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https://github.com/ranfysvalle02/chatlas-prototype

Talk with your data using MongoDB + AI
https://github.com/ranfysvalle02/chatlas-prototype

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Talk with your data using MongoDB + AI

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README 

          
# chatlas-prototype



![](https://honeydew.ai/wp-content/uploads/2024/06/Blog_8web.jpg)



---



# Chatlas: Talk with Your Data



In today's data-driven world, the ability to efficiently query and analyze large datasets is more critical than ever. MongoDB's aggregation framework offers powerful tools for data analysis, but constructing optimized aggregation pipelines can be complex. Integrating natural language interfaces with verified queries can simplify this process, allowing you to "talk with your data" while ensuring consistency, performance, and security.



Integrating natural language interfaces with verified queries allows users to interact with data intuitively while maintaining high standards of performance and security. By leveraging optimized aggregation pipelines in MongoDB, you ensure that your applications are scalable and responsive.



This approach has immense potential in various fields, including:



- **Data Analysis**: Analysts can query large datasets without needing to understand complex query languages.

- **Customer Service**: Assistants can provide customers with real-time data insights based on their queries.

- **Business Intelligence**: Decision-makers can access and analyze data quickly and efficiently.



---



## The Value of Conversational Data Interaction



### Enhanced Accessibility



- **Democratization of Data**: Natural language interfaces allow users of all technical backgrounds to access and interact with data effortlessly.

- **Intuitive Engagement**: Users can pose questions in plain English, eliminating the need to learn complex query syntax.



### Increased Efficiency



- **Faster Insights**: Immediate data retrieval accelerates decision-making processes, enabling organizations to respond swiftly to changing conditions.

- **Reduced Training Costs**: Minimizes the need for extensive training on query languages, saving time and resources.



### Improved Collaboration



- **Unified Communication**: Facilitates better communication among teams by providing a common, understandable way to interact with data.

- **Customer Engagement**: Enhances customer service by allowing representatives to access information quickly during interactions.



---



## The Challenge of Natural Language to Database Queries



Translating natural language into database queries bridges the gap between user intent and data retrieval. However, this translation must ensure:



- **Consistency**: Queries should produce reliable and expected results.

- **Generalization**: The system should handle a variety of similar queries effectively.

- **Performance**: Queries must be optimized to minimize resource usage and execution time.



Unverified or poorly constructed queries can lead to inefficiencies, inaccurate results, and potential security risks. This is where **verified queries**—predefined, tested, and optimized query patterns—play a crucial role.



---



## The Importance of Verified Queries



### Consistency



- **Reliable Results**: Pre-tested queries produce expected outcomes.

- **Standardization**: Adheres to established query patterns within your applications.



### Generalization



- **Adaptability**: The assistant can handle variations of similar queries effectively.

- **Scalability**: New queries can be generated based on existing verified templates.



### Performance



- **Optimization**: Verified queries are fine-tuned for efficiency.

- **Resource Management**: Reduces database load and improves execution times.



### Security



- **Risk Mitigation**: Limits exposure to injection attacks and unauthorized data access.

- **Controlled Environment**: Ensures queries only perform intended operations.



## Understanding the Implementation



- **System Prompt**: We instruct the assistant to translate English into a MongoDB aggregation pipeline array.

- **Verified Pipelines**: We provide a verified pipeline for a similar query (`What are the best 1337 movies`) within the `[verified pipelines]` section. This serves as a template.

- **User Input**: The user asks, `What are the best 5 movies?`

- **Assistant's Task**: Use the verified pipeline to construct a new pipeline that answers the user's question, ensuring it adheres to the response criteria.



---



## Integrating Verified Queries with Azure OpenAI and MongoDB



```python

from openai import AzureOpenAI

import json



# Set up Azure OpenAI credentials

azure_endpoint = "https://.openai.azure.com"

azure_deployment = "gpt-4o"

api_key = ""

api_version = "2024-10-21"



az_client = AzureOpenAI(

    azure_endpoint=azure_endpoint,

    api_version=api_version,

    api_key=api_key

)



user_input = 'What are the best 5 movies?'



messages = [

    {

        "role": "system",

        "content": """

You are a helpful assistant that translates English to MongoDB Aggregation Pipeline array. Only respond with the array, ready to use.

"""

    },

    {

        "role": "user",

        "content": f"""

[context]

collection = "movies"

[verified pipelines]

[user_input=`What are the best 1337 movies`]

{[

    {

        '$project': {

            'title': 1,

            'imdb_rating': '$imdb.rating',

            'tomatoes_viewer_rating': '$tomatoes.viewer.rating'

        }

    },

    {

        '$addFields': {

            'combined_rating': {

                '$avg': [

                    '$imdb_rating',

                    '$tomatoes_viewer_rating'

                ]

            }

        }

    },

    {

        '$sort': {

            'combined_rating': -1

        }

    },

    {

        '$limit': 1337

    }

]}

[/verified pipelines]

[/context]



USE THE [verified pipelines] IN THE [context] TO RESPOND TO [user_input=`{user_input}`] AND RETURN A JSON ARRAY MONGODB AGGREGATION PIPELINE



[response criteria]

- JSON object with keys 'pipeline' and 'user_input'

- response must be JSON

[/response criteria]

"""

    }

]



response = az_client.chat.completions.create(

    model="gpt-4o",

    messages=messages,

    response_format={"type": "json_object"}

)



ai_msg = json.loads(response.choices[0].message.content)

print(ai_msg['pipeline'])

```



**Output:**



```json

[

    {

        "$project": {

            "title": 1,

            "imdb_rating": "$imdb.rating",

            "tomatoes_viewer_rating": "$tomatoes.viewer.rating"

        }

    },

    {

        "$addFields": {

            "combined_rating": {

                "$avg": [

                    "$imdb_rating",

                    "$tomatoes_viewer_rating"

                ]

            }

        }

    },

    {

        "$sort": {

            "combined_rating": -1

        }

    },

    {

        "$limit": 5

    }

]

```



---



## Understanding the Implementation



In the code above:



- **System Prompt**: We instruct the assistant to translate English into a MongoDB aggregation pipeline array.

- **Verified Pipelines**: We provide a verified pipeline for a similar query (`What are the best 1337 movies`) within the `[verified pipelines]` section. This serves as a template.

- **User Input**: The user asks, `What are the best 5 movies?`

- **Assistant's Task**: Use the verified pipeline to construct a new pipeline that answers the user's question, ensuring it adheres to the response criteria.



By adjusting the `$limit` stage from `1337` to `5`, the assistant provides an optimized and verified pipeline tailored to the user's request.



---



## Optimizing Aggregation Stages in MongoDB



Optimization is key to performance in MongoDB's aggregation framework. Here's how each stage in the pipeline contributes:



1. **$project**



   - **Purpose**: Includes or excludes specific fields from documents.

   - **Optimization**: Reduces data size early in the pipeline, improving subsequent stage performance.



2. **$addFields**



   - **Purpose**: Adds new fields or resets existing fields with computed values.

   - **Optimization**: Calculates necessary data within the pipeline, avoiding extra queries.



3. **$sort**



   - **Purpose**: Orders documents based on specified fields.

   - **Optimization**: Efficient sorting mechanisms enhance performance, especially with proper indexing.



4. **$limit**



   - **Purpose**: Restricts the number of documents passing through the pipeline.

   - **Optimization**: Minimizes data processed in later stages, conserving resources.



By carefully structuring these stages, you can create pipelines that are both efficient and effective.



---



# The Power of Optimizing Access Patterns



When working with databases, one of the most effective ways to enhance performance is by identifying access patterns and optimizing for them. This involves understanding how your application queries data and structuring your database accordingly. In this blog post, we'll explore how creating appropriate indexes in MongoDB can significantly reduce query execution times, demonstrating the tangible benefits of optimizing access patterns.



## The Scenario



Suppose you have a MongoDB collection named `movies` within the `sample_mflix` database. You want to find the top 5 movies based on a combined rating calculated from IMDb and Rotten Tomatoes viewer ratings. To achieve this, you use an aggregation pipeline that:



1. Projects the necessary fields (`title`, `imdb.rating`, and `tomatoes.viewer.rating`).

2. Calculates a new field `combined_rating` as the average of the IMDb and Rotten Tomatoes ratings.

3. Sorts the documents in descending order based on the `combined_rating`.

4. Limits the results to the top 5 movies.



To measure the performance of this aggregation, you run the pipeline multiple times under different conditions: without indexes (both cold and warm starts) and with indexes on the rating fields.



