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doc:add integrations documents (#2382)
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@@ -0,0 +1 @@
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# Claude
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37
docs/docs/installation/integrations/clickhouse_install.md
Normal file
37
docs/docs/installation/integrations/clickhouse_install.md
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@@ -0,0 +1,37 @@
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# ClickHouse
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In this example, we will show how to use the ClickHouse as in DB-GPT Datasource. Using a column-oriented database to implement Datasource can, to some extent, alleviate the uncertainty and interpretability issues brought about by vector database retrieval.
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### Install Dependencies
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First, you need to install the `dbgpt clickhouse datasource` library.
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```bash
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uv sync --all-packages --frozen \
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--extra "base" \
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--extra "datasource_clickhouse" \
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--extra "rag" \
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--extra "storage_chromadb" \
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--extra "dbgpts"
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```
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### Prepare ClickHouse
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Prepare ClickHouse database service, reference-[ClickHouse Installation](https://clickhouse.tech/docs/en/getting-started/install/).
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Then run the following command to start the webserver:
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```bash
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uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
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```
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Optionally, you can also use the following command to start the webserver:
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```bash
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uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
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```
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### ClickHouse Configuration
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<p align="left">
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<img src={'https://github.com/user-attachments/assets/b506dc5e-2930-49da-b0c0-5ca051cb6c3f'} width="1000px"/>
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</p>
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@@ -0,0 +1 @@
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# DeepSeek
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42
docs/docs/installation/integrations/duckdb_install.md
Normal file
42
docs/docs/installation/integrations/duckdb_install.md
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@@ -0,0 +1,42 @@
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# DuckDB
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DuckDB is a high-performance analytical database system. It is designed to execute analytical SQL queries fast and efficiently, and it can also be used as an embedded analytical database.
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In this example, we will show how to use DuckDB as in DB-GPT Datasource. Using DuckDB to implement Datasource can, to some extent, alleviate the uncertainty and interpretability issues brought about by vector database retrieval.
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### Install Dependencies
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First, you need to install the `dbgpt duckdb datasource` library.
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```bash
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uv sync --all-packages --frozen \
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--extra "base" \
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--extra "datasource_duckdb" \
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--extra "rag" \
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--extra "storage_chromadb" \
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```
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### Prepare DuckDB
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Prepare DuckDB database service, reference-[DuckDB Installation](https://duckdb.org/docs/installation).
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Then run the following command to start the webserver:
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```bash
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uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
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```
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Optionally, you can also use the following command to start the webserver:
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```bash
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uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
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```
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### DuckDB Configuration
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<p align="left">
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<img src={'https://github.com/user-attachments/assets/bc5ffc20-4b5b-4e24-8c29-bf5702b0e840'} width="1000px"/>
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</p>
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1
docs/docs/installation/integrations/gitee_llm_install.md
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1
docs/docs/installation/integrations/gitee_llm_install.md
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@@ -0,0 +1 @@
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# Gitee
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@@ -1,4 +1,4 @@
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# Graph RAG Installation
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# Graph RAG
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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.
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@@ -12,6 +12,7 @@ First, you need to install the `dbgpt graph_rag` library.
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```bash
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uv sync --all-packages --frozen \
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--extra "base" \
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--extra "proxy_openai" \
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--extra "rag" \
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--extra "storage_chromadb" \
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@@ -68,41 +69,3 @@ uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/
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### Load into Knowledge Graph
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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.
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We created a knowledge graph with graph community summaries based on `CommunitySummaryKnowledgeGraph`.
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### Chat Knowledge via GraphRAG
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> Note: The current test data is in Chinese.
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Here we demonstrate how to achieve chat knowledge through Graph RAG on web page.
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First, create a knowledge base using the `Knowledge Graph` type.
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<p align="left">
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<img src={'/img/chat_knowledge/graph_rag/create_knowledge_graph.png'} width="1000px"/>
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</p>
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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).
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<p align="left">
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<img src={'/img/chat_knowledge/graph_rag/upload_file.png'} width="1000px"/>
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</p>
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After indexing, the graph data may look like this.
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<p align="left">
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<img src={'/img/chat_knowledge/graph_rag/graph_data.png'} width="1000px"/>
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</p>
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Start to chat on knowledge graph.
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<p align="left">
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<img src={'/img/chat_knowledge/graph_rag/graph_rag_chat.png'} width="1000px"/>
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</p>
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38
docs/docs/installation/integrations/hive_install.md
Normal file
38
docs/docs/installation/integrations/hive_install.md
Normal file
@@ -0,0 +1,38 @@
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# Hive
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In this example, we will show how to use the Hive as in DB-GPT Datasource. Using Hive to implement Datasource can, to some extent, alleviate the uncertainty and interpretability issues brought about by vector database retrieval.
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### Install Dependencies
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First, you need to install the `dbgpt hive datasource` library.
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```bash
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uv sync --all-packages --frozen \
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--extra "base" \
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--extra "datasource_hive" \
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--extra "rag" \
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--extra "storage_chromadb" \
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--extra "dbgpts"
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```
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### Prepare Hive
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Prepare Hive database service, reference-[Hive Installation](https://cwiki.apache.org/confluence/display/Hive/GettingStarted).
