产品动态
damoxing=> CREATE EXTENSION tencentdb_ai CASCADE;NOTICE: installing required extension "pgcrypto"CREATE EXTENSIONdamoxing=> SELECT * FROM pg_extension;oid | extname | extowner | extnamespace | extrelocatable | extversion | extconfig | extcondition-------+--------------+----------+--------------+----------------+------------+-----------+--------------14275 | plpgsql | 10 | 11 | f | 1.0 | |16631 | pgcrypto | 16615 | 2200 | t | 1.3 | |16668 | tencentdb_ai | 16615 | 2200 | t | 1.0 | {16670} | {""}(3 rows)
DeepSeek-V3 模型。damoxing=> SELECT tencentdb_ai.add_model('deepseek-v3', '2024-05-22', 'ap-guangzhou', '$.Response.Choices[*].Message.Content');add_model-----------(1 row)damoxing=> SELECT tencentdb_ai.update_model_attr('deepseek-v3', 'SecretId', 'AKID**************');update_model_attr-------------------(1 row)damoxing=> SELECT tencentdb_ai.update_model_attr('deepseek-v3', 'SecretKey', '*******************');update_model_attr-------------------(1 row)
PostgreSQL 对象。damoxing=> CREATE TABLE chat_logs (id SERIAL PRIMARY KEY,user_input TEXT,system_response TEXT,timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP);CREATE TABLEdamoxing=>
deepseek-v3 的 API 接口聊天：damoxing=> CREATE OR REPLACE FUNCTION chat_with_deepseek(user_input TEXT)RETURNS TEXT AS $$DECLAREsystem_response TEXT;BEGINSELECT tencentdb_ai.chat_completions('deepseek-v3', user_input) INTO system_response;INSERT INTO chat_logs (user_input, system_response)VALUES (user_input, system_response);RETURN system_response;END;$$ LANGUAGE plpgsql;CREATE FUNCTIONdamoxing=>
damoxing=> SELECT chat_with_deepseek('hi，deepseek');chat_with_deepseek-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"Hello! It seems like you're trying to reach DeepSeek, but I’m not sure what you’re looking for. Could you clarify? Are you referring to a specific tool, service, or something else? Let me know how I can assist!"(1 row)damoxing=>
damoxing=> SELECT * FROM chat_logs ORDER BY timestamp DESC;id | user_input | system_response| timestamp----+--------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------3 | hi，deepseek | "Hello! It seems like you're trying to reach DeepSeek, but I’m not sure what you’re looking for. Could you clarify?Are you referring to a specific tool, service, or something else? Let me know how I can assist!" | 2025-02-18 16:02:39.145322(1 row)damoxing=>
tencentdb_ai 插件。damoxing_lke_embdding=> CREATE EXTENSION tencentdb_ai CASCADE;NOTICE: installing required extension "pgcrypto"CREATE EXTENSIONdamoxing_lke_embdding=> SELECT * FROM pg_extension;oid | extname | extowner | extnamespace | extrelocatable | extversion | extconfig | extcondition-------+--------------+----------+--------------+----------------+------------+-----------+--------------14275 | plpgsql | 10 | 11 | f | 1.0 | |16631 | pgcrypto | 16615 | 2200 | t | 1.3 | |16668 | tencentdb_ai | 16615 | 2200 | t | 1.0 | {16670} | {""}(3 rows)
lke-text-embedding-v1 模型。damoxing_lke_embdding=> SELECT tencentdb_ai.add_model('lke-text-embedding-v1', '2024-05-22', 'ap-guangzhou', NULL);-[ RECORD 1 ]add_model |damoxing_lke_embdding=> SELECT tencentdb_ai.update_model_attr('lke-text-embedding-v1', 'SecretId', 'AKID**************');-[ RECORD 1 ]-----+-update_model_attr |damoxing_lke_embdding=> SELECT tencentdb_ai.update_model_attr('lke-text-embedding-v1', 'SecretKey', '*******************');-[ RECORD 1 ]-----+-update_model_attr |damoxing_lke_embdding=>
