\\nplanSummary: COLLSCAN keysExamined:0\\ndocsExamined: 10000 cursorExhausted:1 numYields:234\\nnreturned:4 protocol:op_query 358ms\\n```\\n\\nThis query scanned 10,000 documents and returned only 4 for a ratio of 2500, which is highly inefficient. No index keys were examined, so MongoDB scanned all documents in the collection, known as a collection scan.\\n\\n## Common Triggers\\n\\nThe query targeting alert typically occurs when there is no index to support a query or queries or when an existing index only partially supports a query or queries.\\n\\nThe change streams cursors that the MongoDB Search process (`mongot`) uses to keep MongoDB Search indexes updated can contribute to the query targeting ratio and trigger query targeting alerts if the ratio is high.\\n\\n## Fix the Immediate Problem\\n\\nAdd one or more indexes to better serve the inefficient queries.\\n\\nThe Performance Advisor provides the easiest and quickest way to create an index. The Performance Advisor monitors queries that MongoDB considers slow and recommends indexes to improve performance. Atlas dynamically adjusts your slow query threshold based on the execution time of operations across your cluster.\\n\\nClick Create Index on a slow query for instructions on how to create the recommended index.\\n\\nIt is possible to receive a Query Targeting alert for an inefficient query without receiving index suggestions from the Performance Advisor if the query exceeds the slow query threshold and the ratio of scanned to returned documents is greater than the threshold specified in the alert.\\n\\nIn addition, you can use the following resources to determine which query generated the alert:\\n\\n- The Real-Time Performance Panel monitors and displays current network traffic and database operations on machines hosting MongoDB in your Atlas clusters.\\n\\n- The MongoDB logs maintain an account of activity, including queries, for each `mongod` instance in your Atlas clusters.\\n\\n- The cursor.explain() command for `mongosh` provides performance details for all queries.\\n\\n- Namespace Insights monitors collection-level query latency.\\n\\n- The Atlas Query Profiler records operations that Atlas considers slow when compared to average execution time for all operations on your cluster.\\n\\n## Implement a Long-Term Solution\\n\\nRefer to the following for more information on query performance:\\n\\n- MongoDB Indexing Strategies\\n\\n- Query Optimization\\n\\n- Analyze Query Plan\\n\\n## Monitor Your Progress\\n\\nAtlas provides the following methods to visualize query targeting:\\n\\n- Query Targeting metrics, which highlight high ratios of objects scanned to objects returned.\\n\\n- Namespace Insights, which monitors collection-level query latency.\\n\\n- The Query Profiler, which describes specific inefficient queries executed on the cluster.\\n\\n### Query Targeting Metrics\\n\\nYou can view historical metrics to help you visualize the query performance of your cluster. To view Query Targeting metrics in the Atlas UI:\\n\\n1. Click Database in the top-left corner of Atlas.\\n\\n2. Click View Monitoring on the dashboard for the cluster.\\n\\n3. On the Metrics page, click the Add Chart dropdown menu and select Query Targeting.\\n\\nThe Query Targeting chart displays the following metrics for queries executed on the server:\\n\\n\\n\\n| \\nMetric\\n\\n | \\n\\nDescription\\n\\n | \\n
\\n\\n| \\nScanned Objects / Returned\\n\\n | \\n\\nIndicates the average number of documents examined relative to the average number of returned documents.\\n\\n | \\n
\\n\\n| \\nScanned / Returned\\n\\n | \\n\\nIndicates the number of index keys examined to fulfill a query relative to the actual number of returned documents.\\n\\n | \\n
\\n
The change streams cursors that the MongoDB Search process (`mongot`) uses to keep MongoDB Search indexes updated can contribute to the query targeting ratio and trigger query targeting alerts if the ratio is high.\\n\\nIf either of these metrics exceed the user-defined threshold, Atlas generates the corresponding `Query Targeting: Scanned Objects / Returned` or `Query Targeting: Scanned / Returned` alert.\\n\\nYou can also view Query Targeting ratios of operations in real-time using the Real-Time Performance Panel.\\n\\n### Namespace Insights\\n\\nNamespace Insights monitors collection-level query latency. You can view query latency metrics and statistics for certain hosts and operation types. Manage pinned namespaces and choose up to five namespaces to show in the corresponding query latency charts.\\n\\nTo access Namespace Insights:\\n\\n1. Click Database in the top-left corner of Atlas.\\n\\n2. Click View Monitoring on the dashboard for the cluster.\\n\\n3. Click the Query Insights tab.\\n\\n4. Click the Namespace Insights tab.\\n\\n### Query Profiler\\n\\nThe Query Profiler contains several metrics you can use to pinpoint specific inefficient queries. You can visualize up to the past 24 hours of query operations. The Query Profiler can show the Examined : Returned Ratio (index keys examined to documents returned) of logged queries, which might help you identify the queries that triggered a `Query Targeting: Scanned / Returned` alert. The chart shows the number of index keys examined to fulfill a query relative to the actual number of returned documents.\\n\\nThe default\\n`Query Targeting: Scanned Objects / Returned` alert ratio differs slightly. The ratio of the average number of documents scanned to the average number of documents returned during a sampling period triggers this alert.\\n\\nAtlas might not log the individual operations that contribute to the Query Targeting ratios due to automatically set thresholds. However, you can still use the Query Profiler and Query Targeting metrics to analyze and optimize query performance.\\n\\nTo access the Query Profiler:\\n\\n1. Click Database in the top-left corner of Atlas.\\n\\n2. Click View Monitoring on the dashboard for the cluster.\\n\\n3. Click the Query Insights tab.\\n\\n4. Click the Query Profiler tab.\\n\\n\\n\\n# Analyze Slow Queries\\n\\nAtlas provides several tools to help analyze slow queries executed on your clusters. See the following sections for descriptions of each tool. To optimize your query performance, review the best practices for query performance.\\n\\n## Performance Advisor\\n\\nThe Performance Advisor monitors queries that MongoDB considers slow and suggests new indexes to improve query performance.\\n\\nYou can use the Performance Advisor to review the following information:\\n\\n- Index Ranking\\n\\n- Drop Index Recommendations\\n\\n## Namespace Insights\\n\\nMonitor collection-level query latency with Namespace Insights. You can view query latency metrics and statistics for certain hosts and operation types. Manage pinned namespaces and choose up to five namespaces to show in the corresponding query latency charts.\\n\\n## Query Profiler\\n\\nThe Query Profiler displays slow-running operations and their key performance statistics. You can explore a sample of historical queries for up to the last 24 hours without additional cost or performance overhead. Before you enable the Query Profiler, see Considerations.\\n\\n## Real-Time Performance Panel (RTPP)\\n\\nThe Real-Time Performance Panel identifies relevant database operations, evaluates query execution times, and shows the ratio of documents scanned to documents returned during query execution. RTPP (Real-Time Performance Panel) is enabled by default.\\n\\nTo enable or disable Real-Time Performance Panel for a project, you must have the `Project Owner` role for the project.\\n\\n## Best Practices for Query Performance\\n\\nTo optimize query performance, review the following best practices:\\n\\n- Create queries that your current indexes support to reduce the time needed to search for your results.\\n\\n- Avoid creating documents with large array fields that require a lot of processing to search and index.\\n\\n- Optimize your indexes and remove unused or inefficent indexes. Too many indexes can negatively impact write performance.\\n\\n- Consider the suggested indexes from the Performance Advisor with the highest Impact scores and lowest Average Query Targeting scores.\\n\\n- Create the indexes that the Performance Advisor suggests when they align with your Indexing Strategies.\\n\\n- The Performance Advisor cannot suggest indexes for MongoDB databases configured to use the ctime timestamp format. As a workaround, set the timestamp format for such databases to either iso8601-utc or iso8601-local.\\n\\n- Perform rolling index builds to reduce the performance impact of building indexes on replica sets and sharded clusters.\\n\\n- Drop unused, redundant, and hidden indexes to improve write performance and free storage space.\\n\\n', name='get_info_about_mongodb', id='58b5fb08-1776-49d8-a6f6-956431f77388', tool_call_id='call_sifH0mrhbpesQie4BTnQytNk')]}\n",
"Node agent:\n",
"{'messages': [AIMessage(content=\"To improve slow queries in MongoDB, you can follow these steps:\\n\\n### 1. **Analyze the Problem**\\n - Use the **Performance Advisor** to monitor slow queries and get index recommendations.\\n - Check the **Query Profiler** to identify slow-running operations and their key performance statistics.\\n - Use **Namespace Insights** to monitor collection-level query latency.\\n - Analyze the **Real-Time Performance Panel (RTPP)** for real-time query execution metrics.\\n\\n### 2. **Common Causes of Slow Queries**\\n - Queries are not supported by existing indexes.\\n - Large array fields in documents that are costly to search and index.\\n - Queries involving multiple collections using `$lookup`.\\n\\n### 3. **Fix Immediate Issues**\\n - **Add Indexes**: Create indexes to support inefficient queries. The Performance Advisor provides suggestions for indexes with high impact.\\n - **Optimize Queries**: Ensure queries are designed to utilize existing indexes effectively.\\n - **Avoid Collection Scans**: If a query scans all documents in a collection (COLLSCAN), it indicates the need for an index.\\n\\n### 4. **Long-Term Solutions**\\n - **Optimize Indexes**: Remove unused or redundant indexes to improve write performance.\\n - **Monitor Query Targeting**: Keep the ratio of documents scanned to documents returned close to 1.\\n - **Avoid Large Arrays**: Minimize the use of large array fields in documents.\\n\\n### 5. **Best Practices**\\n - Use the **Query Targeting Metrics** to identify inefficiencies.\\n - Perform **rolling index builds** to minimize performance impact on replica sets and sharded clusters.\\n - Drop unused or hidden indexes to free up storage and improve write performance.\\n\\n### 6. **Tools for Monitoring and Optimization**\\n - **Performance Advisor**: Suggests indexes and provides query insights.\\n - **Query Profiler**: Displays slow-running queries and their statistics.\\n - **Namespace Insights**: Monitors query latency at the collection level.\\n - **Real-Time Performance Panel**: Provides real-time metrics for query execution.\\n\\nBy following these steps and utilizing MongoDB's built-in tools, you can significantly improve the performance of slow queries.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 451, 'prompt_tokens': 5355, 'total_tokens': 5806, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-11-20', 'system_fingerprint': 'fp_d924043139', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad3b553a-e5e6-4c9e-9246-d6e0f7286abb-0', usage_metadata={'input_tokens': 5355, 'output_tokens': 451, 'total_tokens': 5806, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})]}\n",
"---FINAL ANSWER---\n",
diff --git a/notebooks/advanced_techniques/retrieval_strategies_mongodb_llamaindex.ipynb b/notebooks/advanced_techniques/retrieval_strategies_mongodb_llamaindex.ipynb
index 8b6a65f8..e13450b9 100644
--- a/notebooks/advanced_techniques/retrieval_strategies_mongodb_llamaindex.ipynb
+++ b/notebooks/advanced_techniques/retrieval_strategies_mongodb_llamaindex.ipynb
@@ -462,7 +462,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 4: Create MongoDB Atlas vector store"
+ "## Step 4: Create MongoDB Vector Store"
]
},
{
@@ -531,7 +531,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 5: Create Atlas Search indexes"
+ "## Step 5: Create MongoDB Search indexes"
]
},
{
diff --git a/notebooks/advanced_techniques/retrieval_strategies_mongodb_llamaindex_togetherai.ipynb b/notebooks/advanced_techniques/retrieval_strategies_mongodb_llamaindex_togetherai.ipynb
index 22e7992f..e64d06d4 100644
--- a/notebooks/advanced_techniques/retrieval_strategies_mongodb_llamaindex_togetherai.ipynb
+++ b/notebooks/advanced_techniques/retrieval_strategies_mongodb_llamaindex_togetherai.ipynb
@@ -271,7 +271,7 @@
"id": "sFUS0_EURg2Q"
},
"source": [
- "## Step 5: Create MongoDB Atlas Vector store"
+ "## Step 5: Create MongoDB Vector Store"
]
},
{
@@ -332,7 +332,7 @@
"id": "I0vBccQaj-o6"
},
"source": [
- "### Create Atlas Search Index"
+ "### Create MongoDB Search Index"
]
},
{
diff --git a/notebooks/agents/Gemini2_0_multi_modality_with_mongodb_atlas_vector_store.ipynb b/notebooks/agents/Gemini2_0_multi_modality_with_mongodb_atlas_vector_store.ipynb
index cc4128a8..347440f4 100644
--- a/notebooks/agents/Gemini2_0_multi_modality_with_mongodb_atlas_vector_store.ipynb
+++ b/notebooks/agents/Gemini2_0_multi_modality_with_mongodb_atlas_vector_store.ipynb
@@ -6,7 +6,7 @@
"id": "3hp_P0cDzTWp"
},
"source": [
- "# Gemini 2.0 - Multimodal live API and MongoDB Atlas Vector store as tools\n",
+ "# Gemini 2.0 - Multimodal live API and MongoDB Vector store as tools\n",
"\n",
"Inspired and built on top of the following Google [example notebook](https://github.com/google-gemini/cookbook/blob/main/gemini-2/live_api_tool_use.ipynb)."
