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Commit 409192b5 authored by Wade's avatar Wade
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add ollama models

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agent_chat.log
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{"level":"info","pid":7027,"time":1749267392,"caller":"/Users/wade/project/wuban/agentchat/log.go:68","message":"This message appears when log level set to Debug or Info"} {"level":"info","pid":16602,"time":1749295282,"caller":"/Users/wade/project/wuban/agentchat/log.go:68","message":"This message appears when log level set to Debug or Info"}
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{"level":"info","pid":7027,"method":"DeepSeek.DefineModel","model_name":"deepseek-coder","time":1749267392,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:128","message":"Defining DeepSeek model"} {"level":"info","pid":16602,"method":"DeepSeek.DefineModel","model_name":"deepseek-coder","time":1749295282,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":7027,"method":"Deep lantern","model_name":"deepseek-coder","time":1749267392,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:158","message":"Model defined successfully"} {"level":"info","pid":16602,"method":"Deep lantern","model_name":"deepseek-coder","time":1749295282,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":7027,"method":"DeepSeek.DefineModel","model_name":"deepseek-reasoner","time":1749267392,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:128","message":"Defining DeepSeek model"} {"level":"info","pid":16602,"method":"DeepSeek.DefineModel","model_name":"deepseek-reasoner","time":1749295282,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
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{"level":"info","pid":7027,"method":"Milvus.Init","time":1749267392,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:75","message":"Initializing Milvus plugin"} {"level":"info","pid":16602,"method":"Milvus.Init","time":1749295282,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:75","message":"Initializing Milvus plugin"}
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{"level":"info","pid":7027,"method":"GraphKnowledge.Init","time":1749267393,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:299","message":"Initializing GraphKnowledge plugin"} {"level":"info","pid":16602,"method":"GraphKnowledge.Init","time":1749295283,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:299","message":"Initializing GraphKnowledge plugin"}
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{"level":"info","pid":7027,"method":"generator.generate","model_name":"deepseek-chat","content_parts":1,"time":1749267441,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:252","message":"Model generation completed successfully"} {"level":"info","pid":16732,"method":"Milvus.Init","time":1749295322,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:75","message":"Initializing Milvus plugin"}
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{"level":"info","pid":16732,"method":"KnowledgeClient.Init","time":1749295324,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:109","message":"Initialization successful"}
{"level":"info","pid":16732,"time":1749295324,"caller":"/Users/wade/project/wuban/agentchat/flow.go:200","message":"KnowledgeClient initialized successfully"}
{"level":"info","pid":16732,"method":"DefineModelsFlow","provider":"","time":1749295332,"caller":"/Users/wade/project/wuban/agentchat/flow.go:38","message":"Listing models and embedders for provider"}
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{"level":"info","pid":17042,"method":"Deep lantern","model_name":"deepseek-chat","time":1749295409,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":17042,"method":"DeepSeek.DefineModel","model_name":"deepseek-coder","time":1749295409,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":17042,"method":"Deep lantern","model_name":"deepseek-coder","time":1749295409,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":17042,"method":"DeepSeek.DefineModel","model_name":"deepseek-reasoner","time":1749295409,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":17042,"method":"Deep lantern","model_name":"deepseek-reasoner","time":1749295409,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":17042,"method":"DeepSeek.Init","time":1749295409,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:123","message":"Initialization successful"}
{"level":"info","pid":17042,"method":"Milvus.Init","time":1749295409,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:75","message":"Initializing Milvus plugin"}
{"level":"info","pid":17042,"method":"Milvus.Init","time":1749295410,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:87","message":"Initialization successful"}
{"level":"info","pid":17042,"method":"GraphKnowledge.Init","time":1749295410,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:299","message":"Initializing GraphKnowledge plugin"}
{"level":"info","pid":17042,"method":"NewClient","ip":"54.92.111.204","port":5670,"time":1749295410,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:93","message":"Creating new GraphRAG client"}
{"level":"info","pid":17042,"method":"GraphKnowledge.Init","time":1749295410,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:311","message":"Initialization successful"}
{"level":"info","pid":17042,"method":"DefineIndexerAndRetriever","collection":"chatRag1","dimension":768,"time":1749295410,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:152","message":"Defining indexer and retriever"}
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{"level":"info","pid":17042,"method":"Milvus.newDocStore","collection":"chatRag1","time":1749295411,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:302","message":"Doc store created successfully"}
{"level":"info","pid":17042,"method":"DefineIndexerAndRetriever","collection":"chatRag1","time":1749295411,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:182","message":"Indexer and retriever defined successfully"}
{"level":"info","pid":17042,"method":"DefineIndexerAndRetriever","time":1749295411,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:357","message":"Defining indexer and retriever"}
{"level":"info","pid":17042,"method":"GraphKnowledge.newDocStore","space_id":"","model_name":"Qwen/Qwen2.5-Coder-32B-Instruct","time":1749295411,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:393","message":"Creating new doc store"}
{"level":"info","pid":17042,"method":"GraphKnowledge.newDocStore","time":1749295411,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:399","message":"Doc store created successfully"}
{"level":"info","pid":17042,"method":"DefineIndexerAndRetriever","time":1749295411,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:376","message":"Indexer and retriever defined successfully"}
{"level":"info","pid":17042,"method":"NewKnowledgeClient","endpoint":"lkeap.tencentcloudapi.com","region":"ap-guangzhou","secret_id":"","token":"","time":1749295412,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:43","message":"Creating new KnowledgeClient"}
{"level":"info","pid":17042,"method":"KnowledgeClient.Init","time":1749295412,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:51","message":"Initializing KnowledgeClient"}
{"level":"info","pid":17042,"method":"KnowledgeClient.Init","time":1749295412,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:109","message":"Initialization successful"}
{"level":"info","pid":17042,"time":1749295412,"caller":"/Users/wade/project/wuban/agentchat/flow.go:207","message":"KnowledgeClient initialized successfully"}
{"level":"info","pid":17042,"method":"DefineModelsFlow","provider":"","time":1749295413,"caller":"/Users/wade/project/wuban/agentchat/flow.go:38","message":"Listing models and embedders for provider"}
{"level":"info","pid":17311,"time":1749295462,"caller":"/Users/wade/project/wuban/agentchat/log.go:68","message":"This message appears when log level set to Debug or Info"}
{"level":"info","pid":17311,"method":"DeepSeek.Init","time":1749295462,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:98","message":"Initializing DeepSeek plugin"}
{"level":"info","pid":17311,"method":"DeepSeek.DefineModel","model_name":"deepseek-chat","time":1749295462,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":17311,"method":"Deep lantern","model_name":"deepseek-chat","time":1749295462,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":17311,"method":"DeepSeek.DefineModel","model_name":"deepseek-coder","time":1749295462,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":17311,"method":"Deep lantern","model_name":"deepseek-coder","time":1749295462,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":17311,"method":"DeepSeek.DefineModel","model_name":"deepseek-reasoner","time":1749295462,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":17311,"method":"Deep lantern","model_name":"deepseek-reasoner","time":1749295462,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":17311,"method":"DeepSeek.Init","time":1749295462,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:123","message":"Initialization successful"}
