extraction.
+
+## Overview
+
+The pipeline consists of several main steps, orchestrated by the `ragu.triplet.pipeline.Pipeline` class:
+
+1.  **Named Entity Recognition (NER):** Identifies entities in the text.
+2.  **Named Entity Normalization (NEN):** Normalizes the extracted entities.
+3.  **Entity Description:** Generates a description for each entity based on its context.
+4.  **Relation Extraction (RE):** Extracts relations between the normalized entities.
+5.  **Relation Description:** Generates descriptions for the extracted relations, creating the final triplets.
+
+Each step is implemented as a `PipelineStep` that communicates with a dedicated microservice.
+
+## Docker Compose and Service Configuration
+
+To run the full pipeline, you need to use the `docker-compose.yml` file located in the project root. This file defines the microservices required for the different pipeline stages.
+
+### Discrepancy with the Project's `docker-compose.yml`
+
+The configuration below is a generic template to illustrate the architecture. The actual `docker-compose.yml` in this repository is slightly different and more optimized:
+
+1.  **Consolidated Services:** The `nen_service` and `description_service` are consolidated into a single `custom_service`. This service runs the `RaguTeam/RAGU-lm` model, which is capable of handling both Named Entity Normalization (NEN) and description generation for both entities and relations.
+2.  **Specific Ports:** The `ner_service` in the project's actual `docker-compose.yml` uses port `8010`, not `8001`.
+
+This consolidation is a practical optimization that reduces the number of required services. The `custom_service` is built from the local `./services` directory.
+
+### Generic `docker-compose.yml` Structure
+
+```yaml
+version: '3.8'
+
+services:
+  ner_service:
+    image: your_ner_image:latest
+    ports:
+      - "8001:8000"
+
+  nen_service:
+    image: your_nen_image:latest
+    ports:
+      - "8002:8000"
+
+  re_service:
+    image: your_re_image:latest
+    ports:
+      - "8003:8000"
+
+  description_service:
+    image: your_description_image:latest
+    ports:
+      - "8004:8000"
+```
+
+### Environment Configuration (`.env`)
+
+You need to create a `.env` file with the base URLs for each running service. Based on the project's actual `docker-compose.yml`, the file should look like this:
+
+```
+NER_SERVICE_BASE_URL=http://localhost:8010
+NEN_SERVICE_BASE_URL=http://localhost:8002
+RE_SERVICE_BASE_URL=http://localhost:8003
+DESCRIPTION_SERVICE_BASE_URL=http://localhost:8002
+```
+*Note that `NEN_SERVICE_BASE_URL` and `DESCRIPTION_SERVICE_BASE_URL` point to the same `custom_service`.*
+
+## Models Used
+
+The pipeline relies on a combination of models served via Docker containers.
+
+### RaguTeam Hugging Face Models
+
+*   **`RaguTeam/RAGU-lm`**: This is a fine-tuned model specifically for Russian language tasks. It is served by the `custom_service` and performs several key steps in the pipeline:
+    *   Named Entity Normalization (NEN)
+    *   Entity Description Generation
+    *   Relation Description Generation
+
+### Docker Hub Images
+
+The following images are pulled from Docker Hub and are used for specialized NLP tasks:
+
+*   **`mrpzzios/runne_contrastive_ner_tf:fixed`**: This image is used for the **Named Entity Recognition (NER)** step. It appears to be a private or custom-built image and is not publicly documented.
+*   **`mrpzzios/bertre:1.3`**: This image is used for the **Relation Extraction (RE)** step. Similar to the NER image, it seems to be a private or custom-built model.
+
+## Example Usage
+
+The `examples/pipeline/` directory contains scripts that demonstrate how to use the pipeline.
+
+*   **[examples/pipeline/test_pipeline.py](examples/pipeline/test_pipeline.py)**: A lightweight script that shows how to initialize all the clients and run the full pipeline on a single text chunk. This is useful for quick verification of the services.
+
+*   **[examples/pipeline/build_kg_with_pipeline.py](examples/pipeline/build_kg_with_pipeline.py)**: A more comprehensive example that demonstrates the end-to-end process of building a complete Knowledge Graph from a collection of documents. It integrates the extraction pipeline with the chunker, embedder, and graph builder components.
+
+### Basic Python Implementation
+
+```python
+import asyncio
+import os
+from dotenv import load_dotenv
+from ragu.triplet.pipeline import (
+    Pipeline,
+    NERClient,
+    NENClient,
+    REClient,
+    DescriptionClient,
+    NERStep,
+    NENStep,
+    REStep,
+    EntityDescriptionStep,
+    RelationDescriptionStep,
+)
+from ragu.chunker.types import Chunk
+
+load_dotenv()
+
+async def main():
+    # Create clients for each service
+    ner_client = NERClient(os.getenv("NER_SERVICE_BASE_URL"))
+    nen_client = NENClient(os.getenv("NEN_SERVICE_BASE_URL"))
+    re_client = REClient(os.getenv("RE_SERVICE_BASE_URL"))
+    description_client = DescriptionClient(os.getenv("DESCRIPTION_SERVICE_BASE_URL"))
+
+    # Create the pipeline steps
+    steps = [
+        NERStep(ner_client),
+        NENStep(nen_client),
+        EntityDescriptionStep(description_client),
+        REStep(re_client),
+        RelationDescriptionStep(description_client),
+    ]
+
+    # Create the pipeline
+    pipeline = Pipeline(steps)
+
+    # Run the pipeline on a sample chunk
+    chunk = Chunk(
+        content="Главным борцом с пробками назначен заместитель министра транспорта России Николай Лямов.",
+        chunk_order_idx=0,
+        doc_id="test_doc"
+    )
+    entities, relations = await pipeline.extract([chunk])
+
+    print("--- Entities ---")
+    print(entities)
+    print("\n--- Relations ---")
+    print(relations)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
\ No newline at end of file
diff --git a/docs/en/pipeline_io_format.md b/docs/en/pipeline_io_format.md
new file mode 100644
index 0000000..3478029
--- /dev/null
+++ b/docs/en/pipeline_io_format.md
@@ -0,0 +1,161 @@
+# NER and RE I/O Formats
+
+This document describes the standard input and output formats for the Named Entity Recognition (NER) and Relation Extraction (RE) models used in the RAGU project.
+
+## NER (Named Entity Recognition)
+
+### NER Input (`NER_IN`)
+
+The input for the NER model is a single JSON string containing the text to be processed.
+
+**Example:**
+```json
+"Главным борцом с пробками назначен заместитель министра транспорта России Николай Лямов."
+```
+
+### NER Output (`NER_OUT`)
+
+The output of the NER model is a JSON object containing the original text and the extracted entities.
+
+- `text`: The original input string.
+- `ners`: A list of extracted entities. Each entity is represented as a list with three elements:
+    1.  `start_char_index` (integer): The starting character offset of the entity in the text.
+    2.  `end_char_index` (integer): The ending character offset of the entity in the text.
+    3.  `entity_type` (string): The type of the entity (e.g., "COUNTRY", "PERSON", "PROFESSION").
