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| 1,825,231,457 |
PR_kwDOIPDwls5Wl4Pj
| 8,381 |
Update documentation for prompts
|
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[] | null | 2 | 2023-07-27T21:06:32 | 2023-08-04T21:25:04 | 2023-08-04T21:25:03 |
COLLABORATOR
| null |
* Documentation to favor creation without declaring input_variables
* Cut out obvious examples, but add more description in a few places
|
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| 8,380 |
RetrievalQA: Submit different queries to Retriever and LLM
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[] | null | 3 | 2023-07-27T20:32:27 | 2023-10-27T16:18:45 | 2023-10-27T16:18:36 |
NONE
| null |
### Feature request
This is related to [HyDE](https://python.langchain.com/docs/modules/chains/additional/hyde). But I feel my feature request puts the HyDE example in a more realistic context, and naturally extends the RetrievalQA chain.
My suggestion would be allow the text passed to the retriever to be different from the query passed to the LLM. This would be useful as fallback when the original query, as provided by the user, yields a response of "I don't know" from the LLM; or if it returns no or poor-scoring documents during the retrieval step.
Step 1, Embed original query
Step 2, retrieve candidate documents based on embedded original query
Step 3, pass original query & retrieved documents to LLM for answer
If Step 2 yields 0/low score documents OR Step 3 yields no/low confidence answer...
Step 4, generate an alternate _hypothetical_ answer (i.e., as described in [HyDE](https://arxiv.org/abs/2212.10496)
Step 5, embed _hypothetical_ answer
Step 6, retrieve candidate documents based on embedded _hypothetical_ answer
Step 7, pass original query & _newly_ retrieved documents to LLM for answer
### Motivation
Perhaps I have a query that does not yield good retrieval documents from my main knowledgebase corpus. It is poorly formed, it has spelling errors, it is vague, it is brief. Even though the corpus does contain the factual answer to the intent of the query. Instead, a different form of the original query would yield better retrieved documents, and therefore produce the correct & complete answer desired.
For example, very simple questions like "Who's Character X?" may yield a wide range of sub-optimal documents from a QA Retriever. As a result, the LLM's response may be muddled or impossible to generate.
### Your contribution
Happy to refine the suggestion and provide concrete examples.
|
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PR_kwDOIPDwls5WlqCK
| 8,379 |
cleanup warnings
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I_kwDOIPDwls5syX1p
| 8,378 |
GCP Matching Engine support for public index endpoints
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[] | null | 7 | 2023-07-27T20:14:21 | 2023-11-21T02:11:30 | 2023-09-19T23:16:05 |
NONE
| null |
### System Info
langchain==0.0.244
### Who can help?
_No response_
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Create a new Matching Engine Index Endpoint that is public.
Follow the tutorial to make a similarity search:
```
vector_store = MatchingEngine.from_components(
project_id="",
region="us-central1",
gcs_bucket_name="",
index_id="",
endpoint_id="",
embedding=embeddings,
)
vector_store.similarity_search("what is a cat?", k=5)
```
Error:
```
File ~/code/gcp-langchain-retrieval-augmentation/embeddings/.venv/lib/python3.9/site-packages/grpc/_channel.py:1030, in _UnaryUnaryMultiCallable.__call__(self, request, timeout, metadata, credentials, wait_for_ready, compression)
1021 def __call__(self,
1022 request: Any,
1023 timeout: Optional[float] = None,
(...)
1026 wait_for_ready: Optional[bool] = None,
1027 compression: Optional[grpc.Compression] = None) -> Any:
1028 state, call, = self._blocking(request, timeout, metadata, credentials,
1029 wait_for_ready, compression)
-> 1030 return _end_unary_response_blocking(state, call, False, None)
File ~/code/gcp-langchain-retrieval-augmentation/embeddings/.venv/lib/python3.9/site-packages/grpc/_channel.py:910, in _end_unary_response_blocking(state, call, with_call, deadline)
908 return state.response
909 else:
--> 910 raise _InactiveRpcError(state)
_InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = "DNS resolution failed for :10000: unparseable host:port"
debug_error_string = "UNKNOWN:DNS resolution failed for :10000: unparseable host:port {created_time:"2023-07-27T20:12:23.727315699+00:00", grpc_status:14}"
>
```
### Expected behavior
It should be possible to do this. The VertexAI Python SDK supports it with the `endpoint.find_neighbors` function.
I think just changing [the wrapper](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/vectorstores/matching_engine.py#L178) from `.match` to `.find_neighbors` for when the endpoint is public should do it.
|
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PR_kwDOIPDwls5WldZx
| 8,377 |
Update local script for docs build
|
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| 8,376 |
Add summarization use-case
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Retry events on any run type
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PR_kwDOIPDwls5WlGuf
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Add FunctionMessage to _message_from_dict
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- Issue: the issue # it fixes (if applicable),
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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_kwDOIPDwls5WlA9f
| 8,373 |
rename to str output parser
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PR_kwDOIPDwls5WlA4C
| 8,372 |
Add support for CustomAttributes for SageMaker endpoint (required by Llama 2)
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[] | null | 2 | 2023-07-27T18:55:03 | 2023-07-27T19:13:49 | 2023-07-27T19:13:49 |
NONE
| null |
Fixes: #8326
Description: Allows passing "CustomAttributes" to the SageMaker endpoint, see [API documentation](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html) for details.
Issue: The Llama 2 model requires the user to accept the EULA by setting the "CustomAttributes" to "accept_eula=true" in the model invocation. "CustomAttributes" are missing in the current SageMaker endpoint implementation.
Change: This PR allows passing "CustomAttributes" by setting the `custom_attributes` parameter in the endpoint instantiation, e.g.:
```
llm=SagemakerEndpoint(
...,
custom_attributes="accept_eula=true"
)
```
Thanks to @sarthak10193 for reporting the issue and suggesting a fix.
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Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
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- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure 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:
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Link to use cases from tutorials
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Harrison/sql query
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refactor: Code refactoring & simplification for Google Cloud Enterprise Search retriever
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[] | null | 2 | 2023-07-27T18:05:59 | 2023-07-28T00:13:50 | 2023-07-28T00:13:49 |
CONTRIBUTOR
| null |
Followup to https://github.com/langchain-ai/langchain/pull/7857
- Changes `_convert_search_response()` to use object attributes instead of converting to dictionary
- Simplifies logic for readability
|
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| 1,824,906,947 |
PR_kwDOIPDwls5WkwSe
| 8,368 |
Add Runnable.bind method to attach kwargs to a Runnable that will be passed to all invoke/stream/batch calls when it is run
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[] | null | 1 | 2023-07-27T18:02:15 | 2023-07-27T18:16:31 | 2023-07-27T18:16:30 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this 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 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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| 1,824,862,056 |
I_kwDOIPDwls5sxS9o
| 8,367 |
RecursiveUrlLoader is not working
|
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[] | null | 2 | 2023-07-27T17:30:39 | 2023-11-02T16:04:39 | 2023-11-02T16:04:38 |
NONE
| null |
### System Info
**RecursiveUrlLoader** is not working. Please refer to the below code. "docs" size is always 0.
https://python.langchain.com/docs/integrations/document_loaders/recursive_url_loader
from langchain.document_loaders.recursive_url_loader import RecursiveUrlLoader
url = "https://js.langchain.com/docs/modules/memory/examples/"
loader = RecursiveUrlLoader(url=url)
docs = loader.load()
print(len(docs))
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.document_loaders.recursive_url_loader import RecursiveUrlLoader
url = "https://js.langchain.com/docs/modules/memory/examples/"
loader = RecursiveUrlLoader(url=url)
docs = loader.load()
print(len(docs))
Here Length is displaying as zero.
### Expected behavior
All the urls should be scraped
|
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| 1,824,852,567 |
I_kwDOIPDwls5sxQpX
| 8,366 |
OpenSearch bulk ingest with deterministic _id if available
|
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[] | null | 2 | 2023-07-27T17:23:26 | 2023-10-26T16:40:38 | 2023-10-26T16:40:29 |
CONTRIBUTOR
| null |
### Feature request
The `_id` field should be populated with these in order:
1. `ids: Optional[List[str]] = None,` -- already in place
2. Document metadata (filename + start_index) -- new feature
3. Leave `_id` empty (instead of uuid) -- new feature
https://github.com/langchain-ai/langchain/blob/cf608f876b0ada9ac965fe5b25b5ca6e5e47feeb/libs/langchain/langchain/vectorstores/opensearch_vector_search.py#L125
### Motivation
Deterministic _id is very important to keep a robust data pipeline. Duplicate load will thus result in version increment instead of creating new document with the same content.
### Your contribution
I can do a PR if the idea is approved.
|
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| 1,824,731,594 |
PR_kwDOIPDwls5WkKIw
| 8,364 |
update release yml
|
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[] | null | 1 | 2023-07-27T16:18:46 | 2023-07-27T23:49:05 | 2023-07-27T23:49:04 |
COLLABORATOR
| null |
only do the step that tags and adds release notes if its langchain
|
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Arbitrary code execution in LLMMathChain
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NONE
| null |
### System Info
Langchain version: 0.0.244
Numexpr version: 2.8.4
Python version: 3.10.11
### Who can help?
@hwchase17 @vowelparrot
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Numexpr's evaluate function that Langchain uses [here](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/llm_math/base.py#L80) in the LLMMathChain is susceptible to arbitrary code execution with eval in the latest released version. See this [issue](https://github.com/pydata/numexpr/issues/442) where PoC for numexpr's evaluate is also provided.
This vulnerability allows an arbitrary code execution, that is to run code and commands on target machine, via LLMMathChain's run method with the right prompt. I'd like to ask the Langchain's maintainers to confirm if they want a full PoC with Langchain posted here publicly.
### Expected behavior
Numerical expressions should be evaluated securely so as to not allow code execution.
|
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I_kwDOIPDwls5swEIc
| 8,362 |
DOC: Contribution guidelines not working
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[] | null | 1 | 2023-07-27T14:46:12 | 2023-10-26T19:39:35 | 2023-10-26T19:39:26 |
CONTRIBUTOR
| null |
### Issue with current documentation:
Following this:https://github.com/levalencia/langchainLuisValencia/blob/master/.github/CONTRIBUTING.md#-common-tasks
On Windows.
Forked the repo, cloned it locally, created a conda environment, (Poetry was already installed), then installed all dependencies.
Error received:```
• Installing xmltodict (0.13.0)
• Installing zstandard (0.21.0)
ChefBuildError
Backend subprocess exited when trying to invoke get_requires_for_build_wheel
Traceback (most recent call last):
File "C:\Users\xx\AppData\Roaming\pypoetry\venv\Lib\site-packages\pyproject_hooks\_in_process\_in_process.py", line 353, in <module>
main()
File "C:\Users\xx\AppData\Roaming\pypoetry\venv\Lib\site-packages\pyproject_hooks\_in_process\_in_process.py", line 335, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
File "C:\Users\xx\AppData\Roaming\pypoetry\venv\Lib\site-packages\pyproject_hooks\_in_process\_in_process.py", line 118, in get_requires_for_build_wheel
return hook(config_settings)
File "C:\Users\LUISVA~1\AppData\Local\Temp\tmpn9w63h_u\.venv\lib\site-packages\setuptools\build_meta.py", line 341, in get_requires_for_build_wheel
return self._get_build_requires(config_settings, requirements=['wheel'])
File "C:\Users\LUISVA~1\AppData\Local\Temp\tmpn9w63h_u\.venv\lib\site-packages\setuptools\build_meta.py", line 323, in _get_build_requires
self.run_setup()
File "C:\Users\LUISVA~1\AppData\Local\Temp\tmpn9w63h_u\.venv\lib\site-packages\setuptools\build_meta.py", line 487, in run_setup
super(_BuildMetaLegacyBackend,
File "C:\Users\LUISVA~1\AppData\Local\Temp\tmpn9w63h_u\.venv\lib\site-packages\setuptools\build_meta.py", line 338, in run_setup
exec(code, locals())
File "<string>", line 8, in <module>
RuntimeError: uvloop does not support Windows at the moment
at ~\AppData\Roaming\pypoetry\venv\Lib\site-packages\poetry\installation\chef.py:147 in _prepare
143│
144│ error = ChefBuildError("\n\n".join(message_parts))
145│
146│ if error is not None:
→ 147│ raise error from None
148│
149│ return path
150│
151│ def _prepare_sdist(self, archive: Path, destination: Path | None = None) -> Path:
Note: This error originates from the build backend, and is likely not a problem with poetry but with uvloop (0.17.0) not supporting PEP 517 builds. You can verify this by running 'pip wheel --use-pep517 "uvloop (==0.17.0)"'.
```
How can I fix this?
### Idea or request for content:
Poetry Version 1.5.1
What do I need to do to be able to test locally? Before I start to contribute?
|
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Argument 'bases' has incorrect type
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[] | null | 2 | 2023-07-27T14:44:05 | 2023-11-03T16:07:03 | 2023-11-03T16:07:02 |
NONE
| null |
### System Info
Mac, command line and jupyter notebook inside VSCode
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Create a new conda environment
Then
`conda install -c conda-forge 'langchain'`
`pip install openai`
Run sample code (can be in jupyter or terminal via a python file):
```
import os
openai_api_key = os.environ.get("OPENAI_API_KEY")
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
llm = OpenAI()
chat_model = ChatOpenAI()
llm.predict("hi!")
```
Get the following error:
`TypeError: Argument 'bases' has incorrect type (expected list, got tuple)`
Full error output:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[2], line 5
1 import os
3 openai_api_key = os.environ.get("OPENAI_API_KEY")
----> 5 from langchain.llms import OpenAI
6 from langchain.chat_models import ChatOpenAI
8 llm = OpenAI()
File [~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/__init__.py:6](https://file+.vscode-resource.vscode-cdn.net/Users/coreyszopinski/Documents/projects/core/experiments-transformers/nlp/langchain/~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/__init__.py:6)
3 from importlib import metadata
4 from typing import Optional
----> 6 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
7 from langchain.cache import BaseCache
8 from langchain.chains import (
9 ConversationChain,
10 LLMBashChain,
(...)
