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

1.Weaviate docs

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()
+```
+