## The Code



```python

import time

from pymongo import MongoClient



# Connect to the MongoDB server

client = MongoClient("")

db = client['sample_mflix']

collection = db['movies']



# Define the first aggregation pipeline

pipeline1 = [{'$project': {'title': 1, 'imdb_rating': '$imdb.rating', 'tomatoes_viewer_rating': '$tomatoes.viewer.rating'}}, {'$addFields': {'combined_rating': {'$avg': ['$imdb_rating', '$tomatoes_viewer_rating']}}}, {'$sort': {'combined_rating': -1}}, {'$limit': 5}]



# Define the second aggregation pipeline

pipeline2 = [{'$project': {'title': 1, 'imdb_rating': '$imdb.rating', 'tomatoes_viewer_rating': '$tomatoes.viewer.rating'}}, {'$addFields': {'combined_rating': {'$avg': ['$imdb_rating', '$tomatoes_viewer_rating']}}}, {'$sort': {'combined_rating': -1}}, {'$limit': 5}]



# Function to measure execution time of a pipeline

def measure_execution_time(pipeline):

    start_time = time.time()

    list(collection.aggregate(pipeline))

    end_time = time.time()

    return end_time - start_time

print("cold start, no index")

time_pipeline1 = measure_execution_time(pipeline1)

print(f"Execution time for pipeline 1: {time_pipeline1:.6f} seconds")

print("warm start, no index")

time_pipeline2 = measure_execution_time(pipeline2)

print(f"Execution time for pipeline 2: {time_pipeline2:.6f} seconds")

print("drop indexes...")

# Drop the indexes

collection.drop_indexes()

time.sleep(10)

print("create indexes...")

# Create indexes on 'imdb.rating' and 'tomatoes.viewer.rating'

collection.create_index('imdb.rating')

collection.create_index('tomatoes.viewer.rating')

time.sleep(10)

time_pipeline1 = measure_execution_time(pipeline1)

print(f"Execution time for pipeline: {time_pipeline1:.6f} seconds")

time_pipeline2 = measure_execution_time(pipeline2)

print(f"Execution time for pipeline: {time_pipeline2:.6f} seconds")



"""

cold start, no index

Execution time for pipeline 1: 0.918224 seconds

warm start, no index

Execution time for pipeline 2: 0.141815 seconds

drop indexes...

create indexes...

Execution time for pipeline: 0.144723 seconds

Execution time for pipeline: 0.141932 seconds

"""

```



## Execution Times Without Indexes



When running the aggregation pipeline without any indexes, the execution times were as follows:



```

Cold start, no index

Execution time: 0.918224 seconds



Warm start, no index

Execution time: 0.141815 seconds

```



- **Cold Start**: The first execution takes longer because MongoDB needs to load data from disk into memory.

- **Warm Start**: Subsequent executions are faster due to data caching.



## Introducing Indexes



By creating indexes on the fields `imdb.rating` and `tomatoes.viewer.rating`, we optimize the access pattern for our queries:



```python

collection.create_index('imdb.rating')

collection.create_index('tomatoes.viewer.rating')

```



These indexes allow MongoDB to quickly access the required rating data without scanning the entire collection.



## Execution Times With Indexes



After indexing, the execution times improved:



```

Execution after indexing

Execution time: 0.144723 seconds

```



The execution time is now consistently low, even for cold starts, because the indexes facilitate faster data retrieval.



## Analysis



- **Performance Improvement**: The cold start execution time dropped from approximately 0.918 seconds to 0.145 seconds—a significant improvement.

- **Consistency**: Execution times became more consistent between cold and warm starts after indexing.

- **Resource Efficiency**: Indexes reduce the load on the database by preventing full collection scans, leading to better resource utilization.



## The Power of Optimizing Access Patterns



- **Identify Critical Queries**: Determine which queries are most frequent or performance-critical.

- **Create Appropriate Indexes**: Index the fields used in filters, sorts, and joins to speed up these operations.

- **Monitor and Adjust**: Use database profiling tools to monitor query performance and adjust indexes as needed.



Optimizing access patterns is a powerful strategy for enhancing database performance. In our example, creating indexes on specific fields used in aggregation pipelines led to dramatic reductions in execution time. This optimization not only improves performance but also contributes to the scalability and efficiency of your application.



---



## Addressing Schema Complexity and Sampling Challenges



While integrating natural language interfaces with MongoDB aggregation pipelines offers significant advantages in accessibility and efficiency, it also introduces challenges related to understanding and mapping complex database schemas. Particularly, sampling a collection and comprehending deeply nested fields or non-intuitive field names can hinder the effectiveness of natural language queries. This section explores these nuances and suggests strategies to overcome them.



### Understanding Collection Schema



#### Heavily Nested Fields



MongoDB's flexibility allows for the storage of deeply nested documents, enabling the representation of complex data structures. However, this complexity poses a challenge for natural language interfaces:



- **Mapping Complexity**: Nested fields require the assistant to understand hierarchical relationships, which can be difficult to express in plain language. For example, querying `user.profile.contact.email` versus simply `user email` requires different handling.

