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https://github.com/csunny/DB-GPT.git
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fix: some typos using typos
Signed-off-by: yihong0618 <zouzou0208@gmail.com>
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@ -111,7 +111,7 @@ RUN if [ "$USE_TSINGHUA_UBUNTU" = "true" ]; then \
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WORKDIR /app
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# Copy the virtual environment from the previous stage
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# Use antoher name to avoid conflict when mounting user's local .venv
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# Use another name to avoid conflict when mounting user's local .venv
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ENV FINAL_VENV_NAME="/opt/.uv.venv"
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COPY --from=builder /app/.venv ${FINAL_VENV_NAME}
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COPY . .
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@ -7,7 +7,7 @@ Model API mainly means that DB-GPT adapts to various models and is uniformly pac
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## Model API
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In the DB-GPT project, we defined a service-oriented multi-model management framework (SMMF). Through the capabilities of SMMF, we can deploy multiple models, and these models provide external services through services. In order to allow clients to achieve seamless switching, we uniformly support the OpenAI SDK standards.
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- Detail useage tutorial: [OpenAI SDK calls local multi-model ](../../installation/advanced_usage/OpenAI_SDK_call.md)
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- Detail usage tutorial: [OpenAI SDK calls local multi-model ](../../installation/advanced_usage/OpenAI_SDK_call.md)
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**Example:** The following is an example of calling through openai sdk
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@ -1,9 +1,9 @@
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# Text2SQL Fine-Tuning
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We have split the Text2SQL-related fine-tuning code into the `DB-GPT-Hub `sub-project, and you can also view the source code directly.
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## Fine-tune pipline
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## Fine-tune pipeline
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Text2SQL pipline mainly includes the following processes:
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Text2SQL pipeline mainly includes the following processes:
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- [Build environment](#build-environment)
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- [Data processing](#data-processing)
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- [Model train](#model-train)
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@ -26,7 +26,7 @@ If you want to use OceanBase, please first start a docker container via the foll
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docker run --name=ob433 -e MODE=slim -p 2881:2881 -d quay.io/oceanbase/oceanbase-ce:4.3.3.0-100000142024101215
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```
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Donwload the partner package:
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Download the partner package:
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```shell
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pip install --upgrade --quiet pyobvector
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```
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@ -383,7 +383,7 @@ def knowledge_delete(
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# Confirm by user
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user_input = (
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input(
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f"Are you sure you want to delete the doucment {doc_name} in "
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f"Are you sure you want to delete the document {doc_name} in "
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f"knowledge space {space_name}? Type 'yes' to confirm: "
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)
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.strip()
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@ -394,6 +394,6 @@ def knowledge_delete(
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return
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client.document_delete(space_name, KnowledgeDocumentRequest(doc_name=doc_name))
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logger.info(
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f"Delete the doucment {doc_name} in knowledge space {space_name} "
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f"Delete the document {doc_name} in knowledge space {space_name} "
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f"successfully!"
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)
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