]
@@ -101,8 +101,7 @@
"import os\n",
"import getpass\n",
"\n",
- "\n",
- "os.environ['GOOGLE_API_KEY'] = getpass.getpass(\"Input your Google API Key\")"
+ "os.environ[\"GOOGLE_API_KEY\"] = getpass.getpass(\"Input your Google API Key\")"
]
},
{
@@ -127,9 +126,7 @@
"source": [
"from google import genai\n",
"\n",
- "client = genai.Client(http_options= {\n",
- " 'api_version': 'v1alpha'\n",
- "})"
+ "client = genai.Client(http_options={\"api_version\": \"v1alpha\"})"
]
},
{
@@ -235,9 +232,10 @@
"outputs": [],
"source": [
"import logging\n",
- "logger = logging.getLogger('Live')\n",
- "#logger.setLevel('DEBUG') # Switch between \"INFO\" and \"DEBUG\" to toggle debug messages.\n",
- "logger.setLevel('INFO')"
+ "\n",
+ "logger = logging.getLogger(\"Live\")\n",
+ "# logger.setLevel('DEBUG') # Switch between \"INFO\" and \"DEBUG\" to toggle debug messages.\n",
+ "logger.setLevel(\"INFO\")"
]
},
{
@@ -269,51 +267,52 @@
"outputs": [],
"source": [
"n = 0\n",
- "async def run(prompt, modality=\"AUDIO\", tools=None):\n",
- " global n\n",
- " if tools is None:\n",
- " tools=[]\n",
"\n",
- " config = {\n",
- " \"tools\": tools,\n",
- " \"system_instruction\" : \"You are a helpful HR assistant who can search employees with atlas_search_tool and create teams in the database with create_team tool\",\n",
- " \"generation_config\": {\n",
- " \"response_modalities\": [modality]}}\n",
- " print(f\"before client invoke {tools}\")\n",
- " async with client.aio.live.connect(model=model_name, config=config) as session:\n",
- " display.display(display.Markdown(prompt))\n",
- " display.display(display.Markdown('-------------------------------'))\n",
- " await session.send(prompt, end_of_turn=True)\n",
"\n",
- " audio = False\n",
- " filename = f'audio_{n}.wav'\n",
- " with wave_file(filename) as wf:\n",
- " async for response in session.receive():\n",
- " logger.debug(str(response))\n",
- " if text:=response.text:\n",
- " display.display(display.Markdown(text))\n",
- " continue\n",
+ "async def run(prompt, modality=\"AUDIO\", tools=None):\n",
+ " global n\n",
+ " if tools is None:\n",
+ " tools = []\n",
+ "\n",
+ " config = {\n",
+ " \"tools\": tools,\n",
+ " \"system_instruction\": \"You are a helpful HR assistant who can search employees with atlas_search_tool and create teams in the database with create_team tool\",\n",
+ " \"generation_config\": {\"response_modalities\": [modality]},\n",
+ " }\n",
+ " print(f\"before client invoke {tools}\")\n",
+ " async with client.aio.live.connect(model=model_name, config=config) as session:\n",
+ " display.display(display.Markdown(prompt))\n",
+ " display.display(display.Markdown(\"-------------------------------\"))\n",
+ " await session.send(prompt, end_of_turn=True)\n",
"\n",
- " if data:=response.data:\n",
- " print('.', end='')\n",
- " wf.writeframes(data)\n",
- " audio = True\n",
- " continue\n",
+ " audio = False\n",
+ " filename = f\"audio_{n}.wav\"\n",
+ " with wave_file(filename) as wf:\n",
+ " async for response in session.receive():\n",
+ " logger.debug(str(response))\n",
+ " if text := response.text:\n",
+ " display.display(display.Markdown(text))\n",
+ " continue\n",
"\n",
- " server_content = response.server_content\n",
- " if server_content is not None:\n",
- " handle_server_content(wf, server_content)\n",
- " continue\n",
- " print(f\"Before tool call {response.tool_call}\")\n",
+ " if data := response.data:\n",
+ " print(\".\", end=\"\")\n",
+ " wf.writeframes(data)\n",
+ " audio = True\n",
+ " continue\n",
"\n",
- " tool_call = response.tool_call\n",
- " if tool_call is not None:\n",
- " await handle_tool_call(session, tool_call)\n",
+ " server_content = response.server_content\n",
+ " if server_content is not None:\n",
+ " handle_server_content(wf, server_content)\n",
+ " continue\n",
+ " print(f\"Before tool call {response.tool_call}\")\n",
"\n",
+ " tool_call = response.tool_call\n",
+ " if tool_call is not None:\n",
+ " await handle_tool_call(session, tool_call)\n",
"\n",
- " if audio:\n",
- " display.display(display.Audio(filename, autoplay=True))\n",
- " n = n+1"
+ " if audio:\n",
+ " display.display(display.Audio(filename, autoplay=True))\n",
+ " n = n + 1"
]
},
{
@@ -339,27 +338,32 @@
"outputs": [],
"source": [
"def handle_server_content(wf, server_content):\n",
- " model_turn = server_content.model_turn\n",
- " if model_turn:\n",
- " for part in model_turn.parts:\n",
- " executable_code = part.executable_code\n",
- " if executable_code is not None:\n",
- " display.display(display.Markdown('-------------------------------'))\n",
- " display.display(display.Markdown(f'``` python\\n{executable_code.code}\\n```'))\n",
- " display.display(display.Markdown('-------------------------------'))\n",
+ " model_turn = server_content.model_turn\n",
+ " if model_turn:\n",
+ " for part in model_turn.parts:\n",
+ " executable_code = part.executable_code\n",
+ " if executable_code is not None:\n",
+ " display.display(display.Markdown(\"-------------------------------\"))\n",
+ " display.display(\n",
+ " display.Markdown(f\"``` python\\n{executable_code.code}\\n```\")\n",
+ " )\n",
+ " display.display(display.Markdown(\"-------------------------------\"))\n",
"\n",
- " code_execution_result = part.code_execution_result\n",
- " if code_execution_result is not None:\n",
- " display.display(display.Markdown('-------------------------------'))\n",
- " display.display(display.Markdown(f'```\\n{code_execution_result.output}\\n```'))\n",
- " display.display(display.Markdown('-------------------------------'))\n",
+ " code_execution_result = part.code_execution_result\n",
+ " if code_execution_result is not None:\n",
+ " display.display(display.Markdown(\"-------------------------------\"))\n",
+ " display.display(\n",
+ " display.Markdown(f\"```\\n{code_execution_result.output}\\n```\")\n",
+ " )\n",
+ " display.display(display.Markdown(\"-------------------------------\"))\n",
"\n",
- " grounding_metadata = getattr(server_content, 'grounding_metadata', None)\n",
- " if grounding_metadata is not None:\n",
- " display.display(\n",
- " display.HTML(grounding_metadata.search_entry_point.rendered_content))\n",
+ " grounding_metadata = getattr(server_content, \"grounding_metadata\", None)\n",
+ " if grounding_metadata is not None:\n",
+ " display.display(\n",
+ " display.HTML(grounding_metadata.search_entry_point.rendered_content)\n",
+ " )\n",
"\n",
- " return"
+ " return"
]
},
{
@@ -382,27 +386,31 @@
"outputs": [],
"source": [
"import json\n",
+ "\n",
+ "\n",
"async def handle_tool_call(session, tool_call):\n",
- " for fc in tool_call.function_calls:\n",
- " function_name = fc.name\n",
- " arguments = fc.args\n",
- " if function_name == \"create_team\":\n",
- " team = arguments.get(\"team_data\")\n",
- " result = create_team(team.get('name'), team.get('people'))\n",
- " elif function_name == \"atlas_search_tool\":\n",
- " result = atlas_search(arguments.get(\"query\"), arguments.get(\"k\", 5))\n",
- " else:\n",
- " result = \"Unknown function\"\n",
- " tool_response = types.LiveClientToolResponse(\n",
- " function_responses=[types.FunctionResponse(\n",
- " name=fc.name,\n",
- " id=fc.id,\n",
- " response={'result': result},\n",
- " )]\n",
- " )\n",
+ " for fc in tool_call.function_calls:\n",
+ " function_name = fc.name\n",
+ " arguments = fc.args\n",
+ " if function_name == \"create_team\":\n",
+ " team = arguments.get(\"team_data\")\n",
+ " result = create_team(team.get(\"name\"), team.get(\"people\"))\n",
+ " elif function_name == \"atlas_search_tool\":\n",
+ " result = atlas_search(arguments.get(\"query\"), arguments.get(\"k\", 5))\n",
+ " else:\n",
+ " result = \"Unknown function\"\n",
+ " tool_response = types.LiveClientToolResponse(\n",
+ " function_responses=[\n",
+ " types.FunctionResponse(\n",
+ " name=fc.name,\n",
+ " id=fc.id,\n",
+ " response={\"result\": result},\n",
+ " )\n",
+ " ]\n",
+ " )\n",
"\n",
- " print('\\n>>> ', tool_response)\n",
- " await session.send(tool_response)"
+ " print(\"\\n>>> \", tool_response)\n",
+ " await session.send(tool_response)"
]
},
{
@@ -483,7 +491,7 @@
}
],
"source": [
- "await run(prompt=\"Hello?\", tools=None, modality = \"AUDIO\")"
+ "await run(prompt=\"Hello?\", tools=None, modality=\"AUDIO\")"
]
},
{
@@ -501,10 +509,7 @@
"source": [
"### MongoDB Vector Database and Connection Setup\n",
"\n",
- "MongoDB acts as both an operational and a vector database for the RAG system.\n",
- "MongoDB Atlas specifically provides a database solution that efficiently stores, queries and retrieves vector embeddings.\n",
- "\n",
- "Creating a database and collection within MongoDB is made simple with MongoDB Atlas.\n",
+ "MongoDB acts as both an operational and a vector database for the RAG system. Creating a database and collection within MongoDB is made simple with MongoDB Atlas.\n",
"\n",
"1. First, register for a [MongoDB Atlas account](https://www.mongodb.com/cloud/atlas/register). For existing users, sign into MongoDB Atlas.\n",
"2. [Follow the instructions](https://www.mongodb.com/docs/atlas/tutorial/deploy-free-tier-cluster/). Select Atlas UI as the procedure to deploy your first cluster."
@@ -665,9 +670,9 @@
"id": "KJT6axzPeUvq"
},
"source": [
- "# Prepare MongoDB Atlas vector store\n",
+ "# Prepare MongoDB vector store\n",
"\n",
- "Run the following code to create the Atlas Vector Search index and insert some vectorised employee records for our database."
+ "Run the following code to create the MongoDB Vector Search index and insert some vectorised employee records for our database."
]
},
{
@@ -713,35 +718,32 @@
"# Create the search index\n",
"## create index\n",
"search_index_model = SearchIndexModel(\n",
- " definition={\n",
- " \"fields\": [\n",
- " {\n",
- " \"type\": \"vector\",\n",
- " \"numDimensions\": 768,\n",
- " \"path\": \"embedding\",\n",
- " \"similarity\": \"cosine\"\n",
- " },\n",
- " ]\n",
- " },\n",
- " name=\"vector_index\",\n",
- " type=\"vectorSearch\",\n",
+ " definition={\n",
+ " \"fields\": [\n",
+ " {\n",
+ " \"type\": \"vector\",\n",
+ " \"numDimensions\": 768,\n",
+ " \"path\": \"embedding\",\n",
+ " \"similarity\": \"cosine\",\n",
+ " },\n",
+ " ]\n",
+ " },\n",
+ " name=\"vector_index\",\n",
+ " type=\"vectorSearch\",\n",
")\n",
"result = collection.create_search_index(model=search_index_model)\n",
"print(\"New search index named \" + result + \" is building.\")\n",
"# Wait for initial sync to complete\n",
"print(\"Polling to check if the index is ready. This may take up to a minute.\")\n",
- "predicate=None\n",
+ "predicate = None\n",
"if predicate is None:\n",
- " predicate = lambda index: index.get(\"queryable\") is True\n",
+ " predicate = lambda index: index.get(\"queryable\") is True\n",
"while True:\n",
- " indices = list(collection.list_search_indexes(result))\n",
- " if len(indices) and predicate(indices[0]):\n",
- " break\n",
- " time.sleep(5)\n",
- "print(result + \" is ready for querying.\")\n",
- "\n",
- "\n",
- "\n"
+ " indices = list(collection.list_search_indexes(result))\n",
+ " if len(indices) and predicate(indices[0]):\n",
+ " break\n",
+ " time.sleep(5)\n",
+ "print(result + \" is ready for querying.\")"
]
},
{
@@ -764,7 +766,7 @@
"source": [
"## Insert Employee Data\n",
"\n",
- "In this section, we will insert sample employee data into the MongoDB Atlas vector store. This data includes employee details such as name, department, location, and salary, along with their respective embeddings."