  

- **User Intent**: Users may not be aware of the underlying nested structure, leading to ambiguities in their queries. Clarifying intent becomes essential to accurately translate natural language into the correct aggregation stages.



#### Field Names without Obvious Mappings



Field names that lack clear, descriptive labels can complicate the translation process:



- **Ambiguous Terminology**: Abbreviations or internal naming conventions (e.g., `usr_id` instead of `user_id`) may not be easily inferred from natural language queries.

  

- **Contextual Understanding**: Without an obvious mapping, the assistant may struggle to associate user queries with the correct fields, resulting in inaccurate or incomplete data retrieval.



### Sampling a Collection



#### Ensuring Representative Data



Sampling is a critical step in understanding a collection's schema and data distribution. However, it comes with its own set of challenges:



- **Data Diversity**: Ensuring that the sample accurately represents the variety and complexity of the entire collection is essential. A non-representative sample can lead to incorrect assumptions about the schema.

  

- **Performance Constraints**: Large collections may make comprehensive sampling resource-intensive. Balancing the depth and breadth of sampling with performance considerations is necessary to maintain efficiency.



#### Performance Considerations



Efficient sampling techniques are vital to minimize the impact on system performance:



- **Incremental Sampling**: Gradually increasing the sample size can help manage resource usage while progressively improving schema understanding.

  

- **Selective Sampling**: Focusing on specific subsets of data based on usage patterns or query frequency can optimize the sampling process, ensuring that the most relevant data structures are prioritized.



### Mapping Complex Structures to Natural Language



#### Limitations of Natural Language Interfaces



Natural language interfaces excel in simplicity but may falter when dealing with intricate data structures:



- **Expressiveness**: Conveying complex queries that involve multiple nested fields or intricate aggregation stages can be challenging in plain language.

  

- **Error Handling**: Misinterpretations or ambiguities in user queries may lead to incorrect pipeline constructions, necessitating robust error detection and correction mechanisms.



#### Strategies to Handle Complexity



Implementing effective strategies can mitigate the challenges posed by complex schemas:



- **Schema Introspection**: Automatically analyzing the collection's schema to identify nested structures and field relationships can inform the assistant's translation process.

  

- **Predefined Mappings**: Establishing a set of mappings between common natural language terms and specific field names or paths can enhance translation accuracy. For example, mapping "user email" to `user.profile.contact.email`.

  

- **User Feedback Loops**: Incorporating mechanisms for users to provide feedback or clarify intents can improve the system's understanding over time, refining the translation of complex queries.

  

- **Schema Simplification**: Where feasible, restructuring the database schema to reduce unnecessary nesting or clarify field names can facilitate more straightforward natural language interactions.



### Best Practices for Managing Schema Complexity



To ensure that natural language interfaces remain effective despite schema complexities, consider the following best practices:



- **Comprehensive Documentation**: Maintain detailed documentation of the database schema, including field descriptions and relationships, to aid in mapping natural language queries accurately.

  

- **Consistent Naming Conventions**: Adopt clear and consistent naming conventions for fields to minimize ambiguities and enhance the intuitiveness of natural language mappings.

  

- **Regular Schema Reviews**: Periodically review and update the schema to align with evolving data requirements and to simplify structures where possible, thereby easing natural language translation.



---



#### Collection Annotations



**Collection annotations** involve adding descriptive metadata to entire collections within a database. These annotations can include:



- **Purpose and Usage**: Explaining the intent behind a collection, its primary use cases, and how it fits into the broader data ecosystem.

- **Data Relationships**: Highlighting relationships between different collections, such as references or dependencies.

- **Access Permissions**: Specifying who can access or modify the collection, ensuring security and compliance.



*Example:*



```json

{

  "collection_name": "movies",

  "description": "Stores information about movies, including titles, ratings, and reviews.",

  "relationships": {

    "actors": "Reference to the actors collection",

    "directors": "Reference to the directors collection"

  },

  "access_permissions": {

    "read": ["admin", "analyst"],

    "write": ["admin"]

  }

}

```



#### Schema Annotations



**Schema annotations** provide detailed metadata about the structure of documents within a collection. They include:



- **Field Descriptions**: Explaining the purpose and type of each field.

- **Constraints and Validation Rules**: Defining rules such as required fields, data types, and value ranges.

- **Default Values**: Specifying default values for fields when none are provided.



*Example:*



```json

{

  "field_name": "imdb_rating",

  "type": "Number",

  "description": "The IMDb rating of the movie on a scale of 1 to 10.",

  "constraints": {

    "required": true,

    "min": 1,

    "max": 10

  },

  "default": 5.0

}

```



#### Data Annotations



**Data annotations** refer to metadata associated with individual data entries. They enhance the context and provide additional layers of information, such as:



- **Tags and Labels**: Categorizing data entries for easier retrieval and analysis.

