add auto-CoT feature (#483)

* 1.refactor the retrieval module. 2.refactor the http service module. 3.upgrade text2sql output format the parse for absolute time related expression in query.

* fix bug.

* upgrade the config module, now support config llm suppoted by langchain.

* fix conflicts.

* update text2sql config reload to be compitable with new config format.

* modify default config.

* 1.add self-consistency feature for text2sql. 2.upgrade llm api call from sync to async. 3.refactor text2sql module. 4. refactor semantical retriever modules.

* merege with upstream master

* add general retrieve service.

* add api service for sql_agent for crud opereations of few-shots examples.

* modify requirements

* add auto-cot feature

---------

Co-authored-by: shaoweigong <shaoweigong@tencent.com>

chat/python/utils/text2vec.py

@@ -0,0 +1,23 @@
+# -*- coding:utf-8 -*-
+from typing import List
+
+from chromadb.api.types import Documents, EmbeddingFunction, Embeddings
+from langchain.embeddings import HuggingFaceEmbeddings
+
+from config.config_parse import HF_TEXT2VEC_MODEL_NAME
+
+hg_embedding = HuggingFaceEmbeddings(model_name=HF_TEXT2VEC_MODEL_NAME)
+
+
+class Text2VecEmbeddingFunction(EmbeddingFunction):
+    def __call__(self, texts: Documents) -> Embeddings:
+
+        embeddings = hg_embedding.embed_documents(texts)
+
+        return embeddings
+
+
+def get_embeddings(documents: List[str]) -> List[List[float]]:
+    embeddings = hg_embedding.embed_documents(documents)
+
+    return embeddings