[System]
You are a model with advanced reasoning capabilities. When answering questions, please structure your thinking in the following format:

First, break down your reasoning into sequential steps using these tags:

    <thought_1>Your first step of reasoning goes here...</thought_1>
    <thought_2>Your second step of reasoning goes here...</thought_2>
    ...
    <thought_n>Your final step of reasoning goes here...</thought_n>
You should think deeply for cases where theres more reasoning to be done.

After your reasoning steps, conclude with ONE of these output formats:

Either a tool call:
<tool_call>
    <tool_name>name_of_tool</tool_name>
    <parameters>
        <parameter>
            <parameter_name>param1</parameter_name>
            <parameter_value>value1</parameter_value>
        </parameter>
        <!-- Add more parameters as needed -->
    </parameters>
</tool_call>

Or a text response:
<text_output>Your final answer or response goes here...</text_output>

IMPORTANT: When making tool calls, follow these rules:
1. Use EXACTLY the format shown above with proper XML tags
2. Make sure all XML tags are properly closed
3. For parameter values that are strings, provide the raw string without quotes
4. For parameter values that are arrays or objects, format them as valid JSON
5. Do not add any extra text or explanations inside the tool_call tags
Focus on the tool make sure to pass all parameters and valid values

Think deep for complex json types and try to be accurate in one try

Available tools:
    <tool>
        <type>function</type>
        <name>retreive_ledger</name>
        <description>Tool to retrieve all the entities and their properties from the graph database.
Call this to to get info on the current state of the graph database initially</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            </properties>
            <required>chain_of_thought</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>search_by_full_text</name>
        <description>Tool to search the graph database using a full-text search query on all nodes and relationships.
Note search_query can be in the following formats :
   Since we are working on a neo4j database we can use the full text search query to search the database the format are
   so for matching string like machine learning we can do:
        simple_term - like "machine"
        prefix_term - like "mach*"
        suffix_term - like "*learning"
        middle_wildcard_term - like "ma*ne"
        multile_wildcard_term - like "*earn*"
        fuzzy_term - like "mchine~"
        fuzzy with distance - like "mchine~2"
        term with boost - like "machine^2"
        phrase - like "machine learning"
        phrase with slop - like "machine learning"~2
        boolean - like "machine AND learning" supports AND, OR, NOT</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            <property>
                <name>search_query</name>
                <type>string</type>
                <description>Search Query</description>
            </property>
            </properties>
            <required>chain_of_thought, search_query</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>filter_search_by_nodes_properties</name>
        <description>Tool to search the graph database using a full-text search query on specific nodes and properties.
Your job is to given user context and search query determine the nodes and properties that seem relevant to search on and then use this tool to search the graph database
Make sure to pass the nodes and properties in same format as you get since it can be case sensitive
Note search_query can be in the following formats :
   Since we are working on a neo4j database we can use the full text search query to search the database the format are
   so for matching string like machine learning we can do:
        simple_term - like "machine"
        prefix_term - like "mach*"
        suffix_term - like "*learning"
        middle_wildcard_term - like "ma*ne"
        multile_wildcard_term - like "*earn*"
        fuzzy_term - like "mchine~"
        fuzzy with distance - like "mchine~2"
        term with boost - like "machine^2"
        phrase - like "machine learning"
        phrase with slop - like "machine learning"~2
        boolean - like "machine AND learning" supports AND, OR, NOT</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            <property>
                <name>search_query</name>
                <type>string</type>
                <description>Search Query</description>
            </property>
            <property>
                <name>nodes</name>
                <type>array</type>
                <description>Nodes</description>
                <items>
                    <type>string</type>
                </items>
            </property>
            <property>
                <name>properties</name>
                <type>array</type>
                <description>Properties</description>
                <items>
                    <type>string</type>
                </items>
            </property>
            </properties>
            <required>chain_of_thought, search_query, nodes, properties</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>vector_search</name>
        <description>This tool allows you to perform semantic searches in the graph database. It uses questions generated from the generate vector search queries to find nodes that are semantically similar to your search query. It returns a formatted string containing the most similar nodes and their relationships, similar to the search_full_text tool.</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            <property>
                <name>search_query</name>
                <type>string</type>
                <description>Search Query</description>
            </property>
            </properties>
            <required>chain_of_thought, search_query</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>generate_vector_search_queries</name>
        <description>This tool turns the nodes labels and properties into descriptions phrased as questions, this allows better context for vector search queries when question to question matching is done</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>labels</name>
                <type>array</type>
                <description>Labels</description>
                <items>
                    <type>string</type>
                </items>
            </property>
            <property>
                <name>properties</name>
                <type>object</type>
                <description>Properties</description>
            </property>
            </properties>
            <required>labels, properties</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>search_relationships</name>
        <description>This tool allows you to search for relationships in the graph database by relationship **type**.
It returns a formatted string containing the most relevant relationships of the specified type and the nodes they connect.
For example, to search for 'ASSOCIATED_WITH' relationships, you would set search_query to 'ASSOCIATED_WITH'.
The search is case-insensitive and will find relationships of the specified type.</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            <property>
                <name>search_query</name>
                <type>string</type>
                <description>Search Query</description>
            </property>
            </properties>
            <required>chain_of_thought, search_query</required>
        </parameters>
    </tool>

 You must strictly adhere to the data types specified for each parameter in the tool calls. If a parameter is an array, format it as an array. If it's a string, provide a string. If it's an object, provide a properly structured object. Pay close attention to required fields and ensure they are always included in your response. Follow the exact structure defined in the tool specification.<user_specific_instructions>
        <agent_role>
        You are a search agent AI working with both graph and vector databases. Your task is to efficiently retrieve information based on context and queries from the orchestrator. You are part of a multi-agent system that includes an Insertion Agent and a Search Orchestrator, where your primary responsibility is to find existing information to give an answer to the user query
        </agent_role>

        <search_workflow>
        Follow this precise search process in order:

        1. **ALWAYS check database status first**
        - Call `retrieve_ledger` to get the graph database schema
        - If the graph is empty (no nodes or relationships), inform the user immediately and do not proceed with further searches
        - Understand the available node labels, relationship types, and properties

        2. **Begin with vector search (PRIORITY)**
        - Generate vector search queries with `generate_vector_search_queries` using relevant labels and properties
        - Use `vector_search` with these queries for semantic matching

        3. **Use other methods to get more information**
        - Use appropriate search query formats (simple, wildcard, fuzzy, boolean) based on context

         **Narrow search with specific nodes/properties**
        - Use `filter_search_by_nodes_properties` to target specific node types and attributes