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Then run the following command to start the webserver:
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```bash
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uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
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```
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Optionally, you can also use the following command to start the webserver:
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```bash
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uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
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```
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### Hive Configuration
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<p align="left">
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<img src={'https://github.com/user-attachments/assets/40fb83c5-9b12-496f-8249-c331adceb76f'} width="1000px"/>
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</p>
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@@ -1,4 +1,4 @@
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# Milvus RAG Installation
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# Milvus RAG
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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.
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@@ -10,6 +10,7 @@ First, you need to install the `dbgpt milvus storage` library.
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```bash
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uv sync --all-packages --frozen \
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--extra "base" \
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--extra "proxy_openai" \
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--extra "rag" \
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--extra "storage_milvus" \
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39
docs/docs/installation/integrations/mssql_install.md
Normal file
39
docs/docs/installation/integrations/mssql_install.md
Normal file
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# MSSQL
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In this example, we will show how to use the MSSQL as in DB-GPT Datasource. Using MSSQL to implement Datasource can, to some extent, alleviate the uncertainty and interpretability issues brought about by vector database retrieval.
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### Install Dependencies
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First, you need to install the `dbgpt mssql datasource` library.
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```bash
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uv sync --all-packages --frozen \
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--extra "base" \
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--extra "datasource_mssql" \
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--extra "rag" \
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--extra "storage_chromadb" \
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--extra "dbgpts"
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```
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### Prepare MSSQL
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Prepare MSSQL database service, reference-[MSSQL Installation](https://docs.microsoft.com/en-us/sql/database-engine/install-windows/install-sql-server?view=sql-server-ver15).
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Then run the following command to start the webserver:
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```bash
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uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
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```
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Optionally, you can also use the following command to start the webserver:
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```bash
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uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
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```
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### MSSQL Configuration
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<p align="left">
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<img src={'https://github.com/user-attachments/assets/2798aaf7-b16f-453e-844a-6ad5dec1d58f'} width="1000px"/>
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</p>
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47
docs/docs/installation/integrations/oceanbase_rag_install.md
Normal file
47
docs/docs/installation/integrations/oceanbase_rag_install.md
Normal file
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# Oceanbase Vector RAG
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In this example, we will show how to use the Oceanbase Vector 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.
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### Install Dependencies
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First, you need to install the `dbgpt Oceanbase Vector storage` library.
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```bash
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uv sync --all-packages --frozen \
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--extra "base" \
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--extra "proxy_openai" \
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--extra "rag" \
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--extra "storage_obvector" \
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--extra "dbgpts"
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````
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### Prepare Oceanbase Vector
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Prepare Oceanbase Vector database service, reference[Oceanbase Vector](https://open.oceanbase.com/) .
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### TuGraph Configuration
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Set rag storage variables below in `configs/dbgpt-proxy-openai.toml` file, let DB-GPT know how to connect to Oceanbase Vector.
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```
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[rag.storage]
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[rag.storage.vector]
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type = "Oceanbase"
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uri = "127.0.0.1"
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port = "19530"
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#username="dbgpt"
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#password=19530
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```
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Then run the following command to start the webserver:
|
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```bash
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uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
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||||
```
|
||||
|
||||
Optionally, you can also use the following command to start the webserver:
|
||||
```bash
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||||
uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
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```
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@@ -0,0 +1 @@
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# Ollama
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40
docs/docs/installation/integrations/postgres_install.md
Normal file
40
docs/docs/installation/integrations/postgres_install.md
Normal file
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# Postgres
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Postgres is a powerful, open source object-relational database system. It is a multi-user database management system and has sophisticated features such as Multi-Version Concurrency Control (MVCC), point in time recovery, tablespaces, asynchronous replication, nested transactions (savepoints), online/hot backups, a sophisticated query planner/optimizer, and write ahead logging for fault tolerance.
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In this example, we will show how to use Postgres as in DB-GPT Datasource. Using Postgres to implement Datasource can, to some extent, alleviate the uncertainty and interpretability issues brought about by vector database retrieval.
|
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||||
### Install Dependencies
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|
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First, you need to install the `dbgpt postgres datasource` library.
|
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|
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```bash
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|
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uv sync --all-packages --frozen \
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||||
--extra "base" \
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||||
--extra "datasource_postgres" \
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||||
--extra "rag" \
|
||||
--extra "storage_chromadb" \
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||||
--extra "dbgpts"
|
||||
```
|
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### Prepare Postgres
|
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Prepare Postgres database service, reference-[Postgres Installation](https://www.postgresql.org/download/).
