how to use deepseek model with wrenai

Open zhenglu696 opened this issue 10 months ago • 1 comments

I am using the deepseek model. When using the demo data from the official website, the following error is reported:

Please help me find out where the problem is in the configuration file.

-----config.yaml:----------- type: llm provider: litellm_llm models:

put DEEPSEEK_API_KEY=<your_api_key> in ~/.wrenai/.env

api_base: https://api.deepseek.com/v1 model: deepseek/deepseek-reasoner alias: default timeout: 120 kwargs: n: 1 temperature: 0 response_format: type: text
api_base: https://api.deepseek.com/v1 model: deepseek/deepseek-chat timeout: 120 kwargs: n: 1 temperature: 0 response_format: type: text
api_base: https://api.deepseek.com/v1 model: deepseek/deepseek-coder timeout: 120 kwargs: n: 1 temperature: 0 response_format: type: json_object

type: embedder provider: litellm_embedder models:

define OPENAI_API_KEY=<api_key> in ~/.wrenai/.env if you are using openai embedding model

please refer to LiteLLM documentation for more details: https://docs.litellm.ai/docs/providers

model: deepseek/deepseek-r1-distill-qwen-14b # put your embedding model name here, if it is not openai embedding model, should be /<model_name> alias: default api_base: https://api.deepseek.com/v1 # change this according to your embedding model timeout: 120

type: engine provider: wren_ui endpoint: http://wren-ui:3000

type: engine provider: wren_ibis endpoint: http://wren-ibis:8000

type: document_store provider: qdrant location: http://qdrant:6333 embedding_model_dim: 3072 # put your embedding model dimension here timeout: 120 recreate_index: true

please change the llm and embedder names to the ones you want to use

the format of llm and embedder should be .<model_name> such as litellm_llm.gpt-4o-2024-08-06

the pipes may be not the latest version, please refer to the latest version: https://raw.githubusercontent.com/canner/WrenAI/<WRENAI_VERSION_NUMBER>/docker/config.example.yaml

type: pipeline pipes:

name: db_schema_indexing embedder: litellm_embedder.default document_store: qdrant
name: historical_question_indexing embedder: litellm_embedder.default document_store: qdrant
name: table_description_indexing embedder: litellm_embedder.default document_store: qdrant
name: db_schema_retrieval llm: litellm_llm.default embedder: litellm_embedder.default document_store: qdrant
name: historical_question_retrieval embedder: litellm_embedder.default document_store: qdrant
name: sql_generation llm: litellm_llm.default engine: wren_ui
name: sql_correction llm: litellm_llm.default engine: wren_ui
name: followup_sql_generation llm: litellm_llm.default engine: wren_ui
name: sql_summary llm: litellm_llm.default
name: sql_answer llm: litellm_llm.default engine: wren_ui
name: sql_breakdown llm: litellm_llm.default engine: wren_ui
name: sql_expansion llm: litellm_llm.default engine: wren_ui
name: semantics_description llm: litellm_llm.default
name: relationship_recommendation llm: litellm_llm.default engine: wren_ui
name: question_recommendation llm: litellm_llm.default
name: question_recommendation_db_schema_retrieval llm: litellm_llm.default embedder: litellm_embedder.default document_store: qdrant
name: question_recommendation_sql_generation llm: litellm_llm.default engine: wren_ui
name: chart_generation llm: litellm_llm.default
name: chart_adjustment llm: litellm_llm.default
name: intent_classification llm: litellm_llm.default embedder: litellm_embedder.default document_store: qdrant
name: data_assistance llm: litellm_llm.default
name: sql_pairs_indexing document_store: qdrant embedder: litellm_embedder.default
name: sql_pairs_retrieval document_store: qdrant embedder: litellm_embedder.default llm: litellm_llm.default
name: preprocess_sql_data llm: litellm_llm.default
name: sql_executor engine: wren_ui
name: sql_question_generation llm: litellm_llm.default
name: sql_generation_reasoning llm: litellm_llm.default
name: followup_sql_generation_reasoning llm: litellm_llm.default
name: sql_regeneration llm: litellm_llm.default engine: wren_ui
name: instructions_indexing embedder: litellm_embedder.default document_store: qdrant
name: instructions_retrieval embedder: litellm_embedder.default document_store: qdrant
name: sql_functions_retrieval engine: wren_ibis document_store: qdrant
name: project_meta_indexing document_store: qdrant

settings: column_indexing_batch_size: 50 table_retrieval_size: 10 table_column_retrieval_size: 100 allow_using_db_schemas_without_pruning: false # if you want to use db schemas without pruning, set this to true. It will be faster allow_intent_classification: true allow_sql_generation_reasoning: true query_cache_maxsize: 1000 query_cache_ttl: 3600 langfuse_host: https://cloud.langfuse.com langfuse_enable: true logging_level: DEBUG development: true historical_question_retrieval_similarity_threshold: 0.9 sql_pairs_similarity_threshold: 0.7 sql_pairs_retrieval_max_size: 10 instructions_similarity_threshold: 0.7 instructions_top_k: 10

Mar 27 '25 11:03 zhenglu696

@zhenglu696 please use WREN_AI_SERVICE_VERSION=0.19.2 in ~/.wrenai/.env and use this config yaml as example

https://github.com/Canner/WrenAI/blob/main/wren-ai-service/docs/config_examples/config.deepseek.yaml

Apr 07 '25 03:04 cyyeh