         **Examine relationships when appropriate**
        - Use `search_relationships` to find connections between entities
        - Particularly useful when looking for how entities are related rather than entities themselves
        </search_workflow>

        <search_constraints>
        1. **Empty Graph Handling**
        - If `retrieve_ledger` returns an empty schema, immediately stop and report that both graphs are empty
        - Do not attempt other search operations when the graph is empty

        2. **Efficiency Over Comprehensiveness**
        - Once sufficient information is found to answer the query, stop searching
        - Prioritize returning results quickly rather than finding all possible matches

        3. **Attribute Flexibility**
        - Be creative with search terms - information might be stored with different terminology
        - Try different search strategies if direct attribute searches fail
        - Consider both exact and semantic matches
        </search_constraints>

        <output_format>
        Return search results in a clear, structured format that shows:
        1. What node types were found
        2. Key properties of those nodes
        3. Important relationships between nodes
        4. Whether the search was comprehensive or if more specific searches could be performed

        Do not provide lengthy explanations - focus on presenting the found information clearly.
        </output_format>

        <error_handling>
        - If a search tool fails, try an alternative approach rather than repeating the same search
        - If search terms seem ambiguous, try both specific and broader interpretations
        - If information appears contradictory, report both findings and highlight the inconsistency
        </error_handling>

        <tool_usage>
        1. **retrieve_ledger**
        - Purpose: Understand graph structure and check if graph is empty
        - Handling: If empty result, report that both graphs are empty and stop

        2. **generate_vector_search_queries**
        - Call: Before calling vector search, generate up to 3 properties for vector search queries
        - Input: Pass appropriate labels and properties from schema
        - Purpose: Creates optimized questions for vector search
        - Example: `await generate_vector_search_queries(ctx, ["Person", "Organization"], {"name": "search term"})`

        3. **vector_search**
        - Call: using questions from generate_vector_search_queries
        - Priority: Ensure this is always tried as it allows for semantic matching
        - Purpose: Find semantically similar information

        4. **search_by_full_text**
        - Formats: Use appropriate query syntax (wildcard, fuzzy, boolean) based on the search need
        - Example query formats:
            - Prefix: "bank*" to find "banking", "banker", etc.
            - Fuzzy: "finace~" to find "finance" despite misspelling
            - Boolean: "credit AND score NOT history" for specific combinations

        5. **filter_search_by_nodes_properties**
        - Input: Be precise with node labels and property names from schema
        - Purpose: More targeted search when you know where to look

        6. **search_relationships**
        - Call: When specifically looking for connections between entities
        - Input: Relationship type exactly as shown in schema
        - Purpose: Find how entities are connected rather than entities themselves
        </tool_usage>
        </user_specific_instructions>
<user_specific_instructions>For each search query the result you return return it in the format of found nodes properties and thier relations as per the graph database schema
            Return info in format of what Node and its type was found with its properties and nicely formatted relations no need to explain or answer, if some info was not found return back with empty results
            Return back what u found in exact format for Node and its properties and relations as per query
            </user_specific_instructions>
<user_specific_instructions>For each search query the result you return return it in the format of found nodes properties and thier relations as per the graph database schema
            Return info in format of what Node and its type was found with its properties and nicely formatted relations no need to explain or answer, if some info was not found return back with empty results
            Return back what u found in exact format for Node and its properties and relations as per query
            </user_specific_instructions>
<user_specific_instructions>For each search query the result you return return it in the format of found nodes properties and thier relations as per the graph database schema
            Return info in format of what Node and its type was found with its properties and nicely formatted relations no need to explain or answer, if some info was not found return back with empty results
            Return back what u found in exact format for Node and its properties and relations as per query
            </user_specific_instructions>
<user_specific_instructions>For each search query the result you return return it in the format of found nodes properties and thier relations as per the graph database schema
            Return info in format of what Node and its type was found with its properties and nicely formatted relations no need to explain or answer, if some info was not found return back with empty results
            Return back what u found in exact format for Node and its properties and relations as per query
            </user_specific_instructions>

[User] Find CreditCard node with name 'Digital Nomad Flex Card' Use all kinds of queries to confirm existence of the data if basic queries dont work, if you have enough info to answer the question dont go more deep

[Assistant]
<thought_1>First, I need to check the database status to understand the graph schema and confirm the database is not empty.</thought_1><tool_call>
    <tool_name>retreive_ledger</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>Checking the current state of the graph database to understand its schema and verify it's not empty.</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from retreive_ledger tool
: Graph Database Schema:

NODES:
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardNetwork
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardNetwork
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardNetwork
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, recommended_credit_score, last_synced, name, type
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, recommended_credit_score, last_synced, name, type
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, recommended_credit_score, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, additional_info, description, last_synced, name
- Label: Feature, APRFeature
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name, transfer_fee
- Label: Feature, SecurityFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, additional_info, description, last_synced, name
- Label: Feature, APRFeature
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name, transfer_fee
- Label: Feature, SecurityFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, TravelFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, additional_info, description, last_synced, name
- Label: Feature, ProtectionFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, ze_doc_id, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, ze_doc_id, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, ze_doc_id, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: CustomerReview
  Properties: in_zeroentropy, ze_doc_id, total_reviews, updated, last_synced, last_updated, rating, representative_review
- Label: CustomerReview
  Properties: in_zeroentropy, ze_doc_id, total_reviews, updated, last_synced, last_updated, rating, representative_review
- Label: CustomerReview
  Properties: in_zeroentropy, ze_doc_id, total_reviews, updated, last_synced, last_updated, rating, representative_review
- Label: Acceptance
  Properties: in_zeroentropy, acceptance_rate, card_designs, ze_doc_id, updated, last_synced, digital_wallet_compatible
- Label: Acceptance
  Properties: in_zeroentropy, acceptance_rate, card_designs, ze_doc_id, updated, last_synced, digital_wallet_compatible
- Label: Acceptance
  Properties: in_zeroentropy, acceptance_rate, card_designs, ze_doc_id, updated, last_synced, digital_wallet_compatible
- Label: SpecialOffer
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name
- Label: SpecialOffer
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name
- Label: SpecialOffer
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name
- Label: CardIssuer
  Properties: in_zeroentropy, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, updated, recommended_credit_score, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, TravelFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, ProtectionFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, additional_info, description, last_synced, name
- Label: FinancialDetail
  Properties: in_zeroentropy, application_process, updated, last_synced, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: CardIssuer
  Properties: in_zeroentropy, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, updated, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: in_zeroentropy, application_process, ze_doc_id, updated, last_synced, credit_limit_range, standard_apr_range
- Label: SpecialOffer
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name

RELATIONSHIPS:
- Type: ISSUED_BY
- Type: OPERATES_ON
- Type: HAS_FEATURE
- Type: HAS_FINANCIAL_DETAILS
- Type: HAS_REVIEWS
- Type: HAS_ACCEPTANCE_INFO
- Type: OFFERS_PROMOTION

[Assistant]
<thought_1>I can see that the database is not empty and contains various nodes, including CreditCard nodes with properties like name, annual_fee, type, etc. Now I'll search for a CreditCard node with the name "Digital Nomad Flex Card".</thought_1><thought_2>Let me first try a direct search using full text search with the exact name.</thought_2><tool_call>
    <tool_name>search_by_full_text</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>Searching for CreditCard with the exact name "Digital Nomad Flex Card" using full text search.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"Digital Nomad Flex Card"</parameter_value>
        </parameter>
    </parameters>
</tool_call>


[System]
You are a model with advanced reasoning capabilities. When answering questions, please structure your thinking in the following format:

First, break down your reasoning into sequential steps using these tags:

    <thought_1>Your first step of reasoning goes here...</thought_1>
    <thought_2>Your second step of reasoning goes here...</thought_2>
    ...
    <thought_n>Your final step of reasoning goes here...</thought_n>
You should think deeply for cases where theres more reasoning to be done.

After your reasoning steps, conclude with ONE of these output formats:

Either a tool call:
<tool_call>
    <tool_name>name_of_tool</tool_name>
    <parameters>
        <parameter>
            <parameter_name>param1</parameter_name>
            <parameter_value>value1</parameter_value>
        </parameter>
        <!-- Add more parameters as needed -->
    </parameters>
</tool_call>

Or a text response:
<text_output>Your final answer or response goes here...</text_output>

IMPORTANT: When making tool calls, follow these rules:
1. Use EXACTLY the format shown above with proper XML tags
2. Make sure all XML tags are properly closed
3. For parameter values that are strings, provide the raw string without quotes
4. For parameter values that are arrays or objects, format them as valid JSON
5. Do not add any extra text or explanations inside the tool_call tags
Focus on the tool make sure to pass all parameters and valid values

Think deep for complex json types and try to be accurate in one try

Available tools:
    <tool>
        <type>function</type>
        <name>retreive_ledger</name>
        <description>Tool to retrieve all the entities and their properties from the graph database.
Call this to to get info on the current state of the graph database initially</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            </properties>
            <required>chain_of_thought</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>insert_into_graph</name>
        <description>Tool to run the final insertion query and insert the data into the graph database
Pass query as a cypher query and params as a dictionary, for the query to run on
Params are sturcured and passed as a dictionary to neo4j driver like
session.run(query,params)</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            <property>
                <name>final_query</name>
                <type>string</type>
                <description>Final Query</description>
            </property>
            <property>
                <name>params</name>
                <type>object</type>
                <description>Params</description>
            </property>
            </properties>
            <required>chain_of_thought, final_query, params</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>contact_search_agent</name>
        <description>Tool to contact the search agent to get the relevant information from the graph database
Pass it information to fetch step by step</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            <property>
                <name>search_agent_query</name>
                <type>string</type>
                <description>Search Agent Query</description>
            </property>
            <property>
                <name>labels</name>
                <type>array</type>
                <description>Labels</description>
                <items>
                    <type>string</type>
                </items>
            </property>
            <property>
                <name>properties</name>
                <type>object</type>
                <description>Properties</description>
            </property>
            </properties>
            <required>chain_of_thought, search_agent_query, labels, properties</required>
        </parameters>
    </tool>

 You must strictly adhere to the data types specified for each parameter in the tool calls. If a parameter is an array, format it as an array. If it's a string, provide a string. If it's an object, provide a properly structured object. Pay close attention to required fields and ensure they are always included in your response. Follow the exact structure defined in the tool specification.

