[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] Search for RewardFeature nodes related to cashback, carbon offset, and eco-friendly rewards 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 current state of the graph database to understand its schema, available nodes, and relationships. This will help me determine if there are any RewardFeature nodes and what properties they might have.</thought_1><thought_2>Let me retrieve the ledger to get the database schema and check if it's empty.</thought_2><tool_call>
    <tool_name>retreive_ledger</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I need to first understand the database schema and check if there are any nodes in the graph database, particularly looking for RewardFeature nodes.</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

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>Now that I have the database schema, I can see that there are RewardFeature nodes present in the graph. I can see they have properties like name, description, and additional_info. I need to search for RewardFeature nodes specifically related to cashback, carbon offset, and eco-friendly rewards.</thought_1><thought_2>Let me start with a vector search since it can help with semantic matching. This will be especially useful for finding eco-friendly and carbon offset rewards which might be described in different ways.</thought_2><thought_3>Before performing vector search, I need to generate vector search queries using the relevant labels and properties. I'll use the "Feature" and "RewardFeature" labels with search terms related to cashback, carbon offset, and eco-friendly rewards.</thought_3><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': 'cashback carbon offset eco-friendly rewards', 'description': 'cashback carbon offset eco-friendly rewards'}</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from generate_vector_search_queries tool
: 1. Is there a label Feature  with property name 'cashback carbon offset eco-friendly rewards'?
2. Is there a label Feature  with property description 'cashback carbon offset eco-friendly rewards'?
3. Is there a label RewardFeature  with property name 'cashback carbon offset eco-friendly rewards'?

[Assistant]
<thought_1>Now that I have generated vector search queries, I can use them to perform a vector search to find semantically similar nodes related to cashback, carbon offset, and eco-friendly rewards.</thought_1><tool_call>
    <tool_name>vector_search</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I'm searching for RewardFeature nodes that are related to cashback, carbon offset, and eco-friendly rewards using vector search for semantic matching.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>cashback carbon offset eco-friendly rewards</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from vector_search tool
:
Vector search results for: 'cashback carbon offset eco-friendly rewards'
================================================================================

Node Similarity: 1.6552
Main Node: Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc959, updated: False, additional_info: Cashback Match: All cash back earned in first year is matched. Rewards never expire., description: 5% cash back on rotating quarterly categories upon activation (up to quarterly maximum), 1% on all other purchases, last_synced: 1744003901861, name: Discover it Cash Back Rewards)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 0, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc956, updated: False, recommended_credit_score: 670+, last_synced: 1744003756707, name: Discover it Cash Back, type: Rewards)
------------------------------------------------------------

Node Similarity: 1.4312
Main Node: Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9512, updated: False, additional_info: Welcome Bonus: 30,000 points after $3,500 spend in first 90 days, description: 6% on dining, travel, entertainment (up to $2,000 per quarter), 2% on groceries and streaming, 1.5% on other purchases, last_synced: 1744003903948, name: Elevate Platinum Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 95, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc957, updated: False, recommended_credit_score: 700+, last_synced: 1744003757375, name: Elevate Platinum Rewards, type: Rewards)
------------------------------------------------------------

Node Similarity: 1.1873
Main Node: Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9516, updated: False, additional_info: Sign-up Bonus: 50,000 points after first $4,000 spend, description: 5x Points on flights, hotels, rentals; 3x Points on dining, ride-sharing; 1x Points on others, last_synced: 1744003906761, name: Summit Infinite Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 150, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc958, updated: False, recommended_credit_score: 740+, last_synced: 1744003758050, name: Summit Infinite Travel, type: Travel)
------------------------------------------------------------

Node Similarity: 0.9434
Main Node: CreditCard(annual_fee: 0, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc956, updated: False, recommended_credit_score: 670+, last_synced: 1744003756707, name: Discover it Cash Back, type: Rewards)

