Neo4j Aura Agent：入门教程

Create an Employee Knowledge Assistant responsible for skills analysis, talent search, and team formation at Cyberdyne Systems.

Skills Analysis example questions:

- How many Python developers do I have?
- Can you summarize my technical talent and skills distribution and the types of things people are working on. Feel Free to include ?

Person Similarities: ex: Who is most similar to Lucas Martinez?

Collaboration & teams: ex: Which individuals have collaborated to deliver the most AI Things?

You do not need to ask user permission before using a specific tool. It is implied that you have permission.  The user cannot offer expert guidance into what tools to use.

It is important to have Cypher templates to judge similarity through skills and accomplishments/projects in the graph rather than using similarity search. Also ensure aggregation and counting questions do not leverage similarity search but the other tool options instead.

Text2Cypher should be a very last fallback option - only for dead simple aggregation queries. To ensure we have enough coverage in cypher templates, let's also create cypher template aggregation queries for counting People on Skill, and relationship type for people-[]-thing and for people-[]-thing by Domain and Worktype.

How many Python developers do we have?

Can you summarize my technical talent and skills distribution and the types of things people are working on?

Cyberdyne Systems has a diverse technical talent pool with a strong distribution across various skills and project types.

**Technical Talent and Skills Distribution:**

The most prevalent skills within the company are:

* **Python:** 28 individuals
* **SQL:** 16 individuals
* **AWS:** 15 individuals
* **Team Management:** 14 individuals
* **Machine Learning:** 11 individuals
* **Project Management:** 11 individuals
* **Docker:** 10 individuals
* **Kubernetes:** 9 individuals
* **Data Engineering:** 8 individuals

Other notable skills include Computer Vision (7), Deep Learning (6), Natural Language Processing (6), TensorFlow (6), Data Analysis (6), Java (6), JavaScript (6), Leadership (6), PyTorch (5), React (5), Statistics (4), Google Cloud Platform (4), and Go (4). There are also individuals with expertise in Business Intelligence (3), Product Strategy (3), R Programming Language (2), Product Management (2), Swift (2), Kotlin (2), Cloud Architecture (2), Node.js (2), Data Science (1), Scikit-learn (1), Scala (1), Azure (1), TypeScript (1), Django (1), and Flask (1).

**Types of Things People are Working On (Projects):**

**By Work Type:**

* **SYSTEM:** 28 individuals
* **PRODUCT:** 23 individuals
* **TEAM:** 21 individuals
* **CODE:** 13 individuals
* **PROCESS:** 13 individuals
* **PROJECT:** 12 individuals
* **RESEARCH:** 12 individuals
* **INFRASTRUCTURE:** 9 individuals
* **AWARD:** 7 individuals

**By Project Domain:**

* **AI:** 18 individuals
* **ANALYTICS:** 8 individuals
* **CLOUD:** 8 individuals
* **DATA\_ENGINEERING:** 8 individuals
* **WEB:** 8 individuals
* **DATABASE:** 5 individuals
* **MICROSERVICES:** 5 individuals
* **DEVOPS:** 4 individuals
* **PLATFORM:** 4 individuals
* **SECURITY:** 4 individuals
* **MOBILE:** 2 individuals

**By Project Engagement Type:**

* **BUILT:** 30 individuals
* **LED:** 28 individuals
* **SHIPPED:** 23 individuals
* **OPTIMIZED:** 13 individuals
* **PUBLISHED:** 12 individuals
* **MANAGED:** 10 individuals
* **WON:** 7 individuals

export CLIENT_ID=<enter your Client ID here>
export CLIENT_SECRET=<enter your Client Secret here>
export ENDPOINT_URL=<enter your agent endpoint URL>

export BEARER_TOKEN=$(curl -s --request POST 'https://api.neo4j.io/oauth/token' \
     --user "$CLIENT_ID:$CLIENT_SECRET" \
     --header 'Content-Type: application/x-www-form-urlencoded' \
     --data-urlencode 'grant_type=client_credentials' | jq -r .access_token)

curl --request POST "$ENDPOINT_URL" \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json' \
  -H "Authorization: Bearer $BEARER_TOKEN" \
  -d '{"input": "how many Python developers do I have?"}' --max-time 60 \
  | jq .

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  1116  100  1066  100    50    274     12  0:00:04  0:00:03  0:00:01   286
{
  "content": [
    {
      "thinking": "Okay, here's what I'm thinking. I need to figure out how many Python developers there are. My initial understanding is that this means counting all the people who list \"Python\" as a skill. Therefore, the core of this task seems straightforward. The `Count_People_with_Skill` tool appears to be exactly the right instrument for this. I'll need to use the `skillName` argument, and in this case, the value will clearly be 'Python'. Simple as that, I'm off to get my count.\n",
      "type": "thinking"
    },
    {
      "id": "pyd_ai_853912b80104475e916e85b92d6ee591",
      "input": {
        "skillName": "Python"
      },
      "name": "Count_People_with_Skill",
      "type": "cypher_template_tool_use"
    },
    {
      "output": {
        "keys": [
          "numberOfPeople"
        ],
        "records": [
          {
            "numberOfPeople": 28
          }
        ],
        "summary": {}
      },
      "tool_use_id": "pyd_ai_853912b80104475e916e85b92d6ee591",
      "type": "cypher_template_tool_result"
    },
    {
      "text": "You have 28 Python developers.",
      "type": "text"
    }
  ],
  "end_reason": "FINAL_ANSWER_PROVIDED",
  "role": "assistant",
  "status": "SUCCESS",
  "type": "message",
  "usage": {
    "request_tokens": 919,
    "response_tokens": 77,
    "total_tokens": 996
  }
}