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| 1,847,942,408
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I_kwDOIPDwls5uJV0I
| 9,152
|
Able to query contents of a PG vector table, ever after dropping the table.
|
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| 2023-08-12T11:13:01
| 2023-08-12T12:07:34
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|
NONE
| null |
### System Info
Hello,
Thanks for the wonderful extension on PG. I am using on DigitialOcean for my SAAS app where i have different schemas for each tenant. I had installed PG vector in public schema, so that all the tenants could make use of them.
My app is built on OpenAI api and I am using the default method from langchain to define the db
**from langchain.vectorstores.pgvector import DistanceStrategy
db = PGVector.from_documents(
documents= docs,
embedding = embeddings,
collection_name= "schema.unique_value",
distance_strategy = DistanceStrategy.COSINE,
connection_string=CONNECTION_STRING)**
This create two files in the public scema and i could see and query the contents. below are the tables
langchain_pg_colllection
langchain_pg_embedding
But when i wish to rewrite the contents by deleteing and even dropping the tables, i could still query the content using the below code:
**db = PGVector(
collection_name=collection_name,
connection_string=CONNECTION_STRING,
embedding_function=embeddings,
)
retriever = db.as_retriever(
search_kwargs={"k": 5}
)**
The deleted table gets automatically restored while query. This feature is strange and not sure what creates it. Also when i pass a non existent value, instead of failing, it creates a new record in the collection table.
I raise this issue with PG_VECTOR but was rightly directed to reach out to you.
Would seek any inputs to resolve this plase.
### Who can help?
@eyurtsev
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**db = PGVector(
collection_name=collection_name,
connection_string=CONNECTION_STRING,
embedding_function=embeddings,
)
retriever = db.as_retriever(
search_kwargs={"k": 5}
)**
### Expected behavior
The retriever should get only the contents from the table. If not in the table (or table not available) it should fail. Thanks in anticipation
|
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| 1,847,930,416
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I_kwDOIPDwls5uJS4w
| 9,151
|
Custom Parameter/ Json Payload for from_llm_and_api_docs() function
|
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| 2023-08-12T10:53:09
| 2023-11-18T16:04:52
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|
NONE
| null |
### Feature request
I am trying to use the from_llm_and_api_docs() function with an API that only supports parameters via the "params" parameter in the request library:
The from_llm_and_api_docs works well with apis that encode parameters in the URl. Eg: https://api.open-meteo.com/v1/forecast/latitude=123&longitude=123
I would need langchain to generate a json_payload and input it into the "params" parameter in the request header. I looked into the Langchain code and it seems like that this line:
requests_wrapper = TextRequestsWrapper(headers=headers)
needs to be augmented with a "params" parameter.
Example:
r = requests.get('https://www.domain.com/endpoint',params=params,cookies=cookies, headers=headers,json=json_data)
json_data = {
'field_ids': field_ids,
'order': [
{
'field_id': 'rank_org_company',
'sort': sort_order,
},
],
'query': [
{
'type': 'predicate',
'field_id': 'founded_on',
'operator_id': 'gte',
'values': [
'01/01/2018',
],
},
{
'type': 'predicate',
'field_id': 'equity_funding_total',
'operator_id': 'lte',
'values': [
{
'value': 15000000,
'currency': 'usd',
},
],
},
{
'type': 'predicate',
'field_id': 'location_identifiers',
'operator_id': 'includes',
'values': [
'6106f5dc-823e-5da8-40d7-51612c0b2c4e',
],
}
],
'field_aggregators': [],
'collection_id': 'organization.companies',
'limit': 1000,
}
How do I need to change the from_llm_and_api_docs() accordingly?
### Motivation
Many APIs are controlled via a json payload and not solely via parameters in the URL.
### Your contribution
I could look into changing the request wrapper but maybe there is a more elegant way of doing so.
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I_kwDOIPDwls5uInsf
| 9,150
|
Error in AimCallbackHandler.on_chain_start callback: 'input'
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| 2023-08-12T04:58:36
| 2023-11-12T07:23:03
| null |
NONE
| null |
### System Info
langchain==0.0.262
aim==3.17.5
aim-ui==3.17.5
aimrecords==0.0.7
aimrocks==0.4.0
python: 3.11.4
env: MacOS
### Who can help?
@agola11
### 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
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
The AIM Callback example: https://python.langchain.com/docs/integrations/providers/aim_tracking
Running only with the LLM works fine. However when using any CHAIN example it fails with the error:
> Entering new LLMChain chain...
Error in AimCallbackHandler.on_chain_start callback: 'input'
> Finished chain.
Error in AimCallbackHandler.on_chain_end callback: 'output'
No further exceptions are displayed
And the trace recorded in AIM is also incomplete.
### Expected behavior
Trace should be recorded completely. And no errors should occur on callback for chain start and chain end
|
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PR_kwDOIPDwls5XxTxQ
| 9,149
|
Update debugging image link for LangSmith Run
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| 2023-08-12T01:37:36
| 2023-09-22T02:36:33
| 2023-09-22T02:36:30
|
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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5Xwyg_
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docstrings: document_loaders consistency 2
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| 2023-08-11T22:17:46
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|
COLLABORATOR
| null |
This is Part 2. See #9139 (Part 1).
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I_kwDOIPDwls5uHgAl
| 9,146
|
Langchain text_generation client hits: pydantic.error_wrappers.ValidationError: 1 validation error for Response
|
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[] | null | 2
| 2023-08-11T21:41:36
| 2023-08-11T21:45:22
| 2023-08-11T21:45:08
|
NONE
| null |
### System Info
langchain 0.0.262
text_generation_server. https://github.com/huggingface/text-generation-inference
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
start text_generation_inference server on local host, verify it is working
===============
from langchain import PromptTemplate, HuggingFaceTextGenInference
llm = HuggingFaceTextGenInference(
inference_server_url="http://127.0.0.1:80",
max_new_tokens=64,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=1,
repetition_penalty=1.03,
)
output = llm("What is Machine Learning?")
print(output)
=================
root@0b801769b7bd:~/langchain_client# python langchain-client.py
Traceback (most recent call last):
File "/root/langchain_client/langchain-client.py", line 59, in <module>
output = llm("What is Machine Learning?")
File "/usr/local/lib/python3.10/dist-packages/langchain/llms/base.py", line 802, in __call__
self.generate(
File "/usr/local/lib/python3.10/dist-packages/langchain/llms/base.py", line 598, in generate
output = self._generate_helper(
File "/usr/local/lib/python3.10/dist-packages/langchain/llms/base.py", line 504, in _generate_helper
raise e
File "/usr/local/lib/python3.10/dist-packages/langchain/llms/base.py", line 491, in _generate_helper
self._generate(
File "/usr/local/lib/python3.10/dist-packages/langchain/llms/base.py", line 977, in _generate
self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/langchain/llms/huggingface_text_gen_inference.py", line 164, in _call
res = self.client.generate(prompt, **invocation_params)
File "/usr/local/lib/python3.10/dist-packages/text_generation/client.py", line 150, in generate
return Response(**payload[0])
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for Response
details -> tokens -> 6 -> logprob
none is not an allowed value (type=type_error.none.not_allowed)
### Expected behavior
output text from text generation inference server.
|
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| 1,847,406,363
|
PR_kwDOIPDwls5Xwfv6
| 9,145
|
fix
|
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| 2023-08-11T20:43:24
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| 2023-08-11T20:58:24
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COLLABORATOR
| null | null |
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I_kwDOIPDwls5uHCP0
| 9,144
|
GoogleSerperRun._arun() got an unexpected keyword argument 'arg1'
|
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| 2023-08-11T19:44:55
| 2023-08-13T23:07:17
| 2023-08-13T23:07:17
|
NONE
| null |
### Issue you'd like to raise.
llm = OpenAI(temperature=0, model_name = args.ModelName)
system_message = SystemMessage(content=template)
agent_kwargs = {
"extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
"system_message": system_message,
}
tools = load_tools(["google-serper"], llm=llm)
agent_executor = initialize_agent(
tools,
llm,
agent=AgentType.OPENAI_FUNCTIONS,
agent_kwargs= agent_kwargs,
verbose = True,
memory = memory,
handle_parsing_errors=True,
)
I am getting this error GoogleSerperRun._arun() got an unexpected keyword argument 'arg1'.
Can anyone explain why is it so
### Suggestion:
_No response_
|
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| 1,847,312,252
|
PR_kwDOIPDwls5XwLFk
| 9,143
|
RFC: remove things from init
|
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| 2023-08-11T19:20:11
| 2023-10-03T18:23:54
| 2023-10-03T18:23:53
|
COLLABORATOR
| null |
will speed up imports, cause less weird transient import errors, and set us up to split up the package
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| 1,847,309,075
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PR_kwDOIPDwls5XwKZV
| 9,142
|
rm integration deps
|
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PR_kwDOIPDwls5XwG1G
| 9,141
|
Fix for SVM retriever discarding document metadata
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| 2023-08-11T19:01:43
| 2023-08-11T20:08:18
| 2023-08-11T20:08:17
|
CONTRIBUTOR
| null |
As stated in the title the SVM retriever discarded the metadata of passed in docs. This code fixes that. I also added one unit test that should test that.
Ultimately it's a simple fix, but unfortunately poetry was not correctly installing dependencies in my environment so I was unable to run the unit test and linting (took way longer to try to set up environment than make my change). Would appreciate if someone with the environment set up could quickly try it out. I did test by directly modifying the langchain source code in my miniconda environment where I initially found the issue and it fixed the bug there, so it's not completely untested.
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PR_kwDOIPDwls5XwGmn
| 9,140
|
Bagatur/vectara nit
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PR_kwDOIPDwls5XwD6P
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docstrings: document_loaders consistency
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| 2023-08-11T18:48:05
| 2023-08-14T22:48:52
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|
COLLABORATOR
| null |
Formatted docstrings from different formats to consistent format, lile:
>Loads processed docs from Docugami.
"Load from `Docugami`."
>Loader that uses Unstructured to load HTML files.
"Load `HTML` files using `Unstructured`."
>Load documents from a directory.
"Load from a directory."