18 VectorDBQAWithSourcesChain,
19 )
File [~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/agents/__init__.py:2](https://file+.vscode-resource.vscode-cdn.net/Users/coreyszopinski/Documents/projects/core/experiments-transformers/nlp/langchain/~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/agents/__init__.py:2)
1 """Interface for agents."""
----> 2 from langchain.agents.agent import (
3 Agent,
4 AgentExecutor,
5 AgentOutputParser,
6 BaseMultiActionAgent,
7 BaseSingleActionAgent,
8 LLMSingleActionAgent,
9 )
10 from langchain.agents.agent_toolkits import (
11 create_csv_agent,
12 create_json_agent,
(...)
22 create_xorbits_agent,
23 )
24 from langchain.agents.agent_types import AgentType
File [~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/agents/agent.py:16](https://file+.vscode-resource.vscode-cdn.net/Users/coreyszopinski/Documents/projects/core/experiments-transformers/nlp/langchain/~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/agents/agent.py:16)
13 from pydantic import BaseModel, root_validator
15 from langchain.agents.agent_types import AgentType
---> 16 from langchain.agents.tools import InvalidTool
17 from langchain.callbacks.base import BaseCallbackManager
18 from langchain.callbacks.manager import (
19 AsyncCallbackManagerForChainRun,
20 AsyncCallbackManagerForToolRun,
(...)
23 Callbacks,
24 )
File [~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/agents/tools.py:4](https://file+.vscode-resource.vscode-cdn.net/Users/coreyszopinski/Documents/projects/core/experiments-transformers/nlp/langchain/~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/agents/tools.py:4)
1 """Interface for tools."""
2 from typing import Optional
----> 4 from langchain.callbacks.manager import (
5 AsyncCallbackManagerForToolRun,
6 CallbackManagerForToolRun,
7 )
8 from langchain.tools.base import BaseTool, Tool, tool
11 class InvalidTool(BaseTool):
File [~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/callbacks/__init__.py:3](https://file+.vscode-resource.vscode-cdn.net/Users/coreyszopinski/Documents/projects/core/experiments-transformers/nlp/langchain/~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/callbacks/__init__.py:3)
1 """Callback handlers that allow listening to events in LangChain."""
----> 3 from langchain.callbacks.aim_callback import AimCallbackHandler
4 from langchain.callbacks.argilla_callback import ArgillaCallbackHandler
5 from langchain.callbacks.arize_callback import ArizeCallbackHandler
File [~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/callbacks/aim_callback.py:4](https://file+.vscode-resource.vscode-cdn.net/Users/coreyszopinski/Documents/projects/core/experiments-transformers/nlp/langchain/~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/callbacks/aim_callback.py:4)
1 from copy import deepcopy
2 from typing import Any, Dict, List, Optional, Union
----> 4 from langchain.callbacks.base import BaseCallbackHandler
5 from langchain.schema import AgentAction, AgentFinish, LLMResult
8 def import_aim() -> Any:
File [~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/callbacks/base.py:7](https://file+.vscode-resource.vscode-cdn.net/Users/coreyszopinski/Documents/projects/core/experiments-transformers/nlp/langchain/~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/callbacks/base.py:7)
4 from typing import Any, Dict, List, Optional, Sequence, Union
5 from uuid import UUID
----> 7 from langchain.schema.agent import AgentAction, AgentFinish
8 from langchain.schema.document import Document
9 from langchain.schema.messages import BaseMessage
File [~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/schema/__init__.py:2](https://file+.vscode-resource.vscode-cdn.net/Users/coreyszopinski/Documents/projects/core/experiments-transformers/nlp/langchain/~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/schema/__init__.py:2)
1 from langchain.schema.agent import AgentAction, AgentFinish
----> 2 from langchain.schema.document import BaseDocumentTransformer, Document
3 from langchain.schema.language_model import BaseLanguageModel
4 from langchain.schema.memory import BaseChatMessageHistory, BaseMemory
File [~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/schema/document.py:11](https://file+.vscode-resource.vscode-cdn.net/Users/coreyszopinski/Documents/projects/core/experiments-transformers/nlp/langchain/~/anaconda3/envs/core_ml/lib/python3.11/site-packages/langchain/schema/document.py:11)
6 from pydantic import Field
8 from langchain.load.serializable import Serializable
---> 11 class Document(Serializable):
12 """Class for storing a piece of text and associated metadata."""
14 page_content: str
File [~/anaconda3/envs/core_ml/lib/python3.11/site-packages/pydantic/main.py:186](https://file+.vscode-resource.vscode-cdn.net/Users/coreyszopinski/Documents/projects/core/experiments-transformers/nlp/langchain/~/anaconda3/envs/core_ml/lib/python3.11/site-packages/pydantic/main.py:186), in pydantic.main.ModelMetaclass.__new__()
TypeError: Argument 'bases' has incorrect type (expected list, got tuple)
```
### Expected behavior
Expected sample code to run without errors.
|
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PR_kwDOIPDwls5WjBGZ
| 8,359 |
bump 245
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[] | null | 1 | 2023-07-27T13:37:49 | 2023-07-27T13:53:25 | 2023-07-27T13:53:25 |
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LLM output parsing error for answers including code sections.
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[] | null | 4 | 2023-07-27T12:40:32 | 2023-08-03T09:05:28 | 2023-08-03T09:05:28 |
NONE
| null |
### System Info
LangChain version v0.0.5, Python 3.9.13
### Who can help?
@hwchase17
### 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
Issue was reported before, see [issue 5428](https://github.com/langchain-ai/langchain/issues/5428)
It is still not solved.
The problem is the regular expression used in libs/langchain/langchain/output_parsers/json.py
```python
match = re.search(r"```(json)?(.*?)```", json_string, re.DOTALL)
```
`.*?` does non-greedy matching, so the parsing does not stop at the final triple backticks, but
at the first occurrence of triple backticks which can be the start of a code section like ```python
Which leads errors like:
`Error while running agent: Could not parse LLM output: json { "action": "Final Answer", "action_input": "Here is a simple Python script that includes a main function and prints 'Hello':\n\npython\ndef say_hello():\n print('Hello')\n\ndef main():\n say_hello()\n\nif name == 'main':\n main()\n```\nThis script defines a function say_hello that prints the string 'Hello'. The main function calls say_hello. The final lines check if this script is being run directly (as opposed to being imported as a module), and if so, calls the main function." }`
### Expected behavior
Correct LLM output parsing for answers including code sections.
See [issue 5428](https://github.com/langchain-ai/langchain/issues/5428)
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| 1,824,225,325 |
PR_kwDOIPDwls5Wicnu
| 8,356 |
fix(petals) allows to run models that aren't Bloom (Support for LLama and newer models)
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[] | null | 1 | 2023-07-27T12:02:52 | 2023-07-28T01:01:05 | 2023-07-28T01:01:04 |
CONTRIBUTOR
| null |
In this PR:
- Removed restricted model loading logic for Petals-Bloom
- Removed petals imports (DistributedBloomForCausalLM, BloomTokenizerFast)
- Instead imported more generalized versions of loader (AutoDistributedModelForCausalLM, AutoTokenizer)
- Updated the Petals example notebook to allow for a successful installation of Petals in Apple Silicon Macs
- Tag maintainer: @hwchase17, @baskaryan
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I_kwDOIPDwls5su1LI
| 8,355 |
Chat History Issue
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[] | null | 2 | 2023-07-27T11:56:41 | 2023-10-25T15:29:27 | null |
NONE
| null |
### Issue you'd like to raise.
if we are passing chathistory as a input in agent
template = """You are a chatbot having a conversation with a human.
{chat_history}
{human_input}
Chatbot:"""
prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
suffix = """Begin!"
{chat_history}
Question: {input}
{agent_scratchpad}
"""
SO will it exceed the max token limit of the agent ??
If Yes what alternatives can be used for this
### Suggestion:
_No response_
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PR_kwDOIPDwls5WiaR8
| 8,354 |
Update sql database agent example
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[] | null | 1 | 2023-07-27T11:55:52 | 2023-07-27T20:44:03 | 2023-07-27T20:44:03 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this 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 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
-->
This PR fixes a minor documentation issue on the SQL database toolkit example notebook.
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PR_kwDOIPDwls5WiZz3
| 8,353 |
Add embeddings for AwaEmbedding
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[] | null | 1 | 2023-07-27T11:54:23 | 2023-07-28T00:08:00 | 2023-07-28T00:08:00 |
CONTRIBUTOR
| null |
- Description: Adds AwaEmbeddings class for embeddings, which provides users with a convenient way to do fine-tuning, as well as the potential need for multimodality
- Tag maintainer: @baskaryan
Create `Awa.ipynb`: an example notebook for AwaEmbeddings class
Modify `embeddings/__init__.py`: Import the class
Create `embeddings/awa.py`: The embedding class
Create `embeddings/test_awa.py`: The test file.
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I_kwDOIPDwls5suvmu
| 8,352 |
Loading vectorstore from disk still calls to OpenAI's embeddings API
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[] | null | 1 | 2023-07-27T11:41:07 | 2023-11-02T16:17:17 | 2023-11-02T16:17:16 |
NONE
| null |
### System Info
I'm trying to achieve the least calls to openAI-s API, So I've tried to save my vectorstorage to the disk and then reload it (like its in the "if" part). But if I use OpenAIEmbeddings() as embedding_funcion it still calls to openai's embeddings API. (Maybe it should be a feature request) Is this a bug? Am I doing something wrong?
import os
from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.indexes import VectorstoreIndexCreator
from langchain.indexes.vectorstore import VectorStoreIndexWrapper
from langchain.vectorstores import Chroma
loaders = [TextLoader("text1.txt"), TextLoader("text2.txt")]
indexes = []
for loader in loaders:
path = os.path.splitext(loader.file_path)[0]
if os.path.exists(path):
indexes.append(VectorStoreIndexWrapper(
vectorstore=Chroma(
persist_directory=f"./{path}", embedding_function=OpenAIEmbeddings())))
else:
indexes.append(VectorstoreIndexCreator(
vectorstore_kwargs={"persist_directory": f"./{path}"}).from_loaders([loader]))
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The code I provided above creates folders when there are text1.txt and text2.txt in the same folder. (something should be in these files, like a poem or an article or something).
### Expected behavior
After the script ran the folders ("text1" and "text2") should be created with a {guid} folder and a chroma.sqlite3 file in them.
I checked my request usage on https://platform.openai.com/account/usage, and there were 2 requests to text-embedding-ada-002-v2 since there are 2 text files
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PR_kwDOIPDwls5WiP_G
| 8,351 |
fix(openapi): openapi chain will work without/empty description/summa…
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[] | null | 1 | 2023-07-27T11:23:13 | 2023-07-28T00:58:43 | 2023-07-28T00:58:43 |
CONTRIBUTOR
| null |
Description:
This PR will enable the Open API chain to work with valid Open API specifications missing `description` and `summary` properties for path and operation nodes in open api specs.
Since both `description` and `summary` property are declared optional we cannot be sure they are defined. This PR resolves this problem by providing an empty (`''`) description as fallback.
The previous behavior of the Open API chain was that the underlying LLM (OpenAI) throw ed an exception since `None` is not of type string:
```
openai.error.InvalidRequestError: None is not of type 'string' - 'functions.0.description'
```
Using this PR the Open API chain will succeed also using Open API specs lacking `description` and `summary` properties for path and operation nodes.
Thanks for your amazing work !
Tag maintainer: @baskaryan
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| 1,824,121,928 |
I_kwDOIPDwls5sueRI
| 8,349 |
No function message compatability in PostgresChatMessageHistory
|
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| false | null |
[] | null | 4 | 2023-07-27T10:53:51 | 2023-07-28T08:24:55 | 2023-07-28T08:24:55 |
NONE
| null |
### System Info
LangChain 0.0.235
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.schema import FunctionMessage
from langchain.memory import PostgresChatMessageHistory
history = PostgresChatMessageHistory(
connection_string="postgresql://postgres:mypassword@localhost/chat_history", # needs configuring
session_id="test_session_id",
)
function_message = FunctionMessage(
content='A Message for passing the result of executing a function back to a model',
name='name of function'
)
history.add_message(function_message)
history.messages
```
### Expected behavior
Output:
FunctionMessage(content='A Message for passing the result of executing a function back to a model', additional_kwargs={}, name='name of function')
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| 1,824,041,459 |
I_kwDOIPDwls5suKnz
| 8,348 |
Custom tool return_direct except when a ToolException occurs.
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[] | null | 1 | 2023-07-27T10:03:00 | 2023-10-25T17:08:19 | null |
NONE
| null |
### Feature request
Some kind of possibility to make a custom tool with return direct not do so when faced with a error such as a ToolException.
### Motivation
Currently, when a tool with return_direct faces a handled error, it returns the error directly. I would want a tool to return an output directly but only if there are no errors, since the errors aren't useful to be directly returned.
### Your contribution
I am not sure how this could be implemented, but would gladly help working on any ideas. Found this issue which would also enable this https://github.com/langchain-ai/langchain/issues/8306
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| 1,824,020,139 |
I_kwDOIPDwls5suFar
| 8,347 |
Playwright Browser Tools: add screenshot feature
|
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[] | null | 1 | 2023-07-27T09:51:26 | 2023-11-02T16:14:07 | 2023-11-02T16:14:07 |
NONE
| null |
### Feature request
The Playwright toolkit is awesome. But it would be great to be able to see what the agent is doing. We could add a feature that takes screenshoots of the current state of the web page
### Motivation
It feels strange to use the toolkit but being blind, we cannot see what the agent is doing. The original idea is that by taking screenshoot we could use agents to perform UI testing.
### Your contribution
I could help writing a PR, but I’ll need help from the Playwright community. I also want to know what the community thinks of this feature.
|
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| 1,823,850,168 |
I_kwDOIPDwls5stb64
| 8,346 |
BabyAGI implementation with tools with multiple inputs (requiring Structured Tool Chat Agent)
|
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[] | null | 2 | 2023-07-27T08:14:36 | 2023-11-02T16:16:00 | 2023-11-02T16:15:59 |
NONE
| null |
### Discussed in https://github.com/langchain-ai/langchain/discussions/5410
<div type='discussions-op-text'>
<sup>Originally posted by **luca-git** May 29, 2023</sup>
My goal is to extend the tools used by baby AGI, more specifically to use at least the basic WriteFileTool() and ReadFileTool(). They use two inputs though, so I cannot stick with the vanilla ZeroShotAgent. So i resorted to using: AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION and replace it in the original code in the link below. Agent initialization is the only modification I've made. Can anyone kindly help me understand what I'm missing in the code in order to be able to leverage multiple input tools or provide guidance/resources?
https://python.langchain.com/en/latest/use_cases/agents/baby_agi_with_agent.html?highlight=babyagi%20with%20tools
```python
from langchain.agents import AgentType
from langchain.agents import initialize_agent
@classmethod
def from_llm(
cls, llm: BaseLLM, vectorstore: VectorStore, verbose: bool = False, **kwargs
) -> "BabyAGI":
"""Initialize the BabyAGI Controller."""
task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose)
task_prioritization_chain = TaskPrioritizationChain.from_llm(
llm, verbose=verbose
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = initialize_agent(agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,llm=llm_chain, tools=tool_names)
agent_executor = AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True
)
return cls(
task_creation_chain=task_creation_chain,
task_prioritization_chain=task_prioritization_chain,
execution_chain=agent_executor,
vectorstore=vectorstore,
**kwargs,
)
```
I get the error
```python
File ~\anaconda3\envs\aagi\lib\site-packages\langchain\agents\structured_chat\base.py:83 in create_prompt args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args)))
AttributeError: 'str' object has no attribute 'args'
```
</div>
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PR_kwDOIPDwls5WhBIq
| 8,345 |
fixed the bug #8343
|
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[] | null | 2 | 2023-07-27T07:43:38 | 2023-07-27T13:33:16 | 2023-07-27T13:33:15 |
CONTRIBUTOR
| null |
- Issue: #8343
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| 1,823,686,379 |
PR_kwDOIPDwls5Wgnxm
| 8,344 |
Fix broken `make` targets `format_diff` and `lint_diff`
|
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[] | null | 2 | 2023-07-27T06:13:06 | 2023-07-27T08:56:56 | 2023-07-27T08:56:55 |
CONTRIBUTOR
| null |
Since the refactoring into sub-projects `libs/langchain` and `libs/experimental`, the `make` targets `format_diff` and `lint_diff` do not work anymore when running `make` from these subdirectories. Reason is that
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- Tag maintainer: @baskaryan
|
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| 1,823,677,777 |
I_kwDOIPDwls5ssx1R
| 8,343 |
GPTCache is getting created multiple times for the same llm_string
|
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[] | null | 1 | 2023-07-27T06:04:49 | 2023-07-27T14:47:28 | 2023-07-27T14:47:28 |
CONTRIBUTOR
| null |
### System Info
gptcache==0.1.37
langchain==0.0.240
python 3.8
### Who can help?
_No response_
### 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
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from gptcache import Cache
from gptcache.adapter.api import init_similar_cache
from langchain.cache import GPTCache
# Every time llm updates the cache, it is creating a new cache object for the existing llm_string
def init_gptcache(cache_obj: Cache, llm_str: str):
init_similar_cache(cache_obj=cache_obj, data_dir=f"similar_cache_{llm_str}")
langchain.llm_cache = GPTCache(init_gptcache)
llm = OpenAI(model_name="text-davinci-002", temperature=0.2)
llm.predict("tell me a joke")
```
### Expected behavior
It should not recreate new GPTCache object for the same llm_string.
|
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| 1,823,673,129 |
I_kwDOIPDwls5sswsp
| 8,342 |
Using URL-PlayWriter/PlayWright Browser Toolkit in Jupyter Notebook
|
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closed
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[] | null | 5 | 2023-07-27T05:59:45 | 2023-11-06T16:06:19 | 2023-11-06T16:06:18 |
NONE
| null |
### System Info
While using both URL-Playwriter or PlayWright Browser Toolkit I am getting this error.
The solution I used:
1. I used asyncio.create_task
2. I used nest_asyncio.apply
but the error is the same. Please help if someone is also getting the same error and found some solution
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
### Expected behavior
This code needs to return the documents of the html content, but I am getting error.
|
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| 1,823,631,962 |
PR_kwDOIPDwls5WgcBa
| 8,341 |
Fix exception caused by restrictions in OWL
|
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[] | null | 3 | 2023-07-27T05:20:26 | 2023-07-27T23:51:32 | 2023-07-27T23:51:32 |
CONTRIBUTOR
| null |
Description: Fix exception caused by restrictions in OWL
Issue: #8331
Dependencies: none
Maintainer: @baskaryan
|
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| 1,823,619,378 |
PR_kwDOIPDwls5WgZWc
| 8,340 |
Fix typo in Etherscan.ipynb
|
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[] | null | 2 | 2023-07-27T05:05:12 | 2023-07-27T08:57:19 | 2023-07-27T08:57:19 |
CONTRIBUTOR
| null |
specifc -> specific
<!-- Thank you for contributing to LangChain!
Replace this 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 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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| 1,823,591,946 |
PR_kwDOIPDwls5WgTnL
| 8,339 |
bump experimental to 005
|
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[] | null | 1 | 2023-07-27T04:30:38 | 2023-07-27T04:46:29 | 2023-07-27T04:46:28 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5ssV69
| 8,338 |
Issue: ModuleNotFoundError: No module named 'playwright'
|
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[] | null | 4 | 2023-07-27T03:55:02 | 2023-08-11T06:53:07 | 2023-07-27T05:08:53 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I encountered the issue while reproducing the section related to **[Structured tool chat](https://python.langchain.com/docs/modules/agents/agent_types/structured_chat)** in the documentation.
## This is the code (from docs)
```python
from langchain.tools.playwright.utils import create_async_playwright_browser
...
async_browser = create_async_playwright_browser() # raise error here
...
```
## And this is the source code
```python
# ...\site-packages\langchain\tools\playwright\utils.py
def create_async_playwright_browser(headless: bool = True) -> AsyncBrowser:
"""
Create an async playwright browser.
Args:
headless: Whether to run the browser in headless mode. Defaults to True.
Returns:
AsyncBrowser: The playwright browser.
"""
from playwright.async_api import async_playwright # raise error here
browser = run_async(async_playwright().start())
return run_async(browser.chromium.launch(headless=headless))
```
## The error info
```
Traceback (most recent call last):
File "...\08_structured_tool_chat.py", line 16, in <module>
async_browser = create_async_playwright_browser()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "...\site-packages\langchain\tools\playwright\utils.py", line 63, in create_async_playwright_browser
from playwright.async_api import async_playwright
ModuleNotFoundError: No module named 'playwright'
```
---
I'm confused.
就有点迷@_@
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5WgKci
| 8,337 |
Add support custom http headers in AzureMLOnlineEndpoint
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[] | null | 4 | 2023-07-27T03:32:18 | 2023-08-01T03:14:27 | 2023-08-01T02:57:22 |
NONE
| null |
Description:
support adding custom http headers when use AzureMLOnlineEndpoint through ContentFormatterBase
Issue:
when using AzureMLOnlineEndpoint through ContentFormatterBase, I am unable to add any HTTP request header elements that I want.
Dependencies:
no dependencies required
Details:
1. Add a dictionary-type member variable named 'headers' to class ContentFormatterBase
2. Pass the 'headers' of the instance object of "ContentFormatterBase" to the "client" member in class AzureMLOnlineEndpoint
Possible tag maintainer：
@baskaryan @hwchase17 @agola11
Thanks!
|
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PR_kwDOIPDwls5WgHk5
| 8,336 |
Update tagging.ipynb
|
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[] | null | 2 | 2023-07-27T03:12:33 | 2023-07-27T23:54:31 | 2023-07-27T23:54:31 |
NONE
| null |
for langchain version 0.0.240 the create_tagging_chain and create_tagging_chain_pydantic will come under langchain.chains.openai_functions.tagging.
<!-- Thank you for contributing to LangChain!
Replace this 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 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_kwDOIPDwls5WgAYA
| 8,335 |
support adding custom http headers when use AzureMLOnlineEndpoint through ContentFormatterBase
|
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[] | null | 2 | 2023-07-27T02:25:20 | 2023-07-27T02:29:45 | 2023-07-27T02:28:43 |
NONE
| null |
Description: support adding custom http headers when use AzureMLOnlineEndpoint through ContentFormatterBase
Issue: hen using AzureMLOnlineEndpoint through ContentFormatterBase, I am unable to add any HTTP request header elements that I want.
Dependencies: no dependencies required
Details:
1. Add a dictionary-type member variable named 'headers' to class ContentFormatterBase
2. Pass the 'headers' of the instance object of "ContentFormatterBase" to the "client" member in class AzureMLOnlineEndpoint
Thank you for your review!
|
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| 1,823,486,705 |
I_kwDOIPDwls5ssDLx
| 8,334 |
Error finding in multiple pdfs
|
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[] | null | 2 | 2023-07-27T02:05:37 | 2023-11-02T16:05:14 | 2023-11-02T16:05:13 |
NONE
| null |
### Issue you'd like to raise.
When I have a single PDF file it works very well, when I have several files it does not find the same question as if it did when there was only one pdf file, I have the following code
store = PGVector(
collection_name=COLLECTION_NAME,
connection_string=CONNECTION_STRING,
embedding_function=embeddings,
)
pdf_qa = ConversationalRetrievalChain.from_llm(
ChatOpenAI(temperature=0.9, model_name="gpt-3.5-turbo"),
store.as_retriever(search_kwargs={'k': 2}),
return_source_documents=True,
verbose=False
)
When I increase the value of search_kwargs to 7 it works, because I think it takes a bigger chunk and finds the answer.
Please help
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5Wf7Sl
| 8,333 |
Fix notebook that 'cannot convert' via nbdoc_build
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[] | null | 1 | 2023-07-27T01:54:17 | 2023-07-27T01:54:25 | 2023-07-27T01:54:24 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5sr_Ja
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Issue: Max CSV Size
|
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[] | null | 2 | 2023-07-27T01:49:19 | 2023-11-02T16:05:40 | 2023-11-02T16:05:39 |
NONE
| null |
### Issue you'd like to raise.
I have a csv file I would like to interact with but when I start using it says:
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised APIError: OpenAI API returned an empty embedding.
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised APIError: OpenAI API returned an empty embedding.
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised APIError: OpenAI API returned an empty embedding.
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 8.0 seconds as it raised APIError: OpenAI API returned an empty embedding.
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 10.0 seconds as it raised APIError: OpenAI API returned an empty embedding.
Do you know the max size for a csv file or for any file for that matter?
My file is 12 MB.
Thanks
### Suggestion:
_No response_
|
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I_kwDOIPDwls5sr4JU
| 8,331 |
RDFGraph throws exception when ontology contains blank nodes
|
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[] | null | 1 | 2023-07-27T01:25:11 | 2023-08-09T07:44:41 | 2023-08-09T07:44:41 |
CONTRIBUTOR
| null |
### System Info
langchain version=0.0.237
python version = 3.11.4
### Who can help?
@hwchase17
The RDFGraph class appears to throw an exception when an OWL ontology contains a Restriction.
For example, a Turtle ontology contains the following restriction causes the error "Unexpected IRI 'n16c4af590.........", contains neither '#' nor '/'
```
:Topping rdf:type owl:Class ;
rdfs:subClassOf :Object ,
[ rdf:type owl:Restriction ;
owl:onProperty :hasTopping ;
owl:someValuesFrom :Topping
] .
```
Theis exception occurs in the code:
```
graph = RdfGraph(
source_file="example.ttl",
serialization="ttl",
standard="owl"
)
```
This appears to happen because the rdflib library inserts necessary blank nodes when it reads an ontology.
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The steps to reproduce are:
1. Create an OWL ontology called example.ttl with the following:
``` turtle
@prefix : <http://example.org/test-ontology#> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix xml: <http://www.w3.org/XML/1998/namespace> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
<http://example.org/test-ontology> rdf:type owl:Ontology .
:hasTopping rdf:type owl:ObjectProperty .
:Object rdf:type owl:Class .
:Pizza rdf:type owl:Class ;
rdfs:subClassOf :Object .
:Topping rdf:type owl:Class ;
rdfs:subClassOf :Object ,
[ rdf:type owl:Restriction ;
owl:onProperty :hasTopping ;
owl:someValuesFrom :Topping
] .
:pizza_001 rdf:type owl:NamedIndividual ,
:Pizza ;
:hasTopping :topping_001 ,
:topping_002 ,
:topping_003 ;
rdfs:label "margharita pizza" .
:pizza_002 rdf:type owl:NamedIndividual ,
:Pizza ;
:hasTopping :topping_001 ,
:topping_004 ;
rdfs:label "cheese and bacon pizza" .
:topping_001 rdf:type owl:NamedIndividual ,
:Topping ;
rdfs:label "cheese" .
:topping_002 rdf:type owl:NamedIndividual ,
:Topping ;
rdfs:label "tomato sauce" .
:topping_003 rdf:type owl:NamedIndividual ,
:Topping ;
rdfs:label "tomato" .
:topping_004 rdf:type owl:NamedIndividual ,
:Topping ;
:hasTopping :topping_002 ;
rdfs:label "bacon" .