+ "In this section, we will insert sample employee data into the MongoDB Vector Store. This data includes employee details such as name, department, location, and salary, along with their respective embeddings."
]
},
{
@@ -792,4652 +794,4654 @@
"source": [
"## Insert data\n",
"\n",
- "collection.insert_many([{\n",
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+ " \"_id\": \"54636\",\n",
+ " \"content\": \"Employee number 54636, name Jane Smith, department Finance, location Chicago, salary 130000\",\n",
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+ " 0.0052247434,\n",
+ " -0.013826671,\n",
+ " 0.06707814,\n",
+ " 0.0406519,\n",
+ " 0.03318739,\n",
+ " 0.010909002,\n",
+ " 0.029758368,\n",
+ " ],\n",
+ " },\n",
+ " ]\n",
+ ")"
]
},
{
@@ -5446,9 +5450,9 @@
"id": "EVb8Ia6LCv3B"
},
"source": [
- "### MongoDB Atlas Vector Search with Gemini 2.0\n",
+ "### MongoDB Vector Search with Gemini 2.0\n",
"\n",
- "A vector similarity search implementation that leverages MongoDB Atlas Vector Search and Google's Gemini 2.0 embeddings to perform semantic document searches, returning the k-most similar documents based on query embedding comparison."
+ "A vector similarity search implementation that leverages MongoDB Vector Search and Google's Gemini 2.0 embeddings to perform semantic document searches, returning the k-most similar documents based on query embedding comparison."
]
},
{
@@ -5459,8 +5463,6 @@
},
"outputs": [],
"source": [
- "\n",
- "\n",
"from langchain_google_genai import GoogleGenerativeAIEmbeddings\n",
"from langchain.vectorstores import MongoDBAtlasVectorSearch\n",
"import os\n",
@@ -5468,20 +5470,24 @@
"# Assuming you have set your MongoDB connection string as an environment variable\n",
"embeddings = GoogleGenerativeAIEmbeddings(model=\"models/embedding-001\")\n",
"vector_store = MongoDBAtlasVectorSearch.from_connection_string(\n",
- " connection_string=MONGO_URI,\n",
- " namespace=\"google-ai.embedded_docs\",\n",
- " embedding_key=\"embedding\",\n",
- " text_key=\"content\",\n",
- " index_name=\"vector_index\",\n",
- " embedding=embeddings\n",
- " )\n",
+ " connection_string=MONGO_URI,\n",
+ " namespace=\"google-ai.embedded_docs\",\n",
+ " embedding_key=\"embedding\",\n",
+ " text_key=\"content\",\n",
+ " index_name=\"vector_index\",\n",
+ " embedding=embeddings,\n",
+ ")\n",
+ "\n",
+ "\n",
"def atlas_search(query: str, k: int = 5):\n",
" \"\"\"\n",
- " Perform a vector similarity search using MongoDB Atlas Vector Search.\n",
+ " Perform a vector similarity search using MongoDB Vector Search.\n",
" \"\"\"\n",
" try:\n",
"\n",
- " vector_search_results = vector_store.similarity_search_with_score(query=query, k=k)\n",
+ " vector_search_results = vector_store.similarity_search_with_score(\n",
+ " query=query, k=k\n",
+ " )\n",
" ## Remove \"embedding\" key\n",
" modified_results = []\n",
" for doc, score in vector_search_results:\n",
@@ -5518,33 +5524,26 @@
"teams_collection = db[\"team\"]\n",
"\n",
"\n",
- "\n",
"@retry.Retry()\n",
"def create_team(name, people):\n",
- " \"\"\"\n",
- " Creates a new team in the teams collection.\n",
+ " \"\"\"\n",
+ " Creates a new team in the teams collection.\n",
"\n",
- " Args:\n",
- " name : Name of the team\n",
- " people : A list of people in the team.\n",
+ " Args:\n",
+ " name : Name of the team\n",
+ " people : A list of people in the team.\n",
"\n",
- " Returns:\n",
- " A message indicating whether the order was successfully created or an error message.\n",
- " \"\"\"\n",
- " try:\n",
- " result = teams_collection.insert_one({'name': name,\n",
- " 'people' : people\n",
+ " Returns:\n",
+ " A message indicating whether the order was successfully created or an error message.\n",
+ " \"\"\"\n",
+ " try:\n",
+ " result = teams_collection.insert_one({\"name\": name, \"people\": people})\n",
+ " return f\"Team created successfully with ID: {result.inserted_id}\"\n",
+ " except Exception as e:\n",
+ " return f\"Error creating order: {e}\"\n",
"\n",
- " })\n",
- " return f\"Team created successfully with ID: {result.inserted_id}\"\n",
- " except Exception as e:\n",
- " return f\"Error creating order: {e}\"\n",
"\n",
- "tool_calls = {\n",
- " 'atlas_search_tool': atlas_search,\n",
- " 'create_order': create_team\n",
- "}\n",
- "\n"
+ "tool_calls = {\"atlas_search_tool\": atlas_search, \"create_order\": create_team}"
]
},
{
@@ -5574,54 +5573,40 @@
" \"type\": \"object\",\n",
" \"description\": \"A dictionary containing the team details.\",\n",
" \"properties\": {\n",
- " \"name\": {\n",
- " \"type\": \"string\",\n",
- " \"description\": \"team name\"\n",
- " },\n",
+ " \"name\": {\"type\": \"string\", \"description\": \"team name\"},\n",
" \"people\": {\n",
" \"type\": \"array\",\n",
" \"description\": \"A list of people in the team.\",\n",
" \"items\": {\n",
" \"type\": \"string\",\n",
- " \"description\": \"A person in the team.\"\n",
- " }\n",
- " }\n",
+ " \"description\": \"A person in the team.\",\n",
+ " },\n",
+ " },\n",
" },\n",
- " \"required\": [\"name\",\"people\"]\n",
+ " \"required\": [\"name\", \"people\"],\n",
" }\n",
" },\n",
- " \"required\": [\"team_data\"]\n",
- " }\n",
+ " \"required\": [\"team_data\"],\n",
+ " },\n",
"}\n",
"\n",
"atlas_search_tool = {\n",
" \"name\": \"atlas_search_tool\",\n",
- " \"description\": \" Perform a vector similarity search for employees using MongoDB Atlas Vector\",\n",
+ " \"description\": \" Perform a vector similarity search for employees using MongoDB Vector Store\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
- " \"query\": {\n",
- " \"type\": \"string\",\n",
- " \"description\": \"The search query.\"\n",
- " },\n",
- " \"k\": {\n",
- " \"type\": \"integer\",\n",
- " \"description\": \"The number of results to return.\"\n",
- " }\n",
+ " \"query\": {\"type\": \"string\", \"description\": \"The search query.\"},\n",
+ " \"k\": {\"type\": \"integer\", \"description\": \"The number of results to return.\"},\n",
" },\n",
- " \"required\": [\"query\"]\n",
- " }\n",
+ " \"required\": [\"query\"],\n",
+ " },\n",
"}\n",
"\n",
"\n",
- "tools = [\n",
- " {'function_declarations': [team_tool, atlas_search_tool]}\n",
- "]\n",
+ "tools = [{\"function_declarations\": [team_tool, atlas_search_tool]}]\n",
"\n",
- "tool_calls = {\n",
- " 'atlas_search_tool': atlas_search,\n",
- " 'create_team': create_team\n",
- "}"
+ "tool_calls = {\"atlas_search_tool\": atlas_search, \"create_team\": create_team}"
]
},
{
@@ -5649,7 +5634,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "before client invoke [{'function_declarations': [{'name': 'create_team', 'description': 'Creates a new team in the teams collection.', 'parameters': {'type': 'object', 'properties': {'team_data': {'type': 'object', 'description': 'A dictionary containing the team details.', 'properties': {'name': {'type': 'string', 'description': 'team name'}, 'people': {'type': 'array', 'description': 'A list of people in the team.', 'items': {'type': 'string', 'description': 'A person in the team.'}}}, 'required': ['name', 'people']}}, 'required': ['team_data']}}, {'name': 'atlas_search_tool', 'description': ' Perform a vector similarity search for employees using MongoDB Atlas Vector', 'parameters': {'type': 'object', 'properties': {'query': {'type': 'string', 'description': 'The search query.'}, 'k': {'type': 'integer', 'description': 'The number of results to return.'}}, 'required': ['query']}}]}]\n"
+ "before client invoke [{'function_declarations': [{'name': 'create_team', 'description': 'Creates a new team in the teams collection.', 'parameters': {'type': 'object', 'properties': {'team_data': {'type': 'object', 'description': 'A dictionary containing the team details.', 'properties': {'name': {'type': 'string', 'description': 'team name'}, 'people': {'type': 'array', 'description': 'A list of people in the team.', 'items': {'type': 'string', 'description': 'A person in the team.'}}}, 'required': ['name', 'people']}}, 'required': ['team_data']}}, {'name': 'atlas_search_tool', 'description': ' Perform a vector similarity search for employees using MongoDB Vector Store', 'parameters': {'type': 'object', 'properties': {'query': {'type': 'string', 'description': 'The search query.'}, 'k': {'type': 'integer', 'description': 'The number of results to return.'}}, 'required': ['query']}}]}]\n"
]
},
{
@@ -5709,8 +5694,7 @@
"\"\"\"\n",
"\n",
"\n",
- "\n",
- "await run(prompt, tools=tools, modality = \"AUDIO\")"
+ "await run(prompt, tools=tools, modality=\"AUDIO\")"
]
},
{
@@ -5738,7 +5722,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "before client invoke [{'code_execution': {}}, {'function_declarations': [{'name': 'create_team', 'description': 'Creates a new team in the teams collection.', 'parameters': {'type': 'object', 'properties': {'team_data': {'type': 'object', 'description': 'A dictionary containing the team details.', 'properties': {'name': {'type': 'string', 'description': 'team name'}, 'people': {'type': 'array', 'description': 'A list of people in the team.', 'items': {'type': 'string', 'description': 'A person in the team.'}}}, 'required': ['name', 'people']}}, 'required': ['team_data']}}, {'name': 'atlas_search_tool', 'description': ' Perform a vector similarity search for employees using MongoDB Atlas Vector', 'parameters': {'type': 'object', 'properties': {'query': {'type': 'string', 'description': 'The search query.'}, 'k': {'type': 'integer', 'description': 'The number of results to return.'}}, 'required': ['query']}}]}]\n"
+ "before client invoke [{'code_execution': {}}, {'function_declarations': [{'name': 'create_team', 'description': 'Creates a new team in the teams collection.', 'parameters': {'type': 'object', 'properties': {'team_data': {'type': 'object', 'description': 'A dictionary containing the team details.', 'properties': {'name': {'type': 'string', 'description': 'team name'}, 'people': {'type': 'array', 'description': 'A list of people in the team.', 'items': {'type': 'string', 'description': 'A person in the team.'}}}, 'required': ['name', 'people']}}, 'required': ['team_data']}}, {'name': 'atlas_search_tool', 'description': ' Perform a vector similarity search for employees using MongoDB Vector Store', 'parameters': {'type': 'object', 'properties': {'query': {'type': 'string', 'description': 'The search query.'}, 'k': {'type': 'integer', 'description': 'The number of results to return.'}}, 'required': ['query']}}]}]\n"