- `Load` - no `Loads`
- DocumentLoader always loads Documents, so no more "documents/docs/texts/ etc"
- integrated systems and APIs enclosed in backticks,
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PR_kwDOIPDwls5Xv9tA
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feat: Docusaurus Loader
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| 2023-11-10T22:21:56
| 2023-11-10T22:21:55
|
CONTRIBUTOR
| null |
Added a Docusaurus Loader
Issue: #6353
I had to implement this for working with the Ionic documentation, and wanted to open this up as a draft to get some guidance on building this out further. I wasn't sure if having it be a light extension of the SitemapLoader was in the spirit of a proper feature for the library -- but I'm grateful for the opportunities Langchain has given me and I'd love to build this out properly for the sake of the community.
Any feedback welcome!
|
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PR_kwDOIPDwls5XvzuU
| 9,137
|
Move self-check use case to "more" section
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PR_kwDOIPDwls5XvzR6
| 9,136
|
fix sparql queries for relations in schema description
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| 2023-11-15T20:34:54
| 2023-10-25T20:36:58
|
CONTRIBUTOR
| null |
- **Description**: Fix for the SPARQL QA chain: fixed SPARQL queries for retrieving information about relations in the graph to create a textual description of the schema for the language model. This should resolve #8907
- **Issue**: #8907
- **Dependencies**: None
- **Tag maintainer**: @baskaryan, @hwchase17
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PR_kwDOIPDwls5Xvxld
| 9,135
|
rfc zep search mem
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|
Move code-writing use case to "more" section
|
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[] | null | 2
| 2023-08-11T17:34:02
| 2023-08-15T01:27:20
| 2023-08-15T01:27:19
|
COLLABORATOR
| null | null |
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I_kwDOIPDwls5uGZuO
| 9,133
|
DOC: `API Reference` `langchain.agents Functions ` table unreadable
|
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[] | null | 5
| 2023-08-11T17:16:33
| 2023-11-19T22:54:10
| 2023-11-19T22:52:01
|
COLLABORATOR
| null |
### Issue with current documentation:
`API Reference` `langchain.agents Functions` table unreadable:
The `name` column is soo long, which makes the `description` column unreadable.
It is because of this value:
`agents.agent_toolkits.conversational_retrieval.openai_functions.create_conversational_retrieval_agent`
### Idea or request for content:
Make the namespace [and name] of this function shorter.
|
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| 1,847,163,390
|
I_kwDOIPDwls5uGXn-
| 9,132
|
Make intent identification in SPARQL QA chain more resilient
|
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open
| false
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[] | null | 3
| 2023-08-11T17:09:45
| 2023-11-16T00:40:56
| null |
CONTRIBUTOR
| null |
### Feature request
**Make the intent identification (SELECT vs UPDATE) within the SPARQL QA chain more resilient**
As originally described in #7758 and further discussed in #8521, some models struggle with providing an unambiguous response to the intent identification prompt, i.e., they return a sentence that contains both keywords instead of returning exactly one keyword. \
This should be resolvable with a loop that reprompts the model if its response is ambiguous.
The loop should probably be limited to one or two retries.
### Motivation
Make the intent identification within the SPARQL QA chain more resilient, so that it can more easily be used with models other than the OpenAI ones, e.g., StarCoder and NeoGPT3 mentioned in #7758, which are still able to generate sensible SPARQL
### Your contribution
I should be able to create a PR soonish, but if somebody else wants to give this a go I would also be happy to review
|
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PR_kwDOIPDwls5XvlYa
| 9,131
|
zep/zep-python 1.0 compat
|
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[] | null | 2
| 2023-08-11T16:49:30
| 2023-08-15T13:20:49
| 2023-08-15T02:11:01
|
CONTRIBUTOR
| null |
Updating Zep's ZepRetriever and ZepChatHistory classes. The former to be compatible with VectorStoreRetrieverMemory. Both for compatibility with zep-python 1.0.
- update zep-python to 1.0.1
- ZepChatHistory, ZepRetriever to zep-python 1.0 compat
- Update ZepRetriever + ZepChatHistory unit tests
|
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I_kwDOIPDwls5uGRP_
| 9,130
|
LangChain tools as gpt func, always fails at the JSON decode
|
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[] | null | 4
| 2023-08-11T16:48:08
| 2023-11-19T16:05:06
| 2023-11-19T16:05:05
|
NONE
| null |
### System Info
Python 3.10
LangChain: 0.0.245
### 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
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I write a 'Python interpreter' tool, as below sample code:
```
class PythonInterpreterTool(BaseTool):
name = "python_interpreter"
name_display = "Python Interpreter"
description = """Give me the Python to execute，it returns the execution result"""
def _run(self, code: str) -> str:
with PythonInterpreter() as executor: # type: ignore
output = executor.run_python_code(code)
return output
async def _arun(self, code: str) -> str:
raise NotImplementedError
```
But, for many times, I always got "Could not parse tool input... because the `arguments` is not valid JSON." error:
Could not parse tool input: {'name': 'python_interpreter', 'arguments': '{\n "__arg1": "\nimport moviepy.editor as mp\n\nvideo_path = \'https://video-clip.oss.com/upload/40ae1ae0-3799-11ee-9c80-476f00f64986.mp4\'\noutput_path = \'output.mp3\'\n\nvideo = mp.VideoFileClip(video_path)\nvideo.audio.write_audiofile(output_path)\n"\n}'} because the `arguments` is not valid JSON.
### Expected behavior
I write a 'Python interpreter' tool, as below sample code:
```
class PythonInterpreterTool(BaseTool):
name = "python_interpreter"
name_display = "Python Interpreter"
description = """Give me the Python to execute，it returns the execution result"""
def _run(self, code: str) -> str:
with PythonInterpreter() as executor: # type: ignore
output = executor.run_python_code(code)
return output
async def _arun(self, code: str) -> str:
raise NotImplementedError
```
But, for many times, I always got "Could not parse tool input... because the `arguments` is not valid JSON." error:
Could not parse tool input: {'name': 'python_interpreter', 'arguments': '{\n "__arg1": "\nimport moviepy.editor as mp\n\nvideo_path = \'https://video-clip.oss.com/upload/40ae1ae0-3799-11ee-9c80-476f00f64986.mp4\'\noutput_path = \'output.mp3\'\n\nvideo = mp.VideoFileClip(video_path)\nvideo.audio.write_audiofile(output_path)\n"\n}'} because the `arguments` is not valid JSON.
|
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| 1,847,115,156
|
I_kwDOIPDwls5uGL2U
| 9,129
|
[Request] Note that the 'base branch' needs to match the repository's 'default branch'
|
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[] | null | 4
| 2023-08-11T16:31:15
| 2023-11-20T16:05:11
| 2023-11-20T16:05:10
|
NONE
| null |
### System Info
I have an extremely simple setup where I created a GH app following the [Github App Quickstart Guide](https://docs.github.com/en/apps/creating-github-apps/writing-code-for-a-github-app/quickstart). I then start a blank repo with the template `README.md` (just the name of the repo in a title).
The agent can successfully create a pull request from one request where I ask the agent to make an ascii art of a whale & insert a new section in the README.md, but after manually merging that pull request myself the agent keeps incorrectly finding the initial value of the `README.md` with (what I'm assuming is) an incorrect commit hash that's never existed in [my repo](https://github.com/Zak-Alford/gh-test-repo2):
```
> Entering new AgentExecutor chain...
First, I need to read the current README.md to know its contents. Then, I will update the file with the riddle. Finally, I will create a pull request.
Action: Read File
Action Input: README.md
Observation: # gh-test-repo2
Thought:The file opens with a header for the repository. Now, I will add the riddle to the 'Weekly Challenge' section.
Action: Update File
Action Input: README.md
OLD <<<<
# gh-test-repo2
>>>> OLD
NEW <<<<
# gh-test-repo2
## Weekly Challenge
Riddle: What has keys but can't open locks?
>>>> NEW
Observation: Unable to update file due to error:
409 {"message": "README.md does not match 87f560c4c6fd9eecce7374b3ac8f5517a975bf63", "documentation_url": "https://docs.github.com/rest/repos/contents#create-or-update-file-contents"}
Thought:It seems like there was a conflict when trying to update the file. The file might have been updated after I last read it. I will try to read the file again and then update it.
Action: Read File
Action Input: README.md
Observation: # gh-test-repo2
Thought:The file content hasn't changed, so I'll attempt to update it again.
Action: Update File
Action Input: README.md
OLD <<<<
# gh-test-repo2
>>>> OLD
NEW <<<<
# gh-test-repo2
## Weekly Challenge
Riddle: What has keys but can't open locks?
>>>> NEW
Observation: Unable to update file due to error:
409 {"message": "README.md does not match 87f560c4c6fd9eecce7374b3ac8f5517a975bf63", "documentation_url": "https://docs.github.com/rest/repos/contents#create-or-update-file-contents"}
Thought:There seems to be a problem with updating the file. Maybe the file was updated by someone else in the meantime, or there's an issue with the way I'm trying to update it. I'll try to create a new file with the riddle instead, and then make a pull request to merge it with the README.md.
Action: Create File
Action Input: WeeklyChallenge.md
## Weekly Challenge
Riddle: What has keys but can't open locks?
Observation: Unable to make file due to error:
404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/contents#get-repository-content"}
Thought:The creation of the file failed, perhaps the filepath or file name is incorrect. I will try to create the pull request directly and mention the changes proposed in the pull request description.
Action: Create Pull Request
Action Input: Update README.md with Weekly Challenge
In this pull request, I propose adding the following riddle to the 'Weekly Challenge' section of the README.md:
Riddle: What has keys but can't open locks?
Observation: Unable to make pull request due to error:
422 {"message": "Validation Failed", "errors": [{"resource": "PullRequest", "code": "custom", "message": "No commits between test and bot-branch"}], "documentation_url": "https://docs.github.com/rest/pulls/pulls#create-a-pull-request"}
Thought:It seems like the pull request failed because there are no commits between the test and the bot-branch. This could be because I haven't successfully updated or created any files, so there's no changes to commit. I'll try to update the README.md file again, making sure to include the entire content of the file in the OLD <<<< and >>>> OLD tags, in case that was the issue.
Action: Update File
Action Input: README.md
OLD <<<<
# gh-test-repo2
>>>> OLD
NEW <<<<
# gh-test-repo2
## Weekly Challenge
Riddle: What has keys but can't open locks?
>>>> NEW
Observation: Unable to update file due to error:
409 {"message": "README.md does not match 87f560c4c6fd9eecce7374b3ac8f5517a975bf63", "documentation_url": "https://docs.github.com/rest/repos/contents#create-or-update-file-contents"}
Thought:It seems like the error persists despite multiple attempts. There might be an issue with the GitHub repository or the API. At this point, I am unable to complete the task as instructed.