```
2. Run the following code:
``` python
from langchain.chat_models import ChatOpenAI
from langchain.chains import GraphSparqlQAChain
from langchain.graphs import RdfGraph
graph = RdfGraph(
source_file="example.ttl",
serialization="ttl",
standard="owl"
)
```
### Expected behavior
The RDFGraph object is created successfully.
|
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PR_kwDOIPDwls5WfsUF
| 8,330 |
Upgrade to Pydantic v2
|
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[] | null | 5 | 2023-07-27T00:38:18 | 2023-08-24T17:25:44 | 2023-08-24T17:25:43 |
NONE
| null |
This PR should encompass (most of) the changes needed to support [Pydantic v2](https://docs.pydantic.dev/2.0/blog/pydantic-v2-final/)
Relevant Issue: https://github.com/langchain-ai/langchain/issues/6841
Dependency wise, had to bump Pydantic (ofc) and `autodoc-pydantic` which has a yanked 2.0.0 pypi deploy which is being used as a Release Candidate for pydantic v2 support.
Currently, the following deps are preventing poetry from resolving a lock file:
- zep-python (issue opened here - https://github.com/getzep/zep-python/issues/58)
- octoai-sdk (closed source xdd)
- chromadb (issue opened here - https://github.com/chroma-core/chroma/issues/893)
- spacy (issue here - https://github.com/explosion/spaCy/issues/12611)
- steamship (issue opened here - https://github.com/steamship-core/python-client/issues/503)
- langsmith (closed source xdd)
@baskaryan
Opened as a draft because until poetry resolves the lock file and downstream packages get resolved, it shouldn't be merged.
Also since PR guidelines suggests, twitter handle is SexualRhino_
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I_kwDOIPDwls5sruWZ
| 8,329 |
Async Memory
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[] | null | 1 | 2023-07-27T00:31:00 | 2023-11-02T16:05:19 | 2023-11-02T16:05:18 |
NONE
| null |
### Feature request
I propose to make BaseMemoryAsync, BaseChatMessageHistoryAsync, SummarizerMixinAsync, BaseChatMemoryAsync and add support for async save_context to the main Chain class. For web development, everything is asynchronous, and this is critical.
### Motivation
I've faced a problem when writing a chatbot using the asynchronous aiogram library, I can't use Memory asynchronously, so I want to add additional classes that support asynchronous memory work. I am using asynchronous sqlalchemy and cannot work with memory. Besides, I think this is a really critical feature.
### Your contribution
I tried to implement this, but it turned out to be too difficult, so I'm asking you very much, give me and other people the opportunity to work with memory asynchronously.
|
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Issue: How to extract quotes from a large file
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[] | null | 2 | 2023-07-26T23:19:52 | 2023-11-01T16:04:50 | 2023-11-01T16:04:49 |
NONE
| null |
### Issue you'd like to raise.
This might be a general issue with LLM tools but I wanted to pose it here since this community is very active.
When you feed a huge CSV file with thousands of comments, you are trying to summarize them but also extract some quotes.
When feeding this file into Langchain, it will summarize just fine. However, the quotes will get lost when reducing the result. The quotes will end up being summarized and lose their data integrity
Is there a way around this issue? Can this solved by tweaking the system prompt?
### Suggestion:
_No response_
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SagemakerEndpoint LLM does not support InvokeEndpoint CustomAttributes needed for Llama-V2
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[] | null | 8 | 2023-07-26T22:59:57 | 2023-11-15T00:04:44 | null |
NONE
| null |
### Feature request
Add support for passing CustomAttributes to the `SagemakerEnpiontLLM` wrapper around `client.invoke_enpoint()`
With the release of LLama-v2 on Sagemaker Jumpstart, we are required to pass EULA=TRUE # END USER LICENSE AGREEMENT
Endstate
```python
class SagemakerEndpoint(LLM):
...
...
custom_attributes: Dict
custom_attributes =
# send request
try:
response = self.client.invoke_endpoint(
EndpointName=self.endpoint_name,
Body=body,
ContentType=content_type,
Accept=accepts,
Custom_Attributes=self.custom_attributes
**_endpoint_kwargs,
)
except Exception as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
```
### Motivation
Lanchain is incompatible with llama-v2 model using SagemakerEnpointLLM in langchain
without the fix, Lanchain throws an error
`Unknown parameter in input: "accept_eula", must be one of: EndpointName, Body, ContentType, Accept, CustomAttributes, TargetModel, TargetVariant, TargetContainerHostname, InferenceId, EnableExplanations`
### Your contribution
Happy to create a PR
|
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I_kwDOIPDwls5sra-i
| 8,325 |
DOC: How to use OPENAI_MULTI_FUNCTIONS with custom prompt?
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[] | null | 2 | 2023-07-26T22:58:19 | 2023-09-08T17:18:24 | 2023-09-08T17:18:23 |
NONE
| null |
### Issue with current documentation:
I am converting from CONVERSATIONAL_REACT agent to Functions and having a tough time figuring out how to customize the prompt. Memory was not obvious, but worked as expected. However, adding additional inputs does not:
``` Python
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=False)
template = """
The Human is named {user_name}. Always answer questions by directing it at the Human by name.
For example, if the Human asks "What is the weather like?", you should answer "It is sunny, {user_name}." instead of just "It is sunny.".
History of the conversation:
{chat_history}
"""
extra_prompt = SystemMessagePromptTemplate.from_template(
template=template,
input_variables=["chat_history", "user_name"],
)
agent_args = {
"extra_prompt_messages": [extra_prompt],
}
mrkl_chat = initialize_agent(
tools,
llm,
agent=AgentType.OPENAI_MULTI_FUNCTIONS,
memory=memory,
agent_kwargs=agent_args,
verbose=True,
)
```
This works, but trying to run using
``` Python
mrkl_chat.run(input="What is fity six times twenty seven?", user_name="John")
```
executes the function call correctly, but then errors:
```
ValueError: One input key expected got ['input', 'user_name']
```
How do I pass in the extra inputs?
### Idea or request for content:
Please extend the examples for the FUNCTIONS agent to include customizing the prompt.
|
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PR_kwDOIPDwls5WfIoj
| 8,324 |
Add an example for azure ml managed feature store
|
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[] | null | 2 | 2023-07-26T21:55:24 | 2023-07-27T23:56:07 | 2023-07-27T23:56:07 |
CONTRIBUTOR
| null |
We are adding an example of how one can connect to azure ml managed feature store and use such a prompt template in a llm chain. @baskaryan
|
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PR_kwDOIPDwls5WfHOH
| 8,323 |
Async memory
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[] | null | 3 | 2023-07-26T21:49:35 | 2023-11-10T22:55:43 | 2023-11-10T22:55:43 |
NONE
| null |
I have faced a problem when writing a chatbot using the asynchronous aiogram library, I cannot use Memory asynchronously, so I want to add additional classes that support asynchronous work with Memory. I created several classes that allow you to call functions asynchronously BaseMemoryAsync, BaseChatMessageHistoryAsync, SummarizerMixinAsync, BaseChatMemoryAsync. Using them, I will be able to make asynchronous queries to the database and summarize the chat history.
@agola11, @hwchase17
|
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PR_kwDOIPDwls5WfEaN
| 8,322 |
Fireworks integration
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[] | null | 3 | 2023-07-26T21:36:00 | 2023-08-12T20:13:49 | 2023-08-02T04:17:26 |
CONTRIBUTOR
| null |
Description - Integrates Fireworks within Langchain LLMs to allow users to use Fireworks models with Langchain, mainly for summarization.
Issue - Not applicable
Dependencies - None
Tag maintainer - @rlancemartin
|
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PR_kwDOIPDwls5WevOJ
| 8,321 |
added unit tests for mrkl output_parser.py
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[] | null | 2 | 2023-07-26T20:38:14 | 2023-07-27T20:46:07 | 2023-07-27T20:46:07 |
CONTRIBUTOR
| null |
- Description: added unit tests for mrkl output_parser.py,
- Tag maintainer: @hinthornw
- Twitter handle: EdenEmarco177
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added error attribute to textloader function
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NONE
| null |
Description - FIxes the issue of Unicode decode encoding problem in a file if the file has decode byte 0x9d or any other such as 0x90
Issue - Not applicable
Dependencies - None
Tag maintainer - @rlancemartin , @eyurtsev
Twitter handle- @carpx05
|
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| 1,823,113,323 |
I_kwDOIPDwls5sqoBr
| 8,319 |
Whisper with word-level timestamps
|
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[] | null | 1 | 2023-07-26T20:28:13 | 2023-11-01T16:04:55 | 2023-11-01T16:04:54 |
NONE
| null |
### Feature request
Hi,
this repo on huggingface implements word-level timestamps into locally run whisper models: https://huggingface.co/spaces/Matthijs/whisper_word_timestamps/tree/main
I have created a simple modification of this for generating simple CSVs with words and timestamps, a functionality i'd love to see in langchain too: https://github.com/SimonB97/whisper-word-ts-csv (currently only running with cuda support, but should be modifiable to dynamically support both cpu and gpu).
### Motivation
It would be amazing to be able to generate word-level timestamps together with transcriptions, for example to be able to pin-point when some information in a youtube video was given.
### Your contribution
I'm unsure how to implement this in langchain, would be willing to help though!
|
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PR_kwDOIPDwls5Weq5V
| 8,318 |
update api ref style
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PR_kwDOIPDwls5WenyU
| 8,317 |
Support using RunnableMap directly
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[] | null | 1 | 2023-07-26T20:11:32 | 2023-07-27T16:24:30 | 2023-07-27T16:24:29 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this 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 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_kwDOIPDwls5WekCf
| 8,316 |
Fix entity extraction not to include \nEND OF EXAMPLE in each entity
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[] | null | 4 | 2023-07-26T19:58:06 | 2023-09-19T22:39:50 | null |
NONE
| null |
Description: Revise the default entity extraction template so that each extracted entity doesn't have unnecessary part '\nEND OF EXAMPLE' at the end.
Issue: Run an example in the Entity memory documentation. When an entity is extracted, it has unnecessary part '\nEND OF EXAMPLE' at the end, e.g., 'Sam\nEND OF EXAMPLE'.
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I_kwDOIPDwls5sqQdY
| 8,315 |
In the AmazonKendraRetriever, allow selection of which API to use: query or retrieve (default)
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[] | null | 2 | 2023-07-26T19:22:38 | 2023-11-01T16:05:00 | 2023-11-01T16:04:59 |
CONTRIBUTOR
| null |
### Feature request
According to the [Amazon Kendra documentation](https://docs.aws.amazon.com/kendra/latest/APIReference/API_Query.html), a query response contains three types of results:
- **Relevant suggested answers.** The answers can be either a text excerpt or table excerpt. The answer can be highlighted in the excerpt.
- **Matching FAQs** or questions-answer from your FAQ file.
- Relevant documents. This result type includes an excerpt of the document with the document title. The searched terms can be highlighted in the excerpt.
Therefore, the AmazonKendraRetriever should be flexible to allow a retrieval pattern that combines the query API with the retrieve API or not.
For instance, the application could:
- use the query API to check for relevant answers or matching FAQs before calling another retriever with the retrieve API;
- explore the [Merger Retriever](https://python.langchain.com/docs/integrations/retrievers/merger_retriever) to combine the results of the retrieve and query APIs.
### Motivation
Currently, the AmazonKendraRetrieve uses the retrieve API by default and fall backs to the query API in case the retrieve API results are empty.
### Your contribution
I could submit a PR.
|
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bump 244
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PR_kwDOIPDwls5WeNz5
| 8,313 |
Fixes #8310 Fix maximum recursion depth exceeded error
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[] | null | 1 | 2023-07-26T18:49:57 | 2023-07-26T19:15:37 | 2023-07-26T19:15:37 |
CONTRIBUTOR
| null |
ElasticsearchVectorStore.as_retriever() method is returning
`RecursionError: maximum recursion depth exceeded`
because of incorrect field reference in
`embeddings()` method
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Fix RecursionError because of a typo
- Issue: the issue #8310
- Dependencies: None,
- Tag maintainer: @eyurtsev
- Twitter handle: bpatel
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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Fixes #8310 Fix maximum recursion depth exceeded error
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[] | null | 4 | 2023-07-26T18:36:30 | 2023-07-26T18:50:34 | 2023-07-26T18:45:00 |
CONTRIBUTOR
| null |
ElasticsearchVectorStore.as_retriever() method is returning RecursionError: maximum recursion depth exceeded because of incorrect field reference in
embeddings() method
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Fix RecursionError
- Issue: the issue #8310
- Dependencies: any dependencies required for this change,
- Tag maintainer: @eyurtsev
- Twitter handle: bpatel
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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| 8,311 |
docstrings cleanup
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[] | null | 2 | 2023-07-26T18:35:23 | 2023-07-26T22:03:51 | 2023-07-26T21:13:10 |
COLLABORATOR
| null |
- added missed docstrings
- changed docstrings into consistent format
@baskaryan
|
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I_kwDOIPDwls5sqBG6
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ElasticVectorSearch().as_retriever() throwingRecursionError: maximum recursion depth exceeded
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[] | null | 1 | 2023-07-26T18:35:13 | 2023-07-26T19:15:41 | 2023-07-26T19:15:41 |
CONTRIBUTOR
| null |
### System Info
langchain==0.0.243
```
@property
def embeddings(self) -> Embeddings:
return self.embeddings
```
is referencing itself instead of `self.embedding` resulting in Recursion error.
```
return self.embeddings
^^^^^^^^^^^^^^^
[Previous line repeated 993 more times]
RecursionError: maximum recursion depth exceeded
```
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
vectordb = ElasticVectorSearch(
elasticsearch_url=elasticsearch_url,
embedding=OpenAIEmbeddings()
index_name="some-index"
)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
```
### Expected behavior
Should not get RecursionError
|
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PR_kwDOIPDwls5WeHuJ
| 8,309 |
Clean queries prior to search
|
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[] | null | 1 | 2023-07-26T18:28:00 | 2023-07-26T19:07:47 | 2023-07-26T18:48:28 |
COLLABORATOR
| null |
With some search tools, we see no results returned if the query is formatted as a string following a number.