{"level":"info","pid":17311,"method":"Milvus.Init","time":1749295462,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:75","message":"Initializing Milvus plugin"}
{"level":"info","pid":17311,"method":"Milvus.Init","time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:87","message":"Initialization successful"}
{"level":"info","pid":17311,"method":"GraphKnowledge.Init","time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:299","message":"Initializing GraphKnowledge plugin"}
{"level":"info","pid":17311,"method":"NewClient","ip":"54.92.111.204","port":5670,"time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:93","message":"Creating new GraphRAG client"}
{"level":"info","pid":17311,"method":"GraphKnowledge.Init","time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:311","message":"Initialization successful"}
{"level":"info","pid":17311,"method":"DefineIndexerAndRetriever","collection":"chatRag1","dimension":768,"time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:152","message":"Defining indexer and retriever"}
{"level":"info","pid":17311,"method":"Milvus.newDocStore","collection":"chatRag1","dimension":768,"time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:201","message":"Creating new doc store"}
{"level":"info","pid":17311,"method":"Milvus.newDocStore","collection":"chatRag1","time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:302","message":"Doc store created successfully"}
{"level":"info","pid":17311,"method":"DefineIndexerAndRetriever","collection":"chatRag1","time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:182","message":"Indexer and retriever defined successfully"}
{"level":"info","pid":17311,"method":"DefineIndexerAndRetriever","time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:357","message":"Defining indexer and retriever"}
{"level":"info","pid":17311,"method":"GraphKnowledge.newDocStore","space_id":"","model_name":"Qwen/Qwen2.5-Coder-32B-Instruct","time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:393","message":"Creating new doc store"}
{"level":"info","pid":17311,"method":"GraphKnowledge.newDocStore","time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:399","message":"Doc store created successfully"}
{"level":"info","pid":17311,"method":"DefineIndexerAndRetriever","time":1749295464,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:376","message":"Indexer and retriever defined successfully"}
{"level":"info","pid":17311,"method":"NewKnowledgeClient","endpoint":"lkeap.tencentcloudapi.com","region":"ap-guangzhou","secret_id":"","token":"","time":1749295465,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:43","message":"Creating new KnowledgeClient"}
{"level":"info","pid":17311,"method":"KnowledgeClient.Init","time":1749295465,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:51","message":"Initializing KnowledgeClient"}
{"level":"info","pid":17311,"method":"KnowledgeClient.Init","time":1749295465,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:109","message":"Initialization successful"}
{"level":"info","pid":17311,"time":1749295465,"caller":"/Users/wade/project/wuban/agentchat/flow.go:201","message":"KnowledgeClient initialized successfully"}
{"level":"info","pid":17311,"method":"DefineModelsFlow","provider":"","time":1749295477,"caller":"/Users/wade/project/wuban/agentchat/flow.go:38","message":"Listing models and embedders for provider"}
{"level":"info","pid":17408,"time":1749295519,"caller":"/Users/wade/project/wuban/agentchat/log.go:68","message":"This message appears when log level set to Debug or Info"}
{"level":"info","pid":17408,"method":"DeepSeek.Init","time":1749295519,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:98","message":"Initializing DeepSeek plugin"}
{"level":"info","pid":17408,"method":"DeepSeek.DefineModel","model_name":"deepseek-chat","time":1749295519,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":17408,"method":"Deep lantern","model_name":"deepseek-chat","time":1749295519,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":17408,"method":"DeepSeek.DefineModel","model_name":"deepseek-coder","time":1749295519,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":17408,"method":"Deep lantern","model_name":"deepseek-coder","time":1749295519,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":17408,"method":"DeepSeek.DefineModel","model_name":"deepseek-reasoner","time":1749295519,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":17408,"method":"Deep lantern","model_name":"deepseek-reasoner","time":1749295519,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":17408,"method":"DeepSeek.Init","time":1749295519,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:123","message":"Initialization successful"}
{"level":"info","pid":17408,"method":"Milvus.Init","time":1749295519,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:75","message":"Initializing Milvus plugin"}
{"level":"info","pid":17408,"method":"Milvus.Init","time":1749295520,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:87","message":"Initialization successful"}
{"level":"info","pid":17408,"method":"GraphKnowledge.Init","time":1749295520,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:299","message":"Initializing GraphKnowledge plugin"}
{"level":"info","pid":17408,"method":"NewClient","ip":"54.92.111.204","port":5670,"time":1749295520,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:93","message":"Creating new GraphRAG client"}
{"level":"info","pid":17408,"method":"GraphKnowledge.Init","time":1749295520,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:311","message":"Initialization successful"}
{"level":"info","pid":17408,"method":"DefineIndexerAndRetriever","collection":"chatRag1","dimension":768,"time":1749295520,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:152","message":"Defining indexer and retriever"}
{"level":"info","pid":17408,"method":"Milvus.newDocStore","collection":"chatRag1","dimension":768,"time":1749295520,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:201","message":"Creating new doc store"}
{"level":"info","pid":17408,"method":"Milvus.newDocStore","collection":"chatRag1","time":1749295521,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:302","message":"Doc store created successfully"}
{"level":"info","pid":17408,"method":"DefineIndexerAndRetriever","collection":"chatRag1","time":1749295521,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:182","message":"Indexer and retriever defined successfully"}
{"level":"info","pid":17408,"method":"DefineIndexerAndRetriever","time":1749295521,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:357","message":"Defining indexer and retriever"}
{"level":"info","pid":17408,"method":"GraphKnowledge.newDocStore","space_id":"","model_name":"Qwen/Qwen2.5-Coder-32B-Instruct","time":1749295521,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:393","message":"Creating new doc store"}
{"level":"info","pid":17408,"method":"GraphKnowledge.newDocStore","time":1749295521,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:399","message":"Doc store created successfully"}
{"level":"info","pid":17408,"method":"DefineIndexerAndRetriever","time":1749295521,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:376","message":"Indexer and retriever defined successfully"}
{"level":"info","pid":17408,"method":"NewKnowledgeClient","endpoint":"lkeap.tencentcloudapi.com","region":"ap-guangzhou","secret_id":"","token":"","time":1749295522,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:43","message":"Creating new KnowledgeClient"}
{"level":"info","pid":17408,"method":"KnowledgeClient.Init","time":1749295522,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:51","message":"Initializing KnowledgeClient"}
{"level":"info","pid":17408,"method":"KnowledgeClient.Init","time":1749295522,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:109","message":"Initialization successful"}
{"level":"info","pid":17408,"time":1749295522,"caller":"/Users/wade/project/wuban/agentchat/flow.go:201","message":"KnowledgeClient initialized successfully"}
{"level":"info","pid":17408,"method":"DefineModelsFlow","provider":"","time":1749295526,"caller":"/Users/wade/project/wuban/agentchat/flow.go:38","message":"Listing models and embedders for provider"}
{"level":"info","pid":19897,"time":1749296374,"caller":"/Users/wade/project/wuban/agentchat/log.go:68","message":"This message appears when log level set to Debug or Info"}
{"level":"info","pid":19897,"method":"DeepSeek.Init","time":1749296374,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:98","message":"Initializing DeepSeek plugin"}
{"level":"info","pid":19897,"method":"DeepSeek.DefineModel","model_name":"deepseek-chat","time":1749296374,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":19897,"method":"Deep lantern","model_name":"deepseek-chat","time":1749296374,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":19897,"method":"DeepSeek.DefineModel","model_name":"deepseek-coder","time":1749296374,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":19897,"method":"Deep lantern","model_name":"deepseek-coder","time":1749296374,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":19897,"method":"DeepSeek.DefineModel","model_name":"deepseek-reasoner","time":1749296374,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:132","message":"Defining DeepSeek model"}
{"level":"info","pid":19897,"method":"Deep lantern","model_name":"deepseek-reasoner","time":1749296374,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:162","message":"Model defined successfully"}
{"level":"info","pid":19897,"method":"DeepSeek.Init","time":1749296374,"caller":"/Users/wade/project/wuban/agentchat/plugins/deepseek/deepseek.go:123","message":"Initialization successful"}
{"level":"info","pid":19897,"method":"Milvus.Init","time":1749296374,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:75","message":"Initializing Milvus plugin"}
{"level":"info","pid":19897,"method":"Milvus.Init","time":1749296376,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:87","message":"Initialization successful"}
{"level":"info","pid":19897,"method":"GraphKnowledge.Init","time":1749296376,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:299","message":"Initializing GraphKnowledge plugin"}
{"level":"info","pid":19897,"method":"NewClient","ip":"54.92.111.204","port":5670,"time":1749296376,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:93","message":"Creating new GraphRAG client"}
{"level":"info","pid":19897,"method":"GraphKnowledge.Init","time":1749296376,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:311","message":"Initialization successful"}
{"level":"info","pid":19897,"method":"DefineIndexerAndRetriever","collection":"chatRag1","dimension":768,"time":1749296376,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:152","message":"Defining indexer and retriever"}
{"level":"info","pid":19897,"method":"Milvus.newDocStore","collection":"chatRag1","dimension":768,"time":1749296376,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:201","message":"Creating new doc store"}
{"level":"info","pid":19897,"method":"Milvus.newDocStore","collection":"chatRag1","time":1749296377,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:302","message":"Doc store created successfully"}
{"level":"info","pid":19897,"method":"DefineIndexerAndRetriever","collection":"chatRag1","time":1749296377,"caller":"/Users/wade/project/wuban/agentchat/plugins/milvus/milvus.go:182","message":"Indexer and retriever defined successfully"}
{"level":"info","pid":19897,"method":"DefineIndexerAndRetriever","time":1749296377,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:357","message":"Defining indexer and retriever"}
{"level":"info","pid":19897,"method":"GraphKnowledge.newDocStore","space_id":"","model_name":"Qwen/Qwen2.5-Coder-32B-Instruct","time":1749296377,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:393","message":"Creating new doc store"}
{"level":"info","pid":19897,"method":"GraphKnowledge.newDocStore","time":1749296377,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:399","message":"Doc store created successfully"}
{"level":"info","pid":19897,"method":"DefineIndexerAndRetriever","time":1749296377,"caller":"/Users/wade/project/wuban/agentchat/plugins/graphrag/graph.go:376","message":"Indexer and retriever defined successfully"}
{"level":"info","pid":19897,"method":"NewKnowledgeClient","endpoint":"lkeap.tencentcloudapi.com","region":"ap-guangzhou","secret_id":"","token":"","time":1749296377,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:43","message":"Creating new KnowledgeClient"}
{"level":"info","pid":19897,"method":"KnowledgeClient.Init","time":1749296377,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:51","message":"Initializing KnowledgeClient"}
{"level":"info","pid":19897,"method":"KnowledgeClient.Init","time":1749296377,"caller":"/Users/wade/project/wuban/agentchat/plugins/knowledge/knowledge.go:109","message":"Initialization successful"}
{"level":"info","pid":19897,"time":1749296377,"caller":"/Users/wade/project/wuban/agentchat/flow.go:327","message":"KnowledgeClient initialized successfully"}
{"level":"info","pid":19897,"method":"DefineModelsFlow","provider":"","time":1749296388,"caller":"/Users/wade/project/wuban/agentchat/flow.go:39","message":"Listing models and embedders for provider"}
{"level":"info","pid":19897,"method":"DefineModelsFlow","provider":"","time":1749296388,"caller":"/Users/wade/project/wuban/agentchat/flow.go:149","message":"------default---"}
{"level":"info","pid":19897,"method":"DefineModelsFlow","provider":"ollama","time":1749296437,"caller":"/Users/wade/project/wuban/agentchat/flow.go:39","message":"Listing models and embedders for provider"}
docs/swagger.yaml
View file @ 409192b5
...@@ -149,6 +149,33 @@ paths: ...@@ -149,6 +149,33 @@ paths:
data: '{"id": 1}' data: '{"id": 1}'
code: 200 code: 200
msg: "GraphRAG index data stored successfully" msg: "GraphRAG index data stored successfully"
/models:
post:
summary: List available models for a provider
description: Returns a list of available models for the specified provider (e.g., "deepseek" or "googleai").
tags:
- Chat
requestBody:
required: true
content:
application/json:
schema:
type: string
description: The provider to list models for
example: "deepseek"
responses:
'200':
description: Successful response with the list of models
content:
application/json:
schema:
$ref: '#/components/schemas/Response'
examples:
success:
value:
data: '{"deepseek-chat":{"label":"DeepSeek Chat","supports":{"multiturn":true,"systemRole":true,"media":false,"tools":true},"versions":[]},"deepseek-coder":{"label":"DeepSeek Coder","supports":{"multiturn":true,"systemRole":true,"media":false,"tools":true},"versions":[]},"deepseek-reasoner":{"label":"DeepSeek Reasoner","supports":{"multiturn":true,"systemRole":true,"media":false,"tools":true},"versions":[]}}'
code: 200
msg: "Models listed successfully"
/chat: /chat:
post: post:
summary: Send a chat message summary: Send a chat message
...@@ -228,8 +255,29 @@ components: ...@@ -228,8 +255,29 @@ components:
msg: msg:
type: string type: string
description: A message describing the result description: A message describing the result
example: "Milvus index data stored successfully" example: "Models listed successfully"
required: required:
- data - data
- code - code
- msg - msg
# components:
# schemas:
# Response:
# type: object
# properties:
# data:
# type: string
# description: The response data, typically a JSON string or message
# example: '{"id": 1}'
# code:
# type: integer
# description: The response code (200 for success, 400 for invalid input, etc.)
# example: 200
# msg:
# type: string
# description: A message describing the result
# example: "Milvus index data stored successfully"
# required:
# - data
# - code
# - msg
flow.go 0 → 100644
View file @ 409192b5
package main
import (
"context"
"encoding/json"
"fmt"
"strings"
"time"
"github.com/firebase/genkit/go/ai"
"github.com/firebase/genkit/go/genkit"
"github.com/wade-liwei/agentchat/plugins/deepseek"
"github.com/wade-liwei/agentchat/plugins/graphrag"
"github.com/wade-liwei/agentchat/plugins/knowledge"
"github.com/wade-liwei/agentchat/plugins/question"
"github.com/wade-liwei/agentchat/util"
"google.golang.org/genai"
// Import knowledge package
"github.com/firebase/genkit/go/plugins/googlegenai"
"github.com/firebase/genkit/go/plugins/ollama"
"github.com/rs/zerolog/log"
_ "github.com/wade-liwei/agentchat/docs" // 导入生成的 Swagger 文档
)
// ProviderInfo 封装模型和嵌入器信息
type ProviderInfo struct {
Models map[string]ai.ModelInfo `json:"models,omitempty"`
Embedders []string `json:"embedders,omitempty"`
Provider string `json:"provider,omitempty"`
}
// DefineModelsFlow 定义 models 流
func DefineModelsFlow(g *genkit.Genkit) {
genkit.DefineFlow(g, "models", func(ctx context.Context, provider string) ([]ProviderInfo, error) {
log.Info().
Str("method", "DefineModelsFlow").
Str("provider", provider).
Msg("Listing models and embedders for provider")
infos := make([]ProviderInfo, 0, 3)
switch provider {
case googlegenai.GoogleAIProvider:
googleModels, err := googlegenai.ListModels(googlegenai.GoogleAIProvider)
if err != nil {
log.Error().
Str("method", "DefineModelsFlow").
Str("provider", provider).
Err(err).
Msg("Failed to list Google AI provider info")
return nil, err
}
googleEmbedders, err := googlegenai.ListEmbedders(genai.BackendGeminiAPI)
if err != nil {
log.Error().
Str("method", "DefineModelsFlow").
Str("provider", provider).
Err(err).
Msg("Failed to list Google AI embedders")
return nil, err
}
googleInfo := ProviderInfo{
Provider: googlegenai.GoogleAIProvider,
Models: googleModels,
Embedders: googleEmbedders,
}
infos = append(infos, googleInfo)
return infos, nil
case deepseek.Provider:
dsModels := deepseek.ListModels()
infos = append(infos, ProviderInfo{
Provider: deepseek.Provider,
Models: dsModels,
Embedders: []string{}, // DeepSeek 不支持嵌入器
})
return infos, nil
case ollama.Provider:
ollamaModels, err := ollama.ListModels()
if err != nil {
log.Error().
