docs: Add Weaviate docs, provide how to use Weaviate vector database in DB-GPT.

1.Weaviate docs

docs/locales/zh_CN/LC_MESSAGES/getting_started/tutorials.po

@@ -8,7 +8,7 @@ msgid ""
 msgstr ""
 "Project-Id-Version: DB-GPT 0.1.0\n"
 "Report-Msgid-Bugs-To: \n"
-"POT-Creation-Date: 2023-06-14 17:19+0800\n"
+"POT-Creation-Date: 2023-06-15 21:28+0800\n"
 "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
 "Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
 "Language: zh_CN\n"
@@ -19,25 +19,25 @@ msgstr ""
 "Content-Transfer-Encoding: 8bit\n"
 "Generated-By: Babel 2.12.1\n"
 
-#: ../../getting_started/tutorials.md:1 f80ad98cf2d444a3a159655b37ae4d4f
+#: ../../getting_started/tutorials.md:1 12844381da6e4aeab07dbccf4a6ae020
 msgid "Tutorials"
 msgstr "教程"
 
-#: ../../getting_started/tutorials.md:4 ff8771c31b024e538dbbaaeaccec8aa7
+#: ../../getting_started/tutorials.md:4 6312ba6a99a04bfdb04b9407fb92d967
 msgid "This is a collection of DB-GPT tutorials on Medium."
 msgstr "这是知乎上DB-GPT教程的集合。．"
 
-#: ../../getting_started/tutorials.md:6 0c1c544a5d184375aabd7fe6918042e2
+#: ../../getting_started/tutorials.md:6 2fc56b41dec94e4c8ba56014e72ad527
 msgid ""
 "DB-GPT is divided into several functions, including chat with knowledge "
 "base, execute SQL, chat with database, and execute plugins."
 msgstr ""
 
-#: ../../getting_started/tutorials.md:8 b5e106a97bbd4467b071bb05d837d7d9
+#: ../../getting_started/tutorials.md:8 ae3ad1badc0948f8a0c271ca1bd58399
-msgid "Introduction"
+msgid "Introduce"
 msgstr ""
 
-#: ../../getting_started/tutorials.md:9 9df43436183d4b41a75a09fec15743d7
+#: ../../getting_started/tutorials.md:9 c646d59a9b3b49459d8f8e6d17f7ea05
 #, fuzzy
 msgid "[What is DB-GPT](https://www.youtube.com/watch?v=QszhVJerc0I)"
 msgstr ""
@@ -45,11 +45,12 @@ msgstr ""
 "GPT](https://www.bilibili.com/video/BV1SM4y1a7Nj/?buvid=551b023900b290f9497610b2155a2668&is_story_h5=false&mid=%2BVyE%2Fwau5woPcUKieCWS0A%3D%3D&p=1&plat_id=116&share_from=ugc&share_medium=iphone&share_plat=ios&share_session_id=5D08B533-82A4-4D40-9615-7826065B4574&share_source=GENERIC&share_tag=s_i&timestamp=1686307943&unique_k=bhO3lgQ&up_id=31375446)"
 "  by csunny (https://github.com/csunny/DB-GPT)"
 
-#: ../../getting_started/tutorials.md:11 3096811f862649bf84ff3cd29cdf14db
+#: ../../getting_started/tutorials.md:11 7446c2e65eb249fa8d2486604a218a63
-msgid "Knowledge Base"
+#, fuzzy
+msgid "Knowledge"
 msgstr "知识库"
 
-#: ../../getting_started/tutorials.md:13 ea00f3de8c754bf2950e735a2f14043a
+#: ../../getting_started/tutorials.md:13 7911e97570c6467095e3d8277c81625e
 #, fuzzy
 msgid ""
 "[How to Create your own knowledge repository](https://db-"
@@ -58,55 +59,58 @@ msgstr ""
 "[怎么创建自己的知识库](https://db-"
 "gpt.readthedocs.io/en/latest/modules/knowledge.html)"
 
-#: ../../getting_started/tutorials.md:15 07195f11314945989eeeb9400c8a9b43
+#: ../../getting_started/tutorials.md:15 39a1d08f301f4266a4043e0eab501825
 #, fuzzy
 msgid "![Add new Knowledge demonstration](../../assets/new_knownledge.gif)"
 msgstr "[新增知识库演示](../../assets/new_knownledge_en.gif)"
 