+
+**Example:**
+```json
+{
+  "ners": [
+    [67, 73, "COUNTRY"],
+    [74, 87, "PERSON"],
+    [35, 73, "PROFESSION"]
+  ],
+  "text": "Главным борцом с пробками назначен заместитель министра транспорта России Николай Лямов."
+}
+```
+
+## RE (Relation Extraction)
+
+### RE Input (`RE_IN`)
+
+The input for the RE model is a JSON object containing text chunks and their corresponding entities.
+
+- `chunks`: A list of text strings (e.g., sentences or paragraphs).
+- `entities_list`: A list where each element is a list of entities found in the corresponding chunk in the `chunks` list. The format for each entity is the same as in the `NER_OUT`.
+
+**Example:**
+```json
+{
+  "chunks": [
+    "Главным борцом с пробками назначен заместитель министра транспорта России Николай Лямов.",
+    "Президент Башкирии Муртаза Рахимов решил поменять главу своей администрации. Он уволил Азамата Сагитова."
+  ],
+  "entities_list": [
+    [
+      [67, 73, "COUNTRY"],
+      [74, 87, "PERSON"],
+      [35, 73, "PROFESSION"]
+    ],
+    [
+      [19, 34, "PERSON"],
+      [0, 18, "PROFESSION"],
+      [50, 75, "PROFESSION"],
+      [10, 18, "STATE_OR_PROVINCE"],
+      [80, 86, "EVENT"],
+      [87, 103, "PERSON"]
+    ]
+  ]
+}
+```
+
+### RE Output (`RE_OUT`)
+
+The output of the RE model is a JSON list of extracted relationships. Each object in the list represents a single relationship and contains the following fields:
+
+- `source_entity` (string): The text of the source entity in the relationship.
+- `target_entity` (string): The text of the target entity in the relationship.
+- `relationship_type` (string): The type of the relationship (e.g., "FOUNDED_BY", "WORKPLACE").
+- `relationship_description` (string or null): A natural language description of the relationship.
+- `relationship_strength` (float): A confidence score for the extracted relationship, typically between 0.0 and 1.0.
+- `chunk_id` (integer): The index of the chunk from the `RE_IN` `chunks` list where this relationship was found.
+
+**Example:**
+```json
+[
+  {
+    "source_entity": "России",
+    "target_entity": "Николай Лямов",
+    "relationship_type": "FOUNDED_BY",
+    "relationship_description": null,
+    "relationship_strength": 0.04831777885556221,
+    "chunk_id": 0
+  },
+  {
+    "source_entity": "Николай Лямов",
+    "target_entity": "России",
+    "relationship_type": "WORKPLACE",
+    "relationship_description": null,
+    "relationship_strength": 0.999497652053833,
+    "chunk_id": 0
+  }
+]
+```
+
+## RAGU-lm I/O Formats
+
+The `RAGU-lm` model uses a prompt-based format for its tasks.
+
+### Named Entity Normalization (NEN)
+
+**Input:**
+The input is a formatted string (prompt) that includes the unnormalized entity and the source text.
+
+- **Prompt Template:**
+  '''
+  Выполните нормализацию именованной сущности, встретившейся в тексте.
+
+  Исходная (ненормализованная) именованная сущность: {source_entity}
+
+  Текст: {source_text}
+
+  Нормализованная именованная сущность:
+  '''
+- **Parameters:**
+  - `{source_entity}`: The unnormalized entity to be normalized.
+  - `{source_text}`: The original text containing the entity.
+
+**Output:**
+The output is a string containing the normalized entity.
+
+- **Example Output:**
+  '''
+  пресс-секретарь
+  '''
+
+### Description Generation (DG)
+
+**Input:**
+The input is a formatted string (prompt) that includes the normalized entity and the source text.
+
+- **Prompt Template:**
+  '''
+  Напишите, что означает именованная сущность в тексте, то есть раскройте её смысл относительно текста.
+
+  Именованная сущность: {normalized_entity}
+
+  Текст: {source_text}
+
+  Смысл именованной сущности:
+  '''
+- **Parameters:**
+  - `{normalized_entity}`: The normalized entity for which to generate a description.
+  - `{source_text}`: The original text containing the entity.
+
+**Output:**
+The output is a string containing the generated description for the entity.
+
+- **Example Output:**
+  '''
+  Бывший представитель СМИ экс-президента США Билла Клинтона.
+  '''
\ No newline at end of file
diff --git a/examples/data/ru/4.txt b/examples/data/ru/4.txt.bad
similarity index 100%
rename from examples/data/ru/4.txt
rename to examples/data/ru/4.txt.bad
diff --git a/examples/pipeline/build_kg_with_pipeline.py b/examples/pipeline/build_kg_with_pipeline.py
new file mode 100644
index 0000000..f502e22
--- /dev/null
+++ b/examples/pipeline/build_kg_with_pipeline.py
@@ -0,0 +1,125 @@
+import asyncio
+import os
+from dotenv import load_dotenv
+
+from ragu.chunker import SimpleChunker
+from ragu.embedder import STEmbedder
+from ragu.graph import KnowledgeGraph, InMemoryGraphBuilder
+from ragu.llm.local_client import LocalTransformerClient
+from ragu.storage import Index
+from ragu.triplet.pipeline import (
+    Pipeline,
+    NERClient,
+    NENClient,
+    REClient,
+    DescriptionClient,
+    NERStep,
+    NENStep,
+    REStep,
+    EntityDescriptionStep,
+    RelationDescriptionStep,
+)
+from ragu.search_engine import LocalSearchEngine
+from ragu.utils.ragu_utils import read_text_from_files
+
+load_dotenv()
+
+# Configuration for the local model
+LLM_MODEL_NAME = "Qwen/Qwen2.5-7B-Instruct"
+
+# Configuration for other services
+NER_SERVICE_BASE_URL = os.getenv("NER_SERVICE_BASE_URL", "http://localhost:8010")
+RE_SERVICE_BASE_URL = os.getenv("RE_SERVICE_BASE_URL", "http://localhost:8003")
+NEN_SERVICE_BASE_URL = os.getenv("NEN_SERVICE_BASE_URL", "http://localhost:8002")
+
+async def main():
+    print("Starting RAGU engine test...")
+
+    # 1. Load Data
+    print("Loading data from examples/data/ru/...")
+    docs = read_text_from_files("examples/data/ru/", file_extensions=[".txt"])
+    if not docs:
+        print("No documents found. Please ensure 'examples/data/ru/' contains .txt files.")
+        return
+
+    # 2. Initialize Local LLM Client
+    print(f"Initializing LocalTransformerClient with model: {LLM_MODEL_NAME}...")
+    # This will download the model from Hugging Face hub if not cached
+    llm_client = LocalTransformerClient(model_name=LLM_MODEL_NAME, max_memory={0: '12GB'})
+
+    # 3. Initialize Triplet Extraction Pipeline Clients and Steps
+    print("Initializing Triplet Extraction Pipeline...")