Str("method", "DefineModelsFlow").
Str("provider", provider).
Err(err).
Msg("Failed to list Ollama provider info")
return nil, err
}
infos = append(infos, ProviderInfo{
Provider: ollama.Provider,
Models: ollamaModels,
})
return infos, nil
default:
// Google AI
googleModels, err := googlegenai.ListModels(googlegenai.GoogleAIProvider)
if err != nil {
log.Error().
Str("method", "DefineModelsFlow").
Str("provider", googlegenai.GoogleAIProvider).
Err(err).
Msg("Failed to list Google AI provider info")
return nil, err
}
googleEmbedders, err := googlegenai.ListEmbedders(genai.BackendGeminiAPI)
if err != nil {
log.Error().
Str("method", "DefineModelsFlow").
Str("provider", googlegenai.GoogleAIProvider).
Err(err).
Msg("Failed to list Google AI embedders")
return nil, err
}
infos = append(infos, ProviderInfo{
Provider: googlegenai.GoogleAIProvider,
Models: googleModels,
Embedders: googleEmbedders,
})
// DeepSeek
dsModels := deepseek.ListModels()
infos = append(infos, ProviderInfo{
Provider: deepseek.Provider,
Models: dsModels,
Embedders: []string{}, // DeepSeek 不支持嵌入器
})
ollamaModels, err := ollama.ListModels()
if err != nil {
log.Error().
Str("method", "DefineModelsFlow").
Str("provider", provider).
Err(err).
Msg("Failed to list Ollama provider info")
return nil, err
}
infos = append(infos, ProviderInfo{
Provider: ollama.Provider,
Models: ollamaModels,
})
log.Info().
Str("method", "DefineModelsFlow").
Str("provider", provider).
Msg("------default---")
return infos, nil
}
})
}
// DefineModelsFlow 定义 models 流
// func DefineModelsFlow(g *genkit.Genkit) {
// genkit.DefineFlow(g, "models", func(ctx context.Context, provider string) ([]ProviderInfo, error) {
// log.Info().
// Str("method", "DefineModelsFlow").
// Str("provider", provider).
// Msg("Listing models and embedders for provider")
// infos := make([]ProviderInfo, 0, 2)
// switch provider {
// case googlegenai.GoogleAIProvider:
// goggleModels, err := googlegenai.ListModels(googlegenai.GoogleAIProvider)
// if err != nil {
// log.Error().
// Str("method", "DefineModelsFlow").
// Str("provider", provider).
// Err(err).
// Msg("Failed to list Google AI provider info")
// return nil, err
// }
// goggleEmbedders, err := googlegenai.ListEmbedders(genai.BackendGeminiAPI) //ListModels(googlegenai.GoogleAIProvider)
// if err != nil {
// log.Error().
// Str("method", "DefineModelsFlow").
// Str("provider", provider).
// Err(err).
// Msg("Failed to list Google AI provider info")
// return nil, err
// }
// googleInfo := ProviderInfo{
// Provider: googlegenai.GoogleAIProvider,
// Models: goggleModels,
// Embedders: goggleEmbedders,
// }
// infos = append(infos, googleInfo)
// return infos, nil
// case deepseek.Provider:
// dsModels := deepseek.ListModels()
// infos = append(infos, ProviderInfo{
// Provider: deepseek.Provider,
// Models: dsModels,
// })
// return infos, nil
// default:
// infos := make([]ProviderInfo, 0, 2)
// goggleModels, err := googlegenai.ListModels(googlegenai.GoogleAIProvider)
// if err != nil {
// return nil, err
// }
// goggleEmbedders, err := googlegenai.ListEmbedders(genai.BackendGeminiAPI) //ListModels(googlegenai.GoogleAIProvider)
// if err != nil {
// return nil, err
// }
// googleInfo := ProviderInfo{
// Provider: googlegenai.GoogleAIProvider,
// Models: goggleModels,
// Embedders: goggleEmbedders,
// }
// infos = append(infos, googleInfo)
// dsModels := deepseek.ListModels()
// infos = append(infos, ProviderInfo{
// Provider: deepseek.Provider,
// Models: dsModels,
// })
// fmt.Println(provider,"------default---")
// return infos, err
// }
// })
// }
func DefineGraphFlow(g *genkit.Genkit, graphIndexer ai.Indexer) {
genkit.DefineFlow(g, "index/graph", func(ctx context.Context, input *GraphInput) (Response, error) {
opt := graphrag.IndexReqOption{
UserId: input.UserID,
UserName: input.Username,
}
resDocName := ""
if v, ok := input.Metadata[graphrag.DocNameKey]; !ok {
// Generate random docName.
docName, err := graphrag.GenerateRandomDocName(8)
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("generate random docName for document %w", err),
}, nil
}
input.Metadata[graphrag.DocNameKey] = docName
resDocName = docName
} else {
if str, isString := v.(string); isString {
resDocName = str
}
}
doc := ai.DocumentFromText(input.Content, input.Metadata)
err := graphIndexer.Index(ctx, &ai.IndexerRequest{
Documents: []*ai.Document{doc},
Options: &opt,
})
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("index document: %w", err),
}, nil
}
return Response{
Code: 200,
Msg: fmt.Sprintf("Document indexed successfully, docname %s", resDocName),
}, nil
})
}
func DefineDocumentFlow(g *genkit.Genkit, indexer ai.Indexer) {
// 定义文档索引流
genkit.DefineFlow(g, "index/document", func(ctx context.Context, input *DocumentInput) (Response, error) {
if input.Metadata == nil {
input.Metadata = make(map[string]any)
}
input.Metadata[util.UserIdKey] = input.UserID
input.Metadata[util.UserNameKey] = input.Username
doc := ai.DocumentFromText(input.Content, input.Metadata)
err := indexer.Index(ctx, &ai.IndexerRequest{
Documents: []*ai.Document{doc},
})
if err != nil {
return Response{
Code: 500,
Msg: err.Error(),
}, nil
}
return Response{
Code: 200,
Msg: "Document indexed successfully",
}, nil
})
}
func DefineChatFlow(g *genkit.Genkit, retriever ai.Retriever, graphRetriever ai.Retriever, pgConnString string) {
qa, err := question.InitQAStore(pgConnString)
if err != nil {
log.Fatal().Msgf("InitQAStore failed: %v", err)
}
// Initialize KnowledgeClient with test parameters
kc := knowledge.NewKnowledgeClient(knowledge.ClientConfig{
Endpoint: "lkeap.tencentcloudapi.com",
Region: "ap-guangzhou",
})
if err := kc.Init(context.Background()); err != nil {
log.Fatal().Msgf("Failed to initialize KnowledgeClient: %v", err)
}
log.Info().Msg("KnowledgeClient initialized successfully")
// Define a simple flow that generates jokes about a given topic
genkit.DefineFlow(g, "chat", func(ctx context.Context, input *ChatInput) (Response, error) {
ctxAsJson, _ := json.Marshal(ctx)
log.Info().Msgf("input----ctxAsJson----%s", string(ctxAsJson))
inputAsJson, err := json.Marshal(input)
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("json.Marshal: %w", err),
}, nil
}
log.Info().Msgf("input--------%s", string(inputAsJson))
idx, lastQa, lastok, err := qa.WriteAndGetLatestQA(context.Background(), question.QA{
FromID: &input.FromID,
From: &input.From,
Question: &input.Content,
To: &input.To,
ToID: &input.ToID,
Milvus: &input.Milvus,
Graph: &input.Graph,
})
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("WriteAndGetLatestQA: %w", err),
}, nil
}
qaAsJson, err := json.Marshal(lastQa)
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("json.Marshal(lastQa): %w", err),
}, nil
}
log.Info().Msgf("qaAsJson--------%s", string(qaAsJson))
promptInput := &simpleQaPromptInput{
Query: input.Content,
}
if lastok && lastQa.Summary != nil {
promptInput.Summary = *lastQa.Summary
}
metaData := make(map[string]any)
metaData[util.UserIdKey] = input.ToID
metaData[util.UserNameKey] = input.To
dRequest := ai.DocumentFromText(input.Content, metaData)
response, err := ai.Retrieve(ctx, retriever, ai.WithDocs(dRequest))
if err != nil {
log.Error().Msgf("milvus Retrieve err.Error() %s", err.Error())
} else {
var sb strings.Builder
for _, d := range response.Documents {
sb.WriteString(d.Content[0].Text)
sb.WriteByte('\n')
}
promptInput.Context = sb.String()
log.Info().Msgf("promptInput.Context: %s", promptInput.Context)
}
begin := time.Now()
graphResponse, err := ai.Retrieve(ctx, graphRetriever, ai.WithDocs(dRequest))
if err != nil {
log.Error().Msgf("graph Retrieve err.Error() %s", err.Error())
} else {
var sb strings.Builder
for _, d := range graphResponse.Documents {
sb.WriteString(d.Content[0].Text)
sb.WriteByte('\n')
}
promptInput.Graph = sb.String()
log.Info().Msgf("promptInput.Graph : %s", promptInput.Graph)
}
fmt.Println("graph time", time.Since(begin).Seconds())
simpleQaPrompt, err := defineSimpleQaPrompt(g, input.Model)
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("index document: %w", err),
}, nil
}
resp, err := simpleQaPrompt.Execute(ctx, ai.WithInput(promptInput))
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("index document: %w", err),
}, nil
}
if lastok {
if promptInput.Summary == "" {
promptInput.Summary = resp.Text()
}
log.Info().
