Feat plugin init (#10)
* chore: Move directory 'workflows' into init_data. * feat: Initialize plugin. * chore: Deepseek plugin. * chore: Update entrypoint.sh * fix: Check if plugin already installed. * chore: Update entrypoint.sh * chore: Update entrypoint.shpull/17608/head
MisluNotFound
1 year ago
committed by
GitHub
parent
3187c887b8
commit
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<path id="path" d="M55.613,3.471C55.018,3.179 54.761,3.735 54.413,4.018C54.294,4.109 54.193,4.227 54.092,4.337C53.222,5.266 52.206,5.876 50.878,5.803C48.936,5.694 47.278,6.305 45.813,7.79C45.501,5.959 44.466,4.865 42.891,4.164C42.067,3.799 41.233,3.435 40.656,2.642C40.253,2.077 40.143,1.448 39.942,0.829C39.814,0.455 39.685,0.073 39.255,0.009C38.788,-0.064 38.605,0.328 38.421,0.656C37.689,1.995 37.405,3.471 37.432,4.965C37.496,8.327 38.916,11.006 41.737,12.91C42.058,13.129 42.14,13.347 42.039,13.666C41.847,14.323 41.618,14.96 41.416,15.616C41.288,16.035 41.096,16.127 40.647,15.944C39.099,15.297 37.762,14.341 36.58,13.184C34.575,11.243 32.761,9.102 30.499,7.425C29.968,7.033 29.436,6.669 28.887,6.323C26.579,4.082 29.189,2.241 29.794,2.022C30.425,1.795 30.013,1.011 27.971,1.02C25.928,1.029 24.06,1.713 21.679,2.624C21.331,2.761 20.964,2.861 20.589,2.943C18.427,2.533 16.183,2.441 13.838,2.706C9.424,3.198 5.898,5.284 3.306,8.847C0.191,13.129 -0.541,17.994 0.356,23.069C1.3,28.417 4.029,32.845 8.224,36.308C12.575,39.897 17.584,41.656 23.3,41.319C26.771,41.118 30.636,40.654 34.996,36.964C36.095,37.51 37.249,37.729 39.163,37.893C40.638,38.03 42.058,37.82 43.157,37.592C44.878,37.228 44.759,35.633 44.137,35.342C39.09,32.991 40.198,33.948 39.191,33.173C41.755,30.139 45.62,26.987 47.132,16.773C47.251,15.962 47.15,15.452 47.132,14.796C47.122,14.395 47.214,14.241 47.672,14.195C48.936,14.049 50.163,13.703 51.29,13.083C54.56,11.298 55.878,8.364 56.19,4.847C56.236,4.309 56.181,3.753 55.613,3.471ZM27.119,35.123C22.228,31.278 19.856,30.012 18.876,30.066C17.96,30.121 18.125,31.169 18.326,31.852C18.537,32.526 18.812,32.991 19.196,33.583C19.462,33.975 19.645,34.558 18.931,34.996C17.356,35.971 14.617,34.667 14.489,34.604C11.301,32.727 8.636,30.249 6.758,26.859C4.945,23.597 3.892,20.099 3.718,16.363C3.672,15.461 3.938,15.142 4.835,14.979C6.017,14.76 7.235,14.714 8.416,14.887C13.408,15.616 17.658,17.848 21.221,21.384C23.254,23.397 24.793,25.802 26.377,28.153C28.062,30.649 29.876,33.028 32.184,34.977C32.999,35.661 33.649,36.18 34.272,36.563C32.395,36.772 29.262,36.818 27.119,35.123ZM29.464,20.044C29.464,19.643 29.784,19.325 30.187,19.325C30.279,19.325 30.361,19.343 30.435,19.37C30.535,19.407 30.627,19.461 30.7,19.543C30.828,19.671 30.902,19.853 30.902,20.044C30.902,20.445 30.581,20.764 30.178,20.764C29.775,20.764 29.464,20.445 29.464,20.044ZM36.745,23.78C36.278,23.971 35.811,24.135 35.362,24.153C34.666,24.19 33.906,23.907 33.494,23.561C32.853,23.023 32.395,22.723 32.202,21.784C32.12,21.384 32.166,20.764 32.239,20.409C32.404,19.643 32.221,19.151 31.68,18.705C31.241,18.34 30.682,18.24 30.068,18.24C29.839,18.24 29.629,18.14 29.473,18.058C29.216,17.93 29.006,17.612 29.207,17.22C29.271,17.092 29.583,16.783 29.656,16.728C30.49,16.254 31.451,16.409 32.34,16.764C33.164,17.101 33.787,17.721 34.684,18.596C35.6,19.652 35.765,19.944 36.287,20.737C36.7,21.356 37.075,21.994 37.331,22.723C37.487,23.179 37.286,23.552 36.745,23.78Z" style="fill:rgb(77,107,254);fill-rule:nonzero;"/>
|
||||||
|
</svg>
|
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|
||||||
|
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|
|||||||
|
INSTALL_METHOD=remote
|
||||||
|
REMOTE_INSTALL_HOST=debug-plugin.dify.dev
|
||||||
|
REMOTE_INSTALL_PORT=5003
|
||||||
|
REMOTE_INSTALL_KEY=ae1aa1c9-0af4-43db-b6d4-4fa9e6bfb646
|
||||||
@ -0,0 +1,24 @@
|
|||||||
|
# Overview
|
||||||
|
The Agent node in Dify Chatflow/Workflow lets LLMs autonomously use tools. This plugin features two official Dify Agent reasoning strategies, enabling LLMs to dynamically select and run tools during runtime for multi-step problem-solving.
|
||||||
|
|
||||||
|
## Strategies
|
||||||
|
|
||||||
|
### 1. Function Calling
|
||||||
|
Function Calling maps user commands to specific functions or tools. The LLM identifies the user's intent, decides which function to call, and extracts the required parameters. It is a straightforward mechanism for invoking external capabilities.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#### Pros:
|
||||||
|
- **Precise:** Directly calls the right tool for defined tasks, avoiding complex reasoning.
|
||||||
|
- **Easy External Integration:** Integrates external APIs and tools as callable functions.
|
||||||
|
- **Structured Output:** Provides structured function call information for easy processing.
|
||||||
|
|
||||||
|
### 2. ReAct (Reason + Act)
|
||||||
|
ReAct alternates between the LLM reasoning about the situation and taking actions. The LLM analyzes the current state and goal, selects and uses a tool, and then uses the tool's output for the next thought and action. This cycle repeats until the problem is resolved.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#### Pros:
|
||||||
|
- **Leverages External Information:** Effectively uses external tools to gather information for tasks the model cannot handle alone.
|
||||||
|
- **Explainable Reasoning:** Interwoven reasoning and action steps allow some tracking of the Agent's process.
|
||||||
|
- **Wide Applicability:** Suitable for tasks requiring external knowledge or specific actions, such as Q&A, information retrieval, and task execution.
|
||||||
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|
|||||||
|
import sys
|
||||||
|
|
||||||
|
sys.path.append("../..")
|
||||||
|
|
||||||
|
from dify_plugin import DifyPluginEnv, Plugin
|
||||||
|
|
||||||
|
plugin = Plugin(DifyPluginEnv(MAX_REQUEST_TIMEOUT=240))
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
plugin.run()
|
||||||
@ -0,0 +1,34 @@
|
|||||||
|
version: 0.0.11
|
||||||
|
type: plugin
|
||||||
|
author: "langgenius"
|
||||||
|
name: "agent"
|
||||||
|
label:
|
||||||
|
en_US: "Dify Agent Strategies"
|
||||||
|
zh_Hans: "Dify Agent 策略"
|
||||||
|
created_at: "2024-07-12T08:03:44.658609186Z"
|
||||||
|
icon: icon.svg
|
||||||
|
description:
|
||||||
|
en_US: Dify official Agent strategies collection
|
||||||
|
zh_Hans: Dify 官方 Agent 策略集合
|
||||||
|
tags:
|
||||||
|
- "agent"
|
||||||
|
resource:
|
||||||
|
memory: 1048576
|
||||||
|
permission:
|
||||||
|
tool:
|
||||||
|
enabled: true
|
||||||
|
model:
|
||||||
|
enabled: true
|
||||||
|
llm: true
|
||||||
|
plugins:
|
||||||
|
agent_strategies:
|
||||||
|
- "provider/agent.yaml"
|
||||||
|
meta:
|
||||||
|
version: 0.0.1
|
||||||
|
arch:
|
||||||
|
- "amd64"
|
||||||
|
- "arm64"
|
||||||
|
runner:
|
||||||
|
language: "python"
|
||||||
|
version: "3.12"
|
||||||
|
entrypoint: "main"
|
||||||
@ -0,0 +1,212 @@
|
|||||||
|
import json
|
||||||
|
import re
|
||||||
|
from collections.abc import Generator
|
||||||
|
from typing import Union
|
||||||
|
|
||||||
|
from dify_plugin.entities.model.llm import LLMResultChunk
|
||||||
|
from dify_plugin.interfaces.agent import AgentScratchpadUnit
|
||||||
|
|
||||||
|
|
||||||
|
class CotAgentOutputParser:
|
||||||
|
@classmethod
|
||||||
|
def handle_react_stream_output(
|
||||||
|
cls, llm_response: Generator[LLMResultChunk, None, None], usage_dict: dict
|
||||||
|
) -> Generator[Union[str, AgentScratchpadUnit.Action], None, None]:
|
||||||
|
def parse_action(json_str):
|
||||||
|
try:
|
||||||
|
action = json.loads(json_str, strict=False)
|
||||||
|
action_name = None
|
||||||
|
action_input = None
|
||||||
|
|
||||||
|
# cohere always returns a list
|
||||||
|
if isinstance(action, list) and len(action) == 1:
|
||||||
|
action = action[0]
|
||||||
|
|
||||||
|
for key, value in action.items():
|
||||||
|
if "input" in key.lower():
|
||||||
|
action_input = value
|
||||||
|
else:
|
||||||
|
action_name = value
|
||||||
|
|
||||||
|
if action_name is not None and action_input is not None:
|
||||||
|
return AgentScratchpadUnit.Action(
|
||||||
|
action_name=action_name,
|
||||||
|
action_input=action_input,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
return json_str or ""
|
||||||
|
except:
|
||||||
|
return json_str or ""
|
||||||
|
|
||||||
|
def extra_json_from_code_block(
|
||||||
|
code_block,
|
||||||
|
) -> Generator[Union[str, AgentScratchpadUnit.Action], None, None]:
|
||||||
|
code_blocks = re.findall(r"```(.*?)```", code_block, re.DOTALL)
|
||||||
|
if not code_blocks:
|
||||||
|
return
|
||||||
|
for block in code_blocks:
|
||||||
|
json_text = re.sub(
|
||||||
|
r"^[a-zA-Z]+\n", "", block.strip(), flags=re.MULTILINE
|
||||||
|
)
|
||||||
|
yield parse_action(json_text)
|
||||||
|
|
||||||
|
code_block_cache = ""
|
||||||
|
code_block_delimiter_count = 0
|
||||||
|
in_code_block = False
|
||||||
|
json_cache = ""
|
||||||
|
json_quote_count = 0
|
||||||
|
in_json = False
|
||||||
|
got_json = False
|
||||||
|
|
||||||
|
action_cache = ""
|
||||||
|
action_str = "action:"
|
||||||
|
action_idx = 0
|
||||||
|
|
||||||
|
thought_cache = ""
|
||||||
|
thought_str = "thought:"
|
||||||
|
thought_idx = 0
|
||||||
|
|
||||||
|
last_character = ""
|
||||||
|
|
||||||
|
for response in llm_response:
|
||||||
|
if response.delta.usage:
|
||||||
|
usage_dict["usage"] = response.delta.usage
|
||||||
|
response_content = response.delta.message.content
|
||||||
|
if not isinstance(response_content, str):
|
||||||
|
continue
|
||||||
|
|
||||||
|
# stream
|
||||||
|
index = 0
|
||||||
|
while index < len(response_content):
|
||||||
|
steps = 1
|
||||||
|
delta = response_content[index : index + steps]
|
||||||
|
yield_delta = False
|
||||||
|
|
||||||
|
if delta == "`":
|
||||||
|
last_character = delta
|
||||||
|
code_block_cache += delta
|
||||||
|
code_block_delimiter_count += 1
|
||||||
|
else:
|
||||||
|
if not in_code_block:
|
||||||
|
if code_block_delimiter_count > 0:
|
||||||
|
last_character = delta
|
||||||
|
yield code_block_cache
|
||||||
|
code_block_cache = ""
|
||||||
|
else:
|
||||||
|
last_character = delta
|
||||||
|
code_block_cache += delta
|
||||||
|
code_block_delimiter_count = 0
|
||||||
|
|
||||||
|
if not in_code_block and not in_json:
|
||||||
|
if delta.lower() == action_str[action_idx] and action_idx == 0:
|
||||||
|
if last_character not in {"\n", " ", ""}:
|
||||||
|
yield_delta = True
|
||||||
|
else:
|
||||||
|
last_character = delta
|
||||||
|
action_cache += delta
|
||||||
|
action_idx += 1
|
||||||
|
if action_idx == len(action_str):
|
||||||
|
action_cache = ""
|
||||||
|
action_idx = 0
|
||||||
|
index += steps
|
||||||
|
continue
|
||||||
|
elif delta.lower() == action_str[action_idx] and action_idx > 0:
|
||||||
|
last_character = delta
|
||||||
|
action_cache += delta
|
||||||
|
action_idx += 1
|
||||||
|
if action_idx == len(action_str):
|
||||||
|
action_cache = ""
|
||||||
|
action_idx = 0
|
||||||
|
index += steps
|
||||||
|
continue
|
||||||
|
else:
|
||||||
|
if action_cache:
|
||||||
|
last_character = delta
|
||||||
|
yield action_cache
|
||||||
|
action_cache = ""
|
||||||
|
action_idx = 0
|
||||||
|
|
||||||
|
if delta.lower() == thought_str[thought_idx] and thought_idx == 0:
|
||||||
|
if last_character not in {"\n", " ", ""}:
|
||||||
|
yield_delta = True
|
||||||
|
else:
|
||||||
|
last_character = delta
|
||||||
|
thought_cache += delta
|
||||||
|
thought_idx += 1
|
||||||
|
if thought_idx == len(thought_str):
|
||||||
|
thought_cache = ""
|
||||||
|
thought_idx = 0
|
||||||
|
index += steps
|
||||||
|
continue
|
||||||
|
elif delta.lower() == thought_str[thought_idx] and thought_idx > 0:
|
||||||
|
last_character = delta
|
||||||
|
thought_cache += delta
|
||||||
|
thought_idx += 1
|
||||||
|
if thought_idx == len(thought_str):
|
||||||
|
thought_cache = ""
|
||||||
|
thought_idx = 0
|
||||||
|
index += steps
|
||||||
|
continue
|
||||||
|
else:
|
||||||
|
if thought_cache:
|
||||||
|
last_character = delta
|
||||||
|
yield thought_cache
|
||||||
|
thought_cache = ""
|
||||||
|
thought_idx = 0
|
||||||
|
|
||||||
|
if yield_delta:
|
||||||
|
index += steps
|
||||||
|
last_character = delta
|
||||||
|
yield delta
|
||||||
|
continue
|
||||||
|
|
||||||
|
if code_block_delimiter_count == 3:
|
||||||
|
if in_code_block:
|
||||||
|
last_character = delta
|
||||||
|
yield from extra_json_from_code_block(code_block_cache)
|
||||||
|
code_block_cache = ""
|
||||||
|
|
||||||
|
in_code_block = not in_code_block
|
||||||
|
code_block_delimiter_count = 0
|
||||||
|
|
||||||
|
if not in_code_block:
|
||||||
|
# handle single json
|
||||||
|
if delta == "{":
|
||||||
|
json_quote_count += 1
|
||||||
|
in_json = True
|
||||||
|
last_character = delta
|
||||||
|
json_cache += delta
|
||||||
|
elif delta == "}":
|
||||||
|
last_character = delta
|
||||||
|
json_cache += delta
|
||||||
|
if json_quote_count > 0:
|
||||||
|
json_quote_count -= 1
|
||||||
|
if json_quote_count == 0:
|
||||||
|
in_json = False
|
||||||
|
got_json = True
|
||||||
|
index += steps
|
||||||
|
continue
|
||||||
|
else:
|
||||||
|
if in_json:
|
||||||
|
last_character = delta
|
||||||
|
json_cache += delta
|
||||||
|
|
||||||
|
if got_json:
|
||||||
|
got_json = False
|
||||||
|
last_character = delta
|
||||||
|
yield parse_action(json_cache)
|
||||||
|
json_cache = ""
|
||||||
|
json_quote_count = 0
|
||||||
|
in_json = False
|
||||||
|
|
||||||
|
if not in_code_block and not in_json:
|
||||||
|
last_character = delta
|
||||||
|
yield delta.replace("`", "")
|
||||||
|
|
||||||
|
index += steps
|
||||||
|
|
||||||
|
if code_block_cache:
|
||||||
|
yield code_block_cache
|
||||||
|
|
||||||
|
if json_cache:
|
||||||
|
yield parse_action(json_cache)
|
||||||
@ -0,0 +1,106 @@
|
|||||||
|
ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
|
||||||
|
|
||||||
|
{{instruction}}
|
||||||
|
|
||||||
|
You have access to the following tools:
|
||||||
|
|
||||||
|
{{tools}}
|
||||||
|
|
||||||