+    ner_client = NERClient(NER_SERVICE_BASE_URL)
+    nen_client = NENClient(NEN_SERVICE_BASE_URL) # Uses vLLM
+    re_client = REClient(RE_SERVICE_BASE_URL)
+    description_client = DescriptionClient(NEN_SERVICE_BASE_URL) # Uses vLLM
+
+    pipeline_steps = [
+        NERStep(ner_client),
+        NENStep(nen_client),
+        EntityDescriptionStep(description_client),
+        REStep(re_client),
+        RelationDescriptionStep(description_client),
+    ]
+    triplet_extraction_pipeline = Pipeline(pipeline_steps)
+
+    # 4. Initialize Chunker and Embedder
+    print("Initializing Chunker and Embedder...")
+    chunker = SimpleChunker(max_chunk_size=2048, overlap=0)
+    embedder = STEmbedder(
+        "Alibaba-NLP/gte-multilingual-base",
+        trust_remote_code=True
+    )
+
+    # 5. Set up Graph Builder and Index
+    print("Setting up Graph Builder and Index...")
+    graph_builder = InMemoryGraphBuilder(
+        client=llm_client, # This LLM client is for summarization within graph building
+        chunker=chunker,
+        artifact_extractor=triplet_extraction_pipeline,
+        language="russian",
+    )
+    index = Index(
+        embedder,
+        graph_storage_kwargs={"clustering_params": {"max_cluster_size": 6}}
+    )
+
+    # 6. Build Knowledge Graph
+    print("Building Knowledge Graph from documents (this may take a while)...")
+    knowledge_graph = await KnowledgeGraph(
+        extraction_pipeline=graph_builder,
+        index=index,
+        make_community_summary=True,
+        language="russian",
+    ).build_from_docs(docs)
+    print("Knowledge Graph built successfully.")
+
+    # 7. Initialize Search Engine
+    print("Initializing LocalSearchEngine...")
+    search_engine = LocalSearchEngine(
+        client=llm_client, # This LLM client is for answering queries
+        knowledge_graph=knowledge_graph,
+        embedder=embedder
+    )
+    print("LocalSearchEngine initialized.")
+
+    # 8. Define Questions and Query the Engine
+    print("\nQuerying the RAGU engine with questions...")
+    questions = [
+        "Кто такой Бьёрнстьерне Бьёрнсон?",
+        "Что такое Ла Фениче?",
+        "Какой роман написал Генрик Сенкевич?",
+        "Где обитают ревуны?",
+        "Как переводится 'Камо грядеши'?",
+        "Кто написал гимн Норвегии?",
+    ]
+
+    for i, question in enumerate(questions):
+        print(f"\n--- Question {i+1}: {question} ---")
+        answer = await search_engine.a_query(question)
+        print(f"Answer: {answer}")
+
+    print("\nRAGU engine test complete.")
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/examples/pipeline/test_pipeline.py b/examples/pipeline/test_pipeline.py
new file mode 100644
index 0000000..86cccd9
--- /dev/null
+++ b/examples/pipeline/test_pipeline.py
@@ -0,0 +1,78 @@
+import asyncio
+import os
+from dotenv import load_dotenv
+
+from ragu.llm.openai_client import OpenAIClient
+from ragu.triplet.pipeline import (
+    Pipeline,
+    NERClient,
+    NENClient,
+    REClient,
+    DescriptionClient,
+    NERStep,
+    NENStep,
+    REStep,
+    EntityDescriptionStep,
+    RelationDescriptionStep,
+)
+from ragu.chunker.types import Chunk
+
+load_dotenv()
+
+LLM_MODEL_NAME = "ragu-lm"
+LLM_BASE_URL = os.getenv("NEN_SERVICE_BASE_URL", "http://localhost:8002")
+LLM_API_KEY = "EMPTY"
+
+NER_SERVICE_BASE_URL = os.getenv("NER_SERVICE_BASE_URL", "http://localhost:8010")
+RE_SERVICE_BASE_URL = os.getenv("RE_SERVICE_BASE_URL", "http://localhost:8003")
+
+async def main():
+    print("Starting pipeline test...")
+
+    # Create OpenAI client for vLLM
+    llm_client = OpenAIClient(
+        model_name=LLM_MODEL_NAME,
+        base_url=LLM_BASE_URL,
+        api_token=LLM_API_KEY,
+        max_requests_per_second=20,
+        max_requests_per_minute=400,
+    )
+
+    # Create clients for each service
+    ner_client = NERClient(NER_SERVICE_BASE_URL)
+    nen_client = NENClient(LLM_BASE_URL)
+    re_client = REClient(RE_SERVICE_BASE_URL)
+    description_client = DescriptionClient(LLM_BASE_URL)
+
+    # Create the pipeline steps in the correct order
+    steps = [
+        NERStep(ner_client),
+        NENStep(nen_client),
+        EntityDescriptionStep(description_client),
+        REStep(re_client),
+        RelationDescriptionStep(description_client),
+    ]
+
+    # Create the pipeline
+    pipeline = Pipeline(steps)
+
+    # Run the pipeline on a sample chunk
+    print("Running pipeline on a sample chunk...")
+    chunk = Chunk(
+        content="Главным борцом с пробками назначен заместитель министра транспорта России Николай Лямов.",
+        chunk_order_idx=0,
+        doc_id="test_doc"
+    )
+    entities, relations = await pipeline.extract([chunk])
+
+    print("\n--- Extraction Complete ---")
+    print(f"Extracted {len(entities)} entities:")
+    for entity in entities:
+        print(entity)
+    
+    print(f"\nExtracted {len(relations)} relations:")
+    for relation in relations:
+        print(relation)
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/pyproject.toml b/pyproject.toml
index 9548135..d753e42 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,6 +25,7 @@ dependencies = [
     "tenacity>=9.1.2",
     "tiktoken>=0.11.0",
     "tqdm>=4.67.1",
+    "python-dotenv>=1.0.0",
 ]
 
 [project.optional-dependencies]
diff --git a/ragu/common/prompts/pipeline_templates.py b/ragu/common/prompts/pipeline_templates.py
new file mode 100644
index 0000000..79935dc
--- /dev/null
+++ b/ragu/common/prompts/pipeline_templates.py
@@ -0,0 +1,21 @@
+DEFAULT_SYSTEM_PROMPT = 'Вы - эксперт в области анализа текстов и извлечения семантической информации из них.'
+
+DEFAULT_ENTITY_NORMALIZATION_PROMPT = """
+Выполните нормализацию именованной сущности, встретившейся в тексте.
+
+Исходная (ненормализованная) именованная сущность: {source_entity}
+
+Текст: {source_text}
+
+Нормализованная именованная сущность: 
+"""
+
+DEFAULT_ENTITY_DESCRIPTION_GENERATION_PROMPT = """
+Напишите, что означает именованная сущность в тексте, то есть раскройте её смысл относительно текста.
+
+Именованная сущность: {normalized_entity}
+
+Текст: {source_text}
+
+Смысл именованной сущности: 
+"""
diff --git a/ragu/graph/types.py b/ragu/graph/types.py
index 8bee125..d9b4cf2 100644
--- a/ragu/graph/types.py
+++ b/ragu/graph/types.py
@@ -96,6 +96,7 @@ class Relation:
     subject_name: str
     object_name: str
     description: str
+    relationship_type: str = ""
     relation_strength: int | float = 1.0
     source_chunk_id: list[str] = field(default_factory=list)
     id: str | None = None
@@ -160,4 +161,4 @@ class CommunitySummary:
     :param summary: Generated textual summary of the community content.
