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移除书签Gemini 2.5 Flash·续
{
"candidates": [
{
"avgLogprobs": -0.20984612667199337,
"content": {
"parts": [
{
"text": "Reading a JSON (JavaScript Object Notation) file in Python is straightforward, thanks to the built-in `json` module.\n\nHere's a step-by-step guide with examples:\n\n---\n\n### 1. The `json` Module\n\nPython's `json` module provides methods for working with JSON data. The two primary methods you'll use for reading are:\n\n* **`json.load()`**: Reads JSON data from a file-like object (e.g., a file opened with `open()`).\n* **`json.loads()`**: Reads JSON data from a string.\n\n---\n\n### 2. Example: Reading a JSON File (`json.load()`)\n\nThis is the most common way to read a JSON file.\n\n**Step 1: Create a sample JSON file**\n\nLet's create a file named `data.json` with the following content:\n\n```json\n{\n \"name\": \"Alice\",\n \"age\": 30,\n \"isStudent\": false,\n \"courses\": [\n {\"title\": \"Math\", \"credits\": 3},\n {\"title\": \"Science\", \"credits\": 4}\n ],\n \"address\": null\n}\n```\n\n**Step 2: Python code to read the file**\n\n```python\nimport json\n\nfile_path = 'data.json'\n\ntry:\n # Open the JSON file in read mode ('r')\n # The 'with' statement ensures the file is properly closed even if errors occur.\n with open(file_path, 'r', encoding='utf-8') as file:\n # Use json.load() to parse the JSON data from the file\n data = json.load(file)\n\n print(\"JSON data successfully loaded:\")\n print(data)\n print(f\"\\nType of loaded data: {type(data)}\")\n\n # You can now access the data like a Python dictionary or list\n print(f\"Name: {data['name']}\")\n print(f\"Age: {data['age']}\")\n print(f\"First course title: {data['courses'][0]['title']}\")\n\nexcept FileNotFoundError:\n print(f\"Error: The file '{file_path}' was not found.\")\nexcept json.JSONDecodeError:\n print(f\"Error: Could not decode JSON from '{file_path}'. Check its format.\")\nexcept Exception as e:\n print(f\"An unexpected error occurred: {e}\")\n\n```\n\n**Explanation:**\n\n1. **`import json`**: Imports the necessary module.\n2. **`with open(file_path, 'r', encoding='utf-8') as file:`**:\n * Opens the file specified by `file_path`.\n * `'r'` indicates read mode.\n * `encoding='utf-8'` is a good practice, especially if your JSON might contain non-ASCII characters.\n * The `with` statement is a context manager that ensures the file is automatically closed when the block is exited, even if errors occur.\n3. **`data = json.load(file)`**: This is the core line. It reads the entire content of the `file` object, parses it as JSON, and converts it into a corresponding Python object (usually a dictionary or a list).\n4. **Error Handling**: The `try...except` block is crucial for robust code:\n * `FileNotFoundError`: Catches the error if the specified file doesn't exist.\n * `json.JSONDecodeError`: Catches errors if the file content is not valid JSON.\n * `Exception as e`: Catches any other unexpected errors.\n\n---\n\n### 3. Example: Reading JSON from a String (`json.loads()`)\n\nSometimes, you might receive JSON data as a string (e.g., from a web API response). In such cases, you use `json.loads()`.\n\n```python\nimport json\n\njson_string_data = '''\n{\n \"product\": \"Laptop\",\n \"price\": 1200.50,\n \"inStock\": true,\n \"features\": [\"lightweight\", \"fast processor\"]\n}\n'''\n\ntry:\n # Use json.loads() to parse the JSON data from a string\n data_from_string = json.loads(json_string_data)\n\n print(\"\\nJSON data from string successfully loaded:\")\n print(data_from_string)\n print(f\"Type of loaded data: {type(data_from_string)}\")\n\n print(f\"Product: {data_from_string['product']}\")\n print(f\"Price: ${data_from_string['price']}\")\n\nexcept json.JSONDecodeError:\n print(\"Error: Could not decode JSON from the string. Check its format.\")\nexcept Exception as e:\n print(f\"An unexpected error occurred: {e}\")\n```\n\n---\n\n### 4. JSON to Python Type Conversion\n\nWhen `json.load()` or `json.loads()` parses JSON, it converts JSON data types to their corresponding Python types:\n\n| JSON Type | Python Type |\n| :------------ | :-------------- |\n| object | `dict` |\n| array | `list` |\n| string | `str` |\n| number | `int` or `float` |\n| boolean | `bool` |\n| null | `None` |\n\n---\n\n### Best Practices:\n\n* **Always use `with open(...)`**: It's safer and handles file closing automatically.\n* **Include `encoding='utf-8'`**: This prevents issues with character encoding, especially for international data.\n* **Implement Error Handling**: Use `try...except` blocks to gracefully handle `FileNotFoundError` and `json.JSONDecodeError`.\n* **Understand `load` vs. `loads`**: Use `load` for file objects and `loads` for strings."
