Langchain integration (#512)

* Added LangChain integration

* Fixed issue created by git checkin process

* Added ':' to characters to remove from end of file path

* Tested initial migration to LangChain, removed comments and logging used for debugging

* Tested initial migration to LangChain, removed comments and logging used for debugging

* Converted camelCase to snake_case

* Turns out we need the exception handling

* Testing Hugging Face Integrations via LangChain

* Added LangChain loadable models

* Renames "qa" prompt to "clarify", since it's used in the "clarify" step, asking for clarification

* Fixed loading model yaml files

* Fixed streaming

* Added modeldir cli option

* Fixed typing

* Fixed interaction with token logging

* Fix spelling + dependency issues + typing

* Fix spelling + tests

* Removed unneeded logging which caused test to fail

* Cleaned up code

* Incorporated feedback

- deleted unnecessary functions & logger.info
- used LangChain ChatLLM instead of LLM to naturally communicate with gpt-4
- deleted loading model from yaml file, as LC doesn't offer this for ChatModels

* Update gpt_engineer/steps.py

Co-authored-by: Anton Osika <anton.osika@gmail.com>

* Incorporated feedback

- Fixed failing test
- Removed parsing complexity by using # type: ignore
- Replace every ocurence of ai.last_message_content with its content

* Fixed test

* Update gpt_engineer/steps.py

---------

Co-authored-by: H <holden.robbins@gmail.com>
Co-authored-by: Anton Osika <anton.osika@gmail.com>

gpt_engineer/learning.py

@@ -100,11 +100,11 @@ def check_consent():
     path = Path(".gpte_consent")
     if path.exists() and path.read_text() == "true":
         return
-    ans = input("Is it ok if we store your prompts to learn? (y/n)")
-    while ans.lower() not in ("y", "n"):
-        ans = input("Invalid input. Please enter y or n: ")
+    answer = input("Is it ok if we store your prompts to learn? (y/n)")
+    while answer.lower() not in ("y", "n"):
+        answer = input("Invalid input. Please enter y or n: ")
 
-    if ans.lower() == "y":
+    if answer.lower() == "y":
         path.write_text("true")
         print(colored("Thank you️", "light_green"))
         print()
@@ -153,21 +153,14 @@ def ask_if_can_store() -> bool:
     return can_store == "y"
 
 
-def logs_to_string(steps: List[Step], logs: DB):
+def logs_to_string(steps: List[Step], logs: DB) -> str:
     chunks = []
     for step in steps:
         chunks.append(f"--- {step.__name__} ---\n")
-        messages = json.loads(logs[step.__name__])
-        chunks.append(format_messages(messages))
+        chunks.append(logs[step.__name__])
     return "\n".join(chunks)
 
 
-def format_messages(messages: List[dict]) -> str:
-    return "\n".join(
-        [f"{message['role']}:\n\n{message['content']}" for message in messages]
-    )
-
-
 def extract_learning(
     model: str, temperature: float, steps: List[Step], dbs: DBs, steps_file_hash
 ) -> Learning: