Tree of Thought introducing a new ToTChain. (#5167)

# [WIP] Tree of Thought introducing a new ToTChain.

This PR adds a new chain called ToTChain that implements the ["Large
Language Model Guided
Tree-of-Though"](https://arxiv.org/pdf/2305.08291.pdf) paper.

There's a notebook example `docs/modules/chains/examples/tot.ipynb` that
shows how to use it.


Implements #4975


## Who can review?

Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:

- @hwchase17
- @vowelparrot

---------

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

libs/experimental/tests/unit_tests/fake_llm.py

@@ -0,0 +1,61 @@
+"""Fake LLM wrapper for testing purposes."""
+from typing import Any, Dict, List, Mapping, Optional, cast
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+from pydantic import validator
+
+
+class FakeLLM(LLM):
+    """Fake LLM wrapper for testing purposes."""
+
+    queries: Optional[Mapping] = None
+    sequential_responses: Optional[bool] = False
+    response_index: int = 0
+
+    @validator("queries", always=True)
+    def check_queries_required(
+        cls, queries: Optional[Mapping], values: Mapping[str, Any]
+    ) -> Optional[Mapping]:
+        if values.get("sequential_response") and not queries:
+            raise ValueError(
+                "queries is required when sequential_response is set to True"
+            )
+        return queries
+
+    def get_num_tokens(self, text: str) -> int:
+        """Return number of tokens."""
+        return len(text.split())
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "fake"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        if self.sequential_responses:
+            return self._get_next_response_in_sequence
+
+        if self.queries is not None:
+            return self.queries[prompt]
+        if stop is None:
+            return "foo"
+        else:
+            return "bar"
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        return {}
+
+    @property
+    def _get_next_response_in_sequence(self) -> str:
+        queries = cast(Mapping, self.queries)
+        response = queries[list(queries.keys())[self.response_index]]
+        self.response_index = self.response_index + 1
+        return response