]
},
{
@@ -6007,9 +5991,10 @@
}
],
"source": [
- "tools = [ {'code_execution': {}},\n",
- " {'function_declarations': [team_tool, atlas_search_tool]}\n",
- " ]\n",
+ "tools = [\n",
+ " {\"code_execution\": {}},\n",
+ " {\"function_declarations\": [team_tool, atlas_search_tool]},\n",
+ "]\n",
"\n",
"prompt = \"\"\"Search for \"marketing\" in the database and use thier names to create a team :\n",
"1. Search for \"marketing\"\n",
@@ -6018,8 +6003,7 @@
"\"\"\"\n",
"\n",
"\n",
- "\n",
- "await run(prompt, tools=tools, modality = \"TEXT\")"
+ "await run(prompt, tools=tools, modality=\"TEXT\")"
]
},
{
diff --git a/notebooks/agents/MongoDB_Haystack_self_reflecting_Cooking_agent.ipynb b/notebooks/agents/MongoDB_Haystack_self_reflecting_Cooking_agent.ipynb
index 216e3d53..9a22ca66 100644
--- a/notebooks/agents/MongoDB_Haystack_self_reflecting_Cooking_agent.ipynb
+++ b/notebooks/agents/MongoDB_Haystack_self_reflecting_Cooking_agent.ipynb
@@ -17,7 +17,7 @@
"source": [
"# Haystack and MongoDB Atlas Agentic RAG pipelines\n",
"\n",
- "Haystack and MongoDB enhanced example building on top of the basic RAG pipeline demonstrated on the following [notebook](https://github.com/mongodb-developer/GenAI-Showcase/blob/main/notebooks/rag/haystack_mongodb_cooking_advisor_pipeline.ipynb). Here the pipelines uses advanced technics of self reflection to advise on reciepes considering prices associated from the MongoDB Atlas vector store.\n",
+ "Haystack and MongoDB enhanced example building on top of the basic RAG pipeline demonstrated on the following [notebook](https://github.com/mongodb-developer/GenAI-Showcase/blob/main/notebooks/rag/haystack_mongodb_cooking_advisor_pipeline.ipynb). Here the pipelines uses advanced technics of self reflection to advise on reciepes considering prices associated from the MongoDB Vector Store.\n",
"\n",
"Install dependencies:"
]
diff --git a/notebooks/agents/Pragmatic_LLM_Application_Introduction_From_RAG_to_Agents_with_MongoDB.ipynb b/notebooks/agents/Pragmatic_LLM_Application_Introduction_From_RAG_to_Agents_with_MongoDB.ipynb
index 94c9c744..8f162f3b 100644
--- a/notebooks/agents/Pragmatic_LLM_Application_Introduction_From_RAG_to_Agents_with_MongoDB.ipynb
+++ b/notebooks/agents/Pragmatic_LLM_Application_Introduction_From_RAG_to_Agents_with_MongoDB.ipynb
@@ -1612,7 +1612,7 @@
"source": [
"## 1.4 Vector Index Creation\n",
"\n",
- "- [Create an Atlas Vector Search Index](https://www.mongodb.com/docs/compass/current/indexes/create-vector-search-index/)\n",
+ "- [Create an MongoDB Vector Search Index](https://www.mongodb.com/docs/compass/current/indexes/create-vector-search-index/)\n",
"\n",
"- If you are following this notebook ensure that you are creating a vector search index for the right database(demo_company_employees) and collection(employees_records)\n",
"\n",
diff --git a/notebooks/agents/airbnb_agent_openai_llamaindex_mongodb.ipynb b/notebooks/agents/airbnb_agent_openai_llamaindex_mongodb.ipynb
index 3278687a..316babb6 100644
--- a/notebooks/agents/airbnb_agent_openai_llamaindex_mongodb.ipynb
+++ b/notebooks/agents/airbnb_agent_openai_llamaindex_mongodb.ipynb
@@ -829,7 +829,7 @@
"id": "dC7CDZGhzPLn"
},
"source": [
- "## Create MongoDB Atlas Vector Store"
+ "## Create MongoDB Vector Store"
]
},
{
diff --git a/notebooks/agents/hr_agentic_chatbot_with_langgraph_claude.ipynb b/notebooks/agents/hr_agentic_chatbot_with_langgraph_claude.ipynb
index f1348227..aa655650 100644
--- a/notebooks/agents/hr_agentic_chatbot_with_langgraph_claude.ipynb
+++ b/notebooks/agents/hr_agentic_chatbot_with_langgraph_claude.ipynb
@@ -1544,7 +1544,7 @@
"source": [
"1.4 Vector Index Creation\n",
"\n",
- "- [Create an Atlas Vector Search Index](https://www.mongodb.com/docs/compass/current/indexes/create-vector-search-index/)\n",
+ "- [Create an MongoDB Vector Search Index](https://www.mongodb.com/docs/compass/current/indexes/create-vector-search-index/)\n",
"\n",
"- If you are following this notebook ensure that you are creating a vector search index for the right database(demo_company_employees) and collection(employees_records)\n",
"\n",
diff --git a/notebooks/agents/implementing_working_memory_with_tavily_and_mongodb.ipynb b/notebooks/agents/implementing_working_memory_with_tavily_and_mongodb.ipynb
index 70824a08..b937ac95 100644
--- a/notebooks/agents/implementing_working_memory_with_tavily_and_mongodb.ipynb
+++ b/notebooks/agents/implementing_working_memory_with_tavily_and_mongodb.ipynb
@@ -1936,7 +1936,7 @@
"source": [
"Retrieving data from MongoDB involves leveraging both traditional queries and vector search. For traditional queries, the pymongo library provides methods like `find_one()` and `find()` to retrieve documents based on specific criteria.\n",
"\n",
- "MongoDB Atlas Vector Search is used for semantic-based retrieval. This feature allows for efficient similarity searches using the pre-calculated product embeddings. The system can retrieve products that are semantically similar to the query by querying the' embedding' field with a target embedding.\n",
+ "MongoDB Vector Search is used for semantic-based retrieval. This feature allows for efficient similarity searches using the pre-calculated product embeddings. The system can retrieve products that are semantically similar to the query by querying the' embedding' field with a target embedding.\n",
"\n",
"This approach significantly enhances the AI sales assistant's ability to understand user intent and offer relevant product suggestions. Variables like `embedding_field_name` and `vector_search_index_name` are used to configure and interact with the vector search index within MongoDB, ensuring efficient retrieval of similar products.\n"
]
@@ -1960,7 +1960,7 @@
"source": [
"# The field containing the text embeddings on each document\n",
"embedding_field_name = \"embedding\"\n",
- "# MongoDB Atlas Vector Search index name\n",
+ "# MongoDB Vector Search index name\n",
"vector_search_index_name = \"vector_index\""
]
},
diff --git a/notebooks/agents/mongodb_as_a_toolbox_for_llamaindex_agents.ipynb b/notebooks/agents/mongodb_as_a_toolbox_for_llamaindex_agents.ipynb
index ba5e382a..c5a76fbd 100644
--- a/notebooks/agents/mongodb_as_a_toolbox_for_llamaindex_agents.ipynb
+++ b/notebooks/agents/mongodb_as_a_toolbox_for_llamaindex_agents.ipynb
@@ -23,7 +23,7 @@
"**Key Features:**\n",
"\n",
"* **MongoDB as a Tool Registry:** Instead of hardcoding tool definitions within the agent, this application stores tool metadata (name, description, parameters) directly in a MongoDB collection.\n",
- "* **MongoDB Atlas Vector Search for Tool Discovery:** LlamaIndex uses the vector embeddings of tool descriptions stored in MongoDB to perform semantic searches based on user queries. This allows the agent to dynamically discover and select the most relevant tools for a given task.\n",
+ "* **MongoDB Vector Search for Tool Discovery:** LlamaIndex uses the vector embeddings of tool descriptions stored in MongoDB to perform semantic searches based on user queries. This allows the agent to dynamically discover and select the most relevant tools for a given task.\n",
"* **LlamaIndex Agent with Function Calling:** The LlamaIndex agent is configured to use the retrieved tool definitions from MongoDB to enable function calling. This means the agent can understand the user's intent and execute the appropriate Python function (tool) stored in the application.\n",
"* **Data Storage in MongoDB:** Besides tool definitions, the application also uses separate MongoDB collections to store operational data like customer orders, return requests, and policy documents.\n",
"* **Integration with External Services:** The tools defined and managed in MongoDB can interact with external services (e.g., fetching real-time data, processing requests) or perform operations on the data stored within MongoDB itself (e.g., looking up order details, creating return requests).\n",
@@ -48,7 +48,7 @@
"- `llama-index-core`: The core LlamaIndex library.\n",
"- `llama-index-llms-openai`: LlamaIndex integration with OpenAI LLMs.\n",
"- `llama-index-embeddings-voyageai`: LlamaIndex integration with VoyageAI embeddings.\n",
- "- `llama-index-vector-stores-mongodb`: LlamaIndex integration with MongoDB Atlas Vector Search.\n",
+ "- `llama-index-vector-stores-mongodb`: LlamaIndex integration with MongoDB Vector Search.\n",
"- `llama-index-readers-file`: LlamaIndex file readers."
]
},
@@ -165,9 +165,9 @@
"id": "b6f3012b"
},
"source": [
- "## Download and store policy documents into MongoDB Atlas vector store\n",
+ "## Download and store policy documents into MongoDB Vector Store\n",
"\n",
- "This cell downloads policy documents and stores them in a MongoDB Atlas vector store. It initializes a vector store, checks if the collection is empty, downloads PDF documents, loads them, adds metadata, initializes embedding and node parsing, parses documents into nodes, creates a storage context, creates a vector index, and ingests the documents."
+ "This cell downloads policy documents and stores them in a MongoDB Vector Store. It initializes a vector store, checks if the collection is empty, downloads PDF documents, loads them, adds metadata, initializes embedding and node parsing, parses documents into nodes, creates a storage context, creates a vector index, and ingests the documents."
]
},
{
diff --git a/notebooks/agents/mongodb_voyage_ai_openai_rag_hybrid_agentic_sports_scores.ipynb b/notebooks/agents/mongodb_voyage_ai_openai_rag_hybrid_agentic_sports_scores.ipynb
index 8d8e91b0..8eed32f4 100644
--- a/notebooks/agents/mongodb_voyage_ai_openai_rag_hybrid_agentic_sports_scores.ipynb
+++ b/notebooks/agents/mongodb_voyage_ai_openai_rag_hybrid_agentic_sports_scores.ipynb
@@ -6,9 +6,9 @@
"id": "Pff8TULfBfmW"
},
"source": [
- "# MongoDB Atlas Vector Search with VoyageAI Embeddings for Sports Scores and Stories\n",
+ "# MongoDB Vector Search with VoyageAI Embeddings for Sports Scores and Stories\n",
"\n",
- "This notebook demonstrates how to use VoyageAI embeddings with MongoDB Atlas Vector Search for retrieving relevant sports scores and stories based on user queries."
+ "This notebook demonstrates how to use VoyageAI embeddings with MongoDB Vector Search for retrieving relevant sports scores and stories based on user queries."
