Add new types of document transformers (#7379)

- Description: Add two new document transformers that translates
documents into different languages and converts documents into q&a
format to improve vector search results. Uses OpenAI function calling
via the [doctran](https://github.com/psychic-api/doctran/tree/main)
library.
  - Issue: N/A
  - Dependencies: `doctran = "^0.0.5"`
  - Tag maintainer: @rlancemartin @eyurtsev @hwchase17 
  - Twitter handle: @psychicapi or @jfan001

Notes
- Adheres to the `DocumentTransformer` abstraction set by @dev2049 in
#3182
- refactored `EmbeddingsRedundantFilter` to put it in a file under a new
`document_transformers` module
- Added basic docs for `DocumentInterrogator`, `DocumentTransformer` as
well as the existing `EmbeddingsRedundantFilter`

---------

Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>

tests/integration_tests/retrievers/document_compressors/test_embeddings_filter.py

@@ -1,7 +1,9 @@
 """Integration test for embedding-based relevant doc filtering."""
 import numpy as np
 
-from langchain.document_transformers import _DocumentWithState
+from langchain.document_transformers.embeddings_redundant_filter import (
+    _DocumentWithState,
+)
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.retrievers.document_compressors import EmbeddingsFilter
 from langchain.schema import Document

tests/integration_tests/test_document_transformers.py

@@ -1,5 +1,5 @@
 """Integration test for embedding-based redundant doc filtering."""
-from langchain.document_transformers import (
+from langchain.document_transformers.embeddings_redundant_filter import (
     EmbeddingsClusteringFilter,
     EmbeddingsRedundantFilter,
     _DocumentWithState,

tests/unit_tests/test_document_transformers.py

@@ -1,5 +1,7 @@
 """Unit tests for document transformers."""
-from langchain.document_transformers import _filter_similar_embeddings
+from langchain.document_transformers.embeddings_redundant_filter import (
+    _filter_similar_embeddings,
+)
 from langchain.math_utils import cosine_similarity