Final Answer: I was unable to complete the task due to an error while updating the README.md file.
> Finished chain.
```
Code I'm using to initialize the GH agent:
```
import os
from langchain.agents import AgentType
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits.github.toolkit import GitHubToolkit
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.utilities.github import GitHubAPIWrapper
import json
import custom_tools
# List of required environment variables:
env_vars = [
"GITHUB_APP_ID",
"GITHUB_APP_PRIVATE_KEY",
"GITHUB_REPOSITORY",
"GITHUB_BRANCH",
"GITHUB_BASE_BRANCH",
"OPENAI_API_KEY",
]
# Check your json file for key values
with open("envvars.json", "r") as f:
env_var_values = json.load(f)
for var in env_vars:
# Check that each key exists. If it doesn't, set it to be "" and then complain later
if env_var_values.get(var, "") != "":
os.environ[var] = env_var_values[var]
else: # Complaint line
raise Exception(f"The environment variable {var} was not set. You must set this value to continue.")
bot_branch = os.environ["GITHUB_BRANCH"]
gh_base_branch = os.environ["GITHUB_BASE_BRANCH"]
llm = ChatOpenAI(model="gpt-4")
github = GitHubAPIWrapper()
toolkit = GitHubToolkit.from_github_api_wrapper(github)
tools = []
# unwanted_tools = ['Get Issue','Delete File']
unwanted_tools = []
for tool in toolkit.get_tools():
if tool.name not in unwanted_tools:
tools.append(tool)
agent = initialize_agent(
tools=tools, llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
# request = f"Draw an ascii art whale and add it once to the 'Nature' section of the README.md file. Make a pull request back to {gh_base_branch}."
request = f"Do a git pull first. Then write a riddle and it once to the 'Weekly Challenge' section of the README.md file. Don't write the answer to the riddle. Make a pull request back to {gh_base_branch}."
agent.run(request)
```
scrubbed JSON file I'm using to grab env vars from:
```
{
"GITHUB_APP_ID": "<my-app-id>",
"GITHUB_APP_PRIVATE_KEY": "<my-path-to-pem>",
"GITHUB_REPOSITORY": "Zak-Alford/gh-test-repo2",
"GITHUB_BRANCH": "bot-branch",
"GITHUB_BASE_BRANCH": "test",
"OPENAI_API_KEY": "<my-openai-api-key>"
}
```
### Who can help?
@hw
### Information
- [ ] The official example notebooks/scripts
- [X] 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Copy my script included above and name the file
2. Run `py (or python3) <filename>.py`
### Expected behavior
The github agent should find the latest commit that it itself committed to the branch before creating a new commit with changes.
|
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| 1,847,094,855
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PR_kwDOIPDwls5Xvbtn
| 9,128
|
add basic support for redis cluster server
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[] | null | 2
| 2023-08-11T16:14:51
| 2023-08-11T18:37:44
| 2023-08-11T18:37:44
|
CONTRIBUTOR
| null |
This change updates the central utility class to recognize a Redis cluster server after connection and returns an new cluster aware Redis client. The "normal" Redis client would not be able to talk to a cluster node because keys might be stored on other shards of the Redis cluster and therefor not readable or writable.
With this patch clients do not need to know what Redis server it is, they just connect though the same API calls for standalone and cluster server.
There are no dependencies added due to this MR.
Remark - with current redis-py client library (4.6.0) a cluster cannot be used as VectorStore. It can be used for other use-cases. There is a bug / missing feature(?) in the Redis client breaking the VectorStore implementation. I opened an issue at the client library too (redis/redis-py#2888) to fix this. As soon as this is fixed in `redis-py` library it should be usable there too.
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I_kwDOIPDwls5uF5RU
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|
inference configurations are invalid forBedrockEmbeddings models
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[] | null | 11
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| 2023-12-13T16:07:43
| 2023-12-13T16:07:42
|
NONE
| null |
### System Info
in sagemaker.
langchain==0.0.256 or 0.0.249 (I tried both)
Image: Data Science 3.0
Kernel: Python 3
Instance type: ml.t3.medium 2 vCPU + 4 GiB
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] 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
https://github.com/aws-samples/amazon-bedrock-workshop/blob/main/03_QuestionAnswering/01_qa_w_rag_claude.ipynb
im trying to following this notebook:
i increase the data input size:
from urllib.request import urlretrieve
```
os.makedirs("data", exist_ok=True)
files = [
"https://www.irs.gov/pub/irs-pdf/p1544.pdf",
"https://www.irs.gov/pub/irs-pdf/p15.pdf",
"https://www.irs.gov/pub/irs-pdf/p1212.pdf",
"https://www.irs.gov/pub/irs-pdf/p3.pdf",
"https://www.irs.gov/pub/irs-pdf/p17.pdf",
"https://www.irs.gov/pub/irs-pdf/p51.pdf",
"https://www.irs.gov/pub/irs-pdf/p54.pdf",
]
for url in files:
file_path = os.path.join("data", url.rpartition("/")[2])
urlretrieve(url, file_path)
```
my data input:
Average length among 1012 documents loaded is 2320 characters.
After the split we have 1167 documents more than the original 1012.
Average length among 1167 documents (after split) is 2011 characters.
```
import numpy as np
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader, PyPDFDirectoryLoader
loader = PyPDFDirectoryLoader("./data/")
documents = loader.load()
# - in our testing Character split works better with this PDF data set
text_splitter = RecursiveCharacterTextSplitter(
# Set a really small chunk size, just to show.
chunk_size = 1000,
chunk_overlap = 100,
)
docs = text_splitter.split_documents(documents)
avg_doc_length = lambda documents: sum([len(doc.page_content) for doc in documents])//len(documents)
avg_char_count_pre = avg_doc_length(documents)
avg_char_count_post = avg_doc_length(docs)
print(f'Average length among {len(documents)} documents loaded is {avg_char_count_pre} characters.')
print(f'After the split we have {len(docs)} documents more than the original {len(documents)}.')
print(f'Average length among {len(docs)} documents (after split) is {avg_char_count_post} characters.')
from langchain.chains.question_answering import load_qa_chain
from langchain.vectorstores import FAISS
from langchain.indexes import VectorstoreIndexCreator
from langchain.indexes.vectorstore import VectorStoreIndexWrapper
vectorstore_faiss = FAISS.from_documents(
docs,
bedrock_embeddings,
)
wrapper_store_faiss = VectorStoreIndexWrapper(vectorstore=vectorstore_faiss)
```
funny thing is if my doc is smaller(docs[:5]), it worked.
vectorstore_faiss = FAISS.from_documents(
**docs[:5]**,
bedrock_embeddings,
)
error:
---------------------------------------------------------------------------
ValidationException Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/langchain/embeddings/bedrock.py:120, in BedrockEmbeddings._embedding_func(self, text)
119 try:
--> 120 response = self.client.invoke_model(
121 body=body,
122 modelId=self.model_id,
123 accept="application/json",
124 contentType="application/json",
125 )
126 response_body = json.loads(response.get("body").read())
File /opt/conda/lib/python3.10/site-packages/botocore/client.py:535, in ClientCreator._create_api_method.<locals>._api_call(self, *args, **kwargs)
534 # The "self" in this scope is referring to the BaseClient.
--> 535 return self._make_api_call(operation_name, kwargs)
File /opt/conda/lib/python3.10/site-packages/botocore/client.py:980, in BaseClient._make_api_call(self, operation_name, api_params)
979 error_class = self.exceptions.from_code(error_code)
--> 980 raise error_class(parsed_response, operation_name)
981 else:
ValidationException: An error occurred (ValidationException) when calling the InvokeModel operation: The provided inference configurations are invalid
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
Cell In[35], line 10
4 from langchain.indexes.vectorstore import VectorStoreIndexWrapper
6
7
8 #
9 #
---> 10 vectorstore_faiss = FAISS.from_documents(
11 docs,
12 bedrock_embeddings,
13 )
15 wrapper_store_faiss = VectorStoreIndexWrapper(vectorstore=vectorstore_faiss)
File /opt/conda/lib/python3.10/site-packages/langchain/vectorstores/base.py:420, in VectorStore.from_documents(cls, documents, embedding, **kwargs)
418 texts = [d.page_content for d in documents]
419 metadatas = [d.metadata for d in documents]
--> 420 return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
File /opt/conda/lib/python3.10/site-packages/langchain/vectorstores/faiss.py:607, in FAISS.from_texts(cls, texts, embedding, metadatas, ids, **kwargs)
581 @classmethod
582 def from_texts(
583 cls,
(...)
588 **kwargs: Any,
589 ) -> FAISS:
590 """Construct FAISS wrapper from raw documents.
591
592 This is a user friendly interface that:
(...)
605 faiss = FAISS.from_texts(texts, embeddings)
606 """
--> 607 embeddings = embedding.embed_documents(texts)
608 return cls.__from(
609 texts,
610 embeddings,
(...)
614 **kwargs,
615 )
File /opt/conda/lib/python3.10/site-packages/langchain/embeddings/bedrock.py:148, in BedrockEmbeddings.embed_documents(self, texts, chunk_size)
146 results = []
147 for text in texts:
--> 148 response = self._embedding_func(text)
149 results.append(response)
150 return results
File /opt/conda/lib/python3.10/site-packages/langchain/embeddings/bedrock.py:129, in BedrockEmbeddings._embedding_func(self, text)
127 return response_body.get("embedding")
128 except Exception as e:
--> 129 raise ValueError(f"Error raised by inference endpoint: {e}")
ValueError: Error raised by inference endpoint: An error occurred (ValidationException) when calling the InvokeModel operation: The provided inference configurations are invalid
### Expected behavior
I would like to generate embeddings for the entire corpus and stored in a vector store.
|
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| 1,847,029,475
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PR_kwDOIPDwls5XvNXp
| 9,126
|
fix MRKLOutputParser
|
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| null |
[] | null | 4
| 2023-08-11T15:26:43
| 2024-01-11T16:46:09
| 2024-01-11T16:46:09
|
NONE
| null |
# Description:
This PR aims to fix the output parser when the agent hallucinates.
Indeed, sometimes the agent outputs an hallucinated **Observation** resulting to errors.
Also, when the agent have found the **Final Answer**, it happens that the agent loops to a new hallucinated question and start the MRKL process again.
Then, this PR fix the `MRKLOutputParser`:
- when agent has hallucinated an observation
- when agent has hallucinated a next question after the final answer
# Issue:
Multiple issues are about errors returned by the output parser, in my case it fixed many parsing errors.
@baskaryan
|
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PR_kwDOIPDwls5XvC11
| 9,125
|
dalle nb fix
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| 2023-08-11T15:21:49
| 2023-08-11T15:21:49
|
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5XvAng
| 9,123
|
Create pydantic v1 namespace, add partial compatibility for pydantic v2
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| 2023-08-14T13:37:34
| 2023-08-14T13:37:33
|
COLLABORATOR
| null |
First of a few PRs to add full compatibility to both pydantic v1 and v2.
This PR creates pydantic v1 namespace and adds it to sys.modules.
Upcoming changes:
1. Handle `openapi-schema-pydantic = "^1.2"` and dependent chains/tools
2. bump dependencies to versions that are cross compatible for pydantic or remove them (see below)
3. Add tests to github workflows to test with pydantic v1 and v2
**Dependencies**
From a quick look (could be wrong since was done manually)
**dependencies pinning pydantic below 2** (some of these can be bumped to newer versions are provide cross-compatible code)
anthropic
bentoml
confection
fastapi
langsmith
octoai-sdk
openapi-schema-pydantic
qdrant-client
spacy
steamship
thinc
zep-python
Unpinned
marqo (*)
nomic (*)
xinference(*)
|
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| 1,846,866,526
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I_kwDOIPDwls5uFPJe
| 9,122
|
Intermediate answer from STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION received as a final answer
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[] | null | 13
| 2023-08-11T13:52:19
| 2023-12-28T16:02:48
| null |
NONE
| null |
### System Info
langchain==0.0.260
model = "gpt-3.5-turbo-16k"
temperature = 0.0
### Who can help?
@hwchase17 and @agola11
### Information
- [ ] 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
- [X] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
search_agent = initialize_agent(
tools=tools,
llm=llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
memory = memory,
agent_kwargs = {
#"suffix": NEW_SUFFIX,
"memory_prompts": [chat_history],
"input_variables": ["input", "agent_scratchpad", "chat_history"]
},
#prompt=cf_template,
#handle_parsing_errors=True,
#max_iterations=2,
#max_execution_time=10,
verbose = True,
handle_parsing_errors=True,
)
from pprint import pprint
#planner = load_chat_planner(llm)
#executor = load_agent_executor(llm, tools, verbose=True)
#search_agent = PlanAndExecute(planner=planner, executor=executor, verbose=True)
#print(search_agent.agent.llm_chain.prompt)
search_agent.agent.llm_chain.verbose=True
question = "What are the stock price for Apple"
response = search_agent.run(input = question)
print(response)
#pprint(search_agent.agent.llm_chain.prompt)
question = "What about Google."
response = search_agent.run(input = question)
print(response)
#pprint(search_agent.agent.llm_chain.prompt)
question = "Show me news for fires in Greece in the last month."
response = search_agent.run(input = question)
print(response)
question = "what about Macedonia in the previous week."
response = search_agent.run(input = question)
print(response)
```
### Expected behavior
For the:
```
question = "What about Google."
response = search_agent.run(input = question)
```
The Agent response is:
```
Human: What about Google.
AI: To provide the current stock price for Google, I will use the "get_current_stock_price" tool. Please wait a moment.
```
Instead it should execute the tool and get the results similar as Apple which is the first query...
|
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I_kwDOIPDwls5uE1Q_
| 9,120
|
Add `do_sample` to HuggingFaceTextGenInference
|
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[] | null | 3
| 2023-08-11T12:42:50
| 2023-11-13T08:45:47
| 2023-11-13T08:45:38
|
NONE
| null |
### Feature request
Many parameters described on the TGI [Swagger](https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/generate) find their direct equivalent in the corresponding LangChain [API](https://api.python.langchain.com/en/latest/llms/langchain.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html?highlight=textgeninference#langchain.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference).
### Motivation
At least one of those parameters is missing: the `do_sample` parameter which I needed.
### Your contribution
Why is this parameter missing? How hard would it be to add this and can this be circumvented?
|
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I_kwDOIPDwls5uEwbG
| 9,119
|
Chain for planning using PDDL
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| 2023-08-11T12:29:14
| 2023-11-17T16:05:14
| 2023-11-17T16:05:13
|
CONTRIBUTOR
| null |
### Feature request
__A chain for planning using the [Planning Domain Definition Language](https://en.wikipedia.org/wiki/Planning_Domain_Definition_Language) and a dedicated solver__
As described in this [paper by Liu et al.](https://arxiv.org/abs/2304.11477), the LLM can generate the PDDL inputs required by a solver (they used [FastDownward](https://github.com/aibasel/downward), which has PDDL support, see [this info page](https://www.fast-downward.org/PddlSupport)). \
There is an implementation available from the authors, see [llm-pddl](https://github.com/Cranial-XIX/llm-pddl). However, I did not see any license information. \
It would be great having this readily available as a part of langchain. \
I guess implementing this as a chain would make the most sense, since iterations for reformulating the problem might be necessary.
### Motivation
LLMs are limited regarding planning in an optimal way and dedicated solvers require inputs that are tedious to create - putting the pieces together just makes sense
### Your contribution
I could create a PR, but this might take some time
|
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| 1,846,659,504
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I_kwDOIPDwls5uEcmw
| 9,118
|
StructuredOutputParser.get_format_instructions with only_json=True doesn't return the closing backticks
|
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[] | null | 1
| 2023-08-11T11:26:35
| 2023-11-17T16:05:19
| 2023-11-17T16:05:18
|
NONE
| null |
### System Info
- langchain version 0.0.262
- Python version 3.10
### Who can help?
Users I've found through blame ([exact commit](https://github.com/langchain-ai/langchain/commit/1d649b127eb10c426f9b9a67cbd1fe6ec8e6befa)):
- @MassimilianoBiancucci
- @baskaryan
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just use the function or look into the source code ([this seems to be the exact commit](https://github.com/langchain-ai/langchain/commit/1d649b127eb10c426f9b9a67cbd1fe6ec8e6befa)).
### Expected behavior
Either return the sole JSON or the whole markdown block (including the closing three backticks).
|
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| 1,846,559,683
|
I_kwDOIPDwls5uEEPD
| 9,117
|
BedrockEmbeddings can't load aws credential profile
|
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[] | null | 8
| 2023-08-11T10:10:30
| 2023-10-09T07:00:40
| 2023-08-11T14:08:13
|
NONE
| null |
### System Info
langchain==0.0.261
python 3.10
------------------
ValidationError Traceback (most recent call last)
Cell In[17], line 1
----> 1 embeddings = BedrockEmbeddings(
2 credentials_profile_name="monisdas-ibm", region_name="us-east-1"
3 )
4 vectorstore = Chroma.from_documents(docs, embeddings)
File ~/Documents/moni/knowlege/unstructured/examples/chroma-news-of-the-day/.py310_unstrcutured/lib/python3.10/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for BedrockEmbeddings
__root__
Could not load credentials to authenticate with AWS client. Please check that credentials in the specified profile name are valid. (type=value_error)
-------------------
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
try to use BedrockEmbeddings
### Expected behavior
it should work.
|
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| 1,846,494,110
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PR_kwDOIPDwls5XtXjt
| 9,115
|
Fix web research retriever for unknown links in results
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| 2023-08-11T09:24:52
| 2023-08-11T17:50:38
| 2023-08-11T17:50:37
|
CONTRIBUTOR
| null |
Fixes an issue with web research retriever for unknown links in results. This is currently making the retrieve crash sometimes.
@rlancemartin
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| 1,846,440,414
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PR_kwDOIPDwls5XtLy8
| 9,114
|
feat(llms): support ernie chat
|
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| 2023-08-11T08:45:11
| 2023-08-15T08:05:46
| 2023-08-15T08:05:46
|
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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
Description: support ernie (文心一言) chat model
Related issue: #7990
Dependencies: None
Tag maintainer: @baskaryan
|
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PR_kwDOIPDwls5XtIfz
| 9,113
|
`ArgillaCallbackHandler` to properly use default values for `api_url` and `api_key`
|
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| 2023-08-11T08:33:37
| 2023-08-11T16:48:47
| 2023-08-11T16:37:07
|
CONTRIBUTOR
| null |
Hi to @agola11 or whoever is reading this! 🤗
## What's in this PR?
As of the recent PR at #9043, after some testing we've realised that the default values were not being used for `api_key` and `api_url`. Besides that, the default for `api_key` was set to `argilla.apikey`, but since the default values are intended for people using the Argilla Quickstart (easy to run and setup), the defaults should be instead `owner.apikey` if using Argilla 1.11.0 or higher, or `admin.apikey` if using a lower version of Argilla.
Additionally, we've removed the f-string replacements from the docstrings.
P.S. Regarding the Twitter/X mention feel free to do so at either https://twitter.com/argilla_io or https://twitter.com/alvarobartt and https://twitter.com/gabrielmbmb_, or both if applicable, otherwise, just the first Twitter/X handle.
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I_kwDOIPDwls5uDjSf
| 9,112
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openai.error.InvalidRequestError: Too many inputs.
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[] | null | 3
| 2023-08-11T08:33:13
| 2023-12-28T16:06:57
| 2023-12-28T16:06:56
|
NONE
| null |
HI,
Is that azure Openai embedding had limitation ?
