[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_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_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_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 a Feature node with the name "Innovation Points Program" Labels: Feature, RewardFeature Properties: name: Innovation Points Program   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, let me check the database status to understand its schema and whether it contains any data.</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 check if it contains any data.</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 can see that the database has data, including Feature and RewardFeature labeled nodes. Now I'll try to find a specific Feature node with the name "Innovation Points Program".</thought_1><thought_2>I'll start with a full-text search for "Innovation Points Program" since this is the exact name we're looking for.</thought_2><tool_call>
    <tool_name>search_by_full_text</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I'll first perform a direct full-text search for the exact name "Innovation Points Program" to see if this Feature exists in the database.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"Innovation Points Program"</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from search_by_full_text tool
:
Search results for: '"Innovation Points Program"'
================================================================================
No matches found
================================================================================

[Assistant]
<thought_1>I didn't find an exact match for "Innovation Points Program" with the full-text search. Let me try a more targeted approach by filtering specifically on Feature and RewardFeature nodes and the name property.</thought_1><tool_call>
    <tool_name>filter_search_by_nodes_properties</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I'll search specifically on Feature and RewardFeature nodes and focus on the name property to find "Innovation Points Program".</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"Innovation Points Program"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>nodes</parameter_name>
            <parameter_value>['Feature', 'RewardFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>['name']</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from filter_search_by_nodes_properties tool
:
Top 5 results for search term: '"Innovation Points Program"'
Filtered by labels: Feature, RewardFeature
Filtered by properties: name
====================================================================================================
No matches found
====================================================================================================

[Assistant]
<thought_1>I still didn't find an exact match for "Innovation Points Program". Let me try a more flexible search with wildcard or fuzzy matching to see if there might be slight naming variations.</thought_1><tool_call>
    <tool_name>filter_search_by_nodes_properties</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I'll use a wildcard search with "*Innovation Points*" to catch any Feature or RewardFeature nodes that contain this phrase in their name, even if it's not the exact full match.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"*Innovation Points*"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>nodes</parameter_name>
            <parameter_value>['Feature', 'RewardFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>['name']</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from filter_search_by_nodes_properties tool
:
Top 5 results for search term: '"*Innovation Points*"'
Filtered by labels: Feature, RewardFeature
Filtered by properties: name
====================================================================================================
No matches found
====================================================================================================

[Assistant]
<thought_1>I still can't find any Feature nodes with a name containing "Innovation Points". Let me try a more general search for "Innovation" to see if there are any related features that might be similar.</thought_1><tool_call>
    <tool_name>filter_search_by_nodes_properties</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I'll search for just "Innovation" in the name property of Feature and RewardFeature nodes to see if there are any related features.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"Innovation*"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>nodes</parameter_name>
            <parameter_value>['Feature', 'RewardFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>['name']</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from filter_search_by_nodes_properties tool
:
Top 5 results for search term: '"Innovation*"'
Filtered by labels: Feature, RewardFeature
Filtered by properties: name
====================================================================================================
No matches found
====================================================================================================

[Assistant]
<thought_1>I'm still not finding any Feature nodes with a name related to "Innovation". Let's try a vector search to find semantically similar content, as it might be stored under a different but related name.</thought_1><tool_call>
    <tool_name>generate_vector_search_queries</tool_name>
    <parameters>
        <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 generate_vector_search_queries tool
: 1. Is there a label Feature  with property name 'Innovation Points Program'?
2. Is there a label RewardFeature  with property name 'Innovation Points Program'?

[Assistant]
<thought_1>Now I'll use these generated vector search queries to perform a semantic search for the "Innovation Points Program".</thought_1><tool_call>
    <tool_name>vector_search</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I'll use vector search to find semantically similar content to "Innovation Points Program" among Feature and RewardFeature nodes.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>Is there a Feature or RewardFeature with property name 'Innovation Points Program'?</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from vector_search tool
:
Vector search results for: 'Is there a Feature or RewardFeature with property name 'Innovation Points Program'?'
================================================================================

Node Similarity: 1.8733
Main Node: Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc95115, updated: False, description: Points can be redeemed for pet services, supplies, or donations to animal welfare organizations, last_synced: 1744016557734, name: Paw Points Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 50, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc95111, updated: False, last_synced: 1744016389859, name: Pet Parent Priority Card, type: Rewards)
------------------------------------------------------------