]
},
{
diff --git a/notebooks/agents/self_reflecting_gift_agent_haystack.ipynb b/notebooks/agents/self_reflecting_gift_agent_haystack.ipynb
index cc2fe933..0880d38f 100644
--- a/notebooks/agents/self_reflecting_gift_agent_haystack.ipynb
+++ b/notebooks/agents/self_reflecting_gift_agent_haystack.ipynb
@@ -9,7 +9,7 @@
"# Self-Reflecting Gift Agent with Haystack and MongoDB Atlas\n",
"This notebook demonstrates how to build a self-reflecting gift selection agent using [Haystack](https://haystack.deepset.ai/) and MongoDB Atlas!\n",
"\n",
- "The agent will help optimize gift selections based on children's wishlists and budget constraints, using MongoDB Atlas vector search for semantic matching and implementing self-reflection to ensure the best possible gift combinations.\n",
+ "The agent will help optimize gift selections based on children's wishlists and budget constraints, using MongoDB Vector Search for semantic matching and implementing self-reflection to ensure the best possible gift combinations.\n",
"\n",
"**Components to use in this notebook:**\n",
"- [`OpenAITextEmbedder`](https://docs.haystack.deepset.ai/docs/openaitextembedder) for query embedding\n",
diff --git a/notebooks/agents/smolagents_hf_with_mongodb.ipynb b/notebooks/agents/smolagents_hf_with_mongodb.ipynb
index 80bc9693..04f5960c 100644
--- a/notebooks/agents/smolagents_hf_with_mongodb.ipynb
+++ b/notebooks/agents/smolagents_hf_with_mongodb.ipynb
@@ -1273,9 +1273,9 @@
"id": "YL83jmaPB-iu"
},
"source": [
- "## Vector Search based RAG with Atlas Search\n",
+ "## Vector Search based RAG with MongoDB Search\n",
"\n",
- "Vector search allows us to find relevant documents based on the semantic meaning of the query rather than just keyword matching. In this section, we demonstrate how to build a Retrieval-Augmented Generation (RAG) agent that leverages MongoDB Atlas Search's vector search capabilities.\n",
+ "Vector search allows us to find relevant documents based on the semantic meaning of the query rather than just keyword matching. In this section, we demonstrate how to build a Retrieval-Augmented Generation (RAG) agent that leverages MongoDB Search's vector search capabilities.\n",
"\n",
"The RAG agent uses the `vector_search_rentals` tool to find relevant documents based on the query's embeddings. This approach enhances the search results by considering the context and meaning of the query, providing more accurate and relevant results.\n",
"\n",
@@ -1390,7 +1390,7 @@
" response = embedding(model=\"text-embedding-3-small\", input=[query])\n",
" query_embedding = response[\"data\"][0][\"embedding\"]\n",
"\n",
- " # Perform vector search using Atlas Search\n",
+ " # Perform vector search using MongoDB Search\n",
" pipeline = [\n",
" {\n",
" \"$vectorSearch\": {\n",
@@ -2432,7 +2432,7 @@
"\n",
"* **Robust Tool Design:** The tools now incorporate error handling, providing more informative feedback to the user in case of issues. The exclusion of embedding fields from queries enhances performance and readability of results.\n",
"* **Enhanced Query Handling:** The inclusion of an initial projection stage in the aggregation pipeline, specifically designed to remove embedding fields (`text_embeddings` and `image_embeddings`) prior to other stages, ensures more efficient query execution and smaller response sizes. The use of `json.loads()` ensures that the pipeline string received from the LLM is correctly parsed.\n",
- "Atlas Search excels at finding relevant documents quickly, thanks to its vector search capabilities. This is particularly beneficial for large datasets where traditional keyword search may be insufficient.\n",
+ "MongoDB Search excels at finding relevant documents quickly, thanks to its vector search capabilities. This is particularly beneficial for large datasets where traditional keyword search may be insufficient.\n",
"* **Improved User Experience:** Clearer tool documentation and example usage further enhance the user's ability to interact with the agent and interpret results.\n",
"* **Practical Application:** The demonstration showcases a practical application for analyzing data within a MongoDB Atlas database using an LLM-powered agent.\n",
"\n",
diff --git a/notebooks/agents/video_intelligence_agent.ipynb b/notebooks/agents/video_intelligence_agent.ipynb
index 6c109bda..a3667335 100644
--- a/notebooks/agents/video_intelligence_agent.ipynb
+++ b/notebooks/agents/video_intelligence_agent.ipynb
@@ -1163,7 +1163,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The code below is a helper wraps MongoDB Atlas Search index creation: \n",
+ "The code below is a helper wraps MongoDB Search index creation: \n",
"- given a collection, an index-definition dict, and a name, \n",
"- it builds a SearchIndexModel, calls create_search_index, \n",
"- and returns the result—printing success or catching errors and returning None.\n"
@@ -1369,7 +1369,7 @@
"\n",
"By tuning `numCandidates` and `limit`, you can balance throughput, resource usage, and retrieval fidelity for your specific dataset.\n",
"\n",
- "[1]: https://www.mongodb.com/docs/drivers/rust/v3.1/fundamentals/aggregation/vector-search/ \"Atlas Vector Search - Rust Driver v3.1 - MongoDB Docs\"\n",
+ "[1]: https://www.mongodb.com/docs/drivers/rust/v3.1/fundamentals/aggregation/vector-search/ \"MongoDB Vector Search - Rust Driver v3.1 - MongoDB Docs\"\n",
"[2]: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/ \"Run Vector Search Queries - Atlas - MongoDB Docs\"\n"
]
},
@@ -1615,7 +1615,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "**`hybrid_search`** combines semantic vector search and traditional text search in MongoDB using the `$rankFusion` operator. It first converts the `user_query` into an embedding via `get_voyage_embedding`, then defines two sub-pipelines—one using `$vectorSearch` on the specified `vector_search_index_name`, the other using Atlas Search’s `$search` on `text_search_index_name`. These pipelines each retrieve up to 20 candidates, which are then merged and re-ranked according to specified weights, producing a unified list of the top-`top_n` results enriched with detailed scoring information.\n",
+ "**`hybrid_search`** combines semantic vector search and traditional text search in MongoDB using the `$rankFusion` operator. It first converts the `user_query` into an embedding via `get_voyage_embedding`, then defines two sub-pipelines—one using `$vectorSearch` on the specified `vector_search_index_name`, the other using MongoDB Search’s `$search` on `text_search_index_name`. These pipelines each retrieve up to 20 candidates, which are then merged and re-ranked according to specified weights, producing a unified list of the top-`top_n` results enriched with detailed scoring information.\n",
"\n",
"The `$rankFusion` stage supports key parameters for fine-tuning relevance blending:\n",
"\n",
diff --git a/notebooks/agents/zero_to_hero_with_genai_with_mongodb_openai.ipynb b/notebooks/agents/zero_to_hero_with_genai_with_mongodb_openai.ipynb
index 9006ac1d..c2c7292a 100644
--- a/notebooks/agents/zero_to_hero_with_genai_with_mongodb_openai.ipynb
+++ b/notebooks/agents/zero_to_hero_with_genai_with_mongodb_openai.ipynb
@@ -688,7 +688,7 @@
"A vector search index organizes high-dimensional embeddings for efficient similarity searches. Without it, finding similar vectors would require exhaustive comparisons against every vector in your database—becoming impractical at scale. These indexes enable fast semantic searches by organizing vectors based on their geometric relationships, essential for RAG, recommendation systems, and semantic search.\n",
"\n",
"#### Understanding HNSW (Hierarchical Navigable Small Worlds)\n",
- "HNSW is MongoDB Atlas Vector Search's algorithm of choice for approximate nearest neighbor searches:\n",
+ "HNSW is MongoDB Vector Search's algorithm of choice for approximate nearest neighbor searches:\n",
"- Creates a multi-layered graph connecting vectors to their nearest neighbors\n",
"- Enables logarithmic search complexity through a hierarchical approach\n",
"- Balances speed and accuracy via configurable parameters\n",
@@ -1035,7 +1035,7 @@
" paths = [paths]\n",
"\n",
" # Define the text search stage using MongoDB's $search operator.\n",
- " # This is part of Atlas Search and provides more powerful text search capabilities\n",
+ " # This is part of MongoDB Search and provides more powerful text search capabilities\n",
" # than MongoDB's standard text index.\n",
" text_search_stage = {\n",
" \"$search\": {\n",
@@ -1264,7 +1264,7 @@
"### Step 10: Define Hybrid Search Function\n",
"\n",
"\n",
- "The `hybrid_search_with_mongodb` function conducts a hybrid search on a MongoDB Atlas collection that combines a vector search and a full-text search using Atlas Search.\n",
+ "The `hybrid_search_with_mongodb` function conducts a hybrid search on a MongoDB Atlas collection that combines a vector search and a full-text search using MongoDB Search.\n",
"\n",
"In the MongoDB hybrid search function, there are two weights:\n",
"\n",
@@ -1338,7 +1338,7 @@
"):\n",
" \"\"\"\n",
" Conduct a hybrid search on a MongoDB Atlas collection that combines a vector search\n",
- " and a full-text search using Atlas Search.\n",
+ " and a full-text search using MongoDB Search.\n",
"\n",
" Args:\n",
" user_query (str): The user's query string.\n",
@@ -1419,7 +1419,7 @@
" \"$unionWith\": {\n",
" \"coll\": collection_name, # Collection to search\n",
" \"pipeline\": [\n",
- " # Perform full text search using Atlas Search\n",
+ " # Perform full text search using MongoDB Search\n",
" {\n",
" \"$search\": {\n",
" \"index\": text_search_index_name, # Name of the text search index\n",
diff --git a/notebooks/evals/Patronus_MongoDB.ipynb b/notebooks/evals/Patronus_MongoDB.ipynb
index 4802ba12..30775714 100644
--- a/notebooks/evals/Patronus_MongoDB.ipynb
+++ b/notebooks/evals/Patronus_MongoDB.ipynb
@@ -8,7 +8,7 @@
"\n",
"## How to Query and Retrieve Results from Atlas Vector Store\n",
"\n",
- "To query and retrieve results from MongoDB Atlas vector store, follow these three steps:\n",
+ "To query and retrieve results from MongoDB Vector Store, follow these three steps:\n",
"\n",
"### Set Up the Database on Atlas\n",
"First, you need to create an account on MongoDB Atlas. This involves signing in to your MongoDB Atlas account, creating a new cluster, and adding a database and collection. You can skip this step if you have already have your collection for vector search.\n",
diff --git a/notebooks/evals/ragas-evaluation.ipynb b/notebooks/evals/ragas-evaluation.ipynb
index 2f4b85b8..c71d00fa 100644
--- a/notebooks/evals/ragas-evaluation.ipynb
+++ b/notebooks/evals/ragas-evaluation.ipynb
@@ -33,7 +33,7 @@
"\n",
"- **langchain**: Python library to develop LLM applications using LangChain\n",
"
\n",
- "- **langchain-mongodb**: Python package to use MongoDB Atlas vector Search with LangChain\n",
+ "- **langchain-mongodb**: Python package to use MongoDB Vector Search with LangChain\n",
"
\n",
"- **langchain-openai**: Python package to use OpenAI models in LangChain\n",
"
\n",
diff --git a/notebooks/performance_guidance/README.md b/notebooks/performance_guidance/README.md
index 860d4cd6..2ddc0743 100644
--- a/notebooks/performance_guidance/README.md
+++ b/notebooks/performance_guidance/README.md
@@ -1,8 +1,8 @@
-Performance guidance showing how MongoDB Atlas Vector Search compares against other vector databases.
+Performance guidance showing how MongoDB Vector Search compares against other vector databases.
-Jupyter Notebooks comparing MongoDB Atlas Vector Search with other vector databases and search engines.
+Jupyter Notebooks comparing MongoDB Vector Search with other vector databases and search engines.