This works -
`query="1. What is the working mechanism of LLM Powered Autonomous Agents?"`
```
QUERY
1. What is the working mechanism of LLM Powered Autonomous Agents?
RESULT
[{'title': "LLM Powered Autonomous Agents | Lil'Log", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': "Jun 23, 2023 ... Fig. 1. Overview of a LLM-powered autonomous agent system. ... Then reflections are added into the agent's working memory, up to three,\xa0..."}]
[{'title': "LLM Powered Autonomous Agents | Lil'Log", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': "Jun 23, 2023 ... Fig. 1. Overview of a LLM-powered autonomous agent system. ... Then reflections are added into the agent's working memory, up to three,\xa0..."}]
```
This fails -
`query='1. "What is the working mechanism of LLM Powered Autonomous Agents?"'`
```
QUERY
1. "What is the working mechanism of LLM Powered Autonomous Agents?"
RESULT
[{'Result': 'No good Google Search Result was found'}]
[{'Result': 'No good Google Search Result was found'}]
```
Local testing w/ Streamlit:
|
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ChatPromptTemplate: Update doc-strings, update from_role_strings behavior
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[] | null | 1 | 2023-07-26T18:23:30 | 2023-07-26T19:02:37 | 2023-07-26T19:02:36 |
COLLABORATOR
| null |
* Update doc-strings in ChatPromptTemplate
* Update from_role_strings classmethod to use well known roles
|
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| 1,822,932,353 |
I_kwDOIPDwls5sp72B
| 8,307 |
ImportError: cannot import name 'ApifyWrapper' from 'langchain.utilities'
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[] | null | 11 | 2023-07-26T18:18:22 | 2023-12-08T16:05:50 | 2023-12-08T16:05:49 |
NONE
| null |
### System Info
Hi All,
I tried to run Apify tutorial and I ran on the issue of ImportError: cannot import name 'ApifyWrapper' from 'langchain.utilities'. I checked the Utilities library under utilities/__init__.py and I couldn't find anything under the Generic integrations with third-party systems and packages.
Any thoughts or support?
### Who can help?
@hwchase17, @agola
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
import os
openai.api_key = os.environ["OPEN_API_KEY"]
os.environ["APIFY_API_TOKEN"] = "apify_api_qNa00bcYGUYFwIZltWiOuhskmer7E61VE6GN"
apify = ApifyWrapper()
loader = apify.call_actor(
actor_id="apify/website-content-crawler",
run_input={"startUrls": [{"url": "https://python.langchain.com/en/latest/"}]},
dataset_mapping_function=lambda item: Document(
page_content=item["text"] or "", metadata={"source": item["url"]}
),
)
index = VectorstoreIndexCreator().from_loaders([loader])
query = "What is LangChain?"
result = index.query_with_sources(query)
print(result["answer"])
print(result["sources"])
### Expected behavior
LangChain is a standard interface through which you can interact with a variety of large language models (LLMs). It provides modules that can be used to build language model applications, and it also provides chains and agents with memory capabilities.
https://python.langchain.com/en/latest/modules/models/llms.html, https://python.langchain.com/en/latest/getting_started/getting_started.html
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| 1,822,926,870 |
I_kwDOIPDwls5sp6gW
| 8,306 |
Make a return_direct of a tool's output under certain conditions
|
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[] | null | 3 | 2023-07-26T18:13:52 | 2023-10-25T17:08:45 | null |
CONTRIBUTOR
| null |
### Feature request
Possibility to make a direct return of a tool's output on certain cases.
### Motivation
Under specific circumstances, it would be extremely beneficial to be able to return a tool's output directly to the user. For instance, this feature would be helpful when using a search tool. If a search made no matches, a response can be directly sent to the user. Without being able to make a direct return, the tool's output is sent to the LLM, making the LLM prone to hallucinate the final answer.
### Your contribution
I am not sure how this could be implemented, but would gladly help working on any ideas.
|
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| 1,822,884,027 |
PR_kwDOIPDwls5Wd6jl
| 8,305 |
fix geopandas link
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[] | null | 1 | 2023-07-26T17:46:19 | 2023-07-26T18:30:18 | 2023-07-26T18:30:17 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5sprac
| 8,304 |
VertexAI using `text-bison` returns no output
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[] | null | 3 | 2023-07-26T17:33:31 | 2023-12-20T22:59:35 | 2023-11-15T16:07:14 |
CONTRIBUTOR
| null |
### System Info
Pop!_OS 22.04 LTS
Python 3.11.4
Langchain v0.0.242
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`code-bison` works fine, `text-bison` returns no output. I've got full `Vertex AI User` perms, and every other model, including the chat models work fine in my config.
```
>>> from langchain.llms.vertexai import VertexAI
>>> llm = VertexAI(model_name="code-bison")
>>> llm("say hello")
'Hello! How are you doing today?'
>>> llm = VertexAI(model_name="text-bison")
>>> llm("say hello")
''
>>>
```
### Expected behavior
`text-bison` should return content.
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PR_kwDOIPDwls5Wdz9R
| 8,303 |
fix missing _identifying_params() in _VertexAICommon
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[] | null | 3 | 2023-07-26T17:26:37 | 2023-09-02T00:16:21 | 2023-07-27T23:59:10 |
CONTRIBUTOR
| null |
Full set of params are missing from Vertex* LLMs when `dict()` method is called.
```
>>> from langchain.chat_models.vertexai import ChatVertexAI
>>> from langchain.llms.vertexai import VertexAI
>>> chat_llm = ChatVertexAI()
l>>> llm = VertexAI()
>>> chat_llm.dict()
{'_type': 'vertexai'}
>>> llm.dict()
{'_type': 'vertexai'}
```
This PR just uses the same mechanism used elsewhere to expose the full params.
Since `_identifying_params()` is on the `_VertexAICommon` class, it should cover the chat and non-chat cases.
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PR_kwDOIPDwls5WdvoF
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fixes reference to code
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[] | null | 2 | 2023-07-26T17:12:09 | 2023-07-26T18:42:06 | 2023-07-26T18:42:06 |
CONTRIBUTOR
| null |
- Description: corrects typo
- Issue: typo
- Dependencies: none
- Tag maintainer: @hinthornw
- Twitter handle: not active
|
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| 1,822,829,475 |
PR_kwDOIPDwls5Wdusl
| 8,301 |
correction to reference to code
|
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[] | null | 2 | 2023-07-26T17:09:02 | 2023-07-26T17:33:19 | 2023-07-26T17:33:19 |
CONTRIBUTOR
| null |
- Description: fixes typo referencing code
- Issue: typo
- Dependencies: none
- Tag maintainer: @hinthornw
- Twitter handle: not active
|
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PR_kwDOIPDwls5WdroB
| 8,300 |
Bug Fix: AzureChatOpenAI streaming with function calls
|
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[] | null | 2 | 2023-07-26T16:58:58 | 2023-07-26T17:11:51 | 2023-07-26T17:11:51 |
CONTRIBUTOR
| null |
- Description: During streaming, the first chunk may only contain the name of an OpenAI function and not any arguments. In this case, the current code presumes there is a streaming response and tries to append to it, but gets a KeyError. This fixes that case by checking if the arguments key exists, and if not, creates a new entry instead of appending.
- Issue: Related to #6462
- Tag maintainer: @hwchase17, @baskaryan
Sample Code:
```python
llm = AzureChatOpenAI(
deployment_name=deployment_name,
model_name=model_name,
streaming=True
)
tools = [PythonREPLTool()]
callbacks = [StreamingStdOutCallbackHandler()]
agent = initialize_agent(
tools=tools,
llm=llm,
agent=AgentType.OPENAI_FUNCTIONS,
callbacks=callbacks
)
agent('Run some python code to test your interpreter')
```
Previous Result:
```
File ...langchain/chat_models/openai.py:344, in ChatOpenAI._generate(self, messages, stop, run_manager, **kwargs)
342 function_call = _function_call
343 else:
--> 344 function_call["arguments"] += _function_call["arguments"]
345 if run_manager:
346 run_manager.on_llm_new_token(token)
KeyError: 'arguments'
```
New Result:
```python
{'input': 'Run some python code to test your interpreter',
'output': "The Python code `print('Hello, World!')` has been executed successfully, and the output `Hello, World!` has been printed."}
```
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| 1,822,776,944 |
I_kwDOIPDwls5spV5w
| 8,298 |
installing pip install langchain[all] installs the old version of langchain 0.0.39
|
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[] | null | 3 | 2023-07-26T16:32:22 | 2023-08-07T18:29:02 | 2023-08-07T18:29:02 |
NONE
| null |
### System Info
When i install for the first time using this command
```
pip install 'langchain[all]'
```
It installs the older version `langchain 0.0.39`
### Who can help?
_No response_
### 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
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. pip install 'langchain[all]'
### Expected behavior
It should install the latest version of langchain
|
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PR_kwDOIPDwls5Wddwm
| 8,297 |
Refactored `load`
|
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[] | null | 3 | 2023-07-26T16:14:04 | 2023-09-01T15:44:30 | 2023-09-01T15:44:30 |
COLLABORATOR
| null |
Refactored `load`. The same as https://github.com/langchain-ai/langchain/pull/7961 #8098 #8099
`load` is in the root code folder. This creates the `langchain.load: Load` group on the API Reference navigation ToC, on the same level as Chains and Agents which is incorrect.
Refactoring:
- copied `load/serializable.py`, `load/load.py`, `load/dump.py` files into `utils/`
- added the backwards compatibility ref in a new `langchain/load.py`
- updated imports to `load` objects
- applied a standard comment, """DEPRECATED: Kept for backwards compatibility.""" to all "deprecated" files in root folder
@baskaryan
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PR_kwDOIPDwls5Wcs_J
| 8,296 |
fix: ElasticVectorSearch.from_documents failed #8293
|
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[] | null | 3 | 2023-07-26T14:19:34 | 2023-07-26T17:34:03 | 2023-07-26T17:33:53 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: fix ElasticVectorSearch.from_documents with elasticsearch_url param,
- Issue: ElasticVectorSearch.from_documents failed #8293 # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: @rlancemartin, @eyurtsev,
- 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_kwDOIPDwls5Wcqxz
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Generic LLM wrapper to support chat model interface with configurable chat prompt format
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CONTRIBUTOR
| null |
## Update 2023-09-08
This PR now supports further models in addition to Lllama-2 chat models. See [this comment](#issuecomment-1668988543) for further details. The title of this PR has been updated accordingly.
## Original PR description
This PR adds a generic `Llama2Chat` model, a wrapper for LLMs able to serve Llama-2 chat models (like `LlamaCPP`, `HuggingFaceTextGenInference`, ...). It implements `BaseChatModel`, converts a list of chat messages into the [required Llama-2 chat prompt format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and forwards the formatted prompt as `str` to the wrapped `LLM`. Usage example:
```python
# uses a locally hosted Llama2 chat model
llm = HuggingFaceTextGenInference(
inference_server_url="http://127.0.0.1:8080/",
max_new_tokens=512,
top_k=50,
temperature=0.1,
repetition_penalty=1.03,
)
# Wrap llm to support Llama2 chat prompt format.
# Resulting model is a chat model
model = Llama2Chat(llm=llm)
messages = [
SystemMessage(content="You are a helpful assistant."),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{text}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
chain = LLMChain(llm=model, prompt=prompt, memory=memory)
# use chat model in a conversation
# ...
```
Also part of this PR are tests and a demo notebook.
- Tag maintainer: @hwchase17
- Twitter handle: `@mrt1nz`
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PR_kwDOIPDwls5Wcplz
| 8,294 |
giving path to the copy command for *.toml files
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[] | null | 2 | 2023-07-26T14:12:12 | 2023-07-30T02:17:34 | 2023-07-26T17:37:03 |
CONTRIBUTOR
| null |
Description: in the .devcontainer, docker-compose build is currently failing due to the src paths in the COPY command. This change adds the full path to the pyproject.toml and poetry.toml to allow the build to run.
Issue:
You can see the issue if you try to build the dev docker image with:
```
cd .devcontainer
docker-compose build
```
Dependencies: none
Twitter handle: byronsalty
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I_kwDOIPDwls5soSfX
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ElasticVectorSearch.from_documents failed
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[] | null | 1 | 2023-07-26T14:08:01 | 2023-07-27T02:20:53 | 2023-07-27T02:20:53 |
CONTRIBUTOR
| null |
### System Info
python = 3.9
es = 8.9
langchain = 0.0.237
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
url = 'https://elastic:xxx@xxxx:8447'
docsearch = ElasticVectorSearch.from_documents(
split_docs,
embeddings,
elasticsearch_url=url,
index_name="my_index",
ssl_verify={
"verify_certs": True,
"basic_auth": ("elastic", "xxx"),
"ca_certs": "./data/xxxx"
},
)
```
then get error:
`__init__() got multiple values for argument 'elasticsearch_url'`
in: https://github.com/langchain-ai/langchain/blob/5c6dcb1960b717aaf70413ed0b467bffc4fc0be8/libs/langchain/langchain/vectorstores/elastic_vector_search.py#L298
add this then work:
```
del kwargs['elasticsearch_url']
```
### Expected behavior
no error
|
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| 8,292 |
DuckDuckGoSearch News Tool
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[] | null | 2 | 2023-07-26T13:29:57 | 2023-07-26T18:30:03 | 2023-07-26T18:30:02 |
CONTRIBUTOR
| null |
Description:
I wanted to use the DuckDuckGoSearch tool in an agent to let him get the latest news for a topic. DuckDuckGoSearch has already an implemented function for retrieving news articles. But there wasn't a tool to use it. I simply adapted the SearchResult class with an extra argument "backend". You can set it to "news" to only get news articles.
Furthermore, I added an example to the DuckDuckGo Notebook on how to further customize the results by using the DuckDuckGoSearchAPIWrapper.