     """
     id: str
-    summary: str
\ No newline at end of file
+    summary: str
diff --git a/ragu/llm/local_client.py b/ragu/llm/local_client.py
new file mode 100644
index 0000000..b98433b
--- /dev/null
+++ b/ragu/llm/local_client.py
@@ -0,0 +1,122 @@
+from types import SimpleNamespace
+from typing import Any, List, Optional, Union
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from pydantic import BaseModel
+from tqdm import tqdm
+
+from ragu.common.logger import logger
+from ragu.llm.base_llm import BaseLLM
+
+class LocalTransformerClient(BaseLLM):
+    """
+    Client for running a local Hugging Face transformer model.
+    """
+
+    def __init__(
+        self,
+        model_name: str,
+        **model_kwargs: Any,
+    ):
+        """
+        Initialize a new LocalTransformerClient.
+
+        :param model_name: Name of the Hugging Face model to use.
+        :param model_kwargs: Additional keyword arguments passed to AutoModelForCausalLM.from_pretrained.
+        """
+        super().__init__()
+
+        self.model_name = model_name
+
+        try:
+            logger.info(f"Loading local transformer model: {model_name}...")
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+            self.model = AutoModelForCausalLM.from_pretrained(
+                model_name,
+                torch_dtype="auto",
+                device_map="auto",
+                trust_remote_code=True,
+                **model_kwargs
+            )
+            logger.info("Local model loaded successfully.")
+        except Exception as e:
+            logger.error(f"Failed to load local transformer model {model_name}: {e}")
+            raise
+
+    async def _one_call(
+        self,
+        prompt: str,
+        system_prompt: Optional[str] = None,
+        **kwargs: Any,
+    ) -> Optional[str]:
+        """
+        Perform a single generation request.
+        """
+        full_prompt = f"{system_prompt}\n\n{prompt}" if system_prompt else prompt
+
+        try:
+            self.statistics["requests"] += 1
+            
+            inputs = self.tokenizer(full_prompt, return_tensors="pt").to(self.model.device)
+            
+            # Remove arguments that are not part of the transformers.generate method
+            kwargs.pop("schema", None)
+            kwargs.pop("response_model", None)
+
+            gen_kwargs = {
+                "max_new_tokens": 512,
+                "do_sample": True,
+                "top_p": 0.95,
+                "temperature": 0.1,
+                **kwargs
+            }
+
+            output_ids = self.model.generate(**inputs, **gen_kwargs)
+            
+            response = self.tokenizer.decode(output_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip()
+
+            self.statistics["success"] += 1
+            return response
+
+        except Exception as e:
+            logger.error(f"[LocalTransformerClient] request failed: {e}", exc_info=True)
+            self.statistics["fail"] += 1
+            return None
+
+    async def generate(
+        self,
+        prompt: str | list[str],
+        *,
+        system_prompt: Optional[str] = None,
+        pydantic_model: type[BaseModel] = None,
+        model_name: Optional[str] = None,
+        progress_bar_desc: Optional[str] = "Processing with local model",
+        **kwargs: Any,
+    ) -> List[Optional[Union[BaseModel, str]]]:
+        """
+        Generate one or multiple completions asynchronously.
+        """
+        schema = kwargs.get("schema") or pydantic_model
+
+        prompts: List[str] = [prompt] if isinstance(prompt, str) else list(prompt)
+        
+        results = []
+        # A single local transformer model runs sequentially. A progress bar is used to show progress.
+        for p in tqdm(prompts, desc=progress_bar_desc):
+            res_str = await self._one_call(p, system_prompt=system_prompt, **kwargs)
+
+            if res_str is None:
+                results.append(None)
+                continue
+
+            if schema:
+                # HACK: Instead of parsing, we create a mock object that satisfies
+                # the attribute access in the calling code (e.g., .description).
+                # This is brittle but respects the constraint of not changing the graph module.
+                mock_obj = SimpleNamespace(description=res_str)
+                results.append(mock_obj)
+            else:
+                results.append(res_str)
+
+        return results
diff --git a/ragu/triplet/__init__.py b/ragu/triplet/__init__.py
index d86df29..6c4811c 100644
--- a/ragu/triplet/__init__.py
+++ b/ragu/triplet/__init__.py
@@ -1,2 +1,14 @@
 from ragu.triplet.llm_artifact_extractor import ArtifactsExtractorLLM
+from ragu.triplet.pipeline import (
+    Pipeline,
+    NERClient,
+    NENClient,
+    REClient,
+    DescriptionClient,
+    NERStep,
+    NENStep,
+    REStep,
+    EntityDescriptionStep,
+    RelationDescriptionStep,
+)
 from ragu.triplet.ragu_lm_artifact_extractor import RaguLmArtifactExtractor
diff --git a/ragu/triplet/pipeline/__init__.py b/ragu/triplet/pipeline/__init__.py
new file mode 100644
index 0000000..e469b13
--- /dev/null
+++ b/ragu/triplet/pipeline/__init__.py
@@ -0,0 +1,4 @@
+from .base import BasePipeline, PipelineStep
+from .clients import (BaseClient, NERClient, NENClient, REClient, DescriptionClient)
+from .pipeline import Pipeline
+from .steps import (NERStep, NENStep, REStep, EntityDescriptionStep, RelationDescriptionStep)
diff --git a/ragu/triplet/pipeline/base.py b/ragu/triplet/pipeline/base.py
new file mode 100644
index 0000000..dd3934d
--- /dev/null
+++ b/ragu/triplet/pipeline/base.py
@@ -0,0 +1,37 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict
+
+
+class PipelineStep(ABC):
+    """
+    Abstract base class for a step in the triplet extraction pipeline.
+    """
+
+    @abstractmethod
+    async def run(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Runs the pipeline step.
+
+        :param context: The context from the previous step.
+        :return: The context for the next step.
+        """
+        pass
+
+
+class BasePipeline(ABC):
+    """
+    Abstract base class for a triplet extraction pipeline.
+    """
+
+    def __init__(self, steps: list[PipelineStep]):
+        self.steps = steps
+
+    @abstractmethod
+    async def run(self, initial_context: Dict[str, Any]) -> Any:
+        """
+        Runs the entire pipeline.
+
+        :param initial_context: The initial context for the pipeline.
+        :return: The final result of the pipeline.
+        """
+        pass
diff --git a/ragu/triplet/pipeline/clients.py b/ragu/triplet/pipeline/clients.py
new file mode 100644
index 0000000..3d975af
--- /dev/null
+++ b/ragu/triplet/pipeline/clients.py
@@ -0,0 +1,72 @@
+import httpx
+from typing import Any, Dict, List
+
+from ragu.triplet.pipeline.io_models import RE_IN, RE_OUT, NER_OUT, NER_IN
+
+
+class BaseClient:
+    """
+    Base class for a client that communicates with an external service.
+    """
+
+    def __init__(self, base_url: str):
+        self.base_url = base_url
+
+    async def post(self, endpoint: str, data: Any) -> Any:
+        async with httpx.AsyncClient(timeout=1200) as client:
+            response = await client.post(f"{self.base_url}{endpoint}", json=data)
+            try:
+                response.raise_for_status()
+            except httpx.HTTPStatusError as e:
+                print(f"HTTP error occurred: {e}")
+                print(f"Response body: {e.response.text}")
+                raise
+            return response.json()
+
+
+class NERClient(BaseClient):
+    """
+    Client for the Named Entity Recognition service.
