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Fix/docs
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<!-- Thank you for contributing to LangChain!
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Mateusz/fix import
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AIORpcError connection reset errors from Qdrant
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NONE
| null |
### System Info
Python:3.11-slim-bullseye base docker image
Langchain version: 0.0.348
qdrant-client: 1.7.0
Qdrant database 1.7.1 (deployed on AWS cluster)
Reproduces regardless of prefer grpc true or false
### Who can help?
@agola11 @hwchase17
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
1. Use ConversationalRetrievalChain with Qdrant vectordb
2. Use async acall interface
3. Maybe wait some idle time (15 min?)
You will experience a lot of errors from Qdrant:
AioRpcError of RPC that terminated with: status = StatusCode.UNAVAILABLE details = "recvmsg:Connection reset by peer" debug_error_string = "UNKNOWN:Error received from peer {created_time}" >, type: AioRpcError
It's ok after retry of the request but this causes significant delay in response
### Expected behavior
Connection recovery for the vectordb should be handled by LangChain internally and ideally understand what causes the connection issues and resolve it
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I_kwDOIPDwls56Ey4l
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Can I switch my template during my Chain working?
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[] | null | 1 | 2023-12-19T07:25:34 | 2023-12-19T07:34:08 | null |
NONE
| null |
### Issue you'd like to raise.
I construct a Chain using retriever with a certain template, but now I want to use another template when the retriever retrieve nothing back.
Is it possible for langchain? Or maybe it can only be handmade?
### Suggestion:
_No response_
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docstrings `langchain_community` update
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[] | null | 1 | 2023-12-19T04:25:52 | 2023-12-19T16:50:59 | 2023-12-19T13:58:25 |
COLLABORATOR
| null |
Addded missed docstrings. Fixed inconsistency in docstrings.
**Note** CC @efriis
There were PR errors on `langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py`
But, I didn't touch this file in this PR! Can it be some cache problems?
I fixed this error.
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optimization: update core type compare
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I_kwDOIPDwls56ECow
| 14,886 |
Issue: can't use llm sagemaker endpoint as load_qa_chain
|
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[] | null | 4 | 2023-12-19T04:04:06 | 2024-01-09T11:51:54 | null |
NONE
| null |
I've deployed 'mistralai/Mistral-7B-v0.1' model to sagemaker and wanna use it as load_qa_chain
```
from langchain.chains.question_answering import load_qa_chain
from langchain.llms.sagemaker_endpoint import SagemakerEndpoint
content_handler = ContentHandler()
llm = SagemakerEndpoint(
endpoint_name=endpoint_name,
region_name="eu-west-2",
model_kwargs={
"temperature": 0,
"maxTokens": 1024,
"numResults": 2
},
content_handler=content_handler
)
chain = load_qa_chain(llm=llm, chain_type="stuff")
```
Now, running chain for Doc QA.
`chain.run(input_documents = docs, question = "what is dollarama")`
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[56], line 1
----> 1 chain.run(input_documents = docs, question = "what is dollarama")
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/chains/base.py:506, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
501 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
502 _output_key
503 ]
505 if kwargs and not args:
--> 506 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
507 _output_key
508 ]
510 if not kwargs and not args:
511 raise ValueError(
512 "`run` supported with either positional arguments or keyword arguments,"
513 " but none were provided."
514 )
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/chains/base.py:306, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
304 except BaseException as e:
305 run_manager.on_chain_error(e)
--> 306 raise e
307 run_manager.on_chain_end(outputs)
308 final_outputs: Dict[str, Any] = self.prep_outputs(
309 inputs, outputs, return_only_outputs
310 )
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/chains/base.py:300, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
293 run_manager = callback_manager.on_chain_start(
294 dumpd(self),
295 inputs,
296 name=run_name,
297 )
298 try:
299 outputs = (
--> 300 self._call(inputs, run_manager=run_manager)
301 if new_arg_supported
302 else self._call(inputs)
303 )
304 except BaseException as e:
305 run_manager.on_chain_error(e)
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/chains/combine_documents/base.py:119, in BaseCombineDocumentsChain._call(self, inputs, run_manager)
117 # Other keys are assumed to be needed for LLM prediction
118 other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
--> 119 output, extra_return_dict = self.combine_docs(
120 docs, callbacks=_run_manager.get_child(), **other_keys
121 )
122 extra_return_dict[self.output_key] = output
123 return extra_return_dict
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/chains/combine_documents/stuff.py:171, in StuffDocumentsChain.combine_docs(self, docs, callbacks, **kwargs)
169 inputs = self._get_inputs(docs, **kwargs)
170 # Call predict on the LLM.
--> 171 return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/chains/llm.py:257, in LLMChain.predict(self, callbacks, **kwargs)
242 def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
243 """Format prompt with kwargs and pass to LLM.
244
245 Args:
(...)
255 completion = llm.predict(adjective="funny")
256 """
--> 257 return self(kwargs, callbacks=callbacks)[self.output_key]
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/chains/base.py:306, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
304 except BaseException as e:
305 run_manager.on_chain_error(e)
--> 306 raise e
307 run_manager.on_chain_end(outputs)
308 final_outputs: Dict[str, Any] = self.prep_outputs(
309 inputs, outputs, return_only_outputs
310 )
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/chains/base.py:300, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
293 run_manager = callback_manager.on_chain_start(
294 dumpd(self),
295 inputs,
296 name=run_name,
297 )
298 try:
299 outputs = (
--> 300 self._call(inputs, run_manager=run_manager)
301 if new_arg_supported
302 else self._call(inputs)
303 )
304 except BaseException as e:
305 run_manager.on_chain_error(e)
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/chains/llm.py:93, in LLMChain._call(self, inputs, run_manager)
88 def _call(
89 self,
90 inputs: Dict[str, Any],
91 run_manager: Optional[CallbackManagerForChainRun] = None,
92 ) -> Dict[str, str]:
---> 93 response = self.generate([inputs], run_manager=run_manager)
94 return self.create_outputs(response)[0]
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/chains/llm.py:103, in LLMChain.generate(self, input_list, run_manager)
101 """Generate LLM result from inputs."""
102 prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
--> 103 return self.llm.generate_prompt(
104 prompts,
105 stop,
106 callbacks=run_manager.get_child() if run_manager else None,
107 **self.llm_kwargs,
108 )
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/llms/base.py:498, in BaseLLM.generate_prompt(self, prompts, stop, callbacks, **kwargs)
490 def generate_prompt(
491 self,
492 prompts: List[PromptValue],
(...)
495 **kwargs: Any,
496 ) -> LLMResult:
497 prompt_strings = [p.to_string() for p in prompts]
--> 498 return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/llms/base.py:647, in BaseLLM.generate(self, prompts, stop, callbacks, tags, metadata, run_name, **kwargs)
632 raise ValueError(
633 "Asked to cache, but no cache found at `langchain.cache`."
634 )
635 run_managers = [
636 callback_manager.on_llm_start(
637 dumpd(self),
(...)
645 )
646 ]
--> 647 output = self._generate_helper(
648 prompts, stop, run_managers, bool(new_arg_supported), **kwargs
649 )
650 return output
651 if len(missing_prompts) > 0:
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/llms/base.py:535, in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
533 for run_manager in run_managers:
534 run_manager.on_llm_error(e)
--> 535 raise e
536 flattened_outputs = output.flatten()
537 for manager, flattened_output in zip(run_managers, flattened_outputs):
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/llms/base.py:522, in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
512 def _generate_helper(
513 self,
514 prompts: List[str],
(...)
518 **kwargs: Any,
519 ) -> LLMResult:
520 try:
521 output = (
--> 522 self._generate(
523 prompts,
524 stop=stop,
525 # TODO: support multiple run managers
526 run_manager=run_managers[0] if run_managers else None,
527 **kwargs,
528 )
529 if new_arg_supported
530 else self._generate(prompts, stop=stop)
531 )
532 except BaseException as e:
533 for run_manager in run_managers:
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/llms/base.py:1048, in LLM._generate(self, prompts, stop, run_manager, **kwargs)
1042 for prompt in prompts:
1043 text = (
1044 self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
1045 if new_arg_supported
1046 else self._call(prompt, stop=stop, **kwargs)
1047 )
-> 1048 generations.append([Generation(text=text)])
1049 return LLMResult(generations=generations)
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/langchain/load/serializable.py:97, in Serializable.__init__(self, **kwargs)
96 def __init__(self, **kwargs: Any) -> None:
---> 97 super().__init__(**kwargs)
98 self._lc_kwargs = kwargs
File ~/anaconda3/envs/tensorflow2_p310/lib/python3.10/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for Generation
text
str type expected (type=type_error.str)
```
### Suggestion:
When I'm using openai api, it works but, only sagemaker endpoint is problem.
|
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I_kwDOIPDwls56EBtp
| 14,885 |
How to connect LangChain application to a mobile app using an API
|
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[] | null | 1 | 2023-12-19T03:58:57 | 2023-12-19T04:06:57 | null |
NONE
| null |
I have developed a Flutter app with a chatroom feature and have successfully implemented chatting in the chatroom interface using the OpenAI API with ChatGPT. Now, I am looking to utilize an API to establish a connection between LangChain and my mobile app. How or what should i do to achieve this? I am new to this, any help will be thankful.
|
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PR_kwDOIPDwls5iU8M6
| 14,884 |
core[patch]: update langchain-core runtime library name
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[] | null | 3 | 2023-12-19T03:33:43 | 2023-12-20T22:35:49 | 2023-12-20T22:35:48 |
CONTRIBUTOR
| null | null |
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[Partner] Update google integration test
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[] | null | 1 | 2023-12-19T02:45:39 | 2023-12-19T02:46:25 | 2023-12-19T02:46:24 |
COLLABORATOR
| null |
Gemini has decided that pickle rick is unsafe: https://github.com/langchain-ai/langchain/actions/runs/7256642294/job/19769249444#step:8:189
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PR_kwDOIPDwls5iUyBl
| 14,882 |
[Partner] Google GenAi new release
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[] | null | 1 | 2023-12-19T02:30:28 | 2023-12-19T02:35:58 | 2023-12-19T02:35:58 |
COLLABORATOR
| null |
to support the system message merging
Also fix integration tests that weren't passing
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PR_kwDOIPDwls5iUwZW
| 14,881 |
cli[patch]: add embedding to integration template
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I_kwDOIPDwls56DqQ0
| 14,880 |
how to increase the response size of chromadb
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[] | null | 3 | 2023-12-19T01:59:00 | 2023-12-22T06:16:20 | null |
NONE
| null |
I am working in project, where i have to use multiple pdf docs to give respose to the user query.
