delayedkarma/langchain-issues huggingface dataset:
fix: agent CHAT_ZERO_SHOT_REACT can not work normally

# Fix agent CHAT_ZERO_SHOT_REACT can not work normally

Fixes #5299 

In this problem, `ChatOutputParser` must handle text similar to the the following:

```
Thought: I need to use the search tool to find the current price of EPAM in NYSE and then use the calculator tool to raise it to the 0.23 power.

Action:
\```
{
  "action": "Search",
  "action_input": "EPAM NYSE price"
}
\```
```

Initially, `ChatOutputParser` used `parse_json_markdown` to solve the problem but it doesn't work and resulted in #5299 . A better solution is to use `re` to handle text to obtain value for `action` and `action_input` .

The optimized program output content is as follows.
--- 

> Entering new AgentExecutor chain...
Thought: I need to use the search tool to find the current price of EPAM in NYSE and then use the calculator tool to raise it to the 0.23 power.

Action:
```
{
  "action": "Search",
  "action_input": "EPAM NYSE price"
}
```


Observation: EPAM Systems, Inc. is an American company that specializes in software engineering services, digital platform engineering, and digital product design, operating out of Newtown, Pennsylvania. EPAM is a founding member of the MACH Alliance.
Thought:I need to refine my search query to find the current stock price of EPAM on NYSE. 

Action:
```
{
  "action": "Search",
  "action_input": "EPAM stock price NYSE"
}
```


Observation: EPAM Systems, Inc. is an American company that specializes in software engineering services, digital platform engineering, and digital product design, operating out of Newtown, Pennsylvania. EPAM is a founding member of the MACH Alliance.
Thought:I need to check the search results to find the current stock price of EPAM on NYSE.

Action:
```
{
  "action": "Search",
  "action_input": "EPAM stock price NYSE"
}
```



Observation: EPAM Systems, Inc. is an American company that specializes in software engineering services, digital platform engineering, and digital product design, operating out of Newtown, Pennsylvania. EPAM is a founding member of the MACH Alliance.
Thought:I found the current stock price of EPAM on NYSE, now I need to use the calculator tool to raise it to the 0.23 power.

Action:
```
{
  "action": "Calculator",
  "action_input": "pow(123.45, 0.23)"
}
```


Observation: Answer: 3.027212804346112
Final Answer: 3.027212804346112

> Finished chain.
Validation Error importing OpenAPI planner when OpenAI credentials not in environment

### System Info

Name: langchain, Version: 0.0.180
Name: openai, Version: 0.27.7

macOS Mojave 10.14.6

### Who can help?

@vowelparrot 

### Information

- [X] The official example notebooks/scripts
- [X] My own modified scripts

### Related Components

- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

Steps:

1. Do _not_ load open ai key into env with the intention of wanting to pass it as a parameter when instantiating the llm

```
from dotenv import dotenv_values
openai_api_key = dotenv_values('.env')['OPENAI_API_KEY']
```

2. Load the planner:

```
from langchain.llms.openai import OpenAI
from langchain.agents.agent_toolkits.openapi import planner
``` 

### Expected behavior

A validation error should not be raised during the importing of the module.
We should be able to pass the open api key as an argument.

That is, the following should work:
```
from langchain.llms.openai import OpenAI
from langchain.agents.agent_toolkits.openapi import planner

llm = OpenAI(model_name="gpt-4", temperature=0.0, open_api_key=open_api_key)
```
change in the template string is causing the SSL connection or the server response to hang

### System Info

Pycharm in macOS

### Who can help?

_No response_

### Information

- [X] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

# change in the template string will cause the SSL connection or the server response to hang,code here:


`question = "Who won the FIFA World Cup in the year 1994? "
template = """Question: {question}

Answer: Let's think step by step."""
def execute_llm(repo_id:str,template:str,question:str):
    prompt = PromptTemplate(template=template, input_variables=["question"])
    llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0, "max_length": 64})
    llm_chain = LLMChain(prompt=prompt, llm=llm)
    print(llm_chain.run(question))`
    
    
execute_llm(repo_id="google/flan-t5-xl",template=template,question=question)
Upon executing llm_chain.run(question), it accesses the HuggingFaceHub service. This results in the code getting stuck at the following line in ssl.py, which is part of the SSLSocket class: self._sslobj.read(len, buffer). This method of the SSLSocket class holds a _sslobj object, which is also of SSLSocket class. The SSLSocket class defines a method read(self, len=1024, buffer=None) which internally calls _sslobj's read(self, len=1024, buffer=None) method.

The problem is that any modification to the template (whether it's adding spaces, newline characters, or changing the string) will cause self._sslobj.read(len, buffer) to block until it times out. Since it's completely impossible to step into it for tracking and debugging, I'm unable to determine the specific reason.