+    """
+
+    async def extract_entities(self, text: NER_IN) -> NER_OUT:
+        """
+        Input: "..." (a string)
+        Output: {'ners': [[start, end, 'TYPE'], ...], 'text': '...'}
+        """
+        response = await self.post("/recognize", data=text.dict())
+        return NER_OUT.parse_obj(response)
+
+
+class NENClient(BaseClient):
+    """
+    Client for the Named Entity Normalization service.
+    """
+
+    async def normalize_entities(self, entities: List[Dict[str, Any]], source_text: str) -> List[Dict[str, Any]]:
+        response = await self.post("/nen", data={"entities": entities, "source_text": source_text})
+        return response.get("normalized_entities", [])
+
+
+class REClient(BaseClient):
+    """
+    Client for the Relation Extraction service.
+    """
+
+    async def extract_relations(self, data: RE_IN) -> RE_OUT:
+        relations = await self.post("/predict", data=data.dict())
+        return RE_OUT.parse_obj(relations)
+
+
+class DescriptionClient(BaseClient):
+    """
+    Client for the Description Generation service.
+    """
+
+    async def generate_entity_descriptions(self, entities: List[Dict[str, Any]], source_text: str) -> List[Dict[str, Any]]:
+        response = await self.post("/describe", data={"entities": entities, "source_text": source_text})
+        return response.get("described_entities", [])
+
+    async def generate_relation_description(self, relations: List[Dict[str, Any]], source_text: str) -> List[Dict[str, Any]]:
+        response = await self.post("/describe_relation", data={"relations": relations, "source_text": source_text})
+        return response.get("triplets", [])
diff --git a/ragu/triplet/pipeline/io_models.py b/ragu/triplet/pipeline/io_models.py
new file mode 100644
index 0000000..36a8bcd
--- /dev/null
+++ b/ragu/triplet/pipeline/io_models.py
@@ -0,0 +1,60 @@
+"""
+This module contains Pydantic models that define the I/O formats for the 
+NER and RE services, as described in the IO_FORMAT.md document.
+"""
+from typing import List, Optional, Tuple
+
+from pydantic import BaseModel, Field, RootModel
+
+
+class NEREntity(RootModel[Tuple[int, int, str]]):
+    """
+    Represents a single entity extracted by the NER model.
+    It's a list with three elements: [start_char_index, end_char_index, entity_type].
+    """
+    pass
+
+
+class NER_IN(RootModel[str]):
+    """
+    Input for the NER model. A single string.
+    """
+    pass
+
+
+class NER_OUT(BaseModel):
+    """
+    Output of the NER model.
+    """
+    ners: List[NEREntity] = Field(..., description="A list of extracted entities.")
+    text: str = Field(..., description="The original input string.")
+
+
+class RE_IN(BaseModel):
+    """
+    Input for the RE model.
+    """
+    chunks: List[str] = Field(..., description="A list of text strings (e.g., sentences or paragraphs).")
+    entities_list: List[List[NEREntity]] = Field(
+        ...,
+        description="A list where each element is a list of entities found in the corresponding chunk."
+    )
+
+
+class RE_OUT_Item(BaseModel):
+    """
+    An item in the output of the RE model.
+    """
+    source_entity: str = Field(..., description="The text of the source entity in the relationship.")
+    target_entity: str = Field(..., description="The text of the target entity in the relationship.")
+    relationship_type: str = Field(..., description="The type of the relationship.")
+    relationship_description: Optional[str] = Field(None, description="A natural language description of the relationship.")
+    relationship_strength: float = Field(..., description="A confidence score for the extracted relationship.")
+    chunk_id: int = Field(..., description="The index of the chunk from the RE_IN chunks list where this relationship was found.")
+
+
+class RE_OUT(RootModel[List[RE_OUT_Item]]):
+    """
+    Output of the RE model. A list of extracted relationships.
+    """
+    pass
diff --git a/ragu/triplet/pipeline/models.py b/ragu/triplet/pipeline/models.py
new file mode 100644
index 0000000..c018bcc
--- /dev/null
+++ b/ragu/triplet/pipeline/models.py
@@ -0,0 +1,11 @@
+from typing import Optional
+from pydantic import BaseModel, Field
+
+
+class NormalizedEntity(BaseModel):
+    name: str = Field(..., description="The name of the entity.")
+    type: str = Field(..., description="The type of the entity.")
+    start: int = Field(..., description="The starting index of the entity in the text.")
+    end: int = Field(..., description="The ending index of the entity in the text.")
+    normalized_name: str = Field(..., description="The normalized name of the entity.")
+    ontology_id: Optional[str] = Field(None, description="The ID of the entity in the ontology.")
\ No newline at end of file
diff --git a/ragu/triplet/pipeline/pipeline.py b/ragu/triplet/pipeline/pipeline.py
new file mode 100644
index 0000000..58c6041
--- /dev/null
+++ b/ragu/triplet/pipeline/pipeline.py
@@ -0,0 +1,223 @@
+from typing import Any, Dict, List, Tuple, Iterable, Optional, Union
+import re
+import asyncio
+from tqdm import tqdm
+from collections import defaultdict
+
+from ragu.triplet.base_artifact_extractor import BaseArtifactExtractor
+from ragu.chunker.types import Chunk
+from ragu.graph.types import Entity, Relation
+from ragu.triplet.pipeline.base import PipelineStep
+from ragu.common.logger import logger
+
+
+class Pipeline(BaseArtifactExtractor):
+    """
+    A pipeline for triplet extraction from text.
+    """
+
+    def __init__(self, steps: list[PipelineStep]):
+        super().__init__(prompts=[])
+        self.steps = steps
+
+    async def extract(
+        self,
+        chunks: Iterable[Chunk],
+        *args,
+        **kwargs
+    ) -> Tuple[List[Entity], List[Relation]]:
+        """
+        Runs the entire pipeline in a structured, multi-pass process.
+        """
+        from ragu.triplet.pipeline.steps import (
+            NERStep, NENStep, REStep, EntityDescriptionStep, RelationDescriptionStep
+        )
+
+        logger.info("Starting triplet extraction pipeline.")
+
+        # 1. Get step instances from the pipeline
+        ner_step = next((s for s in self.steps if isinstance(s, NERStep)), None)
+        nen_step = next((s for s in self.steps if isinstance(s, NENStep)), None)
+        entity_desc_step = next((s for s in self.steps if isinstance(s, EntityDescriptionStep)), None)
+        re_step = next((s for s in self.steps if isinstance(s, REStep)), None)
+        relation_desc_step = next((s for s in self.steps if isinstance(s, RelationDescriptionStep)), None)
+
+        if not all([ner_step, nen_step, entity_desc_step, re_step, relation_desc_step]):
+            raise ValueError("Missing one or more required pipeline steps.")
