Merge remote-tracking branch 'upstream/main'

Author: dchourasia

examples/kfto_feast_rag/README.md

@@ -20,9 +20,9 @@ This example notebook provides a step-by-step demonstration of building and usin
 
 4. RAG System Implementation
    - **Embedding Model**: `all-MiniLM-L6-v2` (configurable)
-   - **Generator Model**: `granite-3.2-2b-instruct` (configurable)
-   - **Vector Store**: Custom implementation with Feast integration
-   - **Retriever**: Custom implementation with Feast integration extending HuggingFace's RagRetriever
+   - **Generator Model**: `granite-3.2-2b-instruct` (configurable) 
+   - **Vector Store**: Feast’s built-in FeastVectorStore backed by Milvus
+   - **Retriever**: Feast’s native RAG retriever FeastRAGRetriever
 
 5. Query Demonstration
    - Perform inference with retrieved context
@@ -37,17 +37,6 @@ This example notebook provides a step-by-step demonstration of building and usin
 From the workbench, clone this repository: https://github.com/opendatahub-io/distributed-workloads.git
 Navigate to the distributed-workloads/examples/kfto-feast-rag directory. Here you will find the following files: 
 
-* **feast_rag_retriever.py**
-  This module implements a custom RAG retriever by combining Feast feature store capabilities with HuggingFace transformer-based models. The implementation provides:
-
-  - A flexible vector store interface with Feast integration (`FeastVectorStore`)
-  - A custom RAG retriever (`FeastRAGRetriever`) that supports three search modes:
-    - Text-based search
-    - Vector-based search
-    - Hybrid search
-  - Seamless integration with HuggingFace transformers library and sentence-transformers
-  - Configurable document formatting and retrieval options
-
 * **feature_repo/feature_store.yaml**
   This is the core configuration file for the RAG project's feature store, configuring a Milvus online store on a local provider. 
   * In order to configure Milvus you should:
@@ -56,7 +45,7 @@ Navigate to the distributed-workloads/examples/kfto-feast-rag directory. Here yo
        - port (default: 19530)
        - credentials (if required)
 
-* **__feature_repo/rag_project_repo.py__**
+* **__feature_repo/ragproject_repo.py__**
   This is the Feast feature repository configuration that defines the schema and data source for Wikipedia passage embeddings. 
 
 * **__rag_feast_kfto.ipynb__**

examples/kfto_feast_rag/feast_rag_retriever.py

@@ -3,7 +3,7 @@ provider: local
 registry: data/registry.db
 online_store:
   type: milvus
-  host: # Insert Milvus route host
+  host: http:// # Insert Milvus route host
   username: # Insert Milvus username if required
   password: # Insert Milvus password if required
   port: 19530