Str("from", input.From).
Str("from_id", input.FromID).
Str("to", input.To).
Str("to_id", input.ToID).
Str("promptInput.Query", promptInput.Query).
Str("resp.Text()", resp.Text()).
Str("promptInput.Summary", promptInput.Summary).Msg("QueryRewriteWithSummary")
res, err := kc.QueryRewriteWithSummary(context.Background(), promptInput.Query, resp.Text(), promptInput.Summary)
if err != nil {
log.Error().Msg(err.Error())
} else {
qa.UpdateQAFields(context.Background(), idx, res.RewrittenQuery, resp.Text())
/*
{"RewrittenQuery":"Conversation summary: The available knowledge base does not contain information about the capital of the UK.","RawResponse":{"Response":{"Content":"Conversation summary: The available knowledge base does not contain information about the capital of the UK.","Usage":{"InputTokens":74,"OutputTokens":19,"TotalTokens":93},"RequestId":"15f1ce0c-a83f-4d95-af22-33a3bd829e8d"}}}
*/
}
} else {
qa.UpdateQAFields(context.Background(), idx, "", resp.Text())
}
log.Info().
Str("from", input.From).
Str("from_id", input.FromID).
Str("to", input.To).
Str("to_id", input.ToID).
Str("question", promptInput.Query).
Str("context", promptInput.Context).
Str("graph", promptInput.Graph).
Str("last summary", promptInput.Summary).
Str("answer", resp.Text()).
Msg("Question and answer pair recorded")
return Response{
Data: resp.Text(),
Code: 200,
}, nil
})
}
// defineSimpleQaPrompt 加载或定义指定名称的 Prompt
func defineSimpleQaPrompt(g *genkit.Genkit, promptName string) (*ai.Prompt, error) {
// 步骤 1：尝试查找现有的 Prompt
log.Info().
Str("method", "defineSimpleQaPrompt").
Str("prompt_name", promptName).
Msg("Attempting to lookup prompt")
prompt := genkit.LookupPrompt(g, promptName)
if prompt != nil {
log.Info().
Str("method", "defineSimpleQaPrompt").
Str("prompt_name", promptName).
Msg("Prompt found and loaded")
return prompt, nil
}
log.Info().
Str("method", "defineSimpleQaPrompt").
Str("prompt_name", promptName).
Msg("Prompt not found, defining new prompt")
// 步骤 2：如果未找到，定义新的 Prompt
simpleQaPrompt, err := genkit.DefinePrompt(g, promptName,
// ai.WithModelName("deepseek/deepseek-chat"),
ai.WithModelName(promptName),
ai.WithPrompt(simpleQaPromptTemplate),
ai.WithInputType(simpleQaPromptInput{}),
ai.WithOutputFormat(ai.OutputFormatText),
)
if err != nil {
log.Error().
Str("method", "defineSimpleQaPrompt").
Str("prompt_name", promptName).
Err(err).
Msg("Failed to define prompt")
return nil, err
}
log.Info().
Str("method", "defineSimpleQaPrompt").
Str("prompt_name", promptName).
Msg("Prompt defined successfully")
return simpleQaPrompt, nil
}
main.go
View file @ 409192b5
...@@ -2,22 +2,15 @@ package main ...@@ -2,22 +2,15 @@ package main
import ( import (
"context" "context"
"encoding/json"
"flag" "flag"
"fmt"
"net/http" "net/http"
"os" "os"
"strings"
"time"
"github.com/firebase/genkit/go/ai"
"github.com/firebase/genkit/go/genkit" "github.com/firebase/genkit/go/genkit"
"github.com/rs/zerolog" "github.com/rs/zerolog"
"github.com/wade-liwei/agentchat/plugins/deepseek" "github.com/wade-liwei/agentchat/plugins/deepseek"
"github.com/wade-liwei/agentchat/plugins/graphrag" "github.com/wade-liwei/agentchat/plugins/graphrag" // Import knowledge package
"github.com/wade-liwei/agentchat/plugins/knowledge" // Import knowledge package
"github.com/wade-liwei/agentchat/plugins/milvus" "github.com/wade-liwei/agentchat/plugins/milvus"
"github.com/wade-liwei/agentchat/plugins/question"
"github.com/firebase/genkit/go/plugins/evaluators" "github.com/firebase/genkit/go/plugins/evaluators"
"github.com/firebase/genkit/go/plugins/googlegenai" "github.com/firebase/genkit/go/plugins/googlegenai"
...@@ -27,7 +20,6 @@ import ( ...@@ -27,7 +20,6 @@ import (
_ "github.com/wade-liwei/agentchat/docs" // 导入生成的 Swagger 文档 _ "github.com/wade-liwei/agentchat/docs" // 导入生成的 Swagger 文档
"github.com/rs/zerolog/log" "github.com/rs/zerolog/log"
"github.com/wade-liwei/agentchat/util"
) )
type ChatInput struct { type ChatInput struct {
...@@ -145,8 +137,6 @@ func main() { ...@@ -145,8 +137,6 @@ func main() {
log.Fatal().Msg(err.Error()) log.Fatal().Msg(err.Error())
} }
embedder := googlegenai.GoogleAIEmbedder(g, "embedding-001") embedder := googlegenai.GoogleAIEmbedder(g, "embedding-001")
if embedder == nil { if embedder == nil {
log.Fatal().Msg(err.Error()) log.Fatal().Msg(err.Error())
...@@ -166,253 +156,16 @@ func main() { ...@@ -166,253 +156,16 @@ func main() {
log.Fatal().Msgf("DefineIndexerAndRetriever failed: %v", err) log.Fatal().Msgf("DefineIndexerAndRetriever failed: %v", err)
} }
// 定义文档索引流
genkit.DefineFlow(g, "index/document", func(ctx context.Context, input *DocumentInput) (Response, error) {
if input.Metadata == nil {
input.Metadata = make(map[string]any)
}
input.Metadata[util.UserIdKey] = input.UserID
input.Metadata[util.UserNameKey] = input.Username
doc := ai.DocumentFromText(input.Content, input.Metadata)
err := indexer.Index(ctx, &ai.IndexerRequest{
Documents: []*ai.Document{doc},
})
if err != nil {
return Response{
Code: 500,
Msg: err.Error(),
}, nil
}
return Response{
Code: 200,
Msg: "Document indexed successfully",
}, nil
})
graphIndexer, graphRetriever, err := graphrag.DefineIndexerAndRetriever(ctx, g) graphIndexer, graphRetriever, err := graphrag.DefineIndexerAndRetriever(ctx, g)
if err != nil { if err != nil {
log.Fatal().Msgf("graphrag.DefineIndexerAndRetriever failed: %v", err) log.Fatal().Msgf("graphrag.DefineIndexerAndRetriever failed: %v", err)
} }
genkit.DefineFlow(g, "index/graph", func(ctx context.Context, input *GraphInput) (Response, error) { DefineDocumentFlow(g, indexer)
DefineGraphFlow(g, graphIndexer)
opt := graphrag.IndexReqOption{ DefineModelsFlow(g)
UserId: input.UserID, DefineChatFlow(g, retriever, graphRetriever, *pgConnString)
UserName: input.Username,
}
resDocName := ""
if v, ok := input.Metadata[graphrag.DocNameKey]; !ok {
// Generate random docName.
