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这里我们进一步尝试将embedding模型也换为本地的，同时熟悉一下流程和学一些新的东西

1.环境还是用之前的，这里我们先下载LLM  

然后你会在下载nomic模型的时候崩溃，因为无法搜索，无法下载

解决办法如下
lm studio 0.2.24国内下载模型_lm studio 国内源-CSDN博客

按照上面的教程依旧无法下载模型，但是可以搜索了，没什么用

直接hugging face下载，然后导入llm  models文件夹

C:\Users\Administrator\.cache\lm-studio\models

注意有格式要求

C:\Users\Administrator\.cache\lm-studio\models\Publisher\Repository
将模型放在这个文件夹里才能被识别，然后加在模型

然后修改配置

settings.yaml

##我这里用到是我的另一个电脑运行LLM Studio  ，所以IP是127
encoding_model: cl100k_base
skip_workflows: []
llm:api_key: ollamatype: openai_chat # or azure_openai_chatmodel: llama3model_supports_json: true # recommended if this is available for your model.# max_tokens: 4000# request_timeout: 180.0api_base: http://127.0.0.1:11434/v1# api_version: 2024-02-15-preview# organization: <organization_id># deployment_name: <azure_model_deployment_name># tokens_per_minute: 150_000 # set a leaky bucket throttle# requests_per_minute: 10_000 # set a leaky bucket throttle# max_retries: 10# max_retry_wait: 10.0# sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times# concurrent_requests: 25 # the number of parallel inflight requests that may be madeparallelization:stagger: 0.3# num_threads: 50 # the number of threads to use for parallel processingasync_mode: threaded # or asyncioembeddings:## parallelization: override the global parallelization settings for embeddingsasync_mode: threaded # or asynciollm:api_key: lm-studiotype: openai_embedding # or azure_openai_embeddingmodel: Publisher/Repository/nomic-embed-text-v1.5.Q5_K_M.ggufapi_base: http://192.168.1.127:1234/v1# api_version: 2024-02-15-preview# organization: <organization_id># deployment_name: <azure_model_deployment_name># tokens_per_minute: 150_000 # set a leaky bucket throttle# requests_per_minute: 10_000 # set a leaky bucket throttle# max_retries: 10# max_retry_wait: 10.0# sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times# concurrent_requests: 25 # the number of parallel inflight requests that may be made# batch_size: 16 # the number of documents to send in a single request# batch_max_tokens: 8191 # the maximum number of tokens to send in a single request# target: required # or optionalchunks:size: 300overlap: 100group_by_columns: [id] # by default, we don't allow chunks to cross documentsinput:type: file # or blobfile_type: text # or csvbase_dir: "input"file_encoding: utf-8file_pattern: ".*\\.txt$"cache:type: file # or blobbase_dir: "cache"# connection_string: <azure_blob_storage_connection_string># container_name: <azure_blob_storage_container_name>storage:type: file # or blobbase_dir: "output/${timestamp}/artifacts"# connection_string: <azure_blob_storage_connection_string># container_name: <azure_blob_storage_container_name>reporting:type: file # or console, blobbase_dir: "output/${timestamp}/reports"# connection_string: <azure_blob_storage_connection_string># container_name: <azure_blob_storage_container_name>entity_extraction:## llm: override the global llm settings for this task## parallelization: override the global parallelization settings for this task## async_mode: override the global async_mode settings for this taskprompt: "prompts/entity_extraction.txt"entity_types: [organization,person,geo,event]max_gleanings: 0summarize_descriptions:## llm: override the global llm settings for this task## parallelization: override the global parallelization settings for this task## async_mode: override the global async_mode settings for this taskprompt: "prompts/summarize_descriptions.txt"max_length: 500claim_extraction:## llm: override the global llm settings for this task## parallelization: override the global parallelization settings for this task## async_mode: override the global async_mode settings for this task# enabled: trueprompt: "prompts/claim_extraction.txt"description: "Any claims or facts that could be relevant to information discovery."max_gleanings: 0community_report:## llm: override the global llm settings for this task## parallelization: override the global parallelization settings for this task## async_mode: override the global async_mode settings for this taskprompt: "prompts/community_report.txt"max_length: 2000max_input_length: 8000cluster_graph:max_cluster_size: 10embed_graph:enabled: false # if true, will generate node2vec embeddings for nodes# num_walks: 10# walk_length: 40# window_size: 2# iterations: 3# random_seed: 597832umap:enabled: false # if true, will generate UMAP embeddings for nodessnapshots:graphml: falseraw_entities: falsetop_level_nodes: falselocal_search:# text_unit_prop: 0.5# community_prop: 0.1# conversation_history_max_turns: 5# top_k_mapped_entities: 10# top_k_relationships: 10# max_tokens: 12000global_search:# max_tokens: 12000# data_max_tokens: 12000# map_max_tokens: 1000# reduce_max_tokens: 2000# concurrency: 32

pdf转markdown，markdown转txt

#测试文档 https://github.com/win4r/mytest/blob/main/book.pdfpip install marker-pdfmarker_single ./book.pdf ./pdftxt --batch_multiplier 2 --max_pages 60 --langs English#markdown转txt
python markdown_to_text.py book.md book.txt