I have a load method to load pdf from directory.
```
def loadFiles():
loader = DirectoryLoader('./static/upload/', glob="./*.pdf", loader_cls=PyPDFLoader)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
texts = text_splitter.split_documents(documents)
return texts
```
I am creating chromadb by below code:
```
def createDb(load,embeddings,persist_directory):
max_input_size = 3000
num_output = 256
chunk_size_limit = 1000 # token window size per document
max_chunk_overlap = 80 # overlap for each token fragment
vectordb = Chroma.from_documents(documents=load, embedding=embeddings, persist_directory=persist_directory)
vectordb.persist()
return vectordb
```
now , I am quering chromadb ,
```
qa_chain = RetrievalQA.from_chain_type(
llm=OpenAI(temperature=0,model_name = "text-davinci-003"),
retriever=vectordb.as_retriever(),chain_type="stuff",
chain_type_kwargs=chain_type_kwargs,
return_source_documents=True
)
```
However, i am getting the response , but not full response in some cases, like below :
My source pdf has following contents:
[source file](https://i.stack.imgur.com/Xaz7U.png)
while my response is showing only some parts as shown below:
[chromadb response](https://i.stack.imgur.com/CY4dL.png)
I tried incresing the chunk_ovarlap size as shown in createdb(), but it does not worked. I am expecting from chromadb full response and response should be comming from given pdf.
I am new to this, any help will be thankful.
|
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| 2,047,778,788 |
PR_kwDOIPDwls5iUtGs
| 14,879 |
Docs `tencent` pages update
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[] | null | 1 | 2023-12-19T01:57:34 | 2023-12-19T02:45:51 | 2023-12-19T02:21:39 |
COLLABORATOR
| null |
- updated `Tencent` provider page: added a chat model and document loader references; company description
- updated Chat model and Document loader pages with descriptions, links
- renamed files to consistent formats; redirected file names
Note:
I was getting this linting error on code that **was not changed in my PR**!
> Error: docs/docs/guides/safety/hugging_face_prompt_injection.ipynb:1:1: I001 Import block is un-sorted or un-formatted
> make: *** [Makefile:47: lint_package] Error 1
I've fixed this error in the notebook
|
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Harrison/agent docs custom
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NONE
| null |
### System Info
Platform: Ubuntu 22.04
Python: 3.11.6
Langchain: 0.0.351
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When the program is first initialized with `__setup_client()` and `__should_reingest()` returns `True`, `__get_new_client()` works as intended. However, if `reingest()` is called afterward, `__get_new_client()` returns the error below.
Relevant code:
```python
def __setup_client(self) -> None:
if self.__should_reingest():
self.db = self.__get_new_client()
else:
self.db = self.__get_existing_client()
def reingest(self) -> None:
self.db = self.__get_new_client()
def __get_new_client(self):
if os.path.exists(self.persist_directory):
shutil.rmtree(self.persist_directory)
docs = self.__get_docs()
client = Chroma.from_documents(
docs, self.embedding_function, persist_directory=self.persist_directory)
with open(f'{self.persist_directory}/date.txt', 'w') as f:
f.write(f'{datetime.date.today()}')
return client
```
Error:
```
Traceback (most recent call last):
...
File line 26, in reingest
self.cauldron.reingest()
File line 19, in reingest
self.db = self.__get_new_client()
^^^^^^^^^^^^^^^^^^^^^^^
File line 51, in __get_new_client
client = Chroma.from_documents(
^^^^^^^^^^^^^^^^^^^^^^
File ".../.pyenv/versions/3.11.6/lib/python3.11/site-packages/langchain_community/vectorstores/chroma.py", line 771, in from_documents
return cls.from_texts(
^^^^^^^^^^^^^^^
File ".../.pyenv/versions/3.11.6/lib/python3.11/site-packages/langchain_community/vectorstores/chroma.py", line 729, in from_texts
chroma_collection.add_texts(
File ".../.pyenv/versions/3.11.6/lib/python3.11/site-packages/langchain_community/vectorstores/chroma.py", line 297, in add_texts
self._collection.upsert(
File ".../.pyenv/versions/3.11.6/lib/python3.11/site-packages/chromadb/api/models/Collection.py", line 459, in upsert
self._client._upsert(
File ".../.pyenv/versions/3.11.6/lib/python3.11/site-packages/chromadb/telemetry/opentelemetry/__init__.py", line 127, in wrapper
return f(*args, **kwargs)
^^^^^^^^^^^^^^^^^^
File ".../.pyenv/versions/3.11.6/lib/python3.11/site-packages/chromadb/api/segment.py", line 446, in _upsert
self._producer.submit_embeddings(coll["topic"], records_to_submit)
File ".../.pyenv/versions/3.11.6/lib/python3.11/site-packages/chromadb/telemetry/opentelemetry/__init__.py", line 127, in wrapper
return f(*args, **kwargs)
^^^^^^^^^^^^^^^^^^
File ".../.pyenv/versions/3.11.6/lib/python3.11/site-packages/chromadb/db/mixins/embeddings_queue.py", line 172, in submit_embeddings
results = cur.execute(sql, params).fetchall()
^^^^^^^^^^^^^^^^^^^^^^^^
sqlite3.OperationalError: attempt to write a readonly database
```
### Expected behavior
No error returned
|
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COLLABORATOR
| null |
Templates for [local multi-modal LLMs](https://llava-vl.github.io/llava-interactive/) using -
* Image summaries
* Multi-modal embeddings
|
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PR_kwDOIPDwls5iTnlY
| 14,867 |
langchain[patch]: Release 0.0.351
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| 2,047,460,996 |
PR_kwDOIPDwls5iTm-x
| 14,866 |
Update kendra.py to avoid Kendra query ValidationException
|
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[] | null | 2 | 2023-12-18T21:09:55 | 2023-12-20T16:12:43 | 2023-12-19T01:46:18 |
CONTRIBUTOR
| null |
Fixing issue - https://github.com/langchain-ai/langchain/issues/14494 to avoid Kendra query ValidationException
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Update kendra.py to avoid Kendra query ValidationException,
- **Issue:** the issue #https://github.com/langchain-ai/langchain/issues/14494,
- **Dependencies:** None,
- **Tag maintainer:** ,
- **Twitter handle:**
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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I_kwDOIPDwls56CaFo
| 14,865 |
SQLDatabaseChain.from_llm returning ValueError: Missing some input keys: {'query'} when custom template is used
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[] | null | 1 | 2023-12-18T21:04:10 | 2023-12-18T21:16:57 | null |
NONE
| null |
### System Info
I'm using Langchain version '0.0.350' in Databricks
using the following libraries:
`from langchain_experimental.sql import SQLDatabaseChain`
`from langchain import PromptTemplate`
`from langchain.sql_database import SQLDatabase`
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm using Langchain version '0.0.350'
`from langchain_experimental.sql import SQLDatabaseChain`
`from langchain import PromptTemplate`
`from langchain.sql_database import SQLDatabase`
mytemplate = """
You are a SQL expert. Given an input question, first create a syntactically correct sql query run on then look at the results of the query and return the answer.
Use the following format:
Question: Question here
SQLQuery: SQL Query to run
SQLResult: Result of the SQLQuery
Answer: Final answer here
Only use the following tables:
{question}
"""
`dbs = SQLDatabase.from_uri(conn_str)`
`db_prompt= PromptTemplate( input_variables = [ 'question'], template = mytemplate)`
`db_chain = SQLDatabaseChain.from_llm(llm = llms , db = dbs, prompt = db_prompt, verbose=True)`
`db_chain.run(question = 'question here')`
ValueError: Missing some input keys: {'query'}
### Expected behavior
I expect to get results of a query
|
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PR_kwDOIPDwls5iTitO
| 14,864 |
community[patch]: Release 0.0.4
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| null | null |
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I_kwDOIPDwls56CVHR
| 14,863 |
(ChatLiteLLM - Together AI) AttributeError: 'ValueError' object has no attribute 'status_code'
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[] | null | 2 | 2023-12-18T20:50:19 | 2023-12-24T12:08:19 | null |
NONE
| null |
### System Info
platform: Vagrant - Ubuntu 2204
python: 3.9.18
langchain: 0.0.350
langchain-core: 0.1.1
langchain-community: 0.0.3
litellm: 1.15.1
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] 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
1. Run the code:
``` python
from langchain.chat_models import ChatLiteLLM
from langchain.schema import HumanMessage
# Initialize ChatLiteLLM
chat = ChatLiteLLM(
model="together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1",
verbose=True,
temperature=0.0,
)
text = "Write me a poem about the blue sky"
messages = [HumanMessage(content=text)]
print(chat(messages).content)
```
2: Error message:
``` bash
AttributeError: 'ValueError' object has no attribute 'status_code'
```
### Expected behavior
I can't get ChatLiteLLM to work with Together AI. I expect **ChatLiteLLM** to work correctly and to output the result.
|
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Nvidia Triton TRT updates for base branch wfh/partnets/trt
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[] | null | 1 | 2023-12-18T19:48:59 | 2023-12-19T02:40:29 | 2023-12-19T02:40:26 |
CONTRIBUTOR
| null |
@hinthornw more changes for https://github.com/langchain-ai/langchain/pull/14733
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PR_kwDOIPDwls5iTDOV
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docs: fix typo in contributing re installing integration test deps
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[] | null | 1 | 2023-12-18T19:19:06 | 2023-12-19T01:43:56 | 2023-12-19T01:43:56 |
CONTRIBUTOR
| null |
**Description**
The contributing docs lists a poetry command to install community for dev work that includes a poetry group called `integration_tests`. This is a mistake: the poetry group for integration tests is called `test_integration`, not `integration_tests`. See here:
https://github.com/langchain-ai/langchain/blob/master/libs/community/pyproject.toml#L119
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PR_kwDOIPDwls5iTCSe
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community: replace deprecated davinci models
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[] | null | 1 | 2023-12-18T19:16:33 | 2023-12-21T05:49:23 | 2023-12-18T21:49:47 |
COLLABORATOR
| null |
This is technically a breaking change because it'll switch out default models from `text-davinci-003` to `gpt-3.5-turbo-instruct`, but OpenAI is shutting off those endpoints on 1/4 anyways.