### Expected behavior

Could you explain what might be causing this issue? It seems to be related to the SSL network connection, not the content of the template string. However, it's puzzling that the change in the template string is causing the SSL connection or the server response to hang. This behavior is not entirely reasonable and may be a bug or a problem caused by other factors.
Fix update_document function, add test and documentation.

# Fix for `update_document` Function in Chroma

## Summary
This pull request addresses an issue with the `update_document` function in the Chroma class, as described in [#5031](https://github.com/hwchase17/langchain/issues/5031#issuecomment-1562577947). The issue was identified as an `AttributeError` raised when calling `update_document` due to a missing corresponding method in the `Collection` object. This fix refactors the `update_document` method in `Chroma` to correctly interact with the `Collection` object.

## Changes
1. Fixed the `update_document` method in the `Chroma` class to correctly call methods on the `Collection` object.
2. Added the corresponding test `test_chroma_update_document` in `tests/integration_tests/vectorstores/test_chroma.py` to reflect the updated method call.
3. Added an example and explanation of how to use the `update_document` function in the Jupyter notebook tutorial for Chroma.

## Test Plan
All existing tests pass after this change. In addition, the `test_chroma_update_document` test case now correctly checks the functionality of `update_document`, ensuring that the function works as expected and updates the content of documents correctly.

## Reviewers
@dev2049

This fix will ensure that users are able to use the `update_document` function as expected, without encountering the previous `AttributeError`. This will enhance the usability and reliability of the Chroma class for all users. 

Thank you for considering this pull request. I look forward to your feedback and suggestions.
DOC: SteamshipImageGenerationTool returns Config Error in multi_modal_output_agent.ipynb

### Issue with current documentation:

When running multi_modal_output_agent.ipynb: https://python.langchain.com/en/latest/use_cases/agents/multi_modal_output_agent.html, I get ConfigError: field "steamship" not yet prepared so type is still a ForwardRef, you might need to call SteamshipImageGenerationTool.update_forward_refs().


tools = [
    SteamshipImageGenerationTool(model_name= "dall-e")
]
---------------------------------------------------------------------------
ConfigError                               Traceback (most recent call last)
Cell In[7], line 2
      1 tools = [
----> 2     SteamshipImageGenerationTool(model_name= "dall-e")
      3 ]

File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/main.py:339, in pydantic.main.BaseModel.__init__()

File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/main.py:1076, in pydantic.main.validate_model()

File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/fields.py:860, in pydantic.fields.ModelField.validate()

ConfigError: field "steamship" not yet prepared so type is still a ForwardRef, you might need to call SteamshipImageGenerationTool.update_forward_refs().


After I call SteamshipImageGenerationTool.update_forward_refs(), I get another error. 

SteamshipImageGenerationTool.update_forward_refs()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[10], line 1
----> 1 SteamshipImageGenerationTool.update_forward_refs()

File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/main.py:815, in pydantic.main.BaseModel.update_forward_refs()

File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/typing.py:562, in pydantic.typing.update_model_forward_refs()

File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/typing.py:528, in pydantic.typing.update_field_forward_refs()

File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/typing.py:66, in pydantic.typing.evaluate_forwardref()

File ~/miniconda3/envs/langchain/lib/python3.11/typing.py:864, in ForwardRef._evaluate(self, globalns, localns, recursive_guard)
    859 if self.__forward_module__ is not None:
    860     globalns = getattr(
    861         sys.modules.get(self.__forward_module__, None), '__dict__', globalns
    862     )
    863 type_ = _type_check(
--> 864     eval(self.__forward_code__, globalns, localns),
    865     "Forward references must evaluate to types.",
    866     is_argument=self.__forward_is_argument__,
    867     allow_special_forms=self.__forward_is_class__,
    868 )
    869 self.__forward_value__ = _eval_type(
    870     type_, globalns, localns, recursive_guard | {self.__forward_arg__}
    871 )
    872 self.__forward_evaluated__ = True

File <string>:1

NameError: name 'Steamship' is not defined

### Idea or request for content:

_No response_
"parse_json_markdown" function improved / fixed.
Labels: name: needs test

# "parse_json_markdown" function improved / fixed.

I've run into an LLm output parsing issue with an agent (type: conversational-react-description), it always raises llm output error/exception with this type of agent.
I couldn't wait for a fix to be released, so I fixed/improved the "parse_json_markdown" function to ensure parsing the json from LLM output, now it's working great for me, and I would like to contribute the fix :D

Note: I'm a Python expert, and I would like to contribute more, is there anyone that can help/guide me to do the testing for more complex contributions (any articles/blogs may help)?

