More permissive requirements.txt (#43)

* Bump versions

* More flexible requirements.txt

* Fix tests

* Revert back test profile for testing

requirements.txt

@@ -1,4 +1,2 @@
-steamship==2.16.2
-langchain==0.0.137
-tiktoken==0.2.0
-pydantic==1.10.2
+steamship~=2.16.4
+langchain==0.0.152

src/steamship_langchain/cache/cache.py

@@ -1,6 +1,6 @@
 import hashlib
 import logging
-from typing import Dict, Optional
+from typing import Any, Dict, Optional
 
 from langchain.cache import RETURN_VAL_TYPE, BaseCache
 from langchain.schema import Generation
@@ -53,6 +53,9 @@ def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
         logging.debug(f"cache miss for {prompt}")
         return None
 
+    def clear(self, **kwargs: Any) -> None:
+        raise NotImplementedError()
+
     def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
         """Update cache based on prompt and llm_string.
 

src/steamship_langchain/vectorstores/steamship_vector_store.py

@@ -176,12 +176,12 @@ def similarity_search_by_vector(
         raise NotImplementedError("similarity_search_by_vector not supported yet.")
 
     def max_marginal_relevance_search(
-        self, query: str, k: int = 4, fetch_k: int = 20
+        self, query: str, k: int = 4, fetch_k: int = 20, **kwargs
     ) -> List[Document]:
         raise NotImplementedError("Max marginal relevance search not supported yet.")
 
     def max_marginal_relevance_search_by_vector(
-        self, embedding: List[float], k: int = 4, fetch_k: int = 20
+        self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs
     ) -> List[Document]:
         raise NotImplementedError("Max marginal relevance search not supported yet.")