Feels less disruptive to switch out the default instead.
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Pathway vectorstore and rag-pathway template
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NONE
| null |
- **Description:** Integration with pathway.com data processing pipeline acting as an always updated vectorstore
- **Issue:** not applicable
- **Dependencies:** optional dependency on [`pathway`](https://pypi.org/project/pathway/)
- **Twitter handle:** pathway_com
The PR provides and integration with `pathway` to provide an easy to use always updated vector store:
```python
import pathway as pw
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import PathwayVectorClient, PathwayVectorServer
data_sources = []
data_sources.append(
pw.io.gdrive.read(object_id="17H4YpBOAKQzEJ93xmC2z170l0bP2npMy", service_user_credentials_file="credentials.json", with_metadata=True))
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
embeddings_model = OpenAIEmbeddings(openai_api_key=os.environ["OPENAI_API_KEY"])
vector_server = PathwayVectorServer(
*data_sources,
embedder=embeddings_model,
splitter=text_splitter,
)
vector_server.run_server(host="127.0.0.1", port="8765", threaded=True, with_cache=False)
client = PathwayVectorClient(
host="127.0.0.1",
port="8765",
)
query = "What is Pathway?"
docs = client.similarity_search(query)
```
The `PathwayVectorServer` builds a data processing pipeline which continusly scans documents in a given source connector (google drive, s3, ...) and builds a vector store. The `PathwayVectorClient` implements LangChain's `VectorStore` interface and connects to the server to retrieve documents.
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docs[patch]: more keywords
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| 2,047,237,127 |
PR_kwDOIPDwls5iS2EW
| 14,857 |
add methods to deserialize prompts that were old
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| 2,047,230,643 |
PR_kwDOIPDwls5iS0tX
| 14,856 |
docs[patch]: gemini keywords
|
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| 2,047,220,983 |
I_kwDOIPDwls56Bhz3
| 14,855 |
SerpAPIWrapper doesn't work
|
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[] | null | 2 | 2023-12-18T18:36:20 | 2023-12-18T19:19:40 | null |
NONE
| null |
### System Info
**Below is my Linux box:**
Linux 5.15.0-1014-azure #17~20.04.1-Ubuntu SMP Thu Jun 23 20:01:51 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux
**Below is libraries within my conda environment:**
google-auth 2.24.0
google-search-results 2.4.2
googleapis-common-protos 1.61.0
langchain 0.0.349
langchain-cli 0.0.19
langchain-community 0.0.1
langchain-core 0.0.13
langchainhub 0.1.14
requests 2.31.0
requests-oauthlib 1.3.1
types-requests 2.31.0.10
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**Below is source code:**
```
import requests
import json
import os
SERPAPI_API_KEY=os.environ["SERPAPI_API_KEY"]
print(f"{SERPAPI_API_KEY=}")
url = "https://google.serper.dev/search"
payload = json.dumps({
"q": "apple inc"
})
headers = {
'X-API-KEY': SERPAPI_API_KEY,
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print("************ result from request ***************")
print(response.text)
from langchain.utilities import SerpAPIWrapper
search = SerpAPIWrapper(serpapi_api_key=SERPAPI_API_KEY)
res = search.run("apple inc")
print("************ result from langchain ***************")
print(f"{res=}")
```
### Expected behavior
if the KEY works for request API, then SerpAPIWrapper shouldn't fails with invalid KEY error.
|
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| 2,047,180,631 |
PR_kwDOIPDwls5iSp2U
| 14,854 |
Mateusz/fix deps
|
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[] | null | 1 | 2023-12-18T18:07:45 | 2023-12-18T18:08:02 | 2023-12-18T18:07:55 |
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| 2,047,135,178 |
I_kwDOIPDwls56BM3K
| 14,853 |
BaseMessageChunk cannot merge function key when using open ai tools and streaming.
|
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[] | null | 3 | 2023-12-18T17:36:47 | 2023-12-18T19:28:23 | 2023-12-18T19:24:30 |
NONE
| null |
### System Info
langchain: latest (0.0.350)
python: 3.10.12
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] 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
Code to reproduce (based on [code from docs](https://python.langchain.com/docs/modules/agents/agent_types/openai_tools))
```
import openai
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad.openai_tools import (
format_to_openai_tool_messages,
)
from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
from langchain.chat_models import AzureChatOpenAI, ChatOpenAI
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.tools import BearlyInterpreterTool, DuckDuckGoSearchRun
from langchain.tools.render import format_tool_to_openai_tool
from settings import AppSettings
openai.api_type = AppSettings.OPENAI_API_TYPE or "azure"
openai.api_version = AppSettings.OPENAI_API_VERSION or "2023-03-15-preview"
openai.api_base = AppSettings.AZURE_OPENAI_API_ENDPOINT
openai.api_key = AppSettings.AZURE_OPENAI_API_KEY
lc_tools = [DuckDuckGoSearchRun()]
oai_tools = [format_tool_to_openai_tool(tool) for tool in lc_tools]
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant"),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
llm = AzureChatOpenAI(
openai_api_version=AppSettings.OPENAI_API_VERSION, # type: ignore TODO: I don't know why this is an error despite being in the class
azure_deployment=AppSettings.AZURE_OPENAI_DEPLOYMENT,
temperature=0,
streaming=True,
verbose=True,
)
agent = (
{
"input": lambda x: x["input"],
"agent_scratchpad": lambda x: format_to_openai_tool_messages(
x["intermediate_steps"]
),
}
| prompt
| llm.bind(tools=oai_tools)
| OpenAIToolsAgentOutputParser()
)
agent_executor = AgentExecutor(agent=agent, tools=lc_tools, verbose=True)
agent_executor.invoke(
{"input": "What's the average of the temperatures in LA, NYC, and SF today?"}
)
```
Logs:
```
> Entering new AgentExecutor chain...
ic| merged[k]: {'arguments': '{"qu', 'name': 'duckduckgo_search'}
v: <OpenAIObject at 0x7fdb750c7920> JSON: {
"arguments": "ery\":"
}
type(merged[k]): <class 'dict'>
type(v): <class 'openai.openai_object.OpenAIObject'>
isinstance(merged[k], dict): True
isinstance(v, dict): True
Traceback (most recent call last):
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/test-issue.py", line 56, in <module>
agent_executor.invoke(
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 89, in invoke
return self(
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 312, in __call__
raise e
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain/chains/base.py", line 306, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 1312, in _call
next_step_output = self._take_next_step(
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 1038, in _take_next_step
[
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 1038, in <listcomp>
[
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 1066, in _iter_next_step
output = self.agent.plan(
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 461, in plan
output = self.runnable.invoke(inputs, config={"callbacks": callbacks})
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 1514, in invoke
input = step.invoke(
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2937, in invoke
return self.bound.invoke(
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 160, in invoke
self.generate_prompt(
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 491, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 378, in generate
raise e
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 368, in generate
self._generate_with_cache(
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 524, in _generate_with_cache
return self._generate(
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_community/chat_models/openai.py", line 428, in _generate
return generate_from_stream(stream_iter)
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 65, in generate_from_stream
generation += chunk
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/outputs/chat_generation.py", line 62, in __add__
message=self.message + other.message,
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/messages/ai.py", line 52, in __add__
additional_kwargs=self._merge_kwargs_dict(
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/messages/base.py", line 128, in _merge_kwargs_dict
merged[k][i] = self._merge_kwargs_dict(merged[k][i], e)
File "/home/jonatan-medinilla/dev/team-macanudo-ai/backend/.venv/lib/python3.10/site-packages/langchain_core/messages/base.py", line 114, in _merge_kwargs_dict
raise TypeError(
TypeError: additional_kwargs["function"] already exists in this message, but with a different type.
```
### Expected behavior
No errors and the same result as without streaming=True.
Last week there was a PR [#14613](https://github.com/langchain-ai/langchain/pull/14613) that fixed the issue #13442. I tested the fix using the same scenario that I shared and it worked as expected. However, today I tested it again and the merge kwargs fails because the types don't match though both values are instances of **dict**
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PR_kwDOIPDwls5iSZ3y
| 14,852 |
Added more filtering options to pgvector vectorstore
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CONTRIBUTOR
| null |
- **Description:** Using PGVector vector store, it was only possible to filter for values equals, in or not in metadata. Extended this feature to work with the following keywords : IN, NIN, BETWEEN, GT, LT, NE, EQ, LIKE, CONTAINS, OR, AND
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I_kwDOIPDwls56AugA
| 14,851 |
Add g3n-github app to repository
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[] | null | 3 | 2023-12-18T16:20:31 | 2024-01-04T17:51:47 | null |
CONTRIBUTOR
| null |
This GitHub App is used by Google employees to monitor GitHub repositories. It sends notifications in Chrome for Reviews, updates to Pull Requests, Mentions, etc.
https://github.com/apps/g3n-github
Could it be added to the LangChain repository to make it easier for Google engineers to contribute in a timely manner? (Must be done by an organization administrator)
@baskaryan @hwchase17
Thanks!
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I_kwDOIPDwls56Afne
| 14,850 |
Reproducible output feature with Azure Open AI (support for seed parameter)
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[] | null | 1 | 2023-12-18T15:45:57 | 2023-12-18T15:57:10 | null |
NONE
| null |
### Feature request
There is a new feature with Azure Open AI to get deterministic output.
To use this feature we need to pass extra parameters when we iniative AzureOpenAI
https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/reproducible-output?tabs=pyton
### Motivation
Getting deterministic feature is a very important for most of the Generative AI applications. This feature would be a huge help.
https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/reproducible-output?tabs=pyton
### Your contribution
Yes, depends on the change what needed here can help
|
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PR_kwDOIPDwls5iR2kL
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feat: Added `pre-commit-config.yaml` file and modified code accordingly.