[User]
current insertion step is :Create the following Feature nodes: 1) RewardFeature with name='4% Back on Tech Purchases', description='4% back on electronics and software purchases', updated=false; 2) RewardFeature with name='Innovation Points Program', description='Redeem points for latest gadgets, coding courses, or tech conference tickets', updated=false; 3) ProtectionFeature with name='Extended Tech Warranties', description='Extended warranties on tech purchases', updated=false; 4) ProtectionFeature with name='Device Protection Insurance', description='Insurance coverage for device protection', updated=false; 5) Feature with name='Early Tech Access', description='Early access to product launches', updated=false. Create HAS_FEATURE relationships from the CreditCard 'Tech Enthusiast Edge Card' to each of these Feature nodes.
                    context to work on is : ```json
{
  "Tech Enthusiast Edge Card": {
    "description": "Digital Banking Solutions' card.",
    "annual_fee": "$125",
    "early_access": "Product launches",
    "rewards": [
      {
        "name": "4% Back on Tech Purchases",
        "benefits": "4% back on electronics and software purchases"
      },
      {
        "name": "Innovation Points Program",
        "benefits": "Redeem points for gadgets, courses, or conference tickets"
      }
    ],
    "protections": [
      {
        "name": "Extended Tech Warranties",
        "benefits": "Extended warranties on tech purchases"
      },
      {
        "name": "Device Protection Insurance",
        "benefits": "Insurance coverage for device protection"
      }
    ],
    "features": [
      {
        "name": "Early Tech Access",
        "benefits": "Early access to product launches"
      }
    ]
  }
}
```

[Assistant]
<thought_1>I need to retrieve the ledger again to understand the current state of the graph database, especially to confirm the existence of the "Tech Enthusiast Edge Card" which we'll be connecting the Feature nodes to.</thought_1><tool_call>
    <tool_name>retreive_ledger</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I need to retrieve the current state of the graph database to understand its schema and to confirm that the "Tech Enthusiast Edge Card" exists before creating Feature nodes and relationships to it.</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from retreive_ledger tool
: Graph Database Schema:

NODES:
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardNetwork
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardNetwork
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardNetwork
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, recommended_credit_score, last_synced, name, type
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, recommended_credit_score, last_synced, name, type
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, recommended_credit_score, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, additional_info, description, last_synced, name
- Label: Feature, APRFeature
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name, transfer_fee
- Label: Feature, SecurityFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, additional_info, description, last_synced, name
- Label: Feature, APRFeature
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name, transfer_fee
- Label: Feature, SecurityFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, TravelFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, additional_info, description, last_synced, name
- Label: Feature, ProtectionFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, ze_doc_id, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, ze_doc_id, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, ze_doc_id, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: CustomerReview
  Properties: in_zeroentropy, ze_doc_id, total_reviews, updated, last_synced, last_updated, rating, representative_review
- Label: CustomerReview
  Properties: in_zeroentropy, ze_doc_id, total_reviews, updated, last_synced, last_updated, rating, representative_review
- Label: CustomerReview
  Properties: in_zeroentropy, ze_doc_id, total_reviews, updated, last_synced, last_updated, rating, representative_review
- Label: Acceptance
  Properties: in_zeroentropy, acceptance_rate, card_designs, ze_doc_id, updated, last_synced, digital_wallet_compatible
- Label: Acceptance
  Properties: in_zeroentropy, acceptance_rate, card_designs, ze_doc_id, updated, last_synced, digital_wallet_compatible
- Label: Acceptance
  Properties: in_zeroentropy, acceptance_rate, card_designs, ze_doc_id, updated, last_synced, digital_wallet_compatible
- Label: SpecialOffer
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name
- Label: SpecialOffer
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name
- Label: SpecialOffer
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name
- Label: CardIssuer
  Properties: in_zeroentropy, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, updated, recommended_credit_score, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, TravelFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, ProtectionFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, additional_info, description, last_synced, name
- Label: FinancialDetail
  Properties: in_zeroentropy, application_process, updated, last_synced, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: CardIssuer
  Properties: in_zeroentropy, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, updated, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: in_zeroentropy, application_process, ze_doc_id, updated, last_synced, credit_limit_range, standard_apr_range
- Label: SpecialOffer
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: CardIssuer
  Properties: in_zeroentropy, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, updated, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, TravelFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, updated, last_synced, minimum_income_requirement, standard_apr_range
- Label: CardIssuer
  Properties: in_zeroentropy, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, updated, recommended_credit_score, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, ProtectionFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, ProtectionFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: in_zeroentropy, application_process, updated, last_synced, credit_limit_range, standard_apr_range
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, recommended_credit_score, last_synced, name, eligibility, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: annual_fee, in_zeroentropy, application_process, ze_doc_id, updated, description, last_synced
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, last_synced, name, eligibility, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, ProtectionFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: annual_fee, in_zeroentropy, application_process, ze_doc_id, updated, last_synced
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, description, last_synced
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, last_synced, name, type
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: Feature, APRFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: annual_fee, in_zeroentropy, application_process, updated, last_synced
- Label: CardIssuer
  Properties: in_zeroentropy, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, additional_info, description, last_synced, name
- Label: FinancialDetail
  Properties: annual_fee, in_zeroentropy, updated, last_synced, standard_apr_range
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, last_synced
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, last_synced, name, type

RELATIONSHIPS:
- Type: ISSUED_BY
- Type: OPERATES_ON
- Type: HAS_FEATURE
- Type: HAS_FINANCIAL_DETAILS
- Type: HAS_REVIEWS
- Type: HAS_ACCEPTANCE_INFO
- Type: OFFERS_PROMOTION
- Type: HAS_FINANCIAL_DETAIL

[Assistant]
<thought_1>I need to first confirm that the CreditCard "Tech Enthusiast Edge Card" exists in the database before proceeding with creating Feature nodes and relationships. Let me search for it specifically.</thought_1><tool_call>
    <tool_name>contact_search_agent</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I need to verify the existence of the CreditCard node with name "Tech Enthusiast Edge Card" before creating Feature nodes and relationships to it.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_agent_query</parameter_name>
            <parameter_value>Find a CreditCard node with the name "Tech Enthusiast Edge Card"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>labels</parameter_name>
            <parameter_value>['CreditCard']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>{'name': 'Tech Enthusiast Edge Card'}</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from tool: Found:

Node: CreditCard
Properties:
- annual_fee: 125
- name: Tech Enthusiast Edge Card
- type: Rewards

Relationships:
- [ISSUED_BY] → CardIssuer(name: Digital Banking Solutions)

The search confirms the existence of a CreditCard node with the name "Tech Enthusiast Edge Card" that has an annual fee of $125 and is a Rewards type card. This card is issued by "Digital Banking Solutions".

[Assistant]
<thought_1>Great, I've confirmed that the CreditCard node "Tech Enthusiast Edge Card" exists in the database and is connected to the CardIssuer "Digital Banking Solutions" via an ISSUED_BY relationship. Now I need to check if any of the Feature nodes described in the current insertion step already exist to avoid creating duplicates.</thought_1><thought_2>Let me check for the existence of the first feature node: "4% Back on Tech Purchases"</thought_2><tool_call>
    <tool_name>contact_search_agent</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I need to check if a Feature node named "4% Back on Tech Purchases" already exists to avoid creating duplicates.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_agent_query</parameter_name>
            <parameter_value>Find a Feature node with the name "4% Back on Tech Purchases"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>labels</parameter_name>
            <parameter_value>['Feature', 'RewardFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>{'name': '4% Back on Tech Purchases'}</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from tool: Results:

No Feature node with the name "4% Back on Tech Purchases" was found in the database after multiple search methods including:
- Exact text search
- Filtered node and property search
- Wildcard searches
- Vector semantic search
- Partial text search using "tech~"

The closest related nodes found were:
1. A Feature, RewardFeature node named "4% Cash Back on Medical Equipment"
2. A Feature, RewardFeature node named "Restaurant Cashback" with 4% cashback
3. A CreditCard node named "Tech Enthusiast Edge Card" (but without the specific 4% back feature)

The specific Feature node with name "4% Back on Tech Purchases" does not exist in the current database.

[Assistant]
<thought_1>I've confirmed that the Feature node "4% Back on Tech Purchases" doesn't exist in the database. Now I'll check for the second feature node: "Innovation Points Program".</thought_1><tool_call>