|
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|
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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
|
||||
```
|
||||
|
||||
### Postgres Configuration
|
||||
<p align="left">
|
||||
<img src={'https://github.com/user-attachments/assets/affa5ef2-09d6-404c-951e-1220a0dce235'} width="1000px"/>
|
||||
</p>
|
||||
@@ -0,0 +1 @@
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# SiliconFlow
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||||
@@ -8,365 +8,224 @@
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| Local model | 8C * 32G | 24G | It is best to start locally with a GPU of 24G or above |
|
||||
|
||||
|
||||
## Environment Preparation
|
||||
|
||||
|
||||
|
||||
|
||||
### Download source code
|
||||
### Download Source Code
|
||||
|
||||
:::tip
|
||||
Download DB-GPT
|
||||
:::
|
||||
|
||||
|
||||
|
||||
```bash
|
||||
git clone https://github.com/eosphoros-ai/DB-GPT.git
|
||||
```
|
||||
|
||||
### Miniconda environment installation
|
||||
|
||||
- The default database uses SQLite, so there is no need to install a database in the default startup mode. If you need to use other databases, you can read the [advanced tutorials](/docs/application_manual/advanced_tutorial) below. We recommend installing the Python virtual environment through the conda virtual environment. For the installation of Miniconda environment, please refer to the [Miniconda installation tutorial](https://docs.conda.io/projects/miniconda/en/latest/).
|
||||
|
||||
:::tip
|
||||
Create a Python virtual environment
|
||||
:::
|
||||
|
||||
```bash
|
||||
python >= 3.10
|
||||
conda create -n dbgpt_env python=3.10
|
||||
conda activate dbgpt_env
|
||||
|
||||
# it will take some minutes
|
||||
pip install -e ".[default]"
|
||||
```
|
||||
|
||||
:::tip
|
||||
Copy environment variables
|
||||
:::
|
||||
```bash
|
||||
cp .env.template .env
|
||||
```
|
||||
|
||||
|
||||
## Model deployment
|
||||
|
||||
DB-GPT can be deployed on servers with lower hardware through proxy model, or as a private local model under the GPU environment. If your hardware configuration is low, you can use third-party large language model API services, such as OpenAI, Azure, Qwen, ERNIE Bot, etc.
|
||||
|
||||
:::info note
|
||||
|
||||
⚠️ You need to ensure that git-lfs is installed
|
||||
```bash
|
||||
● CentOS installation: yum install git-lfs
|
||||
● Ubuntu installation: apt-get install git-lfs
|
||||
● MacOS installation: brew install git-lfs
|
||||
```
|
||||
There are some ways to install uv:
|
||||
:::
|
||||
### Proxy model
|
||||
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
|
||||
<Tabs
|
||||
defaultValue="uv_sh"
|
||||
values={[
|
||||
{label: 'Command (macOS And Linux)', value: 'uv_sh'},
|
||||
{label: 'PyPI', value: 'uv_pypi'},
|
||||
{label: 'Other', value: 'uv_other'},
|
||||
]}>
|
||||
<TabItem value="uv_sh" label="Command">
|
||||
```bash
|
||||
curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
```
|
||||
</TabItem>
|
||||
|
||||
<TabItem value="uv_pypi" label="Pypi">
|
||||
Install uv using pipx.
|
||||
|
||||
```bash
|
||||
python -m pip install --upgrade pip
|
||||
python -m pip install --upgrade pipx
|
||||
python -m pipx ensurepath
|
||||
pipx install uv --global
|
||||
```
|
||||
</TabItem>
|
||||
|
||||
<TabItem value="uv_other" label="Other">
|
||||
|
||||
You can see more installation methods on the [uv installation](https://docs.astral.sh/uv/getting-started/installation/)
|
||||
</TabItem>
|
||||
|
||||
</Tabs>
|
||||
|
||||
Then, you can run `uv --version` to check if uv is installed successfully.
|
||||
|
||||
```bash
|
||||
uv --version
|
||||
```
|
||||
|
||||
## Deploy DB-GPT
|
||||
|
||||
### Install Dependencies
|
||||
|
||||
<Tabs
|
||||
defaultValue="openai"
|
||||
values={[
|
||||
{label: 'Open AI', value: 'openai'},
|
||||
{label: 'Qwen', value: 'qwen'},
|
||||
{label: 'ChatGLM', value: 'chatglm'},
|
||||
{label: 'WenXin', value: 'erniebot'},
|
||||
{label: 'Yi', value: 'yi'},
|
||||
{label: 'OpenAI (proxy)', value: 'openai'},
|
||||
{label: 'DeepSeek (proxy)', value: 'deepseek'},
|
||||
{label: 'GLM4 (local)', value: 'glm-4'},
|
||||
]}>
|
||||
<TabItem value="openai" label="open ai">
|
||||
Install dependencies
|
||||
|
||||
<TabItem value="openai" label="OpenAI(proxy)">
|
||||
|
||||
```bash
|
||||
pip install -e ".[openai]"
|
||||
# Use uv to install dependencies needed for OpenAI proxy
|
||||
uv sync --all-packages --frozen \
|
||||
--extra "base" \
|
||||
--extra "proxy_openai" \
|
||||
--extra "rag" \
|
||||
--extra "storage_chromadb" \
|
||||
--extra "dbgpts"
|
||||
```
|
||||
|
||||
Download embedding model
|
||||
### Run Webserver
|
||||
|
||||
To run DB-GPT with OpenAI proxy, you must provide the OpenAI API key in the `configs/dbgpt-proxy-openai.toml` configuration file or privide it in the environment variable with key `OPENAI_API_KEY`.
|
||||
|
||||
```toml
|
||||
# Model Configurations
|
||||
[models]
|
||||
[[models.llms]]
|
||||
...