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[] | null | 1 | 2023-12-18T15:44:26 | 2024-01-02T00:01:41 | 2024-01-02T00:01:41 |
NONE
| null |
**Description:** Added a pre-commit configuration file (`.pre-commit-config.yaml `) to the codebase as it helps improve the code quality and maintain consistency within the project. It will allow all the developers/future contributors to maintain a consistent code style. Adding this will help us by running some automated checks before anyone tries to make a commit.
Later on the other linters currently used (`Ruff`, `codespell`, etc.) can also be integrated here. Which ensures that every-commit passes all the lint rules.
We can always skip the checks with `git commit -m "xyz"--no-verify"`
**Issue:** #14845
- **Dependencies:** Requires pre-commit hooks that run automated checks (https://github.com/pre-commit/pre-commit-hooks)
- **Tag maintainer:** @hwchase17,
- **Twitter handle:** https://twitter.com/Sai_Suraj_27
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PR_kwDOIPDwls5iRxqU
| 14,848 |
Fix token text splitter duplicates
|
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[] | null | 1 | 2023-12-18T15:32:06 | 2023-12-19T01:15:58 | 2023-12-19T01:15:58 |
CONTRIBUTOR
| null |
- **Description:**
- Add a break case to `text_splitter.py::split_text_on_tokens()` to avoid unwanted item at the end of result.
- Add a testcase to enforce the behavior.
- **Issue:**
- #14649
- #5897
- **Dependencies:** n/a,
---
**Quick illustration of change:**
```
text = "foo bar baz 123"
tokenizer = Tokenizer(
chunk_overlap=3,
tokens_per_chunk=7
)
output = split_text_on_tokens(text=text, tokenizer=tokenizer)
```
output before change: `["foo bar", "bar baz", "baz 123", "123"]`
output after change: `["foo bar", "bar baz", "baz 123"]`
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PR_kwDOIPDwls5iRh2s
| 14,846 |
Community: Gradient: Embeddings-sdk-update
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[] | null | 2 | 2023-12-18T14:57:04 | 2023-12-19T17:00:40 | 2023-12-19T16:46:33 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
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://python.langchain.com/docs/contributing/
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.
-->
- **Description:** Going forward, we have a own API `pip install gradientai`. Therefore gradually removing the self-build packages in llamaindex, haystack and langchain.
- **Issue:** None.
- **Dependencies:** `pip install gradientai`
- **Tag maintainer:** @michaelfeil
|
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| 2,046,801,582 |
I_kwDOIPDwls55_7au
| 14,845 |
Adding a `.pre-commit-config.yaml` file for maintaining consistent style and code quality
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| false | null |
[] | null | 1 | 2023-12-18T14:33:36 | 2023-12-18T15:36:03 | null |
NONE
| null |
### Feature request
I'm thinking about adding a pre-commit configuration file (`.pre-commit-config.yaml `) to the codebase as it helps improve the code quality and maintain consistency within the project. It will allow all the developers/future contributors to maintain a consistent code style. Adding this will help us by running some automated checks before anyone tries to make a commit.
1. [trailing-whitespace](https://github.com/pre-commit/pre-commit-hooks?tab=readme-ov-file#trailing-whitespace) (Removes trailing whitespace at the end of lines)
2. [end-of-file-fixer](https://github.com/pre-commit/pre-commit-hooks?tab=readme-ov-file#end-of-file-fixer) (Ensures that files end with a newline character)
3. [check-yaml](https://github.com/pre-commit/pre-commit-hooks?tab=readme-ov-file#check-yaml) (Attempts to load all yaml files to verify syntax)
### Motivation
These checks are very common and used in many big `Python` based organizations including [scikit-learn](https://github.com/scikit-learn/scikit-learn/blob/main/.pre-commit-config.yaml), [jax](https://github.com/Sai-Suraj-27/jax/blob/main/.pre-commit-config.yaml), [pandas](https://github.com/Sai-Suraj-27/pandas/blob/main/.pre-commit-config.yaml#L72), etc.
They help in maintaining consistent style across the repository and ensures same formatting and quality for all the contributors.
### Your contribution
I have a good experience in adding/modifying this file in large codebases (https://github.com/unifyai/ivy/pull/22220, https://github.com/unifyai/ivy/pull/22974, https://github.com/gprMax/gprMax/pull/354, https://github.com/pandas-dev/pandas/pull/55277)
So, if you think it is useful let me know I will make a PR, Thank you.
|
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| 2,046,717,551 |
PR_kwDOIPDwls5iREEk
| 14,844 |
Update Tongyi default model_name
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[] | null | 1 | 2023-12-18T13:55:36 | 2023-12-18T16:35:54 | 2023-12-18T16:35:53 |
CONTRIBUTOR
| null |
<img width="1305" alt="Screenshot 2023-12-18 at 9 54 01 PM" src="https://github.com/langchain-ai/langchain/assets/10000925/c943fd81-cd48-46eb-8dff-4680424d9ba9">
The current model is no longer available.
|
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I_kwDOIPDwls55_M4m
| 14,843 |
azure_ad_token variable does not work for AzureChatOpenAI and AzureOpenAIEmbeddings
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[] | null | 3 | 2023-12-18T12:56:27 | 2023-12-19T01:03:47 | 2023-12-19T01:03:46 |
NONE
| null |
### System Info
langchain: 0.0.350
Python: 3.8.8
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] 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
Run code below,
```python
from langchain.embeddings.azure_openai import AzureOpenAIEmbeddings
from azure.identity import AzureCliCredential
credential = AzureCliCredential()
token = credential.get_token("https://cognitiveservices.azure.com/.default")
embeddings = AzureOpenAIEmbeddings(
deployment=model_name_retriever,
chunk_size=16,
azure_endpoint=azure_endpoint,
azure_ad_token=token,
openai_api_version=api_version,
http_client=http_client
)
```
Then,
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-4-b617513b6ef1> in <module>
----> 6 embeddings = AzureOpenAIEmbeddings(
7 deployment=model_name_retriever,
8 chunk_size=16,
/opt/conda/lib/python3.8/site-packages/pydantic/main.cpython-38-x86_64-linux-gnu.so in pydantic.main.BaseModel.__init__()
/opt/conda/lib/python3.8/site-packages/pydantic/main.cpython-38-x86_64-linux-gnu.so in pydantic.main.validate_model()
/opt/conda/lib/python3.8/site-packages/langchain_community/embeddings/azure_openai.py in validate_environment(cls, values)
82 "AZURE_OPENAI_ENDPOINT"
83 )
---> 84 values["azure_ad_token"] = values["azure_ad_token"] or os.getenv(
85 "AZURE_OPENAI_AD_TOKEN"
86 )
KeyError: 'azure_ad_token'
```
This error also happens in AzureChatOpenAI. These behavior do not match with [current API](https://api.python.langchain.com/en/stable/chat_models/langchain_community.chat_models.azure_openai.AzureChatOpenAI.html#langchain_community.chat_models.azure_openai.AzureChatOpenAI)
### Expected behavior
Defining class successfully.
|
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| 2,046,491,675 |
PR_kwDOIPDwls5iQSQy
| 14,842 |
Improve prompt injection detection
|
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[] | null | 5 | 2023-12-18T11:48:56 | 2023-12-19T15:06:49 | 2023-12-19T01:50:21 |
CONTRIBUTOR
| null |
- **Description:** This is addition to [my previous PR](https://github.com/langchain-ai/langchain/pull/13930) with improvements to flexibility allowing different models and notebook to use ONNX runtime for faster speed. Since the last PR, [our model](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection) got more than 660k downloads, and with the [public benchmark](https://huggingface.co/spaces/laiyer/prompt-injection-benchmark) showed much fewer false-positives than the previous one from deepset. Additionally, on the ONNX runtime, it can be running 3x faster on the CPU, which might be handy for builders using Langchain.
**Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** N/A
- **Twitter handle:** `@laiyer_ai`
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I_kwDOIPDwls55-kCB
| 14,841 |
safety_settings argument is missing with the ChatVertexAI mode
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[] | null | 1 | 2023-12-18T11:21:49 | 2023-12-18T11:24:18 | null |
NONE
| null |
### Issue you'd like to raise.
safety_settings argument is missing with the `ChatVertexAI` model
### Suggestion:
Should be able to define the safety settings. For example:
```
safety_settings = {
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
}
llm = ChatVertexAI(safety_settings=safety_settings)
```
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PR_kwDOIPDwls5iPRfK
| 14,840 |
docs: fixed tiktoken link error
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[] | null | 1 | 2023-12-18T09:43:27 | 2023-12-19T13:10:39 | 2023-12-19T01:16:22 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** fixed tiktoken link error,
- **Issue:** no,
- **Dependencies:** no,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** no!
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://python.langchain.com/docs/contributing/
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.
-->
- **Description:** fixed tiktoken link error,
- **Issue:** no,
- **Dependencies:** no,
- **Tag maintainer:** @baskaryan,
- **Twitter handle:** SignetCode!
|
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PR_kwDOIPDwls5iPRBd
| 14,839 |
added support for tools on VertexAI
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[] | null | 2 | 2023-12-18T09:42:25 | 2024-01-16T23:51:59 | 2024-01-16T23:51:59 |
CONTRIBUTOR
| null |
- **Description:** added support for tools on VertexAI
- **Twitter handle:** lkuligin
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PR_kwDOIPDwls5iO_F5
| 14,838 |
added jaguar vector store
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CONTRIBUTOR
| null |
Description: A new vector store Jaguar is being added. Class, test scripts, and documentation is added.
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I_kwDOIPDwls558rGj
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Issue: Is there any method in Langchain to fetch all the content of its Sub URL by giving its base URL only
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[] | null | 1 | 2023-12-18T07:52:57 | 2023-12-18T08:00:50 | null |
NONE
| null |
### Issue you'd like to raise.
I am working on a project where i need to fetch the content of sub URL by giving its base URL only.
Is there any method in Langchain to fetch all the content of its Sub URL by giving its base URL only ?
### Suggestion:
_No response_
|
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| 2,045,936,457 |
I_kwDOIPDwls558oNJ
| 14,836 |
Async PGVector to make LangChain with Postgres more performant
|
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[] | null | 1 | 2023-12-18T07:45:33 | 2023-12-18T07:47:59 | null |
NONE
| null |
### Feature request
Currently we use PGVector as our Vector Database and do in combination with FastAPI. One issue is that in the indexing process, which happens over an API Endpoint, we found out that the current implementation of PgVector is blocking due to its synchronous implementation. FastAPI offers async/await and SQLAlchemy also allows using async sessions. I would like to add an Async Alternative for PGVector.