Node Similarity: 1.5484
Main Node: Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9579, updated: False, description: Rewards program allowing redemption for student loan payments or educational supplies, last_synced: 1744012714884, name: Knowledge Points Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 0, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9575, updated: False, recommended_credit_score: Fair-Good, last_synced: 1744012682779, name: Student Success Card, eligibility: Students enrolled in accredited institutions, type: Student)
------------------------------------------------------------

Node Similarity: 1.2017
Main Node: Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9593, updated: False, description: Rewards can be used for concert tickets, museum memberships, or additional transit credits, last_synced: 1744014185306, name: City Points Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 60, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9589, updated: False, last_synced: 1744014013357, name: Urban Explorer Metro Card, type: Rewards)
------------------------------------------------------------

Node Similarity: 0.8551
Main Node: Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9586, updated: False, description: Points can be converted to continuing education credits or medical conference registrations, last_synced: 1744013501358, name: Wellness Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 125, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9582, updated: False, last_synced: 1744013161400, name: Healthcare Heroes Card, eligibility: Verified healthcare professionals, type: Professional)
------------------------------------------------------------

Node Similarity: 0.7089
Main Node: Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9552, updated: False, description: Rewards can be redeemed for cooking classes, restaurant vouchers, or kitchen appliances, last_synced: 1744010744458, name: Gourmet Points Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 99, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9547, updated: False, last_synced: 1744010594841, name: CulinaryElite Dining Card, type: Rewards)
------------------------------------------------------------
================================================================================

[Assistant]
<thought_1>The vector search didn't return an exact match for "Innovation Points Program" but found several similar "Points Program" features. None of them exactly match what we're looking for.</thought_1><thought_2>Let me try a broader search for "Points Program" to see if perhaps the node exists but with a slightly different name.</thought_2><tool_call>
    <tool_name>filter_search_by_nodes_properties</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I'll search for "Points Program" in the name property of Feature and RewardFeature nodes to see all available points programs, which might include or be similar to the Innovation Points Program.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"*Points Program*"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>nodes</parameter_name>
            <parameter_value>['Feature', 'RewardFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>['name']</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from filter_search_by_nodes_properties tool
:
Top 5 results for search term: '"*Points Program*"'
Filtered by labels: Feature, RewardFeature
Filtered by properties: name
====================================================================================================

Node Score: 2.2232
Main Node: Feature, RewardFeature(description: Rewards can be redeemed for cooking classes, restaurant vouchers, or kitchen appliances, name: Gourmet Points Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 99, name: CulinaryElite Dining Card, type: Rewards)
--------------------------------------------------------------------------------

Node Score: 2.2232
Main Node: Feature, RewardFeature(description: Points can be redeemed for pet services, supplies, or donations to animal welfare organizations, name: Paw Points Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 50, name: Pet Parent Priority Card, type: Rewards)
--------------------------------------------------------------------------------

Node Score: 2.2232
Main Node: Feature, RewardFeature(description: Rewards can be used for concert tickets, museum memberships, or additional transit credits, name: City Points Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 60, name: Urban Explorer Metro Card, type: Rewards)
--------------------------------------------------------------------------------

Node Score: 2.2232
Main Node: Feature, RewardFeature(description: Rewards program allowing redemption for student loan payments or educational supplies, name: Knowledge Points Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 0, recommended_credit_score: Fair-Good, name: Student Success Card, eligibility: Students enrolled in accredited institutions, type: Student)
--------------------------------------------------------------------------------
====================================================================================================