    <tool_name>contact_search_agent</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I need to check if a Feature node named "Innovation Points Program" already exists to avoid creating duplicates.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_agent_query</parameter_name>
            <parameter_value>Find a Feature node with the name "Innovation Points Program"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>labels</parameter_name>
            <parameter_value>['Feature', 'RewardFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>{'name': 'Innovation Points Program'}</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from tool: Based on comprehensive searching through the database, I could not find a Feature node with the name "Innovation Points Program".

I searched using various methods including:
- Exact name search
- Wildcard searches for "Innovation Points"
- Prefix searches for "Innovation"
- Vector semantic search
- Broader searches for all "Points Program" features
- Comprehensive search for all "Program" related features

The database contains several other "Points Program" features:
- Gourmet Points Program
- Paw Points Program
- City Points Program
- Knowledge Points Program
- Nomad Miles Program
- BuildUp Rewards Program

But there is no Feature or RewardFeature node with the name "Innovation Points Program" in the current database.

[Assistant]
<thought_1>Now that I've confirmed that both the "4% Back on Tech Purchases" and "Innovation Points Program" features don't exist, I'll check for "Extended Tech Warranties" feature.</thought_1><tool_call>
    <tool_name>contact_search_agent</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I need to check if a Feature node named "Extended Tech Warranties" already exists to avoid creating duplicates.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_agent_query</parameter_name>
            <parameter_value>Find a Feature node with the name "Extended Tech Warranties"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>labels</parameter_name>
            <parameter_value>['Feature', 'ProtectionFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>{'name': 'Extended Tech Warranties'}</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from tool: Based on multiple search approaches, I could not find a Feature or ProtectionFeature node specifically named "Extended Tech Warranties" in the database.

The searches returned several protection-related features including:

1. Feature, ProtectionFeature(name: Summit Infinite Premium Protections) - has "Purchase Protection" in its description but nothing specific to tech warranties
2. Feature, ProtectionFeature(name: Premium Travel Insurance) - focused on travel insurance
3. Feature, ProtectionFeature(name: Discounted Malpractice Insurance) - for healthcare professionals
4. Feature, SecurityFeature(name: Elevate Platinum Security Features) - security features but no tech warranties

No nodes matching the specific criteria of "Extended Tech Warranties" as a Feature or ProtectionFeature were found in the database.

[Assistant]
<thought_1>I've confirmed that the "Extended Tech Warranties" feature doesn't exist. Next, I'll check for the "Device Protection Insurance" feature.</thought_1><tool_call>
    <tool_name>contact_search_agent</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I need to check if a Feature node named "Device Protection Insurance" already exists to avoid creating duplicates.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_agent_query</parameter_name>
            <parameter_value>Find a Feature node with the name "Device Protection Insurance"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>labels</parameter_name>
            <parameter_value>['Feature', 'ProtectionFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>{'name': 'Device Protection Insurance'}</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from tool: Based on multiple search attempts, I could not find any Feature node with the exact name "Device Protection Insurance" in the graph database.

Here are the protection and insurance-related features that exist instead:

1. Protection Features:
   - "Summit Infinite Premium Protections" (Feature, ProtectionFeature)
     - Description: Trip Delay/Cancellation Protection, Lost Baggage Insurance, Purchase Protection, Free TSA PreCheck/Global Entry Credit
     - Related to: CreditCard "Summit Infinite Travel"

   - "Premium Travel Insurance" (Feature, ProtectionFeature)
     - Description: Comprehensive travel insurance coverage including trip cancellation, interruption, delay coverage, lost luggage reimbursement, and emergency assistance
     - Related to: CreditCard "Horizon Unlimited Platinum Card"

   - "Discounted Malpractice Insurance" (Feature, ProtectionFeature)
     - Description: Special rates on malpractice insurance for cardholders
     - Related to: CreditCard "Healthcare Heroes Card"

2. Security Features:
   - "Elevate Platinum Security Features" (Feature, SecurityFeature)
     - Description: Zero Liability Protection, ID Guard Plus for identity theft protection, Free Dark Web Monitoring
     - Related to: CreditCard "Elevate Platinum Rewards"

No nodes containing "device" were found in the database.

[Assistant]
<thought_1>I've confirmed that the "Device Protection Insurance" feature doesn't exist. Finally, I'll check for the "Early Tech Access" feature.</thought_1><tool_call>
    <tool_name>contact_search_agent</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I need to check if a Feature node named "Early Tech Access" already exists to avoid creating duplicates.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_agent_query</parameter_name>
            <parameter_value>Find a Feature node with the name "Early Tech Access"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>labels</parameter_name>
            <parameter_value>['Feature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>{'name': 'Early Tech Access'}</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from tool: After searching the database using multiple methods (exact match, filtered search, vector search, wildcards, and fuzzy matching), no Feature node with the name "Early Tech Access" was found in the database.

The closest matches found were:
- Feature node "Worldwide Airport Lounge Access" (TravelFeature)
- Feature node "Global Wifi Hotspot Access" (TravelFeature)
- Feature node "VC Networking Events" (RewardFeature)

No exact or close match for "Early Tech Access" was found among Feature nodes.

[Assistant]
<thought_1>Now I've confirmed that none of the five Feature nodes I need to create ("4% Back on Tech Purchases", "Innovation Points Program", "Extended Tech Warranties", "Device Protection Insurance", and "Early Tech Access") already exist in the database. I can proceed with creating all these nodes and connecting them to the "Tech Enthusiast Edge Card" CreditCard via HAS_FEATURE relationships.</thought_1><tool_call>
    <tool_name>insert_into_graph</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I've confirmed the CreditCard "Tech Enthusiast Edge Card" exists in the database and none of the five Feature nodes I need to create already exist. I'll create all five Feature nodes with their respective labels and properties, and connect them to the CreditCard with HAS_FEATURE relationships. I'm using MERGE for the CreditCard to ensure we find the existing node, and CREATE for the Feature nodes since they're new.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>final_query</parameter_name>
            <parameter_value>MERGE (card:CreditCard {name: $cardName})
CREATE (reward1:Feature:RewardFeature {name: $reward1Name, description: $reward1Desc, updated: $updated})
CREATE (reward2:Feature:RewardFeature {name: $reward2Name, description: $reward2Desc, updated: $updated})
CREATE (protection1:Feature:ProtectionFeature {name: $protection1Name, description: $protection1Desc, updated: $updated})
CREATE (protection2:Feature:ProtectionFeature {name: $protection2Name, description: $protection2Desc, updated: $updated})
CREATE (feature:Feature {name: $featureName, description: $featureDesc, updated: $updated})
CREATE (card)-[:HAS_FEATURE]->(reward1)
CREATE (card)-[:HAS_FEATURE]->(reward2)
CREATE (card)-[:HAS_FEATURE]->(protection1)
CREATE (card)-[:HAS_FEATURE]->(protection2)
CREATE (card)-[:HAS_FEATURE]->(feature)
RETURN card, reward1, reward2, protection1, protection2, feature</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>params</parameter_name>
            <parameter_value>{'cardName': 'Tech Enthusiast Edge Card', 'reward1Name': '4% Back on Tech Purchases', 'reward1Desc': '4% back on electronics and software purchases', 'reward2Name': 'Innovation Points Program', 'reward2Desc': 'Redeem points for latest gadgets, coding courses, or tech conference tickets', 'protection1Name': 'Extended Tech Warranties', 'protection1Desc': 'Extended warranties on tech purchases', 'protection2Name': 'Device Protection Insurance', 'protection2Desc': 'Insurance coverage for device protection', 'featureName': 'Early Tech Access', 'featureDesc': 'Early access to product launches', 'updated': False}</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from insert_into_graph tool
: 5 nodes created; 5 relationships created; 15 properties set; 9 labels added

[Assistant]
<thought_1>The insertion was successful. According to the result, 5 nodes were created (the five Feature nodes), 5 relationships were created (the HAS_FEATURE relationships from the CreditCard to each Feature node), 15 properties were set (name, description, and updated for each Feature node), and 9 labels were added (Feature label for all five nodes, plus RewardFeature label for two nodes and ProtectionFeature label for two nodes).</thought_1><text_output>I've successfully completed the requested insertion task. Here's a summary of what was accomplished:

1. Created five Feature nodes with their respective labels and properties:
   - RewardFeature: "4% Back on Tech Purchases" (description: "4% back on electronics and software purchases")
   - RewardFeature: "Innovation Points Program" (description: "Redeem points for latest gadgets, coding courses, or tech conference tickets")
   - ProtectionFeature: "Extended Tech Warranties" (description: "Extended warranties on tech purchases")
   - ProtectionFeature: "Device Protection Insurance" (description: "Insurance coverage for device protection")
   - Feature: "Early Tech Access" (description: "Early access to product launches")

2. Connected all five Feature nodes to the existing "Tech Enthusiast Edge Card" CreditCard via HAS_FEATURE relationships.

All nodes were created with updated=false as specified. The operation resulted in 5 nodes created, 5 relationships created, 15 properties set, and 9 labels added.
</text_output>

[User]
current insertion step is :Create a FinancialDetail node with annual_fee=125, updated=false. Create HAS_FINANCIAL_DETAILS relationship from the CreditCard 'Tech Enthusiast Edge Card' to this FinancialDetail node.
                    context to work on is : ```json
{
  "Tech Enthusiast Edge Card": {
    "annual_fee": 125,
    "financial_details": {
      "HAS_FINANCIAL_DETAILS": true,
      "updated": false
    },
    "benefits": [
      "Early access to product launches",
      "4% cashback on electronics and software",
      "Extended warranties on tech purchases",
      "Device protection insurance",
      "Innovation Points for gadgets, courses, tech conferences"
    ]
  }
}
```