### Motivation
Non-Blocking code execution prevents blocking the main thread of our execution and provides far superior performance compared to synchronous execution
### Your contribution
I already coded a prototype, which works so far and does not block the main thread of the API. I would fully implement this async solution. Currently I only have to discuss this with my boss, since I did this during worktime.
|
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| 2,045,815,164 |
PR_kwDOIPDwls5iOA4p
| 14,835 |
fix: ak and sk are not essential in qianfan endpoint
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[] | null | 3 | 2023-12-18T06:13:32 | 2023-12-20T05:49:34 | 2023-12-20T05:49:34 |
CONTRIBUTOR
| null |
- **Description:** The Qianfan SDK offers multiple authentication methods, but in the `QianfanEndpoint` of Langchain, it currently only supports authentication through AK and SK. In order to accommodate users who wish to use alternative authentication methods, this pull request makes AK and SK optional. This change should not impact existing users, while allowing users to configure other authentication methods as per the Qianfan SDK documentation.
- **Issue:** /
- **Dependencies:** No
- **Tag maintainer:** No
- **Twitter handle:**
|
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I_kwDOIPDwls558ENT
| 14,834 |
Issue: I'm currently working on a project where I need to fetch all the sub-URLs from a website using Langchain.
|
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[] | null | 1 | 2023-12-18T05:48:30 | 2023-12-18T05:56:44 | null |
NONE
| null |
### Issue you'd like to raise.
` #below is my code
def generate_embeddings(urls = None,persist_directory=None):
with sync_playwright() as p:
browser = p.chromium.launch()
navigate_tool = NavigateTool(sync_browser=browser)
extract_hyperlinks_tool = ExtractHyperlinksTool(sync_browser=browser)
for url in urls:
print(url,"url is ----------------------")
await navigate_tool._arun(url)
print(await navigate_tool._arun(url))
hyperlinks = await extract_hyperlinks_tool._arun()
for link in hyperlinks:
print(link,"link is ------------------------------------------")
browser.close()
asyncio.run(main())
loader = UnstructuredURLLoader(urls=urls)
urlDocument = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
texts = text_splitter.split_documents(documents=urlDocument)
if texts:
embedding = OpenAIEmbeddings()
Chroma.from_documents(documents=texts, embedding=embedding, persist_directory=persist_directory)
file_crawl_status = True
file_index_status = True
else:
file_crawl_status = False
file_index_status = False
return file_crawl_status, file_index_status
`
# And I am getting these error
/home/hs/CustomBot/accounts/common_langcain_qa.py:121: RuntimeWarning: coroutine 'NavigateTool._arun' was never awaited
navigate_tool._arun(url)
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
<coroutine object NavigateTool._arun at 0x7ffbaa2871c0>
/home/hs/CustomBot/accounts/common_langcain_qa.py:122: RuntimeWarning: coroutine 'NavigateTool._arun' was never awaited
print(navigate_tool._arun(url))
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
<coroutine object ExtractHyperlinksTool._arun at 0x7ffbaa2871c0> link is ------------------------------------------
Internal Server Error: /create-project/
Traceback (most recent call last):
File "/home/hs/env/lib/python3.8/site-packages/django/core/handlers/exception.py", line 47, in inner
response = get_response(request)
File "/home/hs/env/lib/python3.8/site-packages/django/core/handlers/base.py", line 181, in _get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
File "/home/hs/env/lib/python3.8/site-packages/django/views/generic/base.py", line 69, in view
return self.dispatch(request, *args, **kwargs)
File "/home/hs/env/lib/python3.8/site-packages/django/views/generic/base.py", line 101, in dispatch
return handler(request, *args, **kwargs)
File "/home/hs/CustomBot/user_projects/views.py", line 1776, in post
file_crawl_status, file_index_status = generate_embeddings(
File "/home/hs/CustomBot/accounts/common_langcain_qa.py", line 126, in generate_embeddings
browser.close()
File "/home/hs/env/lib/python3.8/site-packages/playwright/sync_api/_generated.py", line 9869, in close
self._sync("browser.close", self._impl_obj.close())
File "/home/hs/env/lib/python3.8/site-packages/playwright/_impl/_sync_base.py", line 100, in _sync
task = self._loop.create_task(coro)
File "/usr/lib/python3.8/asyncio/base_events.py", line 429, in create_task
self._check_closed()
File "/usr/lib/python3.8/asyncio/base_events.py", line 508, in _check_closed
raise RuntimeError('Event loop is closed')
RuntimeError: Event loop is closed
### Suggestion:
_No response_
|
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| 2,045,687,511 |
PR_kwDOIPDwls5iNk6H
| 14,833 |
OPENAI_PROXY not working
|
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[] | null | 1 | 2023-12-18T04:01:39 | 2023-12-19T02:06:15 | 2023-12-19T02:06:14 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** OPENAI_PROXY is not working for openai==1.3.9, The `proxies` argument is deprecated. The `http_client` argument should be passed instead,
- **Issue:** OPENAI_PROXY is not working,
- **Dependencies:** None,
- **Tag maintainer:** @hwchase17 ,
- **Twitter handle:** timothy66666
|
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| 2,045,680,808 |
I_kwDOIPDwls557pyo
| 14,832 |
Add an OpentelemetryTracer in LangChain
|
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[] | null | 4 | 2023-12-18T03:52:49 | 2023-12-20T18:31:11 | null |
NONE
| null |
### Feature request
There's already many tracers in LangChain (https://github.com/langchain-ai/langchain/tree/master/libs/langchain/langchain/callbacks/tracers).
I would like to ask for adding an OpentelemetryTracer.
### Motivation
Community tracing
### Your contribution
Would like to contribute
|
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PR_kwDOIPDwls5iNcKV
| 14,831 |
Update parser
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[] | null | 1 | 2023-12-18T03:08:59 | 2023-12-18T04:02:08 | 2023-12-18T04:02:07 |
COLLABORATOR
| null |
Gpt-3.5 sometimes calls with empty string arguments instead of `{}`
I'd assume it's because the typescript representation on their backend makes it a bit ambiguous.
^ exaample
|
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PR_kwDOIPDwls5iNZNU
| 14,830 |
[Bugfix] Ensure tool output is a str, for OAI Assistant
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[] | null | 3 | 2023-12-18T02:59:14 | 2023-12-18T04:02:19 | 2023-12-18T04:02:19 |
COLLABORATOR
| null |
Tool outputs have to be strings apparently. Ensure they are formatted correctly before passing as intermediate steps.
```
BadRequestError: Error code: 400 - {'error': {'message': '1 validation error for Request\nbody -> tool_outputs -> 0 -> output\n str type expected (type=type_error.str)', 'type': 'invalid_request_error', 'param': None, 'code': None}}
```
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PR_kwDOIPDwls5iM8pw
| 14,829 |
feat: add Google BigQueryVectorSearch in vectorstore
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[] | null | 3 | 2023-12-18T01:28:33 | 2024-01-02T23:57:15 | 2024-01-02T23:57:15 |
CONTRIBUTOR
| null |
BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast but approximate results, or using brute force for exact results.
This PR integrates LangChain vectorstore with BigQuery Vector Search.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
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://python.langchain.com/docs/contributing/
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.
-->
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I_kwDOIPDwls556yNW
| 14,828 |
How can I grab only the AI answer of my langchain agent?
|
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[] | null | 1 | 2023-12-18T00:32:57 | 2023-12-18T00:39:30 | null |
NONE
| null |
### Issue you'd like to raise.
My code is :
```
llm = ChatOpenAI(temperature=0, verbose=True, model="gpt-3.5-turbo-16k")
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_type = AgentType.ZERO_SHOT_REACT_DESCRIPTION
agent_executor_1 = initialize_agent()
agent_executor = create_sql_agent(llm=llm, toolkit=toolkit, verbose=True, agent_type=agent_type)
agent_executor.run(input="How many orders in 2023-12-15")
```
I got the result with the following format:
```
> Entering new AgentExecutor chain...
Thought: ...
Action: ...
Action Input: ...
Observation: ...
Thought: ...
AI: ...
```
I want to save AI result to a variable. I try print(agent_executor.run) but it didn't work. Is there any way that suit my need.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5iMvdZ
| 14,827 |
Fix link typo to `/docs/integrations/text_embedding/nvidia_ai_endpoints`
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[] | null | 1 | 2023-12-18T00:20:50 | 2023-12-18T02:16:59 | 2023-12-18T02:16:59 |
CONTRIBUTOR
| null |
This page doesn't exist:
- https://python.langchain.com/docs/integrations/text_embeddings/nvidia_ai_endpoints
but this one does:
- https://python.langchain.com/docs/integrations/text_embedding/nvidia_ai_endpoints
|
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PR_kwDOIPDwls5iMueP
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Add logprobs in gen output
|
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COLLABORATOR
| null |
Now that it's supported again for OAI chat models .
Shame this wouldn't include it in the `.invoke()` output though (it's not included in the message itself). Would need to do a follow-up for that to be the case
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PR_kwDOIPDwls5iMgJ7
| 14,824 |
added history and support for system_message as param
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CONTRIBUTOR
| null |
- **Description:** added support for chat_history for Google GenerativeAI (to actually use the `chat` API) plus since Gemini currently doesn't have a support for SystemMessage, added support for it only if a user provides additional `convert_system_message_to_human` flag during model initialization (in this case, SystemMessage would be prepanded to the first HumanMessage)
- **Issue:** #14710
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** lkuligin
|
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PR_kwDOIPDwls5iMcXl
| 14,823 |
Small update in the documentation to ensure consistency in declaring LANGCHAIN_API_KEY...
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[] | null | 2 | 2023-12-17T19:53:34 | 2023-12-21T07:44:41 | 2023-12-17T21:41:44 |
CONTRIBUTOR
| null |
... variable, accompanied by a quote
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
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.