|
||||
api_key = "your-openai-api-key"
|
||||
[[models.embeddings]]
|
||||
...
|
||||
api_key = "your-openai-api-key"
|
||||
```
|
||||
|
||||
Then run the following command to start the webserver:
|
||||
|
||||
```bash
|
||||
cd DB-GPT
|
||||
mkdir models and cd models
|
||||
git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese
|
||||
uv run dbgpt start webserver --config configs/dbgpt-proxy-openai.toml
|
||||
```
|
||||
In the above command, `--config` specifies the configuration file, and `configs/dbgpt-proxy-openai.toml` is the configuration file for the OpenAI proxy model, you can also use other configuration files or create your own configuration file according to your needs.
|
||||
|
||||
Configure the proxy and modify LLM_MODEL, PROXY_API_URL and API_KEY in the `.env`file
|
||||
|
||||
Optionally, you can also use the following command to start the webserver:
|
||||
```bash
|
||||
# .env
|
||||
LLM_MODEL=chatgpt_proxyllm
|
||||
PROXY_API_KEY={your-openai-sk}
|
||||
PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions
|
||||
# If you use gpt-4
|
||||
# PROXYLLM_BACKEND=gpt-4
|
||||
uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-openai.toml
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="qwen" label="通义千问">
|
||||
Install dependencies
|
||||
<TabItem value="deepseek" label="DeepSeek(proxy)">
|
||||
|
||||
```bash
|
||||
pip install dashscope
|
||||
# Use uv to install dependencies needed for OpenAI proxy
|
||||
uv sync --all-packages --frozen \
|
||||
--extra "base" \
|
||||
--extra "proxy_openai" \
|
||||
--extra "rag" \
|
||||
--extra "storage_chromadb" \
|
||||
--extra "dbgpts"
|
||||
```
|
||||
|
||||
Download embedding model
|
||||
### Run Webserver
|
||||
|
||||
To run DB-GPT with DeepSeek proxy, you must provide the DeepSeek API key in the `configs/dbgpt-proxy-deepseek.toml`.
|
||||
|
||||
And you can specify your embedding model in the `configs/dbgpt-proxy-deepseek.toml` configuration file, the default embedding model is `BAAI/bge-large-zh-v1.5`. If you want to use other embedding models, you can modify the `configs/dbgpt-proxy-deepseek.toml` configuration file and specify the `name` and `provider` of the embedding model in the `[[models.embeddings]]` section. The provider can be `hf`.
|
||||
|
||||
```toml
|
||||
# Model Configurations
|
||||
[models]
|
||||
[[models.llms]]
|
||||
# name = "deepseek-chat"
|
||||
name = "deepseek-reasoner"
|
||||
provider = "proxy/deepseek"
|
||||
api_key = "your-deepseek-api-key"
|
||||
[[models.embeddings]]
|
||||
name = "BAAI/bge-large-zh-v1.5"
|
||||
provider = "hf"
|
||||
# If not provided, the model will be downloaded from the Hugging Face model hub
|
||||
# uncomment the following line to specify the model path in the local file system
|
||||
# path = "the-model-path-in-the-local-file-system"
|
||||
path = "/data/models/bge-large-zh-v1.5"
|
||||
```
|
||||
|
||||
Then run the following command to start the webserver:
|
||||
|
||||
```bash
|
||||
cd DB-GPT
|
||||
mkdir models and cd models
|
||||
|
||||
# embedding model
|
||||
git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese
|
||||
or
|
||||
git clone https://huggingface.co/moka-ai/m3e-large
|
||||
uv run dbgpt start webserver --config configs/dbgpt-proxy-deepseek.toml
|
||||
```
|
||||
In the above command, `--config` specifies the configuration file, and `configs/dbgpt-proxy-deepseek.toml` is the configuration file for the DeepSeek proxy model, you can also use other configuration files or create your own configuration file according to your needs.
|
||||
|
||||
Configure the proxy and modify LLM_MODEL, PROXY_API_URL and API_KEY in the `.env`file
|
||||
|
||||
Optionally, you can also use the following command to start the webserver:
|
||||
```bash
|
||||
# .env
|
||||
# Aliyun tongyiqianwen
|
||||
LLM_MODEL=tongyi_proxyllm
|
||||
TONGYI_PROXY_API_KEY={your-tongyi-sk}
|
||||
PROXY_SERVER_URL={your_service_url}
|
||||
uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-proxy-deepseek.toml
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="chatglm" label="chatglm" >
|
||||
Install dependencies
|
||||
<TabItem value="glm-4" label="GLM4(local)">
|
||||
|
||||
```bash
|
||||
pip install zhipuai
|
||||
# Use uv to install dependencies needed for GLM4
|
||||
# Install core dependencies and select desired extensions
|
||||
uv sync --all-packages --frozen \
|
||||
--extra "base" \
|
||||
--extra "hf" \
|
||||
--extra "rag" \
|
||||
--extra "storage_chromadb" \
|
||||
--extra "quant_bnb" \
|
||||
--extra "dbgpts"
|
||||
```
|
||||
|
||||
Download embedding model
|
||||
### Run Webserver
|
||||
|
||||
To run DB-GPT with the local model. You can modify the `configs/dbgpt-local-glm.toml` configuration file to specify the model path and other parameters.
|
||||
|
||||
```toml
|
||||
# Model Configurations
|
||||
[models]
|
||||
[[models.llms]]
|
||||
name = "THUDM/glm-4-9b-chat-hf"
|
||||
provider = "hf"
|
||||
# If not provided, the model will be downloaded from the Hugging Face model hub
|
||||
# uncomment the following line to specify the model path in the local file system
|
||||
# path = "the-model-path-in-the-local-file-system"
|
||||
|
||||
[[models.embeddings]]
|
||||
name = "BAAI/bge-large-zh-v1.5"
|
||||
provider = "hf"
|
||||
# If not provided, the model will be downloaded from the Hugging Face model hub
|
||||
# uncomment the following line to specify the model path in the local file system
|
||||
# path = "the-model-path-in-the-local-file-system"
|
||||
```
|
||||
In the above configuration file, `[[models.llms]]` specifies the LLM model, and `[[models.embeddings]]` specifies the embedding model. If you not provide the `path` parameter, the model will be downloaded from the Hugging Face model hub according to the `name` parameter.
|
||||
|
||||
Then run the following command to start the webserver:
|
||||
|
||||
```bash
|
||||
cd DB-GPT
|
||||
mkdir models and cd models
|
||||
|
||||
# embedding model
|
||||
git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese
|
||||
or
|
||||
git clone https://huggingface.co/moka-ai/m3e-large
|
||||
uv run dbgpt start webserver --config configs/dbgpt-local-glm.toml
|
||||
```
|
||||
|
||||
Configure the proxy and modify LLM_MODEL, PROXY_API_URL and API_KEY in the `.env`file
|
||||
|
||||
```bash
|
||||
# .env
|
||||
LLM_MODEL=zhipu_proxyllm
|
||||
PROXY_SERVER_URL={your_service_url}
|
||||
ZHIPU_MODEL_VERSION={version}
|
||||
ZHIPU_PROXY_API_KEY={your-zhipu-sk}
|
||||
```
|
||||
</TabItem>
|
||||
|
||||
<TabItem value="erniebot" label="文心一言" default>
|
||||
|
||||
Download embedding model
|
||||
|
||||
```bash
|
||||
cd DB-GPT
|
||||
mkdir models and cd models
|
||||
|
||||
# embedding model
|
||||
git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese
|
||||
or
|
||||
git clone https://huggingface.co/moka-ai/m3e-large
|
||||
```
|
||||
|
||||
Configure the proxy and modify LLM_MODEL, MODEL_VERSION, API_KEY and API_SECRET in the `.env`file
|
||||
|
||||
```bash
|
||||
# .env
|
||||
LLM_MODEL=wenxin_proxyllm
|
||||
WEN_XIN_MODEL_VERSION={version} # ERNIE-Bot or ERNIE-Bot-turbo
|
||||
WEN_XIN_API_KEY={your-wenxin-sk}
|
||||
WEN_XIN_API_SECRET={your-wenxin-sct}
|
||||
```
|
||||
</TabItem>
|
||||
<TabItem value="yi" label="Yi">
|
||||
Install dependencies
|
||||
|
||||
Yi's API is compatible with OpenAI's API, so you can use the same dependencies as OpenAI's API.
|
||||
|
||||
```bash
|
||||
pip install -e ".[openai]"
|
||||
```
|
||||
|
||||
Download embedding model
|
||||
|
||||
```shell
|
||||
cd DB-GPT
|
||||
mkdir models and cd models
|
||||
git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese
|
||||
```
|
||||
|
||||
Configure the proxy and modify LLM_MODEL, YI_API_BASE and YI_API_KEY in the `.env`file
|
||||
|
||||
```shell
|
||||
# .env
|
||||
LLM_MODEL=yi_proxyllm
|
||||
YI_MODEL_VERSION=yi-34b-chat-0205
|
||||
YI_API_BASE=https://api.lingyiwanwu.com/v1
|
||||
YI_API_KEY={your-yi-api-key}
|
||||
```
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
|
||||
:::info note
|
||||
## Visit Website
|
||||
|
||||
⚠️ Be careful not to overwrite the contents of the `.env` configuration file
|
||||
:::
|
||||
Open your browser and visit [`http://localhost:5670`](http://localhost:5670)
|
||||
|
||||
### (Optional) Run Web Front-end Separately
|
||||
|
||||
### Local model
|
||||
<Tabs
|
||||
defaultValue="vicuna"
|
||||
values={[
|
||||
{label: 'ChatGLM', value: 'chatglm'},
|
||||
{label: 'Vicuna', value: 'vicuna'},
|
||||
{label: 'Baichuan', value: 'baichuan'},
|
||||
]}>
|
||||
<TabItem value="vicuna" label="vicuna">
|
||||
|
||||
##### Hardware requirements description
|
||||
| Model | Quantize | VRAM Size |
|
||||
|------------------ |--------------|----------------|
|
||||
|Vicuna-7b-1.5 | 4-bit | 8GB |
|
||||
|Vicuna-7b-1.5 | 8-bit | 12GB |
|
||||
|Vicuna-13b-v1.5 | 4-bit | 12GB |
|
||||
|Vicuna-13b-v1.5 | 8-bit | 24GB |
|
||||
|
||||
##### Download LLM
|
||||
You can also run the web front-end separately:
|
||||
|