[Assistant]
<thought_1>I need to check if there's already a FinancialDetail node connected to the "Tech Enthusiast Edge Card" CreditCard before creating a new one to avoid duplication.</thought_1><tool_call>
    <tool_name>contact_search_agent</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I need to check if there's already a FinancialDetail node connected to the "Tech Enthusiast Edge Card" CreditCard via a HAS_FINANCIAL_DETAILS relationship.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_agent_query</parameter_name>
            <parameter_value>Find a FinancialDetail node connected to the CreditCard "Tech Enthusiast Edge Card" through a HAS_FINANCIAL_DETAILS relationship</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>labels</parameter_name>
            <parameter_value>['FinancialDetail']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>{}</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from tool: Node: CreditCard(annual_fee: 125, name: Tech Enthusiast Edge Card, type: Rewards)

Relationships:
  → [ISSUED_BY] → CardIssuer(name: Digital Banking Solutions)
  → [HAS_FEATURE] → Feature, RewardFeature(description: 4% back on electronics and software purchases, name: 4% Back on Tech Purchases)
  → [HAS_FEATURE] → Feature, RewardFeature(description: Redeem points for latest gadgets, coding courses, or tech conference tickets, name: Innovation Points Program)
  → [HAS_FEATURE] → Feature, ProtectionFeature(description: Extended warranties on tech purchases, name: Extended Tech Warranties)
  → [HAS_FEATURE] → Feature, ProtectionFeature(description: Insurance coverage for device protection, name: Device Protection Insurance)
  → [HAS_FEATURE] → Feature(description: Early access to product launches, name: Early Tech Access)

No FinancialDetail node connected to "Tech Enthusiast Edge Card" through a HAS_FINANCIAL_DETAILS relationship was found in the database.

[Assistant]
<thought_1>I've confirmed that there is no FinancialDetail node connected to the "Tech Enthusiast Edge Card" via a HAS_FINANCIAL_DETAILS relationship. Now I can proceed with creating the FinancialDetail node with annual_fee=125, updated=false and connect it to the CreditCard with a HAS_FINANCIAL_DETAILS relationship.</thought_1><tool_call>
    <tool_name>insert_into_graph</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I've verified that there is no FinancialDetail node currently connected to the "Tech Enthusiast Edge Card". Now I will create a new FinancialDetail node with annual_fee=125, updated=false and connect it to the CreditCard with a HAS_FINANCIAL_DETAILS relationship.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>final_query</parameter_name>
            <parameter_value>MERGE (card:CreditCard {name: $cardName})
CREATE (fd:FinancialDetail {annual_fee: $annualFee, updated: $updated})
CREATE (card)-[:HAS_FINANCIAL_DETAILS]->(fd)
RETURN card, fd</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>params</parameter_name>
            <parameter_value>{"cardName": "Tech Enthusiast Edge Card", "annualFee": 125, "updated": false}</parameter_value>
        </parameter>
    </parameters>
</tool_call>


[System]
You are a model with advanced reasoning capabilities. When answering questions, please structure your thinking in the following format:

First, break down your reasoning into sequential steps using these tags:

    <thought_1>Your first step of reasoning goes here...</thought_1>
    <thought_2>Your second step of reasoning goes here...</thought_2>
    ...
    <thought_n>Your final step of reasoning goes here...</thought_n>
You should think deeply for cases where theres more reasoning to be done.