docName, err := graphrag.GenerateRandomDocName(8)
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("generate random docName for document %w", err),
}, nil
}
input.Metadata[graphrag.DocNameKey] = docName
resDocName = docName
} else {
if str, isString := v.(string); isString {
resDocName = str
}
}
doc := ai.DocumentFromText(input.Content, input.Metadata)
err := graphIndexer.Index(ctx, &ai.IndexerRequest{
Documents: []*ai.Document{doc},
Options: &opt,
})
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("index document: %w", err),
}, nil
}
return Response{
Code: 200,
Msg: fmt.Sprintf("Document indexed successfully, docname %s", resDocName),
}, nil
})
qa, err := question.InitQAStore(*pgConnString)
if err != nil {
log.Fatal().Msgf("InitQAStore failed: %v", err)
}
// Initialize KnowledgeClient with test parameters
kc := knowledge.NewKnowledgeClient(knowledge.ClientConfig{
Endpoint: "lkeap.tencentcloudapi.com",
Region: "ap-guangzhou",
})
if err := kc.Init(ctx); err != nil {
log.Fatal().Msgf("Failed to initialize KnowledgeClient: %v", err)
}
log.Info().Msg("KnowledgeClient initialized successfully")
// Define a simple flow that generates jokes about a given topic
genkit.DefineFlow(g, "chat", func(ctx context.Context, input *ChatInput) (Response, error) {
ctxAsJson, _ := json.Marshal(ctx)
log.Info().Msgf("input----ctxAsJson----%s", string(ctxAsJson))
inputAsJson, err := json.Marshal(input)
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("json.Marshal: %w", err),
}, nil
}
log.Info().Msgf("input--------%s", string(inputAsJson))
idx, lastQa, lastok, err := qa.WriteAndGetLatestQA(context.Background(), question.QA{
FromID: &input.FromID,
From: &input.From,
Question: &input.Content,
To: &input.To,
ToID: &input.ToID,
Milvus: &input.Milvus,
Graph: &input.Graph,
})
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("WriteAndGetLatestQA: %w", err),
}, nil
}
qaAsJson, err := json.Marshal(lastQa)
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("json.Marshal(lastQa): %w", err),
}, nil
}
log.Info().Msgf("qaAsJson--------%s", string(qaAsJson))
promptInput := &simpleQaPromptInput{
Query: input.Content,
}
if lastok && lastQa.Summary != nil {
promptInput.Summary = *lastQa.Summary
}
metaData := make(map[string]any)
metaData[util.UserIdKey] = input.ToID
metaData[util.UserNameKey] = input.To
dRequest := ai.DocumentFromText(input.Content, metaData)
response, err := ai.Retrieve(ctx, retriever, ai.WithDocs(dRequest))
if err != nil {
log.Error().Msgf("milvus Retrieve err.Error() %s", err.Error())
} else {
var sb strings.Builder
for _, d := range response.Documents {
sb.WriteString(d.Content[0].Text)
sb.WriteByte('\n')
}
promptInput.Context = sb.String()
log.Info().Msgf("promptInput.Context: %s", promptInput.Context)
}
begin := time.Now()
graphResponse, err := ai.Retrieve(ctx, graphRetriever, ai.WithDocs(dRequest))
if err != nil {
log.Error().Msgf("graph Retrieve err.Error() %s", err.Error())
} else {
var sb strings.Builder
for _, d := range graphResponse.Documents {
sb.WriteString(d.Content[0].Text)
sb.WriteByte('\n')
}
promptInput.Graph = sb.String()
log.Info().Msgf("promptInput.Graph : %s", promptInput.Graph)
}
fmt.Println("graph time", time.Since(begin).Seconds())
simpleQaPrompt,err := defineSimpleQaPrompt(g,input.Model)
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("index document: %w", err),
}, nil
}
resp, err := simpleQaPrompt.Execute(ctx, ai.WithInput(promptInput))
if err != nil {
return Response{
Code: 500,
Msg: fmt.Sprintf("index document: %w", err),
}, nil
}
if lastok {
if promptInput.Summary == "" {
promptInput.Summary = resp.Text()
}
log.Info().
Str("from", input.From).
Str("from_id", input.FromID).
Str("to", input.To).
Str("to_id", input.ToID).
Str("promptInput.Query", promptInput.Query).
Str("resp.Text()", resp.Text()).
Str("promptInput.Summary", promptInput.Summary).Msg("QueryRewriteWithSummary")
res, err := kc.QueryRewriteWithSummary(context.Background(), promptInput.Query, resp.Text(), promptInput.Summary)
if err != nil {
log.Error().Msg(err.Error())
} else {
qa.UpdateQAFields(context.Background(), idx, res.RewrittenQuery, resp.Text())
/*
{"RewrittenQuery":"Conversation summary: The available knowledge base does not contain information about the capital of the UK.","RawResponse":{"Response":{"Content":"Conversation summary: The available knowledge base does not contain information about the capital of the UK.","Usage":{"InputTokens":74,"OutputTokens":19,"TotalTokens":93},"RequestId":"15f1ce0c-a83f-4d95-af22-33a3bd829e8d"}}}
*/
}
} else {
qa.UpdateQAFields(context.Background(), idx, "", resp.Text())
}
log.Info().
Str("from", input.From).
Str("from_id", input.FromID).
Str("to", input.To).
Str("to_id", input.ToID).
Str("question", promptInput.Query).
Str("context", promptInput.Context).
Str("graph", promptInput.Graph).
Str("last summary", promptInput.Summary).
Str("answer", resp.Text()).
Msg("Question and answer pair recorded")
return Response{
Data: resp.Text(),
Code: 200,
}, nil
})
// 配置限速器：每秒 10 次请求，突发容量 20，最大并发 5 // 配置限速器：每秒 10 次请求，突发容量 20，最大并发 5
rl := NewRateLimiter(10, 20, 5) rl := NewRateLimiter(10, 20, 5)
...@@ -445,49 +198,3 @@ type Response struct { ...@@ -445,49 +198,3 @@ type Response struct {
Code int `json:"code"` Code int `json:"code"`
Msg string `json:"msg"` Msg string `json:"msg"`
} }
// defineSimpleQaPrompt 加载或定义指定名称的 Prompt
func defineSimpleQaPrompt(g *genkit.Genkit, promptName string) (*ai.Prompt, error) {
// 步骤 1：尝试查找现有的 Prompt
log.Info().
Str("method", "defineSimpleQaPrompt").
Str("prompt_name", promptName).
Msg("Attempting to lookup prompt")
prompt := genkit.LookupPrompt(g, promptName)
if prompt != nil {
log.Info().
Str("method", "defineSimpleQaPrompt").
Str("prompt_name", promptName).
Msg("Prompt found and loaded")
return prompt, nil
}
log.Info().
Str("method", "defineSimpleQaPrompt").
Str("prompt_name", promptName).