-#: ../../getting_started/tutorials.md:15 333cdda401df4509a11d14535391b8a8
+#: ../../getting_started/tutorials.md:15 f37788a8907b4463a8a7fc4145c6ca1e
 #, fuzzy
 msgid "Add new Knowledge demonstration"
 msgstr "[新增知识库演示](../../assets/new_knownledge_en.gif)"
 
-#: ../../getting_started/tutorials.md:17 5245cd247a184f63a10f735f414f303f
+#: ../../getting_started/tutorials.md:17 1e3d3188b0d2459186290df1f849390a
 msgid "SQL Generation"
 msgstr ""
 
-#: ../../getting_started/tutorials.md:18 9a980e7625d34b98bf318851c43fb13d
+#: ../../getting_started/tutorials.md:18 6cec0866942b4bd1beaec17355b370ca
 #, fuzzy
 msgid "![sql generation demonstration](../../assets/demo_en.gif)"
 msgstr "[sql生成演示](../../assets/demo_en.gif)"
 
-#: ../../getting_started/tutorials.md:18 952c680cf62140978b4e94d36c49134a
+#: ../../getting_started/tutorials.md:18 41c5f06251cb4149a5ff47e6b800bc65
 #, fuzzy
 msgid "sql generation demonstration"
 msgstr "[sql生成演示](../../assets/demo_en.gif)"
 
-#: ../../getting_started/tutorials.md:20 c0a6f9fefbb9404695fe3bffb6ecc577
+#: ../../getting_started/tutorials.md:20 862e024da7cb4e8b9d3e7396664d34ce
 msgid "SQL Execute"
 msgstr "SQL执行"
 
-#: ../../getting_started/tutorials.md:21 e959cc6ca356407d854ee5541233c19a
+#: ../../getting_started/tutorials.md:21 381350353517454cb800e5b804319027
 #, fuzzy
 msgid "![sql execute demonstration](../../assets/auto_sql_en.gif)"
 msgstr "[sql execute 演示](../../assets/auto_sql_en.gif)"
 
-#: ../../getting_started/tutorials.md:21 69247d51ccd349b082ea452f6d74d2b3
+#: ../../getting_started/tutorials.md:21 523a107500394147bb2051cddb5d9ad9
 #, fuzzy
 msgid "sql execute demonstration"
 msgstr "SQL执行"
 
-#: ../../getting_started/tutorials.md:23 0fd9770dbf3c49b0b644599dc70187a7
+#: ../../getting_started/tutorials.md:23 105d8c6319754b7794daf2993a10ed87
 #, fuzzy
 msgid "Plugins"
 msgstr "DB Plugins"
 
-#: ../../getting_started/tutorials.md:24 cf58eb1ee13f49f69e501c0e221b4bed
+#: ../../getting_started/tutorials.md:24 45a1bdf9424a4cd48b0f361d46c64f1e
 #, fuzzy
 msgid "![db plugins demonstration](../../assets/dbgpt_bytebase_plugin.gif)"
 msgstr "[db plugins 演示](../../assets/dbgpt_bytebase_plugin.gif)"
 
-#: ../../getting_started/tutorials.md:24 9e474caadb87481ba51f8595067f7edd
+#: ../../getting_started/tutorials.md:24 275b5132954a44e983a7c4a69e914142
 msgid "db plugins demonstration"
 msgstr ""
 
+#~ msgid "Introduction"
+#~ msgstr ""
+

docs/locales/zh_CN/LC_MESSAGES/modules/vector.po

@@ -50,17 +50,21 @@ msgid ""
 "differences and underlying details of different vector data. For example,"
 " it can be used to connect to databases such as Milvus, Chroma, "
 "Elasticsearch, and Weaviate."
-msgstr "vectorconnector是一个向量数据库连接适配器，你可以通过它来连接不同的向量数据库，并且屏蔽掉了不同的向量数据的是实现差异和底层细节。例如Milvus, Chroma, Elasticsearch, Weaviate..."
+msgstr ""
+"vectorconnector是一个向量数据库连接适配器，你可以通过它来连接不同的向量数据库，并且屏蔽掉了不同的向量数据的是实现差异和底层细节。例如Milvus,"
+" Chroma, Elasticsearch, Weaviate..."
 
 #: ../../modules/vector.rst:10 4d359cf0cf254b9392e9d3b963bc1071
 msgid "DB-GPT VectorConnector currently support milvus and chroma vector database"
 msgstr "DB-GPT VectorConnector当前支持Milvus和Chroma，未来会越来越多."
 
