Minimal RAG recipe

recipes/rag-example/README.md

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+# Minimal RAG example
+
+## Context
+
+This recipe is intended as a minimal example for Retrieval Augmented Generation using Eole models.
+It relies on additional tools, such as Langchain's [loaders](https://python.langchain.com/docs/integrations/document_loaders/pymupdf/) and [splitters](https://python.langchain.com/v0.1/docs/modules/data_connection/document_transformers/recursive_text_splitter/), as well as [ChromaDB](https://docs.trychroma.com/getting-started) for vector search.
+
+The example is using the rather hard to digest ["EU AI Act" full text](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai#:~:text=The%20AI%20Act%20(Regulation%20(EU,regarding%20specific%20uses%20of%20AI.) for the sake of the exercise.
+
+This is just a very quickly put together proof of concept, and is not expected to give perfect answers.
+
+## Usage
+
+### 0. Install requirements
+
+```bash
+pip install langchain pymupdf chromadb
+```
+
+### 1. Convert the model you want to use (Llama-3.1-8B by default)
+
+**Set environment variables**
+
+```bash
+export EOLE_MODEL_DIR=<where_to_store_models>
+export HF_TOKEN=<your_hf_token>
+```
+
+**Download and convert model**
+
+```bash
+eole convert HF --model_dir meta-llama/Meta-Llama-3.1-8B --output $EOLE_MODEL_DIR/llama3.1-8b --token $HF_TOKEN
+```
+
+### 2. Adapt and run the script
+
+Modify, if needed, the model_path in `PredictConfig`:
+```python
+...
+config = PredictConfig(
+    model_path=os.path.expandvars("${EOLE_MODEL_DIR}/llama3.1-8b"), # <------ change if needed
+    src="dummy",
+    max_length=500,
+...
+```
+
+**Run the script**
+```bash
+python3 test_rag.py
+```
+
+Note: You can test various queries by changing the `QUERY` variable.

recipes/rag-example/test_rag.py

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+# flake8: noqa
+
+import os
+from rich import print
+from tqdm import tqdm
+from langchain_community.document_loaders import PyMuPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import chromadb
+
+from eole.utils.logging import init_logger
+from eole.config.run import PredictConfig
+from eole.inference_engine import InferenceEnginePY
+
+# Set up logging
+logger = init_logger()
+
+# 1. Load and Split the Document
+logger.info("Loading and splitting the document...")
+loader = PyMuPDFLoader("./OJ_L_202401689_EN_TXT.pdf")
+docs = loader.load()
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)
+documents = text_splitter.split_documents(documents=docs)
+print(f"[INFO] Total chunks: {len(documents)}")
+
+# 2. Set Up ChromaDB Client and Collection
+logger.info("Setting up ChromaDB client...")
+chroma_client = chromadb.PersistentClient(path="chromadb_data")
+collection = chroma_client.get_or_create_collection(name="test-eu")
+
+# 3. Insert Documents into Collection
+logger.info("Checking for existing data in the collection...")
+
+# Retrieve all existing IDs from the collection
+existing_ids = set(collection.get(ids=None)["ids"])  # Fetches all IDs in the collection
+logger.info(f"Found {len(existing_ids)} existing documents in the collection.")
+batch_size = 100
+for i in tqdm(
+    range(0, len(documents), batch_size),
+    desc="Upserting batches in the vector database",
+):
+    batch_ids = [str(k) for k in range(i, min(len(documents), i + batch_size))]
+    new_ids = [
+        id_ for id_ in batch_ids if id_ not in existing_ids
+    ]  # Filter out existing IDs
+
+    if new_ids:  # Only upsert if there are new IDs
+        new_docs = [documents[int(id_)] for id_ in new_ids]
+        collection.upsert(
+            ids=new_ids,
+            documents=[doc.page_content for doc in new_docs],
+            metadatas=[doc.metadata for doc in new_docs],
+        )
+        logger.info(f"Upserted {len(new_ids)} new documents.")
+    else:
+        logger.info(f"Skipping batch {i // batch_size + 1}, all IDs already exist.")
+
+# print(collection.peek(10))
+
+# 4. Query the Collection
+QUERY = "What is the general position around using biometrics and facial recognition in public places?"
+# QUERY = "Are there any derogations for specific actors?"
+# QUERY = "What are the main obligations of importers?"
+# QUERY = "What are the main risks and penalties incurred?"
+# QUERY = "What is the maximum fine for potential offenders?"
+# QUERY = "What are the main prohibited practices coverd by the act?"
+# QUERY = "What are the main accepted practices covered by the act?"
+
+print(f"[INFO] Querying collection with: {QUERY}")
+results = collection.query(query_texts=[QUERY], n_results=5)
+
+best_id = int(results["ids"][0][0])
+print(f"[INFO] Best result ID: {best_id}")
+context_docs = collection.get(ids=[str(best_id - 1), str(best_id), str(best_id + 1)])
+
+# 5. Prepare the Prompt for Inference
+PROMPT = """You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise. The answer should be understandable outside of its context.
+The context comes from this document: "Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act)Text with EEA relevance."
+Question: {question}
+Context:  {context}
+Answer:"""
+
+context = "\n".join(context_docs["documents"])
+prompt = PROMPT.format(question=QUERY, context=context)
+logger.info("Generated Prompt:")
+print(prompt)
+
+
+# 6. Perform Inference
+logger.info("Running inference...")
+config = PredictConfig(
+    model_path=os.path.expandvars("${EOLE_MODEL_DIR}/llama3.1-8b"),
+    src="dummy",
+    max_length=500,
+    gpu_ranks=[0],
+    # Uncomment to activate bnb quantization
+    # quant_type="bnb_NF4",
+    # quant_layers=[
+    #     "gate_up_proj",
+    #     "down_proj",
+    #     "up_proj",
+    #     "linear_values",
+    #     "linear_query",
+    #     "linear_keys",
+    #     "final_linear",
+    #     "w_in",
+    #     "w_out",
+    # ],
+    top_p=0.3,
+    temperature=0.35,
+    beam_size=5,
+    seed=42,
+    batch_size=1,
+    batch_type="sents",
+)
+
+engine = InferenceEnginePY(config)
+
+_, _, predictions = engine.infer_list([prompt])
+
+# 7. Display the Prediction
+answer = predictions[0][0].replace("｟newline｠", "\n")
+logger.info("Final Answer:")
+print(answer)