+
+        # 2. Pass 1: Process entities for each chunk in parallel
+        chunk_list = list(chunks)
+        chunk_map = {i: chunk for i, chunk in enumerate(chunk_list)}
+        all_entities_by_chunk_id: Dict[str, List[Entity]] = defaultdict(list)
+        all_normalized_entities_for_re: List[List[Dict[str, Any]]] = [[] for _ in chunk_list]
+
+        async def _process_chunk_entities(chunk: Chunk, index: int):
+            context = {"text": chunk.content}
+            context = await ner_step.run(context)
+            context = await nen_step.run(context)
+            context = await entity_desc_step.run(context)
+
+            described_entities = context.get("described_entities", [])
+            chunk_entities = []
+            for entity_data in described_entities:
+                entity = Entity(
+                    entity_name=entity_data["name"],
+                    entity_type=entity_data["type"],
+                    description=entity_data.get("description", ""),
+                    source_chunk_id=[chunk.id],
+                )
+                chunk_entities.append(entity)
+            
+            normalized_for_re = [[e['start'], e['end'], e['type']] for e in described_entities]
+            return index, chunk.id, chunk_entities, normalized_for_re
+
+        entity_processing_tasks = [_process_chunk_entities(chunk, i) for i, chunk in enumerate(chunk_list)]
+        
+        for task in tqdm(asyncio.as_completed(entity_processing_tasks), total=len(entity_processing_tasks), desc="Pass 1/3: Processing Entities"):
+            index, chunk_id, chunk_entities, normalized_for_re = await task
+            all_entities_by_chunk_id[chunk_id].extend(chunk_entities)
+            if index < len(all_normalized_entities_for_re):
+                all_normalized_entities_for_re[index] = normalized_for_re
+
+        # Flatten the list of all entities
+        final_entities = [entity for entities in all_entities_by_chunk_id.values() for entity in entities]
+
+        # 3. Pass 2: Extract relations from all chunks
+        logger.info("Pass 2/3: Extracting Relations...")
+        
+        def batch_generator(data, batch_size):
+            for i in range(0, len(data), batch_size):
+                yield data[i:i + batch_size]
+
+        batch_size = 5
+        chunk_batches = list(batch_generator(chunk_list, batch_size))
+        entity_batches = list(batch_generator(all_normalized_entities_for_re, batch_size))
+        
+        raw_relations = []
+        for i, (chunk_batch, entity_batch) in enumerate(tqdm(zip(chunk_batches, entity_batches), desc="Pass 2/3: Extracting Relations in Batches", total=len(chunk_batches))):
+            batch_start_index = i * batch_size
+            re_context = {
+                "chunks": [c.content for c in chunk_batch],
+                "entities_list": entity_batch
+            }
+            re_output_context = await re_step.run(re_context)
+            relations_in_batch = re_output_context.get("relations", [])
+            for rel in relations_in_batch:
+                rel.chunk_id += batch_start_index
+            raw_relations.extend(relations_in_batch)
+
+        # 4. Pass 3: Process and describe relations
+        logger.info("Pass 3/3: Processing and Describing Relations...")
+        final_relations: List[Relation] = []
+        relations_for_description_by_chunk: Dict[int, List[Dict[str, Any]]] = defaultdict(list)
+
+        for raw_rel in tqdm(raw_relations, desc="Processing Relations"):
+            chunk_idx = raw_rel.chunk_id
+            if chunk_idx in chunk_map:
+                chunk = chunk_map[chunk_idx]
+                subject_entity = next((e for e in all_entities_by_chunk_id.get(chunk.id, []) if e.entity_name == raw_rel.source_entity), None)
+                object_entity = next((e for e in all_entities_by_chunk_id.get(chunk.id, []) if e.entity_name == raw_rel.target_entity), None)
+
+                if subject_entity and object_entity:
+                    relation = Relation(
+                        subject_id=subject_entity.id,
+                        object_id=object_entity.id,
+                        subject_name=subject_entity.entity_name,
+                        object_name=object_entity.entity_name,
+                        relationship_type=raw_rel.relationship_type,
+                        description="",  # Placeholder for now
+                        relation_strength=raw_rel.relationship_strength,
+                        source_chunk_id=[chunk.id]
+                    )
+                    final_relations.append(relation)
+                    relations_for_description_by_chunk[chunk_idx].append({
+                        "source_entity": relation.subject_name,
+                        "target_entity": relation.object_name,
+                        "relationship_type": relation.relationship_type,
+                    })
+
+        # Generate descriptions for relations chunk by chunk
+        all_described_triplets = []
+        for chunk_idx, relations_in_chunk in tqdm(relations_for_description_by_chunk.items(), desc="Describing Relations"):
+            if not relations_in_chunk:
+                continue
+            desc_context = {
+                "text": chunk_map[chunk_idx].content,
+                "relations_for_description": relations_in_chunk
+            }
+            described_context = await relation_desc_step.run(desc_context)
+            all_described_triplets.extend(described_context.get("triplets", []))
+
+        # Map descriptions back to the final Relation objects
+        for triplet in all_described_triplets:
+            for relation in final_relations:
+                if (relation.subject_name == triplet["source_entity"] and
+                    relation.object_name == triplet["target_entity"] and
+                    relation.relationship_type == triplet["relationship_type"]):
+                    relation.description = triplet.get("description", "")
+                    break
+
+        logger.info(f"Triplet extraction pipeline complete. Total extracted: {len(final_entities)} entities, {len(final_relations)} relations.")
+
+        final_relations = self.filter_relations(final_relations)
+        logger.info(f"Number of relations after filtering: {len(final_relations)}")
+
+        return final_entities, final_relations
+
+    @staticmethod
+    def filter_relations(
+            relations: List[Relation],
+            negative_pattern: Optional[Union[str, re.Pattern[str]]] = None,
+    ) -> List[Relation]:
+        """
+        Filter out relations extracted by RAGU-LM that are empty, irrelevant, or explicitly negated.
+
+        This function applies a combined regular expression pattern that detects
+        negations and absence phrases such as "нет связи", "не содержит информации",
+        "отсутствует отношение", etc.
+
+        :param relations: List of extracted `Relation` objects.
+        :param negative_pattern: Optional custom regular expression pattern to override default.
+        :returns: Filtered list of relations with only meaningful descriptions.
