| Name | llm-json-adapter JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | None |
| upload_time | 2024-04-27 09:58:20 |
| maintainer | None |
| docs_url | None |
| author | Takaaki Mizuno |
| requires_python | <4.0,>=3.10 |
| license | None |
| keywords |
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| requirements |
No requirements were recorded.
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# LLM JSON Adapter
## What is it ?
When using LLMs from the system, you often expect to get output results in JSON: OpenAPI's GPT API has a mechanism called Function Calling, which can return JSON, but Google's Gemini does not seem to have that functionality.
Therefore, I have created a wrapper library to switch LLMs and get results in JSON. What this library can do is as follows.
- Allows you to define the results you want to get in JSON Schema
- Switch between LLMs (currently supports OpenAI's GPT, Google's Gemini, Ollama and Bedrock for Llama and Anthropic Claude).
- Retry a specified number of times if the JSON retrieval fails
## How to use
Use the following code to get the results in JSON.
| Parameter | Description |
|-----------------|-------------------------------------------------------------------------------------------|
| provider_name | The name of the LLM provider to use. Currently, only "google" and "openai" are supported. |
| max_retry_count | The number of times to retry if the JSON retrieval fails. |
| attributes | The attributes to pass to the LLM provider. |
### OpenAI
#### Libraries
You need to install `openai`.
#### Attributes
| Parameter | Description |
|-------------------|------------------------------------|
| api_key | The API key to use. |
| model | Model name. Default: gpt-3.5-turbo |
| temperature | Default: 0.67 |
| presence_penalty | Default: 0 |
| frequency_penalty | Default: 0 |
#### Example
```python
from llm_json_adapter import LLMJsonAdapter, Response
adapter = LLMJsonAdapter(provider_name="openai", max_retry_count=3, attributes={
"api_key": "Your API Key",
"model": "gpt-3.5-turbo",
})
result = adapter.generate(
prompt="prompt",
language="en",
act_as="Professional Software Service Business Analyst",
function=Response(
name="response name",
description="response description",
parameters={
"type": "object",
"properties": {
"data": {
"type": "array",
"items": {
"type": "object",
"properties": {
"title": {
"type": "string",
},
"description": {
"type": "string",
},
},
"required": ["title", "description"],
},
},
},
"required": ["data"]
},
)
)
```
### Gemini
#### Libraries
You need to install `google-generativeai`.
#### Attributes
| Parameter | Description |
|-----------|--------------------------------------------|
| api_key | The API key to use. |
| model | Model name( Default: gemini-1.5-pro-latest |
#### Example
```python
from llm_json_adapter import LLMJsonAdapter, Response
adapter = LLMJsonAdapter(provider_name="google", max_retry_count=3, attributes={
"api_key": "Your API Key",
"model": "gemini-1.5-pro-latest",
})
result = adapter.generate(
prompt="prompt",
language="en",
act_as="Professional Software Service Business Analyst",
function=Response(
name="response name",
description="response description",
parameters={
"type": "object",
"properties": {
"data": {
"type": "array",
"items": {
"type": "object",
"properties": {
"title": {
"type": "string",
},
"description": {
"type": "string",
},
},
"required": ["title", "description"],
},
},
},
"required": ["data"]
},
)
)
```
### Ollama
You need to prepare the Ollama server. ( https://ollama.com/ )
#### Libraries
You need to install `ollama`.
#### Attributes
| Parameter | Description |
|-----------|---------------------------------|
| url | http://localhost:11434 |
| model | Model name ( Default: llama3 ). |
#### Example
```python
from llm_json_adapter import LLMJsonAdapter, Response
adapter = LLMJsonAdapter(provider_name="ollama", max_retry_count=3, attributes={
"url": "http://localhost:11434",
"model": "llama3",
})
result = adapter.generate(
prompt="prompt",
language="en",
act_as="Professional Software Service Business Analyst",
function=Response(
name="response name",
description="response description",
parameters={
"type": "object",
"properties": {
"data": {
"type": "array",
"items": {
"type": "object",
"properties": {
"title": {
"type": "string",
},
"description": {
"type": "string",
},
},
"required": ["title", "description"],
},
},
},
"required": ["data"]
},
)
)
```
### Bedrock
You need to setup the AWS Bedrock( https://aws.amazon.com/bedrock/ )
#### Libraries
You need to install `boto3`.
