diff --git a/libs/langchain/langchain/cache.py b/libs/langchain/langchain/cache.py
index bbcc87bd39..be96bdf441 100644
--- a/libs/langchain/langchain/cache.py
+++ b/libs/langchain/langchain/cache.py
@@ -25,6 +25,7 @@ import hashlib
 import inspect
 import json
 import logging
+import uuid
 from datetime import timedelta
 from functools import lru_cache
 from typing import (
@@ -40,7 +41,7 @@ from typing import (
     cast,
 )
 
-from sqlalchemy import Column, Integer, String, create_engine, select
+from sqlalchemy import Column, Integer, Row, String, create_engine, select
 from sqlalchemy.engine.base import Engine
 from sqlalchemy.orm import Session
 
@@ -49,7 +50,6 @@ try:
 except ImportError:
     from sqlalchemy.ext.declarative import declarative_base
 
-
 from langchain.llms.base import LLM, get_prompts
 from langchain.load.dump import dumps
 from langchain.load.load import loads
@@ -1066,3 +1066,87 @@ class CassandraSemanticCache(BaseCache):
     def clear(self, **kwargs: Any) -> None:
         """Clear the *whole* semantic cache."""
         self.table.clear()
+
+
+class FullMd5LLMCache(Base):  # type: ignore
+    """SQLite table for full LLM Cache (all generations)."""
+
+    __tablename__ = "full_md5_llm_cache"
+    id = Column(String, primary_key=True)
+    prompt_md5 = Column(String, index=True)
+    llm = Column(String, index=True)
+    idx = Column(Integer, index=True)
+    prompt = Column(String)
+    response = Column(String)
+
+
+class SQLAlchemyMd5Cache(BaseCache):
+    """Cache that uses SQAlchemy as a backend."""
+
+    def __init__(
+        self, engine: Engine, cache_schema: Type[FullMd5LLMCache] = FullMd5LLMCache
+    ):
+        """Initialize by creating all tables."""
+        self.engine = engine
+        self.cache_schema = cache_schema
+        self.cache_schema.metadata.create_all(self.engine)
+
+    def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
+        """Look up based on prompt and llm_string."""
+        rows = self._search_rows(prompt, llm_string)
+        if rows:
+            return [loads(row[0]) for row in rows]
+        return None
+
+    def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
+        """Update based on prompt and llm_string."""
+        self._delete_previous(prompt, llm_string)
+        prompt_md5 = self.get_md5(prompt)
+        items = [
+            self.cache_schema(
+                id=str(uuid.uuid1()),
+                prompt=prompt,
+                prompt_md5=prompt_md5,
+                llm=llm_string,
+                response=dumps(gen),
+                idx=i,
+            )
+            for i, gen in enumerate(return_val)
+        ]
+        with Session(self.engine) as session, session.begin():
+            for item in items:
+                session.merge(item)
+
+    def _delete_previous(self, prompt: str, llm_string: str) -> None:
+        stmt = (
+            select(self.cache_schema.response)
+            .where(self.cache_schema.prompt_md5 == self.get_md5(prompt))  # type: ignore
+            .where(self.cache_schema.llm == llm_string)
+            .where(self.cache_schema.prompt == prompt)
+            .order_by(self.cache_schema.idx)
+        )
+        with Session(self.engine) as session, session.begin():
+            rows = session.execute(stmt).fetchall()
+            for item in rows:
+                session.delete(item)
+
+    def _search_rows(self, prompt: str, llm_string: str) -> List[Row]:
+        prompt_pd5 = self.get_md5(prompt)
+        stmt = (
+            select(self.cache_schema.response)
+            .where(self.cache_schema.prompt_md5 == prompt_pd5)  # type: ignore
+            .where(self.cache_schema.llm == llm_string)
+            .where(self.cache_schema.prompt == prompt)
+            .order_by(self.cache_schema.idx)
+        )
+        with Session(self.engine) as session:
+            return session.execute(stmt).fetchall()
+
+    def clear(self, **kwargs: Any) -> None:
+        """Clear cache."""
+        with Session(self.engine) as session:
+            session.execute(self.cache_schema.delete())
+
+    @staticmethod
+    def get_md5(input_string: str) -> str:
+        return hashlib.md5(input_string.encode()).hexdigest()