Traceback (most recent call last):
File "D:\Corent\AI\LangChain\azure\azure_connection.py", line 45, in <module>
VectorStore = Milvus.from_texts(
^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\vectorstores\milvus.py", line 822, in from_texts
vector_db.add_texts(texts=texts, metadatas=metadatas)
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\vectorstores\milvus.py", line 422, in add_texts
embeddings = self.embedding_func.embed_documents(texts)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\embeddings\openai.py", line 478, in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\embeddings\openai.py", line 364, in _get_len_safe_embeddings
response = embed_with_retry(
^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\embeddings\openai.py", line 107, in embed_with_retry
return _embed_with_retry(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\tenacity\__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\tenacity\__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\tenacity\__init__.py", line 314, in iter
return fut.result()
^^^^^^^^^^^^
File "C:\Users\donbosco\AppData\Local\Programs\Python\Python311\Lib\concurrent\futures\_base.py", line 449, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "C:\Users\donbosco\AppData\Local\Programs\Python\Python311\Lib\concurrent\futures\_base.py", line 401, in __get_result
raise self._exception
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\tenacity\__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\embeddings\openai.py", line 104, in _embed_with_retry
response = embeddings.client.create(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\openai\api_resources\embedding.py", line 33, in create
response = super().create(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 153, in create
response, _, api_key = requestor.request(
^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\openai\api_requestor.py", line 298, in request
resp, got_stream = self._interpret_response(result, stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\openai\api_requestor.py", line 700, in _interpret_response
self._interpret_response_line(
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\openai\api_requestor.py", line 763, in _interpret_response_line
raise self.handle_error_response(
openai.error.InvalidRequestError: Too many inputs. The max number of inputs is 16. We hope to increase the number of inputs per request soon. Please contact us through an Azure support request at: https://go.microsoft.com/fwlink/?linkid=2213926 for further questions.
|
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| 1,846,415,172
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I_kwDOIPDwls5uDg9E
| 9,111
|
Issue: when using the map_rerank & refine, occur the " list index out of range" (already modify the llm.py file, but only map_reduce can work well)
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[] | null | 6
| 2023-08-11T08:26:10
| 2023-12-06T17:44:20
| 2023-12-06T17:44:19
|
NONE
| null |
### Issue you'd like to raise.
I just tried the simple example with the latest version of langchain, but I meet the problem with " list index out of the range", when I modify the chain_type in "RetrievalQA.from_chain_type" function, the error messages are as follows :
map_rerank :
-----------------------------------------------------------------------------------------------------------------------------
File ~\anaconda3\envs\pytorch\Lib\site-packages\langchain\chains\combine_documents\map_rerank.py:194, in MapRerankDocumentsChain._process_results(self, docs, results)
190 typed_results = cast(List[dict], results)
191 sorted_res = sorted(
192 zip(typed_results, docs), key=lambda x: -int(x[0][self.rank_key])
193 )
--> 194 output, document = sorted_res[0]
195 extra_info = {}
196 if self.metadata_keys is not None:
IndexError: list index out of range
-----------------------------------------------------------------------------------------------------------------------------
refine:
-----------------------------------------------------------------------------------------------------------------------------
File ~\anaconda3\envs\pytorch\Lib\site-packages\langchain\chains\combine_documents\refine.py:203, in RefineDocumentsChain._construct_initial_inputs(self, docs, **kwargs)
200 def _construct_initial_inputs(
201 self, docs: List[Document], **kwargs: Any
202 ) -> Dict[str, Any]:
--> 203 base_info = {"page_content": docs[0].page_content}
204 base_info.update(docs[0].metadata)
205 document_info = {k: base_info[k] for k in self.document_prompt.input_variables}
IndexError: list index out of range
-----------------------------------------------------------------------------------------------------------------------------
Thanks for your help!
### Suggestion:
_No response_
|
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| 1,846,399,416
|
I_kwDOIPDwls5uDdG4
| 9,110
|
LLM with empty list of document
|
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[] | null | 1
| 2023-08-11T08:14:57
| 2023-08-16T07:14:35
| 2023-08-16T07:14:35
|
CONTRIBUTOR
| null |
### System Info
If, for some reason, the list of document is empty, an exception is sent from llm.py.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
llm.prep_prompts(input_list=[])
raise exception
### Expected behavior
Continue
See the pull [request #9109](https://github.com/langchain-ai/langchain/pull/9109)
|
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PR_kwDOIPDwls5XtCvZ
| 9,109
|
Fix bug in llm.py when the list of documents is empty
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| 2023-08-11T08:14:26
| 2023-08-11T16:38:30
| 2023-08-11T16:38:29
|
CONTRIBUTOR
| null |
llm.prep_prompts(input_list=[])
throw exception.
I fix this bug.
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PR_kwDOIPDwls5XtCQk
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bump 262
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| 2023-08-11T08:12:42
| 2023-08-11T08:29:09
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COLLABORATOR
| null | null |
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PR_kwDOIPDwls5Xs77Q
| 9,105
|
fix: chunk_size passing
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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:
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If you're adding a new integration, please include:
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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,846,358,624
|
PR_kwDOIPDwls5Xs59G
| 9,104
|
fix: chunk_size passing
|
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| 2023-08-11T07:45:27
| 2023-08-11T07:45:59
| 2023-08-11T07:45:33
|
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://github.com/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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https://api.github.com/repos/langchain-ai/langchain/issues/9103
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https://github.com/langchain-ai/langchain/issues/9103
| 1,846,353,415
|
I_kwDOIPDwls5uDR4H
| 9,103
|
Issue: Elasticsearch BM25
|
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| 2023-08-11T07:41:41
| 2023-11-06T19:45:03
| null |
NONE
| null |
### Issue you'd like to raise.
I am using Elasticsearch BM25 to fetch relevant documents. How can I add a parameter to tell the retriever to return only first n matching docs?
### Suggestion:
_No response_
|
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| 1,846,350,706
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PR_kwDOIPDwls5Xs4Or
| 9,102
|
Minor grammatical error
|
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|
CONTRIBUTOR
| null |
Have corrected a grammatical error in: https://python.langchain.com/docs/modules/model_io/models/llms/ document 😄
|
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I_kwDOIPDwls5uDQj4
| 9,101
|
ConversationalRetrievalChain having trouble with vector store retriever 'as_retriever()'
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| 2023-08-11T07:37:03
| 2023-10-21T09:24:07
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NONE
| null |
### System Info
Code snapshot:
Data in Azure Cognitive Search Vector Store:
data ={
"id": " ",
"content": "",
"content_vector": [],
"metadata": "{}",
"a": "",
"b": "",
"c": 20.4,
"d": ""
}
Issue: with langchain==0.0.261, but it is working fine with langchain==0.0.242
Issue Description : SerializationError: (', DeserializationError: (", AttributeError: \'float\' object has no attribute \'lower\'", \'Unable to deserialize to object: type\', AttributeError("\'float\' object has no attribute \'lower\'"))', 'Unable to build a model: (", AttributeError: \'float\' object has no attribute \'lower\'", \'Unable to deserialize to object: type\', AttributeError("\'float\' object has no attribute \'lower\'"))', DeserializationError(", AttributeError: 'float' object has no attribute 'lower'", 'Unable to deserialize to object: type', AttributeError("'float' object has no attribute 'lower'")))
Note: In the upgraded version the response time also increases.
### Who can help?
_No response_
### 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
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
def __llm_client(self, streaming: bool =False):
# LLM connection
if streaming is False:
return AzureOpenAI(
deployment_name=AZURE_OPENAI_LLM_MODEL_DEPLOYMENT_NAME,
model=AZURE_OPENAI_LLM_MODEL_NAME,
openai_api_type=self.__azure_openAI_type,
openai_api_version=self.__azure_openAI_version,
openai_api_base=self.__azure_openAI_base,
openai_api_key=self.__azure_openAI_key,
temperature=0.0,
max_tokens=2000
)
return AzureOpenAI(
deployment_name=AZURE_OPENAI_LLM_MODEL_DEPLOYMENT_NAME,
model=AZURE_OPENAI_LLM_MODEL_NAME,
openai_api_type=self.__azure_openAI_type,
openai_api_version=self.__azure_openAI_version,
openai_api_base=self.__azure_openAI_base,
openai_api_key=self.__azure_openAI_key,
temperature=0.0,
max_tokens=2000,
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()]
)
# LLM Embeddings Client
def __embeddings_client(self):
return OpenAIEmbeddings(
model=AZURE_OPENAI_LLM_EMBEDDING_NAME,
deployment=AZURE_OPENAI_LLM_EMBEDDING_DEPLOYMENT_NAME,
openai_api_type=self.__azure_openAI_type,
openai_api_version=self.__azure_openAI_version,
openai_api_base=self.__azure_openAI_base,
openai_api_key=self.__azure_openAI_key,
chunk_size=1536
)
# Embedding vector store client
def __vector_store_client(self):
acs_vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=self.__acs_endpoint,
azure_search_key=self.__acs_key,
index_name=self.__acs_index_name,
embedding_function=self.__embeddings_client().embed_query,
)
return acs_vector_store
# Langchain Chain Client
def __chain_client(self):
chain_type = "stuff"
return load_qa_chain(
llm=self.__llm_client(streaming=True),
chain_type=chain_type,
document_variable_name='context',
prompt=QA_PROMPT
# verbose=True
)
def __conversational_retrieval_chain_client(self):
# print(self.__vector_store_client().as_retriever())
return ConversationalRetrievalChain(
retriever=self.__vector_store_client().as_retriever(),
question_generator=self.__question_generator(),
combine_docs_chain=self.__chain_client(),
memory=ConversationBufferMemory(
memory_key="chat_history",
return_messages=False
)
)
def __question_generator(self):
return LLMChain(
llm=self.__llm_client(),
prompt=CONDENSE_QUESTION_PROMPT
)
# Main function
def smart_chat_bot(self, query: str="*", conversation: list=[]):
self.user_input = query
print(f"Human Input: {self.user_input}", end="\n")
result = self.__conversational_retrieval_chain_client()({"question": self.user_input, "chat_history": conversation})
return result
### Expected behavior
It should return the response on the bases of user input and the documents that came from the vector store. And the latency will be lesser.
|
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| 1,846,343,465
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I_kwDOIPDwls5uDPcp
| 9,100
|
sync problem
|
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open
| false
| null |
[] | null | 3
| 2023-08-11T07:33:15
| 2023-10-12T14:08:49
| null |
NONE
| null |
https://github.com/langchain-ai/langchain/blob/991b448dfce7fa326e70774b5f38c9576f1f304c/libs/langchain/langchain/chains/base.py#L327C25-L327C25
IMPORTANT: chain.acall is a async function.
Chain.prep_inputs will invoke memory.load_memory_variables
in ConversationBufferWindowMemory.load_memory_variables.
```python
buffer: Any = self.buffer[-self.k * 2 :] if self.k > 0 else []
```
```python
@property
def buffer(self) -> List[BaseMessage]:
"""String buffer of memory."""
return self.chat_memory.messages
```
if chat_memory is type of RedisChatMessageHistory.
