docs: Add example with MongoDB

docs/examples/rag_mongodb.ipynb

@@ -68,7 +68,7 @@
     "import warnings\n",
     "\n",
     "warnings.filterwarnings(\"ignore\")\n",
-    "logging.getLogger(\"pymongo\").setLevel(logging.ERROR)\n"
+    "logging.getLogger(\"pymongo\").setLevel(logging.ERROR)"
    ]
   },
   {
@@ -134,8 +134,8 @@
    "source": [
     "# Influential machine learning papers\n",
     "source_urls = [\n",
-    "    \"https://arxiv.org/pdf/1706.03762\" # Attention is All You Need\n",
+    "    \"https://arxiv.org/pdf/1706.03762\"  # Attention is All You Need\n",
-    "    ]"
+    "]"
    ]
   },
   {
@@ -185,8 +185,10 @@
     }
    ],
    "source": [
-    "from docling.document_converter import DocumentConverter\n",
     "from pprint import pprint\n",
+    "\n",
+    "from docling.document_converter import DocumentConverter\n",
+    "\n",
     "# Instantiate the doc converter\n",
     "doc_converter = DocumentConverter()\n",
     "\n",
@@ -288,11 +290,11 @@
    },
    "outputs": [],
    "source": [
-    "# Voyage API key\n",
-    "VOYAGE_API_KEY=\"**********************\" \n",
-    "\n",
     "import voyageai\n",
     "\n",
+    "# Voyage API key\n",
+    "VOYAGE_API_KEY = \"**********************\"\n",
+    "\n",
     "# Initialize the VoyageAI client\n",
     "vo = voyageai.Client(VOYAGE_API_KEY)\n",
     "result = vo.contextualized_embed(inputs=[chunk_texts], model=\"voyage-context-3\")\n",
@@ -326,7 +328,10 @@
    "outputs": [],
    "source": [
     "# Combine chunks with their embeddings\n",
-    "chunk_data = [{\"text\": text, \"embedding\": emb} for text, emb in zip(chunk_texts, contextualized_chunk_embds)]"
+    "chunk_data = [\n",
+    "    {\"text\": text, \"embedding\": emb}\n",
+    "    for text, emb in zip(chunk_texts, contextualized_chunk_embds)\n",
+    "]"
    ]
   },
   {
@@ -362,9 +367,11 @@
     "# Insert to MongoDB\n",
     "from pymongo import MongoClient\n",
     "\n",
-    "client = MongoClient(\"mongodb+srv://*******.mongodb.net/\") # Replace with your MongoDB connection string\n",
+    "client = MongoClient(\n",
-    "db = client[\"rag_db\"] # Database name\n",
+    "    \"mongodb+srv://*******.mongodb.net/\"\n",
-    "collection = db[\"documents\"] # Collection name\n",
+    ")  # Replace with your MongoDB connection string\n",
+    "db = client[\"rag_db\"]  # Database name\n",
+    "collection = db[\"documents\"]  # Collection name\n",
     "\n",
     "# Insert chunk data into MongoDB\n",
     "response = collection.insert_many(chunk_data)\n",
@@ -399,21 +406,21 @@
     "\n",
     "# Create your index model, then create the search index\n",
     "search_index_model = SearchIndexModel(\n",
-    "  definition={\n",
+    "    definition={\n",
-    "    \"fields\": [\n",
+    "        \"fields\": [\n",
-    "      {\n",
+    "            {\n",
-    "        \"type\": \"vector\",\n",
+    "                \"type\": \"vector\",\n",
-    "        \"path\": \"embedding\",\n",
+    "                \"path\": \"embedding\",\n",
-    "        \"numDimensions\": 1024,\n",
+    "                \"numDimensions\": 1024,\n",
-    "        \"similarity\": \"dotProduct\"\n",
+    "                \"similarity\": \"dotProduct\",\n",
-    "      }\n",
+    "            }\n",
-    "    ]\n",
+    "        ]\n",
-    "  },\n",
+    "    },\n",
-    "  name=\"vector_index\",\n",
+    "    name=\"vector_index\",\n",
-    "  type=\"vectorSearch\"\n",
+    "    type=\"vectorSearch\",\n",
     ")\n",
     "result = collection.create_search_index(model=search_index_model)\n",
-    "print(\"New search index named \" + result + \" is building.\")\n"
+    "print(\"New search index named \" + result + \" is building.\")"
    ]
   },
   {
@@ -524,21 +531,24 @@
     }
    ],
    "source": [
+    "import os\n",
+    "\n",
+    "from openai import AzureOpenAI\n",
     "from rich.console import Console\n",
     "from rich.panel import Panel\n",
-    "from openai import AzureOpenAI\n",