Relationships:
  → [ISSUED_BY] → CardIssuer(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc950, updated: False, last_synced: 1744003752699, name: Discover Financial Services)
  → [OPERATES_ON] → CardNetwork(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc953, updated: False, last_synced: 1744003754704, name: Discover)
  → [HAS_FEATURE] → Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc959, updated: False, additional_info: Cashback Match: All cash back earned in first year is matched. Rewards never expire., description: 5% cash back on rotating quarterly categories upon activation (up to quarterly maximum), 1% on all other purchases, last_synced: 1744003901861, name: Discover it Cash Back Rewards)
  → [HAS_FEATURE] → Feature, APRFeature(in_zeroentropy: True, end_date: June 10, 2025, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9510, updated: False, description: 0% on purchases and balance transfers for 15 months, last_synced: 1744003902558, name: Discover it Intro APR, transfer_fee: 3% Intro Balance Transfer Fee)
  → [HAS_FEATURE] → Feature, SecurityFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9511, updated: False, description: $0 Fraud Liability Guarantee, Freeze it to prevent new transactions, Free SSN Alerts & Dark Web monitoring, last_synced: 1744003903241, name: Discover it Security Features)
  → [HAS_FINANCIAL_DETAILS] → FinancialDetail(foreign_transaction_fee: None, in_zeroentropy: True, application_process: Online application with instant decision available, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9518, updated: False, last_synced: 1744004025347, cash_advance_fee: $10 or 5%, balance_transfer_fee: 3% (intro) to 5% (standard), credit_limit_range: $500-$25,000, minimum_income_requirement: $30,000+, standard_apr_range: 18.24%-27.24%)
  → [HAS_REVIEWS] → CustomerReview(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9521, total_reviews: 33895, updated: False, last_synced: 1744004027435, last_updated: 2025-04-07, rating: 4.7, representative_review: Great rewards and U.S. based customer service.)
  → [HAS_ACCEPTANCE_INFO] → Acceptance(in_zeroentropy: True, acceptance_rate: 99% of U.S. merchants, card_designs: 25, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9524, updated: False, last_synced: 1744004029472, digital_wallet_compatible: True)
  → [OFFERS_PROMOTION] → SpecialOffer(in_zeroentropy: True, end_date: 2026-04-07, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9527, updated: False, description: Discover automatically matches all cash back earned in the first year, last_synced: 1744004031620, name: First Year Cashback Match)
------------------------------------------------------------

Node Similarity: 0.7188
Main Node: CreditCard(annual_fee: 95, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc957, updated: False, recommended_credit_score: 700+, last_synced: 1744003757375, name: Elevate Platinum Rewards, type: Rewards)