```python
@property
def messages(self) -> List[BaseMessage]: # type: ignore
"""Retrieve the messages from Redis"""
_items = self.redis_client.lrange(self.key, 0, -1)
items = [json.loads(m.decode("utf-8")) for m in _items[::-1]]
messages = messages_from_dict(items)
return messages
```
the lrange is a sync invoking...
IMPORTANT: chain.acall is a async function.
in fastapi or other scene.
a chain.acall will blocking by this invoke. can you change the invoke of Chain.prep_inputs to:
```python
inputs = await asyncify(self.prep_inputs)(inputs)
```
|
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I_kwDOIPDwls5uDKjC
| 9,099
|
Lazy import in vectorstore
|
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[] | null | 3
| 2023-08-11T07:16:11
| 2023-11-29T09:35:50
| 2023-11-29T09:35:50
|
CONTRIBUTOR
| null |
### Feature request
To import a big list of documents in a vectorstore, you must load all the documents in memory, split the documents, and import the list of documents.
They consume a lot of memory just to import. Sometime, the number of documents is too big to feet in memory.
I propose to generalize the `lazy_...()` with some generator. Then, only one document at a time can be in memory.
### Motivation
Reduce the memory foot print.
Accept to import, in one loop, a big list of documents.
### Your contribution
- Small contribution. I use lazy approach in my pull request to load google documents.
|
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| 1,846,287,070
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PR_kwDOIPDwls5XsqV5
| 9,098
|
Bagatur/lite llm
|
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| 2023-08-11T06:45:57
| 2023-08-11T20:11:44
| 2023-08-11T20:11:44
|
COLLABORATOR
| null | null |
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| 1,846,233,083
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PR_kwDOIPDwls5Xsen4
| 9,096
|
fix json tool
|
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| 2023-08-11T05:50:06
| 2023-08-11T06:39:26
| 2023-08-11T06:39:26
|
COLLABORATOR
| null | null |
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| 1,846,231,934
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PR_kwDOIPDwls5XseXw
| 9,095
|
Update pydantic format instruction prompt
|
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| 2023-08-11T05:48:27
| 2023-09-20T17:02:33
| 2023-08-11T07:22:14
|
CONTRIBUTOR
| null |
- remove unopened bracket
<!-- 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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
|
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I_kwDOIPDwls5uCxyn
| 9,094
|
Issue: Bedrock Doesn't support streaming
|
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[] | null | 6
| 2023-08-11T05:34:22
| 2023-11-11T13:20:39
| null |
NONE
| null |
I just wondering can i do streaming model bedrock like Claude V2 on Langchain?
because i don't see support streaming on the Bedrock code.
If can, can you give me example for use streaming on bedrock?
|
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PR_kwDOIPDwls5XsaZq
| 9,093
|
minor edits
|
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[] | null | 1
| 2023-08-11T05:23:17
| 2023-08-11T06:40:37
| 2023-08-11T06:40:37
|
CONTRIBUTOR
| null |
Description:
Minor edit to PR#845
@baskaryan - following up on this comment thread:
https://github.com/langchain-ai/langchain/pull/845#issuecomment-1673520449
Thanks!
|
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I_kwDOIPDwls5uCtLV
| 9,092
|
Enums not used with OpenAI Multi Function Agent
|
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[] | null | 2
| 2023-08-11T05:07:36
| 2023-11-17T16:05:25
| 2023-11-17T16:05:24
|
NONE
| null |
### System Info
langchain==0.0.261
### 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
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When running the OpenAI Multi Functions Agent, **enums** in custom structured tools are not being used. Using the enums for data validation works fine, but the LLM does not use enums to produce enumerated parameter values. For reference, OpenAI's Function Calling takes the JSON schema for a function, which can pass the JSON schema enums to the LLM to be used when producing parameter values.
1. Create a custom structured tool with an enum input
2. Give tool to agent
3. Run a query that uses the tool
4. Agent may hallucinate an invalid enum
### Expected behavior
The expected behavior is two-fold:
1. Provide Python type annotations in BaseModel arg_schemas to the LLM instead of just the Field() object
2. Correct the error similar to the `StructuredChatOutputParserWithRetries`
|
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PR_kwDOIPDwls5XsM09
| 9,091
|
add support AsyncIteratorCallbackHandler with buffer
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| 2023-08-11T03:39:05
| 2023-09-20T17:53:52
| null |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description:Add new class [ChunkAsyncIteratorCallbackHandler], like AsyncIteratorCallbackHandler with buffer (chunk)
- Issue: the issue None
- Dependencies: None
- 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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
|
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| 1,846,147,524
|
I_kwDOIPDwls5uCfnE
| 9,090
|
Can the atomicity of multiple write operations be guaranteed through the Agent
|
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[] | null | 2
| 2023-08-11T03:35:52
| 2023-11-17T16:05:30
| 2023-11-17T16:05:29
|
NONE
| null |
### Feature request
Can the atomicity of multiple write operations be guaranteed through the Agent
### Motivation
If two actions need to be executed within an agent and each action modifies the status of external tools or services, then if the first action writing operation is successful and the second action writing fails. How to ensure the revocation of the first write operation through an agent to ensure atomic: "all or nothing".
### Your contribution
Does the default agent runtime of langchain support this operation? If not, can we support it through the following ideas:
1: using langchain callback ?
2: inspired by BabyAGI feature: generate and pretend to execute tasks based on a given objective. I think the pretend operation can meet this requirement. Is this correct？
|
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I_kwDOIPDwls5uCaNH
| 9,089
|
[BUG] module 'signal' has no attribute 'SIGALRM
|
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| 2023-08-11T02:54:44
| 2023-08-11T16:04:49
| 2023-08-11T16:04:49
|
NONE
| null |
### System Info
```py
In [21]: langchain.__version__
Out[21]: '0.0.223'
In [24]: sys.platform
Out[24]: 'win32'
In [25]: !python -V
Python 3.10.10
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [ ] 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
```py
In [18]: from langchain.embeddings.huggingface import HuggingFaceEmbeddings
In [19]: model = HuggingFaceEmbeddings(model_name='d:/src/chatglm2-6b-int4/')
No sentence-transformers model found with name d:/src/chatglm2-6b-int4/. Creating a new one with MEAN pooling.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[19], line 1
----> 1 model = HuggingFaceEmbeddings(model_name='d:/src/chatglm2-6b-int4/')
File D:\Python310\lib\site-packages\langchain\embeddings\huggingface.py:59, in HuggingFaceEmbeddings.__init__(self, **kwargs)
53 except ImportError as exc:
54 raise ImportError(
55 "Could not import sentence_transformers python package. "
56 "Please install it with `pip install sentence_transformers`."
57 ) from exc
---> 59 self.client = sentence_transformers.SentenceTransformer(
60 self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
61 )
File D:\Python310\lib\site-packages\sentence_transformers\SentenceTransformer.py:97, in SentenceTransformer.__init__(self, model_name_or_path, modules, device, cache_folder, use_auth_token)
95 modules = self._load_sbert_model(model_path)
96 else: #Load with AutoModel
---> 97 modules = self._load_auto_model(model_path)
99 if modules is not None and not isinstance(modules, OrderedDict):
100 modules = OrderedDict([(str(idx), module) for idx, module in enumerate(modules)])
File D:\Python310\lib\site-packages\sentence_transformers\SentenceTransformer.py:806, in SentenceTransformer._load_auto_model(self, model_name_or_path)
802 """
803 Creates a simple Transformer + Mean Pooling model and returns the modules
804 """
805 logger.warning("No sentence-transformers model found with name {}. Creating a new one with MEAN pooling.".format(model_name_or_path))
--> 806 transformer_model = Transformer(model_name_or_path)
807 pooling_model = Pooling(transformer_model.get_word_embedding_dimension(), 'mean')
808 return [transformer_model, pooling_model]
File D:\Python310\lib\site-packages\sentence_transformers\models\Transformer.py:28, in Transformer.__init__(self, model_name_or_path, max_seq_length, model_args, cache_dir, tokenizer_args, do_lower_case, tokenizer_name_or_path)
25 self.config_keys = ['max_seq_length', 'do_lower_case']
26 self.do_lower_case = do_lower_case
---> 28 config = AutoConfig.from_pretrained(model_name_or_path, **model_args, cache_dir=cache_dir)
29 self._load_model(model_name_or_path, config, cache_dir)
31 self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path, cache_dir=cache_dir, **tokenizer_args)
File D:\Python310\lib\site-packages\transformers\models\auto\configuration_auto.py:986, in AutoConfig.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
984 has_remote_code = "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]
985 has_local_code = "model_type" in config_dict and config_dict["model_type"] in CONFIG_MAPPING
--> 986 trust_remote_code = resolve_trust_remote_code(
987 trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code
988 )
990 if has_remote_code and trust_remote_code:
991 class_ref = config_dict["auto_map"]["AutoConfig"]
File D:\Python310\lib\site-packages\transformers\dynamic_module_utils.py:535, in resolve_trust_remote_code(trust_remote_code, model_name, has_local_code, has_remote_code)
533 trust_remote_code = False
534 elif has_remote_code and TIME_OUT_REMOTE_CODE > 0:
--> 535 signal.signal(signal.SIGALRM, _raise_timeout_error)
536 signal.alarm(TIME_OUT_REMOTE_CODE)
537 while trust_remote_code is None:
AttributeError: module 'signal' has no attribute 'SIGALRM'
```
### Expected behavior
load successfully
This is a signal not supported in windows, so it's shouldn't be depended.
|
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I_kwDOIPDwls5uCYKD
| 9,088
|
I would like GPT to quickly and accurately embody any characters or tools.
|
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[] | null | 1
| 2023-08-11T02:39:07
| 2023-11-17T16:05:35
| 2023-11-17T16:05:33
|
NONE
| null |
### Feature request
Sometimes, GPT might not accurately portray characters or tools, so before I ask GPT a question, I need to come up with a set of prompts.
However, in most cases, these prompts may not capture the characteristics of the characters or tools effectively. To address this, I've developed a pip package that collects various prompts. How can I make Chain automatically use my package when it needs to embody characters (as a tool)? Or how can I optimize the pip package to better align with LangChain's requirements?
This is the address of my repository:[https://github.com/limaoyi1/GPT-prompt](https://github.com/limaoyi1/GPT-prompt)
### Motivation
Sometimes, GPT might not accurately portray characters or tools, so before I ask GPT a question, I need to come up with a set of prompts.