Dependencies: no new dependencies
@hinthornw
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cannot clear memory when token limit exceeded
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NONE
| null |
### System Info
Langchain version - 0.0.208
openAI - 3.5-turbo
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
i have a chat built on top of OpenAI , i have memory of 1 in the conversation.
the problem is that sometimes when the prompt is very long i hit the token limit from openAI .
i tried to clean the conversation memory to allow next question to succeed with :
agent.memory.clear()
but it doesnt seem to work, and all subsequent queries are failing on openai.error.InvalidRequestError
this is my agent creation code :
chatgpt_chain = LLMChain(
llm=ChatOpenAI(temperature=0, model_name=settings.model_name), # type: ignore
prompt=prompt,
verbose=False,
# memory=ConversationBufferWindowMemory(k=1),
)
tools = [PythonAstREPLTool()]
custom_agent = LLMSingleActionAgent(
llm_chain=chatgpt_chain,
output_parser=my_output_parser,
stop=["\nObservation:"],
)
agent_memory = ConversationBufferWindowMemory(k=1)
return AgentExecutor.from_agent_and_tools(
agent=custom_agent,
tools=tools,
verbose=True,
memory=agent_memory,
)
### Expected behavior
i expected the memory to be clear and continue to work with the agent
|
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| 1,822,328,227 |
PR_kwDOIPDwls5WcCjv
| 8,290 |
Fix for code snippet in documentation
|
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[] | null | 2 | 2023-07-26T12:37:36 | 2023-07-26T18:31:08 | 2023-07-26T18:31:08 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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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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- Description: I fixed an issue in the code snippet related to the variable name and the evaluation of its length. The original code used the variable "docs," but the correct variable name is "docs_svm" after using the SVMRetriever.
- maintainer: @baskaryan
- Twitter handle: @iamreechi_
|
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PR_kwDOIPDwls5WcBNl
| 8,289 |
bump 243
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[] | null | 1 | 2023-07-26T12:34:05 | 2023-07-26T12:41:58 | 2023-07-26T12:41:57 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5Wb_HA
| 8,288 |
fix: overwrite stream for ChatOpenAI in runtime
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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-->
@hwchase17, @baskaryan
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| 1,822,298,506 |
I_kwDOIPDwls5snhGK
| 8,287 |
Issue: Performance of Agents or Chains dropping when used as routers in an Agent.
|
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[] | null | 2 | 2023-07-26T12:20:31 | 2023-11-01T16:05:04 | 2023-11-01T16:05:04 |
NONE
| null |
### Issue you'd like to raise.
I have defined several Chains (LLM Chains, RetrievalQA and ConversationalRelationChain) and have used an agent to route between them after I created for each their respective Tool.
I realised that when each chain was defined outside of the agent, their performance was amazing. But once you add them up as routes in the agent, their performance drops significantly.
The exact same questions that they manage to answer to outside of the routing system (agent) are answered incorrectly when used as a route in the Agent.
Is anyone else facing this issue or is it only me? Is it fixable?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5snfEm
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AttributeError: `Document` object has not attribute `replace`
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[] | null | 2 | 2023-07-26T12:15:13 | 2024-01-17T20:08:24 | 2023-08-05T10:38:05 |
NONE
| null |
### System Info
langchain==0.0.144
python3 == 3.11.4
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am running the following code:
```
from langchain.document_loaders import PyMuPDFLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
# Load the text document
loader = PyMuPDFLoader("path/to/file.pdf")
documents = loader.load()
# Split the document into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
# Generate the embeddings
embeddings = OpenAIEmbeddings()
embedded_docs = embeddings.embed_documents(docs)
# Initialize FAISS
db = FAISS.from_documents(embedded_docs, embeddings)
# Perform a similarity search between the embedding of the query and the embeddings of the documents
query = "Your query here"
similar_docs = db.similarity_search(query)
# Print the content of the most similar document
print(similar_docs[0].page_content)
```
The error occurs as follows:
```
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ /var/folders/q3/9988ccxx13q_x9yfdkv36x2r0000gn/T/ipykernel_22317/1552469458.py:1 in <module> │
│ │
│ [Errno 2] No such file or directory: │
│ '/var/folders/q3/9988ccxx13q_x9yfdkv36x2r0000gn/T/ipykernel_22317/1552469458.py' │
│ │
│ /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain/embedd │
│ ings/openai.py:275 in embed_documents │
│ │
│ 272 │ │ """ │
│ 273 │ │ # handle batches of large input text │
│ 274 │ │ if self.embedding_ctx_length > 0: │
│ ❱ 275 │ │ │ return self._get_len_safe_embeddings(texts, engine=self.document_model_name) │
│ 276 │ │ else: │
│ 277 │ │ │ results = [] │
│ 278 │ │ │ _chunk_size = chunk_size or self.chunk_size │
│ │
│ /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain/embedd │
│ ings/openai.py:209 in _get_len_safe_embeddings │
│ │
│ 206 │ │ │ encoding = tiktoken.model.encoding_for_model(self.document_model_name) │
│ 207 │ │ │ for i, text in enumerate(texts): │
│ 208 │ │ │ │ # replace newlines, which can negatively affect performance. │
│ ❱ 209 │ │ │ │ text = text.replace("\n", " ") │
│ 210 │ │ │ │ token = encoding.encode( │
│ 211 │ │ │ │ │ text, │
│ 212 │ │ │ │ │ allowed_special=self.allowed_special, │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
AttributeError: 'Document' object has no attribute 'replace'
```
### Expected behavior
Expected behaviour is that the variable `embedded_docs` should return a list of vector embeddings for each document.
|
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PR_kwDOIPDwls5WbnKi
| 8,285 |
add test for embedding distance evaluation
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[] | null | 3 | 2023-07-26T11:29:12 | 2023-07-26T21:52:49 | 2023-07-26T18:45:50 |
CONTRIBUTOR
| null |
Add tests for embedding distance evaluation
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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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.
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PR_kwDOIPDwls5WbhdI
| 8,284 |
Avoid re-computation of embedding in weaviate similarity search
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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PR_kwDOIPDwls5WbSJB
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support adding custom http headers when use AzureMLOnlineEndpoint through ContentFormatterBase
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NONE
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support adding custom http headers when use AzureMLOnlineEndpoint through ContentFormatterBase
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I_kwDOIPDwls5smrML
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Issue: OutputParserException when using SQL Agent
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NONE
| null |
### Issue you'd like to raise.
I'm using a SQL Agent that is connected to BigQuery to build a QA model. I get OutputParserException fairly often. The Agent returns the correct answer some times, but I have never got an answer when the option view_support=True in SQLDatabase.from_uri().
For some questions, the agent generates a SQL query and executes it. It gets the results of the query as an Observation, but runs into an OutputParserException. The last line of output is almost always 'Thought:' before the OutputParserException.
Here's the code that I'm running:
```
from google.cloud import bigquery
from sqlalchemy import *
from sqlalchemy.engine import create_engine
from sqlalchemy.schema import *
import os
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain import SQLDatabase
from langchain.agents import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.llms import VertexAI
from vertexai.preview.language_models import TextGenerationModel
service_account_file = "masked.json"
project = "masked_project_name"
dataset = "SAP_REPORTING"
sqlalchemy_url = f'bigquery://{project}/{dataset}?credentials_path={service_account_file}'
llm = VertexAI(temperature=0)
db_2 = SQLDatabase.from_uri(sqlalchemy_url, view_support=True)
toolkit_2 = SQLDatabaseToolkit(db=db_2, llm=llm, use_query_checker=True)
PREFIX = '''You are a SQL expert. You have access to a BigQuery database.
Identify which tables can be used to answer the user's question and write and execute a SQL query accordingly.
'''
FORMAT_INSTRUCTIONS = """Please use the following format:
'''
Thought:
Action: the action to take should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
'''
Provide the output in the Final Answer."""
SUFFIX = '''
Begin!
Previous conversation history:
{chat_history}
Instructions: {input}
{agent_scratchpad}
'''
agent_executor_2 = create_sql_agent(llm=llm,
toolkit=toolkit_2,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
# top_k=1000,
handle_parsing_errors=True,
agent_kwargs={
'prefix': PREFIX,
'format_instructions': FORMAT_INSTRUCTIONS,
'suffix': SUFFIX
}
)
agent_executor_2.run("Which customers are located in Cleveland?")
---------------------------------------------------------------------------
> Entering new AgentExecutor chain...
Action: sql_db_list_tables
Action Input:
Observation: AccountingDocuments, AccountingDocumentsReceivable, AccountsPayable, AccountsPayableTurnover, AddressesMD, AugmentedDemandPlan, AugmentedWeeklySales, BatchesMD, BillOfMaterialsMD, Billing, BillingBlockingReasonsMD, BusinessPartnersMD, CashDiscountUtilization, CompaniesMD, CostCenterAmountsHierarchy_SAMPLE, CostCentersMD, CountriesMD, CurrenciesMD, CurrencyConvUtil, CurrencyConversion, CustomersMD, DaysPayableOutstanding, Deliveries, DeliveriesStatus_PerSalesOrg, DeliveryBlockingReasonsMD, DemandForecast, DemandPlan, DistributionChannelsMD, DivisionsMD, GLAccountsMD, HolidayCalendar, InventoryByPlant, InventoryKeyMetrics, InvoiceDocuments_Flow, Languages_T002, LedgersMD, MaterialGroupsMD, MaterialLedger, MaterialMovementTypesMD, MaterialPlantsMD, MaterialTypesMD, MaterialsBatchMD, MaterialsMD, MaterialsMovement, MaterialsValuation, OneTouchOrder, OrderToCash, POFulfillment, POOrderHistory, POSchedule, POScheduleLine, POVendorConfirmation, PlantsMD, PricingConditions, ProductHierarchiesMD, ProductHierarchy_Flatten, ProductionOrders, ProfitCenterAmountsHierarchy_SAMPLE, ProfitCentersMD, PromotionCalendar, PurchaseDocumentTypesMD, PurchaseDocuments, PurchaseDocumentsHistory, PurchaseDocuments_Flow, PurchasingGroupsMD, PurchasingOrganizationsMD, ReasonForMovementTypesMD, Regions_T005S, SDDocumentFlow, SDStatus_Items, SalesFulfillment, SalesFulfillment_perOrder, SalesOrderDetails_SAMPLE, SalesOrderHeaderStatus, SalesOrderPartnerFunction, SalesOrderPricing, SalesOrderScheduleLine, SalesOrderStatus, SalesOrders, SalesOrders_V2, SalesOrganizationsMD, SalesStatus_Items, SlowMovingThreshold, SpecialStocksMD, StockCharacteristicsConfig, StockInHand, StockMonthlySnapshots, StockWeeklySnapshots, Stock_NonValuated, Stock_PerPlant, Stock_Unrestricted_vs_Sales, StorageLocationsMD, TelephoneCodes_T005K, Trends, UoMConversionUtil, UoMMD, UoMUsage_SAMPLE, ValuationAreasMD, VendorConfig, VendorPerformance, VendorsMD, Weather, WeatherDaily
Thought:I should look at the schema of the CustomersMD table to see what columns I can query.