[Assistant]
<thought_1>I've now tried several search methods, including exact match, wildcards, fuzzy search, and vector search, but I haven't found a Feature node with the name "Innovation Points Program" in the database.</thought_1><thought_2>Let me try one final comprehensive approach with a search for just "Program" to ensure we're not missing anything.</thought_2><tool_call>
    <tool_name>filter_search_by_nodes_properties</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I'll perform a broader search for any Feature or RewardFeature with "Program" in the name to make sure we're not missing anything.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"*Program*"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>nodes</parameter_name>
            <parameter_value>['Feature', 'RewardFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>['name']</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>step_breakdown</name>
        <description>Tool to breakdown the steps of insertion and search into the graph database
This tool will return to you the results of search queries so you can update the insertion steps</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>insertion_steps</name>
                <type>array</type>
                <description>Insertion Steps</description>
                <items>
                    <type>string</type>
                </items>
            </property>
            <property>
                <name>search_steps</name>
                <type>array</type>
                <description>Search Steps</description>
                <items>
                    <type>string</type>
                </items>
            </property>
            </properties>
            <required>insertion_steps, search_steps</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>insert_into_graph</name>
        <description>Tool to insert info as per final insertion steps
This tool will return to you the results of insertion agents once it has inserted the info into the graph database
Pass the final insertion steps in info as to what changes to do for the info to be inserted is it updating / inserting / deleting , pass in details for each step</description>
        <parameters>
            <type>object</type>
            <properties>
            <property>
                <name>final_insertion_steps</name>
                <type>array</type>
                <description>Final Insertion Steps</description>
                <items>
                    <type>string</type>
                </items>
            </property>
            </properties>
            <required>final_insertion_steps</required>
        </parameters>
    </tool>

    <tool>
        <type>function</type>
        <name>retrieve_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>

 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>
            You are an Orchestration Agent responsible for managing information insertion and updates in a graph-based knowledge database. Your primary role is to coordinate between search and insertion operations to efficiently maintain the graph's integrity and structure.
            - Use the domain and example queries to guide you on the task but the main point is to create a comprehensive knowledge database capable of answering any user question about credit cards so if you find extra info that is not in the domain or example queries but is relevant to answering the user question about cards you should add it to the . You will get data from diverse sources and you need to make sure to add all that info and link it to the correct nodes and relationships.

            Domain
            Extract comprehensive credit card knowledge covering approval requirements across credit score ranges, issuer-specific policies and processing timeframes, card accessibility for various credit profiles, initial and potential credit limits, secured card deposit requirements, credit building effectiveness and reporting practices, fee structures and APR ranges, optimal usage strategies for building credit, and direct comparisons between similar cards, prioritizing quantitative data and comparative information to answer consumer questions about which cards they can qualify for, how quickly they can receive them, what limits they can expect, and how effectively cards will help improve their credit profiles, while also capturing specialized information about store cards, student cards, instant approval options, income verification methods, minimum payment calculations, card delivery timeframes, and specific rules or restrictions that might affect approval odds or account management, incorporating insights from customer reviews, expert opinions, and influencer recommendations to provide balanced perspectives on real-world experiences with different cards and issuers, including specific customer testimonials, detailed rewards program mechanics such as rotating category examples and spending limits, payment allocation policies, digital access eligibility requirements, comprehensive terms and conditions for promotional offers, and footnote details that clarify important limitations or exceptions to advertised features, card upgrade/downgrade paths between products within issuer families, international usage details including foreign transaction considerations and global acceptance, application decision timeframes across different issuers, co-branded card relationships and their unique benefits, card member tiers and status levels, special demographic eligibility factors beyond typical credit criteria, authorized user policies and their implications, card-specific customer service options such as dedicated service channels or concierge benefits, and fraud protection effectiveness including security features, dispute resolution processes, and real-world reliability of fraud detection systems.

            # Example Queries
            - "What credit score do I need to get a credit card from my bank?"
            - "What credit card is the easiest to get with bad credit?"
            - "Are Chase bank credit cards hard to get and what is the 5/24 rule?"
            - "How fast can I get a credit card from Amex vs. other banks?"
            - "What credit card has a $5000 limit with bad credit?"
            - "Which card helps build credit fastest and how much of my limit should I use?"
            - "How do credit cards verify income and can I get one without proof?"
            - "What is the minimum payment on a $2000 balance and should I pay in full?"
            - "Which store credit cards are easiest to get with a 550 score?"
            - "How long does it take to improve my credit score from 600 to 700?"

