[Feature]: Add support for creating app using yaml config (#787)

embedchain/vectordb/chroma.py

@@ -37,7 +37,7 @@ class ChromaDB(BaseVectorDB):
             self.config = ChromaDbConfig()
 
         self.settings = Settings()
-        self.settings.allow_reset = self.config.allow_reset
+        self.settings.allow_reset = self.config.allow_reset if hasattr(self.config, "allow_reset") else False
         if self.config.chroma_settings:
             for key, value in self.config.chroma_settings.items():
                 if hasattr(self.settings, key):
@@ -72,6 +72,17 @@ class ChromaDB(BaseVectorDB):
         """Called during initialization"""
         return self.client
 
+    def _generate_where_clause(self, where: Dict[str, any]) -> str:
+        # If only one filter is supplied, return it as is
+        # (no need to wrap in $and based on chroma docs)
+        if len(where.keys()) == 1:
+            return where
+        where_filters = []
+        for k, v in where.items():
+            if isinstance(v, str):
+                where_filters.append({k: v})
+        return {"$and": where_filters}
+
     def _get_or_create_collection(self, name: str) -> Collection:
         """
         Get or create a named collection.
@@ -107,13 +118,14 @@ class ChromaDB(BaseVectorDB):
         if ids:
             args["ids"] = ids
         if where:
-            args["where"] = where
+            args["where"] = self._generate_where_clause(where)
         if limit:
             args["limit"] = limit
         return self.collection.get(**args)
 
     def get_advanced(self, where):
-        return self.collection.get(where=where, limit=1)
+        where_clause = self._generate_where_clause(where)
+        return self.collection.get(where=where_clause, limit=1)
 
     def add(
         self,

embedchain/vectordb/opensearch.py

@@ -110,8 +110,13 @@ class OpenSearchDB(BaseVectorDB):
         return result
 
     def add(
-        self, embeddings: List[List[str]], documents: List[str], metadatas: List[object], ids: List[str],
-            skip_embedding: bool):
+        self,
+        embeddings: List[List[str]],
+        documents: List[str],
+        metadatas: List[object],
+        ids: List[str],
+        skip_embedding: bool,
+    ):
         """add data in vector database
 
         :param embeddings: list of embeddings to add
@@ -162,7 +167,8 @@ class OpenSearchDB(BaseVectorDB):
             embedding_function=embeddings,
             opensearch_url=f"{self.config.opensearch_url}",
             http_auth=self.config.http_auth,
-            use_ssl=True,
+            use_ssl=hasattr(self.config, "use_ssl") and self.config.use_ssl,
+            verify_certs=hasattr(self.config, "verify_certs") and self.config.verify_certs,
         )
 
         pre_filter = {"match_all": {}}  # default

embedchain/vectordb/zilliz.py

@@ -5,8 +5,8 @@ from embedchain.helper.json_serializable import register_deserializable
 from embedchain.vectordb.base import BaseVectorDB
 
 try:
-    from pymilvus import MilvusClient
-    from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection, utility
+    from pymilvus import (Collection, CollectionSchema, DataType, FieldSchema,
+                          MilvusClient, connections, utility)
 except ImportError:
     raise ImportError(
         "Zilliz requires extra dependencies. Install with `pip install --upgrade embedchain[milvus]`"