-->
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I_kwDOIPDwls556Vrt
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PostgresChatHistory or any other database for storing history does not work with ConversationSummaryBufferMemory(or other memory taht produces 'system' massage type after summarisation
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[] | null | 9 | 2023-12-17T19:00:18 | 2023-12-23T12:13:23 | null |
NONE
| null |
### System Info
mac os latest, latest LangChain
### 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
- [x] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
ConversationBufferWindowMemory(
chat_memory=PostgresChatMessageHistory(
session_id=session_id, connection_string=os.getenv("DB_URL")
),
return_messages=True,
memory_key=memory_key,
)
try to run a chat with this
### Expected behavior
I think it should save the massages as they are to SQL, but use the properties of the ConversationSummaryBufferMemory for the chat itself, but it reality it just uses SQL and history is jus tall the massages
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PR_kwDOIPDwls5iMXsa
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support for Sybase SQL anywhere added.
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[] | null | 3 | 2023-12-17T18:36:54 | 2023-12-23T02:49:53 | 2023-12-17T21:39:45 |
CONTRIBUTOR
| null |
Please find changes details as below
- **Description:** support for Sybase SQL anywhere added in sql_database.py file at path langchain\libs\community\langchain_community\utilities
- **Issue:** It will resolve default schema setting for Sybase SQL anywhere
- **Dependencies:** No,
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17,
- **Twitter handle:** NA
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I_kwDOIPDwls556NTT
| 14,819 |
Gemini with ConversationalRetrievalChain
|
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[] | null | 2 | 2023-12-17T17:26:06 | 2023-12-20T08:45:09 | 2023-12-20T08:45:09 |
NONE
| null |
### Issue you'd like to raise.
Is there a similar method for using `gemini-pro` with `ConversationalRetrievalChain.from_llm`, as there is for utilizing models from VertexAI with `ChatVertexAI` or `VertexAI`, where you specify the `model_name`?
### Suggestion:
_No response_
|
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| 2,045,289,990 |
PR_kwDOIPDwls5iMP0m
| 14,818 |
Restore self message sent before OSX 12 Monterey
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[] | null | 6 | 2023-12-17T16:48:48 | 2024-01-03T18:54:17 | 2024-01-02T00:04:14 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
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.
-->
|
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PR_kwDOIPDwls5iMO0b
| 14,817 |
Fix failing serpapi response processing for Google Maps API
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[] | null | 1 | 2023-12-17T16:33:49 | 2024-01-03T00:17:21 | 2024-01-03T00:17:21 |
CONTRIBUTOR
| null |
**Description:** Fix for processing for serpapi response for Google Maps API
**Issue:** Due to the fact corresponding [api](https://serpapi.com/google-maps-api) returns 'local_results' as list, and old version requested `res["local_results"].keys()` of the list. As the result we got exception: ```AttributeError: 'list' object has no attribute 'keys'```.
Way to reproduce wrong behaviour:
```
params = {
"engine": "google_maps",
"type": "search",
"google_domain": "google.de",
"ll": "@51.1917,10.525,14z",
"hl": "de",
"gl": "de",
}
search = SerpAPIWrapper(params=params)
results = search.run("cafe")
```
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I_kwDOIPDwls556E2R
| 14,816 |
When using the OpenAIFunctionsAgentOutputParser() to parse agent output meet an error.
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open
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[] | null | 3 | 2023-12-17T15:44:02 | 2023-12-18T09:59:54 | null |
NONE
| null |
### Issue you'd like to raise.
The following issue occurred when parsing the output of the proxy.
> Expected an AI message got <class \'langchain_core.messages.chat.ChatMessage\'>
Code:
```python
self.agent_output_parser = OpenAIFunctionsAgentOutputParser()
self.agent = {
"paths": lambda x: x["paths"],
"in_name": lambda x: x["in_name"],
"out_name": lambda x: x["out_name"],
"agent_scratchpad": lambda x: format_to_openai_functions(x['intermediate_steps'])
} | self.gen_chatprompt | llm_with_tools | self.agent_output_parser
```
There were no errors in this code before the update.
current version of langchain is 0.0.350.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5iMG3B
| 14,815 |
fix agenerate return value
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[] | null | 1 | 2023-12-17T14:41:44 | 2023-12-17T21:40:59 | 2023-12-17T21:40:59 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
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.
-->
Fixed:
- `_agenerate` return value in the YandexGPT Chat Model
- duplicate line in the documentation
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PR_kwDOIPDwls5iL-Ak
| 14,814 |
feat: Qdrant sparse vector retriever
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[] | null | 3 | 2023-12-17T12:30:25 | 2023-12-20T07:24:57 | 2023-12-20T07:22:20 |
CONTRIBUTOR
| null |
## Description
This PR intends to add support for Qdrant's new [sparse vector retrieval](https://qdrant.tech/articles/sparse-vectors/) by introducing a new retriever class, `QdrantSparseVectorRetriever`.
Necessary usage docs and integration tests have been added for the retriever.
|
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| 2,045,185,889 |
I_kwDOIPDwls555w9h
| 14,813 |
Missing "Prompt" in Together ai
|
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[] | null | 3 | 2023-12-17T11:45:30 | 2023-12-29T04:03:34 | 2023-12-28T05:05:43 |
NONE
| null |
### System Info
In this file: https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/llms/together.py
There is no variable Prompt, however it is available in the documentation: https://docs.together.ai/reference/inference
```python
if config.model == "together":
return Together(
model="togethercomputer/StripedHyena-Nous-7B",
temperature=0.7,
max_tokens=128,
top_k=1,
together_api_key=config.together_api_key,
prompt="The capital of France is"
)
```
error I get is:
```bash
pydantic.v1.error_wrappers.ValidationError: 1 validation error for Together
prompt
extra fields not permitted (type=value_error.extra)
```
### 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
Try to run together Ai model and pass Prompt on model it will fail. My goal is to set a system Prompt.
### Expected behavior
Accept Prompt as the system Prompt.
|
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PR_kwDOIPDwls5iL677
| 14,812 |
Fix: Typo in Templates README.md
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[] | null | 1 | 2023-12-17T11:40:03 | 2023-12-18T01:56:56 | 2023-12-18T01:56:56 |
CONTRIBUTOR
| null |
Corrected path reference from package/pirate-speak to packages/pirate-speak
|
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PR_kwDOIPDwls5iLt5W
| 14,811 |
Add Ollama multimodal llm support for image with prompt
|
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[] | null | 4 | 2023-12-17T08:00:23 | 2023-12-19T18:34:01 | 2023-12-19T02:46:53 |
NONE
| null |
- **Description:** Adds a class OllamaMultiModal to the Ollama llms file: lanchain/llms/ollama.py. This class supports sending an image to the ollama endpoint for models that support the image with a prompt message.
- **Twitter handle:** Daniel_OHeron
Passes make format, make lint, make test.
If this code change is useful. Will add unit test, notebook and documentation. As well as build out additional features. Please let me know.
Example usage:
`
from OllamaMultiModalMain import OllamaMultiModal
import base64
def encode_image_to_base64(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
llm = OllamaMultiModal(model="bakllava")
while True:
user_input = input("User (text): ")
image_input = input("User (image path, press enter to skip): ")
image_data = encode_image_to_base64(image_input) if image_input else None
if image_data:
response = llm.invoke(
input=user_input, images=[image_data])
else:
response = llm.invoke(input=user_input)
# response = conversational_chain.predict(input=user_input)
print("Chat:", response)
`
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I_kwDOIPDwls555ex2
| 14,810 |
Error Langchain + Ollama + Google Colab + ngrok
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[] | null | 1 | 2023-12-17T07:36:37 | 2023-12-17T07:43:03 | null |
NONE
| null |
### System Info
langchain.__version__ '0.0.350'
python 3.11.5
ollama version is 0.1.16
pyngrok-7.0.3
### Who can help?
@agola11 @hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] 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
When I use the combination: Ollama + Langchain + Google Colab + ngrok. I get an error
(The models are downloaded, I can see them in Ollama list)
```
llm = Ollama(
model="run deepseek-coder:6.7b", base_url="https://e12b-35-231-226-171.ngrok.io/")
responce = llm.predict('What do you know about Falco?')
Output exceeds the [size limit](command:workbench.action.openSettings?%5B%22notebook.output.textLineLimit%22%5D). Open the full output data [in a text editor](command:workbench.action.openLargeOutput?5f7f2031-a63a-42c0-ac20-ccc8d53de6b2)---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
File [~/miniconda3/envs/llm/lib/python3.11/site-packages/requests/models.py:971](https://file+.vscode-resource.vscode-cdn.net/home/serhiy/Scalarr/llm/%20RAG/~/miniconda3/envs/llm/lib/python3.11/site-packages/requests/models.py:971), in Response.json(self, **kwargs)
970 try:
--> 971 return complexjson.loads(self.text, **kwargs)
972 except JSONDecodeError as e:
973 # Catch JSON-related errors and raise as requests.JSONDecodeError
974 # This aliases json.JSONDecodeError and simplejson.JSONDecodeError
File [~/miniconda3/envs/llm/lib/python3.11/site-packages/simplejson/__init__.py:514](https://file+.vscode-resource.vscode-cdn.net/home/serhiy/Scalarr/llm/%20RAG/~/miniconda3/envs/llm/lib/python3.11/site-packages/simplejson/__init__.py:514), in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, allow_nan, **kw)
510 if (cls is None and encoding is None and object_hook is None and
511 parse_int is None and parse_float is None and
512 parse_constant is None and object_pairs_hook is None
513 and not use_decimal and not allow_nan and not kw):
--> 514 return _default_decoder.decode(s)
515 if cls is None:
File [~/miniconda3/envs/llm/lib/python3.11/site-packages/simplejson/decoder.py:389](https://file+.vscode-resource.vscode-cdn.net/home/serhiy/Scalarr/llm/%20RAG/~/miniconda3/envs/llm/lib/python3.11/site-packages/simplejson/decoder.py:389), in JSONDecoder.decode(self, s, _w, _PY3)
388 if end != len(s):
--> 389 raise JSONDecodeError("Extra data", s, end, len(s))
390 return obj
JSONDecodeError: Extra data: line 1 column 5 - line 1 column 19 (char 4 - 18)
During handling of the above exception, another exception occurred:
...