||||
```bash
|
||||
cd DB-GPT
|
||||
mkdir models and cd models
|
||||
|
||||
# embedding model
|
||||
git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese
|
||||
or
|
||||
git clone https://huggingface.co/moka-ai/m3e-large
|
||||
|
||||
# llm model, if you use openai or Azure or tongyi llm api service, you don't need to download llm model
|
||||
git clone https://huggingface.co/lmsys/vicuna-13b-v1.5
|
||||
|
||||
cd web && npm install
|
||||
cp .env.template .env
|
||||
// Set API_BASE_URL to your DB-GPT server address, usually http://localhost:5670
|
||||
npm run dev
|
||||
```
|
||||
##### Environment variable configuration, configure the LLM_MODEL parameter in the `.env` file
|
||||
```bash
|
||||
# .env
|
||||
LLM_MODEL=vicuna-13b-v1.5
|
||||
```
|
||||
</TabItem>
|
||||
|
||||
<TabItem value="baichuan" label="baichuan">
|
||||
|
||||
##### Hardware requirements description
|
||||
| Model | Quantize | VRAM Size |
|
||||
|------------------ |--------------|----------------|
|
||||
|Baichuan-7b | 4-bit | 8GB |
|
||||
|Baichuan-7b | 8-bit | 12GB |
|
||||
|Baichuan-13b | 4-bit | 12GB |
|
||||
|Baichuan-13b | 8-bit | 20GB |
|
||||
|
||||
##### Download LLM
|
||||
|
||||
|
||||
```bash
|
||||
cd DB-GPT
|
||||
mkdir models and cd models
|
||||
|
||||
# embedding model
|
||||
git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese
|
||||
or
|
||||
git clone https://huggingface.co/moka-ai/m3e-large
|
||||
|
||||
# llm model
|
||||
git clone https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat
|
||||
or
|
||||
git clone https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat
|
||||
|
||||
```
|
||||
##### Environment variable configuration, configure the LLM_MODEL parameter in the `.env` file
|
||||
```bash
|
||||
# .env
|
||||
LLM_MODEL=baichuan2-13b
|
||||
```
|
||||
</TabItem>
|
||||
|
||||
<TabItem value="chatglm" label="chatglm">
|
||||
|
||||
##### Hardware requirements description
|
||||
| Model | Quantize | VRAM Size |
|
||||
|--------------------|-------------|----------------|
|
||||
| glm-4-9b-chat | Not support | 16GB |
|
||||
| ChatGLM-6b | 4-bit | 7GB |
|
||||
| ChatGLM-6b | 8-bit | 9GB |
|
||||
| ChatGLM-6b | FP16 | 14GB |
|
||||
|
||||
|
||||
##### Download LLM
|
||||
|
||||
```bash
|
||||
cd DB-GPT
|
||||
mkdir models and cd models
|
||||
|
||||
# embedding model
|
||||
git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese
|
||||
or
|
||||
git clone https://huggingface.co/moka-ai/m3e-large
|
||||
|
||||
# llm model
|
||||
git clone https://huggingface.co/THUDM/glm-4-9b-chat
|
||||
|
||||
```
|
||||
##### Environment variable configuration, configure the LLM_MODEL parameter in the `.env` file
|
||||
```bash
|
||||
# .env
|
||||
LLM_MODEL=glm-4-9b-chat
|
||||
```
|
||||
</TabItem>
|
||||
|
||||
</Tabs>
|
||||
|
||||
|
||||
### llama.cpp(CPU)
|
||||
:::info note
|
||||
⚠️ llama.cpp can be run on Mac M1 or Mac M2
|
||||
:::
|
||||
|
||||
DB-GPT also supports the lower-cost inference framework llama.cpp, which can be used through llama-cpp-python.
|
||||
|
||||
|
||||
#### Document preparation
|
||||
Before using llama.cpp, you first need to prepare the model file in gguf format. There are two ways to obtain it. You can choose one method to obtain the corresponding file.
|
||||
|
||||
:::tip
|
||||
Method 1: Download the converted model
|
||||
:::
|
||||
|
||||
If you want to use [Vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5), you can download the converted file [TheBloke/vicuna-13B-v1.5-GGUF](https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF), only this one file is needed. Download the file and put it in the model path. You need to rename the model to: `ggml-model-q4_0.gguf`.
|
||||
```bash
|
||||
wget https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF/resolve/main/vicuna-13b-v1.5.Q4_K_M.gguf -O models/ggml-model-q4_0.gguf
|
||||
```
|
||||
|
||||
:::tip
|
||||
Method 2: Convert files yourself
|
||||
:::
|
||||
During use, you can also convert the model file yourself according to the instructions in [llama.cpp#prepare-data–run](https://github.com/ggerganov/llama.cpp#prepare-data--run), and place the converted file in the models directory and name it `ggml-model-q4_0.gguf`.