| Title | Notebook |
|-------|-------|
-| Vector Database Comparison For AI Workloads: Elasticsearch vs MongoDB Atlas Vector Search | [](https://colab.research.google.com/github/mongodb-developer/GenAI-Showcase/blob/main/notebooks/performance_guidance/ai_workload_database_architecture_mongodb_elastic.ipynb) |
-| AI Database Performance Comparison For AI Workloads: PostgreSQL/PgVector vs MongoDB Atlas Vector Search | [](https://colab.research.google.com/github/mongodb-developer/GenAI-Showcase/blob/main/notebooks/performance_guidance/vector_database_performance_guidance_mongondb_pgvector.ipynb) |
+| Vector Database Comparison For AI Workloads: Elasticsearch vs MongoDB Vector Search | [](https://colab.research.google.com/github/mongodb-developer/GenAI-Showcase/blob/main/notebooks/performance_guidance/ai_workload_database_architecture_mongodb_elastic.ipynb) |
+| AI Database Performance Comparison For AI Workloads: PostgreSQL/PgVector vs MongoDB Vector Search | [](https://colab.research.google.com/github/mongodb-developer/GenAI-Showcase/blob/main/notebooks/performance_guidance/vector_database_performance_guidance_mongondb_pgvector.ipynb) |
diff --git a/notebooks/performance_guidance/ai_workload_database_architecture_mongodb_elastic.ipynb b/notebooks/performance_guidance/ai_workload_database_architecture_mongodb_elastic.ipynb
index 622cf095..92b93326 100644
--- a/notebooks/performance_guidance/ai_workload_database_architecture_mongodb_elastic.ipynb
+++ b/notebooks/performance_guidance/ai_workload_database_architecture_mongodb_elastic.ipynb
@@ -6,7 +6,7 @@
"id": "0ioUyhIh4VMe"
},
"source": [
- "# Vector Database Comparison For AI Workloads: Elasticsearch vs MongoDB Atlas Vector Search\n",
+ "# Vector Database Comparison For AI Workloads: Elasticsearch vs MongoDB Vector Search\n",
"\n",
"\n",
"[](https://colab.research.google.com/github/mongodb-developer/GenAI-Showcase/blob/main/notebooks/performance_guidance/ai_workload_database_architecture_mongodb_elastic.ipynb)\n",
@@ -1049,7 +1049,7 @@
"id": "KZTYPIn34VMi"
},
"source": [
- "## Part 3: Search with MongoDB Atlas Vector Search"
+ "## Part 3: Search with MongoDB Vector Search"
]
},
{
@@ -1094,7 +1094,7 @@
"### Step 2: Installing MongoDB via Atlas CLI\n",
"\n",
"The Atlas CLI is a command line interface built specifically for MongoDB Atlas.\n",
- "Interact with your Atlas database deployments and Atlas Search from the terminal with short, intuitive commands, so you can accomplish complex database management tasks in seconds.\n",
+ "Interact with your Atlas database deployments and MongoDB Search from the terminal with short, intuitive commands, so you can accomplish complex database management tasks in seconds.\n",
"\n",
"You can follow the instructions [here](https://www.mongodb.com/docs/atlas/cli/current/install-atlas-cli/#complete-the-prerequisites-3) to install the Atlas CLI using docker(other options are available) and get a local MongoDB database instance running.\n",
"\n",
@@ -1496,7 +1496,7 @@
"\n",
"Benefits\n",
"The BinData vector format requires about three times less disk space in your cluster compared to arrays of elements.\n",
- "It allows you to index your vectors with alternate types such as int1 or int8 vectors, reducing the memory needed to build the Atlas Vector Search index for your collection.\n",
+ "It allows you to index your vectors with alternate types such as int1 or int8 vectors, reducing the memory needed to build the MongoDB Vector Search index for your collection.\n",
"It reduces the RAM for mongot by 3.75x for scalar and by 24x for binary; the vector values shrink by 4x and 32x respectively, but the Hierarchical Navigable Small Worlds graph itself doesn't shrink.\n",
"\n",
"In this notebook, we will convert the embeddings to the BSON binData vector format by using the `bson.binary` module.\n",
diff --git a/notebooks/performance_guidance/vector_database_performance_guidance_mongondb_pgvector.ipynb b/notebooks/performance_guidance/vector_database_performance_guidance_mongondb_pgvector.ipynb
index 523f0bbe..1a665812 100644
--- a/notebooks/performance_guidance/vector_database_performance_guidance_mongondb_pgvector.ipynb
+++ b/notebooks/performance_guidance/vector_database_performance_guidance_mongondb_pgvector.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# AI Database Performance Comparison For AI Workloads: PostgreSQL/PgVector vs MongoDB Atlas Vector Search\n",
+ "# AI Database Performance Comparison For AI Workloads: PostgreSQL/PgVector vs MongoDB Vector Search\n",
"\n",
"[](https://colab.research.google.com/github/mongodb-developer/GenAI-Showcase/blob/main/notebooks/performance_guidance/vector_database_performance_guidance_mongondb_pgvector.ipynb)\n",
"-----\n",
@@ -35,14 +35,14 @@
"- PostgreSQL with pgvector:\n",
" - How to set up a PostgreSQL database with the pgvector extension.\n",
" - How to run text, vector, and hybrid searches on PostgreSQL.\n",
- "- MongoDB Atlas Vector Search:\n",
+ "- MongoDB Vector Search:\n",
" - How to set up a MongoDB Atlas database with native Vector Search capabilities.\n",
" - How to execute text, vector, and hybrid searches on MongoDB Atlas.\n",
"- AI Workload Overview:\n",
" - This notebook showcases a standard AI workload involving vector embeddings and the retrieval of semantically similar documents. \n",
" - The system leverages two different vector search solutions:\n",
" - PostgreSQL with pgvector: A powerful extension that integrates vector search capabilities directly into PostgreSQL.\n",
- " - MongoDB Atlas Vector Search: A native vector search feature built into MongoDB, optimized for modern, document-based applications.\n",
+ " - MongoDB Vector Search: A native vector search feature built into MongoDB, optimized for modern, document-based applications.\n",
"- AI Workload Metrics:\n",
" - Latency: The time it takes to retrieve the top n results\n",
" - Throughput: The number of queries processed per second\n",
@@ -1345,9 +1345,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "An Atlas Search index is a data structure that categorizes data in an easily searchable format. It is a mapping between terms and the documents that contain those terms. Atlas Search indexes enable faster retrieval of documents using certain identifiers. You must configure an Atlas Search index to query data in your Atlas cluster using Atlas Search.\n",
+ "An MongoDB Search index is a data structure that categorizes data in an easily searchable format. It is a mapping between terms and the documents that contain those terms. MongoDB Search indexes enable faster retrieval of documents using certain identifiers. You must configure an MongoDB Search index to query data in your Atlas cluster using MongoDB Search.\n",
"\n",
- "You can create an Atlas Search index on a single field or on multiple fields. We recommend that you index the fields that you regularly use to sort or filter your data in order to quickly retrieve the documents that contain the relevant data at query-time.\n",
+ "You can create an MongoDB Search index on a single field or on multiple fields. We recommend that you index the fields that you regularly use to sort or filter your data in order to quickly retrieve the documents that contain the relevant data at query-time.\n",
"\n"
]
},
@@ -1681,7 +1681,7 @@
"source": [
"### Step 9: Define Hybrid Search Function\n",
"\n",
- "The `hybrid_search_with_mongodb` function conducts a hybrid search on a MongoDB Atlas collection that combines a vector search and a full-text search using Atlas Search.\n",
+ "The `hybrid_search_with_mongodb` function conducts a hybrid search on a MongoDB Atlas collection that combines a vector search and a full-text search using MongoDB Search.\n",
"\n",
"In the MongoDB hybrid search function, there are two weights:\n",
"\n",
@@ -1753,7 +1753,7 @@
"):\n",
" \"\"\"\n",
" Conduct a hybrid search on a MongoDB Atlas collection that combines a vector search\n",
- " and a full-text search using Atlas Search.\n",
+ " and a full-text search using MongoDB Search.\n",
"\n",
" Args:\n",
" user_query (str): The user's query string.\n",
@@ -2774,7 +2774,7 @@
"\n",
"This notebook implements and benchmarks a standard AI workload that involves vector embeddings and the retreival of semantically similar documents from a database. The system uses two different vector databases:\n",
"- PostgreSQL with pgvector: A vector database extension for PostgreSQL that enables vector search on the database.\n",
- "- MongoDB Atlas Vector Search: A vector search feature for MongoDB Database that enables vector search on the database.\n"
+ "- MongoDB Vector Search: A vector search feature for MongoDB Database that enables vector search on the database.\n"
]
},
{
@@ -4151,7 +4151,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Part 3: Semantic Search with MongoDB Atlas Vector Search"
+ "## Part 3: Semantic Search with MongoDB Vector Search"
]
},
{
@@ -4179,7 +4179,7 @@
"### Step 2: Installing MongoDB via Atlas CLI\n",
"\n",
"The Atlas CLI is a command line interface built specifically for MongoDB Atlas. \n",
- "Interact with your Atlas database deployments and Atlas Search from the terminal with short, intuitive commands, so you can accomplish complex database management tasks in seconds.\n",
+ "Interact with your Atlas database deployments and MongoDB Search from the terminal with short, intuitive commands, so you can accomplish complex database management tasks in seconds.\n",
"\n",
"You can follow the instructions [here](https://www.mongodb.com/docs/atlas/cli/current/install-atlas-cli/#complete-the-prerequisites-3) to install the Atlas CLI using docker(other options are available) and get a local MongoDB database instance running.\n",
"\n",
diff --git a/notebooks/rag/README.md b/notebooks/rag/README.md
index 9c450cf7..bea8b235 100644
--- a/notebooks/rag/README.md
+++ b/notebooks/rag/README.md
@@ -1,4 +1,4 @@
-Jupyter Notebooks demonstrating various Retrieval Augmented Generation (RAG) implementations using LLM providers, frameworks and MongoDB Atlas Vector Search.
+Jupyter Notebooks demonstrating various Retrieval Augmented Generation (RAG) implementations using LLM providers, frameworks and MongoDB Vector Search.
| Title | Stack | Notebook |
|-------|-------|----------|
diff --git a/notebooks/rag/SwigMenu_Playwright_OpenAI_MongoDB.ipynb b/notebooks/rag/SwigMenu_Playwright_OpenAI_MongoDB.ipynb
index 7df39db4..df062126 100644
--- a/notebooks/rag/SwigMenu_Playwright_OpenAI_MongoDB.ipynb
+++ b/notebooks/rag/SwigMenu_Playwright_OpenAI_MongoDB.ipynb
@@ -27,7 +27,7 @@
"source": [
"## Overview\n",
"\n",
- "In this tutorial we are going to scrape the popular Utah \"dirty\" soda website, Swig, using Playwright, then we are going to feed in our drinks into OpenAI using a prompt and their structured outputs to understand which drinks from their menu are best for various seasons with reasonings, and then save this information into MongoDB Atlas so we can use Atlas Search to find specific drinks based on the fall season and ingredients we are craving."
+ "In this tutorial we are going to scrape the popular Utah \"dirty\" soda website, Swig, using Playwright, then we are going to feed in our drinks into OpenAI using a prompt and their structured outputs to understand which drinks from their menu are best for various seasons with reasonings, and then save this information into MongoDB Atlas so we can use MongoDB Search to find specific drinks based on the fall season and ingredients we are craving."
]
},
{
@@ -906,7 +906,7 @@
"id": "rp_xZtC4DSXp"
},
"source": [
- "Now that our drinks with their reasonings are printed out nicely, let's upload them into MongoDB Atlas so we can use Atlas Search and take a look at drinks based off their ingredients!"
+ "Now that our drinks with their reasonings are printed out nicely, let's upload them into MongoDB Atlas so we can use MongoDB Search and take a look at drinks based off their ingredients!"
]
},
{
@@ -915,7 +915,7 @@
"id": "uObYzbmu_Mjd"
},
"source": [
- "## Step 3: Store into MongoDB and use Atlas Search"
+ "## Step 3: Store into MongoDB and use MongoDB Search"
]
},
{
@@ -1030,7 +1030,7 @@
"id": "wFUitWvnFb4Z"
},
"source": [
- "Create an Atlas Search index on your collection\n",
+ "Create an MongoDB Search index on your collection\n",
"and create an aggregation pipeline. We are using the operator $search.\n",
"\n",
"Do NOT run this part in your notebook. This is done in the Atlas UI.\n",
diff --git a/notebooks/rag/TraderJoesFallAIPartyPlanner_PlaywrightLlamaIndexVectorSearch.ipynb b/notebooks/rag/TraderJoesFallAIPartyPlanner_PlaywrightLlamaIndexVectorSearch.ipynb
index 216ec262..47c0dd75 100644
--- a/notebooks/rag/TraderJoesFallAIPartyPlanner_PlaywrightLlamaIndexVectorSearch.ipynb
+++ b/notebooks/rag/TraderJoesFallAIPartyPlanner_PlaywrightLlamaIndexVectorSearch.ipynb
@@ -30,9 +30,9 @@
"\n",
"What's Covered\n",
"\n",
- "* Building a Trader Joe’s AI party planner using Playwright, LlamaIndex, and MongoDB Atlas Vector Search\n",
+ "* Building a Trader Joe’s AI party planner using Playwright, LlamaIndex, and MongoDB Vector Search\n",
"* Scraping Trader Joe’s fall items with Playwright and formatting them for chatbot use\n",
- "* Setting up and embedding product data in MongoDB Atlas Vector Store for semantic search\n",
+ "* Setting up and embedding product data in MongoDB Vector Store for semantic search\n",
"* Creating a Retrieval-Augmented Generation (RAG) chatbot to answer party planning questions\n",
"* Adding interactive Chat Engine functionality for back-and-forth Q&A about fall party items\n",
"\n",
@@ -421,7 +421,7 @@
"id": "qG9xp4g5SQ-7"
},
"source": [
- "Now, let’s go ahead and save our products into a `.txt` file so we can use it later in our tutorial when we are using our LlamaIndex and Atlas Vector Search integration. Go ahead and name the file whatever you like, for sake of tracking I’m naming mine: `tj_fall_faves_oct30.txt`."
+ "Now, let’s go ahead and save our products into a `.txt` file so we can use it later in our tutorial when we are using our LlamaIndex and MongoDB Vector Search integration. Go ahead and name the file whatever you like, for sake of tracking I’m naming mine: `tj_fall_faves_oct30.txt`."
]
},
{
@@ -473,7 +473,7 @@
"id": "LMuoPlEwi2bq"
},
"source": [
- "## Part 2: LlamaIndex and Atlas Vector Search Integration"
+ "## Part 2: LlamaIndex and MongoDB Vector Search Integration"
]
},
{
@@ -482,9 +482,9 @@
"id": "qTkfhJ_3SaNY"
},
"source": [
- "This is the quickstart we are using in order to be successful with this part of the tutorial: https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/llamaindex/#:~:text=You%20can%20integrate%20Atlas%20Vector,RAG). We will be going over how to use Atlas Vector Search with LlamaIndex to build a RAG application with chat capabilities!\n",
+ "This is the quickstart we are using in order to be successful with this part of the tutorial: https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/llamaindex/#:~:text=You%20can%20integrate%20Atlas%20Vector,RAG). We will be going over how to use MongoDB Vector Search with LlamaIndex to build a RAG application with chat capabilities!\n",
"\n",
- "This section will cover in detail how to set up the environment, store our custom data that we previously scraped on Atlas, create an Atlas Vector Search index on top of our data, and to finish up we will implement RAG and will use Atlas Vector Search to answer questions from our unique data store.\n",
+ "This section will cover in detail how to set up the environment, store our custom data that we previously scraped on Atlas, create an MongoDB Vector Search index on top of our data, and to finish up we will implement RAG and will use MongoDB Vector Search to answer questions from our unique data store.\n",
"\n",
"\n",
"Let’s first use `pip` to install all our necessary libraries. We will need to include `llama-index`, `llama-index-vector-stores-mongodb`, and `llama-index-embeddings-openai`.\n"
@@ -775,7 +775,7 @@
"source": [
"Once this cell has run you can go ahead and view your data with the embeddings inside of your Atlas cluster.\n",
"\n",
- "In order to allow for vector search queries on our created vector store, we need to create an Atlas Vector Search index on our tj_products.fall_faves collection. We can do this either through the [Atlas UI](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/) or directly from our notebook:\n"
+ "In order to allow for vector search queries on our created vector store, we need to create an MongoDB Vector Search index on our tj_products.fall_faves collection. We can do this either through the [Atlas UI](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/) or directly from our notebook:\n"
]
},
{
@@ -834,7 +834,7 @@
"id": "F398Wr-aTLUJ"
},
"source": [
- "You’ll be able to see this index once it’s up and running under your “Atlas Search” tab in your Atlas UI. Once it’s done, we can start querying our data and we can do some basic RAG."
+ "You’ll be able to see this index once it’s up and running under your “MongoDB Search” tab in your Atlas UI. Once it’s done, we can start querying our data and we can do some basic RAG."
]
},
{
@@ -852,9 +852,9 @@
"id": "v3rIQipnTPHB"
},
"source": [
- "With our Atlas Vector Search index up and running we are ready to have some fun and bring our AI Party Planner to life! We are going to continue with this dream team where we will use Atlas Vector Search to get our documents and LlamaIndex’s query engine to actually answer our questions based on our documents.\n",
+ "With our MongoDB Vector Search index up and running we are ready to have some fun and bring our AI Party Planner to life! We are going to continue with this dream team where we will use MongoDB Vector Search to get our documents and LlamaIndex’s query engine to actually answer our questions based on our documents.\n",
"\n",
- "To do this, we will need to have Atlas Vector Search become a [vector index retriever](https://docs.llamaindex.ai/en/stable/api_reference/retrievers/vector/) and we will need to initialize a `RetrieverQueryEngine` to handle queries by passing each question through our vector retrieval system. This combination will allow us to ask any questions we want in natural language, and it will match us with the most accurate documents."
+ "To do this, we will need to have MongoDB Vector Search become a [vector index retriever](https://docs.llamaindex.ai/en/stable/api_reference/retrievers/vector/) and we will need to initialize a `RetrieverQueryEngine` to handle queries by passing each question through our vector retrieval system. This combination will allow us to ask any questions we want in natural language, and it will match us with the most accurate documents."
]
},
{
@@ -877,7 +877,7 @@
}
],
"source": [
- "# Instantiate Atlas Vector Search as a retriever\n",
+ "# Instantiate MongoDB Vector Search as a retriever\n",
"vector_store_retriever = VectorIndexRetriever(\n",
" index=vector_store_index, similarity_top_k=5\n",
")\n",
diff --git a/notebooks/rag/chat_with_pdf_mongodb_openai_langchain_POLM_AI_Stack.ipynb b/notebooks/rag/chat_with_pdf_mongodb_openai_langchain_POLM_AI_Stack.ipynb
index a3ad12e7..5b783cb1 100644
--- a/notebooks/rag/chat_with_pdf_mongodb_openai_langchain_POLM_AI_Stack.ipynb
+++ b/notebooks/rag/chat_with_pdf_mongodb_openai_langchain_POLM_AI_Stack.ipynb
@@ -17,7 +17,7 @@
"\n",
"## Vector Index Creation\n",
"\n",
- "- [Create an Atlas Vector Search Index](https://www.mongodb.com/docs/compass/current/indexes/create-vector-search-index/)\n",
+ "- [Create an MongoDB Vector Search Index](https://www.mongodb.com/docs/compass/current/indexes/create-vector-search-index/)\n",
"\n",
"- If you are following this notebook ensure that you are creating a vector search index for the right database(anthropic_demo) and collection(research)\n",
"\n",
diff --git a/notebooks/rag/deepseek_r1_rag_pipeline_with_mongodb.ipynb b/notebooks/rag/deepseek_r1_rag_pipeline_with_mongodb.ipynb
index 2f82a84b..10b1a179 100644
--- a/notebooks/rag/deepseek_r1_rag_pipeline_with_mongodb.ipynb
+++ b/notebooks/rag/deepseek_r1_rag_pipeline_with_mongodb.ipynb
@@ -908,7 +908,7 @@
"source": [
"# The field containing the text embeddings on each document within the shipping_data collection\n",
"embedding_field_name = \"embedding\"\n",
- "# MongoDB Atlas Vector Search index name\n",
+ "# MongoDB Vector Search index name\n",
"vector_search_index_name = \"vector_index\""
]
},
diff --git a/notebooks/rag/openai_text_3_emebdding.ipynb b/notebooks/rag/openai_text_3_emebdding.ipynb
index 288cd587..a35e1c91 100644
--- a/notebooks/rag/openai_text_3_emebdding.ipynb
+++ b/notebooks/rag/openai_text_3_emebdding.ipynb
@@ -17,7 +17,7 @@
"source": [
"# Using OpenAI Latest Embeddings In A RAG System With MongoDB\n",
"\n",
- "OpenAI recently released new embeddings and moderation models. This article explores the step-by-step implementation process of utilizing one of the new embedding models: text-embedding-3-small within a Retrieval Augmented Generation(RAG) System powered by MongoDB Atlas Vector Database.\n"
+ "OpenAI recently released new embeddings and moderation models. This article explores the step-by-step implementation process of utilizing one of the new embedding models: text-embedding-3-small within a Retrieval Augmented Generation(RAG) System powered by MongoDB Vector Store.\n"
]
},
{
diff --git a/notebooks/rag/rag_chunking_strategies.ipynb b/notebooks/rag/rag_chunking_strategies.ipynb
index 403fd13b..d94d483a 100644
--- a/notebooks/rag/rag_chunking_strategies.ipynb
+++ b/notebooks/rag/rag_chunking_strategies.ipynb
@@ -495,13 +495,13 @@
"source": [
"def create_vector_store(docs: List[Document]) -> MongoDBAtlasVectorSearch:\n",
" \"\"\"\n",
- " Create MongoDB Atlas vector store\n",
+ " Create MongoDB Vector Store\n",
"\n",
" Args:\n",
" docs (List[Document]): List of documents to create the vector store\n",
"\n",
" Returns:\n",
- " MongoDBAtlasVectorSearch: MongoDB Atlas vector store\n",
+ " MongoDBAtlasVectorSearch: MongoDB Vector Store\n",
" \"\"\"\n",
" vector_store = MongoDBAtlasVectorSearch.from_documents(\n",
" documents=docs,\n",
diff --git a/notebooks/rag/self_querying_mongodb_unstructured_langgraph.ipynb b/notebooks/rag/self_querying_mongodb_unstructured_langgraph.ipynb
index 5a6e51c8..c2e726ae 100644
--- a/notebooks/rag/self_querying_mongodb_unstructured_langgraph.ipynb
+++ b/notebooks/rag/self_querying_mongodb_unstructured_langgraph.ipynb
@@ -621,7 +621,7 @@
"Next, let's add the graph nodes. Nodes in LangGraph are functions or tools that your system has access to in order to complete the task. Each node updates one or more attributes in the graph state with its return value after it executes. Our assistant has four nodes:\n",
"1. **Metadata Extractor**: Extract metadata from a natural language query\n",
"2. **Filter Generator**: Generate a MongoDB Query API filter definition\n",
- "3. **MongoDB Atlas Vector Search**: Retrieve documents from MongoDB using semantic search\n",
+ "3. **MongoDB Vector Search**: Retrieve documents from MongoDB using semantic search\n",
"4. **Answer Generator**: Generate an answer to the user question\n",
"\n",
"\n"
@@ -823,7 +823,7 @@
"id": "U1nFUwqbtGtS"
},
"source": [
- "### MongoDB Atlas Vector Search"
+ "### MongoDB Vector Search"
]
},
{
@@ -886,7 +886,7 @@
"source": [
"def vector_search(state: Dict) -> Dict:\n",
" \"\"\"\n",
- " Get relevant information using MongoDB Atlas Vector Search\n",
+ " Get relevant information using MongoDB Vector Search\n",
"\n",
" Args:\n",
" state (Dict): The current graph state\n",
diff --git a/partners/README.md b/partners/README.md
index a54aa534..a8934ccf 100644
--- a/partners/README.md
+++ b/partners/README.md
@@ -1,6 +1,6 @@
# MongoDB Gen AI Partners Showcase
-This directory contains collaborative AI projects and showcases developed in partnership with leading AI and technology companies. These partnerships demonstrate real-world applications of MongoDB's AI capabilities across various industries and use cases, showcasing how MongoDB's vector search, Atlas Search, and other AI-powered features can be integrated with cutting-edge AI platforms and frameworks.
+This directory contains collaborative AI projects and showcases developed in partnership with leading AI and technology companies. These partnerships demonstrate real-world applications of MongoDB's AI capabilities across various industries and use cases, showcasing how MongoDB's vector search, MongoDB Search, and other AI-powered features can be integrated with cutting-edge AI platforms and frameworks.
Each partner brings their unique expertise and technology stack to create comprehensive solutions that address specific industry challenges. From regulatory compliance and supply chain management to AI safety and knowledge discovery, these collaborations highlight the versatility and power of MongoDB's AI ecosystem.
diff --git a/partners/galileo/ai_hallucination_detection_and_reduction.ipynb b/partners/galileo/ai_hallucination_detection_and_reduction.ipynb
index c2780976..a87853c2 100644
--- a/partners/galileo/ai_hallucination_detection_and_reduction.ipynb
+++ b/partners/galileo/ai_hallucination_detection_and_reduction.ipynb
@@ -66,7 +66,7 @@
"- ✅ Designing agentic workflows with tool integration\n",
"- ✅ Evaluating and improving AI system quality\n",
"\n",
- "**Key topics explored:** Multi-modal RAG architecture, MongoDB Atlas vector search, embeddings, hybrid search with RankFusion, AI hallucination detection using Galileo, agentic workflows with LangGraph, GitHub repository processing with GitIngest, quality evaluation frameworks, tool development and integration, and production-ready AI observability patterns."
+ "**Key topics explored:** Multi-modal RAG architecture, MongoDB Vector Search, embeddings, hybrid search with RankFusion, AI hallucination detection using Galileo, agentic workflows with LangGraph, GitHub repository processing with GitIngest, quality evaluation frameworks, tool development and integration, and production-ready AI observability patterns."