|
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
|
||||||
|
Valid "action" values: "Final Answer" or {{tool_names}}
|
||||||
|
|
||||||
|
Provide only ONE action per $JSON_BLOB, as shown:
|
||||||
|
|
||||||
|
```
|
||||||
|
{
|
||||||
|
"action": $TOOL_NAME,
|
||||||
|
"action_input": $ACTION_INPUT
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
Follow this format:
|
||||||
|
|
||||||
|
Question: input question to answer
|
||||||
|
Thought: consider previous and subsequent steps
|
||||||
|
Action:
|
||||||
|
```
|
||||||
|
$JSON_BLOB
|
||||||
|
```
|
||||||
|
Observation: action result
|
||||||
|
... (repeat Thought/Action/Observation N times)
|
||||||
|
Thought: I know what to respond
|
||||||
|
Action:
|
||||||
|
```
|
||||||
|
{
|
||||||
|
"action": "Final Answer",
|
||||||
|
"action_input": "Final response to human"
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
|
||||||
|
{{historic_messages}}
|
||||||
|
Question: {{query}}
|
||||||
|
{{agent_scratchpad}}
|
||||||
|
Thought:""" # noqa: E501
|
||||||
|
|
||||||
|
|
||||||
|
ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES = """Observation: {{observation}}
|
||||||
|
Thought:"""
|
||||||
|
|
||||||
|
ENGLISH_REACT_CHAT_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
|
||||||
|
|
||||||
|
{{instruction}}
|
||||||
|
|
||||||
|
You have access to the following tools:
|
||||||
|
|
||||||
|
{{tools}}
|
||||||
|
|
||||||
|
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
|
||||||
|
Valid "action" values: "Final Answer" or {{tool_names}}
|
||||||
|
|
||||||
|
Provide only ONE action per $JSON_BLOB, as shown:
|
||||||
|
|
||||||
|
```
|
||||||
|
{
|
||||||
|
"action": $TOOL_NAME,
|
||||||
|
"action_input": $ACTION_INPUT
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
Follow this format:
|
||||||
|
|
||||||
|
Question: input question to answer
|
||||||
|
Thought: consider previous and subsequent steps
|
||||||
|
Action:
|
||||||
|
```
|
||||||
|
$JSON_BLOB
|
||||||
|
```
|
||||||
|
Observation: action result
|
||||||
|
... (repeat Thought/Action/Observation N times)
|
||||||
|
Thought: I know what to respond
|
||||||
|
Action:
|
||||||
|
```
|
||||||
|
{
|
||||||
|
"action": "Final Answer",
|
||||||
|
"action_input": "Final response to human"
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
|
||||||
|
""" # noqa: E501
|
||||||
|
|
||||||
|
|
||||||
|
ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES = ""
|
||||||
|
|
||||||
|
REACT_PROMPT_TEMPLATES = {
|
||||||
|
"english": {
|
||||||
|
"chat": {
|
||||||
|
"prompt": ENGLISH_REACT_CHAT_PROMPT_TEMPLATES,
|
||||||
|
"agent_scratchpad": ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES,
|
||||||
|
},
|
||||||
|
"completion": {
|
||||||
|
"prompt": ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES,
|
||||||
|
"agent_scratchpad": ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES,
|
||||||
|
},
|
||||||
|
}
|
||||||
|
}
|
||||||
@ -0,0 +1,5 @@
|
|||||||
|
from dify_plugin.interfaces.agent import AgentProvider
|
||||||
|
|
||||||
|
|
||||||
|
class LanggeniusAgentProvider(AgentProvider):
|
||||||
|
pass
|
||||||
@ -0,0 +1,18 @@
|
|||||||
|
identity:
|
||||||
|
author: langgenius
|
||||||
|
name: agent
|
||||||
|
label:
|
||||||
|
en_US: Agent
|
||||||
|
zh_Hans: Agent
|
||||||
|
pt_BR: Agent
|
||||||
|
description:
|
||||||
|
en_US: Agent
|
||||||
|
zh_Hans: Agent
|
||||||
|
pt_BR: Agent
|
||||||
|
icon: icon.svg
|
||||||
|
strategies:
|
||||||
|
- strategies/function_calling.yaml
|
||||||
|
- strategies/ReAct.yaml
|
||||||
|
extra:
|
||||||
|
python:
|
||||||
|
source: provider/agent.py
|
||||||
@ -0,0 +1 @@
|
|||||||
|
dify_plugin==0.0.1b74
|
||||||
@ -0,0 +1,642 @@
|
|||||||
|
import json
|
||||||
|
import time
|
||||||
|
from collections.abc import Generator, Mapping
|
||||||
|
from typing import Any, Optional, cast
|
||||||
|
|
||||||
|
from dify_plugin.entities.agent import AgentInvokeMessage
|
||||||
|
from dify_plugin.entities.model.llm import LLMModelConfig, LLMUsage
|
||||||
|
from dify_plugin.entities.model.message import (
|
||||||
|
AssistantPromptMessage,
|
||||||
|
PromptMessage,
|
||||||
|
SystemPromptMessage,
|
||||||
|
ToolPromptMessage,
|
||||||
|
UserPromptMessage,
|
||||||
|
)
|
||||||
|
from dify_plugin.entities.tool import (
|
||||||
|
LogMetadata,
|
||||||
|
ToolInvokeMessage,
|
||||||
|
ToolParameter,
|
||||||
|
ToolProviderType,
|
||||||
|
)
|
||||||
|
from dify_plugin.interfaces.agent import (
|
||||||
|
AgentModelConfig,
|
||||||
|
AgentScratchpadUnit,
|
||||||
|
AgentStrategy,
|
||||||
|
ToolEntity,
|
||||||
|
)
|
||||||
|
from output_parser.cot_output_parser import CotAgentOutputParser
|
||||||
|
from prompt.template import REACT_PROMPT_TEMPLATES
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
|
||||||
|
ignore_observation_providers = ["wenxin"]
|
||||||
|
|
||||||
|
|
||||||
|
class ReActParams(BaseModel):
|
||||||
|
query: str
|
||||||
|
instruction: str | None
|
||||||
|
model: AgentModelConfig
|
||||||
|
tools: list[ToolEntity] | None
|
||||||
|
inputs: dict[str, Any] = {}
|
||||||
|
maximum_iterations: int = 3
|
||||||
|
|
||||||
|
|
||||||
|
class AgentPromptEntity(BaseModel):
|
||||||
|
"""
|
||||||
|
Agent Prompt Entity.
|
||||||
|
"""
|
||||||
|
|
||||||
|
first_prompt: str
|
||||||
|
next_iteration: str
|
||||||
|
|
||||||
|
|
||||||
|
class ToolInvokeMeta(BaseModel):
|
||||||
|
"""
|
||||||
|
Tool invoke meta
|
||||||
|
"""
|
||||||
|
|
||||||
|
time_cost: float = Field(..., description="The time cost of the tool invoke")
|
||||||
|
error: Optional[str] = None
|
||||||
|
tool_config: Optional[dict] = None
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def empty(cls) -> "ToolInvokeMeta":
|
||||||
|
"""
|
||||||
|
Get an empty instance of ToolInvokeMeta
|
||||||
|
"""
|
||||||
|
return cls(time_cost=0.0, error=None, tool_config={})
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def error_instance(cls, error: str) -> "ToolInvokeMeta":
|
||||||
|
"""
|
||||||
|
Get an instance of ToolInvokeMeta with error
|
||||||
|
"""
|
||||||
|
return cls(time_cost=0.0, error=error, tool_config={})
|
||||||
|
|
||||||
|
def to_dict(self) -> dict:
|
||||||
|
return {
|
||||||
|
"time_cost": self.time_cost,
|
||||||
|
"error": self.error,
|
||||||
|
"tool_config": self.tool_config,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class ReActAgentStrategy(AgentStrategy):
|
||||||
|
def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
|
||||||
|
react_params = ReActParams(**parameters)
|
||||||
|
query = react_params.query
|
||||||
|
model = react_params.model
|
||||||
|
agent_scratchpad = []
|
||||||
|
history_prompt_messages: list[PromptMessage] = []
|
||||||
|
current_session_messages = []
|
||||||
|
self._organize_historic_prompt_messages(
|
||||||
|
history_prompt_messages, current_session_messages=current_session_messages
|
||||||
|
)
|
||||||
|
tools = react_params.tools
|
||||||
|
tool_instances = {tool.identity.name: tool for tool in tools} if tools else {}
|
||||||
|
react_params.model.completion_params = (
|
||||||
|
react_params.model.completion_params or {}
|
||||||
|
)
|
||||||
|
# check model mode
|
||||||
|
stop = (
|
||||||
|
react_params.model.completion_params.get("stop", [])
|
||||||
|
if react_params.model.completion_params
|
||||||
|
else []
|
||||||
|
)
|
||||||
|
|
||||||
|
if (
|
||||||
|
"Observation" not in stop
|
||||||
|
and model.provider not in ignore_observation_providers
|
||||||
|
):
|
||||||
|
stop.append("Observation")
|
||||||
|
# init instruction
|
||||||
|
inputs = react_params.inputs
|
||||||
|
instruction = react_params.instruction or ""
|
||||||
|
self._instruction = self._fill_in_inputs_from_external_data_tools(
|
||||||
|
instruction, inputs
|
||||||
|
)
|
||||||
|
|
||||||
|
iteration_step = 1
|
||||||
|
max_iteration_steps = react_params.maximum_iterations
|
||||||
|
|
||||||
|
# convert tools into ModelRuntime Tool format
|
||||||
|
prompt_messages_tools = self._init_prompt_tools(tools)
|
||||||
|
self._prompt_messages_tools = prompt_messages_tools
|
||||||
|
|
||||||
|
run_agent_state = True
|
||||||
|
llm_usage: dict[str, Optional[LLMUsage]] = {"usage": None}
|
||||||
|
final_answer = ""
|
||||||
|
prompt_messages = []
|
||||||
|
while run_agent_state and iteration_step <= max_iteration_steps:
|
||||||
|
# continue to run until there is not any tool call
|
||||||
|
run_agent_state = False
|
||||||
|
round_started_at = time.perf_counter()
|
||||||
|
round_log = self.create_log_message(
|
||||||
|
label=f"ROUND {iteration_step}",
|
||||||
|
data={},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: round_started_at,
|
||||||
|
},
|
||||||
|
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||||
|
)
|
||||||
|
yield round_log
|
||||||
|
if iteration_step == max_iteration_steps:
|
||||||
|
# the last iteration, remove all tools
|
||||||
|
self._prompt_messages_tools = []
|
||||||
|
|
||||||
|
message_file_ids: list[str] = []
|
||||||
|
|
||||||
|
# recalc llm max tokens
|
||||||
|
prompt_messages = self._organize_prompt_messages(agent_scratchpad, query)
|
||||||
|
if model.completion_params:
|
||||||
|
self.recalc_llm_max_tokens(
|
||||||
|
model.entity, prompt_messages, model.completion_params
|
||||||
|
)
|
||||||
|
# invoke model
|
||||||
|
chunks = self.session.model.llm.invoke(
|
||||||
|
model_config=LLMModelConfig(**model.model_dump(mode="json")),
|
||||||
|
prompt_messages=prompt_messages,
|
||||||
|
stream=True,
|
||||||
|
stop=stop,
|
||||||
|
)
|
||||||
|
|
||||||
|
usage_dict = {}
|
||||||
|
react_chunks = CotAgentOutputParser.handle_react_stream_output(
|
||||||
|
chunks, usage_dict
|
||||||
|
)
|
||||||
|
scratchpad = AgentScratchpadUnit(
|
||||||
|
agent_response="",
|
||||||
|
thought="",
|
||||||
|
action_str="",
|
||||||
|
observation="",
|
||||||
|
action=None,
|
||||||
|
)
|
||||||
|
|
||||||
|
model_started_at = time.perf_counter()
|
||||||
|
model_log = self.create_log_message(
|
||||||
|
label=f"{model.model} Thought",
|
||||||
|
data={},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: model_started_at,
|
||||||
|
LogMetadata.PROVIDER: model.provider,
|
||||||
|
},
|
||||||
|
parent=round_log,
|
||||||
|
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||||
|
)
|
||||||
|
yield model_log
|
||||||
|
|
||||||
|
for chunk in react_chunks:
|
||||||
|
if isinstance(chunk, AgentScratchpadUnit.Action):
|
||||||
|
action = chunk
|
||||||
|
# detect action
|
||||||
|
assert scratchpad.agent_response is not None
|
||||||
|
scratchpad.agent_response += json.dumps(chunk.model_dump())
|
||||||
|
|
||||||
|
scratchpad.action_str = json.dumps(chunk.model_dump())
|
||||||
|
scratchpad.action = action
|
||||||
|
else:
|
||||||
|
scratchpad.agent_response = scratchpad.agent_response or ""
|
||||||
|
scratchpad.thought = scratchpad.thought or ""
|
||||||
|
scratchpad.agent_response += chunk
|
||||||
|
scratchpad.thought += chunk
|
||||||
|
scratchpad.thought = (
|
||||||
|
scratchpad.thought.strip()
|
||||||
|
if scratchpad.thought
|
||||||
|
else "I am thinking about how to help you"
|
||||||
|
)
|
||||||
|
agent_scratchpad.append(scratchpad)
|
||||||
|
|
||||||
|
# get llm usage
|
||||||
|
if "usage" in usage_dict:
|
||||||
|
if usage_dict["usage"] is not None:
|
||||||
|
self.increase_usage(llm_usage, usage_dict["usage"])
|
||||||
|
else:
|
||||||
|
usage_dict["usage"] = LLMUsage.empty_usage()
|
||||||
|
|
||||||
|
action = (
|
||||||
|
scratchpad.action.to_dict()
|
||||||
|
if scratchpad.action
|
||||||
|
else {"action": scratchpad.agent_response}
|
||||||
|
)
|
||||||
|
|
||||||
|
yield self.finish_log_message(
|
||||||
|
log=model_log,
|
||||||
|
data={"thought": scratchpad.thought, **action},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: model_started_at,
|
||||||
|
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||||
|
LogMetadata.ELAPSED_TIME: time.perf_counter() - model_started_at,
|
||||||
|
LogMetadata.PROVIDER: model.provider,
|
||||||
|
LogMetadata.TOTAL_PRICE: usage_dict["usage"].total_price
|
||||||
|
if usage_dict["usage"]
|
||||||
|
else 0,
|
||||||
|
LogMetadata.CURRENCY: usage_dict["usage"].currency
|
||||||
|
if usage_dict["usage"]
|
||||||
|
else "",
|
||||||
|
LogMetadata.TOTAL_TOKENS: usage_dict["usage"].total_tokens
|
||||||
|
if usage_dict["usage"]
|
||||||
|
else 0,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
if not scratchpad.action:
|
||||||
|
final_answer = scratchpad.thought
|
||||||
|
else:
|
||||||
|
if scratchpad.action.action_name.lower() == "final answer":
|
||||||
|
# action is final answer, return final answer directly
|
||||||
|
try:
|
||||||
|
if isinstance(scratchpad.action.action_input, dict):
|
||||||
|
final_answer = json.dumps(scratchpad.action.action_input)
|
||||||
|
elif isinstance(scratchpad.action.action_input, str):
|
||||||
|
final_answer = scratchpad.action.action_input
|
||||||
|
else:
|
||||||
|
final_answer = f"{scratchpad.action.action_input}"
|
||||||
|
except json.JSONDecodeError:
|
||||||
|
final_answer = f"{scratchpad.action.action_input}"
|
||||||
|
else:
|
||||||
|
run_agent_state = True
|
||||||
|
# action is tool call, invoke tool
|
||||||
|
tool_call_started_at = time.perf_counter()
|
||||||
|
tool_name = scratchpad.action.action_name
|
||||||
|
tool_call_log = self.create_log_message(
|
||||||
|
label=f"CALL {tool_name}",
|
||||||
|
data={},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: time.perf_counter(),
|
||||||
|
LogMetadata.PROVIDER: tool_instances[
|
||||||
|
tool_name
|
||||||
|
].identity.provider
|
||||||
|
if tool_instances.get(tool_name)
|
||||||
|
else "",
|
||||||
|
},
|
||||||
|
parent=round_log,
|
||||||
|
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||||
|
)
|
||||||
|
yield tool_call_log
|
||||||
|
tool_invoke_response, tool_invoke_parameters = (
|
||||||
|
self._handle_invoke_action(
|
||||||
|
action=scratchpad.action,
|
||||||
|
tool_instances=tool_instances,
|
||||||
|