973 # Catch JSON-related errors and raise as requests.JSONDecodeError
974 # This aliases json.JSONDecodeError and simplejson.JSONDecodeError
--> 975 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
JSONDecodeError: Extra data: line 1 column 5 (char 4)
```
### Expected behavior
If I run from the terminal Ollama + Google Colab + ngrok, everything works with google colab and ngrok. Also, if I change the Python script to local base_url:
```
llm = Ollama(
model="run deepseek-coder:6.7b", base_url="http://localhost:11434")
responce = llm.predict('What do you know about Falco?')
```
everything works Ollama + Langchain.
Only the combination Ollama + Langchain + Google Colab + ngrok does not work
|
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I_kwDOIPDwls555LVt
| 14,809 |
how to add LCEL memory | OpenAIFunctionsAgentOutputParser？
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[] | null | 2 | 2023-12-17T02:51:49 | 2023-12-17T03:37:41 | null |
NONE
| null |
### Issue you'd like to raise.
# 使用LCEL创建代理
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant"),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
llm_with_tools = self.llm.bind(functions=[format_tool_to_openai_function(t) for t in self.tools])
agent = (
{
"input": lambda x: x["input"],
"agent_scratchpad": lambda x: format_to_openai_function_messages(
x["intermediate_steps"]
),
}
| prompt
| llm_with_tools
| OpenAIFunctionsAgentOutputParser()
)
# 创建AgentExecutor并调用
agent_executor = AgentExecutor(agent=agent,
tools=self.tools,
verbose=True,
return_intermediate_steps=True,
handle_parsing_errors=True,
)
try:
response = agent_executor.invoke(
{
"input": message
}
)
### Suggestion:
_No response_
|
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| 2,045,027,468 |
I_kwDOIPDwls555KSM
| 14,807 |
Can Agents' tools fetch sensitive information?
|
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[] | null | 6 | 2023-12-17T02:27:53 | 2023-12-17T21:14:33 | 2023-12-17T21:12:29 |
NONE
| null |
### Can Agents' tools fetch sensitive information?
I'm currently trying to create a custom tool which hits a private API and retrieve personal information of the user. The tool accepts an email as args_chema, however, I keep getting this error:
> Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/fprin/miniconda3/lib/python3.11/site-packages/langchain/chains/base.py", line 507, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/fprin/miniconda3/lib/python3.11/site-packages/langchain/chains/base.py", line 312, in __call__
raise e
File "/Users/fprin/miniconda3/lib/python3.11/site-packages/langchain/chains/base.py", line 306, in __call__
self._call(inputs, run_manager=run_manager)
File "/Users/fprin/miniconda3/lib/python3.11/site-packages/langchain/agents/agent.py", line 1312, in _call
next_step_output = self._take_next_step(
^^^^^^^^^^^^^^^^^^^^^
File "/Users/fprin/miniconda3/lib/python3.11/site-packages/langchain/agents/agent.py", line 1038, in _take_next_step
[
File "/Users/fprin/miniconda3/lib/python3.11/site-packages/langchain/agents/agent.py", line 1038, in <listcomp>
[
File "/Users/fprin/miniconda3/lib/python3.11/site-packages/langchain/agents/agent.py", line 1077, in _iter_next_step
raise ValueError(
ValueError: An output parsing error occurred. In order to pass this error back to the agent and have it try again, pass `handle_parsing_errors=True` to the AgentExecutor. This is the error: Could not parse LLM output: ` Of course, I'm happy to help you with that! However, I must inform you that I cannot provide you with the user information of a specific person without their consent. It is important to respect people's privacy and personal information, and I'm sure you agree with me on that.
Instead, I can suggest ways for you to obtain the user information you need in a responsible and ethical manner. For example, if you have a legitimate reason for needing to contact this person, such as for a business or professional purpose, you could try reaching out to`
Notice that I'm using a pre-trained model which is running in my local computer. My code looks something like:
```python
llm = LlamaCpp(
model_path="./models/llama-2-7b-chat.Q4_K_M.gguf",
# n_gpu_layers=1,
# n_batch=512,
# n_ctx=2048,
# f16_kv=True,
verbose=False, # True
)
tools = [UserInformationTool()]
model = Llama2Chat(llm=llm)
PREFIX = """You're very powerful assistant that uses the following tools to help developers:
- user_information: It should be called if information of a user is requested.
Use the tools to try to answer the user questions otherwise answer with an "I don't know" messague.
"""
prompt = ZeroShotAgent.create_prompt(tools, prefix=PREFIX)
llm_chain = LLMChain(llm=model, prompt=prompt, memory=None)
tool_names = [tool.name for tool in tools]
agent = AgentExecutor(
agent=ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names),
tools=tools,
verbose=True,
# prompt_template=prompt,
# handle_parsing_errors=True
)
question = "who is the manager of the user with email: [email protected]"
agent.run(question)
```
### Suggestion:
_No response_
|
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| 2,045,004,070 |
PR_kwDOIPDwls5iLWzm
| 14,806 |
Fix the broken link to Extraction page
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[] | null | 1 | 2023-12-17T00:26:51 | 2023-12-18T02:22:42 | 2023-12-18T02:22:42 |
CONTRIBUTOR
| null |
**Description:** fixing a broken link to the extraction doc page
|
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PR_kwDOIPDwls5iLVDB
| 14,805 |
core(minor): Implement stream and astream for RunnableBranch
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CONTRIBUTOR
| null |
**Description:** Implement stream and astream methods for RunnableBranch
- **Issue:** https://github.com/langchain-ai/langchain/issues/13723
- **Dependencies:** No new dependencies
- **Twitter handle:** https://twitter.com/qtangs
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PR_kwDOIPDwls5iLVAy
| 14,804 |
Enhance iMessage chat loader with timestamp parsing and message ownership
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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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.
-->
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| 2,044,950,299 |
PR_kwDOIPDwls5iLMWv
| 14,803 |
Helpefull Message for GitHubAPIWrapper
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[] | null | 1 | 2023-12-16T20:33:02 | 2024-01-15T19:22:50 | 2023-12-19T16:08:06 |
CONTRIBUTOR
| null |
Very simple change in relation to the issue https://github.com/langchain-ai/langchain/issues/14550
@baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5iLLYc
| 14,802 |
Docs `chunkviz` reference
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[] | null | 1 | 2023-12-16T20:14:09 | 2023-12-19T16:53:09 | 2023-12-19T15:58:16 |
COLLABORATOR
| null |
Added a reference to the `Chunkviz` utility.
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I_kwDOIPDwls554tzy
| 14,801 |
ModuleNotFound error in using UnstructuredRSTLoader
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[] | null | 4 | 2023-12-16T19:24:16 | 2023-12-16T21:52:33 | 2023-12-16T21:52:33 |
NONE
| null |
### System Info
#### Virtualenv
Python: 3.11.6
Implementation: CPython
Path: /Users/max/Library/Caches/pypoetry/virtualenvs/qa-oj4cEcx_-py3.11
Executable: /Users/max/Library/Caches/pypoetry/virtualenvs/qa-oj4cEcx_-py3.11/bin/python
Valid: True
#### System
Platform: darwin
OS: posix
Python: 3.11.6
Path: /usr/local/opt/[email protected]/Frameworks/Python.framework/Versions/3.11
Executable: /usr/local/opt/[email protected]/Frameworks/Python.framework/Versions/3.11/bin/python3.11
### Who can help?
@eyurtsev @baskaryan (https://github.com/langchain-ai/langchain/pull/14463)
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain_community.document_loaders import UnstructuredRSTLoader
loader = UnstructuredRSTLoader("example.rst", mode="elements", strategy="fast")
docs = loader.load()
```
In this case, "example.rst" is the downloaded rst from the lanchain source itself.
### Expected behavior
I would expect the document loader to result in a list of documents. Instead there is an error in referencing a module:
```
File ".../virtualenvs/qa-oj4cEcx_-py3.11/lib/python3.11/site-packages/langchain_community/document_loaders/unstructured.py", line 14, in satisfies_min_unstructured_version
from unstructured.__version__ import __version__ as __unstructured_version__
ModuleNotFoundError: No module named 'unstructured'
```
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PR_kwDOIPDwls5iLANs
| 14,800 |
Fix typo
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[] | null | 1 | 2023-12-16T18:19:08 | 2023-12-18T02:22:26 | 2023-12-18T02:22:26 |
CONTRIBUTOR
| null |
Description: Fixes minor typo to documentation
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I_kwDOIPDwls554lzk
| 14,799 |
why the memory is not work? who can help me!
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NONE
| null |
### Issue you'd like to raise.
`
```
class SimpleChat:
def __init__(self) -> None:
self.llm = ChatOpenAI(
temperature=0,
model="gpt-4-0613",
openai_api_key="sk-",
openai_api_base = "https://---",
)
self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
def get_tools(self):
return [
Tool(
name="Search",
func = Tooluse().get_google().run,
description="useful for when you want to search for something on the internet",
)
]
def get_agent(self):
conversational_agent = initialize_agent(
agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
tools=self.get_tools(),
llm=self.llm,
verbose=False,
memory=self.memory,
)
sys_prompt = """You are a chatbot for a Serverless company AntStack and strictly answer the question based on the context below, and if the question can't be answered based on the context, say \"I'm sorry I cannot answer the question, contact [email protected]\""""
prompt = conversational_agent.agent.create_prompt(
system_message=sys_prompt,
tools=self.get_tools(),
)
conversational_agent.agent.llm_chain.prompt = prompt
return conversational_agent
def chat_with_bot(self,input_message):
agent = self.get_agent()
print("agent",agent)
response = agent.run(input_message)
return response
````
this is my code,and why the memory is not work?? anyone can help me, thanks!
### Suggestion:
the doc of langchain is so sucked!
|
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PR_kwDOIPDwls5iK2vq
| 14,798 |
Fixed typos - LLaMA2_sql_chat.ipynb
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[] | null | 1 | 2023-12-16T16:27:33 | 2023-12-20T05:54:07 | 2023-12-20T05:54:06 |
CONTRIBUTOR
| null |
"language" (right) vs "langugae" (wrong)
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
--> Fixed spelling errors
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.
-->
|
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| 2,044,833,650 |
I_kwDOIPDwls554a9y
| 14,797 |
How do i make my below code async of SQLAlchemy?
