Should be able to override the global key if you want to evaluate
different outputs in a single run

## Description
This PR adds an option to allow unsigned requests to the Neptune
database when using the `NeptuneGraph` class.

```python
graph = NeptuneGraph(
    host='<my-cluster>',
    port=8182,
    sign=False
)
```

Also, added is an option in the `NeptuneOpenCypherQAChain` to provide
additional domain instructions to the graph query generation prompt.
This will be injected in the prompt as-is, so you should include any
provider specific tags, for example `<instructions>` or `<INSTR>`.

```python
chain = NeptuneOpenCypherQAChain.from_llm(
    llm=llm,
    graph=graph,
    extra_instructions="""
    Follow these instructions to build the query:
    1. Countries contain airports, not the other way around
    2. Use the airport code for identifying airports
    """
)
```

…on error (#13401)

- **Description:** Added a method `fetch_valid_documents` to
`WebResearchRetriever` class that will test the connection for every url
in `new_urls` and remove those that raise a `ConnectionError`.
- **Issue:** [Previous
PR](#13353),
  - **Dependencies:** None,
  - **Tag maintainer:** @efriis 

Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.

See contribution guidelines for more information on how to write/run
tests, lint, etc:

https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md

If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in `docs/extras`
directory.

If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.

And warn instead of raising an error, since the chain API is too
inconsistent.

… configurable chat prompt format (#8295)

## Update 2023-09-08

This PR now supports further models in addition to Lllama-2 chat models.
See [this comment](#issuecomment-1668988543) for further details. The
title of this PR has been updated accordingly.

## Original PR description

This PR adds a generic `Llama2Chat` model, a wrapper for LLMs able to
serve Llama-2 chat models (like `LlamaCPP`,
`HuggingFaceTextGenInference`, ...). It implements `BaseChatModel`,
converts a list of chat messages into the [required Llama-2 chat prompt
format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and
forwards the formatted prompt as `str` to the wrapped `LLM`. Usage
example:

```python
# uses a locally hosted Llama2 chat model
llm = HuggingFaceTextGenInference(
    inference_server_url="http://127.0.0.1:8080/",
    max_new_tokens=512,
    top_k=50,
    temperature=0.1,
    repetition_penalty=1.03,
)

# Wrap llm to support Llama2 chat prompt format.
# Resulting model is a chat model
model = Llama2Chat(llm=llm)

messages = [
    SystemMessage(content="You are a helpful assistant."),
    MessagesPlaceholder(variable_name="chat_history"),
    HumanMessagePromptTemplate.from_template("{text}"),
]

prompt = ChatPromptTemplate.from_messages(messages)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
chain = LLMChain(llm=model, prompt=prompt, memory=memory)

# use chat model in a conversation
# ...
```

Also part of this PR are tests and a demo notebook.

- Tag maintainer: @hwchase17
- Twitter handle: `@mrt1nz`

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

For default eval llm

- **Description:** a simple typo/extra line break fix
  - **Dependencies:** none

small fix to add support for new OctoAI LLM endpoints

BUG: langchain.agents.openai_assistant has a reference as
`from langchain_experimental.openai_assistant.base import
OpenAIAssistantRunnable`
should be 
`from langchain.agents.openai_assistant.base import
OpenAIAssistantRunnable`

This prevents building of the API Reference docs