message_file_ids=message_file_ids,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
scratchpad.observation = tool_invoke_response
|
||||||
|
scratchpad.agent_response = tool_invoke_response
|
||||||
|
yield self.finish_log_message(
|
||||||
|
log=tool_call_log,
|
||||||
|
data={
|
||||||
|
"tool_name": tool_name,
|
||||||
|
"tool_call_args": tool_invoke_parameters,
|
||||||
|
"output": tool_invoke_response,
|
||||||
|
},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: tool_call_started_at,
|
||||||
|
LogMetadata.PROVIDER: tool_instances[
|
||||||
|
tool_name
|
||||||
|
].identity.provider
|
||||||
|
if tool_instances.get(tool_name)
|
||||||
|
else "",
|
||||||
|
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||||
|
LogMetadata.ELAPSED_TIME: time.perf_counter()
|
||||||
|
- tool_call_started_at,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
|
||||||
|
# update prompt tool message
|
||||||
|
for prompt_tool in self._prompt_messages_tools:
|
||||||
|
self.update_prompt_message_tool(
|
||||||
|
tool_instances[prompt_tool.name], prompt_tool
|
||||||
|
)
|
||||||
|
yield self.finish_log_message(
|
||||||
|
log=round_log,
|
||||||
|
data={
|
||||||
|
"action_name": scratchpad.action.action_name
|
||||||
|
if scratchpad.action
|
||||||
|
else "",
|
||||||
|
"action_input": scratchpad.action.action_input
|
||||||
|
if scratchpad.action
|
||||||
|
else "",
|
||||||
|
"thought": scratchpad.thought,
|
||||||
|
"observation": scratchpad.observation,
|
||||||
|
},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: round_started_at,
|
||||||
|
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||||
|
LogMetadata.ELAPSED_TIME: time.perf_counter() - round_started_at,
|
||||||
|
LogMetadata.TOTAL_PRICE: usage_dict["usage"].total_price
|
||||||
|
if usage_dict["usage"]
|
||||||
|
else 0,
|
||||||
|
LogMetadata.CURRENCY: usage_dict["usage"].currency
|
||||||
|
if usage_dict["usage"]
|
||||||
|
else "",
|
||||||
|
LogMetadata.TOTAL_TOKENS: usage_dict["usage"].total_tokens
|
||||||
|
if usage_dict["usage"]
|
||||||
|
else 0,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
iteration_step += 1
|
||||||
|
|
||||||
|
yield self.create_text_message(final_answer)
|
||||||
|
yield self.create_json_message(
|
||||||
|
{
|
||||||
|
"execution_metadata": {
|
||||||
|
LogMetadata.TOTAL_PRICE: llm_usage["usage"].total_price
|
||||||
|
if llm_usage["usage"] is not None
|
||||||
|
else 0,
|
||||||
|
LogMetadata.CURRENCY: llm_usage["usage"].currency
|
||||||
|
if llm_usage["usage"] is not None
|
||||||
|
else "",
|
||||||
|
LogMetadata.TOTAL_TOKENS: llm_usage["usage"].total_tokens
|
||||||
|
if llm_usage["usage"] is not None
|
||||||
|
else 0,
|
||||||
|
}
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
def _organize_system_prompt(self) -> SystemPromptMessage:
|
||||||
|
"""
|
||||||
|
Organize system prompt
|
||||||
|
"""
|
||||||
|
|
||||||
|
prompt_entity = AgentPromptEntity(
|
||||||
|
first_prompt=REACT_PROMPT_TEMPLATES["english"]["chat"]["prompt"],
|
||||||
|
next_iteration=REACT_PROMPT_TEMPLATES["english"]["chat"][
|
||||||
|
"agent_scratchpad"
|
||||||
|
],
|
||||||
|
)
|
||||||
|
if not prompt_entity:
|
||||||
|
raise ValueError("Agent prompt configuration is not set")
|
||||||
|
first_prompt = prompt_entity.first_prompt
|
||||||
|
|
||||||
|
system_prompt = (
|
||||||
|
first_prompt.replace("{{instruction}}", self._instruction)
|
||||||
|
.replace(
|
||||||
|
"{{tools}}",
|
||||||
|
json.dumps(
|
||||||
|
[
|
||||||
|
tool.model_dump(mode="json")
|
||||||
|
for tool in self._prompt_messages_tools
|
||||||
|
]
|
||||||
|
),
|
||||||
|
)
|
||||||
|
.replace(
|
||||||
|
"{{tool_names}}",
|
||||||
|
", ".join([tool.name for tool in self._prompt_messages_tools]),
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
return SystemPromptMessage(content=system_prompt)
|
||||||
|
|
||||||
|
def _organize_user_query(
|
||||||
|
self, query, prompt_messages: list[PromptMessage]
|
||||||
|
) -> list[PromptMessage]:
|
||||||
|
"""
|
||||||
|
Organize user query
|
||||||
|
"""
|
||||||
|
prompt_messages.append(UserPromptMessage(content=query))
|
||||||
|
|
||||||
|
return prompt_messages
|
||||||
|
|
||||||
|
def _organize_prompt_messages(
|
||||||
|
self, agent_scratchpad: list, query: str
|
||||||
|
) -> list[PromptMessage]:
|
||||||
|
"""
|
||||||
|
Organize
|
||||||
|
"""
|
||||||
|
# organize system prompt
|
||||||
|
system_message = self._organize_system_prompt()
|
||||||
|
|
||||||
|
# organize current assistant messages
|
||||||
|
agent_scratchpad = agent_scratchpad
|
||||||
|
if not agent_scratchpad:
|
||||||
|
assistant_messages = []
|
||||||
|
else:
|
||||||
|
assistant_message = AssistantPromptMessage(content="")
|
||||||
|
assistant_message.content = (
|
||||||
|
"" # FIXME: type check tell mypy that assistant_message.content is str
|
||||||
|
)
|
||||||
|
for unit in agent_scratchpad:
|
||||||
|
if unit.is_final():
|
||||||
|
assert isinstance(assistant_message.content, str)
|
||||||
|
assistant_message.content += f"Final Answer: {unit.agent_response}"
|
||||||
|
else:
|
||||||
|
assert isinstance(assistant_message.content, str)
|
||||||
|
assistant_message.content += f"Thought: {unit.thought}\n\n"
|
||||||
|
if unit.action_str:
|
||||||
|
assistant_message.content += f"Action: {unit.action_str}\n\n"
|
||||||
|
if unit.observation:
|
||||||
|
assistant_message.content += (
|
||||||
|
f"Observation: {unit.observation}\n\n"
|
||||||
|
)
|
||||||
|
|
||||||
|
assistant_messages = [assistant_message]
|
||||||
|
|
||||||
|
# query messages
|
||||||
|
query_messages = self._organize_user_query(query, [])
|
||||||
|
|
||||||
|
if assistant_messages:
|
||||||
|
# organize historic prompt messages
|
||||||
|
historic_messages = self._organize_historic_prompt_messages(
|
||||||
|
[
|
||||||
|
system_message,
|
||||||
|
*query_messages,
|
||||||
|
*assistant_messages,
|
||||||
|
UserPromptMessage(content="continue"),
|
||||||
|
]
|
||||||
|
)
|
||||||
|
messages = [
|
||||||
|
system_message,
|
||||||
|
*historic_messages,
|
||||||
|
*query_messages,
|
||||||
|
*assistant_messages,
|
||||||
|
UserPromptMessage(content="continue"),
|
||||||
|
]
|
||||||
|
else:
|
||||||
|
# organize historic prompt messages
|
||||||
|
historic_messages = self._organize_historic_prompt_messages(
|
||||||
|
[system_message, *query_messages]
|
||||||
|
)
|
||||||
|
messages = [system_message, *historic_messages, *query_messages]
|
||||||
|
|
||||||
|
# join all messages
|
||||||
|
return messages
|
||||||
|
|
||||||
|
def _handle_invoke_action(
|
||||||
|
self,
|
||||||
|
action: AgentScratchpadUnit.Action,
|
||||||
|
tool_instances: Mapping[str, ToolEntity],
|
||||||
|
message_file_ids: list[str],
|
||||||
|
) -> tuple[str, dict[str, Any] | str]:
|
||||||
|
"""
|
||||||
|
handle invoke action
|
||||||
|
:param action: action
|
||||||
|
:param tool_instances: tool instances
|
||||||
|
:param message_file_ids: message file ids
|
||||||
|
:param trace_manager: trace manager
|
||||||
|
:return: observation, meta
|
||||||
|
"""
|
||||||
|
# action is tool call, invoke tool
|
||||||
|
tool_call_name = action.action_name
|
||||||
|
tool_call_args = action.action_input
|
||||||
|
tool_instance = tool_instances.get(tool_call_name)
|
||||||
|
|
||||||
|
if not tool_instance:
|
||||||
|
answer = f"there is not a tool named {tool_call_name}"
|
||||||
|
return answer, tool_call_args
|
||||||
|
|
||||||
|
if isinstance(tool_call_args, str):
|
||||||
|
try:
|
||||||
|
tool_call_args = json.loads(tool_call_args)
|
||||||
|
except json.JSONDecodeError as e:
|
||||||
|
params = [
|
||||||
|
param.name
|
||||||
|
for param in tool_instance.parameters
|
||||||
|
if param.form == ToolParameter.ToolParameterForm.LLM
|
||||||
|
]
|
||||||
|
if len(params) > 1:
|
||||||
|
raise ValueError("tool call args is not a valid json string") from e
|
||||||
|
tool_call_args = {params[0]: tool_call_args} if len(params) == 1 else {}
|
||||||
|
|
||||||
|
tool_invoke_parameters = {**tool_instance.runtime_parameters, **tool_call_args}
|
||||||
|
try:
|
||||||
|
tool_invoke_responses = self.session.tool.invoke(
|
||||||
|
provider_type=ToolProviderType(tool_instance.provider_type),
|
||||||
|
provider=tool_instance.identity.provider,
|
||||||
|
tool_name=tool_instance.identity.name,
|
||||||
|
parameters=tool_invoke_parameters,
|
||||||
|
)
|
||||||
|
result = ""
|
||||||
|
for response in tool_invoke_responses:
|
||||||
|
if response.type == ToolInvokeMessage.MessageType.TEXT:
|
||||||
|
result += cast(ToolInvokeMessage.TextMessage, response.message).text
|
||||||
|
elif response.type == ToolInvokeMessage.MessageType.LINK:
|
||||||
|
result += (
|
||||||
|
f"result link: {cast(ToolInvokeMessage.TextMessage, response.message).text}."
|
||||||
|
+ " please tell user to check it."
|
||||||
|
)
|
||||||
|
elif response.type in {
|
||||||
|
ToolInvokeMessage.MessageType.IMAGE_LINK,
|
||||||
|
ToolInvokeMessage.MessageType.IMAGE,
|
||||||
|
}:
|
||||||
|
result += (
|
||||||
|
"image has been created and sent to user already, "
|
||||||
|
+ "you do not need to create it, just tell the user to check it now."
|
||||||
|
)
|
||||||
|
elif response.type == ToolInvokeMessage.MessageType.JSON:
|
||||||
|
text = json.dumps(
|
||||||
|
cast(
|
||||||
|
ToolInvokeMessage.JsonMessage, response.message
|
||||||
|
).json_object,
|
||||||
|
ensure_ascii=False,
|
||||||
|
)
|
||||||
|
result += f"tool response: {text}."
|
||||||
|
else:
|
||||||
|
result += f"tool response: {response.message!r}."
|
||||||
|
except Exception as e:
|
||||||
|
result = f"tool invoke error: {str(e)}"
|
||||||
|
|
||||||
|
return result, tool_invoke_parameters
|
||||||
|
|
||||||
|
def _convert_dict_to_action(self, action: dict) -> AgentScratchpadUnit.Action:
|
||||||
|
"""
|
||||||
|
convert dict to action
|
||||||
|
"""
|
||||||
|
return AgentScratchpadUnit.Action(
|
||||||
|
action_name=action["action"], action_input=action["action_input"]
|
||||||
|
)
|
||||||
|
|
||||||
|
def _fill_in_inputs_from_external_data_tools(
|
||||||
|
self, instruction: str, inputs: Mapping[str, Any]
|
||||||
|
) -> str:
|
||||||
|
"""
|
||||||
|
fill in inputs from external data tools
|
||||||
|
"""
|
||||||
|
for key, value in inputs.items():
|
||||||
|
try:
|
||||||
|
instruction = instruction.replace(f"{{{{{key}}}}}", str(value))
|
||||||
|
except Exception:
|
||||||
|
continue
|
||||||
|
|
||||||
|
return instruction
|
||||||
|
|
||||||
|
def _format_assistant_message(
|
||||||
|
self, agent_scratchpad: list[AgentScratchpadUnit]
|
||||||
|
) -> str:
|
||||||
|
"""
|
||||||
|
format assistant message
|
||||||
|
"""
|
||||||
|
message = ""
|
||||||
|
for scratchpad in agent_scratchpad:
|
||||||
|
if scratchpad.is_final():
|
||||||
|
message += f"Final Answer: {scratchpad.agent_response}"
|
||||||
|
else:
|
||||||
|
message += f"Thought: {scratchpad.thought}\n\n"
|
||||||
|
if scratchpad.action_str:
|
||||||
|
message += f"Action: {scratchpad.action_str}\n\n"
|
||||||
|
if scratchpad.observation:
|
||||||
|
message += f"Observation: {scratchpad.observation}\n\n"
|
||||||
|
|
||||||
|
return message
|
||||||
|
|
||||||
|
def _organize_historic_prompt_messages(
|
||||||
|
self,
|
||||||
|
history_prompt_messages: list[PromptMessage],
|
||||||
|
current_session_messages: list[PromptMessage] | None = None,
|
||||||
|
) -> list[PromptMessage]:
|
||||||
|
"""
|
||||||
|
organize historic prompt messages
|
||||||
|
"""
|
||||||
|
result: list[PromptMessage] = []
|
||||||
|
scratchpads: list[AgentScratchpadUnit] = []
|
||||||
|
current_scratchpad: AgentScratchpadUnit | None = None
|
||||||
|
|
||||||
|
for message in history_prompt_messages:
|
||||||
|
if isinstance(message, AssistantPromptMessage):
|
||||||
|
if not current_scratchpad:
|
||||||
|
assert isinstance(message.content, str)
|
||||||
|
current_scratchpad = AgentScratchpadUnit(
|
||||||
|
agent_response=message.content,
|
||||||
|
thought=message.content
|
||||||
|
or "I am thinking about how to help you",
|
||||||
|
action_str="",
|
||||||
|
action=None,
|
||||||
|
observation=None,
|
||||||
|
)
|
||||||
|
scratchpads.append(current_scratchpad)
|
||||||
|
if message.tool_calls:
|
||||||
|
try:
|
||||||
|
current_scratchpad.action = AgentScratchpadUnit.Action(
|
||||||
|
action_name=message.tool_calls[0].function.name,
|
||||||
|
action_input=json.loads(
|
||||||
|
message.tool_calls[0].function.arguments
|
||||||
|
),
|
||||||
|
)
|
||||||
|
current_scratchpad.action_str = json.dumps(
|
||||||
|
current_scratchpad.action.to_dict()
|
||||||
|
)
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
elif isinstance(message, ToolPromptMessage):
|
||||||
|
if current_scratchpad:
|
||||||
|
assert isinstance(message.content, str)
|
||||||
|
current_scratchpad.observation = message.content
|
||||||
|
else:
|
||||||
|
raise NotImplementedError("expected str type")
|
||||||
|
elif isinstance(message, UserPromptMessage):
|
||||||
|
if scratchpads:
|
||||||
|
result.append(
|
||||||
|
AssistantPromptMessage(
|
||||||
|
content=self._format_assistant_message(scratchpads)
|
||||||
|
)
|
||||||
|
)
|
||||||
|
scratchpads = []
|
||||||
|
current_scratchpad = None
|
||||||
|
|
||||||
|
result.append(message)
|
||||||
|
|
||||||
|
if scratchpads:
|
||||||
|
result.append(
|
||||||
|
AssistantPromptMessage(
|
||||||
|
content=self._format_assistant_message(scratchpads)
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
return current_session_messages or []
|
||||||
@ -0,0 +1,58 @@
|
|||||||
|
identity:
|
||||||
|
name: ReAct
|
||||||
|
author: Dify
|
||||||
|
label:
|
||||||
|
en_US: ReAct
|
||||||
|
zh_Hans: ReAct
|
||||||
|
pt_BR: ReAct
|
||||||
|
description:
|
||||||
|
en_US: ReAct is a basic strategy for agent, model will use the tools provided to perform the task.
|
||||||
|
zh_Hans: ReAct 是一个基本的 Agent 策略，模型将使用提供的工具来执行任务。
|
||||||
|
pt_BR: ReAct is a basic strategy for agent, model will use the tools provided to perform the task.
|
||||||
|
parameters:
|
||||||
|
- name: model
|
||||||
|
type: model-selector
|
||||||
|
scope: tool-call&llm
|
||||||
|
required: true
|
||||||
|
label:
|
||||||
|
en_US: Model
|
||||||
|
zh_Hans: 模型
|
||||||
|
pt_BR: Model
|
||||||
|
- name: tools
|
||||||
|
type: array[tools]
|
||||||
|
required: true
|
||||||
|
label:
|
||||||
|
en_US: Tool list
|
||||||
|
zh_Hans: 工具列表
|
||||||
|
pt_BR: Tool list
|
||||||
|
- name: instruction
|
||||||
|
type: string
|
||||||
|
required: true
|
||||||
|
label:
|
||||||
|
en_US: Instruction
|
||||||
|
zh_Hans: 指令
|
||||||
|
pt_BR: Instruction
|
||||||
|
auto_generate:
|
||||||
|
type: prompt_instruction
|
||||||
|
template:
|
||||||
|
enabled: true
|
||||||
|
- name: query
|
||||||
|
type: string
|
||||||
|
required: true
|
||||||
|
label:
|
||||||
|
en_US: Query
|
||||||
|
zh_Hans: 查询
|
||||||
|
pt_BR: Query
|
||||||
|
- name: maximum_iterations
|
||||||
|
type: number
|
||||||
|
required: true
|
||||||
|
label:
|
||||||
|
en_US: Maximum Iterations
|
||||||
|
zh_Hans: 最大迭代次数
|
||||||
|
pt_BR: Maximum Iterations
|
||||||
|
default: 3
|
||||||
|
min: 1
|
||||||
|
max: 30
|
||||||
|
extra:
|
||||||
|
python:
|
||||||
|
source: strategies/ReAct.py
|
||||||
@ -0,0 +1,546 @@
|
|||||||
|
import json
|
||||||
|
import time
|
||||||
|
from collections.abc import Generator
|
||||||
|
from copy import deepcopy
|
||||||
|
from typing import Any, Optional, cast
|
||||||
|
|
||||||
|
from dify_plugin.entities.agent import AgentInvokeMessage
|
||||||
|
from dify_plugin.entities.model.llm import (
|
||||||
|
LLMModelConfig,
|
||||||
|
LLMResult,
|
||||||
|
LLMResultChunk,
|
||||||
|
LLMUsage,
|
||||||
|
)
|
||||||
|
from dify_plugin.entities.model.message import (
|
||||||
|
AssistantPromptMessage,
|
||||||
|
PromptMessage,
|
||||||
|
PromptMessageContentType,
|
||||||
|
PromptMessageRole,
|
||||||
|
SystemPromptMessage,
|
||||||
|
ToolPromptMessage,
|
||||||
|
UserPromptMessage,
|
||||||
|
)
|
||||||
|
from dify_plugin.entities.tool import LogMetadata, ToolInvokeMessage, ToolProviderType
|
||||||
|
from dify_plugin.interfaces.agent import AgentModelConfig, AgentStrategy, ToolEntity
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
|
||||||
|
|
||||||
|
class FunctionCallingParams(BaseModel):
|
||||||
|
query: str
|
||||||
|
instruction: str | None
|
||||||
|
model: AgentModelConfig
|
||||||
|
tools: list[ToolEntity] | None
|
||||||
|
maximum_iterations: int = 3
|
||||||
|
|
||||||
|
|
||||||
|
class ToolInvokeMeta(BaseModel):
|
||||||
|
"""
|
||||||
|
Tool invoke meta
|
||||||
|
"""
|
||||||
|
|
||||||
|
time_cost: float = Field(..., description="The time cost of the tool invoke")
|
||||||
|
error: Optional[str] = None
|
||||||
|
tool_config: Optional[dict] = None
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def empty(cls) -> "ToolInvokeMeta":
|
||||||
|
"""
|
||||||
|
Get an empty instance of ToolInvokeMeta
|
||||||
|
"""
|
||||||
|
return cls(time_cost=0.0, error=None, tool_config={})
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def error_instance(cls, error: str) -> "ToolInvokeMeta":
|
||||||
|
"""
|
||||||
|
Get an instance of ToolInvokeMeta with error
|
||||||
|
"""
|
||||||
|
return cls(time_cost=0.0, error=error, tool_config={})
|
||||||
|
|
||||||
|
def to_dict(self) -> dict:
|
||||||
|
return {
|
||||||
|
"time_cost": self.time_cost,
|
||||||
|
"error": self.error,
|
||||||
|
"tool_config": self.tool_config,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class FunctionCallingAgentStrategy(AgentStrategy):
|
||||||
|
def __init__(self, session):
|
||||||
|
super().__init__(session)
|
||||||
|
self.query = ""
|
||||||
|
|
||||||
|
def _invoke(self, parameters: dict[str, Any]) -> Generator[AgentInvokeMessage]:
|
||||||
|
"""
|
||||||
|
Run FunctionCall agent application
|
||||||
|
"""
|
||||||
|
fc_params = FunctionCallingParams(**parameters)
|
||||||
|
query = fc_params.query
|
||||||
|
self.query = query
|
||||||
|
instruction = fc_params.instruction
|
||||||
|
init_prompt_messages = [
|
||||||
|
PromptMessage(role=PromptMessageRole.SYSTEM, content=instruction)
|
||||||
|
]
|
||||||
|
tools = fc_params.tools
|
||||||
|
tool_instances = {tool.identity.name: tool for tool in tools} if tools else {}
|
||||||
|
model = fc_params.model
|
||||||
|
stop = (
|
||||||
|
fc_params.model.completion_params.get("stop", [])
|
||||||
|
if fc_params.model.completion_params
|
||||||
|
else []
|
||||||
|
)
|
||||||
|
# convert tools into ModelRuntime Tool format
|
||||||
|
prompt_messages_tools = self._init_prompt_tools(tools)
|
||||||
|
|
||||||
|
iteration_step = 1
|
||||||
|
max_iteration_steps = fc_params.maximum_iterations
|
||||||
|
current_thoughts: list[PromptMessage] = []
|
||||||
|
# continue to run until there is not any tool call
|
||||||
|
function_call_state = True
|
||||||
|
llm_usage: dict[str, Optional[LLMUsage]] = {"usage": None}
|
||||||
|
final_answer = ""
|
||||||
|
|
||||||
|
while function_call_state and iteration_step <= max_iteration_steps:
|
||||||
|
function_call_state = False
|
||||||
|
round_started_at = time.perf_counter()
|
||||||
|
round_log = self.create_log_message(
|
||||||
|
label=f"ROUND {iteration_step}",
|
||||||
|
data={},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: round_started_at,
|
||||||
|
},
|
||||||
|
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||||
|
)
|
||||||
|
yield round_log
|
||||||
|
if iteration_step == max_iteration_steps:
|
||||||
|
# the last iteration, remove all tools
|
||||||
|
prompt_messages_tools = []
|
||||||
|
|
||||||
|
# recalc llm max tokens
|
||||||
|
prompt_messages = self._organize_prompt_messages(
|
||||||
|
history_prompt_messages=init_prompt_messages,
|
||||||
|
current_thoughts=current_thoughts,
|
||||||
|
)
|
||||||
|
if model.completion_params:
|
||||||
|
self.recalc_llm_max_tokens(
|
||||||
|
model.entity, prompt_messages, model.completion_params
|
||||||
|
)
|
||||||
|
# invoke model
|
||||||
|
model_started_at = time.perf_counter()
|
||||||
|
model_log = self.create_log_message(
|
||||||
|
label=f"{model.model} Thought",
|
||||||
|
data={},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: model_started_at,
|
||||||
|
LogMetadata.PROVIDER: model.provider,
|
||||||
|
},
|
||||||
|
parent=round_log,
|
||||||
|
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||||
|
)
|
||||||
|
yield model_log
|
||||||
|
chunks: Generator[LLMResultChunk, None, None] | LLMResult = (
|
||||||
|
self.session.model.llm.invoke(
|
||||||
|
model_config=LLMModelConfig(**model.model_dump(mode="json")),
|
||||||
|
prompt_messages=prompt_messages,
|
||||||
|
stream=True,
|
||||||
|
stop=stop,
|
||||||
|
tools=prompt_messages_tools,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
tool_calls: list[tuple[str, str, dict[str, Any]]] = []
|
||||||
|
|
||||||
|
# save full response
|
||||||
|
response = ""
|
||||||
|
|
||||||
|
# save tool call names and inputs
|
||||||
|
tool_call_names = ""
|
||||||
|
|
||||||
|
current_llm_usage = None
|
||||||
|
|
||||||
|
if isinstance(chunks, Generator):
|
||||||
|
for chunk in chunks:
|
||||||
|
# check if there is any tool call
|
||||||
|
if self.check_tool_calls(chunk):
|
||||||
|
function_call_state = True
|
||||||
|
tool_calls.extend(self.extract_tool_calls(chunk) or [])
|
||||||
|
tool_call_names = ";".join(
|
||||||
|
[tool_call[1] for tool_call in tool_calls]
|
||||||
|
)
|
||||||
|
|
||||||
|
if chunk.delta.message and chunk.delta.message.content:
|
||||||
|
if isinstance(chunk.delta.message.content, list):
|
||||||
|
for content in chunk.delta.message.content:
|
||||||
|
response += content.data
|
||||||
|
if (
|
||||||
|
not function_call_state
|
||||||
|
or iteration_step == max_iteration_steps
|
||||||
|
):
|
||||||
|
yield self.create_text_message(content.data)
|
||||||
|
else:
|
||||||
|
response += str(chunk.delta.message.content)
|
||||||
|
if (
|
||||||
|
not function_call_state
|
||||||
|
or iteration_step == max_iteration_steps
|
||||||
|
):
|
||||||
|
yield self.create_text_message(
|
||||||
|
str(chunk.delta.message.content)
|
||||||
|
)
|
||||||
|
|
||||||
|
if chunk.delta.usage:
|
||||||
|
self.increase_usage(llm_usage, chunk.delta.usage)
|
||||||
|
current_llm_usage = chunk.delta.usage
|
||||||
|
|
||||||
|
else:
|
||||||
|
result = chunks
|
||||||
|
# check if there is any tool call
|
||||||
|
if self.check_blocking_tool_calls(result):
|
||||||
|
function_call_state = True
|
||||||
|
tool_calls.extend(self.extract_blocking_tool_calls(result) or [])
|
||||||
|
tool_call_names = ";".join(
|
||||||
|
[tool_call[1] for tool_call in tool_calls]
|
||||||
|
)
|
||||||
|
|
||||||
|
if result.usage:
|
||||||
|
self.increase_usage(llm_usage, result.usage)
|
||||||
|
current_llm_usage = result.usage
|
||||||
|
|
||||||
|
if result.message and result.message.content:
|
||||||
|
if isinstance(result.message.content, list):
|
||||||
|
for content in result.message.content:
|
||||||
|
response += content.data
|
||||||
|
else:
|
||||||
|
response += str(result.message.content)
|
||||||
|
|
||||||
|
if not result.message.content:
|
||||||
|
result.message.content = ""
|
||||||
|
yield self.finish_log_message(
|
||||||
|
log=model_log,
|
||||||
|
data={
|
||||||
|
"output": response,
|
||||||
|
"tool_name": tool_call_names,
|
||||||
|
"tool_input": {
|
||||||
|
tool_call[1]: tool_call[2] for tool_call in tool_calls
|
||||||
|
},
|
||||||
|
},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: model_started_at,
|
||||||
|
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||||
|
LogMetadata.ELAPSED_TIME: time.perf_counter() - model_started_at,
|
||||||
|
LogMetadata.PROVIDER: model.provider,
|
||||||
|
LogMetadata.TOTAL_PRICE: current_llm_usage.total_price
|
||||||
|
if current_llm_usage
|
||||||
|
else 0,
|
||||||
|
LogMetadata.CURRENCY: current_llm_usage.currency
|
||||||
|
if current_llm_usage
|
||||||
|
else "",
|
||||||
|
LogMetadata.TOTAL_TOKENS: current_llm_usage.total_tokens
|
||||||
|
if current_llm_usage
|
||||||
|
else 0,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
assistant_message = AssistantPromptMessage(content="", tool_calls=[])
|
||||||
|
if tool_calls:
|
||||||
|
assistant_message.tool_calls = [
|
||||||
|
AssistantPromptMessage.ToolCall(
|
||||||
|
id=tool_call[0],
|
||||||
|
type="function",
|
||||||
|
function=AssistantPromptMessage.ToolCall.ToolCallFunction(
|
||||||
|
name=tool_call[1],
|
||||||
|
arguments=json.dumps(tool_call[2], ensure_ascii=False),
|
||||||
|
),
|
||||||
|
)
|
||||||
|
for tool_call in tool_calls
|
||||||
|
]
|
||||||
|
else:
|
||||||
|
assistant_message.content = response
|
||||||
|
|
||||||
|
current_thoughts.append(assistant_message)
|
||||||
|
|
||||||
|
final_answer += response + "\n"
|
||||||
|
|
||||||
|
# call tools
|
||||||
|
tool_responses = []
|
||||||
|
for tool_call_id, tool_call_name, tool_call_args in tool_calls:
|
||||||
|
tool_instance = tool_instances[tool_call_name]
|
||||||
|
tool_call_started_at = time.perf_counter()
|
||||||
|
tool_call_log = self.create_log_message(
|
||||||
|
label=f"CALL {tool_call_name}",
|
||||||
|
data={},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: time.perf_counter(),
|
||||||
|
LogMetadata.PROVIDER: tool_instance.identity.provider,
|
||||||
|
},
|
||||||
|
parent=round_log,
|
||||||
|
status=ToolInvokeMessage.LogMessage.LogStatus.START,
|
||||||
|
)
|
||||||
|
yield tool_call_log
|
||||||
|
if not tool_instance:
|
||||||
|
tool_response = {
|
||||||
|
"tool_call_id": tool_call_id,
|
||||||
|
"tool_call_name": tool_call_name,
|
||||||
|
"tool_response": f"there is not a tool named {tool_call_name}",
|
||||||
|
"meta": ToolInvokeMeta.error_instance(
|
||||||
|
f"there is not a tool named {tool_call_name}"
|
||||||
|
).to_dict(),
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
# invoke tool
|
||||||
|
try:
|
||||||
|
tool_invoke_responses = self.session.tool.invoke(
|
||||||
|
provider_type=ToolProviderType(tool_instance.provider_type),
|
||||||
|
provider=tool_instance.identity.provider,
|
||||||
|
tool_name=tool_instance.identity.name,
|
||||||
|
parameters={
|
||||||
|
**tool_instance.runtime_parameters,
|
||||||
|
**tool_call_args,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
result = ""
|
||||||
|
for response in tool_invoke_responses:
|
||||||
|
if response.type == ToolInvokeMessage.MessageType.TEXT:
|
||||||
|
result += cast(
|
||||||
|
ToolInvokeMessage.TextMessage, response.message
|
||||||
|
).text
|
||||||
|
elif response.type == ToolInvokeMessage.MessageType.LINK:
|
||||||
|
result += (
|
||||||
|
f"result link: {cast(ToolInvokeMessage.TextMessage, response.message).text}."
|
||||||
|
+ " please tell user to check it."
|
||||||
|
)
|
||||||
|
elif response.type in {
|
||||||
|
ToolInvokeMessage.MessageType.IMAGE_LINK,
|
||||||
|
ToolInvokeMessage.MessageType.IMAGE,
|
||||||
|
}:
|
||||||
|
result += (
|
||||||
|
"image has been created and sent to user already, "
|
||||||
|
+ "you do not need to create it, just tell the user to check it now."
|
||||||
|
)
|
||||||
|
elif response.type == ToolInvokeMessage.MessageType.JSON:
|
||||||
|
text = json.dumps(
|
||||||
|
cast(
|
||||||
|
ToolInvokeMessage.JsonMessage, response.message
|
||||||
|
).json_object,
|
||||||
|
ensure_ascii=False,
|
||||||
|
)
|
||||||
|
result += f"tool response: {text}."
|
||||||
|
else:
|
||||||
|
result += f"tool response: {response.message!r}."
|
||||||
|
except Exception as e:
|
||||||
|
result = f"tool invoke error: {str(e)}"
|
||||||
|
tool_response = {
|
||||||
|
"tool_call_id": tool_call_id,
|
||||||
|
"tool_call_name": tool_call_name,
|
||||||
|
"tool_call_input": {
|
||||||
|
**tool_instance.runtime_parameters,
|
||||||
|
**tool_call_args,
|
||||||
|
},
|
||||||
|
"tool_response": result,
|
||||||
|
}
|
||||||
|
|
||||||
|
yield self.finish_log_message(
|
||||||
|
log=tool_call_log,
|
||||||
|
data={
|
||||||
|
"output": tool_response,
|
||||||
|
},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: tool_call_started_at,
|
||||||
|
LogMetadata.PROVIDER: tool_instance.identity.provider,
|
||||||
|
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||||
|
LogMetadata.ELAPSED_TIME: time.perf_counter()
|
||||||
|
- tool_call_started_at,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
tool_responses.append(tool_response)
|
||||||
|
if tool_response["tool_response"] is not None:
|
||||||
|
current_thoughts.append(
|
||||||
|
ToolPromptMessage(
|
||||||
|
content=str(tool_response["tool_response"]),
|
||||||
|
tool_call_id=tool_call_id,
|
||||||
|
name=tool_call_name,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
# update prompt tool
|
||||||
|
for prompt_tool in prompt_messages_tools:
|
||||||
|
self.update_prompt_message_tool(
|
||||||
|
tool_instances[prompt_tool.name], prompt_tool
|
||||||
|
)
|
||||||
|
yield self.finish_log_message(
|
||||||
|
log=round_log,
|
||||||
|
data={
|
||||||
|
"output": {
|
||||||
|
"llm_response": response,
|
||||||
|
"tool_responses": tool_responses,
|
||||||
|
},
|
||||||
|
},
|
||||||
|
metadata={
|
||||||
|
LogMetadata.STARTED_AT: round_started_at,
|
||||||
|
LogMetadata.FINISHED_AT: time.perf_counter(),
|
||||||
|
LogMetadata.ELAPSED_TIME: time.perf_counter() - round_started_at,
|
||||||
|
LogMetadata.TOTAL_PRICE: current_llm_usage.total_price
|
||||||
|
if current_llm_usage
|
||||||
|
else 0,
|
||||||
|
LogMetadata.CURRENCY: current_llm_usage.currency
|
||||||
|
if current_llm_usage
|
||||||
|
else "",
|
||||||
|
LogMetadata.TOTAL_TOKENS: current_llm_usage.total_tokens
|
||||||
|
if current_llm_usage
|
||||||
|
else 0,
|
||||||
|
},
|
||||||
|
)
|
||||||
|
iteration_step += 1
|
||||||
|
|
||||||
|
yield self.create_json_message(
|
||||||
|
{
|
||||||
|
"execution_metadata": {
|
||||||
|
LogMetadata.TOTAL_PRICE: llm_usage["usage"].total_price
|
||||||
|
if llm_usage["usage"] is not None
|
||||||
|
else 0,
|
||||||
|
LogMetadata.CURRENCY: llm_usage["usage"].currency
|
||||||
|
if llm_usage["usage"] is not None
|
||||||
|
else "",
|
||||||
|
LogMetadata.TOTAL_TOKENS: llm_usage["usage"].total_tokens
|
||||||
|
if llm_usage["usage"] is not None
|
||||||
|
else 0,
|
||||||
|
}
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
|
||||||
|
"""
|
||||||
|
Check if there is any tool call in llm result chunk
|
||||||
|
"""
|
||||||
|
return bool(llm_result_chunk.delta.message.tool_calls)
|
||||||
|
|
||||||
|
def check_blocking_tool_calls(self, llm_result: LLMResult) -> bool:
|
||||||
|
"""
|
||||||
|
Check if there is any blocking tool call in llm result
|
||||||
|
"""
|
||||||
|
return bool(llm_result.message.tool_calls)
|
||||||
|
|
||||||
|
def extract_tool_calls(
|
||||||
|
self, llm_result_chunk: LLMResultChunk
|
||||||
|
) -> list[tuple[str, str, dict[str, Any]]]:
|
||||||
|
"""
|
||||||
|
Extract tool calls from llm result chunk
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
|
||||||
|
"""
|
||||||
|
tool_calls = []
|
||||||
|
for prompt_message in llm_result_chunk.delta.message.tool_calls:
|
||||||
|
args = {}
|
||||||
|
if prompt_message.function.arguments != "":
|
||||||
|
args = json.loads(prompt_message.function.arguments)
|
||||||
|
|
||||||
|
tool_calls.append(
|
||||||
|
(
|
||||||
|
prompt_message.id,
|
||||||
|
prompt_message.function.name,
|
||||||
|
args,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
return tool_calls
|
||||||
|
|
||||||
|
def extract_blocking_tool_calls(
|
||||||
|
self, llm_result: LLMResult
|
||||||
|
) -> list[tuple[str, str, dict[str, Any]]]:
|
||||||
|
"""
|
||||||
|
Extract blocking tool calls from llm result
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
|
||||||
|
"""
|
||||||
|
tool_calls = []
|
||||||
|
for prompt_message in llm_result.message.tool_calls:
|
||||||
|
args = {}
|
||||||
|
if prompt_message.function.arguments != "":
|
||||||
|
args = json.loads(prompt_message.function.arguments)
|
||||||
|
|
||||||
|
tool_calls.append(
|
||||||
|
(
|
||||||
|
prompt_message.id,
|
||||||
|
prompt_message.function.name,
|
||||||
|
args,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
return tool_calls
|
||||||
|
|
||||||
|
def _init_system_message(
|
||||||
|
self, prompt_template: str, prompt_messages: list[PromptMessage]
|
||||||
|
) -> list[PromptMessage]:
|
||||||
|
"""
|
||||||
|
Initialize system message
|
||||||
|
"""
|
||||||
|
if not prompt_messages and prompt_template:
|
||||||
|
return [
|
||||||
|
SystemPromptMessage(content=prompt_template),
|
||||||
|
]
|
||||||
|
|
||||||
|
if (
|
||||||
|
prompt_messages
|
||||||
|
and not isinstance(prompt_messages[0], SystemPromptMessage)
|
||||||
|
and prompt_template
|
||||||
|
):
|
||||||
|
prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
|
||||||
|
|
||||||
|
return prompt_messages or []
|
||||||
|
|
||||||
|
def _organize_user_query(
|
||||||
|
self, query: str, prompt_messages: list[PromptMessage]
|
||||||
|
) -> list[PromptMessage]:
|
||||||
|
"""
|
||||||
|
Organize user query
|
||||||
|
"""
|
||||||
|
|
||||||
|
prompt_messages.append(UserPromptMessage(content=query))
|
||||||
|
|
||||||
|
return prompt_messages
|
||||||
|
|
||||||
|
def _clear_user_prompt_image_messages(
|
||||||
|
self, prompt_messages: list[PromptMessage]
|
||||||
|
) -> list[PromptMessage]:
|
||||||
|
"""
|
||||||
|
As for now, gpt supports both fc and vision at the first iteration.
|
||||||
|
We need to remove the image messages from the prompt messages at the first iteration.
|
||||||
|
"""
|
||||||
|
prompt_messages = deepcopy(prompt_messages)
|
||||||
|
|
||||||
|
for prompt_message in prompt_messages:
|
||||||
|
if isinstance(prompt_message, UserPromptMessage) and isinstance(
|
||||||
|
prompt_message.content, list
|
||||||
|
):
|
||||||
|
prompt_message.content = "\n".join(
|
||||||
|
[
|
||||||
|
content.data
|
||||||
|
if content.type == PromptMessageContentType.TEXT
|
||||||
|
else "[image]"
|
||||||
|
if content.type == PromptMessageContentType.IMAGE
|
||||||
|
else "[file]"
|
||||||
|
for content in prompt_message.content
|
||||||
|
]
|
||||||
|
)
|
||||||
|
|
||||||
|
return prompt_messages
|
||||||
|
|
||||||
|
def _organize_prompt_messages(
|
||||||
|
self,
|
||||||
|
current_thoughts: list[PromptMessage],
|
||||||
|
history_prompt_messages: list[PromptMessage],
|
||||||
|
) -> list[PromptMessage]:
|
||||||
|
prompt_template = ""
|
||||||
|
history_prompt_messages = self._init_system_message(
|
||||||
|
prompt_template, history_prompt_messages
|
||||||
|
)
|
||||||
|
query_prompt_messages = self._organize_user_query(self.query or "", [])
|
||||||
|
|
||||||
|
prompt_messages = [
|
||||||
|
*history_prompt_messages,
|
||||||
|
*query_prompt_messages,
|
||||||
|
*current_thoughts,
|
||||||
|
]
|
||||||
|
if len(current_thoughts) != 0:
|
||||||
|
# clear messages after the first iteration
|
||||||
|
prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
|
||||||
|
return prompt_messages
|
||||||
@ -0,0 +1,58 @@
|
|||||||
|
identity:
|
||||||
|
name: function_calling
|
||||||
|
author: Dify
|
||||||
|
label:
|
||||||
|
en_US: FunctionCalling
|
||||||
|
zh_Hans: FunctionCalling
|
||||||
|
pt_BR: FunctionCalling
|
||||||
|
description:
|
||||||
|
en_US: Function Calling is a basic strategy for agent, model will use the tools provided to perform the task.
|
||||||
|
zh_Hans: Function Calling 是一个基本的 Agent 策略，模型将使用提供的工具来执行任务。
|
||||||
|
pt_BR: Function Calling is a basic strategy for agent, model will use the tools provided to perform the task.
|
||||||
|
parameters:
|
||||||
|
- name: model
|
||||||
|
type: model-selector
|
||||||
|
scope: tool-call&llm
|
||||||
|
required: true
|
||||||
|
label:
|
||||||
|
en_US: Model
|
||||||
|
zh_Hans: 模型
|
||||||
|
pt_BR: Model
|
||||||
|
- name: tools
|
||||||
|
type: array[tools]
|
||||||
|
required: true
|
||||||
|
label:
|
||||||
|
en_US: Tool list
|
||||||
|
zh_Hans: 工具列表
|
||||||
|
pt_BR: Tool list
|
||||||
|
- name: instruction
|
||||||
|
type: string
|
||||||
|
required: true
|
||||||
|
label:
|
||||||
|
en_US: Instruction
|
||||||
|
zh_Hans: 指令
|
||||||
|
pt_BR: Instruction
|
||||||
|
auto_generate:
|
||||||
|
type: prompt_instruction
|
||||||
|
template:
|
||||||
|
enabled: true
|
||||||
|
- name: query
|
||||||
|
type: string
|
||||||
|
required: true
|
||||||
|
label:
|
||||||
|
en_US: Query
|
||||||
|
zh_Hans: 查询
|
||||||
|
pt_BR: Query
|
||||||
|
- name: maximum_iterations
|
||||||
|
type: number
|
||||||
|
required: true
|
||||||
|
label:
|
||||||
|
en_US: Maximum Iterations
|
||||||
|
zh_Hans: 最大迭代次数
|
||||||
|
pt_BR: Maximum Iterations
|
||||||
|
default: 3
|
||||||
|
max: 30
|
||||||
|
min: 1
|
||||||
|
extra:
|
||||||
|
python:
|
||||||
|
source: strategies/function_calling.py
|
||||||
@ -0,0 +1,4 @@
|
|||||||
|
INSTALL_METHOD=remote
|
||||||
|
REMOTE_INSTALL_HOST=debug-plugin.dify.dev
|
||||||
|
REMOTE_INSTALL_PORT=5003
|
||||||
|
REMOTE_INSTALL_KEY=********-****-****-****-************
|
||||||
@ -0,0 +1,7 @@
|
|||||||
|
# Overview
|
||||||
|
DeepSeek provides advanced AI capabilities for chats and completions. This plugin enables developers to integrate DeepSeek's models, including text generation (deepseek-chat and deepseek-code) via the API.
|
||||||
|
|
||||||
|
# Configure
|
||||||
|
After installation, you need to get API keys from [Deepseek](https://platform.deepseek.com/api_keys) and setup in Settings -> Model Provider.
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 66 KiB |
@ -0,0 +1,22 @@
|
|||||||
|
<svg width="195.000000" height="41.359375" viewBox="0 0 195 41.3594" fill="none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
|
||||||
|
<desc>
|
||||||
|
Created with Pixso.
|
||||||
|
</desc>
|
||||||
|
<defs>
|
||||||
|
<clipPath id="clip30_2029">
|
||||||
|
<rect id="_图层_1" width="134.577469" height="25.511124" transform="translate(60.422485 10.022217)" fill="white"/>
|
||||||
|
</clipPath>
|
||||||
|
</defs>
|
||||||
|
<g clip-path="url(#clip30_2029)">
|
||||||
|
<path id="path" d="M119.508 30.113L117.562 30.113L117.562 27.0967L119.508 27.0967C120.713 27.0967 121.931 26.7961 122.715 25.9614C123.5 25.1265 123.796 23.8464 123.796 22.5664C123.796 21.2864 123.512 20.0063 122.715 19.1716C121.919 18.3369 120.713 18.0364 119.508 18.0364C118.302 18.0364 117.085 18.3369 116.3 19.1716C115.515 20.0063 115.219 21.2864 115.219 22.5664L115.219 34.9551L111.806 34.9551L111.806 15.031L115.219 15.031L115.219 16.2998L115.845 16.2998C115.913 16.2219 115.981 16.1553 116.049 16.0884C116.903 15.3093 118.211 15.031 119.496 15.031C121.51 15.031 123.523 15.532 124.843 16.9233C126.162 18.3145 126.629 20.4517 126.629 22.5776C126.629 24.7036 126.151 26.8296 124.843 28.2319C123.535 29.6345 121.51 30.113 119.508 30.113Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||||
|
<path id="path" d="M67.5664 15.5654L69.5117 15.5654L69.5117 18.5818L67.5664 18.5818C66.3606 18.5818 65.1434 18.8823 64.3585 19.717C63.5736 20.552 63.2778 21.832 63.2778 23.1121C63.2778 24.3921 63.5623 25.6721 64.3585 26.5068C65.1548 27.3418 66.3606 27.6423 67.5664 27.6423C68.7722 27.6423 69.9895 27.3418 70.7744 26.5068C71.5593 25.6721 71.8551 24.3921 71.8551 23.1121L71.8551 10.7124L75.2677 10.7124L75.2677 30.6475L71.8551 30.6475L71.8551 29.3787L71.2294 29.3787C71.1611 29.4565 71.0929 29.5234 71.0247 29.5901C70.1715 30.3691 68.8633 30.6475 67.5779 30.6475C65.5643 30.6475 63.5509 30.1467 62.2313 28.7554C60.9117 27.364 60.4453 25.2268 60.4453 23.1008C60.4453 20.9749 60.9231 18.8489 62.2313 17.4465C63.5509 16.0552 65.5643 15.5654 67.5664 15.5654Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||||
|
<path id="path" d="M92.3881 22.845L92.3881 24.0581L83.299 24.0581L83.299 21.6428L89.328 21.6428C89.1914 20.7634 88.8729 19.9397 88.3042 19.3386C87.4851 18.4705 86.2224 18.1589 84.9711 18.1589C83.7198 18.1589 82.4572 18.4705 81.6381 19.3386C80.819 20.2068 80.5232 21.5315 80.5232 22.845C80.5232 24.1582 80.819 25.4939 81.6381 26.3511C82.4572 27.208 83.7198 27.531 84.9711 27.531C86.2224 27.531 87.4851 27.2192 88.3042 26.3511C88.418 26.2285 88.5203 26.095 88.6227 25.9614L91.9899 25.9614C91.6941 27.0078 91.2277 27.9539 90.5225 28.6885C89.1573 30.1243 87.0529 30.6475 84.9711 30.6475C82.8894 30.6475 80.7849 30.1355 79.4198 28.6885C78.0547 27.2415 77.5542 25.0376 77.5542 22.845C77.5542 20.6521 78.0433 18.437 79.4198 17.0012C80.7963 15.5654 82.8894 15.0422 84.9711 15.0422C87.0529 15.0422 89.1573 15.5542 90.5225 17.0012C91.8988 18.4482 92.3881 20.6521 92.3881 22.845Z" fill-rule="nonzero" fill="#4D6BFE"/>
|
||||||
|
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|
</g>
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|
||||||
|
</svg>
|
||||||
|
After Width: | Height: | Size: 10 KiB |
@ -0,0 +1,3 @@
|
|||||||
|
<svg width="60" height="50" viewBox="0 0 60 50" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" xml:space="preserve" xmlns:serif="http://www.serif.com/" style="fill-rule:evenodd;clip-rule:evenodd;stroke-linejoin:round;stroke-miterlimit:2;">
|
||||||
|
<path id="path" d="M55.613,3.471C55.018,3.179 54.761,3.735 54.413,4.018C54.294,4.109 54.193,4.227 54.092,4.337C53.222,5.266 52.206,5.876 50.878,5.803C48.936,5.694 47.278,6.305 45.813,7.79C45.501,5.959 44.466,4.865 42.891,4.164C42.067,3.799 41.233,3.435 40.656,2.642C40.253,2.077 40.143,1.448 39.942,0.829C39.814,0.455 39.685,0.073 39.255,0.009C38.788,-0.064 38.605,0.328 38.421,0.656C37.689,1.995 37.405,3.471 37.432,4.965C37.496,8.327 38.916,11.006 41.737,12.91C42.058,13.129 42.14,13.347 42.039,13.666C41.847,14.323 41.618,14.96 41.416,15.616C41.288,16.035 41.096,16.127 40.647,15.944C39.099,15.297 37.762,14.341 36.58,13.184C34.575,11.243 32.761,9.102 30.499,7.425C29.968,7.033 29.436,6.669 28.887,6.323C26.579,4.082 29.189,2.241 29.794,2.022C30.425,1.795 30.013,1.011 27.971,1.02C25.928,1.029 24.06,1.713 21.679,2.624C21.331,2.761 20.964,2.861 20.589,2.943C18.427,2.533 16.183,2.441 13.838,2.706C9.424,3.198 5.898,5.284 3.306,8.847C0.191,13.129 -0.541,17.994 0.356,23.069C1.3,28.417 4.029,32.845 8.224,36.308C12.575,39.897 17.584,41.656 23.3,41.319C26.771,41.118 30.636,40.654 34.996,36.964C36.095,37.51 37.249,37.729 39.163,37.893C40.638,38.03 42.058,37.82 43.157,37.592C44.878,37.228 44.759,35.633 44.137,35.342C39.09,32.991 40.198,33.948 39.191,33.173C41.755,30.139 45.62,26.987 47.132,16.773C47.251,15.962 47.15,15.452 47.132,14.796C47.122,14.395 47.214,14.241 47.672,14.195C48.936,14.049 50.163,13.703 51.29,13.083C54.56,11.298 55.878,8.364 56.19,4.847C56.236,4.309 56.181,3.753 55.613,3.471ZM27.119,35.123C22.228,31.278 19.856,30.012 18.876,30.066C17.96,30.121 18.125,31.169 18.326,31.852C18.537,32.526 18.812,32.991 19.196,33.583C19.462,33.975 19.645,34.558 18.931,34.996C17.356,35.971 14.617,34.667 14.489,34.604C11.301,32.727 8.636,30.249 6.758,26.859C4.945,23.597 3.892,20.099 3.718,16.363C3.672,15.461 3.938,15.142 4.835,14.979C6.017,14.76 7.235,14.714 8.416,14.887C13.408,15.616 17.658,17.848 21.221,21.384C23.254,23.397 24.793,25.802 26.377,28.153C28.062,30.649 29.876,33.028 32.184,34.977C32.999,35.661 33.649,36.18 34.272,36.563C32.395,36.772 29.262,36.818 27.119,35.123ZM29.464,20.044C29.464,19.643 29.784,19.325 30.187,19.325C30.279,19.325 30.361,19.343 30.435,19.37C30.535,19.407 30.627,19.461 30.7,19.543C30.828,19.671 30.902,19.853 30.902,20.044C30.902,20.445 30.581,20.764 30.178,20.764C29.775,20.764 29.464,20.445 29.464,20.044ZM36.745,23.78C36.278,23.971 35.811,24.135 35.362,24.153C34.666,24.19 33.906,23.907 33.494,23.561C32.853,23.023 32.395,22.723 32.202,21.784C32.12,21.384 32.166,20.764 32.239,20.409C32.404,19.643 32.221,19.151 31.68,18.705C31.241,18.34 30.682,18.24 30.068,18.24C29.839,18.24 29.629,18.14 29.473,18.058C29.216,17.93 29.006,17.612 29.207,17.22C29.271,17.092 29.583,16.783 29.656,16.728C30.49,16.254 31.451,16.409 32.34,16.764C33.164,17.101 33.787,17.721 34.684,18.596C35.6,19.652 35.765,19.944 36.287,20.737C36.7,21.356 37.075,21.994 37.331,22.723C37.487,23.179 37.286,23.552 36.745,23.78Z" style="fill:rgb(77,107,254);fill-rule:nonzero;"/>
|
||||||
|
</svg>
|
||||||
|
After Width: | Height: | Size: 3.2 KiB |
@ -0,0 +1,6 @@
|
|||||||
|
from dify_plugin import Plugin, DifyPluginEnv
|
||||||
|
|
||||||
|
plugin = Plugin(DifyPluginEnv())
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
plugin.run()
|
||||||
@ -0,0 +1,37 @@
|
|||||||
|
author: langgenius
|
||||||
|
created_at: '2024-09-20T00:13:50.29298939-04:00'
|
||||||
|
description:
|
||||||
|
en_US: Models provided by deepseek, such as deepseek-chat、deepseek-coder.
|
||||||
|
zh_Hans: 深度求索提供的模型，例如 deepseek-chat、deepseek-coder 。
|
||||||
|
icon: icon_s_en.svg
|
||||||
|
label:
|
||||||
|
en_US: DeepSeek
|
||||||
|
zh_Hans: 深度求索
|
||||||
|
meta:
|
||||||
|
arch:
|
||||||
|
- amd64
|
||||||
|
- arm64
|
||||||
|
runner:
|
||||||
|
entrypoint: main
|
||||||
|
language: python
|
||||||
|
version: '3.12'
|
||||||
|
version: 0.0.1
|
||||||
|
name: deepseek
|
||||||
|
plugins:
|
||||||
|
models:
|
||||||
|
- provider/deepseek.yaml
|
||||||
|
resource:
|
||||||
|
memory: 268435456
|
||||||
|
permission:
|
||||||
|
model:
|
||||||
|
enabled: true
|
||||||
|
llm: true
|
||||||
|
moderation: false
|
||||||
|
rerank: true
|
||||||
|
speech2text: false
|
||||||
|
text_embedding: true
|
||||||
|
tts: false
|
||||||
|
tool:
|
||||||
|
enabled: true
|
||||||
|
type: plugin
|
||||||
|
version: 0.0.5
|
||||||
@ -0,0 +1,3 @@
|
|||||||
|
- deepseek-chat
|
||||||
|
- deepseek-coder
|
||||||
|
- deepseek-reasoner
|
||||||
@ -0,0 +1,29 @@
|
|||||||
|
model: deepseek-coder
|
||||||
|
label:
|
||||||
|
zh_Hans: deepseek-coder
|
||||||
|
en_US: deepseek-coder
|
||||||
|
model_type: llm
|
||||||
|
features:
|
||||||
|
- agent-thought
|
||||||
|
- tool-call
|
||||||
|
- multi-tool-call
|
||||||
|
- stream-tool-call
|
||||||
|
model_properties:
|
||||||
|
mode: chat
|
||||||
|
context_size: 128000
|
||||||
|
parameter_rules:
|
||||||
|
- name: temperature
|
||||||
|
use_template: temperature
|
||||||
|
min: 0
|
||||||
|
max: 1
|
||||||
|
default: 0.5
|
||||||
|
- name: top_p
|
||||||
|
use_template: top_p
|
||||||
|
min: 0
|
||||||
|
max: 1
|
||||||
|
default: 1
|
||||||
|
- name: max_tokens
|
||||||
|
use_template: max_tokens
|
||||||
|
min: 1
|
||||||
|
max: 4096
|
||||||
|
default: 1024
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
model: deepseek-reasoner
|
||||||
|
label:
|
||||||
|
zh_Hans: deepseek-reasoner
|
||||||
|
en_US: deepseek-reasoner
|
||||||
|
model_type: llm
|
||||||
|
features:
|
||||||
|
- agent-thought
|
||||||
|
model_properties:
|
||||||
|
mode: chat
|
||||||
|
context_size: 128000
|
||||||
|
parameter_rules:
|
||||||
|
- name: max_tokens
|
||||||
|
use_template: max_tokens
|
||||||
|
min: 1
|
||||||
|
max: 8192
|
||||||
|
default: 4096
|
||||||
|
pricing:
|
||||||
|
input: "4"
|
||||||
|
output: "16"
|
||||||
|
unit: "0.000001"
|
||||||
|
currency: RMB
|
||||||
@ -0,0 +1,33 @@
|
|||||||
|
from collections.abc import Generator
|
||||||
|
from typing import Optional, Union
|
||||||
|
from dify_plugin.entities.model.llm import LLMMode, LLMResult
|
||||||
|
from dify_plugin.entities.model.message import PromptMessage, PromptMessageTool
|
||||||
|
from yarl import URL
|
||||||
|
from dify_plugin import OAICompatLargeLanguageModel
|
||||||
|
|
||||||
|
|
||||||
|
class DeepseekLargeLanguageModel(OAICompatLargeLanguageModel):
|
||||||
|
def _invoke(
|
||||||
|
self,
|
||||||
|
model: str,
|
||||||
|
credentials: dict,
|
||||||
|
prompt_messages: list[PromptMessage],
|
||||||
|
model_parameters: dict,
|
||||||
|
tools: Optional[list[PromptMessageTool]] = None,
|
||||||
|
stop: Optional[list[str]] = None,
|
||||||
|
stream: bool = True,
|
||||||
|
user: Optional[str] = None,
|
||||||
|
) -> Union[LLMResult, Generator]:
|
||||||
|
self._add_custom_parameters(credentials)
|
||||||
|
return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
|
||||||
|
|
||||||
|
def validate_credentials(self, model: str, credentials: dict) -> None:
|
||||||
|
self._add_custom_parameters(credentials)
|
||||||
|
super().validate_credentials(model, credentials)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _add_custom_parameters(credentials) -> None:
|
||||||
|
credentials["endpoint_url"] = str(URL(credentials.get("endpoint_url", "https://api.deepseek.com")))
|
||||||
|
credentials["mode"] = LLMMode.CHAT.value
|
||||||
|
credentials["function_calling_type"] = "tool_call"
|
||||||
|
credentials["stream_function_calling"] = "support"
|
||||||
@ -0,0 +1,24 @@
|
|||||||
|
import logging
|
||||||
|
from dify_plugin.entities.model import ModelType
|
||||||
|
from dify_plugin.errors.model import CredentialsValidateFailedError
|
||||||
|
from dify_plugin import ModelProvider
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class DeepSeekProvider(ModelProvider):
|
||||||
|
def validate_provider_credentials(self, credentials: dict) -> None:
|
||||||
|
"""
|
||||||
|
Validate provider credentials
|
||||||
|
if validate failed, raise exception
|
||||||
|
|
||||||
|
:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
model_instance = self.get_model_instance(ModelType.LLM)
|
||||||
|
model_instance.validate_credentials(model="deepseek-chat", credentials=credentials)
|
||||||
|
except CredentialsValidateFailedError as ex:
|
||||||
|
raise ex
|
||||||
|
except Exception as ex:
|
||||||
|
logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
|
||||||
|
raise ex
|
||||||
@ -0,0 +1,51 @@
|
|||||||
|
background: '#c0cdff'
|
||||||
|
configurate_methods:
|
||||||
|
- predefined-model
|
||||||
|
description:
|
||||||
|
en_US: Models provided by deepseek, such as deepseek-reasoner、deepseek-chat、deepseek-coder.
|
||||||
|
zh_Hans: 深度求索提供的模型，例如 deepseek-reasoner、deepseek-chat、deepseek-coder 。
|
||||||
|
extra:
|
||||||
|
python:
|
||||||
|
model_sources:
|
||||||
|
- models/llm/llm.py
|
||||||
|
provider_source: provider/deepseek.py
|
||||||
|
help:
|
||||||
|
title:
|
||||||
|
en_US: Get your API Key from deepseek
|
||||||
|
zh_Hans: 从深度求索获取 API Key
|
||||||
|
url:
|
||||||
|
en_US: https://platform.deepseek.com/api_keys
|
||||||
|
icon_large:
|
||||||
|
en_US: icon_l_en.svg
|
||||||
|
icon_small:
|
||||||
|
en_US: icon_s_en.svg
|
||||||
|
label:
|
||||||
|
en_US: deepseek
|
||||||
|
zh_Hans: 深度求索
|
||||||
|
models:
|
||||||
|
llm:
|
||||||
|
position: models/llm/_position.yaml
|
||||||
|
predefined:
|
||||||
|
- models/llm/*.yaml
|
||||||
|
provider: deepseek
|
||||||
|
provider_credential_schema:
|
||||||
|
credential_form_schemas:
|
||||||
|
- label:
|
||||||
|
en_US: API Key
|
||||||
|
placeholder:
|
||||||
|
en_US: Enter your API Key
|
||||||
|
zh_Hans: 在此输入您的 API Key
|
||||||
|
required: true
|
||||||
|
type: secret-input
|
||||||
|
variable: api_key
|
||||||
|
- label:
|
||||||
|
en_US: Custom API endpoint URL
|
||||||
|
zh_Hans: 自定义 API endpoint 地址
|
||||||
|
placeholder:
|
||||||
|
en_US: Base URL, e.g. https://api.deepseek.com/v1 or https://api.deepseek.com
|
||||||
|
zh_Hans: Base URL, e.g. https://api.deepseek.com/v1 or https://api.deepseek.com
|
||||||
|
required: false
|
||||||
|
type: text-input
|
||||||
|
variable: endpoint_url
|
||||||
|
supported_model_types:
|
||||||
|
- llm
|
||||||
@ -0,0 +1 @@
|
|||||||
|
dify_plugin==0.0.1b65
|
||||||
@ -0,0 +1,4 @@
|
|||||||
|
INSTALL_METHOD=remote
|
||||||
|
REMOTE_INSTALL_HOST=debug-plugin.dify.dev
|
||||||
|
REMOTE_INSTALL_PORT=5003
|
||||||
|
REMOTE_INSTALL_KEY=********-****-****-****-************
|
||||||
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 12 KiB |
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 7.7 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 279 KiB |
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|
After Width: | Height: | Size: 190 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 123 KiB |
@ -0,0 +1,6 @@
|
|||||||
|
from dify_plugin import Plugin, DifyPluginEnv
|
||||||
|
|
||||||
|
plugin = Plugin(DifyPluginEnv())
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
plugin.run()
|
||||||
@ -0,0 +1,35 @@
|
|||||||
|
author: langgenius
|
||||||
|
created_at: '2024-09-20T00:13:50.29298939-04:00'
|
||||||
|
description:
|
||||||
|
en_US: Ollama
|
||||||
|
icon: icon_s_en.svg
|
||||||
|
label:
|
||||||
|
en_US: Ollama
|
||||||
|
meta:
|
||||||
|
arch:
|
||||||
|
- amd64
|
||||||
|
- arm64
|
||||||
|
runner:
|
||||||
|
entrypoint: main
|
||||||
|
language: python
|
||||||
|
version: '3.12'
|
||||||
|
version: 0.0.1
|
||||||
|
name: ollama
|
||||||
|
plugins:
|
||||||
|
models:
|
||||||
|
- provider/ollama.yaml
|
||||||
|
resource:
|
||||||
|
memory: 268435456
|
||||||
|
permission:
|
||||||
|
model:
|
||||||
|
enabled: true
|
||||||
|
llm: true
|
||||||
|
moderation: false
|
||||||
|
rerank: true
|
||||||
|
speech2text: false
|
||||||
|
text_embedding: true
|
||||||
|
tts: false
|
||||||
|
tool:
|
||||||
|
enabled: true
|
||||||
|
type: plugin
|
||||||
|
version: 0.0.3
|
||||||
@ -0,0 +1,200 @@
|
|||||||
|
import json
|
||||||
|
import logging
|
||||||
|
import time
|
||||||
|
from decimal import Decimal
|
||||||
|
from typing import Optional
|
||||||
|
from urllib.parse import urljoin
|
||||||
|
from dify_plugin import TextEmbeddingModel
|
||||||
|
import numpy as np
|
||||||
|
import requests
|
||||||
|
from dify_plugin.entities.model import (
|
||||||
|
AIModelEntity,
|
||||||
|
EmbeddingInputType,
|
||||||
|
FetchFrom,
|
||||||
|
I18nObject,
|
||||||
|
ModelPropertyKey,
|
||||||
|
ModelType,
|
||||||
|
PriceConfig,
|
||||||
|
PriceType,
|
||||||
|
)
|
||||||
|
from dify_plugin.entities.model.text_embedding import (
|
||||||
|
EmbeddingUsage,
|
||||||
|
TextEmbeddingResult,
|
||||||
|
)
|
||||||
|
from dify_plugin.errors.model import (
|
||||||
|
CredentialsValidateFailedError,
|
||||||
|
InvokeAuthorizationError,
|
||||||
|
InvokeBadRequestError,
|
||||||
|
InvokeConnectionError,
|
||||||
|
InvokeError,
|
||||||
|
InvokeRateLimitError,
|
||||||
|
InvokeServerUnavailableError,
|
||||||
|
)
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class OllamaEmbeddingModel(TextEmbeddingModel):
|
||||||
|
"""
|
||||||
|
Model class for an Ollama text embedding model.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def _invoke(
|
||||||
|
self,
|
||||||
|
model: str,
|
||||||
|
credentials: dict,
|
||||||
|
texts: list[str],
|
||||||
|
user: Optional[str] = None,
|
||||||
|
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
|
||||||
|
) -> TextEmbeddingResult:
|
||||||
|
"""
|
||||||
|
Invoke text embedding model
|
||||||
|
|
||||||
|
:param model: model name
|
||||||
|
:param credentials: model credentials
|
||||||
|
:param texts: texts to embed
|
||||||
|
:param user: unique user id
|
||||||
|
:param input_type: input type
|
||||||
|
:return: embeddings result
|
||||||
|
"""
|
||||||
|
headers = {"Content-Type": "application/json"}
|
||||||
|
endpoint_url = credentials.get("base_url", "")
|
||||||
|
if endpoint_url and not endpoint_url.endswith("/"):
|
||||||
|
endpoint_url += "/"
|
||||||
|
endpoint_url = urljoin(endpoint_url, "api/embed")
|
||||||
|
context_size = self._get_context_size(model, credentials)
|
||||||
|
inputs = []
|
||||||
|
used_tokens = 0
|
||||||
|
for text in texts:
|
||||||
|
num_tokens = self._get_num_tokens_by_gpt2(text)
|
||||||
|
if num_tokens >= context_size:
|
||||||
|
cutoff = int(np.floor(len(text) * (context_size / num_tokens)))
|
||||||
|
inputs.append(text[0:cutoff])
|
||||||
|
else:
|
||||||
|
inputs.append(text)
|
||||||
|
payload = {"input": inputs, "model": model, "options": {"use_mmap": True}}
|
||||||
|
response = requests.post(
|
||||||
|
endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300)
|
||||||
|
)
|
||||||
|
response.raise_for_status()
|
||||||
|
response_data = response.json()
|
||||||
|
embeddings = response_data["embeddings"]
|
||||||
|
embedding_used_tokens = self.get_num_tokens(model, credentials, inputs)
|
||||||
|
used_tokens += sum(embedding_used_tokens)
|
||||||
|
usage = self._calc_response_usage(
|
||||||
|
model=model, credentials=credentials, tokens=used_tokens
|
||||||
|
)
|
||||||
|
return TextEmbeddingResult(embeddings=embeddings, usage=usage, model=model)
|
||||||
|
|
||||||
|
def get_num_tokens(
|
||||||
|
self, model: str, credentials: dict, texts: list[str]
|
||||||
|
) -> list[int]:
|
||||||
|
"""
|
||||||
|
Approximate number of tokens for given messages using GPT2 tokenizer
|
||||||
|
|
||||||
|
:param model: model name
|
||||||
|
:param credentials: model credentials
|
||||||
|
:param texts: texts to embed
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
return [self._get_num_tokens_by_gpt2(text) for text in texts]
|
||||||
|
|
||||||
|
def validate_credentials(self, model: str, credentials: dict) -> None:
|
||||||
|
"""
|
||||||
|
Validate model credentials
|
||||||
|
|
||||||
|
:param model: model name
|
||||||
|
:param credentials: model credentials
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
self._invoke(model=model, credentials=credentials, texts=["ping"])
|
||||||
|
except InvokeError as ex:
|
||||||
|
raise CredentialsValidateFailedError(
|
||||||
|
f"An error occurred during credentials validation: {ex.description}"
|
||||||
|
)
|
||||||
|
except Exception as ex:
|
||||||
|
raise CredentialsValidateFailedError(
|
||||||
|
f"An error occurred during credentials validation: {str(ex)}"
|
||||||
|
)
|
||||||
|
|
||||||
|
def get_customizable_model_schema(
|
||||||
|
self, model: str, credentials: dict
|
||||||
|
) -> AIModelEntity:
|
||||||
|
"""
|
||||||
|
generate custom model entities from credentials
|
||||||
|
"""
|
||||||
|
entity = AIModelEntity(
|
||||||
|
model=model,
|
||||||
|
label=I18nObject(en_US=model),
|
||||||
|
model_type=ModelType.TEXT_EMBEDDING,
|
||||||
|
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
|
||||||
|
model_properties={
|
||||||
|
ModelPropertyKey.CONTEXT_SIZE: int(
|
||||||
|
credentials.get("context_size", 512)
|
||||||
|
),
|
||||||
|
ModelPropertyKey.MAX_CHUNKS: 1,
|
||||||
|
},
|
||||||
|
parameter_rules=[],
|
||||||
|
pricing=PriceConfig(
|
||||||
|
input=Decimal(credentials.get("input_price", 0)),
|
||||||
|
unit=Decimal(credentials.get("unit", 0)),
|
||||||
|
currency=credentials.get("currency", "USD"),
|
||||||
|
),
|
||||||
|
)
|
||||||
|
return entity
|
||||||
|
|
||||||
|
def _calc_response_usage(
|
||||||
|
self, model: str, credentials: dict, tokens: int
|
||||||
|
) -> EmbeddingUsage:
|
||||||
|
"""
|
||||||
|
Calculate response usage
|
||||||
|
|
||||||
|
:param model: model name
|
||||||
|
:param credentials: model credentials
|
||||||
|
:param tokens: input tokens
|
||||||
|
:return: usage
|
||||||
|
"""
|
||||||
|
input_price_info = self.get_price(
|
||||||
|
model=model,
|
||||||
|
credentials=credentials,
|
||||||
|
price_type=PriceType.INPUT,
|
||||||
|
tokens=tokens,
|
||||||
|
)
|
||||||
|
usage = EmbeddingUsage(
|
||||||
|
tokens=tokens,
|
||||||
|
total_tokens=tokens,
|
||||||
|
unit_price=input_price_info.unit_price,
|
||||||
|
price_unit=input_price_info.unit,
|
||||||
|
total_price=input_price_info.total_amount,
|
||||||
|
currency=input_price_info.currency,
|
||||||
|
latency=time.perf_counter() - self.started_at,
|
||||||
|
)
|
||||||
|
return usage
|
||||||
|
|
||||||
|
@property
|
||||||
|
def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
|
||||||
|
"""
|
||||||
|
Map model invoke error to unified error
|
||||||
|
The key is the error type thrown to the caller
|
||||||
|
The value is the error type thrown by the model,
|
||||||
|
which needs to be converted into a unified error type for the caller.
|
||||||
|
|
||||||
|
:return: Invoke error mapping
|
||||||
|
"""
|
||||||
|
return {
|
||||||
|
InvokeAuthorizationError: [requests.exceptions.InvalidHeader],
|
||||||
|
InvokeBadRequestError: [
|
||||||
|
requests.exceptions.HTTPError,
|
||||||
|
requests.exceptions.InvalidURL,
|
||||||
|
],
|
||||||
|
InvokeRateLimitError: [requests.exceptions.RetryError],
|
||||||
|
InvokeServerUnavailableError: [
|
||||||
|
requests.exceptions.ConnectionError,
|
||||||
|
requests.exceptions.HTTPError,
|
||||||
|
],
|
||||||
|
InvokeConnectionError: [
|
||||||
|
requests.exceptions.ConnectTimeout,
|
||||||
|
requests.exceptions.ReadTimeout,
|
||||||
|
],
|
||||||
|
}
|
||||||
@ -0,0 +1,15 @@
|
|||||||
|
import logging
|
||||||
|
from dify_plugin import ModelProvider
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class OpenAIProvider(ModelProvider):
|
||||||
|
def validate_provider_credentials(self, credentials: dict) -> None:
|
||||||
|
"""
|
||||||
|
Validate provider credentials
|
||||||
|
if validate failed, raise exception
|
||||||
|
|
||||||
|
:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
|
||||||
|
"""
|
||||||
|
pass
|
||||||
@ -0,0 +1,2 @@
|
|||||||
|
dify_plugin==0.0.1b65
|
||||||
|
numpy~=2.1.3
|
||||||
@ -0,0 +1,9 @@
|
|||||||
|
## Overview
|
||||||
|
|
||||||
|
This plugin provides access to models that are OpenAI-compatible, including LLMs, reranking, text embedding, speech-to-text (STT), and text-to-speech(TTS) models. Developers can easily add models by providing configuration parameters such as the model name and API key.
|
||||||
|
|
||||||
|
## Configure
|
||||||
|
|
||||||
|
Configure the OpenAI-API-compatible model by providing its core details (Type, Name, API Key, URL) and adjusting further options like completion, context, and token limits, as well as streaming and vision settings. Save when done.
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
@ -0,0 +1 @@
|
|||||||
|
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|
||||||
|
After Width: | Height: | Size: 4.2 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 156 KiB |
@ -0,0 +1,6 @@
|
|||||||
|
from dify_plugin import Plugin, DifyPluginEnv
|
||||||
|
|
||||||
|
plugin = Plugin(DifyPluginEnv())
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
plugin.run()
|
||||||
@ -0,0 +1,31 @@
|
|||||||
|
version: 0.0.11
|
||||||
|
type: plugin
|
||||||
|
author: "langgenius"
|
||||||
|
name: "openai_api_compatible"
|
||||||
|
description:
|
||||||
|
en_US: Model providers compatible with OpenAI's API standard, such as LM Studio.
|
||||||
|
zh_Hans: 兼容 OpenAI API 的模型供应商，例如 LM Studio 。
|
||||||
|
label:
|
||||||
|
en_US: "OpenAI-API-compatible"
|
||||||
|
created_at: "2024-07-12T08:03:44.658609186Z"
|
||||||
|
icon: icon.svg
|
||||||
|
resource:
|
||||||
|
memory: 1048576
|
||||||
|
permission:
|
||||||
|
tool:
|
||||||
|
enabled: true
|
||||||
|
model:
|
||||||
|
enabled: true
|
||||||
|
llm: true
|
||||||
|
plugins:
|
||||||
|
models:
|
||||||
|
- "provider/openai_api_compatible.yaml"
|
||||||
|
meta:
|
||||||
|
version: 0.0.1
|
||||||
|
arch:
|
||||||
|
- "amd64"
|
||||||
|
- "arm64"
|
||||||
|
runner:
|
||||||
|
language: "python"
|
||||||
|
version: "3.12"
|
||||||
|
entrypoint: "main"
|
||||||
@ -0,0 +1,53 @@
|
|||||||
|
from collections.abc import Mapping
|
||||||
|
|
||||||
|
import openai
|
||||||
|
from httpx import Timeout
|
||||||
|
|
||||||
|
from dify_plugin.errors.model import InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError, InvokeError, InvokeRateLimitError, InvokeServerUnavailableError
|
||||||
|
|
||||||
|
|
||||||
|
class _CommonOpenAI:
|
||||||
|
def _to_credential_kwargs(self, credentials: Mapping) -> dict:
|
||||||
|
"""
|
||||||
|
Transform credentials to kwargs for model instance
|
||||||
|
|
||||||
|
:param credentials:
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
credentials_kwargs = {
|
||||||
|
"api_key": credentials['openai_api_key'],
|
||||||
|
"timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
|
||||||
|
"max_retries": 1,
|
||||||
|
}
|
||||||
|
|
||||||
|
if credentials.get("openai_api_base"):
|
||||||
|
openai_api_base = credentials["openai_api_base"].rstrip("/")
|
||||||
|
credentials_kwargs["base_url"] = openai_api_base + "/v1"
|
||||||
|
|
||||||
|
if 'openai_organization' in credentials:
|
||||||
|
credentials_kwargs['organization'] = credentials['openai_organization']
|
||||||
|
|
||||||
|
return credentials_kwargs
|
||||||
|
|
||||||
|
@property
|
||||||
|
def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
|
||||||
|
"""
|
||||||
|
Map model invoke error to unified error
|
||||||
|
The key is the error type thrown to the caller
|
||||||
|
The value is the error type thrown by the model,
|
||||||
|
which needs to be converted into a unified error type for the caller.
|
||||||
|
|
||||||
|
:return: Invoke error mapping
|
||||||
|
"""
|
||||||
|
return {
|
||||||
|
InvokeConnectionError: [openai.APIConnectionError, openai.APITimeoutError],
|
||||||
|
InvokeServerUnavailableError: [openai.InternalServerError],
|
||||||
|
InvokeRateLimitError: [openai.RateLimitError],
|
||||||
|
InvokeAuthorizationError: [openai.AuthenticationError, openai.PermissionDeniedError],
|
||||||
|
InvokeBadRequestError: [
|
||||||
|
openai.BadRequestError,
|
||||||
|
openai.NotFoundError,
|
||||||
|
openai.UnprocessableEntityError,
|
||||||
|
openai.APIError,
|
||||||
|
],
|
||||||
|
}
|
||||||
@ -0,0 +1,31 @@
|
|||||||
|
from typing import Mapping
|
||||||
|
|
||||||
|
from dify_plugin.entities.model import (
|
||||||
|
AIModelEntity,
|
||||||
|
I18nObject,
|
||||||
|
ModelFeature
|
||||||
|
)
|
||||||
|
|
||||||
|
from dify_plugin.interfaces.model.openai_compatible.llm import (
|
||||||
|
OAICompatLargeLanguageModel,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class OpenAILargeLanguageModel(OAICompatLargeLanguageModel):
|
||||||
|
def get_customizable_model_schema(self, model: str, credentials: Mapping) -> AIModelEntity:
|
||||||
|
entity = super().get_customizable_model_schema(model, credentials)
|
||||||
|
|
||||||
|
agent_though_support = credentials.get("agent_though_support", "not_supported")
|
||||||
|
if agent_though_support == "supported":
|
||||||
|
try:
|
||||||
|
entity.features.index(ModelFeature.AGENT_THOUGHT)
|
||||||
|
except ValueError:
|
||||||
|
entity.features.append(ModelFeature.AGENT_THOUGHT)
|
||||||
|
|
||||||
|
if "display_name" in credentials and credentials["display_name"] != "":
|
||||||
|
entity.label= I18nObject(
|
||||||
|
en_US=credentials["display_name"],
|
||||||
|
zh_Hans=credentials["display_name"]
|
||||||
|
)
|
||||||
|
|
||||||
|
return entity
|
||||||
@ -0,0 +1,50 @@
|
|||||||
|
from typing import Mapping
|
||||||
|
|
||||||
|
from dify_plugin.entities.model import (
|
||||||
|
AIModelEntity,
|
||||||
|
I18nObject
|
||||||
|
)
|
||||||
|
|
||||||
|
from dify_plugin.interfaces.model.openai_compatible.rerank import (
|
||||||
|
OAICompatRerankModel,
|
||||||
|
)
|
||||||
|
from dify_plugin.errors.model import (
|
||||||
|
CredentialsValidateFailedError,
|
||||||
|
)
|
||||||
|
|
||||||
|
class OpenAIRerankModel(OAICompatRerankModel):
|
||||||
|
def validate_credentials(self, model: str, credentials: dict) -> None:
|
||||||
|
"""
|
||||||
|
Validate model credentials
|
||||||
|
|
||||||
|
:param model: model name
|
||||||
|
:param credentials: model credentials
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
self._invoke(
|
||||||
|
model=model,
|
||||||
|
credentials=credentials,
|
||||||
|
query="What is the capital of the United States?",
|
||||||
|
docs=[
|
||||||
|
"Carson City is the capital city of the American state of Nevada. At the 2010 United States "
|
||||||
|
"Census, Carson City had a population of 55,274.",
|
||||||
|
"The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
|
||||||
|
"are a political division controlled by the United States. Its capital is Saipan.",
|
||||||
|
],
|
||||||
|
score_threshold=0.8,
|
||||||
|
top_n=3,
|
||||||
|
)
|
||||||
|
except Exception as ex:
|
||||||
|
raise CredentialsValidateFailedError(str(ex)) from ex
|
||||||
|
|
||||||
|
def get_customizable_model_schema(self, model: str, credentials: Mapping) -> AIModelEntity:
|
||||||
|
entity = super().get_customizable_model_schema(model, credentials)
|
||||||
|
|
||||||
|
if "display_name" in credentials and credentials["display_name"] != "":
|
||||||
|
entity.label= I18nObject(
|
||||||
|
en_US=credentials["display_name"],
|
||||||
|
zh_Hans=credentials["display_name"]
|
||||||
|
)
|
||||||
|
|
||||||
|
return entity
|
||||||
@ -0,0 +1,28 @@
|
|||||||
|
from typing import Optional
|
||||||
|
from dify_plugin.entities.model import AIModelEntity, FetchFrom, I18nObject, ModelType
|
||||||
|
from dify_plugin.interfaces.model.openai_compatible.speech2text import (
|
||||||
|
OAICompatSpeech2TextModel,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class OpenAISpeech2TextModel(OAICompatSpeech2TextModel):
|
||||||
|
def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
|
||||||
|
"""
|
||||||
|
used to define customizable model schema
|
||||||
|
"""
|
||||||
|
entity = AIModelEntity(
|
||||||
|
model=model,
|
||||||
|
label=I18nObject(en_US=model),
|
||||||
|
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
|
||||||
|
model_type=ModelType.SPEECH2TEXT,
|
||||||
|
model_properties={},
|
||||||
|
parameter_rules=[],
|
||||||
|
)
|
||||||
|
|
||||||
|
if "display_name" in credentials and credentials["display_name"] != "":
|
||||||
|
entity.label= I18nObject(
|
||||||
|
en_US=credentials["display_name"],
|
||||||
|
zh_Hans=credentials["display_name"]
|
||||||
|
)
|
||||||
|
|
||||||
|
return entity
|
||||||
@ -0,0 +1,24 @@
|
|||||||
|
from typing import Mapping
|
||||||
|
|
||||||
|
from dify_plugin.entities.model import (
|
||||||
|
AIModelEntity,
|
||||||
|
I18nObject
|
||||||
|
)
|
||||||
|
|
||||||
|
from dify_plugin.interfaces.model.openai_compatible.text_embedding import (
|
||||||
|
OAICompatEmbeddingModel,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class OpenAITextEmbeddingModel(OAICompatEmbeddingModel):
|
||||||
|
|
||||||
|
def get_customizable_model_schema(self, model: str, credentials: Mapping) -> AIModelEntity:
|
||||||
|
entity = super().get_customizable_model_schema(model, credentials)
|
||||||
|
|
||||||
|
if "display_name" in credentials and credentials["display_name"] != "":
|
||||||
|
entity.label= I18nObject(
|
||||||
|
en_US=credentials["display_name"],
|
||||||
|
zh_Hans=credentials["display_name"]
|
||||||
|
)
|
||||||
|
|
||||||
|
return entity
|
||||||
@ -0,0 +1,22 @@
|
|||||||
|
from typing import Mapping
|
||||||
|
|
||||||
|
from dify_plugin.entities.model import (
|
||||||
|
AIModelEntity,
|
||||||
|
I18nObject
|
||||||
|
)
|
||||||
|
|
||||||
|
from dify_plugin.interfaces.model.openai_compatible.tts import OAICompatText2SpeechModel
|
||||||
|
|
||||||
|
|
||||||
|
class OpenAIText2SpeechModel(OAICompatText2SpeechModel):
|
||||||
|
|
||||||
|
def get_customizable_model_schema(self, model: str, credentials: Mapping) -> AIModelEntity:
|
||||||
|
entity = super().get_customizable_model_schema(model, credentials)
|
||||||
|
|
||||||
|
if "display_name" in credentials and credentials["display_name"] != "":
|
||||||
|
entity.label= I18nObject(
|
||||||
|
en_US=credentials["display_name"],
|
||||||
|
zh_Hans=credentials["display_name"]
|
||||||
|
)
|
||||||
|
|
||||||
|
return entity
|
||||||
@ -0,0 +1,17 @@
|
|||||||
|
import logging
|
||||||
|
from collections.abc import Mapping
|
||||||
|
|
||||||
|
from dify_plugin import ModelProvider
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
class OpenAIProvider(ModelProvider):
|
||||||
|
def validate_provider_credentials(self, credentials: Mapping) -> None:
|
||||||
|
"""
|
||||||
|
Validate provider credentials
|
||||||
|
if validate failed, raise exception
|
||||||
|
|
||||||
|
:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
|
||||||
|
"""
|
||||||
|
pass
|
||||||
@ -0,0 +1,240 @@
|
|||||||
|
provider: openai_api_compatible
|
||||||
|
label:
|
||||||
|
en_US: OpenAI-API-compatible
|
||||||
|
description:
|
||||||
|
en_US: Model providers compatible with OpenAI's API standard, such as LM Studio.
|
||||||
|
zh_Hans: 兼容 OpenAI API 的模型供应商，例如 LM Studio 。
|
||||||
|
icon_small:
|
||||||
|
en_US: icon.svg
|
||||||
|
supported_model_types:
|
||||||
|
- llm
|
||||||
|
- rerank
|
||||||
|
- text-embedding
|
||||||
|
- speech2text
|
||||||
|
- tts
|
||||||
|
configurate_methods:
|
||||||
|
- customizable-model
|
||||||
|
model_credential_schema:
|
||||||
|
model:
|
||||||
|
label:
|
||||||
|
en_US: Model Name
|
||||||
|
zh_Hans: 模型名称
|
||||||
|
placeholder:
|
||||||
|
en_US: Enter full model name
|
||||||
|
zh_Hans: 输入模型全称
|
||||||
|
credential_form_schemas:
|
||||||
|
- variable: display_name
|
||||||
|
label:
|
||||||
|
en_US: Model display name
|
||||||
|
zh_Hans: 模型显示名称
|
||||||
|
type: text-input
|
||||||
|
required: false
|
||||||
|
placeholder:
|
||||||
|
zh_Hans: 模型在界面的显示名称
|
||||||
|
en_US: The display name of the model in the interface.