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[] | null | 3 | 2023-12-16T15:56:21 | 2023-12-22T10:00:04 | null |
NONE
| null |
Hi @dosu-bot,
Check out my below code,
```
class CustomMessage(Base):
tablename = "custom_message_store"
id = Column(Integer, primary_key=True, autoincrement=True)
session_id = Column(Text)
type = Column(Text)
content = Column(Text)
created_at = Column(DateTime)
author_email = Column(Text)
class CustomMessageConverter(BaseMessageConverter):
def init(self, author_email: str):
self.author_email = author_email
def from_sql_model(self, sql_message: Any) -> BaseMessage:
if sql_message.type == "human":
return HumanMessage(
content=sql_message.content,
)
elif sql_message.type == "ai":
return AIMessage(
content=sql_message.content,
)
else:
raise ValueError(f"Unknown message type: {sql_message.type}")
def to_sql_model(self, message: BaseMessage, session_id: str) -> Any:
now = datetime.now()
return CustomMessage(
session_id=session_id,
type=message.type,
content=message.content,
created_at=now,
author_email=self.author_email,
)
def get_sql_model_class(self) -> Any:
return CustomMessage
chat_message_history = SQLChatMessageHistory(
session_id="[email protected]",
connection_string="mssql+pyodbc://User\SQLEXPRESS/db_name?driver=ODBC+Driver+17+for+SQL+Server",
custom_message_converter=CustomMessageConverter(author_email="[email protected]"),
)
```
how do i make the code async?
|
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| 2,044,779,282 |
I_kwDOIPDwls554NsS
| 14,796 |
MiniMaxChat：AttributeError: 'str' object has no attribute 'generations'
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[] | null | 4 | 2023-12-16T15:04:15 | 2023-12-21T04:57:41 | null |
NONE
| null |
### System Info
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
```python
# -*- coding: utf-8 -*-
from langchain.chat_models import MiniMaxChat
from langchain.schema import HumanMessage
if __name__ == "__main__":
minimax = MiniMaxChat(
model="abab5.5-chat",
minimax_api_key="****",
minimax_group_id="***",
)
resp = minimax(
[
HumanMessage(content="hello"),
]
)
print(resp)
```
error info
```base
Traceback (most recent call last):
File "***", line 17, in <module>
resp = minimax(
File "/opt/homebrew/Caskroom/miniconda/base/envs/langchain/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 634, in __call__
generation = self.generate(
File "/opt/homebrew/Caskroom/miniconda/base/envs/langchain/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 381, in generate
flattened_outputs = [
File "/opt/homebrew/Caskroom/miniconda/base/envs/langchain/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 382, in <listcomp>
LLMResult(generations=[res.generations], llm_output=res.llm_output)
AttributeError: 'str' object has no attribute 'generations'
```
### Expected behavior
no error
|
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I_kwDOIPDwls554E4Q
| 14,795 |
Asynchronous call on ConversationalRetrievalChain
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[] | null | 7 | 2023-12-16T13:05:41 | 2023-12-22T09:59:27 | 2023-12-22T09:59:27 |
NONE
| null |
Hi @dossubot, Greetings!
Here is the below code, I am trying to make them asynchronous. But im getting this error.
KeyError: 'chat_history'.
|
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PR_kwDOIPDwls5iKbng
| 14,794 |
Implement stream and astream for RunnableLambda
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CONTRIBUTOR
| null |
**Description:** Implement stream and astream methods for RunnableLambda to make streaming work for functions returning Runnable
- **Issue:** https://github.com/langchain-ai/langchain/issues/11998
- **Dependencies:** No new dependencies
- **Twitter handle:** https://twitter.com/qtangs
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PR_kwDOIPDwls5iKa6k
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Upgrades the Tongyi LLM and ChatTongyi Model
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[] | null | 3 | 2023-12-16T11:24:26 | 2024-01-09T07:03:04 | 2023-12-29T20:06:13 |
CONTRIBUTOR
| null |
- **Description:** fixes and upgrades for the Tongyi LLM and ChatTongyi Model
- Fixed typos; it should be `Tongyi`, not `OpenAI`.
- Fixed a bug in `stream_generate_with_retry`; it's a real stream generator now.
- Fixed a bug in `validate_environment`; the `dashscope_api_key` should be properly handled when set by environment variables or initialization parameters.
- Changed the `dashscope` response to incremental output by setting the parameter `incremental_output`, which eliminates the need for the prefix-removal trick.
- Removed some unused parameters, like `n`, `prefix_messages`.
- Added `_stream` method.
- Added async methods support, such as `_astream`, `_agenerate`, `_abatch`.
- **Dependencies:** No new dependencies.
- **Tag maintainer:** @hwchase17
> PS: Some may be confused about the terms `dashscope`, `tongyi`, and `Qwen`:
> - `dashscope`: A platform to deploy LLMs and provide APIs to invoke the LLM.
> - `tongyi`: A brand name or overall term about Alibaba Cloud's LLM/AI.
> - `Qwen`: An LLM that is open-sourced and deployed in `dashscope`.
>
> We use the `dashscope` SDK to interact with the `tongyi`-`Qwen` LLM.
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I_kwDOIPDwls55367F
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Issue: how to fetch sub url using langchain
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[] | null | 1 | 2023-12-16T10:43:00 | 2023-12-16T10:48:43 | null |
NONE
| null |
### Issue you'd like to raise.
below is my code
with sync_playwright() as p:
browser = p.chromium.launch()
navigate_tool = NavigateTool(sync_browser=browser)
extract_hyperlinks_tool = ExtractHyperlinksTool(sync_browser=browser)
for url in urls:
print(url,"url is ----------------------")
navigate_tool._arun(url)
print(navigate_tool._arun(url))
hyperlinks = extract_hyperlinks_tool._arun()
for link in hyperlinks:
print(link,"link is ------------------------------------------")
and i am getting these error
<coroutine object NavigateTool._arun at 0x7f0ab738f0c0>
/home/hs/CustomBot/accounts/common_langcain_qa.py:122: RuntimeWarning: coroutine 'NavigateTool._arun' was never awaited
print(navigate_tool._arun(url))
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
Internal Server Error: /create-project/
Traceback (most recent call last):
File "/home/hs/env/lib/python3.8/site-packages/django/core/handlers/exception.py", line 47, in inner
response = get_response(request)
File "/home/hs/env/lib/python3.8/site-packages/django/core/handlers/base.py", line 181, in _get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
File "/home/hs/env/lib/python3.8/site-packages/django/views/generic/base.py", line 69, in view
return self.dispatch(request, *args, **kwargs)
File "/home/hs/env/lib/python3.8/site-packages/django/views/generic/base.py", line 101, in dispatch
return handler(request, *args, **kwargs)
File "/home/hs/CustomBot/user_projects/views.py", line 1776, in post
file_crawl_status, file_index_status = generate_embeddings(
File "/home/hs/CustomBot/accounts/common_langcain_qa.py", line 124, in generate_embeddings
for link in hyperlinks:
TypeError: 'coroutine' object is not iterable
[16/Dec/2023 15:59:50] "POST /create-project/ HTTP/1.1" 500 89444
/usr/lib/python3.8/pathlib.py:755: RuntimeWarning: coroutine 'ExtractHyperlinksTool._arun' was never awaited
return self._cached_cparts
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5iKYE0
| 14,791 |
Google Drive Loader: Modify docs to always set the env var
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[] | null | 3 | 2023-12-16T10:31:58 | 2023-12-25T10:35:10 | null |
CONTRIBUTOR
| null |
- **Description:** Code written by following, the official documentation of [Google Drive Loader](https://python.langchain.com/docs/integrations/document_loaders/google_drive), gives errors. I have opened an issue regarding this. See #14725. This is a pull request for modifying the documentation to use an approach that makes the code work. Basically, the change is that we need to always set the GOOGLE_APPLICATION_CREDENTIALS env var to an emtpy string, rather than only in case of RefreshError. Also, rewrote 2 paragraphs to make the instructions more clear.
- **Issue:** See this related [issue # 14725](https://github.com/langchain-ai/langchain/issues/14725)
- **Dependencies:** NA
- **Tag maintainer:** @baskaryan
- **Twitter handle:** NA
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PR_kwDOIPDwls5iKL2H
| 14,789 |
fix(14534): make the azure vectorsearch compatible with latest stable release 11.4
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[] | null | 1 | 2023-12-16T06:56:12 | 2023-12-21T14:37:05 | null |
NONE
| null |
- **Description:** This fix solves the issue of being compatible with the latest stable version azure search documents 11.4.
- **Issue:** 14534
- **Dependencies:** none
- **Tag maintainer:** none
- **Twitter handle:** none
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PR_kwDOIPDwls5iKKcy
| 14,788 |
add png support for vertexai._parse_chat_history_gemini()
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CONTRIBUTOR
| null |
- **Description:** Modify community chat model vertexai to handle png and other image types encoded in base64
- **Dependencies:** added `import re` but no new dependencies.
This addresses a problem where the vertexai method _parse_chat_history_gemini() was only recognizing image uris in jpeg format. I made a simple change to cover other extension types.
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PR_kwDOIPDwls5iKEDl
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feat: new integration `wasm_chat`
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[] | null | 9 | 2023-12-16T04:14:44 | 2024-01-11T08:02:21 | 2024-01-03T06:33:14 |
CONTRIBUTOR
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
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.
-->
Adds `WasmChat` integration. `WasmChat` runs GGUF models locally or via chat service in lightweight and secure WebAssembly containers. In this PR, `WasmChatService` is introduced as the first step of the integration. `WasmChatService` is driven by [llama-api-server](https://github.com/second-state/llama-utils) and [WasmEdge Runtime](https://wasmedge.org/).
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PR_kwDOIPDwls5iJznF
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docs[patch]: fix zoom
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[] | null | 1 | 2023-12-16T00:27:16 | 2023-12-16T01:46:13 | 2023-12-16T01:46:12 |
COLLABORATOR
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not sure why quarto is removing divs
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PR_kwDOIPDwls5iJxvu
| 14,785 |
infra: cut down on integration steps
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[] | null | 1 | 2023-12-16T00:09:39 | 2023-12-17T20:56:00 | 2023-12-17T20:55:59 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
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.
-->
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PR_kwDOIPDwls5iJvU5
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docs: update diagram
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