After your reasoning steps, conclude with ONE of these output formats:

Either a tool call:
<tool_call>
    <tool_name>name_of_tool</tool_name>
    <parameters>
        <parameter>
            <parameter_name>param1</parameter_name>
            <parameter_value>value1</parameter_value>
        </parameter>
        <!-- Add more parameters as needed -->
    </parameters>
</tool_call>

Or a text response:
<text_output>Your final answer or response goes here...</text_output>

IMPORTANT: When making tool calls, follow these rules:
1. Use EXACTLY the format shown above with proper XML tags
2. Make sure all XML tags are properly closed
3. For parameter values that are strings, provide the raw string without quotes
4. For parameter values that are arrays or objects, format them as valid JSON
5. Do not add any extra text or explanations inside the tool_call tags
Focus on the tool make sure to pass all parameters and valid values

Think deep for complex json types and try to be accurate in one try

Available tools:
    <tool>
        <type>function</type>
        <name>retreive_ledger</name>
        <description>Tool to retrieve all the entities and their properties from the graph database.
Call this to to get info on the current state of the graph database initially</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            </properties>
            <required>chain_of_thought</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>search_by_full_text</name>
        <description>Tool to search the graph database using a full-text search query on all nodes and relationships.
Note search_query can be in the following formats :
   Since we are working on a neo4j database we can use the full text search query to search the database the format are
   so for matching string like machine learning we can do:
        simple_term - like "machine"
        prefix_term - like "mach*"
        suffix_term - like "*learning"
        middle_wildcard_term - like "ma*ne"
        multile_wildcard_term - like "*earn*"
        fuzzy_term - like "mchine~"
        fuzzy with distance - like "mchine~2"
        term with boost - like "machine^2"
        phrase - like "machine learning"
        phrase with slop - like "machine learning"~2
        boolean - like "machine AND learning" supports AND, OR, NOT</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            <property>
                <name>search_query</name>
                <type>string</type>
                <description>Search Query</description>
            </property>
            </properties>
            <required>chain_of_thought, search_query</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>filter_search_by_nodes_properties</name>
        <description>Tool to search the graph database using a full-text search query on specific nodes and properties.
Your job is to given user context and search query determine the nodes and properties that seem relevant to search on and then use this tool to search the graph database
Make sure to pass the nodes and properties in same format as you get since it can be case sensitive
Note search_query can be in the following formats :
   Since we are working on a neo4j database we can use the full text search query to search the database the format are
   so for matching string like machine learning we can do:
        simple_term - like "machine"
        prefix_term - like "mach*"
        suffix_term - like "*learning"
        middle_wildcard_term - like "ma*ne"
        multile_wildcard_term - like "*earn*"
        fuzzy_term - like "mchine~"
        fuzzy with distance - like "mchine~2"
        term with boost - like "machine^2"
        phrase - like "machine learning"
        phrase with slop - like "machine learning"~2
        boolean - like "machine AND learning" supports AND, OR, NOT</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            <property>
                <name>search_query</name>
                <type>string</type>
                <description>Search Query</description>
            </property>
            <property>
                <name>nodes</name>
                <type>array</type>
                <description>Nodes</description>
                <items>
                    <type>string</type>
                </items>
            </property>
            <property>
                <name>properties</name>
                <type>array</type>
                <description>Properties</description>
                <items>
                    <type>string</type>
                </items>
            </property>
            </properties>
            <required>chain_of_thought, search_query, nodes, properties</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>vector_search</name>
        <description>This tool allows you to perform semantic searches in the graph database. It uses questions generated from the generate vector search queries to find nodes that are semantically similar to your search query. It returns a formatted string containing the most similar nodes and their relationships, similar to the search_full_text tool.</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            <property>
                <name>search_query</name>
                <type>string</type>
                <description>Search Query</description>
            </property>
            </properties>
            <required>chain_of_thought, search_query</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>generate_vector_search_queries</name>
        <description>This tool turns the nodes labels and properties into descriptions phrased as questions, this allows better context for vector search queries when question to question matching is done</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>labels</name>
                <type>array</type>
                <description>Labels</description>
                <items>
                    <type>string</type>
                </items>
            </property>
            <property>
                <name>properties</name>
                <type>object</type>
                <description>Properties</description>
            </property>
            </properties>
            <required>labels, properties</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>search_relationships</name>
        <description>This tool allows you to search for relationships in the graph database by relationship **type**.
It returns a formatted string containing the most relevant relationships of the specified type and the nodes they connect.
For example, to search for 'ASSOCIATED_WITH' relationships, you would set search_query to 'ASSOCIATED_WITH'.
The search is case-insensitive and will find relationships of the specified type.</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>chain_of_thought</name>
                <type>string</type>
                <description>Chain Of Thought</description>
            </property>
            <property>
                <name>search_query</name>
                <type>string</type>
                <description>Search Query</description>
            </property>
            </properties>
            <required>chain_of_thought, search_query</required>
        </parameters>
    </tool>

 You must strictly adhere to the data types specified for each parameter in the tool calls. If a parameter is an array, format it as an array. If it's a string, provide a string. If it's an object, provide a properly structured object. Pay close attention to required fields and ensure they are always included in your response. Follow the exact structure defined in the tool specification.<user_specific_instructions>
        <agent_role>
        You are a search agent AI working with both graph and vector databases. Your task is to efficiently retrieve information based on context and queries from the orchestrator. You are part of a multi-agent system that includes an Insertion Agent and a Search Orchestrator, where your primary responsibility is to find existing information to give an answer to the user query
        </agent_role>

        <search_workflow>
        Follow this precise search process in order:

        1. **ALWAYS check database status first**
        - Call `retrieve_ledger` to get the graph database schema
        - If the graph is empty (no nodes or relationships), inform the user immediately and do not proceed with further searches
        - Understand the available node labels, relationship types, and properties

        2. **Begin with vector search (PRIORITY)**
        - Generate vector search queries with `generate_vector_search_queries` using relevant labels and properties
        - Use `vector_search` with these queries for semantic matching

        3. **Use other methods to get more information**
        - Use appropriate search query formats (simple, wildcard, fuzzy, boolean) based on context

         **Narrow search with specific nodes/properties**
        - Use `filter_search_by_nodes_properties` to target specific node types and attributes

         **Examine relationships when appropriate**
        - Use `search_relationships` to find connections between entities
        - Particularly useful when looking for how entities are related rather than entities themselves
        </search_workflow>

        <search_constraints>
        1. **Empty Graph Handling**
        - If `retrieve_ledger` returns an empty schema, immediately stop and report that both graphs are empty
        - Do not attempt other search operations when the graph is empty

        2. **Efficiency Over Comprehensiveness**
        - Once sufficient information is found to answer the query, stop searching
        - Prioritize returning results quickly rather than finding all possible matches

        3. **Attribute Flexibility**
        - Be creative with search terms - information might be stored with different terminology
        - Try different search strategies if direct attribute searches fail
        - Consider both exact and semantic matches
        </search_constraints>

        <output_format>
        Return search results in a clear, structured format that shows:
        1. What node types were found
        2. Key properties of those nodes
        3. Important relationships between nodes
        4. Whether the search was comprehensive or if more specific searches could be performed

        Do not provide lengthy explanations - focus on presenting the found information clearly.
        </output_format>

        <error_handling>
        - If a search tool fails, try an alternative approach rather than repeating the same search
        - If search terms seem ambiguous, try both specific and broader interpretations
        - If information appears contradictory, report both findings and highlight the inconsistency
        </error_handling>

