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/main.py

@@ -1,4 +1,3 @@
-import json
 import logging
 
 from pathlib import Path
@@ -28,7 +27,7 @@ def main(
 
     model = fallback_model(model)
     ai = AI(
-        model=model,
+        model_name=model,
         temperature=temperature,
     )
 
@@ -56,7 +55,7 @@ def main(
     steps = STEPS[steps_config]
     for step in steps:
         messages = step(ai, dbs)
-        dbs.logs[step.__name__] = json.dumps(messages)
+        dbs.logs[step.__name__] = AI.serialize_messages(messages)
 
     if collect_consent():
         collect_learnings(model, temperature, steps, dbs)