Action: sql_db_schema
Action Input: CustomersMD
Observation:
CREATE TABLE `CustomersMD` (
`Client_MANDT` STRING,
`CustomerNumber_KUNNR` STRING,
`CountryKey_LAND1` STRING,
`Name1_NAME1` STRING,
`Name2_NAME2` STRING,
`City_ORT01` STRING,
`PostalCode_PSTLZ` STRING,
`CustomerRegion_REGIO` STRING,
`SortField_SORTL` STRING,
`StreetAndNumber_STRAS` STRING,
`FirstTelephoneNumber_TELF1` STRING,
`FaxNumber_TELFX` STRING,
`OneTimeAccount_XCPDK` STRING,
`Address_ADRNR` STRING,
`MatchcodeSearch_MCOD1` STRING,
`MatchcodeSearch_MCOD2` STRING,
`MatchcodeSearch_MCOD3` STRING,
`Title_ANRED` STRING,
`CentralOrderBlockForCustomer_AUFSD` STRING,
`ExpressTrainStation_BAHNE` STRING,
`TrainStation_BAHNS` STRING,
`InternationalLocationNumber_BBBNR` STRING,
`InternationalLocationNumber_BBSNR` STRING,
`AuthorizationGroup_BEGRU` STRING,
`IndustryKey_BRSCH` STRING,
`CheckDigitForTheInternationalLocationNumber_BUBKZ` STRING,
`DataCommunicationLineNo_DATLT` STRING,
`DateCreated_ERDAT` DATE,
`CreatedBy_ERNAM` STRING,
`UnloadingPointsExist_EXABL` STRING,
`CentralBillingBlock_FAKSD` STRING,
`AccountNumberFiscalAddress_FISKN` STRING,
`WorkingTimeCalendar_KNAZK` STRING,
`AlternativePayer_KNRZA` STRING,
`GroupKey_KONZS` STRING,
`CustomerAccountGroup_KTOKD` STRING,
`CustomerClassification_KUKLA` STRING,
`AccountNumberOfVendorOrCreditor_LIFNR` STRING,
`CentralDeliveryBlockForTheCustomer_LIFSD` STRING,
`CityCoordinates_LOCCO` STRING,
`CentralDeletionFlagForMasterRecord_LOEVM` STRING,
`Name3_NAME3` STRING,
`Name4_NAME4` STRING,
`NielsenId_NIELS` STRING,
`District_ORT02` STRING,
`PoBox_PFACH` STRING,
`POBoxPostalCode_PSTL2` STRING,
`CountyCode_COUNC` STRING,
`CityCode_CITYC` STRING,
`RegionalMarket_RPMKR` STRING,
`CentralPostingBlock_SPERR` STRING,
`LanguageKey_SPRAS` STRING,
`TaxNumber1_STCD1` STRING,
`TaxNumber2_STCD2` STRING,
`SubjectToEqualizationTax_STKZA` STRING,
`LiableForVat_STKZU` STRING,
`TeleboxNumber_TELBX` STRING,
`SecondTelephoneNumber_TELF2` STRING,
`TeletexNumber_TELTX` STRING,
`TelexNumber_TELX1` STRING,
`TransportationZone_LZONE` STRING,
`AlternativePayerAllowed_XZEMP` STRING,
`CompanyIdOfTradingPartner_VBUND` STRING,
`VatRegistrationNumber_STCEG` STRING,
`Competitor_DEAR1` STRING,
`SalesPartner_DEAR2` STRING,
`SalesProspect_DEAR3` STRING,
`CustomerType4_DEAR4` STRING,
`IdForDefaultSoldToParty_DEAR5` STRING,
`LegalStatus_GFORM` STRING,
`IndustryCode1_BRAN1` STRING,
`IndustryCode2_BRAN2` STRING,
`IndustryCode3_BRAN3` STRING,
`IndustryCode4_BRAN4` STRING,
`IndustryCode5_BRAN5` STRING,
`InitialContact_EKONT` STRING,
`AnnualSales_UMSAT` NUMERIC,
`YearForWhichSalesAreGiven_UMJAH` STRING,
`CurrencyOfSalesFigure_UWAER` STRING,
`YearlyNumberOfEmployees_JMZAH` STRING,
`YearForWhichTheNumberOfEmployeesIsGiven_JMJAH` STRING,
`Attribute1_KATR1` STRING,
`Attribute2_KATR2` STRING,
`Attribute3_KATR3` STRING,
`Attribute4_KATR4` STRING,
`Attribute5_KATR5` STRING,
`Attribute6_KATR6` STRING,
`Attribute7_KATR7` STRING,
`Attribute8_KATR8` STRING,
`Attribute9_KATR9` STRING,
`Attribute10_KATR10` STRING,
`NaturalPerson_STKZN` STRING,
`AnnualSales_UMSA1` NUMERIC,
`TaxJurisdiction_TXJCD` STRING,
`FiscalYearVariant_PERIV` STRING,
`UsageIndicator_ABRVW` STRING,
`InspectionCarriedOutByCustomer_INSPBYDEBI` STRING,
`InspectionForADeliveryNote_INSPATDEBI` STRING,
`ReferenceAccountGroup_KTOCD` STRING,
`PoBoxCity_PFORT` STRING,
`Plant_WERKS` STRING,
`ReportKeyForDataMediumExchange_DTAMS` STRING,
`InstructionKeyForDataMediumExchange_DTAWS` STRING,
`StatusOfDataTransferIntoSubsequentRelease_DUEFL` STRING,
`AssignmentToHierarchy_HZUOR` STRING,
`PaymentBlock_SPERZ` STRING,
`RLabeling_CustomerplantGroup_ETIKG` STRING,
`IdNonMilitaryUse_CIVVE` STRING,
`IdForMilitaryUse_MILVE` STRING,
`ConditionGroup1_KDKG1` STRING,
`ConditionGroup2_KDKG2` STRING,
`ConditionGroup3_KDKG3` STRING,
`ConditionGroup4_KDKG4` STRING,
`ConditionGroup5_KDKG5` STRING,
`AlternativePayerUsingAccountNumber_XKNZA` STRING,
`TaxType_FITYP` STRING,
`TaxNumberType_STCDT` STRING,
`TaxNumber3_STCD3` STRING,
`TaxNumber4_STCD4` STRING,
`TaxNumber5_STCD5` STRING,
`CustomerIsIcmsExempt_XICMS` STRING,
`CustomerIsIpiExempt_XXIPI` STRING,
`CustomerGroupForSubstituicaoTributariaCalculation_XSUBT` STRING,
`CustomerCfopCategory_CFOPC` STRING,
`TaxLaw_Icms_TXLW1` STRING,
`TaxLaw_Ipi_TXLW2` STRING,
`IndicatorForBiochemicalWarfare_CCC01` STRING,
`IndicatorForNuclearNonproliferation_CCC02` STRING,
`IndicatorForNationalSecurity_CCC03` STRING,
`IndicatorForMissileTechnology_CCC04` STRING,
`CentralSalesBlock_CASSD` STRING,
`UniformResourceLocator_KNURL` STRING,
`NameOfRepresentative_J_1KFREPRE` STRING,
`TypeOfBusiness_J_1KFTBUS` STRING,
`TypeOfIndustry_J_1KFTIND` STRING,
`StatusOfChangeAuthorization_CONFS` STRING,
`DateOnWhichTheChangesWereConfirmed_UPDAT` DATE,
`TimeOfLastChangeConfirmation_UPTIM` TIME,
`CentralDeletionBlockForMasterRecord_NODEL` STRING,
`Indicator_Consumer_DEAR6` STRING,
`BusinessPurposeCompletedFlag_CVP_XBLCK` STRING,
`SuframaCode_SUFRAMA` STRING,
`RgNumber_RG` STRING,
`IssuedBy_EXP` STRING,
`State_UF` STRING,
`RgIssuingDate_RGDATE` DATE,
`RicNumber_RIC` STRING,
`ForeignNationalRegistration_RNE` STRING,
`RneIssuingDate_RNEDATE` DATE,
`Cnae_CNAE` STRING,
`LegalNature_LEGALNAT` STRING,
`CrtNumber_CRTN` STRING,
`IcmsTaxpayer_ICMSTAXPAY` STRING,
`IndustryMainType_INDTYP` STRING,
`TaxDeclarationType_TDT` STRING,
`CompanySize_COMSIZE` STRING,
`DeclarationRegimenForPiscofins_DECREGPC` STRING,
`AgencyLocationCode_ALC` STRING,
`PaymentOffice_PMT_OFFICE` STRING,
`FeeSchedule_FEE_SCHEDULE` STRING,
`DunsNumber_DUNS` STRING,
`Duns4_DUNS4` STRING,
`ProcessorGroup_PSOFG` STRING,
`SubledgerAcctPreprocessingProcedure_PSOIS` STRING,
`Name1_PSON1` STRING,
`Name2_PSON2` STRING,
`Name3_PSON3` STRING,
`FirstName_PSOVN` STRING,
`Title_PSOTL` STRING,
`Description_PSOO1` STRING,
`Description_PSOO2` STRING,
`Description_PSOO3` STRING,
`Description_PSOO4` STRING,
`Description_PSOO5` STRING,
`ValidFromDate_DATE_FROM` DATE,
`VersionIdForInternationalAddresses_NATION` STRING,
`ValidToDate_DATE_TO` DATE,
`FormOfAddressKey_TITLE` STRING,
`Addr_NAME1` STRING,
`Addr_NAME2` STRING,
`Addr_NAME3` STRING,
`Addr_NAME4` STRING,
`City_CITY1` STRING,
`District_CITY2` STRING,
`CityCodeForCitystreetFile_CITY_CODE` STRING,
`DistrictCodeForCityAndStreetFile_CITYP_CODE` STRING,
`City_HOME_CITY` STRING,
`DifferentCityForCitystreetFile_CITYH_CODE` STRING,
`RegionalStructureGrouping_REGIOGROUP` STRING,
`CityPostalCode_POST_CODE1` STRING,
`PoBoxPostalCode_POST_CODE2` STRING,
`CompanyPostalCode_POST_CODE3` STRING,
`PoBox_PO_BOX` STRING,
`PoBoxAddressUndeliverableFlag_DONT_USE_P` STRING,
`Flag_PoBoxWithoutNumber_PO_BOX_NUM` STRING,
`PoBoxCity_PO_BOX_LOC` STRING,
`CityPoBoxCode_CityFile_CITY_CODE2` STRING,
`RegionForPoBox_PO_BOX_REG` STRING,
`PoBoxCountry_PO_BOX_CTY` STRING,
`TransportationZoneToOrFromWhichTheGoodsAreDelivered_TRANSPZONE` STRING,
`Street_STREET` STRING,
`StreetAddressUndeliverableFlag_DONT_USE_S` STRING,
`StreetNumberForCitystreetFile_STREETCODE` STRING,
`HouseNumber_HOUSE_NUM1` STRING,
`HouseNumberSupplement_HOUSE_NUM2` STRING,
`Street2_STR_SUPPL1` STRING,
`Street3_STR_SUPPL2` STRING,
`Street4_STR_SUPPL3` STRING,
`Street5_LOCATION` STRING,
`Building_NumberOrCode_BUILDING` STRING,
`FloorInBuilding_FLOOR` STRING,
`RoomOrAppartmentNumber_ROOMNUMBER` STRING,
`CountryKey_COUNTRY` STRING,
`Language_LANGU` STRING,
`Region__REGION` STRING,
`AddressGroup_Key_BusinessAddressServices_ADDR_GROUP` STRING,
`Flag_ThereAreMoreAddressGroupAssignments_FLAGGROUPS` STRING,
`Flag_ThisIsAPersonalAddress_PERS_ADDR` STRING,
`SearchTerm1_SORT1` STRING,
`SearchTerm2_SORT2` STRING,
`FirstTelephoneNo_DiallingCodenumber_TEL_NUMBER` STRING,
`FirstTelephoneNo_Extension_TEL_EXTENS` STRING,
`CountyCodeForCounty_COUNTY_CODE` STRING,
`County_COUNTY` STRING,
`TownshipCodeForTownship_TOWNSHIP_CODE` STRING,
`Township_TOWNSHIP` STRING,
`CountyNameInUpperCaseForSearchHelp_MC_COUNTY` STRING,
`TownshipNameInUpperCaseForSearchHelp_MC_TOWNSHIP` STRING,
`BusinessPurposeCompletedFlag_XPCPT` STRING
)
OPTIONS(description='Customer Master Data')
/*
3 rows from CustomersMD table:
Client_MANDT CustomerNumber_KUNNR CountryKey_LAND1 Name1_NAME1 Name2_NAME2 City_ORT01 PostalCode_PSTLZ CustomerRegion_REGIO SortField_SORTL StreetAndNumber_STRAS FirstTelephoneNumber_TELF1 FaxNumber_TELFX OneTimeAccount_XCPDK Address_ADRNR MatchcodeSearch_MCOD1 MatchcodeSearch_MCOD2 MatchcodeSearch_MCOD3 Title_ANRED CentralOrderBlockForCustomer_AUFSD ExpressTrainStation_BAHNE TrainStation_BAHNS InternationalLocationNumber_BBBNR InternationalLocationNumber_BBSNR AuthorizationGroup_BEGRU IndustryKey_BRSCH CheckDigitForTheInternationalLocationNumber_BUBKZ DataCommunicationLineNo_DATLT DateCreated_ERDAT CreatedBy_ERNAM UnloadingPointsExist_EXABL CentralBillingBlock_FAKSD AccountNumberFiscalAddress_FISKN WorkingTimeCalendar_KNAZK AlternativePayer_KNRZA GroupKey_KONZS CustomerAccountGroup_KTOKD CustomerClassification_KUKLA AccountNumberOfVendorOrCreditor_LIFNR CentralDeliveryBlockForTheCustomer_LIFSD CityCoordinates_LOCCO CentralDeletionFlagForMasterRecord_LOEVM Name3_NAME3 Name4_NAME4 NielsenId_NIELS District_ORT02 PoBox_PFACH POBoxPostalCode_PSTL2 CountyCode_COUNC CityCode_CITYC RegionalMarket_RPMKR CentralPostingBlock_SPERR LanguageKey_SPRAS TaxNumber1_STCD1 TaxNumber2_STCD2 SubjectToEqualizationTax_STKZA LiableForVat_STKZU TeleboxNumber_TELBX SecondTelephoneNumber_TELF2 TeletexNumber_TELTX TelexNumber_TELX1 TransportationZone_LZONE AlternativePayerAllowed_XZEMP CompanyIdOfTradingPartner_VBUND VatRegistrationNumber_STCEG Competitor_DEAR1 SalesPartner_DEAR2 SalesProspect_DEAR3 CustomerType4_DEAR4 IdForDefaultSoldToParty_DEAR5 LegalStatus_GFORM IndustryCode1_BRAN1 IndustryCode2_BRAN2 IndustryCode3_BRAN3 IndustryCode4_BRAN4 IndustryCode5_BRAN5 InitialContact_EKONT AnnualSales_UMSAT YearForWhichSalesAreGiven_UMJAH CurrencyOfSalesFigure_UWAER YearlyNumberOfEmployees_JMZAH YearForWhichTheNumberOfEmployeesIsGiven_JMJAH Attribute1_KATR1 Attribute2_KATR2 Attribute3_KATR3 Attribute4_KATR4 Attribute5_KATR5 Attribute6_KATR6 Attribute7_KATR7 Attribute8_KATR8 Attribute9_KATR9 Attribute10_KATR10 NaturalPerson_STKZN AnnualSales_UMSA1 TaxJurisdiction_TXJCD FiscalYearVariant_PERIV UsageIndicator_ABRVW InspectionCarriedOutByCustomer_INSPBYDEBI InspectionForADeliveryNote_INSPATDEBI ReferenceAccountGroup_KTOCD PoBoxCity_PFORT Plant_WERKS ReportKeyForDataMediumExchange_DTAMS InstructionKeyForDataMediumExchange_DTAWS StatusOfDataTransferIntoSubsequentRelease_DUEFL AssignmentToHierarchy_HZUOR PaymentBlock_SPERZ RLabeling_CustomerplantGroup_ETIKG IdNonMilitaryUse_CIVVE IdForMilitaryUse_MILVE ConditionGroup1_KDKG1 ConditionGroup2_KDKG2 ConditionGroup3_KDKG3 ConditionGroup4_KDKG4 ConditionGroup5_KDKG5 AlternativePayerUsingAccountNumber_XKNZA TaxType_FITYP TaxNumberType_STCDT TaxNumber3_STCD3 TaxNumber4_STCD4 TaxNumber5_STCD5 CustomerIsIcmsExempt_XICMS CustomerIsIpiExempt_XXIPI CustomerGroupForSubstituicaoTributariaCalculation_XSUBT CustomerCfopCategory_CFOPC TaxLaw_Icms_TXLW1 TaxLaw_Ipi_TXLW2 IndicatorForBiochemicalWarfare_CCC01 IndicatorForNuclearNonproliferation_CCC02 IndicatorForNationalSecurity_CCC03 IndicatorForMissileTechnology_CCC04 CentralSalesBlock_CASSD UniformResourceLocator_KNURL NameOfRepresentative_J_1KFREPRE TypeOfBusiness_J_1KFTBUS TypeOfIndustry_J_1KFTIND StatusOfChangeAuthorization_CONFS DateOnWhichTheChangesWereConfirmed_UPDAT TimeOfLastChangeConfirmation_UPTIM CentralDeletionBlockForMasterRecord_NODEL Indicator_Consumer_DEAR6 BusinessPurposeCompletedFlag_CVP_XBLCK SuframaCode_SUFRAMA RgNumber_RG IssuedBy_EXP State_UF RgIssuingDate_RGDATE RicNumber_RIC ForeignNationalRegistration_RNE RneIssuingDate_RNEDATE Cnae_CNAE LegalNature_LEGALNAT CrtNumber_CRTN IcmsTaxpayer_ICMSTAXPAY IndustryMainType_INDTYP TaxDeclarationType_TDT CompanySize_COMSIZE DeclarationRegimenForPiscofins_DECREGPC AgencyLocationCode_ALC PaymentOffice_PMT_OFFICE FeeSchedule_FEE_SCHEDULE DunsNumber_DUNS Duns4_DUNS4 ProcessorGroup_PSOFG SubledgerAcctPreprocessingProcedure_PSOIS Name1_PSON1 Name2_PSON2 Name3_PSON3 FirstName_PSOVN Title_PSOTL Description_PSOO1 Description_PSOO2 Description_PSOO3 Description_PSOO4 Description_PSOO5 ValidFromDate_DATE_FROM VersionIdForInternationalAddresses_NATION ValidToDate_DATE_TO FormOfAddressKey_TITLE Addr_NAME1 Addr_NAME2 Addr_NAME3 Addr_NAME4 City_CITY1 District_CITY2 CityCodeForCitystreetFile_CITY_CODE DistrictCodeForCityAndStreetFile_CITYP_CODE City_HOME_CITY DifferentCityForCitystreetFile_CITYH_CODE RegionalStructureGrouping_REGIOGROUP CityPostalCode_POST_CODE1 PoBoxPostalCode_POST_CODE2 CompanyPostalCode_POST_CODE3 PoBox_PO_BOX PoBoxAddressUndeliverableFlag_DONT_USE_P Flag_PoBoxWithoutNumber_PO_BOX_NUM PoBoxCity_PO_BOX_LOC CityPoBoxCode_CityFile_CITY_CODE2 RegionForPoBox_PO_BOX_REG PoBoxCountry_PO_BOX_CTY TransportationZoneToOrFromWhichTheGoodsAreDelivered_TRANSPZONE Street_STREET StreetAddressUndeliverableFlag_DONT_USE_S StreetNumberForCitystreetFile_STREETCODE HouseNumber_HOUSE_NUM1 HouseNumberSupplement_HOUSE_NUM2 Street2_STR_SUPPL1 Street3_STR_SUPPL2 Street4_STR_SUPPL3 Street5_LOCATION Building_NumberOrCode_BUILDING FloorInBuilding_FLOOR RoomOrAppartmentNumber_ROOMNUMBER CountryKey_COUNTRY Language_LANGU Region__REGION AddressGroup_Key_BusinessAddressServices_ADDR_GROUP Flag_ThereAreMoreAddressGroupAssignments_FLAGGROUPS Flag_ThisIsAPersonalAddress_PERS_ADDR SearchTerm1_SORT1 SearchTerm2_SORT2 FirstTelephoneNo_DiallingCodenumber_TEL_NUMBER FirstTelephoneNo_Extension_TEL_EXTENS CountyCodeForCounty_COUNTY_CODE County_COUNTY TownshipCodeForTownship_TOWNSHIP_CODE Township_TOWNSHIP CountyNameInUpperCaseForSearchHelp_MC_COUNTY TownshipNameInUpperCaseForSearchHelp_MC_TOWNSHIP BusinessPurposeCompletedFlag_XPCPT