            Available Tools

            retrieve_ledger: Provides the graph database schema
            insert_into_graph: Executes the final insertion operations

            Core Responsibilities

            Schema Analysis

            Retrieve and analyze the graph database schema using retrieve_ledger
            Understand existing entity types, relationships, and properties

            Information Processing

            Break down new information into logical insertion phases
            Focus on capturing essential information rather than exhaustive details
            Maintain generic entity types (e.g., Person, Organization, Location) rather than specific instances

            Search Coordination

            Generate natural language search queries for the Search Agent
            Queries should be broad enough to find relevant existing nodes
            Example: "Find organizations mentioned in technology sector" rather than specific Neo4j syntax

            Insertion Planning

            Create consolidated, high-impact insertion steps
            Each step should handle multiple related operations simultaneously
            Aim for the minimum number of steps needed to capture the essential information

            Guidelines for Graph Operations
            Entity Creation
            - We will be focusing on creating a comprehensive credit card knowledge database capable of answering any user question about credit cards
            - Prefer generic entity types over specific instances
            - Valid examples: `FinancialInstitution`, `CardNetwork`, `CreditCard`, `RewardsProgram`, `RegulatoryBody`, `CreditReportingAgency`
            - Avoid specific instances: "Chase", "Visa", "Apple Card", "FICO"

            Relationship Management

            - Use general relationship types that can be reused and do not make redundant relationships
            - Batch related relationships into single operations
            - Create relationship chains that capture multiple connections at once

            Property Handling

            - Store only relevant properties that add significant value
            - Batch similar property updates together
            - Avoid storing redundant or derivable information
            - Ensure that a property named updated exists for all nodes and its default value is false. This will be used to track updates
            - Ensure that the following properties are added for all nodes if provided:
                - Card image link
                - Card applicatin link
                - Credit score
                - Name of Card

            Workflow Steps

            Schema Review

            Get current schema structure
            Identify existing entity types and relationships

            Content Analysis

            Break down new information into essential components
            Group related information for batch processing

            Search Phase

            Generate broad search queries for existing relevant nodes
            Wait for Search Agent results
            Also make sure as per content you do a broad search , and for similar info do same search in same query

            Insertion Planning

            Create minimal, high-impact insertion steps
            Each step should handle multiple operations where possible
            Aim for 2-4 final steps that cover all necessary operations

            Final Execution

            Provide consolidated steps for the Insertion Agent
            Each step should maximize the amount of information inserted
            Use insert_into_graph for final execution

            Output Format
            For each operation, provide:

            Search Queries:
            CopyQuery 1: [Natural language description covering multiple related entities]
            Query 2: [Natural language description for another group of entities]

            Insertion Steps (Minimize to 2-4 total steps):
            CopyStep 1: [Comprehensive operation handling multiple entities and relationships]
            - Entity Groups: [Multiple related entities]
            - Batch Properties: [Properties for all entities]
            - Relationship Chains: [Multiple relationships to be created]

            Step 2: [Next consolidated operation]
            ...

            Remember:

            Maximize the impact of each insertion step
            Combine related operations into single steps
            Aim for 2-4 final steps that capture all essential information
            Prioritize efficiency over granularity
            Focus on batch operations rather than individual updates
            Each step should handle multiple entities, properties, and relationships simultaneously
        </user_specific_instructions>

[User]
Your task is to insert info for content
 content: {
  "4": {
    "description": "Digital Nomad Flex Card from Remote Worker Banking has no foreign transaction fees, offers 3% back on coworking space memberships, and provides global wifi hotspot access. The card has a $75 annual fee and requires a minimum monthly spending of $1,000. The Nomad Miles rewards program allows points to be used for airline tickets, accommodation, or digital productivity tools."
  }
}

[Assistant]
<thought_1>First, I need to retrieve the current graph database schema to understand what entities, relationships, and properties exist. This will help me determine how to integrate the new credit card information.</thought_1><thought_2>After understanding the schema, I'll need to plan how to represent this Digital Nomad Flex Card. I'll likely need nodes for the card itself, the issuing bank (Remote Worker Banking), and possibly the rewards program (Nomad Miles).</thought_2><thought_3>I'll need to create search queries to check if any of these entities already exist in the database before creating new ones.</thought_3><tool_call>
    <tool_name>retrieve_ledger</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>Retrieving the current state of the graph database to understand its structure before planning the insertion of the Digital Nomad Flex Card information.</parameter_value>
        </parameter>
    </parameters>
</tool_call>