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doc:update knowledge api
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@ -8,7 +8,7 @@ msgid ""
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msgstr ""
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"Project-Id-Version: DB-GPT 0.3.0\n"
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"Report-Msgid-Bugs-To: \n"
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"POT-Creation-Date: 2023-07-10 16:59+0800\n"
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"POT-Creation-Date: 2023-07-12 11:57+0800\n"
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"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
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"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
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"Language: zh_CN\n"
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@ -19,7 +19,7 @@ msgstr ""
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"Content-Transfer-Encoding: 8bit\n"
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"Generated-By: Babel 2.12.1\n"
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#: ../../modules/knowledge.rst:2 ../../modules/knowledge.rst:84
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#: ../../modules/knowledge.rst:2 ../../modules/knowledge.rst:98
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#: ca36c0ca545c4d70b51fe811a3e7caca
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msgid "Knowledge"
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msgstr "知识"
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@ -54,22 +54,29 @@ msgstr "准备"
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#: ../../modules/knowledge.rst:15 515555d13e7548deb596d80ea1514bb2
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msgid "before execution:"
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msgstr ""
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msgstr "开始前"
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#: ../../modules/knowledge.rst:21 8b790c0c37114dfc8eda4863af9314b4
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#: ../../modules/knowledge.rst:21 3333f92965ee41ea9cfa542de6c1e976
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msgid ""
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"2.Update your .env, set your vector store type, VECTOR_STORE_TYPE=Chroma "
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"(now only support Chroma and Milvus, if you set Milvus, please set "
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"MILVUS_URL and MILVUS_PORT)"
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msgstr "2.更新你的.env，设置你的向量存储类型，VECTOR_STORE_TYPE=Chroma(现在只支持Chroma和Milvus，如果你设置了Milvus，请设置MILVUS_URL和MILVUS_PORT)"
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"2.prepare embedding model, you can download from https://huggingface.co/."
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" Notice you have installed git-lfs. eg: git clone "
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"https://huggingface.co/THUDM/chatglm2-6b"
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msgstr "提前准备Embedding Model, 你可以在https://huggingface.co/进行下载，注意：你需要先安装git-lfs.eg: git clone "
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"https://huggingface.co/THUDM/chatglm2-6b"
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#: ../../modules/knowledge.rst:24 058fa57484a64756ab2650b46f4b33bf
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#: ../../modules/knowledge.rst:29 7abcbe007d594f4aaa43ddef88ef4d89
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msgid ""
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"3.prepare vector_store instance and vector store config, now we support "
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"Chroma, Milvus and Weaviate."
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msgstr "提前准备向量数据库环境，目前支持Chroma, Milvus and Weaviate向量数据库"
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#: ../../modules/knowledge.rst:50 058fa57484a64756ab2650b46f4b33bf
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msgid ""
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"3.init Url Type EmbeddingEngine api and embedding your document into "
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"vector store in your code."
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msgstr "初始化 Url类型 EmbeddingEngine api， 将url文档embedding向量化到向量数据库 "
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#: ../../modules/knowledge.rst:40 5f255b96abd346479ab3c371393e47dc
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#: ../../modules/knowledge.rst:62 5f255b96abd346479ab3c371393e47dc
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#, fuzzy
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msgid ""
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"4.init Document Type EmbeddingEngine api and embedding your document into"
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@ -79,17 +86,17 @@ msgstr ""
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"初始化 文档型类型 EmbeddingEngine api， 将文档embedding向量化到向量数据库(文档可以是.txt, .pdf, "
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".md, .html, .doc, .ppt)"
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#: ../../modules/knowledge.rst:57 d8c85ba7714749269714b03857738f70
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#: ../../modules/knowledge.rst:75 d8c85ba7714749269714b03857738f70
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msgid ""
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"5.init TEXT Type EmbeddingEngine api and embedding your document into "
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"vector store in your code."
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msgstr "初始化TEXT类型 EmbeddingEngine api， 将文档embedding向量化到向量数据库"
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#: ../../modules/knowledge.rst:73 c59e4650d57e44ae8d967768dddf908a
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#: ../../modules/knowledge.rst:87 c59e4650d57e44ae8d967768dddf908a
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msgid "4.similar search based on your knowledge base. ::"
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msgstr "在知识库进行相似性搜索"
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#: ../../modules/knowledge.rst:79 f500fcdc791c4286b411819ae9ab3dc6
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#: ../../modules/knowledge.rst:93 f500fcdc791c4286b411819ae9ab3dc6
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msgid ""
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"Note that the default vector model used is text2vec-large-chinese (which "
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"is a large model, so if your personal computer configuration is not "
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@ -99,7 +106,7 @@ msgstr ""
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"注意，这里默认向量模型是text2vec-large-chinese(模型比较大，如果个人电脑配置不够建议采用text2vec-base-"
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"chinese),因此确保需要将模型download下来放到models目录中。"
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#: ../../modules/knowledge.rst:81 62a5e10a19844ba9955113f5c78cb460
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#: ../../modules/knowledge.rst:95 62a5e10a19844ba9955113f5c78cb460
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msgid ""
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"`pdf_embedding <./knowledge/pdf_embedding.html>`_: supported pdf "
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"embedding."