Msg("Prompt not found, defining new prompt")
// 步骤 2：如果未找到，定义新的 Prompt
simpleQaPrompt, err := genkit.DefinePrompt(g, promptName,
// ai.WithModelName("deepseek/deepseek-chat"),
ai.WithModelName(promptName),
ai.WithPrompt(simpleQaPromptTemplate),
ai.WithInputType(simpleQaPromptInput{}),
ai.WithOutputFormat(ai.OutputFormatText),
)
if err != nil {
log.Error().
Str("method", "defineSimpleQaPrompt").
Str("prompt_name", promptName).
Err(err).
Msg("Failed to define prompt")
return nil, err
}
log.Info().
Str("method", "defineSimpleQaPrompt").
Str("prompt_name", promptName).
Msg("Prompt defined successfully")
return simpleQaPrompt, nil
}
plugins/deepseek/deepseek.go
View file @ 409192b5
...@@ -14,304 +14,270 @@ import ( ...@@ -14,304 +14,270 @@ import (
) )
const provider = "deepseek" const provider = "deepseek"
const Provider = "deepseek"
// 常量定义支持的模型 // 常量定义支持的模型
const ( const (
// DeepSeekChat = "deepseek-chat" // DeepSeekChat = "deepseek-chat"
// DeepSeekCoder = "deepseek-coder" // DeepSeekCoder = "deepseek-coder"
// DeepSeekReasoner = "deepseek-reasoner" // DeepSeekReasoner = "deepseek-reasoner"
DeepSeekChat = deepseek.DeepSeekChat DeepSeekChat = deepseek.DeepSeekChat
DeepSeekCoder = deepseek.DeepSeekCoder DeepSeekCoder = deepseek.DeepSeekCoder
DeepSeekReasoner = deepseek.DeepSeekReasoner DeepSeekReasoner = deepseek.DeepSeekReasoner
) )
var ( var (
mediaSupportedModels = []string{DeepSeekChat, DeepSeekCoder, DeepSeekReasoner} mediaSupportedModels = []string{DeepSeekChat, DeepSeekCoder, DeepSeekReasoner}
roleMapping = map[ai.Role]string{ roleMapping = map[ai.Role]string{
ai.RoleUser: deepseek.ChatMessageRoleUser, ai.RoleUser: deepseek.ChatMessageRoleUser,
ai.RoleModel: deepseek.ChatMessageRoleAssistant, ai.RoleModel: deepseek.ChatMessageRoleAssistant,
ai.RoleSystem: deepseek.ChatMessageRoleSystem, ai.RoleSystem: deepseek.ChatMessageRoleSystem,
ai.RoleTool: deepseek.ChatMessageRoleTool, ai.RoleTool: deepseek.ChatMessageRoleTool,
} }
) )
// modelInfoMap 定义支持的模型及其信息 // modelInfoMap 定义支持的模型及其信息
var modelInfoMap = map[string]ai.ModelInfo{ var modelInfoMap = map[string]ai.ModelInfo{
DeepSeekChat: { DeepSeekChat: {
Label: "DeepSeek Chat", Label: "DeepSeek Chat",
Supports: &ai.ModelSupports{ Supports: &ai.ModelSupports{
Multiturn: true, Multiturn: true,
SystemRole: true, SystemRole: true,
Media: false, Media: false,
Tools: true, Tools: true,
}, },
Versions: []string{}, Versions: []string{},
}, },
DeepSeekCoder: { DeepSeekCoder: {
Label: "DeepSeek Coder", Label: "DeepSeek Coder",
Supports: &ai.ModelSupports{ Supports: &ai.ModelSupports{
Multiturn: true, Multiturn: true,
SystemRole: true, SystemRole: true,
Media: false, Media: false,
Tools: true, Tools: true,
}, },
Versions: []string{}, Versions: []string{},
}, },
DeepSeekReasoner: { DeepSeekReasoner: {
Label: "DeepSeek Reasoner", Label: "DeepSeek Reasoner",
Supports: &ai.ModelSupports{ Supports: &ai.ModelSupports{
Multiturn: true, Multiturn: true,
SystemRole: true, SystemRole: true,
Media: false, Media: false,
Tools: true, Tools: true,
}, },
Versions: []string{}, Versions: []string{},
}, },
}
func ListModels() map[string]ai.ModelInfo {
return modelInfoMap
} }
// DeepSeek holds configuration for the plugin. // DeepSeek holds configuration for the plugin.
type DeepSeek struct { type DeepSeek struct {
APIKey string // DeepSeek API key APIKey string // DeepSeek API key
mu sync.Mutex // Mutex to control access. mu sync.Mutex // Mutex to control access.
initted bool // Whether the plugin has been initialized. initted bool // Whether the plugin has been initialized.
} }
// Name returns the provider name. // Name returns the provider name.
func (d *DeepSeek) Name() string { func (d *DeepSeek) Name() string {
return provider return provider
} }
// ModelDefinition represents a model with its name and type. // ModelDefinition represents a model with its name and type.
type ModelDefinition struct { type ModelDefinition struct {
Name string Name string
Type string Type string
} }
// Init initializes the DeepSeek plugin and registers all known models. // Init initializes the DeepSeek plugin and registers all known models.
func (d *DeepSeek) Init(ctx context.Context, g *genkit.Genkit) error { func (d *DeepSeek) Init(ctx context.Context, g *genkit.Genkit) error {
log.Info().Str("method", "DeepSeek.Init").Msg("Initializing DeepSeek plugin") log.Info().Str("method", "DeepSeek.Init").Msg("Initializing DeepSeek plugin")
d.mu.Lock() d.mu.Lock()
defer d.mu.Unlock() defer d.mu.Unlock()
if d.initted { if d.initted {
log.Error().Str("method", "DeepSeek.Init").Msg("Plugin already initialized") log.Error().Str("method", "DeepSeek.Init").Msg("Plugin already initialized")
return fmt.Errorf("deepseek.Init already called") return fmt.Errorf("deepseek.Init already called")
} }
if d == nil || d.APIKey == "" { if d == nil || d.APIKey == "" {
log.Error().Str("method", "DeepSeek.Init").Msg("APIKey is required") log.Error().Str("method", "DeepSeek.Init").Msg("APIKey is required")
return fmt.Errorf("deepseek: need APIKey") return fmt.Errorf("deepseek: need APIKey")
} }
d.initted = true d.initted = true
//d.mu.Unlock() //d.mu.Unlock()
// 自动注册所有支持的模型 // 自动注册所有支持的模型
for _, modelName := range mediaSupportedModels { for _, modelName := range mediaSupportedModels {
modelDef := ModelDefinition{ modelDef := ModelDefinition{
Name: modelName, Name: modelName,
Type: "chat", // 假设所有模型都支持 chat 类型 Type: "chat", // 假设所有模型都支持 chat 类型
} }
d.DefineModel(g, modelDef, nil) d.DefineModel(g, modelDef, nil)
} }
log.Info().Str("method", "DeepSeek.Init").Msg("Initialization successful") log.Info().Str("method", "DeepSeek.Init").Msg("Initialization successful")
return nil return nil
} }
// DefineModel defines a DeepSeek model in Genkit. // DefineModel defines a DeepSeek model in Genkit.
func (d *DeepSeek) DefineModel(g *genkit.Genkit, model ModelDefinition, info *ai.ModelInfo) ai.Model { func (d *DeepSeek) DefineModel(g *genkit.Genkit, model ModelDefinition, info *ai.ModelInfo) ai.Model {
log.Info(). log.Info().
Str("method", "DeepSeek.DefineModel"). Str("method", "DeepSeek.DefineModel").
Str("model_name", model.Name). Str("model_name", model.Name).