Relationships:
  → [ISSUED_BY] → CardIssuer(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc951, updated: False, last_synced: 1744003753382, name: Elevate Financial)
  → [OPERATES_ON] → CardNetwork(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc954, updated: False, last_synced: 1744003755377, name: Visa)
  → [HAS_FEATURE] → Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9512, updated: False, additional_info: Welcome Bonus: 30,000 points after $3,500 spend in first 90 days, description: 6% on dining, travel, entertainment (up to $2,000 per quarter), 2% on groceries and streaming, 1.5% on other purchases, last_synced: 1744003903948, name: Elevate Platinum Rewards Program)
  → [HAS_FEATURE] → Feature, APRFeature(in_zeroentropy: True, end_date: September 15, 2025, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9513, updated: False, description: 0% on purchases for 12 months, 0% on balance transfers for 18 months, last_synced: 1744003904631, name: Elevate Platinum Intro APR, transfer_fee: 4% Intro Balance Transfer Fee)
  → [HAS_FEATURE] → Feature, SecurityFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9514, updated: False, description: Zero Liability Protection, ID Guard Plus for identity theft protection, Free Dark Web Monitoring, last_synced: 1744003905366, name: Elevate Platinum Security Features)
  → [HAS_FINANCIAL_DETAILS] → FinancialDetail(foreign_transaction_fee: 3%, in_zeroentropy: True, application_process: Online application with pre-qualification available, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9519, updated: False, last_synced: 1744004025999, cash_advance_fee: $15 or 6%, balance_transfer_fee: 4% (intro) to 5.5% (standard), credit_limit_range: $1,000-$35,000, minimum_income_requirement: $45,000+, standard_apr_range: 19.99%-28.99%)
  → [HAS_REVIEWS] → CustomerReview(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9522, total_reviews: 27912, updated: False, last_synced: 1744004028113, last_updated: 2025-04-07, rating: 4.6, representative_review: Best travel card with great rewards and ID Guard Plus.)
  → [HAS_ACCEPTANCE_INFO] → Acceptance(in_zeroentropy: True, acceptance_rate: 98.5% of U.S. merchants, card_designs: 15, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9525, updated: False, last_synced: 1744004030168, digital_wallet_compatible: True)
  → [OFFERS_PROMOTION] → SpecialOffer(in_zeroentropy: True, end_date: 2026-04-07, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9528, updated: False, description: Annual fee of $95 waived for the first year, last_synced: 1744004032333, name: First Year Annual Fee Waiver)
------------------------------------------------------------
================================================================================

[Assistant]
<thought_1>I've received results from the vector search, but I don't see specific results for carbon offset or eco-friendly rewards yet. I can see some cashback reward features from the "Discover it Cash Back Rewards" and "Elevate Platinum Rewards Program". Let me try a more specific full-text search to look for eco-friendly and carbon offset reward features.</thought_1><tool_call>
    <tool_name>search_by_full_text</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>I need to specifically search for RewardFeature nodes that mention carbon offset, eco-friendly, or environmental rewards, as they weren't clearly identified in the vector search results.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"carbon offset" OR eco* OR environment* OR green OR sustainable</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from search_by_full_text tool
:
Search results for: '"carbon offset" OR eco* OR environment* OR green OR sustainable'
================================================================================
No matches found
================================================================================

[Assistant]
<thought_1>The full-text search for terms related to carbon offset and eco-friendly features didn't return any results. Let me try another approach by using a filter search on RewardFeature nodes specifically to look for any descriptions or names that might contain relevant information.</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 perform a specific search targeting only RewardFeature nodes and look at their name, description, and additional_info properties to find anything related to cashback, carbon offset, or eco-friendly rewards.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>cashback OR cash OR reward* OR carbon OR eco OR green OR environment*</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>nodes</parameter_name>
            <parameter_value>['RewardFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>['name', 'description', 'additional_info']</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from filter_search_by_nodes_properties tool
:
Top 5 results for search term: 'cashback OR cash OR reward* OR carbon OR eco OR green OR environment*'
Filtered by labels: RewardFeature
Filtered by properties: name, description, additional_info
====================================================================================================

Node Score: 4.7750
Main Node: Feature, RewardFeature(additional_info: Cashback Match: All cash back earned in first year is matched. Rewards never expire., description: 5% cash back on rotating quarterly categories upon activation (up to quarterly maximum), 1% on all other purchases, name: Discover it Cash Back Rewards)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 0, recommended_credit_score: 670+, name: Discover it Cash Back, type: Rewards)
--------------------------------------------------------------------------------

Node Score: 2.1990
Main Node: Feature, RewardFeature(description: Earn 5% cashback on all travel expenses including flights, hotels, car rentals, and cruises, name: 5% Cashback on Travel)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 495, recommended_credit_score: 740, name: Horizon Unlimited Platinum Card, type: Platinum)
--------------------------------------------------------------------------------

Node Score: 1.0000
Main Node: Feature, RewardFeature(additional_info: Points can be redeemed for travel, gift cards, merchandise, or statement credits, description: Points never expire and can be transferred to 25+ airline and hotel partners at 1:1 ratio, name: Horizon Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 495, recommended_credit_score: 740, name: Horizon Unlimited Platinum Card, type: Platinum)
--------------------------------------------------------------------------------