|
||||
|
||||
|
||||
#### Install dependencies
|
||||
llama.cpp is an optional installation item in DB-GPT. You can install it with the following command.
|
||||
|
||||
```bash
|
||||
pip install -e ".[llama_cpp]"
|
||||
```
|
||||
|
||||
#### Modify configuration file
|
||||
Modify the `.env` file to use llama.cpp, and then you can start the service by running the [command](../quickstart.md)
|
||||
Open your browser and visit [`http://localhost:3000`](http://localhost:3000)
|
||||
|
||||
|
||||
#### More descriptions
|
||||
@@ -443,27 +302,6 @@ bash ./scripts/examples/load_examples.sh
|
||||
.\scripts\examples\load_examples.bat
|
||||
```
|
||||
|
||||
## Run service
|
||||
The DB-GPT service is packaged into a server, and the entire DB-GPT service can be started through the following command.
|
||||
```bash
|
||||
python dbgpt/app/dbgpt_server.py
|
||||
```
|
||||
:::info NOTE
|
||||
### Run service
|
||||
|
||||
If you are running version v0.4.3 or earlier, please start with the following command:
|
||||
|
||||
```bash
|
||||
python pilot/server/dbgpt_server.py
|
||||
```
|
||||
### Run DB-GPT with command `dbgpt`
|
||||
|
||||
If you want to run DB-GPT with the command `dbgpt`:
|
||||
|
||||
```bash
|
||||
dbgpt start webserver
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
## Visit website
|
||||
|
||||
@@ -81,6 +81,7 @@ uv --version
|
||||
defaultValue="openai"
|
||||
values={[
|
||||
{label: 'OpenAI (proxy)', value: 'openai'},
|
||||
{label: 'DeepSeek (proxy)', value: 'deepseek'},
|
||||
{label: 'GLM4 (local)', value: 'glm-4'},
|
||||
]}>
|
||||
|
||||
@@ -121,6 +122,54 @@ In the above command, `--config` specifies the configuration file, and `configs/
|
||||
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
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="deepseek" label="DeepSeek(proxy)">
|
||||
|
||||
```bash
|
||||
# Use uv to install dependencies needed for OpenAI proxy
|
||||
uv sync --all-packages --frozen \
|
||||
--extra "base" \
|
||||
--extra "proxy_openai" \
|
||||
--extra "rag" \
|
||||
--extra "storage_chromadb" \
|
||||
--extra "dbgpts"
|
||||
```
|
||||
|
||||
### Run Webserver
|
||||
|
||||
To run DB-GPT with DeepSeek proxy, you must provide the DeepSeek API key in the `configs/dbgpt-proxy-deepseek.toml`.
|
||||
|
||||
And you can specify your embedding model in the `configs/dbgpt-proxy-deepseek.toml` configuration file, the default embedding model is `BAAI/bge-large-zh-v1.5`. If you want to use other embedding models, you can modify the `configs/dbgpt-proxy-deepseek.toml` configuration file and specify the `name` and `provider` of the embedding model in the `[[models.embeddings]]` section. The provider can be `hf`.
|
||||
|
||||
```toml
|
||||
# Model Configurations
|
||||
[models]
|
||||
[[models.llms]]
|
||||
# name = "deepseek-chat"
|
||||
name = "deepseek-reasoner"
|
||||
provider = "proxy/deepseek"
|
||||
api_key = "your-deepseek-api-key"
|
||||
[[models.embeddings]]
|
||||
name = "BAAI/bge-large-zh-v1.5"
|
||||
provider = "hf"
|
||||
# If not provided, the model will be downloaded from the Hugging Face model hub
|
||||
# uncomment the following line to specify the model path in the local file system
|
||||
# path = "the-model-path-in-the-local-file-system"
|
||||
path = "/data/models/bge-large-zh-v1.5"
|
||||
```
|
||||
|
||||
Then run the following command to start the webserver:
|
||||
|
||||
```bash
|
||||
uv run dbgpt start webserver --config configs/dbgpt-proxy-deepseek.toml
|
||||
```
|
||||
In the above command, `--config` specifies the configuration file, and `configs/dbgpt-proxy-deepseek.toml` is the configuration file for the DeepSeek proxy model, you can also use other configuration files or create your own configuration file according to your needs.
