ContextQMD
Back to Db Gpt

RAG Parameter Adjustment

docs/docs/application/advanced_tutorial/rag.md

0.8.24.2 KB
Original Source

RAG Parameter Adjustment

Each knowledge space supports argument customization, including the relevant arguments for vector retrieval and the arguments for knowledge question-answering prompts.

As shown in the figure below, clicking on the "Knowledge" will trigger a pop-up dialog box. Click the "Arguments" button to enter the parameter tuning interface.

<Tabs defaultValue="Embedding" values={[ {label: 'Embedding Argument', value: 'Embedding'}, {label: 'Prompt Argument', value: 'Prompt'}, {label: 'Summary Argument', value: 'Summary'}, ]}> <TabItem value="Embedding" label="Embedding Argument">

:::tip Embedding Arguments

  • topk:the top k vectors based on similarity score.
  • recall_score:set a similarity threshold score for the retrieval of similar vectors. between 0 and 1. default 0.3.
  • recall_type:recall type. now nly support topk by vector similarity.
  • model:A model used to create vector representations of text or other data.
  • chunk_size:The size of the data chunks used in processing.default 500.
  • chunk_overlap:The amount of overlap between adjacent data chunks.default 50. ::: </TabItem>
<TabItem value="Prompt" label="Prompt Argument">

:::tip Prompt Arguments

  • scene:A contextual parameter used to define the setting or environment in which the prompt is being used.
  • template:A pre-defined structure or format for the prompt, which can help ensure that the AI system generates responses that are consistent with the desired style or tone.
  • max_token:The maximum number of tokens or words allowed in a prompt. :::
</TabItem> <TabItem value="Summary" label="Summary Argument">

:::tip summary arguments

  • max_iteration: summary max iteration call with llm, default 5. the bigger and better for document summary but time will cost longer.
  • concurrency_limit: default summary concurrency call with llm, default 3. :::
</TabItem> </Tabs>

Knowledge Query Rewrite

set KNOWLEDGE_SEARCH_REWRITE=True in .env file, and restart the server.

shell
# Whether to enable Chat Knowledge Search Rewrite Mode
KNOWLEDGE_SEARCH_REWRITE=True

Change Vector Database

import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

<Tabs defaultValue="Chroma" values={[ {label: 'Chroma', value: 'Chroma'}, {label: 'Milvus', value: 'Milvus'}, {label: 'Weaviate', value: 'Weaviate'}, {label: 'OceanBase', value: 'OceanBase'}, ]}> <TabItem value="Chroma" label="Chroma">

set VECTOR_STORE_TYPE in .env file.

shell
### Chroma vector db config
VECTOR_STORE_TYPE=Chroma
#CHROMA_PERSIST_PATH=/root/DB-GPT/pilot/data
</TabItem> <TabItem value="Milvus" label="Milvus">

set VECTOR_STORE_TYPE in .env file

shell
### Milvus vector db config
VECTOR_STORE_TYPE=Milvus
MILVUS_URL=127.0.0.1
MILVUS_PORT=19530
#MILVUS_USERNAME
#MILVUS_PASSWORD
#MILVUS_SECURE=
</TabItem> <TabItem value="Weaviate" label="Weaviate">

set VECTOR_STORE_TYPE in .env file

shell
### Weaviate vector db config
VECTOR_STORE_TYPE=Weaviate
#WEAVIATE_URL=https://kt-region-m8hcy0wc.weaviate.network
</TabItem> <TabItem value="OceanBase" label="OceanBase">

set VECTOR_STORE_TYPE in .env file

shell
OB_HOST=127.0.0.1
OB_PORT=2881
OB_USER=root@test
OB_DATABASE=test
## Optional
# OB_PASSWORD=
## Optional: If {OB_ENABLE_NORMALIZE_VECTOR} is set, the vector stored in OceanBase is normalized.
# OB_ENABLE_NORMALIZE_VECTOR=True
</TabItem> </Tabs>

See also