}
],
"role": "model"
},
"finishReason": "STOP"
}
],
"createTime": "2026-04-28T19:34:48.848405Z",
"gatewayMetadata": {
"keySource": "BYOK"
},
"modelVersion": "gemini-2.5-flash",
"responseId": "2AvxaZXkM5mA6tkPxraQ6Qg",
"usageMetadata": {
"candidatesTokenCount": 1320,
"candidatesTokensDetails": [
{
"modality": "TEXT",
"tokenCount": 1320
}
],
"promptTokenCount": 20,
"promptTokensDetails": [
{
"modality": "TEXT",
"tokenCount": 20
}
],
"thoughtsTokenCount": 1357,
"totalTokenCount": 2697,
"trafficType": "ON_DEMAND"
}
}
</details>
<details>
<summary>Multi-turn Conversation — Continuing a conversation with context</summary>
const response = await env.AI.run(
'google/gemini-2.5-flash',
{
contents: [
{
parts: [{ text: 'I need help planning a road trip from San Francisco to Los Angeles.' }],
role: 'user',
},
{
parts: [
{
text: "I'd be happy to help! The drive is about 380 miles and takes roughly 5-6 hours. Would you like suggestions for scenic routes or interesting stops along the way?",
},
],
role: 'model',
},
{ parts: [{ text: 'Yes, what are some good places to stop?' }], role: 'user' },
],
generationConfig: { maxOutputTokens: 2048 },
},
)
console.log(response)
curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run \
--header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
--header "Content-Type: application/json" \
--data '{
"model": "google/gemini-2.5-flash",
"input": {
"contents": [
{
"parts": [
{
"text": "I need help planning a road trip from San Francisco to Los Angeles."
}
],
"role": "user"
},
{
"parts": [
{
"text": "I'\''d be happy to help! The drive is about 380 miles and takes roughly 5-6 hours. Would you like suggestions for scenic routes or interesting stops along the way?"
}
],
"role": "model"
},
{
"parts": [
{
"text": "Yes, what are some good places to stop?"
}
],
"role": "user"
}
],
"generationConfig": {
"maxOutputTokens": 2048
}
}
}'
Okay, you've got two main routes, each with its own character and fantastic stops
{
"candidates": [
{
"avgLogprobs": -10.623407151963976,
"content": {
"parts": [
{
"text": "Okay, you've got two main routes, each with its own character and fantastic stops"
}
],
"role": "model"
},
"finishReason": "MAX_TOKENS"
}
],
"createTime": "2026-04-28T19:34:57.011971Z",
"gatewayMetadata": {
"keySource": "BYOK"
},
"modelVersion": "gemini-2.5-flash",
"responseId": "4QvxacNd9bf33w-WuK35Ag",
"usageMetadata": {
"candidatesTokenCount": 18,
"candidatesTokensDetails": [
{
"modality": "TEXT",
"tokenCount": 18
}
],
"promptTokenCount": 64,
"promptTokensDetails": [
{
"modality": "TEXT",
"tokenCount": 64
}
],
"thoughtsTokenCount": 478,
"totalTokenCount": 560,
"trafficType": "ON_DEMAND"
}
}
</details>
<details>
<summary>Creative Writing — Higher temperature for creative output</summary>
const response = await env.AI.run(
'google/gemini-2.5-flash',
{
contents: [
{
parts: [{ text: 'Write a short story opening about a detective finding an unusual clue.' }],
role: 'user',
},
],
generationConfig: { maxOutputTokens: 1500, temperature: 0.8 },
},
)
console.log(response)
curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run \
--header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
--header "Content-Type: application/json" \
--data '{
"model": "google/gemini-2.5-flash",
"input": {
"contents": [
{
"parts": [
{
"text": "Write a short story opening about a detective finding an unusual clue."
}
],
"role": "user"
}
],
"generationConfig": {
"maxOutputTokens": 1500,
"temperature": 0.8
}
}
}'
The rain had been a relentless drumbeat against
{
"candidates": [
{
"avgLogprobs": -13.157335069444445,
"content": {
"parts": [
{
"text": "The rain had been a relentless drumbeat against"
}
],
"role": "model"
},
"finishReason": "MAX_TOKENS"
}
],
"createTime": "2026-04-28T19:35:01.318796Z",
"gatewayMetadata": {
"keySource": "BYOK"
},
"modelVersion": "gemini-2.5-flash",
"responseId": "5Qvxacy6E6CV9LsP2P_v0Ak",
"usageMetadata": {
"candidatesTokenCount": 9,
"candidatesTokensDetails": [
{
"modality": "TEXT",
"tokenCount": 9
}
],
"promptTokenCount": 13,
"promptTokensDetails": [
{
"modality": "TEXT",
"tokenCount": 13
}
],
"thoughtsTokenCount": 287,
"totalTokenCount": 309,
"trafficType": "ON_DEMAND"
}
}
</details>
Parameters
Schema variant
Generate ContentChat Completions
▶contents[]
arrayrequired
▶systemInstruction{}
object
▶generationConfig{}
object
▶safetySettings[]
array
▶tools[]
array
toolConfig
▶messages[]
arrayrequired
temperature
numberminimum: 0maximum: 2
max_tokens
numberexclusiveMinimum: 0
max_completion_tokens
numberexclusiveMinimum: 0
top_p
numberminimum: 0maximum: 1
frequency_penalty
numberminimum: -2maximum: 2
presence_penalty
numberminimum: -2maximum: 2
stream
boolean
▶stream_options{}
object
▶tools[]
array
tool_choice
response_format
▶modalities[]
array
▶audio{}
object
reasoning_effort
stringOptional reasoning control; availability and accepted values are model-dependent.
▶candidates[]
array
▶usageMetadata{}
object
modelVersion
string
id
string
object
string
created
number
model
string
▶choices[]
array
▶usage{}
object
API Schemas (Raw)
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description: Google's lightest and most cost-efficient Gemini 2.5 model for high-throughput tasks. title: Gemini 2.5 Flash Lite image: https://developers.cloudflare.com/og-docs.png
Documentation Index
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Use this file to discover all available pages before exploring further.