-    "import os\n",
     "\n",
     "# Create MongoDB vector search query for \"Attention is All You Need\"\n",
     "# (prompt already defined above, reuse if present; else keep this definition)\n",
     "prompt = \"Give me top 3 learning points from `Attention is All You Need`, using only the retrieved context.\"\n",
     "\n",
     "# Generate embedding for the query using VoyageAI (vo already initialized earlier)\n",
-    "query_embd_context = vo.contextualized_embed(\n",
+    "query_embd_context = (\n",
-    "    inputs=[[prompt]],\n",
+    "    vo.contextualized_embed(\n",
-    "    model=\"voyage-context-3\",\n",
+    "        inputs=[[prompt]], model=\"voyage-context-3\", input_type=\"query\"\n",
-    "    input_type=\"query\"\n",
+    "    )\n",
-    ").results[0].embeddings[0]\n",
+    "    .results[0]\n",
+    "    .embeddings[0]\n",
+    ")\n",
     "\n",
     "# Vector search pipeline\n",
     "search_pipeline = [\n",
@@ -548,21 +558,17 @@
     "            \"path\": \"embedding\",\n",
     "            \"queryVector\": query_embd_context,\n",
     "            \"numCandidates\": 10,\n",
-    "            \"limit\": 10\n",
+    "            \"limit\": 10,\n",
     "        }\n",
     "    },\n",
-    "    {\n",
+    "    {\"$project\": {\"text\": 1, \"_id\": 0, \"score\": {\"$meta\": \"vectorSearchScore\"}}},\n",
-    "        \"$project\": {\n",
-    "            \"text\": 1,\n",
-    "            \"_id\": 0,\n",
-    "            \"score\": {\"$meta\": \"vectorSearchScore\"}\n",
-    "        }\n",
-    "    }\n",
     "]\n",
     "\n",
     "results = list(collection.aggregate(search_pipeline))\n",
     "if not results:\n",
-    "    raise ValueError(\"No vector search results returned. Verify the index is built before querying.\")\n",
+    "    raise ValueError(\n",
+    "        \"No vector search results returned. Verify the index is built before querying.\"\n",
+    "    )\n",
     "\n",
     "context_texts = [doc[\"text\"] for doc in results]\n",
     "combined_context = \"\\n\\n\".join(context_texts)\n",
@@ -579,7 +585,7 @@
     "client = AzureOpenAI(\n",
     "    api_key=AZURE_OPENAI_API_KEY,\n",
     "    azure_endpoint=AZURE_OPENAI_ENDPOINT.rstrip(\"/\"),\n",
-    "    api_version=AZURE_OPENAI_API_VERSION\n",
+    "    api_version=AZURE_OPENAI_API_VERSION,\n",
     ")\n",
     "\n",
     "# Chat completion using retrieved context\n",
@@ -588,21 +594,23 @@
     "    messages=[\n",
     "        {\n",
     "            \"role\": \"system\",\n",
-    "            \"content\": \"You are a helpful assistant. Use only the provided context to answer questions. If the context is insufficient, say so.\"\n",
+    "            \"content\": \"You are a helpful assistant. Use only the provided context to answer questions. If the context is insufficient, say so.\",\n",
     "        },\n",
     "        {\n",
     "            \"role\": \"user\",\n",
-    "            \"content\": f\"Context:\\n{combined_context}\\n\\nQuestion: {prompt}\"\n",
+    "            \"content\": f\"Context:\\n{combined_context}\\n\\nQuestion: {prompt}\",\n",
-    "        }\n",
+    "        },\n",
     "    ],\n",
-    "    temperature=0.2\n",
+    "    temperature=0.2,\n",
     ")\n",
     "\n",
     "response_text = response.choices[0].message.content\n",
     "\n",
     "console = Console()\n",
     "console.print(Panel(f\"{prompt}\", title=\"Prompt\", border_style=\"bold red\"))\n",
-    "console.print(Panel(response_text, title=\"Generated Content\", border_style=\"bold green\"))"
+    "console.print(\n",
+    "    Panel(response_text, title=\"Generated Content\", border_style=\"bold green\")\n",
+    ")"
    ]
   },
   {

mkdocs.yml

@@ -123,6 +123,7 @@ nav:
       - examples/rag_opensearch.ipynb
       - examples/rag_weaviate.ipynb
       - examples/retrieval_qdrant.ipynb
+      - examples/rag_mongodb.ipynb
   - Integrations:
     - Integrations: integrations/index.md
     - 🤖 Agentic / AI dev frameworks: