docs: Add chroma and milvus connector docs

Add vector docs,  provide how to you vector connector in DB-GPT.
1.chroma docs
2.milvus docs
Closes #230

docs/modules/vector/chroma/chroma.md

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+ChromaStore
+==================================
+ChromaStore is one implementation of the Chroma vector database in VectorConnector.
+
+inheriting the VectorStoreBase and implement similar_search(), vector_name_exists(), load_document().
+```
+class ChromaStore(VectorStoreBase):
+    """chroma database"""
+
+    def __init__(self, ctx: {}) -> None:
+        self.ctx = ctx
+        self.embeddings = ctx["embeddings"]
+        self.persist_dir = os.path.join(
+            KNOWLEDGE_UPLOAD_ROOT_PATH, ctx["vector_store_name"] + ".vectordb"
+        )
+        self.vector_store_client = Chroma(
+            persist_directory=self.persist_dir, embedding_function=self.embeddings
+        )
+```
+
+similar_search()
+
+```
+  def similar_search(self, text, topk) -> None:
+        logger.info("ChromaStore similar search")
+        return self.vector_store_client.similarity_search(text, topk)
+
+```
+
+vector_name_exists()
+
+```
+ def vector_name_exists(self):
+        return (
+            os.path.exists(self.persist_dir) and len(os.listdir(self.persist_dir)) > 0
+        )
+
+```
+
+load_document()
+
+```
+  def load_document(self, documents):
+        logger.info("ChromaStore load document")
+        texts = [doc.page_content for doc in documents]
+        metadatas = [doc.metadata for doc in documents]
+        self.vector_store_client.add_texts(texts=texts, metadatas=metadatas)
+        self.vector_store_client.persist()
+```
+

docs/modules/vector/milvus/milvus.md

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+MilvusStore
+==================================
+MilvusStore is one implementation of the Milvus vector database in VectorConnector.
+
+[Tutorial on how to create a Milvus instance](https://milvus.io/docs/install_standalone-docker.md)
+
+inheriting the VectorStoreBase and implement similar_search(), vector_name_exists(), load_document().
+```
+class MilvusStore(VectorStoreBase):
+    """Milvus database"""
+
+    def __init__(self, ctx: {}) -> None:
+        """init a milvus storage connection.
+
+        Args:
+            ctx ({}): MilvusStore global config.
+        """
+        # self.configure(cfg)
+
+        connect_kwargs = {}
+        self.uri = CFG.MILVUS_URL
+        self.port = CFG.MILVUS_PORT
+        self.username = CFG.MILVUS_USERNAME
+        self.password = CFG.MILVUS_PASSWORD
+        self.collection_name = ctx.get("vector_store_name", None)
+        self.secure = ctx.get("secure", None)
+        self.embedding = ctx.get("embeddings", None)
+        self.fields = []
+        self.alias = "default"
+        )
+```
+
+similar_search()
+
+```
+    def similar_search(self, text, topk) -> None:
+        """similar_search in vector database."""
+        self.col = Collection(self.collection_name)
+        schema = self.col.schema
+        for x in schema.fields:
+            self.fields.append(x.name)
+            if x.auto_id:
+                self.fields.remove(x.name)
+            if x.is_primary:
+                self.primary_field = x.name
+            if x.dtype == DataType.FLOAT_VECTOR or x.dtype == DataType.BINARY_VECTOR:
+                self.vector_field = x.name
+        _, docs_and_scores = self._search(text, topk)
+        return [doc for doc, _, _ in docs_and_scores]
+
+```
+
+vector_name_exists()
+
+```
+   def vector_name_exists(self):
+        """is vector store name exist."""
+        return utility.has_collection(self.collection_name)
+
+```
+
+load_document()
+
+```
+    def load_document(self, documents) -> None:
+        """load document in vector database."""
+        # self.init_schema_and_load(self.collection_name, documents)
+        batch_size = 500
+        batched_list = [
+            documents[i : i + batch_size] for i in range(0, len(documents), batch_size)
+        ]
+        # docs = []
+        for doc_batch in batched_list:
+            self.init_schema_and_load(self.collection_name, doc_batch)
+```
+