chore: update wechat QR code

README.zh.md

@@ -185,6 +185,7 @@ DB-GPT是一个开源的数据域大模型框架。目的是构建大模型领
   <img src="https://contrib.rocks/image?repo=eosphoros-ai/DB-GPT&max=200" />
 </a>
 
+
 ## Licence
 
 The MIT License (MIT)

dbgpt/core/interface/operators/message_operator.py

@@ -416,6 +416,18 @@ def _filter_round_messages(
             ...     ],
             ... ]
 
+            # Test end rounds is zero
+            >>> operator = BufferedConversationMapperOperator(
+            ...     keep_start_rounds=1, keep_end_rounds=0
+            ... )
+            >>> assert operator._filter_round_messages(messages) == [
+            ...     [
+            ...         HumanMessage(content="Hi", round_index=1),
+            ...         AIMessage(content="Hello!", round_index=1),
+            ...     ]
+            ... ]
+
+
         Args:
             messages_by_round (List[List[BaseMessage]]):
                 The messages grouped by round.
@@ -425,7 +437,12 @@ def _filter_round_messages(
 
         """
         total_rounds = len(messages_by_round)
-        if self._keep_start_rounds is not None and self._keep_end_rounds is not None:
+        if (
+            self._keep_start_rounds is not None
+            and self._keep_end_rounds is not None
+            and self._keep_start_rounds > 0
+            and self._keep_end_rounds > 0
+        ):
             if self._keep_start_rounds + self._keep_end_rounds > total_rounds:
                 # Avoid overlapping when the sum of start and end rounds exceeds total
                 # rounds

dbgpt/rag/embedding/embeddings.py

@@ -361,3 +361,70 @@ def embed_query(self, text: str) -> List[float]:
             Embeddings for the text.
         """
         return self.embed_documents([text])[0]
+
+
+class JinaEmbeddings(BaseModel, Embeddings):
+    """
+    This class is used to get embeddings for a list of texts using the Jina AI API.
+    It requires an API key and a model name. The default model name is "jina-embeddings-v2-base-en".
+    """
+
+    api_url: Any  #: :meta private:
+    session: Any  #: :meta private:
+    api_key: str
+    """our API key for the Jina AI API.."""
+    model_name: str = "jina-embeddings-v2-base-en"
+    """he name of the model to use for text embeddings. Defaults to "jina-embeddings-v2-base-en"."""
+
+    def __init__(self, **kwargs):
+        """
+        Initialize the JinaEmbeddings.
+        """
+        super().__init__(**kwargs)
+        try:
+            import requests
+        except ImportError:
+            raise ValueError(
+                "The requests python package is not installed. Please install it with `pip install requests`"
+            )
+        self.api_url = "https://api.jina.ai/v1/embeddings"
+        self.session = requests.Session()
+        self.session.headers.update(
+            {"Authorization": f"Bearer {self.api_key}", "Accept-Encoding": "identity"}
+        )
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Get the embeddings for a list of texts.
+
+        Args:
+            texts (Documents): A list of texts to get embeddings for.
+
+        Returns:
+            Embedded texts as List[List[float]], where each inner List[float]
+                corresponds to a single input text.
+        """
+        # Call Jina AI Embedding API
+        resp = self.session.post(  # type: ignore
+            self.api_url, json={"input": texts, "model": self.model_name}
+        ).json()
+        if "data" not in resp:
+            raise RuntimeError(resp["detail"])
+
+        embeddings = resp["data"]
+
+        # Sort resulting embeddings by index
+        sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])  # type: ignore
+
+        # Return just the embeddings
+        return [result["embedding"] for result in sorted_embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Compute query embeddings using a HuggingFace transformer model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        return self.embed_documents([text])[0]

dbgpt/storage/vector_store/milvus_store.py

@@ -133,8 +133,9 @@ def __init__(self, vector_store_config: MilvusVectorConfig) -> None:
         connections.connect(
             host=self.uri or "127.0.0.1",
             port=self.port or "19530",
-            alias="default"
-            # secure=self.secure,
+            username=self.username,
+            password=self.password,
+            alias="default",
         )
 
     def init_schema_and_load(self, vector_name, documents) -> List[str]:

examples/awel/simple_dbschema_retriever_example.py

@@ -3,7 +3,8 @@
 
 from pydantic import BaseModel, Field
 
-from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH
+from dbgpt._private.config import Config
+from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, MODEL_PATH, PILOT_PATH
 from dbgpt.core.awel import DAG, HttpTrigger, JoinOperator, MapOperator
 from dbgpt.datasource.rdbms.conn_sqlite import SQLiteTempConnect
 from dbgpt.rag.chunk import Chunk
@@ -38,17 +39,19 @@
 """
 
 
+CFG = Config()
+
+
 def _create_vector_connector():
     """Create vector connector."""
-    model_name = os.getenv("EMBEDDING_MODEL", "text2vec")
     return VectorStoreConnector.from_default(
         "Chroma",
         vector_store_config=ChromaVectorConfig(
             name="vector_name",
             persist_path=os.path.join(PILOT_PATH, "data"),
         ),
         embedding_fn=DefaultEmbeddingFactory(
-            default_model_name=os.path.join(MODEL_PATH, model_name),
+            default_model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL],
         ).create(),
     )
 

examples/awel/simple_rag_embedding_example.py

@@ -3,7 +3,8 @@
 
 from pydantic import BaseModel, Field
 
-from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH
+from dbgpt._private.config import Config
+from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, MODEL_PATH, PILOT_PATH
 from dbgpt.core.awel import DAG, HttpTrigger, MapOperator
 from dbgpt.rag.embedding.embedding_factory import DefaultEmbeddingFactory
 from dbgpt.rag.knowledge.base import KnowledgeType
@@ -25,18 +26,19 @@
             }'
 """
 
+CFG = Config()
+
 
 def _create_vector_connector() -> VectorStoreConnector:
     """Create vector connector."""
-    model_name = os.getenv("EMBEDDING_MODEL", "text2vec")
     return VectorStoreConnector.from_default(
         "Chroma",
         vector_store_config=ChromaVectorConfig(
             name="vector_name",
             persist_path=os.path.join(PILOT_PATH, "data"),
         ),
         embedding_fn=DefaultEmbeddingFactory(
-            default_model_name=os.path.join(MODEL_PATH, model_name),
+            default_model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL],
         ).create(),
     )
 

examples/awel/simple_rag_retriever_example.py

@@ -4,7 +4,8 @@
 
 from pydantic import BaseModel, Field
 
-from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH
+from dbgpt._private.config import Config
+from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG, MODEL_PATH, PILOT_PATH
 from dbgpt.core.awel import DAG, HttpTrigger, JoinOperator, MapOperator
 from dbgpt.model.proxy import OpenAILLMClient
 from dbgpt.rag.chunk import Chunk
@@ -43,6 +44,8 @@
             }'
 """
 
+CFG = Config()
+
 
 class TriggerReqBody(BaseModel):
     query: str = Field(..., description="User query")
@@ -83,7 +86,7 @@ def _create_vector_connector():
             persist_path=os.path.join(PILOT_PATH, "data"),
         ),
         embedding_fn=DefaultEmbeddingFactory(
-            default_model_name=os.path.join(MODEL_PATH, model_name),
+            default_model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL],
         ).create(),
     )