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refactor:adapt rag storage and add integration documents. (#2361)

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Aries-ckt
2025-02-24 12:49:36 +08:00
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parent 94b51284e0
commit 22598ca79f
27 changed files with 647 additions and 343 deletions
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6
docs/docs/api/app.md
View File

@@ -34,7 +34,7 @@ import TabItem from '@theme/TabItem';
-H "Authorization: Bearer $DBGPT_API_KEY" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d "{\"messages\":\"Hello\",\"model\":\"chatgpt_proxyllm\", \"chat_mode\": \"chat_app\", \"chat_param\": \"$APP_ID\"}"
-d "{\"messages\":\"Hello\",\"model\":\"gpt-4o\", \"chat_mode\": \"chat_app\", \"chat_param\": \"$APP_ID\"}"
```
</TabItem>
@@ -51,7 +51,7 @@ client = Client(api_key=DBGPT_API_KEY)
async for data in client.chat_stream(
messages="Introduce AWEL",
model="chatgpt_proxyllm",
model="gpt-4o",
chat_mode="chat_app",
chat_param=APP_ID
):
@@ -63,7 +63,7 @@ async for data in client.chat_stream(
### Chat Completion Stream Response
```commandline
data: {"id": "109bfc28-fe87-452c-8e1f-d4fe43283b7d", "created": 1710919480, "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "```agent-plans\n[{\"name\": \"Introduce Awel\", \"num\": 2, \"status\": \"complete\", \"agent\": \"Human\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"Summarizer\\\", \\\"receiver\\\": \\\"Human\\\", \\\"model\\\": \\\"chatgpt_proxyllm\\\", \\\"markdown\\\": \\\"Agentic Workflow Expression Language (AWEL) is a specialized language designed for developing large model applications with intelligent agent workflows. It offers flexibility and functionality, allowing developers to focus on business logic for LLMs applications without getting bogged down in model and environment details. AWEL uses a layered API design architecture, making it easier to work with. You can find examples and source code to get started with AWEL, and it supports various operators and environments. AWEL is a powerful tool for building native data applications through workflows and agents.\"}]\n```"}}]}
data: {"id": "109bfc28-fe87-452c-8e1f-d4fe43283b7d", "created": 1710919480, "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "```agent-plans\n[{\"name\": \"Introduce Awel\", \"num\": 2, \"status\": \"complete\", \"agent\": \"Human\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"Summarizer\\\", \\\"receiver\\\": \\\"Human\\\", \\\"model\\\": \\\"gpt-4o\\\", \\\"markdown\\\": \\\"Agentic Workflow Expression Language (AWEL) is a specialized language designed for developing large model applications with intelligent agent workflows. It offers flexibility and functionality, allowing developers to focus on business logic for LLMs applications without getting bogged down in model and environment details. AWEL uses a layered API design architecture, making it easier to work with. You can find examples and source code to get started with AWEL, and it supports various operators and environments. AWEL is a powerful tool for building native data applications through workflows and agents.\"}]\n```"}}]}
data: [DONE]
```

28
docs/docs/api/chat.md
View File

@@ -34,7 +34,7 @@ import TabItem from '@theme/TabItem';
-H "Authorization: Bearer $DBGPT_API_KEY" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d "{\"messages\":\"Hello\",\"model\":\"chatgpt_proxyllm\", \"stream\": true}"
-d "{\"messages\":\"Hello\",\"model\":\"gpt-4o\", \"stream\": true}"
```
</TabItem>
@@ -48,7 +48,7 @@ DBGPT_API_KEY = "dbgpt"
client = Client(api_key=DBGPT_API_KEY)
async for data in client.chat_stream(
model="chatgpt_proxyllm",
model="gpt-4o",
messages="hello",
):
print(data)
@@ -58,23 +58,23 @@ async for data in client.chat_stream(
### Chat Completion Stream Response
```commandline
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "Hello"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "Hello"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "!"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "!"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " How"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " How"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " can"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " can"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " I"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " I"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " assist"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " assist"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " you"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " you"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " today"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " today"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "?"}}]}
data: {"id": "chatcmpl-ba6fb52e-e5b2-11ee-b031-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "?"}}]}
data: [DONE]
```
@@ -98,7 +98,7 @@ data: [DONE]
-H "Authorization: Bearer $DBGPT_API_KEY" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d "{\"messages\":\"Hello\",\"model\":\"chatgpt_proxyllm\", \"stream\": false}"
-d "{\"messages\":\"Hello\",\"model\":\"gpt-4o\", \"stream\": false}"
```
</TabItem>
@@ -109,7 +109,7 @@ from dbgpt_client import Client
DBGPT_API_KEY = "dbgpt"
client = Client(api_key=DBGPT_API_KEY)
response = await client.chat(model="chatgpt_proxyllm" ,messages="hello")
response = await client.chat(model="gpt-4o" ,messages="hello")
```
</TabItem>
</Tabs>
@@ -120,7 +120,7 @@ response = await client.chat(model="chatgpt_proxyllm" ,messages="hello")
"id": "a8321543-52e9-47a5-a0b6-3d997463f6a3",
"object": "chat.completion",
"created": 1710826792,
"model": "chatgpt_proxyllm",
"model": "gpt-4o",
"choices": [
{
"index": 0,

6
docs/docs/api/datasource.md
View File

@@ -34,7 +34,7 @@ curl -X POST "http://localhost:5670/api/v2/chat/completions" \
-H "Authorization: Bearer $DBGPT_API_KEY" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d "{\"messages\":\"show space datas limit 5\",\"model\":\"chatgpt_proxyllm\", \"chat_mode\": \"chat_data\", \"chat_param\": \"$DB_NAME\"}"
-d "{\"messages\":\"show space datas limit 5\",\"model\":\"gpt-4o\", \"chat_mode\": \"chat_data\", \"chat_param\": \"$DB_NAME\"}"
```
</TabItem>
@@ -50,7 +50,7 @@ DB_NAME="{your_db_name}"
client = Client(api_key=DBGPT_API_KEY)
res = client.chat(
messages="show space datas limit 5",
model="chatgpt_proxyllm",
model="gpt-4o",
chat_mode="chat_data",
chat_param=DB_NAME
)
@@ -64,7 +64,7 @@ res = client.chat(
"id": "2bb80fdd-e47e-4083-8bc9-7ca66ee0931b",
"object": "chat.completion",
"created": 1711509733,
"model": "chatgpt_proxyllm",
"model": "gpt-4o",
"choices": [
{
"index": 0,

230
docs/docs/api/knowledge.md
View File

@@ -34,7 +34,7 @@ curl -X POST "http://localhost:5670/api/v2/chat/completions" \
-H "Authorization: Bearer $DBGPT_API_KEY" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d "{\"messages\":\"Hello\",\"model\":\"chatgpt_proxyllm\", \"chat_mode\": \"chat_knowledge\", \"chat_param\": \"$SPACE_NAME\"}"
-d "{\"messages\":\"Hello\",\"model\":\"gpt-4o\", \"chat_mode\": \"chat_knowledge\", \"chat_param\": \"$SPACE_NAME\"}"
```
</TabItem>
@@ -50,7 +50,7 @@ client = Client(api_key=DBGPT_API_KEY)
async for data in client.chat_stream(
messages="Introduce AWEL",
model="chatgpt_proxyllm",
model="gpt-4o",
chat_mode="chat_knowledge",
chat_param=SPACE_NAME
):
@@ -65,7 +65,7 @@ async for data in client.chat_stream(
"id": "acb050ab-eb2c-4754-97e4-6f3b94b7dac2",
"object": "chat.completion",
"created": 1710917272,
"model": "chatgpt_proxyllm",
"model": "gpt-4o",
"choices": [
{
"index": 0,
@@ -86,229 +86,229 @@ async for data in client.chat_stream(
#### Chat Completion Stream Response
```commandline
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "AW"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "AW"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "EL"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "EL"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": ","}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": ","}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " which"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " which"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " stands"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " stands"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " for"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " for"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " Ag"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " Ag"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "entic"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "entic"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " Workflow"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " Workflow"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " Expression"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " Expression"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " Language"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " Language"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": ","}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": ","}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " is"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " is"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " a"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " a"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " powerful"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " powerful"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " tool"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " tool"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " designed"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " designed"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " for"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " for"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " developing"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " developing"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " large"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " large"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " model"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " model"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " applications"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " applications"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "."}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "."}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " It"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " It"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " simpl"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " simpl"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "ifies"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "ifies"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " the"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " the"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " process"}}]}
data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "gpt-4o", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " process"}}]}
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data: {"id": "chatcmpl-86f60a0c-e686-11ee-9322-acde48001122", "model": "chatgpt_proxyllm", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "\n\n<references title=\"References\" references=\"[{&quot;name&quot;: &quot;AWEL_URL&quot;, &quot;chunks&quot;: [{&quot;id&quot;: 2526, &quot;content&quot;: &quot;Agentic Workflow Expression Language(AWEL) is a set of intelligent agent workflow expression language specially designed for large model applicationdevelopment. It provides great functionality and flexibility. Through the AWEL API, you can focus on the development of business logic for LLMs applicationswithout paying attention to cumbersome model and environment details.AWEL adopts a layered API design. AWEL's layered API design architecture is shown in the figure below.AWEL Design&quot;, &quot;meta_info&quot;: &quot;{'source': 'https://docs.dbgpt.site/docs/awel/', 'title': 'AWEL(Agentic Workflow Expression Language) | DB-GPT', 'description': 'Agentic Workflow Expression Language(AWEL) is a set of intelligent agent workflow expression language specially designed for large model application', 'language': 'en-US'}&quot;, &quot;recall_score&quot;: 0.6579902643967029}, {&quot;id&quot;: 2531, &quot;content&quot;: &quot;ExamplesThe preliminary version of AWEL has alse been released, and we have provided some built-in usage examples.OperatorsExample of API-RAGYou can find source code from examples/awel/simple_rag_example.py&quot;, &quot;meta_info&quot;: &quot;{'source': 'https://docs.dbgpt.site/docs/awel/', 'title': 'AWEL(Agentic Workflow Expression Language) | DB-GPT', 'description': 'Agentic Workflow Expression Language(AWEL) is a set of intelligent agent workflow expression language specially designed for large model application', 'language': 'en-US'}&quot;, &quot;recall_score&quot;: 0.5997033286385491}, {&quot;id&quot;: 2538, &quot;content&quot;: &quot;Stand-alone environmentRay environmentPreviousWhy use AWEL?NextReleased V0.5.0 | Develop native data applications through workflows and agentsAWEL DesignExamplesOperatorsExample of API-RAGAgentFream ExampleDSL ExampleCurrently supported operatorsExecutable environmentCommunityDiscordDockerhubGithubGithubHuggingFaceMoreHacker NewsTwitterCopyright © 2024 DB-GPT&quot;, &quot;meta_info&quot;: &quot;{'source': 'https://docs.dbgpt.site/docs/awel/', 'title': 'AWEL(Agentic Workflow Expression Language) | DB-GPT', 'description': 'Agentic Workflow Expression Language(AWEL) is a set of intelligent agent workflow expression language specially designed for large model application', 'language': 'en-US'}&quot;, &quot;recall_score&quot;: 0.5980204530753225}]}]\" />"}}]}
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data: [DONE]
```

21
docs/docs/cookbook/rag/graph_rag_app_develop.md
View File

@@ -10,7 +10,12 @@ You can refer to the python example file `DB-GPT/examples/rag/graph_rag_example.
First, you need to install the `dbgpt` library.
```bash
pip install "dbgpt[graph_rag]>=0.6.1"
uv sync --all-packages --frozen \
--extra "proxy_openai" \
--extra "rag" \
--extra "storage_chromadb" \
--extra "dbgpts"
--extra "graph_rag"
````
### Prepare Graph Database
@@ -52,11 +57,7 @@ import TabItem from '@theme/TabItem';
]}>
<TabItem value="openai">
First, you should install the `openai` library.
```bash
pip install openai
```
Then set your API key in the environment `OPENAI_API_KEY`.
```python
@@ -70,19 +71,9 @@ llm_client = OpenAILLMClient()
You should have a YI account and get the API key from the YI official website.
First, you should install the `openai` library.
```bash
pip install openai
```
Then set your API key in the environment variable `YI_API_KEY`.
```python
from dbgpt.model.proxy import YiLLMClient
llm_client = YiLLMClient()
```
</TabItem>
<TabItem value="model_service">

108
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View File

@@ -0,0 +1,108 @@
# Graph RAG Installation
In this example, we will show how to use the Graph RAG framework in DB-GPT. Using a graph database to implement RAG can, to some extent, alleviate the uncertainty and interpretability issues brought about by vector database retrieval.
You can refer to the python example file `DB-GPT/examples/rag/graph_rag_example.py` in the source code. This example demonstrates how to load knowledge from a document and store it in a graph store. Subsequently, it recalls knowledge relevant to your question by searching for triplets in the graph store.
### Install Dependencies
First, you need to install the `dbgpt graph_rag` library.
```bash
uv sync --all-packages --frozen \
--extra "proxy_openai" \
--extra "rag" \
--extra "storage_chromadb" \
--extra "dbgpts"
--extra "graph_rag"
````
### Prepare Graph Database
To store the knowledge in graph, we need an graph database, [TuGraph](https://github.com/TuGraph-family/tugraph-db) is the first graph database supported by DB-GPT.
Visit github repository of TuGraph to view [Quick Start](https://tugraph-db.readthedocs.io/zh-cn/latest/3.quick-start/1.preparation.html#id5) document, follow the instructions to pull the TuGraph database docker image (latest / version >= 4.5.1) and launch it.
```
docker pull tugraph/tugraph-runtime-centos7:4.5.1
docker run -d -p 7070:7070 -p 7687:7687 -p 9090:9090 --name tugraph_demo tugraph/tugraph-runtime-centos7:latest lgraph_server -d run --enable_plugin true
```
The default port for the bolt protocol is `7687`.
> **Download Tips:**
>
> There is also a corresponding version of the TuGraph Docker image package on OSS. You can also directly download and import it.
>
> ```
> wget 'https://tugraph-web.oss-cn-beijing.aliyuncs.com/tugraph/tugraph-4.5.1/tugraph-runtime-centos7-4.5.1.tar' -O tugraph-runtime-centos7-4.5.1.tar
> docker load -i tugraph-runtime-centos7-4.5.1.tar
> ```
### TuGraph Configuration
Set variables below in `configs/dbgpt-graphrag.toml` file, let DB-GPT know how to connect to TuGraph.
```
[rag.storage.graph]
type = "TuGraph"
host="127.0.0.1"
port=7687
username="admin"
password="73@TuGraph"
enable_summary="True"
enable_similarity_search="True"
```
Then run the following command to start the webserver:
```bash
uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-graphrag.toml
```
Optionally, you can also use the following command to start the webserver:
uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
### Load into Knowledge Graph
When using a graph database as the underlying knowledge storage platform, it is necessary to build a knowledge graph to facilitate the archiving and retrieval of documents. DB-GPT leverages the capabilities of large language models to implement an integrated knowledge graph, while still maintaining the flexibility to freely connect to other knowledge graph systems and graph database systems.
We created a knowledge graph with graph community summaries based on `CommunitySummaryKnowledgeGraph`.
### Chat Knowledge via GraphRAG
> Note: The current test data is in Chinese.
Here we demonstrate how to achieve chat knowledge through Graph RAG on web page.
First, create a knowledge base using the `Knowledge Graph` type.
<p align="left">
<img src={'/img/chat_knowledge/graph_rag/create_knowledge_graph.png'} width="1000px"/>
</p>
Then, upload the documents ([graphrag-test.md](https://github.com/eosphoros-ai/DB-GPT/blob/main/examples/test_files/graphrag-test.md)) and process them automatically (markdown header by default).
<p align="left">
<img src={'/img/chat_knowledge/graph_rag/upload_file.png'} width="1000px"/>
</p>
After indexing, the graph data may look like this.
<p align="left">
<img src={'/img/chat_knowledge/graph_rag/graph_data.png'} width="1000px"/>
</p>
Start to chat on knowledge graph.
<p align="left">
<img src={'/img/chat_knowledge/graph_rag/graph_rag_chat.png'} width="1000px"/>
</p>

40
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View File

@@ -0,0 +1,40 @@
# DB-GPT Integrations
DB-GPT integrates with many datasources and rag storage providers.
Integration Packages
# Datasource Providers
| Provider | Supported | Install Packages |
|-------------|-----------|----------------------|
| MySQL | ✅ | --extra datasource_mysql |
| OceanBase | ✅ | |
| ClickHouse | ✅ | --extra datasource_clickhouse |
| Hive | ✅ | --extra datasource_hive |
| MSSQL | ✅ | --extra datasource_mssql |
| PostgreSQL | ✅ | --extra datasource_postgres |
| ApacheDoris | ✅ | |
| StarRocks | ✅ | --extra datasource_starroks |
| Spark | ✅ | --extra datasource_spark |
| Oracle | ❌ | |
# RAG Storage Providers
| Provider | Supported | Install Packages |
|-------------|-----------|--------------------------------|
| Chroma | ✅ | --extra storage_chroma |
| Milvus | ✅ | --extra storage_milvus |
| Elasticsearch | ✅ | --extra storage_elasticsearch |
| OceanBase | ✅ | --extra storage_obvector |
# Graph RAG Storage Providers
| Provider | Supported | Install Packages |
|----------|-----------|------------------|
| TuGraph | ✅ | --extra graph_rag|
| Neo4j | ❌ | |

46
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View File

@@ -0,0 +1,46 @@
# Milvus RAG Installation
In this example, we will show how to use the Milvus as in DB-GPT RAG Storage. Using a graph database to implement RAG can, to some extent, alleviate the uncertainty and interpretability issues brought about by vector database retrieval.
### Install Dependencies
First, you need to install the `dbgpt milvus storage` library.
```bash
uv sync --all-packages --frozen \
--extra "proxy_openai" \
--extra "rag" \
--extra "storage_milvus" \
--extra "dbgpts"
````
### Prepare Milvus
Prepare Milvus database service, reference-[Milvus Installation](https://milvus.io/docs/install_standalone-docker-compose.md) .
### TuGraph Configuration
Set rag storage variables below in `configs/dbgpt-proxy-openai.toml` file, let DB-GPT know how to connect to Milvus.
```
[rag.storage]
[rag.storage.vector]
type = "Milvus"
uri = "127.0.0.1"
port = "19530"
#username="dbgpt"
#password=19530
```
Then run the following command to start the webserver:
```bash
uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
```
Optionally, you can also use the following command to start the webserver:
```bash
uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
```

22
docs/sidebars.js
View File

@@ -230,6 +230,28 @@ const sidebars = {
id: 'installation/sourcecode',
},
{
// type: 'doc',
// id: 'installation/integrations',
type: "category",
label: "Integrations",
collapsed: false,
collapsible: false,
items: [
{
type: "doc",
id: "installation/integrations"
},
{
type: "doc",
id: "installation/graph_rag_install"
},
{
type: "doc",
id: "installation/milvus_rag_install"
}
]
},
{
type: 'doc',
id: 'installation/docker',
},