Node Score: 1.0000
Main Node: Feature, RewardFeature(additional_info: Welcome Bonus: 30,000 points after $3,500 spend in first 90 days, description: 6% on dining, travel, entertainment (up to $2,000 per quarter), 2% on groceries and streaming, 1.5% on other purchases, name: Elevate Platinum Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 95, recommended_credit_score: 700+, name: Elevate Platinum Rewards, type: Rewards)
--------------------------------------------------------------------------------

Node Score: 1.0000
Main Node: Feature, RewardFeature(additional_info: Sign-up Bonus: 50,000 points after first $4,000 spend, description: 5x Points on flights, hotels, rentals; 3x Points on dining, ride-sharing; 1x Points on others, name: Summit Infinite Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 150, recommended_credit_score: 740+, name: Summit Infinite Travel, type: Travel)
--------------------------------------------------------------------------------
====================================================================================================

[Assistant]
<thought_1>Now I have more specific results about RewardFeature nodes. Let me check if there are any other RewardFeature nodes that might not have been captured in my previous searches, by doing a more general search for all RewardFeature nodes.</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 do a general search for all RewardFeature nodes to make sure I have a complete picture of all reward features in the database.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>*</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>nodes</parameter_name>
            <parameter_value>['RewardFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>['name', 'description', 'additional_info']</parameter_value>
        </parameter>
    </parameters>
</tool_call>

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

Node Score: 3.0000
Main Node: Feature, RewardFeature(additional_info: Sign-up Bonus: 50,000 points after first $4,000 spend, description: 5x Points on flights, hotels, rentals; 3x Points on dining, ride-sharing; 1x Points on others, name: Summit Infinite Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 150, recommended_credit_score: 740+, name: Summit Infinite Travel, type: Travel)
--------------------------------------------------------------------------------

Node Score: 3.0000
Main Node: Feature, RewardFeature(additional_info: Points can be redeemed for travel, gift cards, merchandise, or statement credits, description: Points never expire and can be transferred to 25+ airline and hotel partners at 1:1 ratio, name: Horizon Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 495, recommended_credit_score: 740, name: Horizon Unlimited Platinum Card, type: Platinum)
--------------------------------------------------------------------------------

Node Score: 3.0000
Main Node: Feature, RewardFeature(additional_info: Cashback Match: All cash back earned in first year is matched. Rewards never expire., description: 5% cash back on rotating quarterly categories upon activation (up to quarterly maximum), 1% on all other purchases, name: Discover it Cash Back Rewards)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 0, recommended_credit_score: 670+, name: Discover it Cash Back, type: Rewards)
--------------------------------------------------------------------------------

Node Score: 3.0000
Main Node: Feature, RewardFeature(additional_info: Welcome Bonus: 30,000 points after $3,500 spend in first 90 days, description: 6% on dining, travel, entertainment (up to $2,000 per quarter), 2% on groceries and streaming, 1.5% on other purchases, name: Elevate Platinum Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 95, recommended_credit_score: 700+, name: Elevate Platinum Rewards, type: Rewards)
--------------------------------------------------------------------------------

Node Score: 2.0000
Main Node: Feature, RewardFeature(description: Earn 5% cashback on all travel expenses including flights, hotels, car rentals, and cruises, name: 5% Cashback on Travel)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 495, recommended_credit_score: 740, name: Horizon Unlimited Platinum Card, type: Platinum)
--------------------------------------------------------------------------------
====================================================================================================

[Assistant]
<thought_1>I have searched for RewardFeature nodes related to cashback, carbon offset, and eco-friendly rewards using various methods. Let me compile what I've found.</thought_1><thought_2>Let me now summarize these findings in a clear format that shows the node types, their properties, and relationships.</thought_2><text_output>
Search Results for RewardFeature nodes related to cashback, carbon offset, and eco-friendly rewards:

CASHBACK REWARDS FOUND:

1. Node Type: Feature, RewardFeature
   Properties:
   - name: Discover it Cash Back Rewards
   - description: 5% cash back on rotating quarterly categories upon activation (up to quarterly maximum), 1% on all other purchases
   - additional_info: Cashback Match: All cash back earned in first year is matched. Rewards never expire.