### Your contribution
Modify my pip package or submit pr
|
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Bagatur/pinecone by vector
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COLLABORATOR
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PR_kwDOIPDwls5XrwWR
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Add string example mapper
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| 2023-08-10T23:26:43
| 2023-08-11T00:07:04
| 2023-08-11T00:07:03
|
COLLABORATOR
| null |
Now that we accept any runnable or arbitrary function to evaluate, we don't always look up the input keys. If an evaluator requires references, we should try to infer if there's one key present. We only have delayed validation here but it's better than nothing
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PR_kwDOIPDwls5Xru4C
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Update Fireworks model names
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| 2023-08-10T23:15:07
| 2023-08-12T20:13:42
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|
CONTRIBUTOR
| null |
Description:
* Remove the "fireworks-" prefix from Fireworks model names
* Switch web app link from platform.fireworks.ai to app.fireworks.ai
* Minor formatting changes
Issue: N/A
Dependencies: N/A
Tag maintainer: @rlancemartin
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I_kwDOIPDwls5uBvQP
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ConversationSummaryBufferMemory broken due to missing token usage
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[] | null | 2
| 2023-08-10T21:57:40
| 2023-08-11T13:18:49
| 2023-08-11T13:15:36
|
NONE
| null |
### System Info
"langchain": "^0.0.125"
Windows 11 x64
### Who can help?
@nfcampos
### Information
- [X] 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
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When trying to retrieve token usage from LangChain, it returns a empty object. There's already an issue open about that.
However, it gets slightly worse: Apparently, this breaks other components too, such as the `ConversationSummaryBufferMemory` which relies on Token count.
Longer explanation and code on this other issue comment: https://github.com/langchain-ai/langchain/issues/2359#issuecomment-1673926888
### Expected behavior
Completion tokens among others are expected to be returned. Maybe fall back to tiktoken ([py](https://pypi.org/project/tiktoken/)/[js](https://www.npmjs.com/package/@dqbd/tiktoken)) or similar when token count is not available? Just an idea.
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I_kwDOIPDwls5uBrKz
| 9,082
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Issue: StructuredOutputParser failures due to invalid JSON
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| 2023-08-10T21:38:22
| 2023-11-17T16:05:39
| 2023-11-17T16:05:38
|
NONE
| null |
### Issue you'd like to raise.
I think the `ResponseScheam` and `StructuredOutputParser` functionality is really convenient but unfortunately, it often fails in my use case due to the model outputting a slightly wrongly formatted JSON. It's usually due to extra or missing quotes.
Any suggestions on how to solve this?
### Suggestion:
Thinking about writing some extra logic to fix the missing/extra quotes issue, but not sure if anyone has a better solution
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Bagatur/bagel
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PR_kwDOIPDwls5XreJH
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add async for python repl
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Bagatur/dingo
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PR_kwDOIPDwls5XrQkw
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Support a few list like operations on ChatPromptTemplate
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Make it easier to work with chat prompt template
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PR_kwDOIPDwls5XrP8t
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Bagatur/refac faiss
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PR_kwDOIPDwls5XrIWA
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docs: (Mendable Search) Fixes stuck when tabbing out issue
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CONTRIBUTOR
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This fixes Mendable not completing when tabbing out and fixes the duplicate message issue as well.
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PR_kwDOIPDwls5XrH2Y
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Fix more incorrect code blocks in doc strings
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Fix 2 more incorrect code blocks in strings
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I_kwDOIPDwls5uBQi9
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Agents final thought as parameter
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| false
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[] | null | 2
| 2023-08-10T20:06:59
| 2023-11-11T21:20:04
| 2023-11-11T21:19:55
|
NONE
| null |
### System Info
I am using langchain in a different language, but I cannot change the following sentence as it is hard-coded into the Agent class
```
thoughts += (
"\n\nI now need to return a final answer based on the previous steps:"
)
```
https://github.com/langchain-ai/langchain/blob/a5a4c53280b4dae8ea2e09430fed88e0cd4e03d2/libs/langchain/langchain/agents/agent.py#L588
### Who can help?
@hwchase17
@agola11
### 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
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
-
### Expected behavior
-
|
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I_kwDOIPDwls5uBPxG
| 9,071
|
Referencing experimental version of SQLDatabaseChain from the production project
|
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| 2023-10-27T19:19:07
|
NONE
| null |
https://github.com/langchain-ai/langchain/blob/a5a4c53280b4dae8ea2e09430fed88e0cd4e03d2/libs/langchain/langchain/chains/loading.py#L376
Work was done earlier this year to move all experimental and vulnerable code over to the experimental project. SQLDatabaseChain had (and still has) an open CVE against it so it was moved to the experimental project. However the experimental version of SQLDatabaseChain is being referenced in the production project on the line above. Our organization would like to start using LangChain but we cannot due to the open High severity CVE. Is it possible to either patch the vulnerability in code or complete the move of SQLDatabaseChain from the production project to the experimental?
|
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PR_kwDOIPDwls5XqxB_
| 9,069
|
Enhance deprecation decorator to modify docs with sphinx directives
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| 2023-08-10T18:55:30
| 2023-08-13T19:35:02
| 2023-08-13T19:35:01
|
COLLABORATOR
| null |
Enhance deprecation decorator
|
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PR_kwDOIPDwls5Xqq0j
| 9,068
|
add ttl to RedisCache
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| 2023-08-14T16:59:18
| 2023-08-14T16:59:18
|
CONTRIBUTOR
| null |
Add `ttl` (time to live) to `RedisCache`
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I_kwDOIPDwls5uAwtI
| 9,067
|
Issue: I want to attach metadata in my PGVector vector store used as retriever for my ConversationChain memory
|
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| 2023-08-10T18:33:16
| 2023-12-30T16:02:19
| null |
NONE
| null |
### Issue you'd like to raise.
I am creating a chatbot that records the history of the conversation through vectors in my vector store per user that uses the chatbot, and for this, I identify who is the owner of the vectors through metadata.
How can I add this metadata to the vectors if I'm using the vector store as a retriever for the memory in my ConversationChain?
This is my code:
```
import os
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from langchain.memory import VectorStoreRetrieverMemory
from langchain.vectorstores.pgvector import PGVector
from langchain.embeddings.openai import OpenAIEmbeddings
exit_conditions = ("q", "quit", "exit")
metadata={"id": "John Doe", "key": 123}
llm = OpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"), temperature=0)
store = PGVector(
collection_name="chatbot_embeddings",
connection_string=os.getenv("POSTGRES_CONNECTION_STRING"),
embedding_function=OpenAIEmbeddings(),
collection_metadata=metadata
)
while True:
query = input("> ")
if query in exit_conditions:
break
conversation_with_summary = ConversationChain(
llm=llm,
memory=VectorStoreRetrieverMemory(retriever=store.as_retriever()),
verbose=True,
metadata=metadata,
)
print(conversation_with_summary.predict(input=query))
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5XqqIx
| 9,066
|
Move from test to supported claude-instant-1 model
|
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| 2023-08-10T18:32:29
| 2023-08-10T18:57:29
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|
CONTRIBUTOR
| null |
Moves from "test" model to "claude-instant-1" model which is supported and has actual capacity
|
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PR_kwDOIPDwls5XqomY
| 9,065
|
update api ref exampels
|
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manually update for now
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PR_kwDOIPDwls5Xqn6v
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Add excludes to FileSystemBlobLoader
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COLLABORATOR
| null |
Add option to specify exclude patterns.
https://github.com/langchain-ai/langchain/discussions/9059
|
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PR_kwDOIPDwls5Xqjgi
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Updating interactive walkthrough link in index.md to resolve 404 error
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| 2023-08-11T20:08:57
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|
CONTRIBUTOR
| null |
Updated interactive walkthrough link in index.md to resolve 404 error. Also, expressing deep gratitude to LangChain library developers for their exceptional efforts 🥇 .
@baskaryan , @hwchase17 , @eyurtsev
|
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PR_kwDOIPDwls5XqiX6
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update pr temp
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PR_kwDOIPDwls5XqgDW
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fix 9046 issue
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COLLABORATOR
| null |
Fixed #9046
Added ut-s for this fix.
@eyurtsev
|
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PR_kwDOIPDwls5Xqd9U
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Fix incorrect code blocks in documentation
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COLLABORATOR
| null |
Fixes incorrect code block syntax in doc strings.
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PR_kwDOIPDwls5XqQGt
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updated default of n_sentence_context to 2
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CONTRIBUTOR
| null |
n_sentences_context determines the amount of sentences (chunks) that are added before/after the matching chunk when Vectara returns results. This additional context usually results in better context in the Grounded Generation (aka retrieval-augmented generation) use-cases.
Replace this comment with:
- Description: updated n_sentences_context to default at 2.
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @ofermend
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I_kwDOIPDwls5uASIq
| 9,057
|
TypeError: expected string or buffer
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[] | null | 2
| 2023-08-10T17:04:41
| 2023-11-08T16:44:34
| null |
NONE
| null |
When I used to connect and azure openai Emabeddings and Milvus used , Below Exception happende
Traceback (most recent call last):
File "D:\Corent\AI\LangChain\azure\azure_connection.py", line 29, in <module>
VectorStore = Milvus.from_texts(
^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\vectorstores\milvus.py", line 822, in from_texts
vector_db.add_texts(texts=texts, metadatas=metadatas)
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\vectorstores\milvus.py", line 422, in add_texts
embeddings = self.embedding_func.embed_documents(texts)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\embeddings\openai.py", line 478, in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\embeddings\openai.py", line 341, in _get_len_safe_embeddings
token = encoding.encode(
^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\tiktoken\core.py", line 116, in encode
if match := _special_token_regex(disallowed_special).search(text):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: expected string or buffer
|
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PR_kwDOIPDwls5XqIf4
| 9,056
|
fix sched ci (more)
|
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COLLABORATOR
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PR_kwDOIPDwls5XqHNW
| 9,055
|
get rid of google-api-python-client as it is has a huge filesystem footprint
|
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[] | null | 6
| 2023-08-10T16:38:00
| 2023-11-13T06:31:43
| null |
CONTRIBUTOR
| null |
**Description:**
Refactored the `GoogleSearchAPIWrapper` to utilize the `requests` library for direct interactions with the Google Custom Search API. This eliminates the dependency on the `google-api-python-client` library, significantly reducing the filesystem footprint.