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@ -131,3 +138,11 @@ msgstr "pdf_embedding <./knowledge/pdf_embedding.html>`_: supported pdf embeddin
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#~ "folders in the pilot/datasets directory."
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#~ msgstr "1.将个人知识文件或文件夹放在pilot/datasets目录中。"
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#~ msgid ""
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#~ "2.Update your .env, set your vector "
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#~ "store type, VECTOR_STORE_TYPE=Chroma (now only"
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#~ " support Chroma and Milvus, if you"
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#~ " set Milvus, please set MILVUS_URL "
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#~ "and MILVUS_PORT)"
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#~ msgstr "2.更新你的.env，设置你的向量存储类型，VECTOR_STORE_TYPE=Chroma(现在只支持Chroma和Milvus，如果你设置了Milvus，请设置MILVUS_URL和MILVUS_PORT)"
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@ -16,20 +16,55 @@ before execution:
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::
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pip install db-gpt -i https://pypi.org/
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python -m spacy download zh_core_web_sm
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from pilot import EmbeddingEngine,KnowledgeType
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2.Update your .env, set your vector store type, VECTOR_STORE_TYPE=Chroma
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(now only support Chroma and Milvus, if you set Milvus, please set MILVUS_URL and MILVUS_PORT)
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2.prepare embedding model, you can download from https://huggingface.co/.
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Notice you have installed git-lfs.
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eg: git clone https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
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::
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embedding_model = "your_embedding_model_path/all-MiniLM-L6-v2"
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3.prepare vector_store instance and vector store config, now we support Chroma, Milvus and Weaviate.
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::
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#Chroma
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vector_store_config = {
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"vector_store_type":"Chroma",
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"vector_store_name":"your_name",#you can define yourself
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"chroma_persist_path":"your_persist_dir"
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}
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#Milvus
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vector_store_config = {
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"vector_store_type":"Milvus",
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"vector_store_name":"your_name",#you can define yourself
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"milvus_url":"your_url",
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"milvus_port":"your_port",
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"milvus_username":"your_username",(optional)
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"milvus_password":"your_password",(optional)
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"milvus_secure":"your_secure"(optional)
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}
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#Weaviate
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vector_store_config = {
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"vector_store_type":"Weaviate",
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"vector_store_name":"your_name",#you can define yourself
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"weaviate_url":"your_url",
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"weaviate_port":"your_port",
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"weaviate_username":"your_username",(optional)
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"weaviate_password":"your_password",(optional)
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}
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3.init Url Type EmbeddingEngine api and embedding your document into vector store in your code.
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::
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url = "https://db-gpt.readthedocs.io/en/latest/getting_started/getting_started.html"
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embedding_model = "your_model_path/all-MiniLM-L6-v2"
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vector_store_config = {
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"vector_store_name": your_name,
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}
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embedding_engine = EmbeddingEngine(
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knowledge_source=url,
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knowledge_type=KnowledgeType.URL.value,
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@ -43,12 +78,6 @@ Document type can be .txt, .pdf, .md, .doc, .ppt.
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::
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document_path = "your_path/test.md"
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embedding_model = "your_model_path/all-MiniLM-L6-v2"
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vector_store_config = {
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"vector_store_name": your_name,
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"vector_store_type": "Chroma",
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"chroma_persist_path": "your_persist_dir",
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}
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embedding_engine = EmbeddingEngine(
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knowledge_source=document_path,
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knowledge_type=KnowledgeType.DOCUMENT.value,
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@ -61,10 +90,6 @@ Document type can be .txt, .pdf, .md, .doc, .ppt.
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::
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raw_text = "a long passage"
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embedding_model = "your_model_path/all-MiniLM-L6-v2"
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vector_store_config = {
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"vector_store_name": your_name,
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}
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embedding_engine = EmbeddingEngine(
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knowledge_source=raw_text,
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knowledge_type=KnowledgeType.TEXT.value,
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