add save and load tfidf vectorizer and docs for TFIDFRetriever (#8112)

This is to add save_local and load_local to tfidf_vectorizer and docs in
tfidf_retriever to make the vectorizer reusable.

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- Description: add save_local and load_local to tfidf_vectorizer and
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  - Issue: None
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  - Tag maintainer: @rlancemartin, @eyurtsev
  - Twitter handle: @MlopsJ

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---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

libs/langchain/langchain/retrievers/tfidf.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import pickle
+from pathlib import Path
 from typing import Any, Dict, Iterable, List, Optional
 
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
@@ -76,3 +78,49 @@ class TFIDFRetriever(BaseRetriever):
         )  # Op -- (n_docs,1) -- Cosine Sim with each doc
         return_docs = [self.docs[i] for i in results.argsort()[-self.k :][::-1]]
         return return_docs
+
+    def save_local(
+        self,
+        folder_path: str,
+        file_name: str = "tfidf_vectorizer",
+    ) -> None:
+        try:
+            import joblib
+        except ImportError:
+            raise ImportError(
+                "Could not import joblib, please install with `pip install joblib`."
+            )
+
+        path = Path(folder_path)
+        path.mkdir(exist_ok=True, parents=True)
+
+        # Save vectorizer with joblib dump.
+        joblib.dump(self.vectorizer, path / f"{file_name}.joblib")
+
+        # Save docs and tfidf array as pickle.
+        with open(path / f"{file_name}.pkl", "wb") as f:
+            pickle.dump((self.docs, self.tfidf_array), f)
+
+    @classmethod
+    def load_local(
+        cls,
+        folder_path: str,
+        file_name: str = "tfidf_vectorizer",
+    ) -> TFIDFRetriever:
+        try:
+            import joblib
+        except ImportError:
+            raise ImportError(
+                "Could not import joblib, please install with `pip install joblib`."
+            )
+
+        path = Path(folder_path)
+
+        # Load vectorizer with joblib load.
+        vectorizer = joblib.load(path / f"{file_name}.joblib")
+
+        # Load docs and tfidf array as pickle.
+        with open(path / f"{file_name}.pkl", "rb") as f:
+            docs, tfidf_array = pickle.load(f)
+
+        return cls(vectorizer=vectorizer, docs=docs, tfidf_array=tfidf_array)

libs/langchain/tests/unit_tests/retrievers/test_tfidf.py

@@ -1,3 +1,7 @@
+import os
+from datetime import datetime
+from tempfile import TemporaryDirectory
+
 import pytest
 
 from langchain.retrievers.tfidf import TFIDFRetriever
@@ -32,3 +36,26 @@ def test_from_documents() -> None:
     tfidf_retriever = TFIDFRetriever.from_documents(documents=input_docs)
     assert len(tfidf_retriever.docs) == 3
     assert tfidf_retriever.tfidf_array.toarray().shape == (3, 5)
+
+
+@pytest.mark.requires("sklearn")
+def test_save_local_load_local() -> None:
+    input_texts = ["I have a pen.", "Do you have a pen?", "I have a bag."]
+    tfidf_retriever = TFIDFRetriever.from_texts(texts=input_texts)
+
+    file_name = "tfidf_vectorizer"
+    temp_timestamp = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
+    with TemporaryDirectory(suffix="_" + temp_timestamp + "/") as temp_folder:
+        tfidf_retriever.save_local(
+            folder_path=temp_folder,
+            file_name=file_name,
+        )
+        assert os.path.exists(os.path.join(temp_folder, f"{file_name}.joblib"))
+        assert os.path.exists(os.path.join(temp_folder, f"{file_name}.pkl"))
+
+        loaded_tfidf_retriever = TFIDFRetriever.load_local(
+            folder_path=temp_folder,
+            file_name=file_name,
+        )
+    assert len(loaded_tfidf_retriever.docs) == 3
+    assert loaded_tfidf_retriever.tfidf_array.toarray().shape == (3, 5)