050 0001000053 US Customer-Oregon None Portland OR OREGON None None None None 0000023139 CUSTOMER-OREGON None PORTLAND Mr. None None None None None None None None None 2022-03-10 DEEPAKV None None None None None None 1000 None None None None None None None None None None None None None None None E None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None X None None None X None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0001-01-01 9999-12-31 0002 Customer-Oregon Portland US E OR BP OREGON
050 0001000060 US Customer- Seattle-Tacoma None Seattle-Tacoma WA SEATTLE-TA None None None None 0000023162 CUSTOMER- SEATTLE-TACOMA None SEATTLE-TACOMA Company None None None None None None None None None 2022-03-20 DEEPAKV None None None None None None 1000 None None None None None None None None None None None None None None None E None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None X None None None X None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0001-01-01 9999-12-31 0003 Customer- Seattle-Tacoma Seattle-Tacoma US E WA BP SEATTLE-TACOMA
050 0000000001 AU Test None Territory 2612 ACT TEST None None None None 0000023110 TEST None TERRITORY Mr. and Mrs. None None None None None None None None None 2022-02-28 MAHITHA None None None None None None 0004 None None None None None None None None None None None None None None None E None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None X None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None 0001-01-01 9999-12-31 0004 Test Territory 2612 AU E ACT BP TEST ING
*/
Thought:
---------------------------------------------------------------------------
OutputParserException Traceback (most recent call last)
Cell In[20], line 1
----> 1 agent_executor_2.run("Which customers are located in Cleveland?")
File /opt/conda/envs/env_preview_lc/lib/python3.11/site-packages/langchain/chains/base.py:436, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
434 if len(args) != 1:
435 raise ValueError("`run` supports only one positional argument.")
--> 436 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
437 _output_key
438 ]
440 if kwargs and not args:
441 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
442 _output_key
443 ]
File /opt/conda/envs/env_preview_lc/lib/python3.11/site-packages/langchain/chains/base.py:243, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
241 except (KeyboardInterrupt, Exception) as e:
242 run_manager.on_chain_error(e)
--> 243 raise e
244 run_manager.on_chain_end(outputs)
245 final_outputs: Dict[str, Any] = self.prep_outputs(
246 inputs, outputs, return_only_outputs
247 )
File /opt/conda/envs/env_preview_lc/lib/python3.11/site-packages/langchain/chains/base.py:237, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
231 run_manager = callback_manager.on_chain_start(
232 dumpd(self),
233 inputs,
234 )
235 try:
236 outputs = (
--> 237 self._call(inputs, run_manager=run_manager)
238 if new_arg_supported
239 else self._call(inputs)
240 )
241 except (KeyboardInterrupt, Exception) as e:
242 run_manager.on_chain_error(e)
File /opt/conda/envs/env_preview_lc/lib/python3.11/site-packages/langchain/agents/agent.py:1029, in AgentExecutor._call(self, inputs, run_manager)
1027 # We now enter the agent loop (until it returns something).
1028 while self._should_continue(iterations, time_elapsed):
-> 1029 next_step_output = self._take_next_step(
1030 name_to_tool_map,
1031 color_mapping,
1032 inputs,
1033 intermediate_steps,
1034 run_manager=run_manager,
1035 )
1036 if isinstance(next_step_output, AgentFinish):
1037 return self._return(
1038 next_step_output, intermediate_steps, run_manager=run_manager
1039 )
File /opt/conda/envs/env_preview_lc/lib/python3.11/site-packages/langchain/agents/agent.py:843, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
841 raise_error = False
842 if raise_error:
--> 843 raise e
844 text = str(e)
845 if isinstance(self.handle_parsing_errors, bool):
File /opt/conda/envs/env_preview_lc/lib/python3.11/site-packages/langchain/agents/agent.py:832, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
829 intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
831 # Call the LLM to see what to do.
--> 832 output = self.agent.plan(
833 intermediate_steps,
834 callbacks=run_manager.get_child() if run_manager else None,
835 **inputs,
836 )
837 except OutputParserException as e:
838 if isinstance(self.handle_parsing_errors, bool):
File /opt/conda/envs/env_preview_lc/lib/python3.11/site-packages/langchain/agents/agent.py:457, in Agent.plan(self, intermediate_steps, callbacks, **kwargs)
455 full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
456 full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
--> 457 return self.output_parser.parse(full_output)
File /opt/conda/envs/env_preview_lc/lib/python3.11/site-packages/langchain/agents/mrkl/output_parser.py:44, in MRKLOutputParser.parse(self, text)
39 return AgentFinish(
40 {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
41 )
43 if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
---> 44 raise OutputParserException(
45 f"Could not parse LLM output: `{text}`",
46 observation="Invalid Format: Missing 'Action:' after 'Thought:'",
47 llm_output=text,
48 send_to_llm=True,
49 )
50 elif not re.search(
51 r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL
52 ):
53 raise OutputParserException(
54 f"Could not parse LLM output: `{text}`",
55 observation="Invalid Format:"
(...)
58 send_to_llm=True,
59 )
OutputParserException: Could not parse LLM output: ````
CREATE TABLE CustomersMD (
Client_MANDT STRING,
CustomerNumber_KUNNR STRING,
CountryKey_LAND1 STRING,
Name1_NAME1 STRING,
Name2_NAME2 STRING,
City_ORT01 STRING,
PostalCode_PSTLZ STRING,
CustomerRegion_REGIO STRING,
SortField_SORTL STRING,
StreetAndNumber_STRAS STRING,
FirstTelephoneNumber_TELF1 STRING,
FaxNumber_TELFX STRING,
OneTimeAccount_XCPDK STRING`
```
Appreciate any help with this :)
### Suggestion:
_No response_
|
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https://github.com/langchain-ai/langchain/pull/8281
| 1,822,054,376 |
PR_kwDOIPDwls5WbHDJ
| 8,281 |
Upgrade the AwaDB from v0.3.7 to v0.3.9 and change the default embeddings
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[] | null | 4 | 2023-07-26T10:08:50 | 2023-07-28T00:20:51 | 2023-07-28T00:20:51 |
CONTRIBUTOR
| null |
1. Upgrade the AwaDB from v0.3.7 to v0.3.9
2. Change the default embedding to AwaEmbedding
@rlancemartin, @eyurtsev Please review, thanks!
|
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| 1,822,048,802 |
I_kwDOIPDwls5smkIi
| 8,280 |
Problem using HuggingFacePipeline by passing pipeline in directly
|
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open
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[] | null | 6 | 2023-07-26T10:05:37 | 2023-11-07T20:51:48 | null |
NONE
| null |
### System Info
langchain 0.0.242
python 3.11.4
### Who can help?
@hwchase17
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I try using a local LLM from HuggingFace:
1. Using from_model_id works perfectly:
llm = HuggingFacePipeline.from_model_id(
model_id="TheBloke/wizardLM-7B-HF",
task="text-generation",
pipeline_kwargs={"max_new_tokens": 1200, "temperature": 0.3, "top_p": 0.95, "repetition_penalty": 1.15,},
device=0,
)
print(llm)
Params: {'model_id': 'TheBloke/wizardLM-7B-HF', 'model_kwargs': {}, 'pipeline_kwargs': {'max_new_tokens': 1200, 'temperature': 0.3, 'top_p': 0.95, 'repetition_penalty': 1.15}}
2. Now the problem is when **passing pipeline directly**:
I just followed the example given in Langchain.
from langchain.llms import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import transformers
model_id = "TheBloke/wizardLM-7B-HF"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens = 1200,
temperature = 0.3,
top_p = 0.95,
repetition_penalty = 1.15,
)
hf = HuggingFacePipeline(pipeline=pipe)
print(hf)
### Now I get this: Params: {'model_id': 'gpt2', 'model_kwargs': None, 'pipeline_kwargs': None}
### Expected behavior
What is the matter?
Why are the paramters that I set not taken?
Why does it use the default ones?
I tried it now over and over and over again, but I can't get it work.
Sb has any idea why? I am a bit desperate, cause I need to pass pipeline directly.
Official langchain documentation:
Example passing pipeline in directly:
from langchain.llms import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
hf = HuggingFacePipeline(pipeline=pipe)
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PR_kwDOIPDwls5WahhS
| 8,279 |
corrected logic referencing code on python toolkit page
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[] | null | 2 | 2023-07-26T08:45:11 | 2023-07-26T19:46:19 | 2023-07-26T19:46:18 |
CONTRIBUTOR
| null |
- Description: PR for typo fix on python toolkit page
- Issue: typo fix on python toolkit page
- Dependencies: none
- Tag maintainer: @hinthornw
- Twitter handle: not active
|
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| 1,821,855,945 |
PR_kwDOIPDwls5WacAl
| 8,278 |
unpack later
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[] | null | 1 | 2023-07-26T08:30:13 | 2023-07-26T08:53:23 | 2023-07-26T08:53:22 |
COLLABORATOR
| null |
Fix https://github.com/langchain-ai/langchain/issues/8272
|
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I_kwDOIPDwls5slqe-
| 8,277 |
devcontainer in vscode is not working
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[] | null | 3 | 2023-07-26T08:03:16 | 2023-12-06T17:44:40 | 2023-12-06T17:44:39 |
NONE
| null |
### System Info
Macos 13.5, vscode 1.80.1
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
As mentioned in the readme if .devcontainer
1. Clone locally
2. In Vscode select Build and open in dev container
### Expected behavior
The Devcontainer is working well, Building and running
but the mounted folder is incorrect and empty, so the vscode file explorer is empty
Maybe due to the devcontainer.json file, referring to a FolderBaseName variable
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PR_kwDOIPDwls5WaPyR
| 8,276 |
Fix "missing key op" RDFGraph OWL serialization
|
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[] | null | 2 | 2023-07-26T07:54:05 | 2023-07-27T00:22:54 | 2023-07-26T19:14:56 |
CONTRIBUTOR
| null |
Replace this comment with:
- Description: Fix "missing key op" error in RDFGraph OWL Serialization
- Issue: #8263
- Dependencies: None
- Tag maintainer: @baskaryan
|
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