#### Attributes
| Parameter | Description |
|-------------------|-------------------------------------------------|
| access_key_id | The access key id to use. |
| secret_access_key | The secret access key to use. |
| region | Region. Default: us-east-1 |
| model | Default: anthropic.claude-3-haiku-20240307-v1:0 |
| max_tokens | Default: 1024 |
#### Example
```python
from llm_json_adapter import LLMJsonAdapter, Response
adapter = LLMJsonAdapter(provider_name="bedrock", max_retry_count=3, attributes={
"access_key_id": "<YOUR AWS ACCESS KEY>",
"secret_access_key": "<YOUR AWS SECRET ACCESS KEY>",
"region": "us-east-1",
"model": "anthropic.claude-3-haiku-20240307-v1:0",
"max_tokens": 1024,
})
result = adapter.generate(
prompt="prompt",
language="en",
act_as="Professional Software Service Business Analyst",
function=Response(
name="response name",
description="response description",
parameters={
"type": "object",
"properties": {
"data": {
"type": "array",
"items": {
"type": "object",
"properties": {
"title": {
"type": "string",
},
"description": {
"type": "string",
},
},
"required": ["title", "description"],
},
},
},
"required": ["data"]
},
)
)
```
Raw data
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"description": "# LLM JSON Adapter\n\n## What is it ?\n\nWhen using LLMs from the system, you often expect to get output results in JSON: OpenAPI's GPT API has a mechanism called Function Calling, which can return JSON, but Google's Gemini does not seem to have that functionality.\n\nTherefore, I have created a wrapper library to switch LLMs and get results in JSON. What this library can do is as follows.\n\n- Allows you to define the results you want to get in JSON Schema\n- Switch between LLMs (currently supports OpenAI's GPT, Google's Gemini, Ollama and Bedrock for Llama and Anthropic Claude).\n- Retry a specified number of times if the JSON retrieval fails\n\n## How to use\n\nUse the following code to get the results in JSON.\n\n| Parameter | Description |\n|-----------------|-------------------------------------------------------------------------------------------|\n| provider_name | The name of the LLM provider to use. Currently, only \"google\" and \"openai\" are supported. |\n| max_retry_count | The number of times to retry if the JSON retrieval fails. |\n| attributes | The attributes to pass to the LLM provider. |\n\n\n\n### OpenAI\n\n#### Libraries\n\nYou need to install `openai`.\n\n#### Attributes\n\n| Parameter | Description |\n|-------------------|------------------------------------|\n| api_key | The API key to use. |\n| model | Model name. Default: gpt-3.5-turbo |\n| temperature | Default: 0.67 |\n| presence_penalty | Default: 0 |\n| frequency_penalty | Default: 0 |\n\n#### Example\n\n```python\nfrom llm_json_adapter import LLMJsonAdapter, Response\n\nadapter = LLMJsonAdapter(provider_name=\"openai\", max_retry_count=3, attributes={\n \"api_key\": \"Your API Key\",\n \"model\": \"gpt-3.5-turbo\",\n})\nresult = adapter.generate(\n prompt=\"prompt\",\n language=\"en\",\n act_as=\"Professional Software Service Business Analyst\",\n function=Response(\n name=\"response name\",\n description=\"response description\",\n parameters={\n \"type\": \"object\",\n \"properties\": {\n \"data\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"title\": {\n \"type\": \"string\",\n },\n \"description\": {\n \"type\": \"string\",\n },\n },\n \"required\": [\"title\", \"description\"],\n },\n },\n },\n \"required\": [\"data\"]\n },\n )\n)\n```\n\n### Gemini\n\n#### Libraries\n\nYou need to install `google-generativeai`.