        <tool_usage>
        1. **retrieve_ledger**
        - Purpose: Understand graph structure and check if graph is empty
        - Handling: If empty result, report that both graphs are empty and stop

        2. **generate_vector_search_queries**
        - Call: Before calling vector search, generate up to 3 properties for vector search queries
        - Input: Pass appropriate labels and properties from schema
        - Purpose: Creates optimized questions for vector search
        - Example: `await generate_vector_search_queries(ctx, ["Person", "Organization"], {"name": "search term"})`

        3. **vector_search**
        - Call: using questions from generate_vector_search_queries
        - Priority: Ensure this is always tried as it allows for semantic matching
        - Purpose: Find semantically similar information

        4. **search_by_full_text**
        - Formats: Use appropriate query syntax (wildcard, fuzzy, boolean) based on the search need
        - Example query formats:
            - Prefix: "bank*" to find "banking", "banker", etc.
            - Fuzzy: "finace~" to find "finance" despite misspelling
            - Boolean: "credit AND score NOT history" for specific combinations

        5. **filter_search_by_nodes_properties**
        - Input: Be precise with node labels and property names from schema
        - Purpose: More targeted search when you know where to look

        6. **search_relationships**
        - Call: When specifically looking for connections between entities
        - Input: Relationship type exactly as shown in schema
        - Purpose: Find how entities are connected rather than entities themselves
        </tool_usage>
        </user_specific_instructions>
<user_specific_instructions>For each search query the result you return return it in the format of found nodes properties and thier relations as per the graph database schema
            Return info in format of what Node and its type was found with its properties and nicely formatted relations no need to explain or answer, if some info was not found return back with empty results
            Return back what u found in exact format for Node and its properties and relations as per query
            </user_specific_instructions>
<user_specific_instructions>For each search query the result you return return it in the format of found nodes properties and thier relations as per the graph database schema
            Return info in format of what Node and its type was found with its properties and nicely formatted relations no need to explain or answer, if some info was not found return back with empty results
            Return back what u found in exact format for Node and its properties and relations as per query
            </user_specific_instructions>

[User] Find Feature node with name "3% Cashback on Eco-Friendly Purchases" or similar to eco-friendly cashback Labels: Feature, RewardFeature Properties: name: 3% Cashback on Eco-Friendly Purchases   Use all kinds of queries to confirm existence of the data if basic queries dont work, if you have enough info to answer the question dont go more deep

[Assistant]
<thought_1>I need to first check the database status to understand the schema and make sure there's data.</thought_1><tool_call>
    <tool_name>retreive_ledger</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>First, I need to retrieve the ledger to understand the current state of the graph database, including available node types, relationships, and properties.</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from retreive_ledger tool
: Graph Database Schema:

NODES:
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardIssuer
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardNetwork
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardNetwork
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CardNetwork
  Properties: in_zeroentropy, ze_doc_id, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, recommended_credit_score, last_synced, name, type
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, recommended_credit_score, last_synced, name, type
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, ze_doc_id, updated, recommended_credit_score, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, additional_info, description, last_synced, name
- Label: Feature, APRFeature
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name, transfer_fee
- Label: Feature, SecurityFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, additional_info, description, last_synced, name
- Label: Feature, APRFeature
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name, transfer_fee
- Label: Feature, SecurityFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, TravelFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, ze_doc_id, updated, additional_info, description, last_synced, name
- Label: Feature, ProtectionFeature
  Properties: in_zeroentropy, ze_doc_id, updated, description, last_synced, name
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, ze_doc_id, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, ze_doc_id, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: FinancialDetail
  Properties: foreign_transaction_fee, in_zeroentropy, application_process, ze_doc_id, updated, last_synced, cash_advance_fee, balance_transfer_fee, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: CustomerReview
  Properties: in_zeroentropy, ze_doc_id, total_reviews, updated, last_synced, last_updated, rating, representative_review
- Label: CustomerReview
  Properties: in_zeroentropy, ze_doc_id, total_reviews, updated, last_synced, last_updated, rating, representative_review
- Label: CustomerReview
  Properties: in_zeroentropy, ze_doc_id, total_reviews, updated, last_synced, last_updated, rating, representative_review
- Label: Acceptance
  Properties: in_zeroentropy, acceptance_rate, card_designs, ze_doc_id, updated, last_synced, digital_wallet_compatible
- Label: Acceptance
  Properties: in_zeroentropy, acceptance_rate, card_designs, ze_doc_id, updated, last_synced, digital_wallet_compatible
- Label: Acceptance
  Properties: in_zeroentropy, acceptance_rate, card_designs, ze_doc_id, updated, last_synced, digital_wallet_compatible
- Label: SpecialOffer
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name
- Label: SpecialOffer
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name
- Label: SpecialOffer
  Properties: in_zeroentropy, end_date, ze_doc_id, updated, description, last_synced, name
- Label: CardIssuer
  Properties: in_zeroentropy, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, updated, recommended_credit_score, last_synced, name, type
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, TravelFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, ProtectionFeature
  Properties: in_zeroentropy, updated, description, last_synced, name
- Label: Feature, RewardFeature
  Properties: in_zeroentropy, updated, additional_info, description, last_synced, name
- Label: FinancialDetail
  Properties: in_zeroentropy, application_process, updated, last_synced, credit_limit_range, minimum_income_requirement, standard_apr_range
- Label: CardIssuer
  Properties: in_zeroentropy, updated, last_synced, name
- Label: CreditCard
  Properties: annual_fee, in_zeroentropy, updated, last_synced, name, type

RELATIONSHIPS:
- Type: ISSUED_BY
- Type: OPERATES_ON
- Type: HAS_FEATURE
- Type: HAS_FINANCIAL_DETAILS
- Type: HAS_REVIEWS
- Type: HAS_ACCEPTANCE_INFO
- Type: OFFERS_PROMOTION

[Assistant]
<thought_1>I now have the database schema. I can see there are Feature nodes with RewardFeature labels, which should contain the kind of cashback feature I'm looking for. My goal is to find a Feature node named "3% Cashback on Eco-Friendly Purchases" or something similar.</thought_1><thought_2>Let me start with a vector search since it's recommended as the priority search method. This should help find semantically similar features related to eco-friendly cashback, even if the exact wording is different.</thought_2><tool_call>
    <tool_name>vector_search</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I'll first try a vector search to find semantically similar features related to eco-friendly cashback rewards. This should help me find relevant nodes even if they don't use the exact same wording as specified in the query.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>3% Cashback on Eco-Friendly Purchases sustainable green environmentally friendly</parameter_value>
        </parameter>
    </parameters>
</tool_call>