+        """
+        def _clean_bullet(s: str) -> str:
+            return re.sub(r"^[\-\u2022]\s*", "", (s or "").strip())
+
+        COMBINED_NEG_RU = (
+            r"(?:"
+            r"^\s*$" 
+            r"|^\s*[\-–—]\s*$"  
+            r"|^(?:[-•]\s*)?(?:отсутств\w*\s+(?:связ\w*|отнош\w*)|"
+            r"нет\s+(?:связ\w*|отнош\w*|информац\w*|данн\w*|сведен\w*))\b"
+            r"|\bтекст\s+не\s+содерж\w*\b"
+            r"|\b(?:текст\s+)?не\s+содерж\w*\s+информац\w*\s+о\b"
+            r"|\bнет\s+(?:информац\w*|сведен\w*|данн\w*)(?:\s+о\b|\b)"
+            r"|\bне\s+явля\w*\s+\w*отнош\w*"
+            r"|\bнет\s+\w*отнош\w*"
+            r"|\bотсутств\w*\s+\w*отнош\w*"
+            r"|\bне\s+содерж\w*\s+\w*отнош\w*"
+            r"|\bнет\s+явн\w*\s+\w*отнош\w*"
+            r"|\bнет\s+\w*связ\w*"
+            r"|\bотсутств\w*\s+\w*связ\w*"
+            r"|\bсвяз\w*\s+не\s+(?:установ\w*|прослежива\w*|подтвержд\w*|обнаруж\w*)"
+            r"|\bотнош\w*\s+не\s+(?:установ\w*|прослежива\w*|подтвержд\w*|обнаруж\w*)"
+            r"|\bне[^.\n]{0,60}(?:содерж\w*|ука\w*|упомина\w*|найд\w*|обнаруж\w*|подтвержд\w*|установ\w*|прослеж\w*)"
+            r"[^.\n]{0,80}(?:связ\w*|отнош\w*|информац\w*)"
+            r")"
+            )
+
+        if isinstance(negative_pattern, re.Pattern):
+            NEG = negative_pattern
+        else:
+            NEG = re.compile(negative_pattern or COMBINED_NEG_RU, flags=re.IGNORECASE | re.UNICODE)
+
+        kept: List[Relation] = []
+        for rel in relations:
+            desc = rel.description
+            cleaned = _clean_bullet(desc)
+            if NEG.search(cleaned):
+                continue
+            rel.description = cleaned
+            kept.append(rel)
+
+        return kept
diff --git a/ragu/triplet/pipeline/steps.py b/ragu/triplet/pipeline/steps.py
new file mode 100644
index 0000000..bfcf234
--- /dev/null
+++ b/ragu/triplet/pipeline/steps.py
@@ -0,0 +1,96 @@
+from typing import Any, Dict, List
+
+from ragu.triplet.pipeline.base import PipelineStep
+from ragu.triplet.pipeline.clients import (
+    DescriptionClient, NERClient, NENClient, REClient
+)
+from ragu.triplet.pipeline.io_models import NER_IN, RE_IN
+
+
+class NERStep(PipelineStep):
+    """
+    Named Entity Recognition step.
+    """
+
+    def __init__(self, client: NERClient):
+        self.client = client
+
+    async def run(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        text = context["text"]
+        ner_input = NER_IN(root=text)
+        ner_output = await self.client.extract_entities(ner_input)
+
+        processed_entities = []
+        for entity_data in ner_output.ners:
+            start, end, entity_type = entity_data.root
+            processed_entities.append({
+                "name": text[start:end],
+                "type": entity_type,
+                "start": start,
+                "end": end,
+            })
+        context["entities"] = processed_entities
+        return context
+
+
+class NENStep(PipelineStep):
+    """
+    Named Entity Normalization step.
+    """
+
+    def __init__(self, client: NENClient):
+        self.client = client
+
+    async def run(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        entities = context["entities"]
+        normalized_entities = await self.client.normalize_entities(entities, context["text"])
+        context["normalized_entities"] = normalized_entities
+        return context
+
+
+class REStep(PipelineStep):
+    """
+    Relation Extraction step.
+    """
+
+    def __init__(self, client: REClient):
+        self.client = client
+
+    async def run(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        chunks = context["chunks"]
+        entities_list = context["entities_list"]
+        re_input = RE_IN(chunks=chunks, entities_list=entities_list)
+        relations = await self.client.extract_relations(re_input)
+        context["relations"] = relations.root
+        return context
+
+class EntityDescriptionStep(PipelineStep):
+    """
+    Entity Description Generation step.
+    """
+
+    def __init__(self, client: DescriptionClient):
+        self.client = client
+
+    async def run(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        entities = context["normalized_entities"]
+        source_text = context["text"]
+        described_entities = await self.client.generate_entity_descriptions(entities, source_text)
+        context["described_entities"] = described_entities
+        return context
+
+
+class RelationDescriptionStep(PipelineStep):
+    """
+    Relation Description Generation step.
+    """
+
+    def __init__(self, client: DescriptionClient):
+        self.client = client
+
+    async def run(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        relations = context["relations_for_description"]
+        source_text = context["text"]
+        triplets = await self.client.generate_relation_description(relations, source_text)
+        context["triplets"] = triplets
+        return context
\ No newline at end of file
diff --git a/services/Dockerfile b/services/Dockerfile
new file mode 100644
index 0000000..88cd8af
--- /dev/null
+++ b/services/Dockerfile
@@ -0,0 +1,55 @@
+# Stage 1: Build the virtual environment
+FROM nvidia/cuda:12.1.1-devel-ubuntu22.04 AS builder
+
+# Install Python, pip, and uv
+RUN apt-get update && \
+    DEBIAN_FRONTEND=noninteractive apt-get install -y software-properties-common && \
+    add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update && \
+    DEBIAN_FRONTEND=noninteractive apt-get install -y python3.12 python3-pip python3.12-venv && \
+    pip3 install uv && \
+    rm -rf /var/lib/apt/lists/*
+
+ENV UV_COMPILE_BYTECODE=1 UV_LINK_MODE=copy
+WORKDIR /app
+
+# Copy project dependency files
+COPY uv.lock pyproject.toml /app/
+
+# Install torch with CUDA 12.1 support
+RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+
+# Create a virtual environment and install dependencies using uv
+RUN uv venv && \
+    . .venv/bin/activate && \
+    uv sync
+
+# Stage 2: Create the final image
+FROM nvidia/cuda:12.1.1-devel-ubuntu22.04
+WORKDIR /app
+
+# Install Python runtime
+RUN apt-get update && \
+    DEBIAN_FRONTEND=noninteractive apt-get install -y software-properties-common && \
+    add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update && \
+    DEBIAN_FRONTEND=noninteractive apt-get install -y python3.12 && \
+    rm -rf /var/lib/apt/lists/*
+
+# Copy the virtual environment from the builder stage
+COPY --from=builder /app/.venv ./.venv
+# Copy the uv executable from the builder stage
+COPY --from=builder /usr/local/bin/uv /usr/local/bin/uv
+
+# Copy application code and model
+COPY ragu-lm ./ragu-lm
+COPY app.py .
+
+# Set PATH to include the virtual environment's bin directory and uv
+ENV PATH="/app/.venv/bin:/usr/local/bin:$PATH"
+ENV NVIDIA_DRIVER_CAPABILITIES=all
+
+EXPOSE 8002
+
+# Run the application using python
+CMD ["python", "app.py"]
diff --git a/services/README.md b/services/README.md
new file mode 100644
index 0000000..bea1716
--- /dev/null
+++ b/services/README.md
@@ -0,0 +1,36 @@
+# RAGU-lm Service
+
+This directory contains a FastAPI application that serves the RAGU-lm model for various NLP tasks.
+
+## Endpoints
+
+The service provides the following endpoints:
+
+### `/extract_entities`
+
+- **Method:** `POST`
+- **Request Body:** `{"text": "..."}`
+- **Response Body:** `{"extracted_entities": ["...", "..."]}`
+- **Description:** Extracts named entities from the input text.