|
||||||
|
- variable: api_key
|
||||||
|
label:
|
||||||
|
en_US: API Key
|
||||||
|
type: secret-input
|
||||||
|
required: false
|
||||||
|
placeholder:
|
||||||
|
zh_Hans: 在此输入您的 API Key
|
||||||
|
en_US: Enter your API Key
|
||||||
|
- variable: endpoint_url
|
||||||
|
label:
|
||||||
|
zh_Hans: API endpoint URL
|
||||||
|
en_US: API endpoint URL
|
||||||
|
type: text-input
|
||||||
|
required: true
|
||||||
|
placeholder:
|
||||||
|
zh_Hans: Base URL, e.g. https://api.openai.com/v1
|
||||||
|
en_US: Base URL, e.g. https://api.openai.com/v1
|
||||||
|
- variable: endpoint_model_name
|
||||||
|
label:
|
||||||
|
zh_Hans: API endpoint中的模型名称
|
||||||
|
en_US: model name for API endpoint
|
||||||
|
type: text-input
|
||||||
|
required: false
|
||||||
|
placeholder:
|
||||||
|
zh_Hans: endpoint model name, e.g. chatgpt4.0
|
||||||
|
en_US: endpoint model name, e.g. chatgpt4.0
|
||||||
|
- variable: mode
|
||||||
|
show_on:
|
||||||
|
- variable: __model_type
|
||||||
|
value: llm
|
||||||
|
label:
|
||||||
|
en_US: Completion mode
|
||||||
|
type: select
|
||||||
|
required: false
|
||||||
|
default: chat
|
||||||
|
placeholder:
|
||||||
|
zh_Hans: 选择对话类型
|
||||||
|
en_US: Select completion mode
|
||||||
|
options:
|
||||||
|
- value: completion
|
||||||
|
label:
|
||||||
|
en_US: Completion
|
||||||
|
zh_Hans: 补全
|
||||||
|
- value: chat
|
||||||
|
label:
|
||||||
|
en_US: Chat
|
||||||
|
zh_Hans: 对话
|
||||||
|
- variable: context_size
|
||||||
|
label:
|
||||||
|
zh_Hans: 模型上下文长度
|
||||||
|
en_US: Model context size
|
||||||
|
required: true
|
||||||
|
show_on:
|
||||||
|
- variable: __model_type
|
||||||
|
value: llm
|
||||||
|
type: text-input
|
||||||
|
default: "4096"
|
||||||
|
placeholder:
|
||||||
|
zh_Hans: 在此输入您的模型上下文长度
|
||||||
|
en_US: Enter your Model context size
|
||||||
|
- variable: context_size
|
||||||
|
label:
|
||||||
|
zh_Hans: 模型上下文长度
|
||||||
|
en_US: Model context size
|
||||||
|
required: true
|
||||||
|
show_on:
|
||||||
|
- variable: __model_type
|
||||||
|
value: text-embedding
|
||||||
|
type: text-input
|
||||||
|
default: "4096"
|
||||||
|
placeholder:
|
||||||
|
zh_Hans: 在此输入您的模型上下文长度
|
||||||
|
en_US: Enter your Model context size
|
||||||
|
- variable: context_size
|
||||||
|
label:
|
||||||
|
zh_Hans: 模型上下文长度
|
||||||
|
en_US: Model context size
|
||||||
|
required: true
|
||||||
|
show_on:
|
||||||
|
- variable: __model_type
|
||||||
|
value: rerank
|
||||||
|
type: text-input
|
||||||
|
default: "4096"
|
||||||
|
placeholder:
|
||||||
|
zh_Hans: 在此输入您的模型上下文长度
|
||||||
|
en_US: Enter your Model context size
|
||||||
|
- variable: max_tokens_to_sample
|
||||||
|
label:
|
||||||
|
zh_Hans: 最大 token 上限
|
||||||
|
en_US: Upper bound for max tokens
|
||||||
|
show_on:
|
||||||
|
- variable: __model_type
|
||||||
|
value: llm
|
||||||
|
default: "4096"
|
||||||
|
type: text-input
|
||||||
|
- variable: agent_though_support
|
||||||
|
show_on:
|
||||||
|
- variable: __model_type
|
||||||
|
value: llm
|
||||||
|
label:
|
||||||
|
en_US: Agent Thought
|
||||||
|
type: select
|
||||||
|
required: false
|
||||||
|
default: not_supported
|
||||||
|
options:
|
||||||
|
- value: supported
|
||||||
|
label:
|
||||||
|
en_US: Support
|
||||||
|
zh_Hans: 支持
|
||||||
|
- value: not_supported
|
||||||
|
label:
|
||||||
|
en_US: Not Support
|
||||||
|
zh_Hans: 不支持
|
||||||
|
- variable: function_calling_type
|
||||||
|
show_on:
|
||||||
|
- variable: __model_type
|
||||||
|
value: llm
|
||||||
|
label:
|
||||||
|
en_US: Function calling
|
||||||
|
type: select
|
||||||
|
required: false
|
||||||
|
default: no_call
|
||||||
|
options:
|
||||||
|
- value: function_call
|
||||||
|
label:
|
||||||
|
en_US: Function Call
|
||||||
|
zh_Hans: Function Call
|
||||||
|
- value: tool_call
|
||||||
|
label:
|
||||||
|
en_US: Tool Call
|
||||||
|
zh_Hans: Tool Call
|
||||||
|
- value: no_call
|
||||||
|
label:
|
||||||
|
en_US: Not Support
|
||||||
|
zh_Hans: 不支持
|
||||||
|
- variable: stream_function_calling
|
||||||
|
show_on:
|
||||||
|
- variable: __model_type
|
||||||
|
value: llm
|
||||||
|
label:
|
||||||
|
en_US: Stream function calling
|
||||||
|
type: select
|
||||||
|
required: false
|
||||||
|
default: not_supported
|
||||||
|
options:
|
||||||
|
- value: supported
|
||||||
|
label:
|
||||||
|
en_US: Support
|
||||||
|
zh_Hans: 支持
|
||||||
|
- value: not_supported
|
||||||
|
label:
|
||||||
|
en_US: Not Support
|
||||||
|
zh_Hans: 不支持
|
||||||
|
- variable: vision_support
|
||||||
|
show_on:
|
||||||
|
- variable: __model_type
|
||||||
|
value: llm
|
||||||
|
label:
|
||||||
|
zh_Hans: Vision 支持
|
||||||
|
en_US: Vision Support
|
||||||
|
type: select
|
||||||
|
required: false
|
||||||
|
default: no_support
|
||||||
|
options:
|
||||||
|
- value: support
|
||||||
|
label:
|
||||||
|
en_US: Support
|
||||||
|
zh_Hans: 支持
|
||||||
|
- value: no_support
|
||||||
|
label:
|
||||||
|
en_US: Not Support
|
||||||
|
zh_Hans: 不支持
|
||||||
|
- variable: stream_mode_delimiter
|
||||||
|
label:
|
||||||
|
zh_Hans: 流模式返回结果的分隔符
|
||||||
|
en_US: Delimiter for streaming results
|
||||||
|
show_on:
|
||||||
|
- variable: __model_type
|
||||||
|
value: llm
|
||||||
|
default: '\n\n'
|
||||||
|
type: text-input
|
||||||
|
- variable: voices
|
||||||
|
show_on:
|
||||||
|
- variable: __model_type
|
||||||
|
value: tts
|
||||||
|
label:
|
||||||
|
en_US: Available Voices (comma-separated)
|
||||||
|
zh_Hans: 可用声音（用英文逗号分隔）
|
||||||
|
type: text-input
|
||||||
|
required: false
|
||||||
|
default: "alloy"
|
||||||
|
placeholder:
|
||||||
|
en_US: "alloy,echo,fable,onyx,nova,shimmer"
|
||||||
|
zh_Hans: "alloy,echo,fable,onyx,nova,shimmer"
|
||||||
|
help:
|
||||||
|
en_US: "List voice names separated by commas. First voice will be used as default."
|
||||||
|
zh_Hans: "用英文逗号分隔的声音列表。第一个声音将作为默认值。"
|
||||||
|
extra:
|
||||||
|
python:
|
||||||
|
provider_source: provider/openai_api_compatible.py
|
||||||
|
model_sources:
|
||||||
|
- "models/llm/llm.py"
|
||||||
|
- "models/text_embedding/text_embedding.py"
|
||||||
|
- "models/rerank/rerank.py"
|
||||||
|
- "models/speech2text/speech2text.py"
|
||||||
|
- "models/tts/tts.py"
|
||||||
@ -0,0 +1,2 @@
|
|||||||
|
dify_plugin==0.0.1b73
|
||||||
|
openai~=1.66.3
|
||||||
@ -0,0 +1,4 @@
|
|||||||
|
INSTALL_METHOD=remote
|
||||||
|
REMOTE_INSTALL_HOST=debug-plugin.dify.dev
|
||||||
|
REMOTE_INSTALL_PORT=5003
|
||||||
|
REMOTE_INSTALL_KEY=********-****-****-****-************
|
||||||
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|
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|
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|
|||||||
|
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|
||||||
|
After Width: | Height: | Size: 1.4 KiB |
@ -0,0 +1,6 @@
|
|||||||
|
from dify_plugin import Plugin, DifyPluginEnv
|
||||||
|
|
||||||
|
plugin = Plugin(DifyPluginEnv())
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
plugin.run()
|
||||||
@ -0,0 +1,44 @@
|
|||||||
|
- Pro/deepseek-ai/DeepSeek-R1
|
||||||
|
- Pro/deepseek-ai/DeepSeek-V3
|
||||||
|
- deepseek-ai/DeepSeek-R1
|
||||||
|
- deepseek-ai/DeepSeek-V3
|
||||||
|
- deepseek-ai/DeepSeek-V2.5
|
||||||
|
- deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
|
||||||
|
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
|
||||||
|
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
|
||||||
|
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
|
||||||
|
- deepseek-ai/DeepSeek-R1-Distill-Llama-70B
|
||||||
|
- deepseek-ai/DeepSeek-R1-Distill-Llama-8B
|
||||||
|
- deepseek-ai/DeepSeek-V2.5
|
||||||
|
- deepseek-ai/Janus-Pro-7B
|
||||||
|
- Qwen/QwQ-32B-Preview
|
||||||
|
- Qwen/QVQ-72B-Preview
|
||||||
|
- Qwen/Qwen2.5-72B-Instruct
|
||||||
|
- Qwen/Qwen2.5-72B-Instruct-128K
|
||||||
|
- Qwen/Qwen2.5-32B-Instruct
|
||||||
|
- Qwen/Qwen2.5-14B-Instruct
|
||||||
|
- Qwen/Qwen2.5-7B-Instruct
|
||||||
|
- Qwen/Qwen2.5-Coder-32B-Instruct
|
||||||
|
- Qwen/Qwen2.5-Coder-7B-Instruct
|
||||||
|
- Qwen/Qwen2-VL-72B-Instruct
|
||||||
|
- Qwen/Qwen2-1.5B-Instruct
|
||||||
|
- Qwen/Qwen2.5-72B-Instruct-128K
|
||||||
|
- Vendor-A/Qwen/Qwen2.5-72B-Instruct
|
||||||
|
- Pro/Qwen/Qwen2-VL-7B-Instruct
|
||||||
|
- OpenGVLab/InternVL2-26B
|
||||||
|
- Pro/OpenGVLab/InternVL2-8B
|
||||||
|
- Vendor-A/Qwen/Qwen2.5-72B-Instruct
|
||||||
|
- Pro/Qwen/Qwen2-VL-7B-Instruct
|
||||||
|
- Pro/OpenGVLab/InternVL2-8B
|
||||||
|
- OpenGVLab/InternVL2-26B
|
||||||
|
- THUDM/glm-4-9b-chat
|
||||||
|
- 01-ai/Yi-1.5-34B-Chat-16K
|
||||||
|
- 01-ai/Yi-1.5-9B-Chat-16K
|
||||||
|
- 01-ai/Yi-1.5-6B-Chat
|
||||||
|
- internlm/internlm2_5-20b-chat
|
||||||
|
- internlm/internlm2_5-7b-chat
|
||||||
|
- meta-llama/Llama-3.3-70B-Instruct
|
||||||
|
- meta-llama/Meta-Llama-3.1-70B-Instruct
|
||||||
|
- meta-llama/Meta-Llama-3.1-8B-Instruct
|
||||||
|
- google/gemma-2-27b-it
|
||||||
|
- google/gemma-2-9b-it
|
||||||
@ -0,0 +1,40 @@
|
|||||||
|
model: deepseek-ai/DeepSeek-Coder-V2-Instruct
|
||||||
|
label:
|
||||||
|
en_US: deepseek-ai/DeepSeek-Coder-V2-Instruct
|
||||||
|
model_type: llm
|
||||||
|
features:
|
||||||
|
- agent-thought
|
||||||
|
model_properties:
|
||||||
|
mode: chat
|
||||||
|
context_size: 32768
|
||||||
|
parameter_rules:
|
||||||
|
- name: temperature
|
||||||
|
use_template: temperature
|
||||||
|
- name: max_tokens
|
||||||
|
use_template: max_tokens
|
||||||
|
type: int
|
||||||
|
default: 512
|
||||||
|
min: 1
|
||||||
|
max: 4096
|
||||||
|
help:
|
||||||
|
zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
|
||||||
|
en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.
|
||||||
|
- name: top_p
|
||||||
|
use_template: top_p
|
||||||
|
- name: top_k
|
||||||
|
label:
|
||||||
|
zh_Hans: 取样数量
|
||||||
|
en_US: Top k
|
||||||
|
type: int
|
||||||
|
help:
|
||||||
|
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
|
||||||
|
en_US: Only sample from the top K options for each subsequent token.
|
||||||
|
required: false
|
||||||
|
- name: frequency_penalty
|
||||||
|
use_template: frequency_penalty
|
||||||
|
pricing:
|
||||||
|
input: '1.33'
|
||||||
|
output: '1.33'
|
||||||
|
unit: '0.000001'
|
||||||
|
currency: RMB
|
||||||
|
deprecated: true
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
|
||||||
|
label:
|
||||||
|
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
|
||||||
|
en_US: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
|
||||||
|
model_type: llm
|
||||||
|
features:
|
||||||
|
- agent-thought
|
||||||
|
model_properties:
|
||||||
|
mode: chat
|
||||||
|
context_size: 32000
|
||||||
|
parameter_rules:
|
||||||
|
- name: max_tokens
|
||||||
|
use_template: max_tokens
|
||||||
|
min: 1
|
||||||
|
max: 8192
|
||||||
|
default: 4096
|
||||||
|
pricing:
|
||||||
|
input: "0.00"
|
||||||
|
output: "4.3"
|
||||||
|
unit: "0.000001"
|
||||||
|
currency: RMB
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
|
||||||
|
label:
|
||||||
|
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
|
||||||
|
en_US: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
|
||||||
|
model_type: llm
|
||||||
|
features:
|
||||||
|
- agent-thought
|
||||||
|
model_properties:
|
||||||
|
mode: chat
|
||||||
|
context_size: 32000
|
||||||
|
parameter_rules:
|
||||||
|
- name: max_tokens
|
||||||
|
use_template: max_tokens
|
||||||
|
min: 1
|
||||||
|
max: 8192
|
||||||
|
default: 4096
|
||||||
|
pricing:
|
||||||
|
input: "0.00"
|
||||||
|
output: "0.00"
|
||||||
|
unit: "0.000001"
|
||||||
|
currency: RMB
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
|
||||||
|
label:
|
||||||
|
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
|
||||||
|
en_US: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
|
||||||
|
model_type: llm
|
||||||
|
features:
|
||||||
|
- agent-thought
|
||||||
|
model_properties:
|
||||||
|
mode: chat
|
||||||
|
context_size: 32000
|
||||||
|
parameter_rules:
|
||||||
|
- name: max_tokens
|
||||||
|
use_template: max_tokens
|
||||||
|
min: 1
|
||||||
|
max: 8192
|
||||||
|
default: 4096
|
||||||
|
pricing:
|
||||||
|
input: "0.00"
|
||||||
|
output: "1.26"
|
||||||
|
unit: "0.000001"
|
||||||
|
currency: RMB
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
|
||||||
|
label:
|
||||||
|
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
|
||||||
|
en_US: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
|
||||||
|
model_type: llm
|
||||||
|
features:
|
||||||
|
- agent-thought
|
||||||
|
model_properties:
|
||||||
|
mode: chat
|
||||||
|
context_size: 32000
|
||||||
|
parameter_rules:
|
||||||
|
- name: max_tokens
|
||||||
|
use_template: max_tokens
|
||||||
|
min: 1
|
||||||
|
max: 8192
|
||||||
|
default: 4096
|
||||||
|
pricing:
|
||||||
|
input: "0.00"
|
||||||
|
output: "0.70"
|
||||||
|
unit: "0.000001"
|
||||||
|
currency: RMB
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
|
||||||
|
label:
|
||||||
|
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
|
||||||
|
en_US: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
|
||||||
|
model_type: llm
|
||||||
|
features:
|
||||||
|
- agent-thought
|
||||||
|
model_properties:
|
||||||
|
mode: chat
|
||||||
|
context_size: 32000
|
||||||
|
parameter_rules:
|
||||||
|
- name: max_tokens
|
||||||
|
use_template: max_tokens
|
||||||
|
min: 1
|
||||||
|
max: 8192
|
||||||
|
default: 4096
|
||||||
|
pricing:
|
||||||
|
input: "0.00"
|
||||||
|
output: "1.26"
|
||||||
|
unit: "0.000001"
|
||||||
|
currency: RMB
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
|
||||||
|
label:
|
||||||
|
zh_Hans: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
|
||||||
|
en_US: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
|
||||||
|
model_type: llm
|
||||||
|
features:
|
||||||
|
- agent-thought
|
||||||
|
model_properties:
|
||||||
|
mode: chat
|
||||||
|
context_size: 32000
|
||||||
|
parameter_rules:
|
||||||
|
- name: max_tokens
|
||||||
|
use_template: max_tokens
|
||||||
|
min: 1
|
||||||
|
max: 8192
|
||||||
|
default: 4096
|
||||||
|
pricing:
|
||||||
|
input: "0.00"
|
||||||
|
output: "0.00"
|
||||||
|
unit: "0.000001"
|
||||||
|
currency: RMB
|
||||||
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Loading…
Reference in New Issue