examples/kfto_feast_rag/feature_repo/feature_store.yaml

@@ -20,9 +20,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Note: `faiss-cpu` is included here due to an assumption in the Hugging Face `RagRetriever` class\n",
-    "# that the FAISS library is required, even though it's not directly used in this example.\n",
-    "%pip install --quiet feast[milvus] sentence-transformers datasets faiss-cpu\n",
+    "%pip install --quiet feast[milvus] sentence-transformers datasets\n",
     "%pip install bigtree==0.19.2\n",
     "%pip install marshmallow==3.10.0 "
    ]
@@ -51,31 +49,50 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset is chunked to contain a preset number of chars, which is the max supported by Feast. Ensuring the chunk only contains whole words, thus the retrieved context can form sentences without incomplete words."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def chunk_dataset(examples, chunk_size=100, overlap=20, max_chars=500):\n",
+    "def chunk_dataset(examples, max_chars=380):\n",
     "    all_chunks = []\n",
     "    all_ids = []\n",
     "    all_titles = []\n",
     "\n",
-    "    for i, text in enumerate(examples['text']):  # Iterate over texts in the batch\n",
+    "    for i, text in enumerate(examples['text']): # Iterate over texts in the batch\n",
     "        words = text.split()\n",
-    "        chunks = []\n",
-    "        for j in range(0, len(words), chunk_size - overlap):\n",
-    "            chunk_words = words[j:j + chunk_size]\n",
-    "            if len(chunk_words) < 20:\n",
-    "                continue\n",
-    "            chunk_text_value = ' '.join(chunk_words)  # Store the chunk text\n",
-    "            chunk_text_value = chunk_text_value[:max_chars]\n",
-    "            chunks.append(chunk_text_value)\n",
-    "            all_ids.append(f\"{examples['id'][i]}_{j}\")  # Unique ID for the chunk\n",
-    "            all_titles.append(examples['title'][i])\n",
+    "        if not words:\n",
+    "            continue\n",
     "\n",
-    "        all_chunks.extend(chunks)\n",
+    "        current_chunk_words = []\n",
+    "        for word in words:\n",
+    "            # Check if adding the next word exceeds the character limit\n",
+    "            if len(' '.join(current_chunk_words + [word])) > max_chars:\n",
+    "                # If the current chunk is valid, save it\n",
+    "                if current_chunk_words:\n",
+    "                    chunk_text = ' '.join(current_chunk_words)\n",
+    "                    all_chunks.append(chunk_text)\n",
+    "                    all_ids.append(f\"{examples['id'][i]}_{len(all_chunks)}\") # Unique ID for the chunk\n",
+    "                    all_titles.append(examples['title'][i])\n",
+    "                # Start a new chunk with the current word\n",
+    "                current_chunk_words = [word]\n",
+    "            else:\n",
+    "                current_chunk_words.append(word)\n",
+    "\n",
+    "        # Add the last remaining chunk\n",
+    "        if current_chunk_words:\n",
+    "            chunk_text = ' '.join(current_chunk_words)\n",
+    "            all_chunks.append(chunk_text)\n",
+    "            all_ids.append(f\"{examples['id'][i]}_{len(all_chunks)}\") # Unique ID for the chunk\n",
+    "            all_titles.append(examples['title'][i])\n",
     "\n",
     "    return {'id': all_ids, 'title': all_titles, 'text': all_chunks}\n",
     "\n",
@@ -120,6 +137,15 @@
     "#### Create parquet file as historical data source"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%mkdir feature_repo/data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -145,7 +171,7 @@
     "print(df[\"embedding\"].apply(lambda x: len(x) if isinstance(x, list) else str(type(x))).value_counts())  # Check lengths\n",
     "\n",
     "# Save to Parquet\n",
-    "df.to_parquet(\"wiki_dpr.parquet\", index=False)\n",
+    "df.to_parquet(\"feature_repo/data/wiki_dpr.parquet\", index=False)\n",
     "print(\"Saved to wiki_dpr.parquet\")"
    ]
   },
@@ -231,8 +257,9 @@
    "source": [
     "import sys\n",
     "sys.path.append(\"..\")\n",
-    "from feast_rag_retriever import FeastVectorStore, FeastRAGRetriever, FeastIndex\n",
-    "from rag_project_repo import wiki_passage_feature_view\n",
+    "from ragproject_repo import wiki_passage_feature_view\n",
+    "from feast.vector_store import FeastVectorStore\n",
+    "from feast.rag_retriever import FeastIndex, FeastRAGRetriever\n",
     "\n",
     "generator_config=generator_model.config\n",
     "question_encoder = AutoModel.from_pretrained(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
@@ -245,12 +272,12 @@
     "}\n",
     "\n",
     "vector_store = FeastVectorStore(\n",
-    "    store=store,\n",
+    "    repo_path=\".\",\n",
     "    rag_view=wiki_passage_feature_view,\n",
-    "    features=[\"wiki_passages:passage_text\", \"wiki_passages:embedding\"]\n",
+    "    features=[\"wiki_passages:passage_text\", \"wiki_passages:embedding\", \"wiki_passages:passage_id\"]\n",
     ")\n",
     "\n",
-    "feast_index = FeastIndex(vector_store=vector_store)\n",
+    "feast_index = FeastIndex()\n",
     "\n",
     "config = RagConfig(\n",
     "    question_encoder=query_encoder_config,\n",
@@ -262,10 +289,12 @@
     "    question_encoder_tokenizer=question_encoder_tokenizer,\n",
     "    generator_tokenizer=generator_tokenizer,\n",
     "    feast_repo_path=\".\",\n",
-    "    vector_store=vector_store,\n",
+    "    feature_view=vector_store.rag_view,\n",
+    "    features=vector_store.features,\n",
     "    generator_model=generator_model, \n",
     "    search_type=\"vector\",\n",
     "    id_field=\"passage_id\",\n",
+    "    text_field=\"passage_text\",\n",
     "    config=config,\n",
     "    index=feast_index,\n",
     ")"