doc:update knowledge api document

docs/modules/knowledge.rst

@@ -105,7 +105,12 @@ Document type can be .txt, .pdf, .md, .doc, .ppt.
 
 Note that the default vector model used is text2vec-large-chinese (which is a large model, so if your personal computer configuration is not enough, it is recommended to use text2vec-base-chinese). Therefore, ensure that you download the model and place it in the models directory.
 
-- `pdf_embedding <./knowledge/pdf_embedding.html>`_: supported pdf embedding.
+- `pdf_embedding <./knowledge/pdf/pdf_embedding.html>`_: supported pdf embedding.
+- `markdown_embedding <./knowledge/markdown/markdown_embedding.html>`_: supported markdown embedding.
+- `word_embedding <./knowledge/word/word_embedding.html>`_: supported word embedding.
+- `url_embedding <./knowledge/url/url_embedding.html>`_: supported url embedding.
+- `ppt_embedding <./knowledge/ppt/ppt_embedding.html>`_: supported ppt embedding.
+- `string_embedding <./knowledge/string/string_embedding.html>`_: supported raw text embedding.
 
 
 .. toctree::
@@ -118,4 +123,5 @@ Note that the default vector model used is text2vec-large-chinese (which is a la
    ./knowledge/markdown/markdown_embedding.md
    ./knowledge/word/word_embedding.md
    ./knowledge/url/url_embedding.md
-   ./knowledge/ppt/ppt_embedding.md
+   ./knowledge/ppt/ppt_embedding.md
+   ./knowledge/string/string_embedding.md

docs/modules/knowledge/markdown/markdown_embedding.md

@@ -1,4 +1,4 @@
-MarkdownEmbedding
+Markdown
 ==================================
 markdown embedding can import md text into a vector knowledge base. The entire embedding process includes the read (loading data), data_process (data processing), and index_to_store (embedding to the vector database) methods.
 

docs/modules/knowledge/pdf/pdf_embedding.md

@@ -1,4 +1,4 @@
-PDFEmbedding
+PDF
 ==================================
 pdfembedding can import PDF text into a vector knowledge base. The entire embedding process includes the read (loading data), data_process (data processing), and index_to_store (embedding to the vector database) methods.
 

docs/modules/knowledge/ppt/ppt_embedding.md

@@ -1,4 +1,4 @@
-PPTEmbedding
+PPT
 ==================================
 ppt embedding can import ppt text into a vector knowledge base. The entire embedding process includes the read (loading data), data_process (data processing), and index_to_store (embedding to the vector database) methods.
 

docs/modules/knowledge/string/string_embedding.md

@@ -0,0 +1,41 @@
+String
+==================================
+string embedding can import a long raw text into a vector knowledge base. The entire embedding process includes the read (loading data), data_process (data processing), and index_to_store (embedding to the vector database) methods.
+
+inheriting the SourceEmbedding
+```
+class StringEmbedding(SourceEmbedding):
+    """string embedding for read string document."""
+
+    def __init__(
+        self,
+        file_path,
+        vector_store_config,
+        text_splitter: Optional[TextSplitter] = None,
+    ):
+        """Initialize raw text word path."""
+        super().__init__(file_path=file_path, vector_store_config=vector_store_config)
+        self.file_path = file_path
+        self.vector_store_config = vector_store_config
+        self.text_splitter = text_splitter or None
+```
+
+implement read() and data_process()
+read() method allows you to read data and split data into chunk
+```
+@register
+    def read(self):
+        """Load from String path."""
+        metadata = {"source": "raw text"}
+        return [Document(page_content=self.file_path, metadata=metadata)]
+```
+data_process() method allows you to pre processing your ways
+```
+@register
+    def data_process(self, documents: List[Document]):
+        i = 0
+        for d in documents:
+            documents[i].page_content = d.page_content.replace("\n", "")
+            i += 1
+        return documents
+```

docs/modules/knowledge/url/url_embedding.md

@@ -1,4 +1,4 @@
-URL Embedding
+URL
 ==================================
 url embedding can import PDF text into a vector knowledge base. The entire embedding process includes the read (loading data), data_process (data processing), and index_to_store (embedding to the vector database) methods.
 

docs/modules/knowledge/word/word_embedding.md

@@ -1,4 +1,4 @@
-WordEmbedding
+Word
 ==================================
 word embedding can import word doc/docx text into a vector knowledge base. The entire embedding process includes the read (loading data), data_process (data processing), and index_to_store (embedding to the vector database) methods.