Msg("Defining DeepSeek model") Msg("Defining DeepSeek model")
// d.mu.Lock() // d.mu.Lock()
// defer d.mu.Unlock() // defer d.mu.Unlock()
if !d.initted { if !d.initted {
log.Error().Str("method", "DeepSeek.DefineModel").Msg("DeepSeek not initialized") log.Error().Str("method", "DeepSeek.DefineModel").Msg("DeepSeek not initialized")
return nil return nil
} }
// 获取模型信息 // 获取模型信息
var mi ai.ModelInfo var mi ai.ModelInfo
if info != nil { if info != nil {
mi = *info mi = *info
} else { } else {
var ok bool var ok bool
mi, ok = modelInfoMap[model.Name] mi, ok = modelInfoMap[model.Name]
if !ok { if !ok {
log.Error(). log.Error().
Str("method", "DeepSeek.DefineModel"). Str("method", "DeepSeek.DefineModel").
Str("model_name", model.Name). Str("model_name", model.Name).
Msg("Unknown model and no ModelInfo provided") Msg("Unknown model and no ModelInfo provided")
return nil return nil
} }
} }
// 定义生成器 // 定义生成器
gen := &generator{model: model, apiKey: d.APIKey} gen := &generator{model: model, apiKey: d.APIKey}
modelDef := genkit.DefineModel(g, provider, model.Name, &mi, gen.generate) modelDef := genkit.DefineModel(g, provider, model.Name, &mi, gen.generate)
log.Info(). log.Info().
Str("method", "Deep lantern"). Str("method", "Deep lantern").
Str("model_name", model.Name). Str("model_name", model.Name).
Msg("Model defined successfully") Msg("Model defined successfully")
return modelDef return modelDef
} }
// generator handles model generation. // generator handles model generation.
type generator struct { type generator struct {
model ModelDefinition model ModelDefinition
apiKey string apiKey string
} }
// generate implements the Genkit model generation interface. // generate implements the Genkit model generation interface.
func (g *generator) generate(ctx context.Context, input *ai.ModelRequest, cb func(context.Context, *ai.ModelResponseChunk) error) (*ai.ModelResponse, error) { func (g *generator) generate(ctx context.Context, input *ai.ModelRequest, cb func(context.Context, *ai.ModelResponseChunk) error) (*ai.ModelResponse, error) {
log.Info(). log.Info().
Str("method", "generator.generate"). Str("method", "generator.generate").
Str("model_name", g.model.Name). Str("model_name", g.model.Name).
Int("messages", len(input.Messages)). Int("messages", len(input.Messages)).
Msg("Starting model generation") Msg("Starting model generation")
if len(input.Messages) == 0 { if len(input.Messages) == 0 {
log.Error().Str("method", "generator.generate").Msg("Prompt or messages required") log.Error().Str("method", "generator.generate").Msg("Prompt or messages required")
return nil, fmt.Errorf("prompt or messages required") return nil, fmt.Errorf("prompt or messages required")
} }
// Initialize DeepSeek client. // Initialize DeepSeek client.
client := deepseek.NewClient(g.apiKey) client := deepseek.NewClient(g.apiKey)
log.Debug().Str("method", "generator.generate").Msg("DeepSeek client initialized") log.Debug().Str("method", "generator.generate").Msg("DeepSeek client initialized")
// Create a chat completion request // Create a chat completion request
request := &deepseek.ChatCompletionRequest{ request := &deepseek.ChatCompletionRequest{
Model: g.model.Name, Model: g.model.Name,
} }
for _, msg := range input.Messages { for _, msg := range input.Messages {
role, ok := roleMapping[msg.Role] role, ok := roleMapping[msg.Role]
if !ok { if !ok {
log.Error(). log.Error().
Str("method", "generator.generate"). Str("method", "generator.generate").
Str("role", string(msg.Role)). Str("role", string(msg.Role)).
Msg("Unsupported role") Msg("Unsupported role")
return nil, fmt.Errorf("unsupported role: %s", msg.Role) return nil, fmt.Errorf("unsupported role: %s", msg.Role)
} }
content := concatMessageParts(msg.Content) content := concatMessageParts(msg.Content)
request.Messages = append(request.Messages, deepseek.ChatCompletionMessage{ request.Messages = append(request.Messages, deepseek.ChatCompletionMessage{
Role: role, Role: role,
Content: content, Content: content,
}) })
log.Debug(). log.Debug().
Str("method", "generator.generate"). Str("method", "generator.generate").
Str("role", role). Str("role", role).
Str("content", content). Str("content", content).
Msg("Added message to request") Msg("Added message to request")
} }
// Send the request and handle the response // Send the request and handle the response
response, err := client.CreateChatCompletion(ctx, request) response, err := client.CreateChatCompletion(ctx, request)
if err != nil { if err != nil {
log.Error(). log.Error().
Err(err). Err(err).
Str("method", "generator.generate"). Str("method", "generator.generate").
Msg("Failed to create chat completion") Msg("Failed to create chat completion")
return nil, fmt.Errorf("create chat completion: %w", err) return nil, fmt.Errorf("create chat completion: %w", err)
} }
log.Debug(). log.Debug().
Str("method", "generator.generate"). Str("method", "generator.generate").
Int("choices", len(response.Choices)). Int("choices", len(response.Choices)).
Msg("Received chat completion response") Msg("Received chat completion response")
// Create a final response with the merged chunks // Create a final response with the merged chunks
finalResponse := &ai.ModelResponse{ finalResponse := &ai.ModelResponse{
Request: input, Request: input,
FinishReason: ai.FinishReason("stop"), FinishReason: ai.FinishReason("stop"),
Message: &ai.Message{ Message: &ai.Message{
Role: ai.RoleModel, Role: ai.RoleModel,
}, },
} }
for _, chunk := range response.Choices { for _, chunk := range response.Choices {
log.Debug(). log.Debug().
Str("method", "generator.generate"). Str("method", "generator.generate").
Int("index", chunk.Index). Int("index", chunk.Index).
Str("content", chunk.Message.Content). Str("content", chunk.Message.Content).
Msg("Processing response chunk") Msg("Processing response chunk")
p := ai.Part{ p := ai.Part{
Text: chunk.Message.Content, Text: chunk.Message.Content,
Kind: ai.PartKind(chunk.Index), Kind: ai.PartKind(chunk.Index),
} }
finalResponse.Message.Content = append(finalResponse.Message.Content, &p) finalResponse.Message.Content = append(finalResponse.Message.Content, &p)
} }
log.Info(). log.Info().
Str("method", "generator.generate"). Str("method", "generator.generate").
Str("model_name", g.model.Name). Str("model_name", g.model.Name).
Int("content_parts", len(finalResponse.Message.Content)). Int("content_parts", len(finalResponse.Message.Content)).
Msg("Model generation completed successfully") Msg("Model generation completed successfully")
return finalResponse, nil return finalResponse, nil
} }
// concatMessageParts concatenates message parts into a single string. // concatMessageParts concatenates message parts into a single string.
func concatMessageParts(parts []*ai.Part) string { func concatMessageParts(parts []*ai.Part) string {
log.Debug(). log.Debug().
Str("method", "concatMessageParts"). Str("method", "concatMessageParts").
Int("parts", len(parts)). Int("parts", len(parts)).
Msg("Concatenating message parts") Msg("Concatenating message parts")
var sb strings.Builder var sb strings.Builder
for _, part := range parts { for _, part := range parts {
if part.IsText() { if part.IsText() {
sb.WriteString(part.Text) sb.WriteString(part.Text)
} }
// Ignore non-text parts (e.g., media, tools) as DeepSeek API doesn't support them. // Ignore non-text parts (e.g., media, tools) as DeepSeek API doesn't support them.
} }
result := sb.String() result := sb.String()
log.Debug(). log.Debug().
Str("method", "concatMessageParts"). Str("method", "concatMessageParts").
Str("result", result). Str("result", result).
Msg("Concatenation complete") Msg("Concatenation complete")
return result return result
} }
// package deepseek // package deepseek
// import ( // import (
......
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