 #: ../../modules/vector.rst:12 60809c6ecbb64cc8888b9dc2f9de398a
-msgid "`Chroma <./vector/chroma.html>`_: supported chroma vector database."
+#, fuzzy
-msgstr "msgid "`Chroma <./vector/chroma.html>`_: 支持Chroma向量数据库"
+msgid "`chroma <./vector/chroma.html>`_: supported chroma vector database."
+msgstr "msgid \"`Chroma <./vector/chroma.html>`_: 支持Chroma向量数据库"
 
 #: ../../modules/vector.rst:13 e24aeb84d75741fe9517a0d5ec5d92f1
-msgid "`Milvus <./vector/milvus.html>`_: supported milvus vector database."
+#, fuzzy
+msgid "`milvus <./vector/milvus.html>`_: supported milvus vector database."
 msgstr "Milvus <./vector/milvus.html>`_: 支持Milvus向量数据库"
 

docs/modules/vector.rst

@@ -11,6 +11,7 @@ DB-GPT VectorConnector currently support milvus and chroma vector database
 
 - `chroma <./vector/chroma.html>`_: supported chroma vector database.
 - `milvus <./vector/milvus.html>`_: supported milvus vector database.
+- `weaviate <./vector/weaviate.html>`_: supported weaviate vector database.
 
 
 .. toctree::
@@ -20,4 +21,5 @@ DB-GPT VectorConnector currently support milvus and chroma vector database
    :hidden:
 
    ./vector/chroma/chroma.md
-   ./vector/milvus/milvus.md
+   ./vector/milvus/milvus.md
+   ./vector/weaviate/weaviate.md

docs/modules/vector/weaviate/weaviate.md

@@ -0,0 +1,89 @@
+WeaviateStore
+==================================
+WeaviateStore is one implementation of the Milvus vector database in VectorConnector.
+
+[Tutorial on how to create a Weaviate instance](https://weaviate.io/developers/weaviate/installation)
+
+inheriting the VectorStoreBase and implement similar_search(), vector_name_exists(), load_document().
+```
+class WeaviateStore(VectorStoreBase):
+    """Weaviate database"""
+
+    def __init__(self, ctx: dict) -> None:
+        """Initialize with Weaviate client."""
+        try:
+            import weaviate
+        except ImportError:
+            raise ValueError(
+                "Could not import weaviate python package. "
+                "Please install it with `pip install weaviate-client`."
+            )
+
+        self.ctx = ctx
+        self.weaviate_url = CFG.WEAVIATE_URL
+        self.embedding = ctx.get("embeddings", None)
+        self.vector_name = ctx["vector_store_name"]
+        self.persist_dir = os.path.join(
+            KNOWLEDGE_UPLOAD_ROOT_PATH, self.vector_name + ".vectordb"
+        )
+
+        self.vector_store_client = weaviate.Client(self.weaviate_url)
+```
+
+similar_search()
+
+```
+   def similar_search(self, text: str, topk: int) -> None:
+        """Perform similar search in Weaviate"""
+        logger.info("Weaviate similar search")
+        # nearText = {
+        #     "concepts": [text],
+        #     "distance": 0.75,  # prior to v1.14 use "certainty" instead of "distance"
+        # }
+        # vector = self.embedding.embed_query(text)
+        response = (
+            self.vector_store_client.query.get(self.vector_name, ["metadata", "page_content"])
+            # .with_near_vector({"vector": vector})
+            .with_limit(topk)
+            .do()
+        )
+        docs = response['data']['Get'][list(response['data']['Get'].keys())[0]]
+        return docs
+
+```
+
+vector_name_exists()
+
+```
+  def vector_name_exists(self) -> bool:
+        """Check if a vector name exists for a given class in Weaviate.
+        Returns:
+            bool: True if the vector name exists, False otherwise.
+        """
+        if self.vector_store_client.schema.get(self.vector_name):
+            return True
+        return False
+
+```
+
+load_document()
+
+```
+    def load_document(self, documents: list) -> None:
+        """Load documents into Weaviate"""
+        logger.info("Weaviate load document")
+        texts = [doc.page_content for doc in documents]
+        metadatas = [doc.metadata for doc in documents]
+
+        # Import data
+        with self.vector_store_client.batch as batch:
+            batch.batch_size = 100
+
+            # Batch import all documents
+            for i in range(len(texts)):
+                properties = {"metadata": metadatas[i]['source'], "page_content": texts[i]}
+
+                self.vector_store_client.batch.add_data_object(data_object=properties, class_name=self.vector_name)
+            self.vector_store_client.batch.flush()
+```
+