## Before submitting
Thanks in advance :D

## Who can review?
@hwchase17
@vowelparrot
Move chat agent prompt formatting instructions into human message

# Move chat agent prompt formatting instructions into human message

Partially addresses #5353 around chat agent reliability.

OpenAI's 3.5 turbo model is documented as "not pay[ing] strong attention to the system message, and therefore important instructions are often better placed in a user message.". Could use a bit of testing, opening for discussion at the moment.

## Who can review?

Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:

@vowelparrot 
@agola11
Issue: Chat agents should put format instructions and other important information in a human message

### Chat agent reliability fix:  put format instructions and other important information in a human message

There have been a few raised issues specifically around agent reliability when using chat models. @emilsedgh brought up in the JS Discord that OpenAI's 3.5 turbo model is documented as "not pay[ing] strong attention to the system message, and therefore important instructions are often better placed in a user message.":

https://platform.openai.com/docs/guides/chat/instructing-chat-models

The `ConversationalChatAgent` is implemented this way:

https://github.com/hwchase17/langchain/blob/master/langchain/agents/conversational_chat/base.py#L90

But the base `ChatAgent` and the `StructuredChatAgent` are not:

https://github.com/hwchase17/langchain/blob/master/langchain/agents/chat/base.py#L81
https://github.com/hwchase17/langchain/blob/master/langchain/agents/structured_chat/base.py#L88

Need to do a little bit more experimenting, but moving things into the human message may help with reliability issues.


### Suggestion:

_No response_
Add llama.cpp get_num_tokens support
Labels: name: lgtm

# Adds support for counting tokens using the llama.cpp python interface rather than the default huggingface transformers library

The current implementation of the `LlamaCpp` LLM defaults to the base `LLM` for token counting. This results in the need for the huggingface transformers library to be loaded.

The Llama.cpp python interface provides a method for tokenizing a given string. This PR overloads the `get_num_tokens` method of the base class to use that instead.

Using the native tokenizer should yield more accurate token counts dependent on the loaded model.

For llama.cpp workflows this PR reduces dependencies.

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-->

## Before submitting

Wasn't sure how to setup a test for this without spinning up a particular model. But I have tested it in a project.

## Who can review?

Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:
@hwchase17
@agola11
<!-- For a quicker response, figure out the right person to tag with @

  @hwchase17 - project lead

  Tracing / Callbacks
  - @agola11

  Async
  - @agola11

  DataLoaders
  - @eyurtsev

  Models
  - @hwchase17
  - @agola11

  Agents / Tools / Toolkits
  - @vowelparrot

  VectorStores / Retrievers / Memory
  - @dev2049
        
 -->
Sample Notebook for DynamoDB Chat Message History
Labels: name: lgtm

# Sample Notebook for DynamoDB Chat Message History

@dev2049

Adding a sample notebook for the DynamoDB Chat Message History class.

<!-- For a quicker response, figure out the right person to tag with @

  @hwchase17 - project lead

  Tracing / Callbacks
  - @agola11

  Async
  - @agola11

  DataLoaders
  - @eyurtsev

  Models
  - @hwchase17
  - @agola11

  Agents / Tools / Toolkits
  - @vowelparrot

  VectorStores / Retrievers / Memory
  - @dev2049
        
 -->
implement `Multiagent Debate`

### Feature request

[Improving Factuality and Reasoning in Language
Models through Multiagent Debate](https://arxiv.org/pdf/2305.14325.pdf) - looks very promising

### Motivation

This method is orthogonal to other methods like CoT. It looks like, this method is beneficial almost in any case when we need the highest quality answer.

### Your contribution

I can help with testing. Not sure about quick implementation.
Using OpenLM example giving error: "ValueError: OPENAI_API_KEY is not set or passed as an argument"

### System Info

Running latest versions of langchain, openai, openlm, python 3.10, mac M1, trying this example I saw on Twitter

https://python.langchain.com/en/latest/modules/models/llms/integrations/openlm.html?highlight=openlm

```
from langchain.llms import OpenLM
from langchain.llms import OpenAI
from langchain.llms import openai
from langchain import PromptTemplate, LLMChain

openai.api_key = os.getenv("OPENAI_API_KEY")

question = "What is the capital of France?"
template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate(template=template, input_variables=["question"])


llm1 = OpenAI()

llm2 = OpenLM(model="text-davinci-003")
llm_chain1 = LLMChain(prompt=prompt, llm=llm1)
llm_chain2 = LLMChain(prompt=prompt, llm=llm2)
result1 = llm_chain1.run(question)
result2 = llm_chain2.run(question)
```

result 1 runs, result 2 gives ValueError: OPENAI_API_KEY is not set or passed as an argument

### Who can help?

_No response_

### Information

- [ ] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

```
from langchain.llms import OpenLM
from langchain.llms import OpenAI
from langchain.llms import openai
from langchain import PromptTemplate, LLMChain

openai.api_key = os.getenv("OPENAI_API_KEY")

question = "What is the capital of France?"
template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate(template=template, input_variables=["question"])


llm1 = OpenAI()

llm2 = OpenLM(model="text-davinci-003")
llm_chain1 = LLMChain(prompt=prompt, llm=llm1)
llm_chain2 = LLMChain(prompt=prompt, llm=llm2)
result1 = llm_chain1.run(question)
result2 = llm_chain2.run(question)
```

### Expected behavior

Run without error
Extend elastic_vector_search.py to allow for kNN indexing/searching

### Feature request

Extend `langchain/embeddings/elasticsearch.py` to support kNN indexing and searching. 
The high-level objectives will be:

1. Allow for the [creation of an index with the correct mapping](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#index-vectors-knn-search) to store documents including dense_vectors so they can be used for kNN search
2. Store embeddings in elasticsearch in [dense_vector](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html) field type
3. Perform [kNN search](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html)
4. Perform [Hybrid](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html#_combine_approximate_knn_with_other_features) BM25 (query) + kNN search


### Motivation

Elasticsearch support approximate k-nearest neighbor search with dense vectors. The current module only support script score / exact match vector search. 

### Your contribution

I will work on the code and create the pull request
FLARE | Azure open Ai doesn't work

---------------------------------------------------------------------------
InvalidRequestError                       Traceback (most recent call last)
[<ipython-input-33-f877209e86e7>](https://localhost:8080/#) in <cell line: 1>()
----> 1 flare.run(query)

20 frames
[/usr/local/lib/python3.10/dist-packages/openai/api_resources/abstract/engine_api_resource.py](https://localhost:8080/#) in __prepare_create_request(cls, api_key, api_base, api_type, api_version, organization, **params)
     81         if typed_api_type in (util.ApiType.AZURE, util.ApiType.AZURE_AD):
     82             if deployment_id is None and engine is None:
---> 83                 raise error.InvalidRequestError(
     84                     "Must provide an 'engine' or 'deployment_id' parameter to create a %s"
     85                     % cls,

InvalidRequestError: Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.completion.Completion'>
Update llamacpp demonstration notebook

# Update llamacpp demonstration notebook

Add instructions to install with BLAS backend, and update the example of model usage.

Fixes #5071. However, it is more like a prevention of similar issues in the future, not a fix, since there was no problem in the framework functionality

## Who can review?

Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:

- @hwchase17 
- @agola11
Removed deprecated llm attribute for load_chain

# Removed deprecated llm attribute for load_chain

Currently `load_chain` for some chain types expect `llm` attribute to be present but `llm` is deprecated attribute for those chains and might not be persisted during their `chain.save`.

Fixes #5224 [(issue)](https://github.com/hwchase17/langchain/issues/5224)

## Who can review?
@hwchase17
@dev2049
UnstructuredURLLoader can't load data from url

### System Info

I operate according to the official documentation (https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/url.html), but I can't get the information through loader.load(), but the output is still [], the language I use is Python, and the version of langchain is 0.0.181, I tried reinstalling the dependent environment but it didn't work well, hopefully it can be solved soon

### Who can help?

_No response_

### Information

- [ ] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

 from langchain.document_loaders import UnstructuredURLLoader

urls = [
    "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-8-2023",
    "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-9-2023"
]

loader = UnstructuredURLLoader(urls=urls)

data = loader.load()

### Expected behavior

data not empty
Implement a way to abort / cancel request

### Feature request

I've been playing around with OpenAI GPT-4 and ran into situation when response generation might take quite some time - say 5 minutes. 

I switched over to streaming, but often I can immediately see the response is not what want, and therefore I'd like to cancel request.

Now here is the part that is unclear to me: 
**is there an official way to cancel request in Python's version of LangChain?** I have found this [described](https://js.langchain.com/docs/modules/models/chat/additional_functionality#cancelling-requests) in JS/TS version of the framework, however scanning docs, sources and issues yields nothing for this repo.

For now I simply terminate process, which works good enough for something like Jupyter notebooks, but quite problematic for say web application.

Besides termination, it's also unclear if I may incur any unwanted costs or not for the abandoned request.

Should some sort of feature parity be made with JS LangChain?

### Motivation

Provide a documented way to cancel long-running requests


### Your contribution

At this point I have capacity only to test out potential implementation. May work on the implementation in later weeks.