**Reasoning:**
Upon closer inspection, I discovered that the `googleapiclient` has a filesystem footprint of 84.8 MiB. This is particularly heavy for applications, especially when the primary use-case is just a single call to Google Search. I use langchain in a AWS lambda environment and getting rid of `google-api-python-client` greatly helped me to be able to run my agent serverless. Further reasoning:
1. **Microservice Architecture:** In a microservices ecosystem, each service ideally should be lightweight and singular in function. Including bulky libraries for minimal features increases the overhead without proportionate benefits.
2. **AWS Lambdas:** AWS Lambda has packaging size limits. For languages like Python, the deployment package size (zipped) should be under 50 MB for direct upload and under 250 MB when uploaded from S3. Reducing unnecessary bulk is crucial for efficient AWS Lambda deployments.
3. **Optimized Docker Images:** Large libraries increase the size of Docker images, which subsequently affects deployment times, scaling efficiency, and even costs (especially in serverless environments where you pay per execution time).
4. **Faster Start Times:** In cloud functions or similar serverless architectures, a lighter package means faster cold starts.
Making this change is a step towards optimized, efficient, and cost-effective deployments, especially in serverless and microservice environments.
**Dependencies:**
- `requests`
**Tag maintainer:**
@hinthornw
Please help me to test this, I hope some GitHub actions will test this PR, the change is rather small and inside a private function, so I hope all tests will pass.
|
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Bagatur/fix sched
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PR_kwDOIPDwls5XqEfw
| 9,053
|
fix sched ci
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I_kwDOIPDwls5t_14k
| 9,052
|
ConversationalRetrievalChain having trouble with custom 'combine_docs_chain'
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[] | null | 2
| 2023-08-10T15:45:03
| 2023-11-16T16:05:16
| 2023-11-16T16:05:15
|
CONTRIBUTOR
| null |
### System Info
Langchain 0.247
Python 3.10.11
### Who can help?
@chase
### Information
- [ ] 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
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I wanted to adapt the original combine_docs_chain to some customization where a user input (e.g. 'you act like a HR chatbot') would be added to the original prompt. The workaround I found was to rewrite the original prompt files to take in a user input, which works. However, I'm having trouble making the ConversationRetrievalChain work.
1. Adapt the original templates from the source files and load them in:
I wanted to add the possibility to add some user input to the pre-made prompts from langchain.
So I duplicated the original files and adapted the text of the prompts somewhat
```
from docs.stuff_prompt import create_stuff_prompt_selector
STUFF_PROMPT_SELECTOR = create_stuff_prompt_selector(ui_input= ui_input) #adds the ui_input to the prompt
stuff_prompt = STUFF_PROMPT_SELECTOR.get_prompt(llm) #based on LLM model, it will return either the regular PromptTemplate version or the chat (ChatPromptTemplate). Same working principle as in the source files
combine_docs_chain = load_qa_chain(llm = llm,
chain_type = 'stuff',
prompt = stuff_prompt
) #create a custom combine_docs_chain
```
2. Create the ConversationalRetrievalChain.from_llm() object with the custom combine_docs_chain
Simply create the object. First time might work, but second won't work
```
from langchain.chains import ConversationalRetrievalChain
qa = ConversationalRetrievalChain.from_llm(
llm,
retriever=vectordb.as_retriever(),
return_source_documents = True,
memory=memory,
verbose = True,
combine_docs_chain = combine_docs_chain
)
```
3. This yields an error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[25], line 2
1 from langchain.chains import ConversationalRetrievalChain
----> 2 qa = ConversationalRetrievalChain.from_llm(
3 llm,
4 retriever=vectordb.as_retriever(),
5 return_source_documents = True, # when return_source_documents is True, a workaround needs to be found https://github.com/langchain-ai/langchain/issues/2303
6 memory=memory,
7 #, get_chat_history=lambda h : h
8 verbose = True,
9 combine_docs_chain = combine_docs_chain
10 #question_generator
11 )
File [c:\Users\XXX\Anaconda3\envs\langchain-env\lib\site-packages\langchain\chains\conversational_retrieval\base.py:357](file:///C:/Users/XXX/Anaconda3/envs/langchain-env/lib/site-packages/langchain/chains/conversational_retrieval/base.py:357), in ConversationalRetrievalChain.from_llm(cls, llm, retriever, condense_question_prompt, chain_type, verbose, condense_question_llm, combine_docs_chain_kwargs, callbacks, **kwargs)
350 _llm = condense_question_llm or llm
351 condense_question_chain = LLMChain(
352 llm=_llm,
353 prompt=condense_question_prompt,
354 verbose=verbose,
355 callbacks=callbacks,
356 )
--> 357 return cls(
358 retriever=retriever,
359 combine_docs_chain=doc_chain,
360 question_generator=condense_question_chain,
361 callbacks=callbacks,
362 **kwargs,
363 )
TypeError: langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain() got multiple values for keyword argument 'combine_docs_chain'
```
### Expected behavior
It is expected to take in the combine_docs_chain I made and initiate the ConversationalRetrievalChain. Is this a cache error? Where the ConversationalRetrievalChain cannot be changed after it has been initiated before?
|
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PR_kwDOIPDwls5Xpw9W
| 9,051
|
Fix issue with Metaphor Search Tool throwing error on missing keys in API response
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| 2023-08-10T16:07:01
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|
CONTRIBUTOR
| null |
- Description: Fixes an issue with Metaphor Search Tool throwing when missing keys in API response.
- Issue: #9048
- Tag maintainer: @hinthornw @hwchase17
- Twitter handle: @pelaseyed
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PR_kwDOIPDwls5Xpusf
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remove the last ";" if the SQL is end with ";"
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| 2023-11-10T22:22:59
| 2023-11-10T22:22:58
|
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: remote the useless ; in the end of the sql
- Issue: If the sql is end with ";", it will have some problem for hive with sqlalchemy (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 you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5XpuEZ
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Update faiss.py delete bug fixed
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NONE
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# After invoking the 'remove_ids' function, the indices of vectors in the Faiss Index will remain consecutive.
# To ensure consistency between the keys of 'index_to_docstore_id' and the indices of vectors in the Faiss Index, modify the keys of 'index_to_docstore_id' to consecutive numbers, like 0, 3, 4 - > 0, 1, 2.
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I_kwDOIPDwls5t_s4O
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|
Metaphor Search throws key error when `publishedDate` is missing
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[] | null | 5
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| 2023-11-19T16:05:16
| 2023-11-19T16:05:15
|
CONTRIBUTOR
| null |
### System Info
LangChain: v0.0.260
Python: v3.11
### Who can help?
@hwchase17 @eyurtsev
### 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
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Sometime `Metaphor` tool returns results without publishedDate which makes the Tool wrapper break due not guarding against empty values:
```
langchain/utilities/metaphor_search.py", line 169, in _clean_results
"published_date": result["publishedDate"],
KeyError: 'publishedDate'
```
### Expected behavior
Preferably the Tool should guard agains missing keys in API response.
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I_kwDOIPDwls5t_r35
| 9,047
|
ArxivLoader support searching by arxiv id_list
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| 2023-11-22T16:06:59
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NONE
| null |
### Feature request
If I know exactly what paper(s) I'd like to load, it would be nice to be able to retrieve them by their arxiv IDs. The arxiv library seems to support this:
```
import arxiv
paper = next(arxiv.Search(id_list=["1605.08386v1"]).results())
```
### Motivation
Sometimes searching by exact paper title still returns the incorrect paper. Searching by ID should be more fool-proof.
### Your contribution
🤷🏻♂️
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ArxivLoader incorrect results
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[] | null | 4
| 2023-08-10T15:18:24
| 2023-08-10T18:59:41
| 2023-08-10T18:59:40
|
NONE
| null |
### System Info
Latest pip versions
### Who can help?
@eyurtsev
### 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
I tried searching by exact title in the following way:
```python
docs = ArxivLoader(query="MetaGPT: Meta Programming for Multi-Agent Collaborative Framework", load_max_docs=1).load()
```
But the result is incorrect. The search works properly on the arxiv website.
### Expected behavior
Correct paper returned
|
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PR_kwDOIPDwls5Xprbw
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Takeoff integration
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| 2023-08-10T17:56:06
| 2023-08-10T17:56:06
|
CONTRIBUTOR
| null |
## Description:
This PR adds the Titan Takeoff Server to the available LLMs in LangChain.
Titan Takeoff is an inference server created by [TitanML](https://www.titanml.co/) that allows you to deploy large language models locally on your hardware in a single command. Most generative model architectures are included, such as Falcon, Llama 2, GPT2, T5 and many more.
Read more about Titan Takeoff here:
- [Blog](https://medium.com/@TitanML/introducing-titan-takeoff-6c30e55a8e1e)
- [Docs](https://docs.titanml.co/docs/titan-takeoff/getting-started)
#### Testing
As Titan Takeoff runs locally on port 8000 by default, no network access is needed. Responses are mocked for testing.
- [x] Make Lint
- [x] Make Format
- [x] Make Test
#### Dependencies
No new dependencies are introduced. However, users will need to install the titan-iris package in their local environment and start the Titan Takeoff inferencing server in order to use the Titan Takeoff integration.
Thanks for your help and please let me know if you have any questions.
cc: @hwchase17 @baskaryan
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PR_kwDOIPDwls5Xplt_
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RedisStore: Update init and Documentation updates
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|
COLLABORATOR
| null |
* Update Redis Store to support init from parameters
* Update notebook to show how to use redis store, and some fixes in documentation
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Update `ArgillaCallbackHandler` as of latest `argilla` release
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|
CONTRIBUTOR
| null |
Hi @agola11, or whoever is reviewing this PR 😄
## What's in this PR?
As of the latest Argilla release, we'll change and refactor some things to make some workflows easier, one of those is how everything's pushed to Argilla, so that now there's no need to call `push_to_argilla` over a `FeedbackDataset` when either `push_to_argilla` is called for the first time, or `from_argilla` is called; among others.
We also add some class variables to make sure those are easy to update in case we update those internally in the future, also to make the `warnings.warn` message lighter from the code view.
P.S. Regarding the Twitter/X mention feel free to do so at either https://twitter.com/argilla_io or https://twitter.com/alvarobartt, or both if applicable, otherwise, just the first Twitter/X handle.
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API Reference: Do not document private modules
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COLLABORATOR
| null |
This PR prevents documentation of private modules in the API reference
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bump 261
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