\n\n#### Attributes\n\n| Parameter | Description |\n|-----------|--------------------------------------------|\n| api_key | The API key to use. |\n| model | Model name( Default: gemini-1.5-pro-latest |\n\n#### Example\n\n```python\nfrom llm_json_adapter import LLMJsonAdapter, Response\n\nadapter = LLMJsonAdapter(provider_name=\"google\", max_retry_count=3, attributes={\n \"api_key\": \"Your API Key\",\n \"model\": \"gemini-1.5-pro-latest\",\n})\nresult = adapter.generate(\n prompt=\"prompt\",\n language=\"en\",\n act_as=\"Professional Software Service Business Analyst\",\n function=Response(\n name=\"response name\",\n description=\"response description\",\n parameters={\n \"type\": \"object\",\n \"properties\": {\n \"data\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"title\": {\n \"type\": \"string\",\n },\n \"description\": {\n \"type\": \"string\",\n },\n },\n \"required\": [\"title\", \"description\"],\n },\n },\n },\n \"required\": [\"data\"]\n },\n )\n)\n```\n\n### Ollama\n\nYou need to prepare the Ollama server. ( https://ollama.com/ )\n\n#### Libraries\n\nYou need to install `ollama`.\n\n#### Attributes\n\n| Parameter | Description |\n|-----------|---------------------------------|\n| url | http://localhost:11434 |\n| model | Model name ( Default: llama3 ). |\n\n#### Example\n\n```python\nfrom llm_json_adapter import LLMJsonAdapter, Response\n\nadapter = LLMJsonAdapter(provider_name=\"ollama\", max_retry_count=3, attributes={\n \"url\": \"http://localhost:11434\",\n \"model\": \"llama3\",\n})\nresult = adapter.generate(\n prompt=\"prompt\",\n language=\"en\",\n act_as=\"Professional Software Service Business Analyst\",\n function=Response(\n name=\"response name\",\n description=\"response description\",\n parameters={\n \"type\": \"object\",\n \"properties\": {\n \"data\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"title\": {\n \"type\": \"string\",\n },\n \"description\": {\n \"type\": \"string\",\n },\n },\n \"required\": [\"title\", \"description\"],\n },\n },\n },\n \"required\": [\"data\"]\n },\n )\n)\n```\n\n### Bedrock\n\nYou need to setup the AWS Bedrock( https://aws.amazon.com/bedrock/ )\n\n#### Libraries\n\nYou need to install `boto3`.\n\n#### Attributes\n\n\n| Parameter | Description |\n|-------------------|-------------------------------------------------|\n| access_key_id | The access key id to use. |\n| secret_access_key | The secret access key to use. |\n| region | Region. Default: us-east-1 |\n| model | Default: anthropic.claude-3-haiku-20240307-v1:0 |\n| max_tokens | Default: 1024 |\n\n#### Example\n\n```python\nfrom llm_json_adapter import LLMJsonAdapter, Response\n\nadapter = LLMJsonAdapter(provider_name=\"bedrock\", max_retry_count=3, attributes={\n \"access_key_id\": \"<YOUR AWS ACCESS KEY>\",\n \"secret_access_key\": \"<YOUR AWS SECRET ACCESS KEY>\",\n \"region\": \"us-east-1\",\n \"model\": \"anthropic.claude-3-haiku-20240307-v1:0\",\n \"max_tokens\": 1024,\n})\nresult = adapter.generate(\n prompt=\"prompt\",\n language=\"en\",\n act_as=\"Professional Software Service Business Analyst\",\n function=Response(\n name=\"response name\",\n description=\"response description\",\n parameters={\n \"type\": \"object\",\n \"properties\": {\n \"data\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"title\": {\n \"type\": \"string\",\n },\n \"description\": {\n \"type\": \"string\",\n },\n },\n \"required\": [\"title\", \"description\"],\n },\n },\n },\n \"required\": [\"data\"]\n },\n )\n)\n```\n\n\n\n",
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