- **Timestamping**: Recording the creation or modification times of data entries.

- **Source Information**: Indicating the origin of the data, which is crucial for data provenance and trustworthiness.



*Example:*



```json

{

  "movie_id": "tt0111161",

  "tags": ["drama", "classic"],

  "created_at": "2024-01-15T08:30:00Z",

  "source": "IMDb API"

}

```



### Leveraging Annotations for Improved Natural Language Queries



Annotations significantly enhance the ability of natural language interfaces to understand and accurately translate user queries into database operations. By providing rich metadata:



- **Contextual Understanding**: The system can better interpret user intents by referencing annotated metadata.

- **Disambiguation**: Annotations help resolve ambiguities in natural language, ensuring queries target the correct fields or collections.

- **Enhanced Mapping**: Predefined annotations facilitate more precise mappings between natural language terms and database schema elements.



### The Limitations of Large Language Models: The "Sins of Attention"



While Large Language Models (LLMs) like GPT-4 have revolutionized natural language processing, they come with inherent limitations that must be acknowledged and addressed to ensure the reliability of systems like Chatlas.



#### The "Sins of Attention"



The "sins of attention" refer to the challenges and limitations associated with the attention mechanisms that underpin LLMs. These include:



1. **Limited Context Window**: LLMs can only consider a fixed number of tokens (words or characters) at a time. This constraint means that in extensive conversations or complex queries, earlier parts of the dialogue may be "forgotten," leading to inconsistencies or loss of context.



2. **Attention Biases**: The attention mechanism may disproportionately focus on certain parts of the input, potentially overlooking crucial information. This bias can result in misinterpretations or incomplete understanding of user intents.



3. **Scalability Issues**: As the size of the input grows, the computational resources required for the attention mechanism increase quadratically. This scaling issue can lead to performance bottlenecks in real-time applications.



4. **Difficulty in Long-Range Dependencies**: LLMs may struggle to maintain coherence over long documents or conversations, making it challenging to handle queries that depend on understanding information presented much earlier.



#### The Flawed Pursuit of 100% Accuracy



LLMs operate on probabilistic foundations, predicting the next word or token based on learned patterns from vast datasets. Aiming for **100% accuracy** in such models is fundamentally flawed due to several reasons:



- **Probabilistic Nature**: LLMs generate responses based on probability distributions. Even with extensive training, there is always a chance of generating incorrect or nonsensical outputs.

  

- **Ambiguity in Natural Language**: Human language is inherently ambiguous and context-dependent. LLMs may misinterpret user intents, especially in the absence of clear annotations or structured prompts.

  

- **Knowledge Cutoff and Updates**: LLMs are trained on data up to a certain point and may not have access to the most recent information, leading to outdated or incomplete responses.

  

- **Biases in Training Data**: LLMs can inherit and even amplify biases present in their training data, affecting the fairness and accuracy of their outputs.



---



# Takeaways



Integrating natural language interfaces with verified queries allows users to interact with data intuitively while maintaining high standards of performance and security. By leveraging optimized aggregation pipelines in MongoDB, you ensure that your applications are scalable and responsive.



**Key Takeaways:**



- **Verified Queries Enhance Reliability**: They provide a foundation for consistent and secure data retrieval.

- **Optimized Pipelines Improve Performance**: Thoughtful construction of aggregation stages leads to efficient data processing.

- **Natural Language Integration Simplifies Interaction**: Users can query data without needing in-depth knowledge of query languages.



---

## Practical Use Cases



### Data Analysis



- **Empowering Analysts**: Allows data analysts to extract insights without writing complex queries, focusing on interpretation rather than data retrieval.

- **Ad-Hoc Reporting**: Facilitates quick generation of reports based on spontaneous queries from stakeholders.



### Customer Service



- **Responsive Support**: Enables customer service agents to retrieve customer information and history instantly, improving service quality.

- **Personalized Interactions**: Access to real-time data allows for tailored solutions to customer needs.



### Business Intelligence



- **Executive Access**: Provides leaders with the ability to query key performance indicators directly, aiding strategic decisions.

- **Market Trend Analysis**: Simplifies the process of analyzing market data, helping businesses stay competitive.



### DevOps and Development Teams



- **Codebase Integrity**: Ensures only optimized and approved queries are added to the codebase, enhancing application performance.

- **Developer Efficiency**: Reduces the time developers spend writing and testing queries, allowing them to focus on core functionality.