damoxing_lke_embdding=> CREATE TABLE documents (id SERIAL PRIMARY KEY,content TEXT,embedding FLOAT8[]);CREATE TABLEdamoxing_lke_embdding=>
damoxing_lke_embdding=> CREATE OR REPLACE FUNCTION generate_embedding(doc_contents TEXT[])RETURNS FLOAT8[][] AS $$DECLAREembeddings FLOAT8[][] := '{}';embedding FLOAT8[];doc_content TEXT;BEGINSELECT * from tencentdb_ai.get_embedding('lke-text-embedding-v1', doc_contents) INTO embedding;RETURN embedding;END;$$ LANGUAGE plpgsql;CREATE FUNCTIONdamoxing_lke_embdding=>
damoxing_lke_embdding=> CREATE OR REPLACE FUNCTION insert_documents(doc_contents TEXT[])RETURNS VOID AS $$DECLAREembedding FLOAT8[];doc_content TEXT;BEGINdoc_content := doc_contents[0];embedding := generate_embedding(doc_contents);INSERT INTO documents (content, embedding)VALUES (doc_contents, embedding);END;$$ LANGUAGE plpgsql;CREATE FUNCTIONdamoxing_lke_embdding=>
damoxing_lke_embdding=> CREATE OR REPLACE FUNCTION cosine_similarity(vec1 FLOAT8[], vec2 FLOAT8[])RETURNS FLOAT8 AS $$DECLAREdot_product FLOAT8 := 0;norm1 FLOAT8 := 0;norm2 FLOAT8 := 0;similarity FLOAT8;BEGINFOR i IN 1..array_length(vec1, 1) LOOPdot_product := dot_product + vec1[i] * vec2[i];norm1 := norm1 + vec1[i] * vec1[i];norm2 := norm2 + vec2[i] * vec2[i];END LOOP;similarity := dot_product / (sqrt(norm1) * sqrt(norm2));RETURN similarity;END;$$ LANGUAGE plpgsql;damoxing_lke_embdding=>
damoxing_lke_embdding=> CREATE OR REPLACE FUNCTION search_similar_documents(query TEXT, limit_count INT)RETURNS TABLE(id INT, content TEXT, similarity FLOAT8) AS $$DECLAREquery_embedding FLOAT8[];BEGINquery_embedding := generate_embedding(ARRAY[query]);RETURN QUERYSELECT d.id, d.content, cosine_similarity(d.embedding, query_embedding) AS similarityFROM documents dORDER BY similarity DESCLIMIT limit_count;END;$$ LANGUAGE plpgsql;CREATE FUNCTIONdamoxing_lke_embdding=>
damoxing_lke_embdding=> SELECT insert_documents(ARRAY['This is a sample document.']);-[ RECORD 1 ]----+-insert_documents |damoxing_lke_embdding=> SELECT insert_documents(ARRAY['Another example of a document.']);-[ RECORD 1 ]----+-insert_documents |damoxing_lke_embdding=> SELECT insert_documents(ARRAY['This document is about PostgreSQL and text embeddings.']);-[ RECORD 1 ]----+-insert_documents |damoxing_lke_embdding=> SELECT insert_documents(ARRAY['Deep learning models can generate text embeddings.']);-[ RECORD 1 ]----+-insert_documents |damoxing_lke_embdding=>
damoxing_lke_embdding=> SELECT * FROM search_similar_documents('PostgreSQL text embeddings', 5);-[ RECORD 1 ]----------------------------------------------------------id | 3content | {"This document is about PostgreSQL and text embeddings."}similarity | 0.8649945496222797-[ RECORD 2 ]----------------------------------------------------------id | 4content | {"Deep learning models can generate text embeddings."}similarity | 0.6824638514797066-[ RECORD 3 ]----------------------------------------------------------id | 1content | {"This is a sample document."}similarity | 0.66412244051794-[ RECORD 4 ]----------------------------------------------------------id | 2content | {"Another example of a document."}similarity | 0.6142928906219256damoxing_lke_embdding=>