]
},
{
@@ -1410,7 +1410,7 @@
" 'functions\\n'\n",
" '- 👤 **Persona System**: Create consistent, specialized agent '\n",
" 'personalities\\n'\n",
- " '- 📊 **Vector Search**: MongoDB Atlas Vector Search for efficient '\n",
+ " '- 📊 **Vector Search**: MongoDB Vector Search for efficient '\n",
" 'retrieval\\n'\n",
" '\\n'\n",
" '## Key Features\\n'\n",
diff --git a/partners/gravity9/Agentic_System_Enhanced_Contract_and_Supply_Chain_Management_for_International_Shipping.ipynb b/partners/gravity9/Agentic_System_Enhanced_Contract_and_Supply_Chain_Management_for_International_Shipping.ipynb
index d2a93cb3..37d92a82 100644
--- a/partners/gravity9/Agentic_System_Enhanced_Contract_and_Supply_Chain_Management_for_International_Shipping.ipynb
+++ b/partners/gravity9/Agentic_System_Enhanced_Contract_and_Supply_Chain_Management_for_International_Shipping.ipynb
@@ -1393,7 +1393,7 @@
"source": [
"# The field containing the text embeddings on each document within the shipping_data collection\n",
"embedding_field_name = \"embedding\"\n",
- "# MongoDB Atlas Vector Search index name\n",
+ "# MongoDB Vector Search index name\n",
"vector_search_index_name = \"vector_index\""
]
},
diff --git a/partners/langchain/agentic_knowledge_discovery_notebook.ipynb b/partners/langchain/agentic_knowledge_discovery_notebook.ipynb
index ddc7bee5..0525481c 100644
--- a/partners/langchain/agentic_knowledge_discovery_notebook.ipynb
+++ b/partners/langchain/agentic_knowledge_discovery_notebook.ipynb
@@ -35,7 +35,7 @@
"* **Mobilizing knowledge resources**: Retrieving relevant technical procedures, best practices, and previous incident documentation.\n",
"* **Orchestrating coordinated response**: Generating comprehensive response plans with prioritized action items, team assignments, and communication protocols.\n",
"\n",
- "Built on `MongoDB Atlas Vector Search` for high-performance semantic search and document retrieval, `LangChain` and `LangGraph` for agentic workflow orchestration, this approach delivers an intelligent emergency response system that dramatically reduces incident resolution time and business impact.\n"
+ "Built on `MongoDB Vector Search` for high-performance semantic search and document retrieval, `LangChain` and `LangGraph` for agentic workflow orchestration, this approach delivers an intelligent emergency response system that dramatically reduces incident resolution time and business impact.\n"
]
},
{
@@ -124,7 +124,7 @@
"\n",
"**Key Components:**\n",
"\n",
- "- MongoDB Atlas Vector Search: Dense vector indexing for semantic relevance.\n",
+ "- MongoDB Vector Search: Dense vector indexing for semantic relevance.\n",
"\n",
"- Voyage AI: State of the art embedding models and rerankers\n",
"\n",
@@ -112,7 +112,7 @@ print("All records ingested successfully!")
bookings = db['bookings']
-## Create Atlas Search index
+## Create MongoDB Search index
db.create_collection("bookings")
search_index_model = SearchIndexModel(
diff --git a/apps/voice-openai-mongo-rentals-agent/src/server/tools.py b/apps/voice-openai-mongo-rentals-agent/src/server/tools.py
index 72c3d480..20fd569a 100644
--- a/apps/voice-openai-mongo-rentals-agent/src/server/tools.py
+++ b/apps/voice-openai-mongo-rentals-agent/src/server/tools.py
@@ -37,7 +37,7 @@ class Booking(BaseModel):
@tool
def rentlas_search_tool(query: str, k: int = 5):
"""
- Perform a vector similarity search using MongoDB Atlas Vector Search to find rentals.
+ Perform a vector similarity search using MongoDB Vector Search to find rentals.
Args:
query (str): The search query string.
@@ -48,7 +48,7 @@ def rentlas_search_tool(query: str, k: int = 5):
and score is the similarity score (lower is more similar).
Note:
- Uses MongoDB Atlas Vector Search for semantic search capabilities.
+ Uses MongoDB Vector Search for semantic search capabilities.
"""
vector_store = MongoDBAtlasVectorSearch.from_connection_string(
connection_string=os.environ["MONGODB_ATLAS_URI"],
diff --git a/misc/low-code/BuildShip.md b/misc/low-code/BuildShip.md
index e76b77ec..9faba88a 100644
--- a/misc/low-code/BuildShip.md
+++ b/misc/low-code/BuildShip.md
@@ -63,7 +63,7 @@ This workflow serves as a versatile template for creating various types of AI ag
### Extend MongoDB Integration
- Implement complex aggregation pipelines for advanced queries
-- Add MongoDB Atlas Search for full-text search capabilities
+- Add MongoDB Search for full-text search capabilities
- Utilize MongoDB Change Streams for real-time updates
- Incorporate MongoDB Charts for data visualization
diff --git a/notebooks/advanced_techniques/automatic_quantization_of_nomic_emebddings_with_mongodb.ipynb b/notebooks/advanced_techniques/automatic_quantization_of_nomic_emebddings_with_mongodb.ipynb
index 5ce3debd..41ca17df 100644
--- a/notebooks/advanced_techniques/automatic_quantization_of_nomic_emebddings_with_mongodb.ipynb
+++ b/notebooks/advanced_techniques/automatic_quantization_of_nomic_emebddings_with_mongodb.ipynb
@@ -1186,7 +1186,7 @@
"Cost Reduction and Resource Efficiency: Efficient data storage and retrieval reduce the need for excessive computational resources, leading to cost savings.\n",
"By examining the trade-offs between retrieval accuracy and performance across different embedding formats (float32, int8, and binary), we showcased how MongoDB's capabilities, such as vector indexing and automatic quantization, can streamline data storage, retrieval, and analysis. \n",
"\n",
- "From this tutorial, we’ve explored Atlas Vector Search native capabilities for scalar quantization as well as binary quantization with rescoring. Our implementation showed that automatic quantization increases scalability and cost savings by reducing the storage and computational resources for efficient processing of vectors. In most cases, automatic quantization reduces the RAM for mongot by 3.75x for scalar and by 24x for binary; the vector values shrink by 4x and 32x, respectively, but the Hierarchical Navigable Small Worlds graph itself does not shrink.\n",
+ "From this tutorial, we’ve explored MongoDB Vector Search native capabilities for scalar quantization as well as binary quantization with rescoring. Our implementation showed that automatic quantization increases scalability and cost savings by reducing the storage and computational resources for efficient processing of vectors. In most cases, automatic quantization reduces the RAM for mongot by 3.75x for scalar and by 24x for binary; the vector values shrink by 4x and 32x, respectively, but the Hierarchical Navigable Small Worlds graph itself does not shrink.\n",
"\n",
"We recommend automatic quantization if you have a large number of full-fidelity vectors, typically over 10M vectors. After quantization, you index reduced representation vectors without compromising the accuracy of your retrieval.\n",
"To further explore quantization techniques and their applications, refer to resources like [Ingesting Quantized Vectors with Cohere](https://www.mongodb.com/developer/products/atlas/ingesting_quantized_vectors_with_cohere/). An [additional notebook](https://github.com/mongodb-developer/GenAI-Showcase/blob/main/notebooks/advanced_techniques/advanced_evaluation_of_quantized_vectors_using_cohere_mongodb_beir.ipynb) for comparing retrieval accuracy between quantized and non-quantized vectors is also available to deepen your understanding of these methods."
diff --git a/notebooks/advanced_techniques/evaluation_of_representation_capacity_retention_with_mongodb_voyageai.ipynb b/notebooks/advanced_techniques/evaluation_of_representation_capacity_retention_with_mongodb_voyageai.ipynb
index 35038294..9b9a98d0 100644
--- a/notebooks/advanced_techniques/evaluation_of_representation_capacity_retention_with_mongodb_voyageai.ipynb
+++ b/notebooks/advanced_techniques/evaluation_of_representation_capacity_retention_with_mongodb_voyageai.ipynb
@@ -41,7 +41,7 @@
"- Binary Quantization: A technique used to reduce the precision of a vector by converting it to a lower precision.\n",
"- Representational Capacity Retention: The ability of a vector to retain the information of the original vector.\n",
"\n",
- "In this guide, we demonstrate how to leverage MongoDB Atlas Search with automatic quantization and Voyage AI embeddings to build a scalable, high-performance vector search pipeline. \n",
+ "In this guide, we demonstrate how to leverage MongoDB Search with automatic quantization and Voyage AI embeddings to build a scalable, high-performance vector search pipeline. \n",
"\n",
"By compressing the embedding space—whether through scalar or binary quantization—you can dramatically reduce memory usage while retaining the vast majority of retrieval accuracy compared to a float32 baseline. \n",
"\n",
@@ -593,7 +593,7 @@
"\n",
"Benefits\n",
"The BinData vector format requires about three times less disk space in your cluster compared to arrays of elements. \n",
- "It allows you to index your vectors with alternate types such as int1 or int8 vectors, reducing the memory needed to build the Atlas Vector Search index for your collection. \n",
+ "It allows you to index your vectors with alternate types such as int1 or int8 vectors, reducing the memory needed to build the MongoDB Vector Search index for your collection. \n",
"It reduces the RAM for mongot by 3.75x for scalar and by 24x for binary; the vector values shrink by 4x and 32x respectively, but the Hierarchical Navigable Small Worlds graph itself doesn't shrink.\n",
"\n",
"In this notebook, we will convert the embeddings to the BSON binData vector format by using the `bson.binary` module.\n",
@@ -1321,7 +1321,7 @@
" \"wiki_id\": 1,\n",
" \"url\": 1,\n",
" \"score\": {\n",
- " # Atlas Vector Search assigns a score, in a fixed range from 0 to 1 (\n",
+ " # MongoDB Vector Search assigns a score, in a fixed range from 0 to 1 (\n",
" # where 0 indicates low similarity and 1 indicates high similarity), to every document that it returns.\n",
" # This is the score of the vector search operation score = (1 + cosine(v1,v2)) / 2.\n",
" \"$meta\": \"vectorSearchScore\"\n",
@@ -4211,7 +4211,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "In this guide, we demonstrate how to leverage MongoDB Atlas Search with automatic quantization and Voyage AI embeddings to build a scalable, high-performance vector search pipeline. By compressing the embedding space—whether through scalar or binary quantization—you can dramatically reduce memory usage while retaining the vast majority of retrieval accuracy compared to a float32 baseline. \n",
+ "In this guide, we demonstrate how to leverage MongoDB Search with automatic quantization and Voyage AI embeddings to build a scalable, high-performance vector search pipeline. By compressing the embedding space—whether through scalar or binary quantization—you can dramatically reduce memory usage while retaining the vast majority of retrieval accuracy compared to a float32 baseline. \n",
"\n",
"These techniques not only cut operational costs but also improve throughput, allowing you to handle larger workloads or more complex queries. \n",
"\n",
diff --git a/notebooks/advanced_techniques/geospatialqueries_vectorsearch_spritzes.ipynb b/notebooks/advanced_techniques/geospatialqueries_vectorsearch_spritzes.ipynb
index 62fd3f27..95897794 100644
--- a/notebooks/advanced_techniques/geospatialqueries_vectorsearch_spritzes.ipynb
+++ b/notebooks/advanced_techniques/geospatialqueries_vectorsearch_spritzes.ipynb
@@ -458,9 +458,9 @@
"## Which one comes first, Vector Search or Geospatial Queries?\n",
"Both of these need to be the first stage in their aggregation pipelines, so instead of making one pipeline we are going to do a little loophole. We will do two pipelines. But how will we decide which?!\n",
"\n",
- "When I'm using Google Maps to figure out where to go, I normally first search for what I'm looking for and then I see how far away it is from where I currently am and pick the closest location to me. So let's keep that mindset in place and start off with MongoDB Atlas Vector Search for this tutorial. But, I understand intuitively some of you might prefer to search via all nearby locations and then utilize Vector Search, so I'll highlight that method of searching for your spritz's as well.\n",
+ "When I'm using Google Maps to figure out where to go, I normally first search for what I'm looking for and then I see how far away it is from where I currently am and pick the closest location to me. So let's keep that mindset in place and start off with MongoDB Vector Search for this tutorial. But, I understand intuitively some of you might prefer to search via all nearby locations and then utilize Vector Search, so I'll highlight that method of searching for your spritz's as well.\n",
"\n",
- "## MongoDB Atlas Vector Search\n",
+ "## MongoDB Vector Search\n",
"We have a couple steps here. Our first step is to create a Vector Search Index. Do this inside of MongoDB Atlas by following this documentation. Please keep in mind that your index is NOT run in your script, it lives in your cluster. You'll know it's ready to go when the button turns green and it's activated."
]
},
diff --git a/notebooks/advanced_techniques/langchain_parent_document_retrieval.ipynb b/notebooks/advanced_techniques/langchain_parent_document_retrieval.ipynb
index e51ad28d..80dee015 100644
--- a/notebooks/advanced_techniques/langchain_parent_document_retrieval.ipynb
+++ b/notebooks/advanced_techniques/langchain_parent_document_retrieval.ipynb
@@ -351,7 +351,7 @@
{
"data": {
"text/plain": [
- "Document(metadata={'contentType': None, 'pageDescription': None, 'productName': 'MongoDB Atlas', 'tags': ['atlas', 'docs'], 'version': None, 'updated': {'$date': '2024-05-20T17:30:49.148Z'}, 'url': 'https://mongodb.com/docs/atlas/access-tracking/', 'title': 'View Database Access History'}, page_content='# View Database Access History\\n\\n- This feature is not available for `M0` free clusters, `M2`, and `M5` clusters. To learn more, see Atlas M0 (Free Cluster), M2, and M5 Limits.\\n\\n- This feature is not supported on Serverless instances at this time. To learn more, see Serverless Instance Limitations.\\n\\n## Overview\\n\\nAtlas parses the MongoDB database logs to collect a list of authentication requests made against your clusters through the following methods:\\n\\n- `mongosh`\\n\\n- Compass\\n\\n- Drivers\\n\\nAuthentication requests made with API Keys through the Atlas Administration API are not logged.\\n\\nAtlas logs the following information for each authentication request within the last 7 days:\\n\\n