|
||||
|
||||
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-deepseek.toml
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
|
||||
235
docs/sidebars.js
235
docs/sidebars.js
@@ -32,7 +32,149 @@ const sidebars = {
|
||||
label: "Quickstart",
|
||||
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "Installation",
|
||||
collapsed: false,
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/sourcecode',
|
||||
},
|
||||
{
|
||||
// type: 'doc',
|
||||
// id: 'installation/integrations',
|
||||
type: "category",
|
||||
label: "Other Integrations",
|
||||
collapsed: true,
|
||||
collapsible: true,
|
||||
items: [
|
||||
{
|
||||
type: "doc",
|
||||
id: "installation/integrations"
|
||||
},
|
||||
// {
|
||||
// type: "category",
|
||||
// label: "LLM Integrations",
|
||||
// items: [
|
||||
// {
|
||||
// type: "doc",
|
||||
// id: "installation/integrations/deepseek_llm_install"
|
||||
// },{
|
||||
// type: "doc",
|
||||
// id: "installation/integrations/ollama_llm_install"
|
||||
// },{
|
||||
// type: "doc",
|
||||
// id: "installation/integrations/claude_llm_install"
|
||||
// },{
|
||||
// type: "doc",
|
||||
// id: "installation/integrations/siliconflow_llm_install"
|
||||
// },{
|
||||
// type: "doc",
|
||||
// id: "installation/integrations/gitee_llm_install"
|
||||
// },
|
||||
// ]
|
||||
// },
|
||||
{
|
||||
type: "category",
|
||||
label: "Datasource Integrations",
|
||||
items: [
|
||||
{
|
||||
type: "doc",
|
||||
id: "installation/integrations/clickhouse_install"
|
||||
},{
|
||||
type: "doc",
|
||||
id: "installation/integrations/postgres_install"
|
||||
},{
|
||||
type: "doc",
|
||||
id: "installation/integrations/duckdb_install"
|
||||
},{
|
||||
type: "doc",
|
||||
id: "installation/integrations/mssql_install"
|
||||
},
|
||||
]
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "RAG Integrations",
|
||||
items: [
|
||||
{
|
||||
type: "doc",
|
||||
id: "installation/integrations/graph_rag_install"
|
||||
},
|
||||
{
|
||||
type: "doc",
|
||||
id: "installation/integrations/oceanbase_rag_install"
|
||||
},
|
||||
{
|
||||
type: "doc",
|
||||
id: "installation/integrations/milvus_rag_install"
|
||||
}
|
||||
]
|
||||
},
|
||||
|
||||
]
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/docker',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/docker_compose',
|
||||
},
|
||||
{
|
||||
type: 'category',
|
||||
label: 'Model Service Deployment',
|
||||
items: [
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/model_service/stand_alone',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/model_service/cluster',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/model_service/cluster_ha',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
type: 'category',
|
||||
label: 'Advanced Usage',
|
||||
items: [
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/advanced_usage/More_proxyllms',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/advanced_usage/ollama',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/advanced_usage/vLLM_inference',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/advanced_usage/Llamacpp_server',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/advanced_usage/OpenAI_SDK_call',
|
||||
},
|
||||
],
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: 'generated-index',
|
||||
description: 'DB-GPT provides a wealth of installation and deployment options, supporting source code deployment, Docker deployment, cluster deployment and other modes. At the same time, it can also be deployed and installed based on the AutoDL image.',
|
||||
slug: "installation",
|
||||
},
|
||||
},
|
||||
{
|
||||
type: "category",
|
||||
label: "AWEL(Agentic Workflow Expression Language)",
|
||||
@@ -217,99 +359,6 @@ const sidebars = {
|
||||
description: "AWEL (Agentic Workflow Expression Language) is an intelligent agent workflow expression language specifically designed for the development of large-model applications",
|
||||
},
|
||||
},
|
||||
|
||||
{
|
||||
type: "category",
|
||||
label: "Installation",
|
||||
collapsed: false,
|
||||
collapsible: false,
|
||||
items: [
|
||||
{
|
||||
type: 'doc',
|
||||
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',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/docker_compose',
|
||||
},
|
||||
{
|
||||
type: 'category',
|
||||
label: 'Model Service Deployment',
|
||||
items: [
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/model_service/stand_alone',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/model_service/cluster',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/model_service/cluster_ha',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
type: 'category',
|
||||
label: 'Advanced Usage',
|
||||
items: [
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/advanced_usage/More_proxyllms',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/advanced_usage/ollama',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/advanced_usage/vLLM_inference',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/advanced_usage/Llamacpp_server',
|
||||
},
|
||||
{
|
||||
type: 'doc',
|
||||
id: 'installation/advanced_usage/OpenAI_SDK_call',
|
||||
},
|
||||
],
|
||||
},
|
||||
],
|
||||
link: {
|
||||
type: 'generated-index',
|
||||
description: 'DB-GPT provides a wealth of installation and deployment options, supporting source code deployment, Docker deployment, cluster deployment and other modes. At the same time, it can also be deployed and installed based on the AutoDL image.',
|
||||
slug: "installation",
|
||||
},
|
||||
},
|
||||
|
||||
{
|
||||
type: "category",
|
||||
label: "Application",
|
||||
|
||||
@@ -159,8 +159,8 @@ class DBSchemaAssembler(BaseAssembler):
|
||||
table_chunks.append(chunk)
|
||||
|
||||
if self._field_vector_store_connector and field_chunks:
|
||||
self._field_vector_store_connector.load_document(field_chunks)
|
||||
return self._table_vector_store_connector.load_document(table_chunks)
|
||||
self._field_vector_store_connector.load_document_with_limit(field_chunks)
|
||||
return self._table_vector_store_connector.load_document_with_limit(table_chunks)
|
||||
|
||||
def _extract_info(self, chunks) -> List[Chunk]:
|
||||
"""Extract info from chunks."""
|
||||
|
||||
Reference in New Issue
Block a user