damoxing_rerank=> CREATE EXTENSION tencentdb_ai CASCADE;NOTICE: installing required extension "pgcrypto"CREATE EXTENSIONdamoxing=> SELECT * FROM pg_extension;oid | extname | extowner | extnamespace | extrelocatable | extversion | extconfig | extcondition-------+--------------+----------+--------------+----------------+------------+-----------+--------------14275 | plpgsql | 10 | 11 | f | 1.0 | |16631 | pgcrypto | 16615 | 2200 | t | 1.3 | |16668 | tencentdb_ai | 16615 | 2200 | t | 1.0 | {16670} | {""}(3 rows)
damoxing_rerank=> SELECT tencentdb_ai.add_model('lke-reranker-base', '2024-05-22', 'ap-guangzhou', '$.Response.ScoreList');add_model-----------(1 row)damoxing_rerank=> SELECT tencentdb_ai.update_model_attr('lke-reranker-base', 'SecretId', 'AKID**************');update_model_attr-------------------(1 row)damoxing_rerank=> SELECT tencentdb_ai.update_model_attr('lke-reranker-base', 'SecretKey', '*******************');update_model_attr-------------------(1 row)damoxing_rerank=> SELECT tencentdb_ai.add_model('lke-text-embedding-v1', '2024-05-22', 'ap-guangzhou', NULL);add_model-----------(1 row)damoxing_rerank=> SELECT tencentdb_ai.update_model_attr('lke-text-embedding-v1', 'SecretId', 'AKID**************');update_model_attr-------------------(1 row)damoxing_rerank=> SELECT tencentdb_ai.update_model_attr('lke-text-embedding-v1', 'SecretKey', '*******************');update_model_attr-------------------(1 row)
damoxing_rerank=> CREATE TABLE documents (id SERIAL PRIMARY KEY,content TEXT,embedding FLOAT8[]);CREATE TABLEdamoxing_rerank=>
damoxing_rerank=> CREATE OR REPLACE FUNCTION generate_embedding(doc_contents TEXT[])RETURNS FLOAT8[][] AS $$DECLAREembeddings FLOAT8[][] := '{}';embedding FLOAT8[];doc_content TEXT;BEGINSELECT * from tencentdb_ai.get_embedding('lke-text-embedding-v1', doc_contents) INTO embedding;RETURN embedding;END;$$ LANGUAGE plpgsql;CREATE FUNCTION
damoxing_rerank=> CREATE OR REPLACE FUNCTION insert_documents(doc_contents TEXT[])RETURNS VOID AS $$DECLAREembedding FLOAT8[];doc_content TEXT;BEGINdoc_content := doc_contents[0];embedding := generate_embedding(doc_contents);INSERT INTO documents (content, embedding)VALUES (doc_contents, embedding);END;$$ LANGUAGE plpgsql;CREATE FUNCTION
damoxing_rerank=> CREATE OR REPLACE FUNCTION retrieve_and_rerank(query TEXT)RETURNS TABLE (id INT, content TEXT, score FLOAT8) AS $$DECLAREkeyword_results RECORD;vector_results RECORD;query_vector VECTOR;rerank_content TEXT[];rerank_ids INT[];rerank_scores my_record;i INT;BEGIN-- 生成查询向量SELECT generate_embedding(ARRAY[query])::vector INTO query_vector;-- 初始化重排序内容数组rerank_content := ARRAY[]::TEXT[];rerank_ids := ARRAY[]::INT[];-- 关键词召回FOR keyword_results INSELECT d.id, d.content, d.embeddingFROM documents dWHERE to_tsvector('english', d.content) @@ plainto_tsquery('english', query)LOOP-- 向量召回FOR vector_results INSELECT d.id, d.content, d.embeddingFROM documents dWHERE d.id = keyword_results.idORDER BY d.embedding <-> query_vectorLIMIT 10LOOPrerank_content := array_append(rerank_content, vector_results.content);rerank_ids := array_append(rerank_ids, vector_results.id);END LOOP;END LOOP;-- 大模型重排序SELECT * FROM tencentdb_ai.run_rerank('lke-reranker-base', query, rerank_content) INTO rerank_scores;RETURN QUERYselect text_field, numeric_field from rerank_scores;END;$$ LANGUAGE plpgsql;
damoxing_rerank=>SELECT insert_documents(ARRAY['This is the first document.']);-[ RECORD 1 ]----+-insert_documents |damoxing_rerank=> SELECT * FROM retrieve_and_rerank('document');("{""This is the first document.""}",-1.5107422)
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