+
+### `/nen`
+
+- **Method:** `POST`
+- **Request Body:** `{"entities": [{"name": "...", "type": "...", "start": ..., "end": ...}], "source_text": "..."}`
+- **Response Body:** `{"normalized_entities": [{"name": "...", "type": "...", "start": ..., "end": ..., "normalized_name": "..."}]}`
+- **Description:** Normalizes the extracted entities.
+
+### `/describe_relations`
+
+- **Method:** `POST`
+- **Request Body:** `{"relations": [{"source_entity": "...", "target_entity": "...", "relationship_type": "..."}], "source_text": "..."}`
+- **Response Body:** `{"triplets": [{"source_entity": "...", "target_entity": "...", "relationship_type": "...", "description": "..."}]}`
+- **Description:** Generates descriptions for the extracted relations.
+
+## Running the Service
+
+To run the service, you can use the provided `Dockerfile` and `docker-compose.yml` in the root directory.
+
+```bash
+docker-compose up -d custom_service
+```
diff --git a/services/app.py b/services/app.py
new file mode 100644
index 0000000..b992219
--- /dev/null
+++ b/services/app.py
@@ -0,0 +1,135 @@
+import os
+import torch
+import uvicorn
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Dict, Any
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+import logging
+logging.getLogger("transformers").setLevel(logging.WARNING)
+logging.getLogger("torch").setLevel(logging.WARNING)
+
+app = FastAPI()
+
+# Load the model and tokenizer
+MODEL_DIR = "ragu-lm"
+
+try:
+    tokenizer = AutoTokenizer.from_pretrained(
+        MODEL_DIR,
+        use_fast=True,
+        trust_remote_code=True,
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_DIR,
+        dtype="auto",        
+        device_map="auto",       
+        trust_remote_code=True,
+    )
+    system_prompt = "Вы - эксперт в области анализа текстов и извлечения семантической информации из них."
+except Exception as e:
+    raise RuntimeError(f"Error loading transformers model: {e}")
+
+class EntityRequest(BaseModel):
+    entities: List[Dict[str, Any]]
+    source_text: str
+
+class RelationRequest(BaseModel):
+    relations: List[Dict[str, Any]]
+    source_text: str
+
+class TextRequest(BaseModel):
+    text: str
+
+class DescribeRequest(BaseModel):
+    entities: List[Dict[str, Any]]
+    source_text: str
+
+def generate_text(prompt):
+    messages = [[
+        {'role': 'system', 'content': system_prompt},
+        {'role': 'user', 'content': prompt}
+    ]]
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    gen_ids = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        do_sample=True,
+        top_p=0.95,
+        top_k=100,
+        temperature=0.01,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id,
+    )
+    return tokenizer.decode(
+        gen_ids[0, inputs["input_ids"].shape[1]:],
+        skip_special_tokens=True
+    ).strip()
+
+@app.post("/extract_entities")
+async def extract_entities(request_data: TextRequest):
+    prompt = f"Распознайте все именованные сущности в тексте и выпишите их список с новой строки.\n\nТекст: {request_data.text}\n\nИменованные сущности:"
+    generated_text = generate_text(prompt)
+    extracted_entities = generated_text.split('\n')
+    return {"extracted_entities": extracted_entities}
+
+@app.post("/nen")
+async def normalize_entities(request_data: EntityRequest):
+    normalized_entities = []
+    for entity in request_data.entities:
+        prompt = f"Выполните нормализацию именованной сущности, встретившейся в тексте.\n\nИсходная (ненормализованная) именованная сущность: {entity['name']}\n\nТекст: {request_data.source_text}\n\nНормализованная именованная сущность:"
+        normalized_name = generate_text(prompt)
+        
+        normalized_entities.append({
+            "name": entity["name"],
+            "type": entity["type"],
+            "start": entity["start"],
+            "end": entity["end"],
+            "normalized_name": normalized_name,
+        })
+    return {"normalized_entities": normalized_entities}
+
+@app.post("/describe")
+async def generate_entity_descriptions(request_data: DescribeRequest):
+    try:
+        described_entities = []
+        for entity in request_data.entities:
+            prompt = f"Напишите, что означает именованная сущность в тексте, то есть раскройте её смысл относительно текста.\n\nИменованная сущность: {entity['normalized_name']}\n\nТекст: {request_data.source_text}\n\nСмысл именованной сущности:"
+            description = generate_text(prompt)
+            
+            entity_with_description = entity.copy()
+            entity_with_description["description"] = description
+            described_entities.append(entity_with_description)
+            
+        return {"described_entities": described_entities}
+    except Exception as e:
+        print(e)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/describe_relation")
+async def generate_relation_description(request_data: RelationRequest):
+    try:
+        triplets = []
+        for relation in request_data.relations:
+            prompt = f"Напишите, что означает отношение между двумя именованными сущностями в тексте, то есть раскройте смысл этого отношения относительно текста (либо напишите прочерк, если между двумя именованными сущностями отсутствует отношение).\n\nПервая именованная сущность: {relation['source_entity']}\n\nВторая именованная сущность: {relation['target_entity']}\n\nТекст: {request_data.source_text}\n\nСмысл отношения между двумя именованными сущностями:"
+            description = generate_text(prompt)
+            
+            triplets.append({
+                "source_entity": relation["source_entity"],
+                "target_entity": relation["target_entity"],
+                "relationship_type": relation["relationship_type"],
+                "description": description,
+            })
+        return {"triplets": triplets}
+    except Exception as e:
+        print(e)
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)
diff --git a/services/download_model.py b/services/download_model.py
new file mode 100644
index 0000000..3cd9828
--- /dev/null
+++ b/services/download_model.py
@@ -0,0 +1,21 @@
+from huggingface_hub import snapshot_download
+import os
+
+# The ID of the model repository on the Hugging Face Hub
+model_id = "RaguTeam/RAGU-lm"
+# The target directory to save the model files, relative to this script
+local_dir = "ragu-lm"
+
+# Create the directory if it doesn't exist
+os.makedirs(local_dir, exist_ok=True)
+
+print(f"Downloading model {model_id} to {local_dir}...")
+
+# Download the model snapshot
+snapshot_download(
+    repo_id=model_id,
+    local_dir=local_dir,
+    local_dir_use_symlinks=False
+)
+
+print("Model downloaded successfully!")
diff --git a/services/pyproject.toml b/services/pyproject.toml
new file mode 100644
index 0000000..dd93b19
--- /dev/null
+++ b/services/pyproject.toml
@@ -0,0 +1,12 @@
+[project]
+name = "ragu-lm-docker-service"
+version = "0.1.0"
+requires-python = ">=3.12"
+dependencies = [
+    "fastapi>=0.119.0",
+    "python-dotenv>=1.1.1",
+    "uvicorn>=0.37.0",
+    "transformers>=4.41.2",
+    "accelerate>=0.30.1",
+    "torch>=2.3.0"
+]
diff --git a/services/uv.lock b/services/uv.lock
new file mode 100644
index 0000000..a51447b
--- /dev/null
+++ b/services/uv.lock
@@ -0,0 +1,972 @@
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+revision = 3
+requires-python = ">=3.12"
+
+[[package]]
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+version = "1.11.0"
+source = { registry = "https://pypi.org/simple" }
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+    { name = "huggingface-hub" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "safetensors" },
+    { name = "torch" },
+]
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