   Relationships:
   ← [HAS_FEATURE] ← CreditCard (Discover it Cash Back)

2. Node Type: Feature, RewardFeature
   Properties:
   - name: 5% Cashback on Travel
   - description: Earn 5% cashback on all travel expenses including flights, hotels, car rentals, and cruises

   Relationships:
   ← [HAS_FEATURE] ← CreditCard (Horizon Unlimited Platinum Card)

OTHER REWARD FEATURES (NOT SPECIFICALLY CASHBACK/ECO-FRIENDLY/CARBON OFFSET):

3. Node Type: Feature, RewardFeature
   Properties:
   - name: Elevate Platinum Rewards Program
   - description: 6% on dining, travel, entertainment (up to $2,000 per quarter), 2% on groceries and streaming, 1.5% on other purchases
   - additional_info: Welcome Bonus: 30,000 points after $3,500 spend in first 90 days

   Relationships:
   ← [HAS_FEATURE] ← CreditCard (Elevate Platinum Rewards)

4. Node Type: Feature, RewardFeature
   Properties:
   - name: Summit Infinite Rewards Program
   - description: 5x Points on flights, hotels, rentals; 3x Points on dining, ride-sharing; 1x Points on others
   - additional_info: Sign-up Bonus: 50,000 points after first $4,000 spend

   Relationships:
   ← [HAS_FEATURE] ← CreditCard (Summit Infinite Travel)

5. Node Type: Feature, RewardFeature
   Properties:
   - name: Horizon Rewards Program
   - description: Points never expire and can be transferred to 25+ airline and hotel partners at 1:1 ratio
   - additional_info: Points can be redeemed for travel, gift cards, merchandise, or statement credits

   Relationships:
   ← [HAS_FEATURE] ← CreditCard (Horizon Unlimited Platinum Card)

NO CARBON OFFSET OR ECO-FRIENDLY REWARD FEATURES FOUND
</text_output>


[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] Find Feature or RewardFeature node with name "5% Cashback on Travel" Labels: Feature, RewardFeature Properties: name: 5% Cashback on Travel   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 current state of the graph database to understand its schema and confirm it's not empty.</thought_1><tool_call>
    <tool_name>retreive_ledger</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>Retrieving the ledger to understand the database schema and check if 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

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 graph database has data and includes the labels I'm looking for. I can see there are nodes with Labels "Feature, RewardFeature" which matches what I'm searching for. Now I need to search specifically for a node with the name "5% Cashback on Travel".</thought_1><thought_2>I'll start with a direct search using the filter_search_by_nodes_properties tool to look for Feature and RewardFeature nodes with the name "5% Cashback on Travel".</thought_2><tool_call>
    <tool_name>filter_search_by_nodes_properties</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>Searching specifically for Feature or RewardFeature nodes with the name "5% Cashback on Travel".</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"5% Cashback on Travel"</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: '"5% Cashback on Travel"'
Filtered by labels: Feature, RewardFeature
Filtered by properties: name
====================================================================================================
No matches found
====================================================================================================

[Assistant]
<thought_1>The direct search for "5% Cashback on Travel" didn't return any results. I'll try using a more flexible search approach using wildcards.</thought_1><thought_2>Let me try a search with wildcards to look for any Feature or RewardFeature nodes that might have "Cashback" and "Travel" in their name, as the exact format might be different.</thought_2><tool_call>
    <tool_name>filter_search_by_nodes_properties</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>Trying a broader search with wildcards to find any Feature or RewardFeature nodes related to cashback on travel.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"*Cashback*Travel*"</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', 'description']</parameter_value>
        </parameter>
    </parameters>
</tool_call>

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

[Assistant]
<thought_1>Still no results with the wildcard search. Let's try a more general approach by searching for any nodes that have "Cashback" in their properties, as it's possible the naming or description might not match exactly what I'm looking for.</thought_1><thought_2>I'll try a search on just the term "Cashback" across Feature and RewardFeature nodes to see if any nodes contain this term, which could be related to what I'm looking for.</thought_2><tool_call>
    <tool_name>filter_search_by_nodes_properties</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>Searching for any Feature or RewardFeature nodes containing "Cashback" in their name or description properties.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"*Cashback*"</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', 'description', 'additional_info']</parameter_value>
        </parameter>
    </parameters>
</tool_call>

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

Node Score: 0.4182
Main Node: Feature, RewardFeature(additional_info: Cashback Match: All cash back earned in first year is matched. Rewards never expire., description: 5% cash back on rotating quarterly categories upon activation (up to quarterly maximum), 1% on all other purchases, name: Discover it Cash Back Rewards)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 0, recommended_credit_score: 670+, name: Discover it Cash Back, type: Rewards)
--------------------------------------------------------------------------------
====================================================================================================

[Assistant]
<thought_1>I've found one result related to cashback, but it's not exactly "5% Cashback on Travel". Instead, it's "Discover it Cash Back Rewards" which offers "5% cash back on rotating quarterly categories upon activation (up to quarterly maximum), 1% on all other purchases".</thought_1><thought_2>Let me try another approach and search specifically for travel-related rewards to see if there are any nodes that might match or be similar to what I'm looking for.</thought_2><tool_call>
    <tool_name>filter_search_by_nodes_properties</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>Searching for any Feature or RewardFeature nodes containing "Travel" in their name or description.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>"*Travel*"</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>nodes</parameter_name>
            <parameter_value>['Feature', 'RewardFeature', 'TravelFeature']</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>properties</parameter_name>
            <parameter_value>['name', 'description', 'additional_info']</parameter_value>
        </parameter>
    </parameters>
</tool_call>

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

Node Score: 0.9408
Main Node: Feature, TravelFeature(description: Access to 1,500+ airport lounges, Priority boarding, Baggage fee credits, name: Summit Infinite Travel Benefits)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 150, recommended_credit_score: 740+, name: Summit Infinite Travel, type: Travel)
--------------------------------------------------------------------------------

Node Score: 0.8799
Main Node: Feature, RewardFeature(additional_info: Welcome Bonus: 30,000 points after $3,500 spend in first 90 days, description: 6% on dining, travel, entertainment (up to $2,000 per quarter), 2% on groceries and streaming, 1.5% on other purchases, name: Elevate Platinum Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 95, recommended_credit_score: 700+, name: Elevate Platinum Rewards, type: Rewards)
--------------------------------------------------------------------------------
====================================================================================================

[Assistant]
<thought_1>I've found two travel-related features, but neither matches the "5% Cashback on Travel" that I'm looking for. Let me try a more comprehensive search using the vector search, which might catch semantically similar entries.</thought_1><thought_2>Before I use vector search, I need to generate vector search queries based on relevant labels and properties.</thought_2><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': '5% Cashback on Travel', 'description': 'cashback rewards for travel purchases'}</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from generate_vector_search_queries tool
: 1. Is there a label Feature  with property name '5% Cashback on Travel'?
2. Is there a label Feature  with property description 'cashback rewards for travel purchases'?
3. Is there a label RewardFeature  with property name '5% Cashback on Travel'?

[Assistant]
<thought_1>Great, I now have generated vector search queries. Let me use these queries with the vector_search tool to find semantically similar nodes.</thought_1><tool_call>
    <tool_name>vector_search</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>Using vector search to find semantically similar nodes to "5% Cashback on Travel" in Feature and RewardFeature nodes.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>Is there a Feature or RewardFeature with 5% Cashback on Travel?</parameter_value>
        </parameter>
    </parameters>
</tool_call>

[User]
Result from vector_search tool
:
Vector search results for: 'Is there a Feature or RewardFeature with 5% Cashback on Travel?'
================================================================================

Node Similarity: 1.8984
Main Node: Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc959, updated: False, additional_info: Cashback Match: All cash back earned in first year is matched. Rewards never expire., description: 5% cash back on rotating quarterly categories upon activation (up to quarterly maximum), 1% on all other purchases, last_synced: 1744003901861, name: Discover it Cash Back Rewards)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 0, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc956, updated: False, recommended_credit_score: 670+, last_synced: 1744003756707, name: Discover it Cash Back, type: Rewards)
------------------------------------------------------------

Node Similarity: 1.5654
Main Node: Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9512, updated: False, additional_info: Welcome Bonus: 30,000 points after $3,500 spend in first 90 days, description: 6% on dining, travel, entertainment (up to $2,000 per quarter), 2% on groceries and streaming, 1.5% on other purchases, last_synced: 1744003903948, name: Elevate Platinum Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 95, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc957, updated: False, recommended_credit_score: 700+, last_synced: 1744003757375, name: Elevate Platinum Rewards, type: Rewards)
------------------------------------------------------------

Node Similarity: 1.1952
Main Node: Feature, RewardFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9516, updated: False, additional_info: Sign-up Bonus: 50,000 points after first $4,000 spend, description: 5x Points on flights, hotels, rentals; 3x Points on dining, ride-sharing; 1x Points on others, last_synced: 1744003906761, name: Summit Infinite Rewards Program)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 150, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc958, updated: False, recommended_credit_score: 740+, last_synced: 1744003758050, name: Summit Infinite Travel, type: Travel)
------------------------------------------------------------

Node Similarity: 0.8250
Main Node: Feature, TravelFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9515, updated: False, description: Access to 1,500+ airport lounges, Priority boarding, Baggage fee credits, last_synced: 1744003906072, name: Summit Infinite Travel Benefits)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 150, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc958, updated: False, recommended_credit_score: 740+, last_synced: 1744003758050, name: Summit Infinite Travel, type: Travel)
------------------------------------------------------------

Node Similarity: 0.6695
Main Node: Feature, ProtectionFeature(in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc9517, updated: False, description: Trip Delay/Cancellation Protection, Lost Baggage Insurance, Purchase Protection, Free TSA PreCheck/Global Entry Credit, last_synced: 1744003907445, name: Summit Infinite Premium Protections)

Relationships:
  ← [HAS_FEATURE] ← CreditCard(annual_fee: 150, in_zeroentropy: True, ze_doc_id: node-4d0ccce1e08ac44d9a5312fc28a30dc958, updated: False, recommended_credit_score: 740+, last_synced: 1744003758050, name: Summit Infinite Travel, type: Travel)
------------------------------------------------------------
================================================================================

[Assistant]
<thought_1>Based on the vector search results, I found several reward features related to cashback and travel, but none with the exact name "5% Cashback on Travel" as requested. Let me review the most similar results:</thought_1><thought_2>Let me check a general full text search as a final attempt to make sure I haven't missed anything.</thought_2><tool_call>
    <tool_name>search_by_full_text</tool_name>
    <parameters>
        <parameter>
            <parameter_name>chain_of_thought</parameter_name>
            <parameter_value>Doing a general full text search for "5% Cashback Travel" to ensure we haven't missed any nodes.</parameter_value>
        </parameter>
        <parameter>
            <parameter_name>search_query</parameter_name>
            <parameter_value>5% Cashback Travel</parameter_value>
        </parameter>
    </parameters>
</tool_call>